Update AWS AMIs for v0.15.1

Fix the way a fixed user id is generated

.gitignore

@@ -1,3 +1,4 @@
+syntax: glob
 .idea
 apierrors/errors
 static/build.json
@@ -18,3 +19,4 @@ build
 dist
 code.tar.gz
 server/schema/services/_cache.json
+server/apierrors/errors/*

LICENSE

@@ -1,7 +1,7 @@
                      Server Side Public License
                      VERSION 1, OCTOBER 16, 2018
 
-                    Copyright © 2018 MongoDB, Inc.
+                    Copyright © 2019 allegro.ai, Inc.
 
   Everyone is permitted to copy and distribute verbatim copies of this
   license document, but changing it is not allowed.

README.md

@@ -1,246 +1,216 @@
-# TRAINS Server
+# Trains Server
 
-##  Magic Version Control & Experiment Manager for AI
+##  Auto-Magical Experiment Manager & Version Control for AI - ε Devops Included!
+
+[![GitHub license](https://img.shields.io/badge/license-SSPL-green.svg)](https://img.shields.io/badge/license-SSPL-green.svg)
+[![Python versions](https://img.shields.io/badge/python-3.6%20%7C%203.7-blue.svg)](https://img.shields.io/badge/python-3.6%20%7C%203.7-blue.svg)
+[![GitHub version](https://img.shields.io/github/release-pre/allegroai/trains-server.svg)](https://img.shields.io/github/release-pre/allegroai/trains-server.svg)
+[![PyPI status](https://img.shields.io/badge/status-beta-yellow.svg)](https://img.shields.io/badge/status-beta-yellow.svg)
+
+### Help improve Trains by filling our 2-min [user survey](https://allegro.ai/lp/trains-user-survey/)
+
+## :rocket: Trains-Agent Services is now included, for more information see [services](https://github.com/allegroai/trains-server#services)
 
 ## Introduction
 
-The **trains-server** is the infrastructure for [trains](https://github.com/allegroai/trains).
-It allows multiple users to collaborate and manage their experiments. 
- 
-The **trains-server** contains the following components:
+The **trains-server** is the backend service infrastructure for [Trains](https://github.com/allegroai/trains).
+It allows multiple users to collaborate and manage their experiments.
+By default, **Trains** is set up to work with the **Trains** demo server, which is open to anyone and resets periodically.
+In order to host your own server, you will need to launch **trains-server** and point **Trains** to it.
 
-* the Web-App which is a single-page UI for experiment management and browsing
-* a REST interface for: 
-    * documenting and logging experiment information, statistics and results
-    * querying experiments history, logs and results
-* a locally-hosted file server for storing images and models making them easily accessible using the Web-App
+**trains-server** contains the following components:
 
-You can quickly setup your **trains-server** using a pre-built Docker image (see [Installation](#installation)).
+* The **Trains** Web-App, a single-page UI for experiment management and browsing
+* RESTful API for:
+    * Documenting and logging experiment information, statistics and results
+    * Querying experiments history, logs and results
+* Locally-hosted file server for storing images and models making them easily accessible using the Web-App
 
-When new releases are available, you can upgrade your pre-built Docker image (see [Upgrade](#upgrade)). 
+You can quickly [deploy](#launching-trains-server)  your **trains-server** using Docker, AWS EC2 AMI, or Kubernetes. 
 
-The **trains-server's** code is freely available [here](https://github.com/allegroai/trains-server).
+## System design
 
-## System diagram
 
-<pre>
- TRAINS-server
- +--------------------------------------------------------------------+
- |                                                                    |
- |   Server Docker                   Elastic Docker     Mongo Docker  |
- |  +-------------------------+     +---------------+  +------------+ |
- |  |     Pythonic Server     |     |               |  |            | |
- |  |   +-----------------+   |     | ElasticSearch |  |  MongoDB   | |
- |  |   |   WEB server    |   |     |               |  |            | |
- |  |   |   Port 8080     |   |     |               |  |            | |
- |  |   +--------+--------+   |     |               |  |            | |
- |  |            |            |     |               |  |            | |
- |  |   +--------+--------+   |     |               |  |            | |
- |  |   |   API server    +----------------------------+            | |
- |  |   |   Port 8008     +---------+               |  |            | |
- |  |   +-----------------+   |     +-------+-------+  +-----+------+ |
- |  |                         |             |                |        |
- |  |   +-----------------+   |         +---+----------------+------+ |
- |  |   |   File Server   +-------+     |    Host Storage           | |
- |  |   |   Port 8081     |   |   +-----+                           | |
- |  |   +-----------------+   |         +---------------------------+ |
- |  +------------+------------+                                       |
- +---------------|----------------------------------------------------+
-                 |HTTP
-                 +--------+
- GPU Machine              |
- +------------------------|-------------------------------------------+
- |     +------------------|--------------+                            |
- |     |  Training        |              |    +---------------------+ |
- |     |  Code        +---+------------+ |    | trains configuration| |
- |     |              | TRAINS         | |    | ~/trains.conf       | |
- |     |              |                +------+                     | |
- |     |              +----------------+ |    +---------------------+ |
- |     +---------------------------------+                            |
- +--------------------------------------------------------------------+
-</pre>
+![Alt Text](https://github.com/allegroai/trains/blob/master/docs/system_diagram.png?raw=true)
 
-## Installation
+**trains-server** has two supported configurations:
+- Single IP (domain) with the following open ports
+    - Web application on port 8080
+    - API service on port 8008
+    - File storage service on port 8081
 
-This section contains the instructions to setup and launch a pre-built Docker image for the **trains-server**.
-
-**Note**: This Docker image was tested with Linux, only. For Windows users, we recommend running the server
-on a Linux virtual machine.
+- Sub-Domain configuration with default http/s ports (80 or 443)
+    - Web application on sub-domain: app.\*.\*
+    - API service on sub-domain: api.\*.\*
+    - File storage service on sub-domain: files.\*.\*
+    
+## Launching trains-server
 
 ### Prerequisites
 
-You must be logged in as a user with sudo privileges.
- 
-### Setup
+The ports 8080/8081/8008 must be available for the **trains-server** services.
+   
+For example, to see if port `8080` is in use:
 
-#### Step 1. Install Docker CE
+* Linux or macOS: 
+   
+        sudo lsof -Pn -i4 | grep :8080 | grep LISTEN
 
-You must install Docker to run the pre-packaged **trains-server**.
+* Windows:
 
-* For [Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/) / Mint (x86_64/amd64):
+        netstat -an |find /i "8080"
+   
+### Launching   
+    
+Launch **trains-server** in any of the following formats:
 
-```bash
-sudo apt-get install -y apt-transport-https ca-certificates curl software-properties-common
-curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
-. /etc/os-release
-sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $UBUNTU_CODENAME stable"
-sudo apt-get update
-sudo apt-get install -y docker-ce
-```
+- Pre-built [AWS EC2 AMI](https://github.com/allegroai/trains-server/blob/master/docs/install_aws.md)
+- Pre-built [GCP Custom Image](https://github.com/allegroai/trains-server/blob/master/docs/install_gcp.md)
+- Pre-built Docker Image
+    - [Linux](https://github.com/allegroai/trains-server/blob/master/docs/install_linux_mac.md)
+    - [macOS](https://github.com/allegroai/trains-server/blob/master/docs/install_linux_mac.md)
+    - [Windows 10](https://github.com/allegroai/trains-server/blob/master/docs/install_win.md)
+- Kubernetes    
+    - [Kubernetes Helm](https://github.com/allegroai/trains-server-helm#prerequisites)
+    - Manual [Kubernetes installation](https://github.com/allegroai/trains-server-k8s#prerequisites)
 
-* For other operating systems, see [Supported platforms](https://docs.docker.com/install//#support) in the Docker documentation for instructions.
+## Connecting Trains to your trains-server
 
-#### Step 2. Setup the Docker daemon
+By default, the **Trains** client is set up to work with the [**Trains** demo server](https://demoapp.trains.allegro.ai/).  
+To have the **Trains** client use your **trains-server** instead:
+- Run the `trains-init` command for an interactive setup.
+- Or manually edit `~/trains.conf` file, making sure the server settings (`api_server`, `web_server`, `file_server`) are configured correctly, for example:
 
-To run the ElasticSearch Docker container, you must setup the Docker daemon by modifing the default 
-values required by Elastic in your Docker configuration file
-that are used by the **trains-server**. We provide instructions for the most common Docker configuration files.
+        api {
+            # API server on port 8008
+            api_server: "http://localhost:8008"
 
-You must edit or create a Docker configuration file:
+            # web_server on port 8080
+            web_server: "http://localhost:8080"
 
-* If your Docker configuration file is `/etc/sysconfig/docker`, edit it. 
-
-    Add the options in quotes to the available arguments in the `OPTIONS` section:
-
-```bash
-OPTIONS="--default-ulimit nofile=1024:65536 --default-ulimit memlock=-1:-1"
-```
-
-* Otherwise, edit `/etc/docker/daemon.json` (if it exists) or create it (if it does not exist).
-
-    Add or modify the `defaults-ulimits` section as shown below. Be sure your configuration file contains the `nofile` and `memlock` sub-sections and values shown. 
-
-    **Note**: Your configuration file may contain other sections. If so, confirm that the sections are separated by commas. For more information about Docker configuration files, see an [Daemon configuration file](https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file) in the Docker documentation.
-
-    The **trains-server** required defaults values are:
-
-```json
-{
-    "default-ulimits": {
-        "nofile": {
-            "name": "nofile",
-            "hard": 65536,
-            "soft": 1024
-        },
-        "memlock":
-        {
-            "name": "memlock",
-            "soft": -1,
-            "hard": -1
+            # file server on port 8081
+            files_server: "http://localhost:8081"
         }
-    }
-}
-```
 
-#### Step 3. Restart the Docker daemon
+**Note**: If you have set up **trains-server** in a sub-domain configuration, then there is no need to specify a port number,
+it will be inferred from the http/s scheme.
 
-You must restart the Docker daemon after modifying the configuration file:
+After launching the **trains-server** and configuring the **Trains** client to use the **trains-server**,
+you can [use](https://github.com/allegroai/trains#using-trains) **Trains** in your experiments and view them in your **trains-server** web server,
+for example http://localhost:8080.  
+For more information about the Trains client, see [**Trains**](https://github.com/allegroai/trains).
 
-```bash
-sudo service docker stop
-sudo service docker start
-```
+## Trains-Agent Services  <a name="services"></a> 
 
-#### Step 4. Set the Maximum Number of Memory Map Areas
+As of version 0.15 of **trains-server**, dockerized deployment includes a **Trains-Agent Services** container running as 
+part of the docker container collection.
 
-The maximum number of memory map areas a process can use is defined
-using the `vm.max_map_count` kernel setting. 
+Trains-Agent Services is an extension of Trains-Agent that provides the ability to launch long-lasting jobs 
+that previously had to be executed on local / dedicated machines. It allows a single agent to 
+launch multiple dockers (Tasks) for different use cases. To name a few use cases, auto-scaler service (spinning instances 
+when the need arises and the budget allows), Controllers (Implementing pipelines and more sophisticated DevOps logic),
+Optimizer (such as Hyper-parameter Optimization or sweeping), and Application (such as interactive Bokeh apps for 
+increased data transparency)
 
-Elastic requires that `vm.max_map_count` to be at least 262144.
+Trains-Agent Services container will spin **any** task enqueued into the dedicated `services` queue. 
+Every task launched by Trains-Agent Services  will be registered as a new node in the system, 
+providing tracking and transparency capabilities.  
+You can also run the Trains-Agent Services manually, see details in [trains-agent services mode](https://github.com/allegroai/trains-agent#trains-agent-services-mode-)
 
-* For CentOS 7, Ubuntu 16.04, Mint 18.3, Ubuntu 18.04 and Mint 19 users, we tested the following commands to set
-`vm.max_map_count`:
+**Note**: It is the user's responsibility to make sure the proper tasks are pushed into the `services` queue. 
+Do not enqueue training / inference tasks into the `services` queue, as it will put unnecessary load on the server.
 
-```bash
-sudo echo "vm.max_map_count=262144" > /tmp/99-trains.conf
-sudo mv /tmp/99-trains.conf /etc/sysctl.d/99-trains.conf
-sudo sysctl -w vm.max_map_count=262144
-```
+## Advanced Functionality
 
-* For information about setting this parameter on other systems, see the [elastic](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-cli-run-prod-mode) documentation.
+**trains-server** provides a few additional useful features, which can be manually enabled:
+ 
+* [Web login authentication](https://github.com/allegroai/trains-server/blob/master/docs/faq.md#web-auth)
+* [Non-responsive experiments watchdog](https://github.com/allegroai/trains-server/blob/master/docs/faq.md#watchdog-the-non-responsive-task-watchdog-settings)  
 
-#### Step 5. Choose a Data Directory
+## Restarting trains-server
 
-You must choose a directory on your system in which all data maintained by the **trains-server** is stored,
-create that directory, and set its permissions. The data stored in that directory includes the database, uploaded files and logs.
+To restart the **trains-server**, you must first stop the containers, and then restart them.
 
-For example, if your data directory is `/opt/trains`, then use the following command:
+   ```bash
+   docker-compose down
+   docker-compose -f docker-compose.yml up
+   ```
 
-```bash
-sudo mkdir -p /opt/trains/data/elastic && sudo chown -R 1000:1000 /opt/trains
-```
+## Upgrading <a name="upgrade"></a>
 
-### Launching Docker Containers
+**trains-server** releases are also reflected in the [docker compose configuration file](https://github.com/allegroai/trains-server/blob/master/docker-compose.yml).  
+We strongly encourage you to keep your **trains-server** up to date, by keeping up with the current release.
 
-Launch the Docker containers. For example, if your data directory is `\opt\trains`,
-then use the following commands:
+**Note**: The following upgrade instructions use the Linux OS as an example.
 
-```bash
-sudo docker run -d --restart="always" --name="trains-elastic" -e "ES_JAVA_OPTS=-Xms2g -Xmx2g" -e "bootstrap.memory_lock=true" -e "cluster.name=trains" -e "discovery.zen.minimum_master_nodes=1" -e "node.name=trains" -e "script.inline=true" -e "script.update=true" -e "thread_pool.bulk.queue_size=2000" -e "thread_pool.search.queue_size=10000" -e "xpack.security.enabled=false" -e "xpack.monitoring.enabled=false" -e "cluster.routing.allocation.node_initial_primaries_recoveries=500" -e "node.ingest=true" -e "http.compression_level=7" -e "reindex.remote.whitelist=*.*" -e "script.painless.regex.enabled=true" --network="host" -v /opt/trains/data/elastic:/usr/share/elasticsearch/data docker.elastic.co/elasticsearch/elasticsearch:5.6.16
-```
+To upgrade your existing **trains-server** deployment:
 
-```bash
-sudo docker run -d --restart="always" --name="trains-mongo" -v /opt/trains/data/mongo/db:/data/db -v /opt/trains/data/mongo/configdb:/data/configdb --network="host" mongo:3.6.5
-```
+1. Shut down the docker containers
+   ```bash
+   docker-compose down
+   ```
 
-```bash
-sudo docker run -d --restart="always" --name="trains-fileserver" --network="host" -v /opt/trains/logs:/var/log/trains -v /opt/trains/data/fileserver:/mnt/fileserver allegroai/trains:latest fileserver
-```
+1. We highly recommend backing up your data directory before upgrading.
 
-```bash
-sudo docker run -d --restart="always" --name="trains-apiserver" --network="host" -v /opt/trains/logs:/var/log/trains allegroai/trains:latest apiserver
-```
+   Assuming your data directory is `/opt/trains`, to archive all data into `~/trains_backup.tgz` execute:
 
-```bash
-sudo docker run -d --restart="always" --name="trains-webserver" --network="host" -v /opt/trains/logs:/var/log/trains allegroai/trains:latest webserver
-```
+   ```bash
+   sudo tar czvf ~/trains_backup.tgz /opt/trains/data
+   ```    
 
-After the **trains-server** Dockers are up, the following are available:
+   <details>
+   <summary>Restore instructions:</summary>
 
-* API server on port `8008`
-* Web server on port `8080`
-* File server on port `8081`
+   To restore this example backup, execute:
+   ```bash
+   sudo rm -R /opt/trains/data
+   sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data
+   ```
+   </details>
 
-## Upgrade
+1. Download the latest `docker-compose.yml` file.
 
-We are constantly updating, improving and adding to the **trains-server**.
-New releases will include new pre-built Docker images.
-When we release a new version and include a new pre-built Docker image for it, upgrade as follows:
+   ```bash
+   curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml -o docker-compose.yml 
+   ```
 
-1. Shut down and remove each of your Docker instances using the following commands:
+1. Configure the Trains-Agent Services (not supported on Windows installation). 
+   If `TRAINS_HOST_IP` is not provided, Trains-Agent Services will use the external 
+   public address of the **trains-server**. If `TRAINS_AGENT_GIT_USER` / `TRAINS_AGENT_GIT_PASS` are not provided, 
+   the Trains-Agent Services will not be able to access any private repositories for running service tasks.
+   
+   ```bash
+   export TRAINS_HOST_IP=server_host_ip_here
+   export TRAINS_AGENT_GIT_USER=git_username_here
+   export TRAINS_AGENT_GIT_PASS=git_password_here
+   ```
 
-        sudo docker stop <docker-name>
-        sudo docker rm -v <docker-name>
-    
-    The Docker names are (see [Launching Docker images](##launching-docker-images)):
-    
-       * `trains-elastic`
-       * `trains-mongo`
-       * `trains-fileserver`
-       * `trains-apiserver`
-       * `trains-webserver`
+1. Spin up the docker containers, it will automatically pull the latest **trains-server** build    
+   ```bash
+   docker-compose -f docker-compose.yml pull
+   docker-compose -f docker-compose.yml up
+   ```
 
-2. We highly recommend backing up your data directory!. A simple way to do that is using `tar`:
+**\* If something went wrong along the way, check our FAQ: [Common Docker Upgrade Errors](https://github.com/allegroai/trains-server/blob/master/docs/faq.md#common-docker-upgrade-errors).**
 
-    For example, if your data directory is `/opt/trains`, use the following command:
-    
-        sudo tar czvf ~/trains_backup.tgz /opt/trains/data
-    
-    This back ups all data to an archive in your home directory. 
-    
-    To restore this example backup, use the following command:
-    
-        sudo rm -R /opt/trains/data
-        sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data
 
-3. Launch the newly released Docker image (see [Launching Docker images](#Launching-docker-images)).
+## Community & Support
+
+If you have any questions, look to the Trains server [FAQ](https://github.com/allegroai/trains-server/blob/master/docs/faq.md), or
+tag your questions on [stackoverflow](https://stackoverflow.com/questions/tagged/trains) with '**trains**' tag.
+
+For feature requests or bug reports, please use [GitHub issues](https://github.com/allegroai/trains-server/issues).
+
+Additionally, you can always find us at *trains@allegro.ai*
 
 ## License
 
 [Server Side Public License v1.0](https://github.com/mongodb/mongo/blob/master/LICENSE-Community.txt)
 
-**trains-server** relies *heavily* on both [MongoDB](https://github.com/mongodb/mongo) and [ElasticSearch](https://github.com/elastic/elasticsearch).
-With the recent changes in both MongoDB's and ElasticSearch's OSS license, we feel it is our job as a community to support the projects we love and cherish.
-We feel the cause for the license change in both cases is more than just, and chose [SSPL](https://www.mongodb.com/licensing/server-side-public-license) because it is the more general and flexible of the two.
+**trains-server** relies on both [MongoDB](https://github.com/mongodb/mongo) and [ElasticSearch](https://github.com/elastic/elasticsearch).
+With the recent changes in both MongoDB's and ElasticSearch's OSS license, we feel it is our responsibility as a
+member of the community to support the projects we love and cherish.
+We believe the cause for the license change in both cases is more than just,
+and chose [SSPL](https://www.mongodb.com/licensing/server-side-public-license) because it is the more general and flexible of the two licenses.
 
 This is our way to say - we support you guys!

docker-compose-unified.yml

@@ -0,0 +1,92 @@
+version: "3.6"
+services:
+  trainsserver:
+    command:
+     - -c
+     - "echo \"#!/bin/bash\" > /opt/trains/all.sh && echo \"/opt/trains/wrapper.sh webserver&\" >> /opt/trains/all.sh && echo \"/opt/trains/wrapper.sh fileserver&\" >> /opt/trains/all.sh && echo \"/opt/trains/wrapper.sh apiserver\" >> /opt/trains/all.sh && cat /opt/trains/all.sh && chmod +x /opt/trains/all.sh && /opt/trains/all.sh"
+    entrypoint: /bin/bash
+    container_name: trains-server
+    image: allegroai/trains:latest
+    ports:
+    - 8008:8008
+    - 8080:80
+    - 8081:8081
+    restart: unless-stopped
+    volumes:
+    - /opt/trains/logs:/var/log/trains
+    - /opt/trains/data/fileserver:/mnt/fileserver
+    - /opt/trains/config:/opt/trains/config
+
+    depends_on:
+      - redis
+      - mongo
+      - elasticsearch
+    environment:
+      TRAINS_ELASTIC_SERVICE_HOST: elasticsearch
+      TRAINS_ELASTIC_SERVICE_PORT: 9200
+      TRAINS_MONGODB_SERVICE_HOST: mongo
+      TRAINS_MONGODB_SERVICE_PORT: 27017
+      TRAINS_REDIS_SERVICE_HOST: redis
+      TRAINS_REDIS_SERVICE_PORT: 6379
+    networks:
+      - backend
+  elasticsearch:
+    networks:
+      - backend
+    container_name: trains-elastic
+    environment:
+      ES_JAVA_OPTS: -Xms2g -Xmx2g
+      bootstrap.memory_lock: "true"
+      cluster.name: trains
+      cluster.routing.allocation.node_initial_primaries_recoveries: "500"
+      discovery.zen.minimum_master_nodes: "1"
+      http.compression_level: "7"
+      node.ingest: "true"
+      node.name: trains
+      reindex.remote.whitelist: '*.*'
+      script.inline: "true"
+      script.painless.regex.enabled: "true"
+      script.update: "true"
+      thread_pool.bulk.queue_size: "2000"
+      thread_pool.search.queue_size: "10000"
+      xpack.monitoring.enabled: "false"
+      xpack.security.enabled: "false"
+    ulimits:
+      memlock:
+        soft: -1
+        hard: -1
+      nofile:
+        soft: 65536
+        hard: 65536
+    image: docker.elastic.co/elasticsearch/elasticsearch:5.6.16
+    restart: unless-stopped
+    volumes:
+    - /opt/trains/data/elastic:/usr/share/elasticsearch/data
+    ports:
+    - "9200:9200"
+  mongo:
+    networks:
+      - backend
+    container_name: trains-mongo
+    image: mongo:3.6.5
+    restart: unless-stopped
+    command: --setParameter internalQueryExecMaxBlockingSortBytes=196100200
+    volumes:
+    - /opt/trains/data/mongo/db:/data/db
+    - /opt/trains/data/mongo/configdb:/data/configdb
+    ports:
+    - "27017:27017"
+  redis:
+    networks:
+      - backend
+    container_name: trains-redis
+    image: redis:5.0
+    restart: unless-stopped
+    volumes:
+    - /opt/trains/data/redis:/data
+    ports:
+    - "6379:6379"
+
+networks:
+  backend:
+    driver: bridge

docker-compose-win10.yml

@@ -0,0 +1,123 @@
+version: "3.6"
+services:
+
+  apiserver:
+    command:
+    - apiserver
+    container_name: trains-apiserver
+    image: allegroai/trains:latest
+    restart: unless-stopped
+    volumes:
+    - c:/opt/trains/logs:/var/log/trains
+    - c:/opt/trains/config:/opt/trains/config
+    depends_on:
+      - redis
+      - mongo
+      - elasticsearch
+      - fileserver
+    environment:
+      TRAINS_ELASTIC_SERVICE_HOST: elasticsearch
+      TRAINS_ELASTIC_SERVICE_PORT: 9200
+      TRAINS_MONGODB_SERVICE_HOST: mongo
+      TRAINS_MONGODB_SERVICE_PORT: 27017
+      TRAINS_REDIS_SERVICE_HOST: redis
+      TRAINS_REDIS_SERVICE_PORT: 6379
+      TRAINS_SERVER_DEPLOYMENT_TYPE: ${TRAINS_SERVER_DEPLOYMENT_TYPE:-win10}
+      TRAINS__apiserver__mongo__pre_populate__enabled: "true"
+      TRAINS__apiserver__mongo__pre_populate__zip_file: "/opt/trains/db-pre-populate/export.zip"
+    ports:
+    - "8008:8008"
+    networks:
+      - backend
+
+  elasticsearch:
+    networks:
+      - backend
+    container_name: trains-elastic
+    environment:
+      ES_JAVA_OPTS: -Xms2g -Xmx2g
+      bootstrap.memory_lock: "true"
+      cluster.name: trains
+      cluster.routing.allocation.node_initial_primaries_recoveries: "500"
+      discovery.zen.minimum_master_nodes: "1"
+      http.compression_level: "7"
+      node.ingest: "true"
+      node.name: trains
+      reindex.remote.whitelist: '*.*'
+      script.inline: "true"
+      script.painless.regex.enabled: "true"
+      script.update: "true"
+      thread_pool.bulk.queue_size: "2000"
+      thread_pool.search.queue_size: "10000"
+      xpack.monitoring.enabled: "false"
+      xpack.security.enabled: "false"
+    ulimits:
+      memlock:
+        soft: -1
+        hard: -1
+      nofile:
+        soft: 65536
+        hard: 65536
+    image: docker.elastic.co/elasticsearch/elasticsearch:5.6.16
+    restart: unless-stopped
+    volumes:
+    - c:/opt/trains/data/elastic:/usr/share/elasticsearch/data
+    ports:
+    - "9200:9200"
+
+  fileserver:
+    networks:
+      - backend
+    command:
+    - fileserver
+    container_name: trains-fileserver
+    image: allegroai/trains:latest
+    restart: unless-stopped
+    volumes:
+    - c:/opt/trains/logs:/var/log/trains
+    - c:/opt/trains/data/fileserver:/mnt/fileserver
+    - c:/opt/trains/config:/opt/trains/config
+
+    ports:
+    - "8081:8081"
+
+  mongo:
+    networks:
+      - backend
+    container_name: trains-mongo
+    image: mongo:3.6.5
+    restart: unless-stopped
+    command: --setParameter internalQueryExecMaxBlockingSortBytes=196100200
+    volumes:
+    - c:/opt/trains/data/mongo/db:/data/db
+    - c:/opt/trains/data/mongo/configdb:/data/configdb
+    ports:
+    - "27017:27017"
+
+  redis:
+    networks:
+      - backend
+    container_name: trains-redis
+    image: redis:5.0
+    restart: unless-stopped
+    volumes:
+    - c:/opt/trains/data/redis:/data
+    ports:
+    - "6379:6379"
+
+  webserver:
+    command:
+    - webserver
+    container_name: trains-webserver
+    image: allegroai/trains:latest
+    restart: unless-stopped
+    volumes:
+    - c:/trains/logs:/var/log/trains
+    depends_on:
+      - apiserver
+    ports:
+    - "8080:80"
+
+networks:
+  backend:
+    driver: bridge

docker-compose.yml

@@ -0,0 +1,152 @@
+version: "3.6"
+services:
+
+  apiserver:
+    command:
+    - apiserver
+    container_name: trains-apiserver
+    image: allegroai/trains:latest
+    restart: unless-stopped
+    volumes:
+    - /opt/trains/logs:/var/log/trains
+    - /opt/trains/config:/opt/trains/config
+    depends_on:
+      - redis
+      - mongo
+      - elasticsearch
+      - fileserver
+    environment:
+      TRAINS_ELASTIC_SERVICE_HOST: elasticsearch
+      TRAINS_ELASTIC_SERVICE_PORT: 9200
+      TRAINS_MONGODB_SERVICE_HOST: mongo
+      TRAINS_MONGODB_SERVICE_PORT: 27017
+      TRAINS_REDIS_SERVICE_HOST: redis
+      TRAINS_REDIS_SERVICE_PORT: 6379
+      TRAINS_SERVER_DEPLOYMENT_TYPE: ${TRAINS_SERVER_DEPLOYMENT_TYPE:-linux}
+      TRAINS__apiserver__mongo__pre_populate__enabled: "true"
+      TRAINS__apiserver__mongo__pre_populate__zip_file: "/opt/trains/db-pre-populate/export.zip"
+    ports:
+    - "8008:8008"
+    networks:
+      - backend
+
+  elasticsearch:
+    networks:
+      - backend
+    container_name: trains-elastic
+    environment:
+      ES_JAVA_OPTS: -Xms2g -Xmx2g
+      bootstrap.memory_lock: "true"
+      cluster.name: trains
+      cluster.routing.allocation.node_initial_primaries_recoveries: "500"
+      discovery.zen.minimum_master_nodes: "1"
+      http.compression_level: "7"
+      node.ingest: "true"
+      node.name: trains
+      reindex.remote.whitelist: '*.*'
+      script.inline: "true"
+      script.painless.regex.enabled: "true"
+      script.update: "true"
+      thread_pool.bulk.queue_size: "2000"
+      thread_pool.search.queue_size: "10000"
+      xpack.monitoring.enabled: "false"
+      xpack.security.enabled: "false"
+    ulimits:
+      memlock:
+        soft: -1
+        hard: -1
+      nofile:
+        soft: 65536
+        hard: 65536
+    image: docker.elastic.co/elasticsearch/elasticsearch:5.6.16
+    restart: unless-stopped
+    volumes:
+    - /opt/trains/data/elastic:/usr/share/elasticsearch/data
+    ports:
+    - "9200:9200"
+
+  fileserver:
+    networks:
+      - backend
+    command:
+    - fileserver
+    container_name: trains-fileserver
+    image: allegroai/trains:latest
+    restart: unless-stopped
+    volumes:
+    - /opt/trains/logs:/var/log/trains
+    - /opt/trains/data/fileserver:/mnt/fileserver
+    - /opt/trains/config:/opt/trains/config
+    ports:
+    - "8081:8081"
+
+  mongo:
+    networks:
+      - backend
+    container_name: trains-mongo
+    image: mongo:3.6.5
+    restart: unless-stopped
+    command: --setParameter internalQueryExecMaxBlockingSortBytes=196100200
+    volumes:
+    - /opt/trains/data/mongo/db:/data/db
+    - /opt/trains/data/mongo/configdb:/data/configdb
+    ports:
+    - "27017:27017"
+
+  redis:
+    networks:
+      - backend
+    container_name: trains-redis
+    image: redis:5.0
+    restart: unless-stopped
+    volumes:
+    - /opt/trains/data/redis:/data
+    ports:
+    - "6379:6379"
+
+  webserver:
+    command:
+    - webserver
+    container_name: trains-webserver
+    image: allegroai/trains:latest
+    restart: unless-stopped
+    depends_on:
+      - apiserver
+    ports:
+    - "8080:80"
+
+  agent-services:
+    networks:
+      - backend
+    container_name: trains-agent-services
+    image: allegroai/trains-agent-services:latest
+    restart: unless-stopped
+    privileged: true
+    environment:
+      TRAINS_HOST_IP: ${TRAINS_HOST_IP}
+      TRAINS_WEB_HOST: ${TRAINS_WEB_HOST:-}
+      TRAINS_API_HOST: http://apiserver:8008
+      TRAINS_FILES_HOST: ${TRAINS_FILES_HOST:-}
+      TRAINS_API_ACCESS_KEY: ${TRAINS_API_ACCESS_KEY:-}
+      TRAINS_API_SECRET_KEY: ${TRAINS_API_SECRET_KEY:-}
+      TRAINS_AGENT_GIT_USER: ${TRAINS_AGENT_GIT_USER}
+      TRAINS_AGENT_GIT_PASS: ${TRAINS_AGENT_GIT_PASS}
+      TRAINS_AGENT_UPDATE_VERSION: ${TRAINS_AGENT_UPDATE_VERSION:->=0.15.0}
+      TRAINS_AGENT_DEFAULT_BASE_DOCKER: "ubuntu:18.04"
+      AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID:-}
+      AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY:-}
+      AWS_DEFAULT_REGION: ${AWS_DEFAULT_REGION:-}
+      AZURE_STORAGE_ACCOUNT: ${AZURE_STORAGE_ACCOUNT:-}
+      AZURE_STORAGE_KEY: ${AZURE_STORAGE_KEY:-}
+      GOOGLE_APPLICATION_CREDENTIALS: ${GOOGLE_APPLICATION_CREDENTIALS:-}
+      TRAINS_WORKER_ID: "trains-services"
+      TRAINS_AGENT_DOCKER_HOST_MOUNT: "/opt/trains/agent:/root/.trains"
+    volumes:
+      - /var/run/docker.sock:/var/run/docker.sock
+      - /opt/trains/agent:/root/.trains
+    depends_on:
+      - apiserver
+
+networks:
+  backend:
+    driver: bridge

docs/apiserver.conf

@@ -0,0 +1,19 @@
+auth {
+    # Fixed users login credentials
+    # No other user will be able to login
+    fixed_users {
+        enabled: true
+        users: [
+            {
+                username: "jane"
+                password: "12345678"
+                name: "Jane Doe"
+            },
+            {
+                username: "john"
+                password: "12345678"
+                name: "John Doe"
+            },
+        ]
+    }
+}

docs/faq.md

@@ -0,0 +1,328 @@
+# trains-server FAQ
+
+Launching **trains-server**
+
+* How do I launch **trains-server** on:
+
+    * [Stand alone Linux Ubuntu systems?](#ubuntu)
+    
+    * [macOS?](#mac-osx)
+    
+    * [Windows 10?](#docker_compose_win10)
+
+* [How do I restart trains-server?](#restart)
+
+Kubernetes
+
+* [Can I deploy trains-server on Kubernetes clusters?](#kubernetes)
+
+* [Can I create a Helm Chart for trains-server Kubernetes deployment?](#helm)
+
+Configuration
+
+* [How do I configure trains-server for sub-domains and load balancers?](#sub-domains)
+
+* [Can I add web login authentication to trains-server?](#web-auth)
+
+* [Can I modify the non-responsive experiment watchdog settings?](#watchdog)
+
+Troubleshooting
+
+* [How do I fix Docker upgrade errors?](#common-docker-upgrade-errors)
+
+* [Why is web login authentication not working?](#port-conflict)
+
+## Launching **trains-server**
+
+### How do I launch trains-server on stand alone Linux Ubuntu systems? <a name="ubuntu"></a>
+
+To launch **trains-server** on a stand alone Linux Ubuntu:
+
+1. Install [docker for Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/).
+
+1. Install `docker-compose` using the following commands (for more detailed information, see the [Install Docker Compose](https://docs.docker.com/compose/install/) in the Docker documentation):
+
+        sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
+        sudo chmod +x /usr/local/bin/docker-compose
+
+1. Remove the previous installation of **trains-server**.
+
+    **WARNING**: This clears all existing **Trains** databases.
+
+        sudo rm -R /opt/trains/
+
+1. Create local directories for the databases and storage.
+
+        sudo mkdir -p /opt/trains/data/elastic
+        sudo mkdir -p /opt/trains/data/mongo/db
+        sudo mkdir -p /opt/trains/data/mongo/configdb
+        sudo mkdir -p /opt/trains/logs
+        sudo mkdir -p /opt/trains/config
+        sudo mkdir -p /opt/trains/data/fileserver
+        sudo chown -R 1000:1000 /opt/trains
+
+1. Clone the [trains-server](https://github.com/allegroai/trains-server) repository and change directories to the new **trains-server** directory.
+
+        git clone https://github.com/allegroai/trains-server.git
+        cd trains-server
+
+1. Run `docker-compose`
+
+        /usr/local/bin/docker-compose -f docker-compose.yml up
+
+    Your server is now running on [http://localhost:8080](http://localhost:8080)
+    
+### How do I launch trains-server on macOS? <a name="mac-osx"></a>
+
+To launch **trains-server** on macOS:
+
+1. Install [docker for macOS](https://docs.docker.com/docker-for-mac/install/).
+
+1. Configure [Docker](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-cli-run-prod-mode).
+
+        screen ~/Library/Containers/com.docker.docker/Data/vms/0/tty
+        sysctl -w vm.max_map_count=262144
+
+1. Create local directories for the databases and storage.
+
+        sudo mkdir -p /opt/trains/data/elastic
+        sudo mkdir -p /opt/trains/data/mongo/db
+        sudo mkdir -p /opt/trains/data/mongo/configdb
+        sudo mkdir -p /opt/trains/data/redis
+        sudo mkdir -p /opt/trains/logs
+        sudo mkdir -p /opt/trains/config
+        sudo mkdir -p /opt/trains/data/fileserver
+        sudo chown -R $(whoami):staff /opt/trains
+
+1. Open the Docker app, select **Preferences**, and then on the **File Sharing** tab, add `/opt/trains`.
+
+1. Clone the [trains-server](https://github.com/allegroai/trains-server) repository and change directories to the new **trains-server** directory.
+
+        git clone https://github.com/allegroai/trains-server.git
+        cd trains-server
+
+1. Run `docker-compose` with the docker compose file.
+
+        docker-compose -f docker-compose.yml up
+
+    Your server is now running on [http://localhost:8080](http://localhost:8080)
+
+### How do I launch trains-server on Windows 10? <a name="docker_compose_win10"></a>
+
+You can run **trains-server** on Windows 10 using Docker Desktop for Windows (see the Docker [System Requirements](https://docs.docker.com/docker-for-windows/install/#system-requirements)).
+
+To launch **trains-server** on Windows 10:
+
+1. Install the Docker Desktop for Windows application by either:
+
+    * following the [Install Docker Desktop on Windows](https://docs.docker.com/docker-for-windows/install/) instructions.
+    * running the Docker installation [wizard](https://hub.docker.com/?overlay=onboarding).
+
+1. Increase the memory allocation in Docker Desktop to `4GB`.
+
+    1. In your Windows notification area (system tray), right click the Docker icon.
+    
+    1. Click *Settings*, *Advanced*, and then set the memory to at least `4096`. 
+    
+    1. Click *Apply*.
+
+1. Create local directories for data and logs. Open PowerShell and execute the following commands:
+
+        cd c:
+        mkdir c:\opt\trains\data
+        mkdir c:\opt\trains\logs
+
+1. Download the **trains-server** docker-compose YAML file [docker-compose-win10.yml](https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose-win10.yml) as `c:\opt\trains\docker-compose.yml`.
+
+1. Run `docker-compose`. In PowerShell, execute the following commands:
+
+        docker-compose -f up docker-compose-win10.yml
+
+    Your server is now running on [http://localhost:8080](http://localhost:8080)
+
+### How do I restart trains-server? <a name="restart"></a>
+
+Restart *trains-server* by first stopping the Docker containers and then restarting them.
+
+   ```bash
+   docker-compose down
+   docker-compose up -f docker-compose.yml
+   ```
+   
+   **Note**: If you are using a different docker-compose YAML file, specify that file.
+
+## Kubernetes
+
+### Can I deploy trains-server on Kubernetes clusters? <a name="kubernetes"></a>
+
+**trains-server** supports Kubernetes. See [trains-server-k8s](https://github.com/allegroai/trains-server-k8s)
+which contains the YAML files describing the required services and detailed instructions for deploying
+**trains-server** to a Kubernetes clusters.
+
+### Can I create a Helm Chart for trains-server Kubernetes deployment? <a name="helm"></a>
+
+**trains-server** supports creating a Helm chart for Kubernetes deployment. See [trains-server-helm](https://github.com/allegroai/trains-server-helm)
+which you can use to create a Helm chart for **trains-server** and contains detailed instructions for deploying
+**trains-server** to a Kubernetes clusters using Helm.
+
+## Configuration
+
+### How do I configure trains-server for sub-domains and load balancers? <a name="sub-domains"></a>
+
+You can configure **trains-server** for sub-domains and a load balancer.
+
+For example, if your domain is `trains.mydomain.com` and your sub-domains are `app` and `api`, then do the following:
+
+1. If you are not using the current **trains-server** version, [upgrade](https://github.com/allegroai/trains-server#upgrade) **trains-server**.
+
+1. Add the following to `/opt/trains/config/apiserver.conf`:
+
+        auth {
+          cookies {
+            httponly: true
+            secure: true
+            domain: ".trains.mydomain.com"
+            max_age: 99999999999
+          }
+        }
+
+1. Use the following load balancer configuration:
+
+    * Listeners:
+        * Optional: HTTP listener, that redirects all traffic to HTTPS.
+        * HTTPS listener for `app.` forwarded to `AppTargetGroup`
+        * HTTPS listener for `api.` forwarded to `ApiTargetGroup`
+        * HTTPS listener for `files.` forwarded to `FilesTargetGroup`
+    * Target groups:
+        * `AppTargetGroup`: HTTP based target group, port `8080`
+        * `ApiTargetGroup`: HTTP based target group, port `8008`
+        * `FilesTargetGroup`: HTTP based target group, port `8081`
+    * Security and routing:
+        * Load balancer: make sure the load balancers are able to receive traffic from the relevant IP addresses (Security groups and Subnets definitions).
+        * Instances: make sure the load balancers are able to access the instances, using the relevant ports (Security groups definitions).
+
+1. Run the Docker containers with our updated `docker run` commands (see [Launching Docker Containers](#https://github.com/allegroai/trains-server#launching-docker-containers)).
+
+### Can I add web login authentication to trains-server? <a name="web-auth"></a>
+
+By default, anyone can login to the **trains-server** Web-App.
+You can configure the **trains-server** to allow only a specific set of users to access the system.
+
+To add web login authentication to **trains-server**:
+
+1. If you are not using the current **trains-server** version, then [upgrade](https://github.com/allegroai/trains-server#upgrade).
+
+1. In `/opt/trains/config/apiserver.conf`, add the `auth` section and in it specify the users, for example:
+
+    **Note**: A sample `apiserver.conf` configuration file is also available [here](https://github.com/allegroai/trains-server/blob/master/docs/apiserver.conf).
+
+        auth {
+            # Fixed users login credentials
+            # No other user will be able to login
+            fixed_users {
+                enabled: true
+                users: [
+                    {
+                        username: "jane"
+                        password: "12345678"
+                        name: "Jane Doe"
+                    },
+                    {
+                        username: "john"
+                        password: "12345678"
+                        name: "John Doe"
+                    },
+                ]
+            }
+        }
+
+1. Restart **trains-server** (see the [Restarting trains-server](#restart) FAQ).
+
+### Can I modify the experiment watchdog settings? <a name="watchdog"></a>
+
+The non-responsive experiment watchdog monitors experiments that were not updated for a specified period of time
+and marks them as `aborted`. The watchdog is always active. 
+
+You can modify the following settings for the watchdog:
+ 
+* the time threshold (in seconds) of experiment inactivity (default value is 7200 seconds (2 hours))
+* the time interval (in seconds) between watchdog cycles
+
+To change the watchdog's settings:
+
+1. In `/opt/trains/config`, add the `services.conf` file and in it specify the watchdog settings, for example:
+
+    **Note**: A sample watchdog `services.conf` configuration file is also available [here](https://github.com/allegroai/trains-server/blob/master/docs/services.conf).
+
+        tasks {
+            non_responsive_tasks_watchdog {
+                # In-progress tasks that haven't been updated for at least 'value' seconds will be stopped by the watchdog
+                threshold_sec: 7200
+        
+                # Watchdog will sleep for this number of seconds after each cycle
+                watch_interval_sec: 900
+            }
+        }
+
+1. Restart **trains-server** (see the [Restarting trains-server](#restart) FAQ).
+
+## Troubleshooting
+
+### How do I fix Docker upgrade errors? <a name="common-docker-upgrade-errors"></a>
+
+To resolve the Docker error "... The container name "/trains-???" is already in use by ...", try removing deprecated images:
+
+    docker rm -f $(docker ps -a -q)
+
+### Why is web login authentication not working?
+
+A port conflict between the **trains-server** MongoDB and / or Elastic instances, and other
+instances running on your system may prevent web login authentication
+from working correctly. 
+
+**trains-server** uses the following default ports which may be in conflict with other instances:
+
+* MongoDB port `27017`
+* Elastic port `9200`
+
+You can check for port conflicts in the logs in `/opt/trains/log`.
+
+If a port conflict occurs, change the MongoDB and / or Elastic ports in the `docker-compose.yml`,
+and then run the Docker compose commands to restart the **trains-server** instance.
+
+To change the MongoDB and / or Elastic ports for **trains-server**:
+
+1. Edit the `docker-compose.yml` file.
+
+1. In the `services/trainsserver/environment` section, add the following environment variable(s):
+
+    * For MongoDB:
+    
+            MONGODB_SERVICE_PORT: <new-mongodb-port>
+        
+    * For Elastic:
+            
+            ELASTIC_SERVICE_PORT: <new-elasticsearch-port> 
+        
+    For example:
+    
+        MONGODB_SERVICE_PORT: 27018
+        ELASTIC_SERVICE_PORT: 9201
+            
+1. For MongoDB, in the `services/mongo/ports` section, expose the new MongoDB port:
+
+        <new-mongodb-port>:27017
+        
+    For example:
+    
+        20718:27017
+        
+1. For Elastic, in the `services/elasticsearch/ports` section, expose the new Elastic port:
+
+        <new-elsticsearch-port>:9200
+            
+    For example:
+
+        9201:9200
+    
+2. Restart **trains-server** (see the [Restarting trains-server](#restart) FAQ).

docs/install_aws.md

@@ -0,0 +1,299 @@
+# Deploying **trains-server** on AWS
+
+To easily deploy **trains-server** on AWS, use one of our pre-built Amazon Machine Images (AMIs).  
+We provide AMIs per region for each released version of **trains-server**, see [Released versions](#released-versions) below. 
+
+Once the AMI is up and running, [configure the Trains client](https://github.com/allegroai/trains/blob/master/README.md#configuration) to use your **trains-server**.  
+The service port numbers on our **trains-server** AMIs:
+
+- Web application: `8080`
+- API Server: `8008`
+- File Server: `8081`
+
+The persistent storage configuration:
+
+- MongoDB: `/opt/trains/data/mongo/`
+- ElasticSearch: `/opt/trains/data/elastic/`
+- File Server: `/mnt/fileserver/`
+
+For examples and use cases, check the [Trains usage examples](https://github.com/allegroai/trains/blob/master/docs/trains_examples.md).
+
+For instructions on launching a custom AMI from the EC2 console, see the [AWS Knowledge Center](https://aws.amazon.com/premiumsupport/knowledge-center/launch-instance-custom-ami/) or detailed instructions in the [AWS Documentation](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/launching-instance.html).
+
+The minimum recommended amount of RAM is 8GB. For example, **t3.large** or **t3a.large** would have the minimum recommended amount of resources.
+
+## Upgrading
+
+To upgrade **trains-server** on an existing EC2 instance based on one of these AMIs, SSH into the instance and follow the [upgrade instructions](../README.md#upgrade) for **trains-server**.
+
+### Note on upgrading AMIs to v0.12 
+
+This upgrade includes the automatically updated AMI in Version 0.12. It also includes an additional REDIS docker to the **trains-server** setup.
+
+To upgrade the AMI:
+
+1. SSH to the EC2 machine running one of the `Latest Version AMI's`
+2. Execute the following bash commands
+    ```bash
+    sudo bash
+    echo "" >>  /usr/bin/start_or_update_server.sh
+    echo "sudo mkdir -p \${datadir}/redis" >>  /usr/bin/start_or_update_server.sh
+    echo "sudo docker stop trains-redis || true && sudo docker rm -v trains-redis || true" >>  /usr/bin/start_or_update_server.sh
+    echo "echo never | sudo tee -a  /sys/kernel/mm/transparent_hugepage/enabled" >>  /usr/bin/start_or_update_server.sh
+    echo "sudo sysctl vm.overcommit_memory=1" >>  /usr/bin/start_or_update_server.sh
+    echo "sudo docker run -d --restart=always --name=trains-redis -v \${datadir}/redis:/data --network=host redis:5 redis-server" >>  /usr/bin/start_or_update_server.sh
+    ``` 
+3. Reboot the EC2 machine
+
+
+## Released versions
+
+The following sections contain lists of AMI Image IDs, per region, for each released **trains-server** version.
+
+### Latest version AMI - v0.15.1 (auto update)<a name="autoupdate"></a>
+
+For easier upgrades, the following AMIs automatically update to the latest release every reboot:
+
+* **eu-north-1** : ami-0f63429f8e5d57315 
+* **ap-south-1** : ami-058a2a70b7fb8ec87 
+* **eu-west-3** : ami-0fc9f9e8e986f39c4 
+* **eu-west-2** : ami-0b0bc1ff2f0239bd9 
+* **eu-west-1** : ami-0056ec5d22b0fac91 
+* **ap-northeast-2** : ami-0898c9aa7f580fec7 
+* **ap-northeast-1** : ami-011036ddcc9398871 
+* **sa-east-1** : ami-04feeded12192438c 
+* **ca-central-1** : ami-02c717776c9e75025 
+* **ap-southeast-1** : ami-05b5866e7029bb9f1 
+* **ap-southeast-2** : ami-0384bd2b69467fff8 
+* **eu-central-1** : ami-01f15be85297d6f06 
+* **us-east-2** : ami-094070ca8aa110180 
+* **us-west-1** : ami-0d08ec5bc29eddb29 
+* **us-west-2** : ami-04715cceedaf6eae7 
+* **us-east-1** : ami-071dbaa1847585c4c 
+
+### v0.15.1 (static update)
+
+* **eu-north-1** : ami-0bb36c4dbe61f8c46 
+* **ap-south-1** : ami-0ac93ff85a5c770f9 
+* **eu-west-3** : ami-015ebfa846b8de5bb 
+* **eu-west-2** : ami-082aacd59408713d9 
+* **eu-west-1** : ami-066aad8c6b9b9991b 
+* **ap-northeast-2** : ami-0cb47f1c8591c799d 
+* **ap-northeast-1** : ami-005131d3037da9d2a 
+* **sa-east-1** : ami-0f7fdc4e19c8444a3 
+* **ca-central-1** : ami-07c234dad3ece2d78 
+* **ap-southeast-1** : ami-0d8e0475d7d4897e4 
+* **ap-southeast-2** : ami-053e3f25dee0424b9 
+* **eu-central-1** : ami-00d25558c5242708e 
+* **us-east-2** : ami-0bd45f800dfbde456 
+* **us-west-1** : ami-05e79bf1704721148 
+* **us-west-2** : ami-037c328649048409b 
+* **us-east-1** : ami-0a3cafe46bf085200 
+
+### v0.15.0 (static update)
+
+* **eu-north-1** : ami-0bef15c03eab64c0c 
+* **ap-south-1** : ami-06ac6248e583e2cd2 
+* **eu-west-3** : ami-0541d86ef47a5714e 
+* **eu-west-2** : ami-01381ef4c4ed22482 
+* **eu-west-1** : ami-064626a0dd38b21f1 
+* **ap-northeast-2** : ami-0a2490a7a3a8aa675 
+* **ap-northeast-1** : ami-063f1de819a2524b8 
+* **sa-east-1** : ami-07980486741b94987 
+* **ca-central-1** : ami-0ced3b8b21ded839e 
+* **ap-southeast-1** : ami-0c493c5093fde8741 
+* **ap-southeast-2** : ami-0320a727eccb8dc6c 
+* **eu-central-1** : ami-0aa85cfc78674c526 
+* **us-east-2** : ami-01791485051e1880c 
+* **us-west-1** : ami-0d8eade4d5888ea73 
+* **us-west-2** : ami-02ceaef72cdf60f7e 
+* **us-east-1** : ami-0fc3f9d1d0eba1d62 
+
+### v0.14.2 (static update)
+
+* **eu-north-1** : ami-006d491e9e8869248 
+* **ap-south-1** : ami-0e55ec221687f98e7 
+* **eu-west-3** : ami-06ad9cf3c05c83e91 
+* **eu-west-2** : ami-0d05839268e748cff 
+* **eu-west-1** : ami-0d14c297789ce0d7a 
+* **ap-northeast-2** : ami-0d7fd775f0e76cc6f 
+* **ap-northeast-1** : ami-0c0a6e1daeb3f7a9c 
+* **sa-east-1** : ami-01e0c5e30e94ec887 
+* **ca-central-1** : ami-07a31896832734897 
+* **ap-southeast-1** : ami-0886d5b2d4b7fccd5 
+* **ap-southeast-2** : ami-0397d5a2db3c356fe 
+* **eu-central-1** : ami-0629f26eea22f5c17 
+* **us-east-2** : ami-0499c3d7bb45a1a6e 
+* **us-west-1** : ami-02fa8a961a4daf9f0 
+* **us-west-2** : ami-05c711cfab4342468 
+* **us-east-1** : ami-0b97d99a08012c726 
+
+### v0.14.1 (static update)
+
+* **eu-north-1** : ami-036defe1885dced2e 
+* **ap-south-1** : ami-0b403aa1da6a5dc17 
+* **eu-west-3** : ami-0d30c2d330d1255c4 
+* **eu-west-2** : ami-06f0e8d075e50a029 
+* **eu-west-1** : ami-0da721d874f282b6d 
+* **ap-northeast-2** : ami-03bffe94675dd5f8c 
+* **ap-northeast-1** : ami-0f96520d646423673 
+* **sa-east-1** : ami-0c2f706a3b7d97282 
+* **ca-central-1** : ami-0da74525dcfd74e32 
+* **ap-southeast-1** : ami-066368a21cf6d232b 
+* **ap-southeast-2** : ami-0bfd09170067f7318 
+* **eu-central-1** : ami-06aa99b1c41492986 
+* **us-east-2** : ami-065c1880f59d03272 
+* **us-west-1** : ami-0b7f6b896f5058eba 
+* **us-west-2** : ami-0041e10ca68eef29a 
+* **us-east-1** : ami-0b7125e4305bbd7eb 
+
+### v0.14.0 (static update)
+* **eu-north-1** : ami-02de71586ec496e38 
+* **ap-south-1** : ami-074b03849b51852e5 
+* **eu-west-3** : ami-022c388835e0eeb03 
+* **eu-west-2** : ami-0a151c236c6b27707 
+* **eu-west-1** : ami-06de69b06b4e73312 
+* **ap-northeast-2** : ami-0ee821b72d9f669b1 
+* **ap-northeast-1** : ami-03687ae215e64e100 
+* **sa-east-1** : ami-01eb83364b7f667af 
+* **ca-central-1** : ami-02e9b35f9c90377e6 
+* **ap-southeast-1** : ami-0d3ab5ab0048fea51 
+* **ap-southeast-2** : ami-0bd39d908fe3a9e06 
+* **eu-central-1** : ami-0b8638701311b35c4 
+* **us-east-2** : ami-02ff039693fc3a614 
+* **us-west-1** : ami-08634f7dfb608a9a7 
+* **us-west-2** : ami-034d693ef742b9333 
+* **us-east-1** : ami-0b828b05c323dde7f
+
+### v0.13.0 (static update)
+* **eu-north-1** : ami-0d9c74a015e7510d8 
+* **ap-south-1** : ami-02acd6dd0659bb5c1 
+* **eu-west-3** : ami-0f0cc5cb6d9afd194 
+* **eu-west-2** : ami-0298fdc0860206ed9 
+* **eu-west-1** : ami-0cdc072e528401d5e 
+* **ap-northeast-2** : ami-0055579cc95b0e53e 
+* **ap-northeast-1** : ami-0ced7becb9b83b5d0 
+* **sa-east-1** : ami-033345d0f16a1b5e4 
+* **ca-central-1** : ami-06c63b05aed47ae67 
+* **ap-southeast-1** : ami-09f0355f367f30602 
+* **ap-southeast-2** : ami-0bd2314163ce0fba0 
+* **eu-central-1** : ami-05fbae957df63e366 
+* **us-east-2** : ami-050c51b5b4074d3fc 
+* **us-west-1** : ami-06ad513073d4e5a19 
+* **us-west-2** : ami-0c96e1361d1d4ca94 
+* **us-east-1** : ami-07b669040d1eea213 
+
+### v0.12.1 (static update)
+* **eu-north-1** : ami-003118a8103286d84 
+* **ap-south-1** : ami-02dfe86baa48e096f 
+* **eu-west-3** : ami-0cc1f01267d2a780d 
+* **eu-west-2** : ami-0e4c8332e5ce09585 
+* **eu-west-1** : ami-03459a2f0b0a3b1ab 
+* **ap-northeast-2** : ami-08f6c2aed3a53f24c 
+* **ap-northeast-1** : ami-0b798eab95a7c5435 
+* **sa-east-1** : ami-0d3ee166c09f0d1b2 
+* **ca-central-1** : ami-00a758c56bd63acd5 
+* **ap-southeast-1** : ami-0be64d4988cd03fbb 
+* **ap-southeast-2** : ami-02087310d43a63f31 
+* **eu-central-1** : ami-097bbefeac0c74225 
+* **us-east-2** : ami-07eda256712b90f4d 
+* **us-west-1** : ami-02ef2b55cbd01c7df 
+* **us-west-2** : ami-037c6176ef4735360 
+* **us-east-1** : ami-08715c20c0e3f1c15 
+
+### v0.12.0 (static update)
+
+* **eu-north-1** : ami-03ff8ab48cd43e77e
+* **ap-south-1** : ami-079c1a41ff836487c
+* **eu-west-3** : ami-0121ef0398ae87ab0
+* **eu-west-2** : ami-09f0f97654d8c79de
+* **eu-west-1** : ami-0b7ba303f757bfcd9
+* **ap-northeast-2** : ami-053f416517b5f40a6
+* **ap-northeast-1** : ami-056dff06c698c2d9d
+* **sa-east-1** : ami-017ab655119258639
+* **ca-central-1** : ami-03bf5fa1d86ac97f6
+* **ap-southeast-1** : ami-0e667958002b0360c
+* **ap-southeast-2** : ami-091f1b69cb43b1933
+* **eu-central-1** : ami-068ec2f0e98c26541
+* **us-east-2** : ami-0524bbdc1b64ff83f
+* **us-west-1** : ami-0b4facd7534e393c9
+* **us-west-2** : ami-0018d5a7e58966848
+* **us-east-1** : ami-08f24178fc14a84d2
+
+### v0.11.0 (static update)
+
+* **eu-north-1** : ami-0cbe338f058018c97
+* **ap-south-1** : ami-06d72ff894f7a5e5d
+* **eu-west-3** : ami-00f2a45d67df2d2f3
+* **eu-west-2** : ami-0627ae688f4533237
+* **eu-west-1** : ami-00bf924ccb0354418
+* **ap-northeast-2** : ami-0800edf1d1dec1da8
+* **ap-northeast-1** : ami-07b2ed9709cdc4b15
+* **sa-east-1** : ami-0012c1648618b812c
+* **ca-central-1** : ami-02870b965d002fc8a
+* **ap-southeast-1** : ami-068ec23abf2473192
+* **ap-southeast-2** : ami-06664624728b5e01a
+* **eu-central-1** : ami-05f2a9304f237a6f0
+* **us-east-2** : ami-0ec242e6dca2b72b9
+* **us-west-1** : ami-050b6577acf246ceb
+* **us-west-2** : ami-0e384b6f78bf96ebe
+* **us-east-1** : ami-0a7b46f907d5d9c4a
+
+### v0.10.1 (static update)
+
+* **eu-north-1** : ami-09937ec4d18350c32
+* **ap-south-1** : ami-089d6ba7541ec4c7f
+* **eu-west-3** : ami-0accb1a94bdd5c5c1
+* **eu-west-2** : ami-0dd2c97bc678b8570
+* **eu-west-1** : ami-07a38865cbe7ca3cb
+* **ap-northeast-2** : ami-09aa0b7fe1cf3dd55
+* **ap-northeast-1** : ami-0905e7d1543e5ed36
+* **sa-east-1** : ami-08c0627daa67d7372
+* **ca-central-1** : ami-034add081712ff648
+* **ap-southeast-1** : ami-0c6caee3689b6e066
+* **ap-southeast-2** : ami-04994afd8dae5b417
+* **eu-central-1** : ami-06b10f8c30e1434f1
+* **us-east-2** : ami-0d3abe7a1fec535cc
+* **us-west-1** : ami-02bb610b70c55018b
+* **us-west-2** : ami-0d1cb8ba7de246ff0
+* **us-east-1** : ami-049ccba6abdb40cba
+
+### v0.10.0 (static update)
+
+* **eu-north-1** : ami-05ba33c763877e54e
+* **ap-south-1** : ami-0529eec569161cae5
+* **eu-west-3** : ami-03cb9396f63e26ff6
+* **eu-west-2** : ami-0dd28cc97283cc201
+* **eu-west-1** : ami-059cf379ae14b0a24
+* **ap-northeast-2** : ami-031409d71f1280616
+* **ap-northeast-1** : ami-0171437c68b3660aa
+* **sa-east-1** : ami-0eb440a3b6e591c7a
+* **ca-central-1** : ami-097da9ec155ee654a
+* **ap-southeast-1** : ami-0ab7ff3ea09826e39
+* **ap-southeast-2** : ami-00969c550ef2d1f60
+* **eu-central-1** : ami-02246400c51990acb
+* **us-east-2** : ami-0cafc1d730381d6fa
+* **eu-central-1** : ami-02246400c51990acb
+* **us-west-1** : ami-0e82a98ddbe995a65
+* **us-west-2** : ami-04a522ecb2250fb44
+* **us-east-1** : ami-0a66ddbd50959f91e
+
+### v0.9.0 (static update)
+
+* **us-east-1** : ami-0991ad536ecbacdac
+* **eu-north-1** : ami-07cbcdff501b14afe
+* **ap-south-1** : ami-014cf398b00d4db83
+* **eu-west-3** : ami-0396ba51e9b733581
+* **eu-west-2** : ami-09134f4c7a20bad09
+* **eu-west-1** : ami-00427ed0a1bbfa7b0
+* **ap-northeast-2** : ami-041756675ca1be954
+* **ap-northeast-1** : ami-0c09ebad05c9128ff
+* **sa-east-1** : ami-017a8de4e8d1e8c8e
+* **ca-central-1** : ami-049ec444470f852be
+* **ap-southeast-1** : ami-0c919b8f821a6c635
+* **ap-southeast-2** : ami-04844a0594712d27b
+* **eu-central-1** : ami-0b4e756e0f7c0617d
+* **us-east-2** : ami-03b01914b07428488
+* **us-west-1** : ami-0cf4768e9d47ed076
+* **us-west-2** : ami-0b145f37da31eb9fb
+

docs/install_gcp.md

@@ -0,0 +1,63 @@
+# Deploying Trains Server on Google Cloud Platform
+
+To easily deploy Trains Server on GCP, use one of our pre-built GCP Custom Images.  
+We provide Custom Images for each released version of Trains Server, see [Released versions](#released-versions) below. 
+
+Once your GCP instance is up and running using our Custom Image, [configure the Trains client](https://github.com/allegroai/trains/blob/master/README.md#configuration) to use your **trains-server**.  
+The service port numbers on our Trains Server GCP Custom Image are:
+
+- Web application: `8080`
+- API Server: `8008`
+- File Server: `8081`
+
+The persistent storage configuration:
+
+- MongoDB: `/opt/trains/data/mongo/`
+- ElasticSearch: `/opt/trains/data/elastic/`
+- File Server: `/mnt/fileserver/`
+
+For examples and use cases, check the [Trains usage examples](https://github.com/allegroai/trains/blob/master/docs/trains_examples.md).
+
+## Importing the Custom Image to your GCP account
+
+In order to launch an instance using the Trains Server GCP Custom Image, you'll need to import the image to your custom images list.
+
+**Note:** there's **no need** to upload the image file to Google Cloud Storage - we already provide links to image files stored in Google Storage
+
+To import the image to your custom images list:
+1. In the Cloud Console, go to the [Images](https://console.cloud.google.com/compute/images) page.
+1. At the top of the page, click **Create image**.
+1. In the **Name** field, specify a unique name for the image.
+1. Optionally, specify an image family for your new image, or configure specific encryption settings for the image.
+1. Click the **Source** menu and select **Cloud Storage file**.
+1. Enter the Trains Server image bucket path (see [Trains Server GCP Custom Image](#released-versions)), for example:
+    `allegro-files/trains-server/trains-server.tar.gz`
+1. Click the **Create** button to import the image. The process can take several minutes depending on the size of the boot disk image.
+
+For more information see [Import the image to your custom images list](https://cloud.google.com/compute/docs/import/import-existing-image#import_image) in the [Compute Engine Documentation](https://cloud.google.com/compute/docs).
+
+## Launching an instance with a Custom Image
+
+For instructions on launching an instance using a GCP Custom Image, see the [Manually importing virtual disks](https://cloud.google.com/compute/docs/import/import-existing-image#overview) in the [Compute Engine Documentation](https://cloud.google.com/compute/docs).
+For more information on Custom Images, see [Custom Images](https://cloud.google.com/compute/docs/images#custom_images) in the Compute Engine Documentation.
+
+The minimum recommended requirements for Trains Server are:
+- 2 vCPUs
+- 7.5GB RAM
+
+## Upgrading
+
+To upgrade **trains-server** on an existing GCP instance based on one of these Custom Images, SSH into the instance and follow the [upgrade instructions](../README.md#upgrade) for **trains-server**.
+
+## Released versions
+
+The following sections contain lists of Custom Image URLs (exported in different formats) for each released **trains-server** version.
+
+### Latest version image
+
+- https://storage.googleapis.com/allegro-files/trains-server/trains-server.tar.gz
+
+### All released images 
+
+- v0.15.0 - https://storage.googleapis.com/allegro-files/trains-server/trains-server-0-15-0.tar.gz
+- v0.14.1 - https://storage.googleapis.com/allegro-files/trains-server/trains-server-0-14-1.tar.gz

docs/install_linux_mac.md

@@ -0,0 +1,97 @@
+# Launching the **trains-server** Docker in Linux or macOS
+
+For Linux or macOS, use our pre-built Docker image for easy deployment. The latest Docker images can be found [here](https://hub.docker.com/r/allegroai/trains). 
+
+For Linux users:
+
+* You must be logged in as a user with sudo privileges.
+* Use `bash` for all command-line instructions in this installation.
+
+To launch **trains-server** on Linux or macOS:
+
+1. Install Docker.
+
+    * Linux - see [Docker for Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/).
+    * macOS - see [Docker for macOS](https://docs.docker.com/docker-for-mac/install/).
+
+1. Verify the Docker CE installation. Execute the command:
+
+        sudo docker run hello-world
+   
+    The expected is output is:
+
+        Hello from Docker!
+        This message shows that your installation appears to be working correctly.
+        To generate this message, Docker took the following steps:
+        
+        1. The Docker client contacted the Docker daemon.
+        2. The Docker daemon pulled the "hello-world" image from the Docker Hub. (amd64)
+        3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading.
+        4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal.
+
+1. For Linux only, install `docker-compose`. Execute the following commands (for more information, see [Install Docker Compose](https://docs.docker.com/compose/install/) in the Docker documentation): 
+
+        sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
+        sudo chmod +x /usr/local/bin/docker-compose
+
+1. Increase `vm.max_map_count` for ElasticSearch docker.
+
+    Linux:
+
+        echo "vm.max_map_count=262144" > /tmp/99-trains.conf
+        sudo mv /tmp/99-trains.conf /etc/sysctl.d/99-trains.conf
+        sudo sysctl -w vm.max_map_count=262144
+        sudo service docker restart
+        
+    macOS:
+    
+        screen ~/Library/Containers/com.docker.docker/Data/vms/0/tty
+        sysctl -w vm.max_map_count=262144
+        
+
+1. Remove any previous installation of **trains-server**.
+
+    **WARNING**: This clears all existing **Trains** databases.
+
+        sudo rm -R /opt/trains/
+
+1. Create local directories for the databases and storage.
+
+        sudo mkdir -p /opt/trains/data/elastic
+        sudo mkdir -p /opt/trains/data/mongo/db
+        sudo mkdir -p /opt/trains/data/mongo/configdb
+        sudo mkdir -p /opt/trains/data/redis
+        sudo mkdir -p /opt/trains/logs
+        sudo mkdir -p /opt/trains/config
+        sudo mkdir -p /opt/trains/data/fileserver
+        
+1. For macOS only, open the Docker app, select **Preferences**, and then on the **File Sharing** tab, add `/opt/trains`.
+          
+1. Grant access to the Dockers.
+
+    Linux:
+
+        sudo chown -R 1000:1000 /opt/trains
+        
+    macOS:
+    
+        sudo chown -R $(whoami):staff /opt/trains
+
+1. Download the **trains-server** docker-compose YAML file.
+
+        cd /opt/trains
+        curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml -o docker-compose.yml
+    
+1. Run `docker-compose` with the downloaded configuration file.
+
+        sudo docker-compose -f docker-compose.yml up
+   
+    Your server is now running on [http://localhost:8080](http://localhost:8080) and the following ports are available:
+
+    * Web server on port `8080`
+    * API server on port `8008`
+    * File server on port `8081`
+
+## Next Step
+
+Configure the [Trains client for trains-server](https://github.com/allegroai/trains/blob/master/README.md#configuration).

docs/install_win.md

@@ -0,0 +1,50 @@
+# Launching the **trains-server** Docker in Windows 10
+
+For Windows, we recommend launching our pre-built Docker image on a Linux virtual machine. 
+However, you can launch **trains-server** on Windows 10 using Docker Desktop for Windows (see the Docker [System Requirements](https://docs.docker.com/docker-for-windows/install/#system-requirements)).
+
+To launch **trains-server** on Windows 10:
+
+1. Install the Docker Desktop for Windows application by either:
+
+    * Following the [Install Docker Desktop on Windows](https://docs.docker.com/docker-for-windows/install/) instructions.
+    * Running the Docker installation [wizard](https://hub.docker.com/?overlay=onboarding).
+
+1. Increase the memory allocation in Docker Desktop to `4GB`.
+
+    1. In your Windows notification area (system tray), right click the Docker icon.
+    
+    1. Click *Settings*, *Advanced*, and then set the memory to at least `4096`. 
+    
+    1. Click *Apply*.
+    
+1. Remove any previous installation of **trains-server**.
+
+    **WARNING**: This clears all existing **Trains** databases.
+
+        rmdir c:\opt\trains /s
+
+1. Create local directories for data and logs. Open PowerShell and execute the following commands:
+
+        cd c:
+        mkdir c:\opt\trains\data
+        mkdir c:\opt\trains\logs
+
+1. Save the **trains-server** docker-compose YAML file.
+ 
+        cd c:\opt\trains
+        curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose-win10.yml -o docker-compose-win10.yml 
+ 
+1. Run `docker-compose`. In PowerShell, execute the following commands:
+
+        docker-compose -f docker-compose-win10.yml up
+   
+    Your server is now running on [http://localhost:8080](http://localhost:8080) and the following ports are available:
+
+    * Web server on port `8080`
+    * API server on port `8008`
+    * File server on port `8081`
+
+## Next Step
+
+Configure the [Trains client for trains-server](https://github.com/allegroai/trains/blob/master/README.md#configuration).

docs/services.conf

@@ -0,0 +1,9 @@
+tasks {
+    non_responsive_tasks_watchdog {
+        # In-progress tasks that haven't been updated for at least 'value' seconds will be stopped by the watchdog
+        threshold_sec: 7200
+
+        # Watchdog will sleep for this number of seconds after each cycle
+        watch_interval_sec: 900
+    }
+}

fileserver/LICENSE

@@ -0,0 +1,557 @@
+                     Server Side Public License
+                     VERSION 1, OCTOBER 16, 2018
+
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+  Public License from time to time. Such new versions will be similar in
+  spirit to the present version, but may differ in detail to address new
+  problems or concerns.
+
+  Each version is given a distinguishing version number. If the Program
+  specifies that a certain numbered version of the Server Side Public
+  License “or any later version” applies to it, you have the option of
+  following the terms and conditions either of that numbered version or of
+  any later version published by MongoDB, Inc. If the Program does not
+  specify a version number of the Server Side Public License, you may
+  choose any version ever published by MongoDB, Inc.
+
+  If the Program specifies that a proxy can decide which future versions of
+  the Server Side Public License can be used, that proxy's public statement
+  of acceptance of a version permanently authorizes you to choose that
+  version for the Program.
+
+  Later license versions may give you additional or different permissions.
+  However, no additional obligations are imposed on any author or copyright
+  holder as a result of your choosing to follow a later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+  APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+  HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY
+  OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+  THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+  PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+  IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+  ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+  
+  16. Limitation of Liability.
+  
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+  WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+  THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING
+  ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF
+  THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO
+  LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU
+  OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
+  PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
+  POSSIBILITY OF SUCH DAMAGES.
+  
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided above
+  cannot be given local legal effect according to their terms, reviewing
+  courts shall apply local law that most closely approximates an absolute
+  waiver of all civil liability in connection with the Program, unless a
+  warranty or assumption of liability accompanies a copy of the Program in
+  return for a fee.
+  
+                        END OF TERMS AND CONDITIONS

fileserver/config/basic.py

@@ -1,4 +1,8 @@
 import logging
+import os
+from functools import reduce
+from os import getenv
+from os.path import expandvars
 from pathlib import Path
 
 from pyhocon import ConfigTree, ConfigFactory
@@ -9,6 +13,13 @@ from pyparsing import (
     ParseSyntaxException,
 )
 
+DEFAULT_EXTRA_CONFIG_PATH = "/opt/trains/config"
+EXTRA_CONFIG_PATH_ENV_KEY = "TRAINS_CONFIG_DIR"
+EXTRA_CONFIG_PATH_SEP = ":"
+
+EXTRA_CONFIG_VALUES_ENV_KEY_SEP = "__"
+EXTRA_CONFIG_VALUES_ENV_KEY_PREFIX = f"TRAINS{EXTRA_CONFIG_VALUES_ENV_KEY_SEP}"
+
 
 class BasicConfig:
     NotSet = object()
@@ -39,8 +50,53 @@ class BasicConfig:
         path = ".".join((self.prefix, Path(name).stem))
         return logging.getLogger(path)
 
+    @staticmethod
+    def _read_extra_env_config_values():
+        """ Loads extra configuration from environment-injected values """
+        result = ConfigTree()
+        prefix = EXTRA_CONFIG_VALUES_ENV_KEY_PREFIX
+
+        keys = sorted(k for k in os.environ if k.startswith(prefix))
+        for key in keys:
+            path = key[len(prefix) :].replace(EXTRA_CONFIG_VALUES_ENV_KEY_SEP, ".").lower()
+            result = ConfigTree.merge_configs(
+                result, ConfigFactory.parse_string(f"{path}: {os.environ[key]}")
+            )
+
+        return result
+
+    @staticmethod
+    def _read_env_paths(key):
+        value = getenv(EXTRA_CONFIG_PATH_ENV_KEY, DEFAULT_EXTRA_CONFIG_PATH)
+        if value is None:
+            return
+        paths = [
+            Path(expandvars(v)).expanduser() for v in value.split(EXTRA_CONFIG_PATH_SEP)
+        ]
+        invalid = [
+            path
+            for path in paths
+            if not path.is_dir() and str(path) != DEFAULT_EXTRA_CONFIG_PATH
+        ]
+        if invalid:
+            print(f"WARNING: Invalid paths in {key} env var: {' '.join(invalid)}")
+        return [path for path in paths if path.is_dir()]
+
     def _load(self, verbose=True):
-        self._config = self._read_recursive(self.folder, verbose=verbose)
+        extra_config_paths = self._read_env_paths(EXTRA_CONFIG_PATH_ENV_KEY) or []
+        extra_config_values = self._read_extra_env_config_values()
+        configs = [
+            self._read_recursive(path, verbose=verbose)
+            for path in [self.folder] + extra_config_paths
+        ]
+
+        self._config = reduce(
+            lambda last, config: ConfigTree.merge_configs(
+                last, config, copy_trees=True
+            ),
+            configs + [extra_config_values],
+            ConfigTree(),
+        )
 
     def _read_recursive(self, conf_root, verbose=True):
         conf = ConfigTree()

fileserver/config/default/fileserver.conf

@@ -0,0 +1,11 @@
+download {
+    # Add response headers requesting no caching for served files
+    disable_browser_caching: false
+
+    # Cache timeout to be set for downloaded files
+    cache_timeout_sec: 300
+}
+
+cors {
+    origins: "*"
+}

fileserver/fileserver.py

@@ -1,16 +1,23 @@
 """ A Simple file server for uploading and downloading files """
 import json
-import logging.config
 import os
 from argparse import ArgumentParser
 from pathlib import Path
 
 from flask import Flask, request, send_from_directory, safe_join
-from pyhocon import ConfigFactory
+from flask_compress import Compress
+from flask_cors import CORS
 
-logging.config.dictConfig(ConfigFactory.parse_file("logging.conf"))
+from config import config
+
+DEFAULT_UPLOAD_FOLDER = "/mnt/fileserver"
 
 app = Flask(__name__)
+CORS(app, **config.get("fileserver.cors"))
+Compress(app)
+
+app.config["UPLOAD_FOLDER"] = os.environ.get("TRAINS_UPLOAD_FOLDER") or DEFAULT_UPLOAD_FOLDER
+app.config["SEND_FILE_MAX_AGE_DEFAULT"] = config.get("fileserver.download.cache_timeout_sec", 5 * 60)
 
 
 @app.route("/", methods=["POST"])
@@ -29,7 +36,15 @@ def upload():
 
 @app.route("/<path:path>", methods=["GET"])
 def download(path):
-    return send_from_directory(app.config["UPLOAD_FOLDER"], path)
+    response = send_from_directory(app.config["UPLOAD_FOLDER"], path)
+    if config.get("fileserver.download.disable_browser_caching", False):
+        headers = response.headers
+        headers["Pragma-directive"] = "no-cache"
+        headers["Cache-directive"] = "no-cache"
+        headers["Cache-control"] = "no-cache"
+        headers["Pragma"] = "no-cache"
+        headers["Expires"] = "0"
+    return response
 
 
 def main():
@@ -44,12 +59,13 @@ def main():
     parser.add_argument(
         "--upload-folder",
         "-u",
-        default="/mnt/fileserver",
+        default=DEFAULT_UPLOAD_FOLDER,
         help="Upload folder (default %(default)s)",
     )
     args = parser.parse_args()
 
-    app.config["UPLOAD_FOLDER"] = args.upload_folder
+    if app.config.get("UPLOAD_FOLDER") is None:
+        app.config["UPLOAD_FOLDER"] = args.upload_folder
 
     app.run(debug=args.debug, host=args.ip, port=args.port, threaded=True)
 

fileserver/requirements.txt

@@ -1 +1,4 @@
-Flask
+Flask
+Flask-Cors>=3.0.5
+Flask-Compress>=1.4.0
+pyhocon>=0.3.35

server/LICENSE

@@ -0,0 +1,557 @@
+                     Server Side Public License
+                     VERSION 1, OCTOBER 16, 2018
+
+                    Copyright © 2019 allegro.ai, Inc.
+
+  Everyone is permitted to copy and distribute verbatim copies of this
+  license document, but changing it is not allowed.
+
+                       TERMS AND CONDITIONS
+
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+  license fee, royalty, or other charge for exercise of rights granted
+  under this License, and you may not initiate litigation (including a
+  cross-claim or counterclaim in a lawsuit) alleging that any patent claim
+  is infringed by making, using, selling, offering for sale, or importing
+  the Program or any portion of it.
+
+  11. Patents.
+
+  A “contributor” is a copyright holder who authorizes use under this
+  License of the Program or a work on which the Program is based. The work
+  thus licensed is called the contributor's “contributor version”.
+
+  A contributor's “essential patent claims” are all patent claims owned or
+  controlled by the contributor, whether already acquired or hereafter
+  acquired, that would be infringed by some manner, permitted by this
+  License, of making, using, or selling its contributor version, but do not
+  include claims that would be infringed only as a consequence of further
+  modification of the contributor version. For purposes of this definition,
+  “control” includes the right to grant patent sublicenses in a manner
+  consistent with the requirements of this License.
+
+  Each contributor grants you a non-exclusive, worldwide, royalty-free
+  patent license under the contributor's essential patent claims, to make,
+  use, sell, offer for sale, import and otherwise run, modify and propagate
+  the contents of its contributor version.
+
+  In the following three paragraphs, a “patent license” is any express
+  agreement or commitment, however denominated, not to enforce a patent
+  (such as an express permission to practice a patent or covenant not to
+  sue for patent infringement). To “grant” such a patent license to a party
+  means to make such an agreement or commitment not to enforce a patent
+  against the party.
+
+  If you convey a covered work, knowingly relying on a patent license, and
+  the Corresponding Source of the work is not available for anyone to copy,
+  free of charge and under the terms of this License, through a publicly
+  available network server or other readily accessible means, then you must
+  either (1) cause the Corresponding Source to be so available, or (2)
+  arrange to deprive yourself of the benefit of the patent license for this
+  particular work, or (3) arrange, in a manner consistent with the
+  requirements of this License, to extend the patent license to downstream
+  recipients. “Knowingly relying” means you have actual knowledge that, but
+  for the patent license, your conveying the covered work in a country, or
+  your recipient's use of the covered work in a country, would infringe
+  one or more identifiable patents in that country that you have reason
+  to believe are valid.
+
+  If, pursuant to or in connection with a single transaction or
+  arrangement, you convey, or propagate by procuring conveyance of, a
+  covered work, and grant a patent license to some of the parties receiving
+  the covered work authorizing them to use, propagate, modify or convey a
+  specific copy of the covered work, then the patent license you grant is
+  automatically extended to all recipients of the covered work and works
+  based on it.
+
+  A patent license is “discriminatory” if it does not include within the
+  scope of its coverage, prohibits the exercise of, or is conditioned on
+  the non-exercise of one or more of the rights that are specifically
+  granted under this License. You may not convey a covered work if you are
+  a party to an arrangement with a third party that is in the business of
+  distributing software, under which you make payment to the third party
+  based on the extent of your activity of conveying the work, and under
+  which the third party grants, to any of the parties who would receive the
+  covered work from you, a discriminatory patent license (a) in connection
+  with copies of the covered work conveyed by you (or copies made from
+  those copies), or (b) primarily for and in connection with specific
+  products or compilations that contain the covered work, unless you
+  entered into that arrangement, or that patent license was granted, prior
+  to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting any
+  implied license or other defenses to infringement that may otherwise be
+  available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+  otherwise) that contradict the conditions of this License, they do not
+  excuse you from the conditions of this License. If you cannot use,
+  propagate or convey a covered work so as to satisfy simultaneously your
+  obligations under this License and any other pertinent obligations, then
+  as a consequence you may not use, propagate or convey it at all. For
+  example, if you agree to terms that obligate you to collect a royalty for
+  further conveying from those to whom you convey the Program, the only way
+  you could satisfy both those terms and this License would be to refrain
+  entirely from conveying the Program.
+
+  13. Offering the Program as a Service.
+
+  If you make the functionality of the Program or a modified version
+  available to third parties as a service, you must make the Service Source
+  Code available via network download to everyone at no charge, under the
+  terms of this License. Making the functionality of the Program or
+  modified version available to third parties as a service includes,
+  without limitation, enabling third parties to interact with the
+  functionality of the Program or modified version remotely through a
+  computer network, offering a service the value of which entirely or
+  primarily derives from the value of the Program or modified version, or
+  offering a service that accomplishes for users the primary purpose of the
+  Program or modified version.
+
+  “Service Source Code” means the Corresponding Source for the Program or
+  the modified version, and the Corresponding Source for all programs that
+  you use to make the Program or modified version available as a service,
+  including, without limitation, management software, user interfaces,
+  application program interfaces, automation software, monitoring software,
+  backup software, storage software and hosting software, all such that a
+  user could run an instance of the service using the Service Source Code
+  you make available.  
+
+  14. Revised Versions of this License.
+
+  MongoDB, Inc. may publish revised and/or new versions of the Server Side
+  Public License from time to time. Such new versions will be similar in
+  spirit to the present version, but may differ in detail to address new
+  problems or concerns.
+
+  Each version is given a distinguishing version number. If the Program
+  specifies that a certain numbered version of the Server Side Public
+  License “or any later version” applies to it, you have the option of
+  following the terms and conditions either of that numbered version or of
+  any later version published by MongoDB, Inc. If the Program does not
+  specify a version number of the Server Side Public License, you may
+  choose any version ever published by MongoDB, Inc.
+
+  If the Program specifies that a proxy can decide which future versions of
+  the Server Side Public License can be used, that proxy's public statement
+  of acceptance of a version permanently authorizes you to choose that
+  version for the Program.
+
+  Later license versions may give you additional or different permissions.
+  However, no additional obligations are imposed on any author or copyright
+  holder as a result of your choosing to follow a later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+  APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+  HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY
+  OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+  THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+  PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+  IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+  ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+  
+  16. Limitation of Liability.
+  
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+  WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+  THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING
+  ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF
+  THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO
+  LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU
+  OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
+  PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
+  POSSIBILITY OF SUCH DAMAGES.
+  
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided above
+  cannot be given local legal effect according to their terms, reviewing
+  courts shall apply local law that most closely approximates an absolute
+  waiver of all civil liability in connection with the Program, unless a
+  warranty or assumption of liability accompanies a copy of the Program in
+  return for a fee.
+  
+                        END OF TERMS AND CONDITIONS

server/api_version.py

@@ -0,0 +1 @@
+__version__ = "2.8.0"

server/apierrors/__init__.py

@@ -47,7 +47,7 @@ _error_codes = {
         128: ('invalid_task_output', 'invalid task output'),
         129: ('task_publish_in_progress', 'Task publish in progress'),
         130: ('task_not_found', 'task not found'),
-
+        131: ('events_not_added', 'events not added'),
 
         # Models
         200: ('model_error', 'general task error'),
@@ -70,9 +70,28 @@ _error_codes = {
         403: ('project_not_found', 'project not found'),
         405: ('project_has_models', 'project has associated models'),
 
+        # Queues
+        701: ('invalid_queue_id', 'invalid queue id'),
+        702: ('queue_not_empty', 'queue is not empty'),
+        703: ('invalid_queue_or_task_not_queued', 'invalid queue id or task not in queue'),
+        704: ('removed_during_reposition', 'task was removed by another party during reposition'),
+        705: ('failed_adding_during_reposition', 'failed adding task back to queue during reposition'),
+        706: ('task_already_queued', 'failed adding task to queue since task is already queued'),
+        707: ('no_default_queue', 'no queue is tagged as the default queue for this company'),
+        708: ('multiple_default_queues', 'more than one queue is tagged as the default queue for this company'),
+
         # Database
         800: ('data_validation_error', 'data validation error'),
         801: ('expected_unique_data', 'value combination already exists'),
+
+        # Workers
+        1001: ('invalid_worker_id', 'invalid worker id'),
+        1002: ('worker_registration_failed', 'worker registration failed'),
+        1003: ('worker_registered', 'worker is already registered'),
+        1004: ('worker_not_registered', 'worker is not registered'),
+        1005: ('worker_stats_not_found', 'worker stats not found'),
+
+        1104: ('invalid_scroll_id', 'Invalid scroll id'),
     },
 
     (401, 'unauthorized'): {
@@ -83,12 +102,12 @@ _error_codes = {
         21: ('bad_credentials', 'unauthorized (malformed credentials)'),
         22: ('invalid_credentials', 'unauthorized (invalid credentials)'),
         30: ('invalid_token', 'invalid token'),
-        31: ('blocked_token', 'token is blocked')
+        31: ('blocked_token', 'token is blocked'),
+        40: ('invalid_fixed_user', 'fixed user ID was not found')
     },
 
     (403, 'forbidden'): {
         10: ('routing_error', 'forbidden (routing error)'),
-        11: ('missing_routing_header', 'forbidden (missing routing header)'),
         12: ('blocked_internal_endpoint', 'forbidden (blocked internal endpoint)'),
         20: ('role_not_allowed', 'forbidden (not allowed for role)'),
         21: ('no_write_permission', 'forbidden (modification not allowed)'),
@@ -104,6 +123,7 @@ _error_codes = {
         100: ('data_error', 'general data error'),
         101: ('inconsistent_data', 'inconsistent data encountered in document'),
         102: ('database_unavailable', 'database is temporarily unavailable'),
+        110: ('update_failed', 'update failed'),
 
         # Index-related issues
         201: ('missing_index', 'missing internal index'),

server/apimodels/__init__.py

@@ -4,16 +4,16 @@ from enum import Enum
 from typing import Union, Type, Iterable
 
 import jsonmodels.errors
-import jsonmodels.validators
 import six
-import validators
 from jsonmodels import fields
-from jsonmodels.fields import _LazyType
+from jsonmodels.fields import _LazyType, NotSet
 from jsonmodels.models import Base as ModelBase
 from jsonmodels.validators import Enum as EnumValidator
 from luqum.parser import parser, ParseError
+from validators import email as email_validator, domain as domain_validator
 
 from apierrors import errors
+from utilities.json import loads, dumps
 
 
 def make_default(field_cls, default_value):
@@ -25,6 +25,12 @@ def make_default(field_cls, default_value):
 
 
 class ListField(fields.ListField):
+    def __init__(self, items_types=None, *args, default=NotSet, **kwargs):
+        if default is not NotSet and callable(default):
+            default = default()
+
+        super(ListField, self).__init__(items_types, *args, default=default, **kwargs)
+
     def _cast_value(self, value):
         try:
             return super(ListField, self)._cast_value(value)
@@ -61,9 +67,7 @@ class DictField(fields.BaseField):
             value_types = tuple()
 
         return tuple(
-            _LazyType(type_)
-            if isinstance(type_, six.string_types)
-            else type_
+            _LazyType(type_) if isinstance(type_, six.string_types) else type_
             for type_ in value_types
         )
 
@@ -73,6 +77,9 @@ class DictField(fields.BaseField):
         if not self.value_types:
             return
 
+        if not value:
+            return
+
         for item in value.values():
             self.validate_single_value(item)
 
@@ -99,7 +106,7 @@ class IntField(fields.IntField):
 
 
 def validate_lucene_query(value):
-    if value == '':
+    if value == "":
         return
     try:
         parser.parse(value)
@@ -117,6 +124,7 @@ class LuceneQueryField(fields.StringField):
 
 class NullableEnumValidator(EnumValidator):
     """Validator for enums that allows a None value."""
+
     def validate(self, value):
         if value is not None:
             super(NullableEnumValidator, self).validate(value)
@@ -144,12 +152,49 @@ class EnumField(fields.StringField):
         return super().parse_value(value)
 
 
+class ActualEnumField(fields.StringField):
+    def __init__(
+        self,
+        enum_class: Type[Enum],
+        *args,
+        validators=None,
+        required=False,
+        default=None,
+        **kwargs
+    ):
+        self.__enum = enum_class
+        self.types = (enum_class,)
+        # noinspection PyTypeChecker
+        choices = list(enum_class)
+        validator_cls = EnumValidator if required else NullableEnumValidator
+        validators = [*(validators or []), validator_cls(*choices)]
+        super().__init__(
+            default=self.parse_value(default) if default else NotSet,
+            *args,
+            required=required,
+            validators=validators,
+            **kwargs
+        )
+
+    def parse_value(self, value):
+        if value is None and not self.required:
+            return self.get_default_value()
+        try:
+            # noinspection PyArgumentList
+            return self.__enum(value)
+        except ValueError:
+            return value
+
+    def to_struct(self, value):
+        return super().to_struct(value.value)
+
+
 class EmailField(fields.StringField):
     def validate(self, value):
         super().validate(value)
         if value is None:
             return
-        if validators.email(value) is not True:
+        if email_validator(value) is not True:
             raise errors.bad_request.InvalidEmailAddress()
 
 
@@ -158,5 +203,14 @@ class DomainField(fields.StringField):
         super().validate(value)
         if value is None:
             return
-        if validators.domain(value) is not True:
+        if domain_validator(value) is not True:
             raise errors.bad_request.InvalidDomainName()
+
+
+class JsonSerializableMixin:
+    def to_json(self: ModelBase):
+        return dumps(self.to_struct())
+
+    @classmethod
+    def from_json(cls: Type[ModelBase], s):
+        return cls(**loads(s))

server/apimodels/auth.py

@@ -1,4 +1,4 @@
-from jsonmodels.fields import IntField, StringField, BoolField, EmbeddedField
+from jsonmodels.fields import IntField, StringField, BoolField, EmbeddedField, DateTimeField
 from jsonmodels.models import Base
 from jsonmodels.validators import Max, Enum
 
@@ -79,6 +79,7 @@ class Credentials(Base):
 
 class CredentialsResponse(Credentials):
     secret_key = StringField()
+    last_used = DateTimeField(default=None)
 
 
 class CreateCredentialsResponse(Base):

server/apimodels/base.py

@@ -58,3 +58,7 @@ class UpdateResponse(models.Base):
 class PagedRequest(models.Base):
     page = fields.IntField()
     page_size = fields.IntField()
+
+
+class IdResponse(models.Base):
+    id = fields.StringField(required=True)

server/apimodels/events.py

@@ -0,0 +1,71 @@
+from typing import Sequence
+
+from jsonmodels import validators
+from jsonmodels.fields import StringField, BoolField
+from jsonmodels.models import Base
+from jsonmodels.validators import Length
+
+from apimodels import ListField, IntField, ActualEnumField
+from bll.event.event_metrics import EventType
+from bll.event.scalar_key import ScalarKeyEnum
+
+
+class HistogramRequestBase(Base):
+    samples: int = IntField(default=10000)
+    key: ScalarKeyEnum = ActualEnumField(ScalarKeyEnum, default=ScalarKeyEnum.iter)
+
+
+class ScalarMetricsIterHistogramRequest(HistogramRequestBase):
+    task: str = StringField(required=True)
+
+
+class MultiTaskScalarMetricsIterHistogramRequest(HistogramRequestBase):
+    tasks: Sequence[str] = ListField(
+        items_types=str, validators=[Length(minimum_value=1)]
+    )
+
+
+class TaskMetric(Base):
+    task: str = StringField(required=True)
+    metric: str = StringField(required=True)
+
+
+class DebugImagesRequest(Base):
+    metrics: Sequence[TaskMetric] = ListField(
+        items_types=TaskMetric, validators=[Length(minimum_value=1)]
+    )
+    iters: int = IntField(default=1, validators=validators.Min(1))
+    navigate_earlier: bool = BoolField(default=True)
+    refresh: bool = BoolField(default=False)
+    scroll_id: str = StringField()
+
+
+class LogEventsRequest(Base):
+    task: str = StringField(required=True)
+    batch_size: int = IntField(default=500)
+    navigate_earlier: bool = BoolField(default=True)
+    refresh: bool = BoolField(default=False)
+    scroll_id: str = StringField()
+
+
+class IterationEvents(Base):
+    iter: int = IntField()
+    events: Sequence[dict] = ListField(items_types=dict)
+
+
+class MetricEvents(Base):
+    task: str = StringField()
+    metric: str = StringField()
+    iterations: Sequence[IterationEvents] = ListField(items_types=IterationEvents)
+
+
+class DebugImageResponse(Base):
+    metrics: Sequence[MetricEvents] = ListField(items_types=MetricEvents)
+    scroll_id: str = StringField()
+
+
+class TaskMetricsRequest(Base):
+    tasks: Sequence[str] = ListField(
+        items_types=str, validators=[Length(minimum_value=1)]
+    )
+    event_type: EventType = ActualEnumField(EventType, required=True)

server/apimodels/models.py

@@ -9,8 +9,9 @@ from apimodels.tasks import PublishResponse as TaskPublishResponse
 class CreateModelRequest(models.Base):
     name = fields.StringField(required=True)
     uri = fields.StringField(required=True)
-    labels = DictField(value_types=string_types+(int,), required=True)
+    labels = DictField(value_types=string_types+(int,))
     tags = ListField(items_types=string_types)
+    system_tags = ListField(items_types=string_types)
     comment = fields.StringField()
     public = fields.BoolField(default=False)
     project = fields.StringField()

server/apimodels/organization.py

@@ -0,0 +1,11 @@
+from jsonmodels import fields, models
+
+
+class Filter(models.Base):
+    tags = fields.ListField([str])
+    system_tags = fields.ListField([str])
+
+
+class TagsRequest(models.Base):
+    include_system = fields.BoolField(default=False)
+    filter = fields.EmbeddedField(Filter)

server/apimodels/projects.py

@@ -0,0 +1,23 @@
+from jsonmodels import models, fields
+
+from apimodels import ListField
+from apimodels.organization import TagsRequest
+
+
+class ProjectReq(models.Base):
+    project = fields.StringField()
+
+
+class GetHyperParamReq(ProjectReq):
+    page = fields.IntField(default=0)
+    page_size = fields.IntField(default=500)
+
+
+class GetHyperParamResp(models.Base):
+    parameters = fields.ListField(str)
+    remaining = fields.IntField()
+    total = fields.IntField()
+
+
+class ProjectTagsRequest(TagsRequest):
+    projects = ListField(str)

server/apimodels/queues.py

@@ -0,0 +1,60 @@
+from jsonmodels import validators
+from jsonmodels.fields import StringField, IntField, BoolField, FloatField
+from jsonmodels.models import Base
+
+from apimodels import ListField
+
+
+class GetDefaultResp(Base):
+    id = StringField(required=True)
+    name = StringField(required=True)
+
+
+class CreateRequest(Base):
+    name = StringField(required=True)
+    tags = ListField(items_types=[str])
+    system_tags = ListField(items_types=[str])
+
+
+class QueueRequest(Base):
+    queue = StringField(required=True)
+
+
+class DeleteRequest(QueueRequest):
+    force = BoolField(default=False)
+
+
+class UpdateRequest(QueueRequest):
+    name = StringField()
+    tags = ListField(items_types=[str])
+    system_tags = ListField(items_types=[str])
+
+
+class TaskRequest(QueueRequest):
+    task = StringField(required=True)
+
+
+class MoveTaskRequest(TaskRequest):
+    count = IntField(default=1)
+
+
+class MoveTaskResponse(Base):
+    position = IntField()
+
+
+class GetMetricsRequest(Base):
+    queue_ids = ListField([str])
+    from_date = FloatField(required=True, validators=validators.Min(0))
+    to_date = FloatField(required=True, validators=validators.Min(0))
+    interval = IntField(required=True, validators=validators.Min(1))
+
+
+class QueueMetrics(Base):
+    queue = StringField()
+    dates = ListField(int)
+    avg_waiting_times = ListField([float, int])
+    queue_lengths = ListField(int)
+
+
+class GetMetricsResponse(Base):
+    queues = ListField(QueueMetrics)

server/apimodels/server.py

@@ -0,0 +1,15 @@
+from jsonmodels.fields import BoolField, DateTimeField, StringField
+from jsonmodels.models import Base
+
+
+class ReportStatsOptionRequest(Base):
+    enabled = BoolField(default=None, nullable=True)
+
+
+class ReportStatsOptionResponse(Base):
+    supported = BoolField(default=True)
+    enabled = BoolField()
+    enabled_time = DateTimeField(nullable=True)
+    enabled_version = StringField(nullable=True)
+    enabled_user = StringField(nullable=True)
+    current_version = StringField()

server/apimodels/tasks.py

@@ -1,6 +1,6 @@
 import six
 from jsonmodels import models
-from jsonmodels.fields import StringField, BoolField, IntField
+from jsonmodels.fields import StringField, BoolField, IntField, EmbeddedField
 from jsonmodels.validators import Enum
 
 from apimodels import DictField, ListField
@@ -9,12 +9,39 @@ from database.model.task.task import TaskType
 from database.utils import get_options
 
 
+class ArtifactTypeData(models.Base):
+    preview = StringField()
+    content_type = StringField()
+    data_hash = StringField()
+
+
+class Artifact(models.Base):
+    key = StringField(required=True)
+    type = StringField(required=True)
+    mode = StringField(validators=Enum("input", "output"), default="output")
+    uri = StringField()
+    hash = StringField()
+    content_size = IntField()
+    timestamp = IntField()
+    type_data = EmbeddedField(ArtifactTypeData)
+    display_data = ListField([list])
+
+
 class StartedResponse(UpdateResponse):
     started = IntField()
 
 
+class EnqueueResponse(UpdateResponse):
+    queued = IntField()
+
+
+class DequeueResponse(UpdateResponse):
+    dequeued = IntField()
+
+
 class ResetResponse(UpdateResponse):
     deleted_indices = ListField(items_types=six.string_types)
+    dequeued = DictField()
     frames = DictField()
     events = DictField()
     model_deleted = IntField()
@@ -30,6 +57,10 @@ class UpdateRequest(TaskRequest):
     force = BoolField(default=False)
 
 
+class EnqueueRequest(UpdateRequest):
+    queue = StringField()
+
+
 class DeleteRequest(UpdateRequest):
     move_to_trash = BoolField(default=True)
 
@@ -55,3 +86,35 @@ class TaskData(models.Base):
 class CreateRequest(TaskData):
     name = StringField(required=True)
     type = StringField(required=True, validators=Enum(*get_options(TaskType)))
+
+
+class PingRequest(TaskRequest):
+    pass
+
+
+class GetTypesRequest(models.Base):
+    projects = ListField(items_types=[str])
+
+
+class CloneRequest(TaskRequest):
+    new_task_name = StringField()
+    new_task_comment = StringField()
+    new_task_tags = ListField([str])
+    new_task_system_tags = ListField([str])
+    new_task_parent = StringField()
+    new_task_project = StringField()
+    execution_overrides = DictField()
+    validate_references = BoolField(default=False)
+
+
+class AddOrUpdateArtifactsRequest(TaskRequest):
+    artifacts = ListField([Artifact], required=True)
+
+
+class AddOrUpdateArtifactsResponse(models.Base):
+    added = ListField([str])
+    updated = ListField([str])
+
+
+class ResetRequest(UpdateRequest):
+    clear_all = BoolField(default=False)

server/apimodels/workers.py

@@ -0,0 +1,175 @@
+from enum import Enum
+
+import six
+from jsonmodels import validators
+from jsonmodels.fields import (
+    StringField,
+    EmbeddedField,
+    DateTimeField,
+    IntField,
+    FloatField,
+    BoolField,
+)
+from jsonmodels.models import Base
+
+from apimodels import make_default, ListField, EnumField, JsonSerializableMixin
+
+DEFAULT_TIMEOUT = 10 * 60
+
+
+class WorkerRequest(Base):
+    worker = StringField(required=True)
+
+
+class RegisterRequest(WorkerRequest):
+    timeout = make_default(
+        IntField, DEFAULT_TIMEOUT
+    )()  # registration timeout in seconds (default is 10min)
+    queues = ListField(six.string_types)  # list of queues this worker listens to
+
+
+class MachineStats(Base):
+    cpu_usage = ListField(six.integer_types + (float,))
+    cpu_temperature = ListField(six.integer_types + (float,))
+    gpu_usage = ListField(six.integer_types + (float,))
+    gpu_temperature = ListField(six.integer_types + (float,))
+    gpu_memory_free = ListField(six.integer_types + (float,))
+    gpu_memory_used = ListField(six.integer_types + (float,))
+    memory_used = FloatField()
+    memory_free = FloatField()
+    network_tx = FloatField()
+    network_rx = FloatField()
+    disk_free_home = FloatField()
+    disk_free_temp = FloatField()
+    disk_read = FloatField()
+    disk_write = FloatField()
+
+
+class StatusReportRequest(WorkerRequest):
+    task = StringField()  # task the worker is running on
+    queue = StringField()  # queue from which task was taken
+    queues = ListField(
+        str
+    )  # list of queues this worker listens to. if None, this will not update the worker's queues list.
+    timestamp = IntField(required=True)
+    machine_stats = EmbeddedField(MachineStats)
+
+
+class IdNameEntry(Base):
+    id = StringField(required=True)
+    name = StringField()
+
+
+class WorkerEntry(Base, JsonSerializableMixin):
+    key = StringField()  # not required due to migration issues
+    id = StringField(required=True)
+    user = EmbeddedField(IdNameEntry)
+    company = EmbeddedField(IdNameEntry)
+    ip = StringField()
+    task = EmbeddedField(IdNameEntry)
+    queue = StringField()  # queue from which current task was taken
+    queues = ListField(str)  # list of queues this worker listens to
+    register_time = DateTimeField(required=True)
+    register_timeout = IntField(required=True)
+    last_activity_time = DateTimeField(required=True)
+    last_report_time = DateTimeField()
+
+
+class CurrentTaskEntry(IdNameEntry):
+    running_time = IntField()
+    last_iteration = IntField()
+
+
+class QueueEntry(IdNameEntry):
+    next_task = EmbeddedField(IdNameEntry)
+    num_tasks = IntField()
+
+
+class WorkerResponseEntry(WorkerEntry):
+    task = EmbeddedField(CurrentTaskEntry)
+    queue = EmbeddedField(QueueEntry)
+    queues = ListField(QueueEntry)
+
+
+class GetAllRequest(Base):
+    last_seen = IntField(default=3600)
+
+
+class GetAllResponse(Base):
+    workers = ListField(WorkerResponseEntry)
+
+
+class StatsBase(Base):
+    worker_ids = ListField(str)
+
+
+class StatsReportBase(StatsBase):
+    from_date = FloatField(required=True, validators=validators.Min(0))
+    to_date = FloatField(required=True, validators=validators.Min(0))
+    interval = IntField(required=True, validators=validators.Min(1))
+
+
+class AggregationType(Enum):
+    avg = "avg"
+    min = "min"
+    max = "max"
+
+
+class StatItem(Base):
+    key = StringField(required=True)
+    aggregation = EnumField(AggregationType, default=AggregationType.avg)
+
+
+class GetStatsRequest(StatsReportBase):
+    items = ListField(
+        StatItem, required=True, validators=validators.Length(minimum_value=1)
+    )
+    split_by_variant = BoolField(default=False)
+
+
+class AggregationStats(Base):
+    aggregation = EnumField(AggregationType)
+    values = ListField(float)
+
+
+class MetricStats(Base):
+    metric = StringField()
+    variant = StringField()
+    dates = ListField(int)
+    stats = ListField(AggregationStats)
+
+
+class WorkerStatistics(Base):
+    worker = StringField()
+    metrics = ListField(MetricStats)
+
+
+class GetStatsResponse(Base):
+    workers = ListField(WorkerStatistics)
+
+
+class GetMetricKeysRequest(StatsBase):
+    pass
+
+
+class MetricCategory(Base):
+    name = StringField()
+    metric_keys = ListField(str)
+
+
+class GetMetricKeysResponse(Base):
+    categories = ListField(MetricCategory)
+
+
+class GetActivityReportRequest(StatsReportBase):
+    pass
+
+
+class ActivityReportSeries(Base):
+    dates = ListField(int)
+    counts = ListField(int)
+
+
+class GetActivityReportResponse(Base):
+    total = EmbeddedField(ActivityReportSeries)
+    active = EmbeddedField(ActivityReportSeries)

server/bll/auth/__init__.py

@@ -2,24 +2,16 @@ from datetime import datetime
 
 import database
 from apierrors import errors
-from apimodels.auth import (
-    GetTokenResponse,
-    CreateUserRequest,
-    Credentials as CredModel,
-)
+from apimodels.auth import GetTokenResponse, CreateUserRequest, Credentials as CredModel
 from apimodels.users import CreateRequest as Users_CreateRequest
 from bll.user import UserBLL
 from config import config
+from config.info import get_version, get_build_number
 from database.errors import translate_errors_context
 from database.model.auth import User, Role, Credentials
 from database.model.company import Company
-from service_repo import APICall
-from service_repo.auth import (
-    Identity,
-    Token,
-    get_client_id,
-    get_secret_key,
-)
+from service_repo import APICall, ServiceRepo
+from service_repo.auth import Identity, Token, get_client_id, get_secret_key
 
 log = config.logger("AuthBLL")
 
@@ -62,6 +54,9 @@ class AuthBLL:
                 identity=identity,
                 entities=entities,
                 expiration_sec=expiration_sec,
+                api_version=str(ServiceRepo.max_endpoint_version()),
+                server_version=str(get_version()),
+                server_build=str(get_build_number()),
             )
 
             return GetTokenResponse(token=token.decode("ascii"))

server/bll/event/debug_images_iterator.py

@@ -0,0 +1,462 @@
+from collections import defaultdict
+from concurrent.futures.thread import ThreadPoolExecutor
+from functools import partial
+from itertools import chain
+from operator import attrgetter, itemgetter
+from typing import Sequence, Tuple, Optional, Mapping
+
+import attr
+import dpath
+from boltons.iterutils import bucketize
+from elasticsearch import Elasticsearch
+from jsonmodels.fields import StringField, ListField, IntField
+from jsonmodels.models import Base
+from redis import StrictRedis
+
+from apierrors import errors
+from apimodels import JsonSerializableMixin
+from bll.event.event_metrics import EventMetrics
+from bll.redis_cache_manager import RedisCacheManager
+from config import config
+from database.errors import translate_errors_context
+from database.model.task.metrics import MetricEventStats
+from database.model.task.task import Task
+from timing_context import TimingContext
+
+
+class VariantScrollState(Base):
+    name: str = StringField(required=True)
+    recycle_url_marker: str = StringField()
+    last_invalid_iteration: int = IntField()
+
+
+class MetricScrollState(Base):
+    task: str = StringField(required=True)
+    name: str = StringField(required=True)
+    last_min_iter: Optional[int] = IntField()
+    last_max_iter: Optional[int] = IntField()
+    timestamp: int = IntField(default=0)
+    variants: Sequence[VariantScrollState] = ListField([VariantScrollState])
+
+    def reset(self):
+        """Reset the scrolling state for the metric"""
+        self.last_min_iter = self.last_max_iter = None
+
+
+class DebugImageEventsScrollState(Base, JsonSerializableMixin):
+    id: str = StringField(required=True)
+    metrics: Sequence[MetricScrollState] = ListField([MetricScrollState])
+
+
+@attr.s(auto_attribs=True)
+class DebugImagesResult(object):
+    metric_events: Sequence[tuple] = []
+    next_scroll_id: str = None
+
+
+class DebugImagesIterator:
+    EVENT_TYPE = "training_debug_image"
+
+    @property
+    def state_expiration_sec(self):
+        return config.get(
+            f"services.events.events_retrieval.state_expiration_sec", 3600
+        )
+
+    @property
+    def _max_workers(self):
+        return config.get("services.events.max_metrics_concurrency", 4)
+
+    def __init__(self, redis: StrictRedis, es: Elasticsearch):
+        self.es = es
+        self.cache_manager = RedisCacheManager(
+            state_class=DebugImageEventsScrollState,
+            redis=redis,
+            expiration_interval=self.state_expiration_sec,
+        )
+
+    def get_task_events(
+        self,
+        company_id: str,
+        metrics: Sequence[Tuple[str, str]],
+        iter_count: int,
+        navigate_earlier: bool = True,
+        refresh: bool = False,
+        state_id: str = None,
+    ) -> DebugImagesResult:
+        es_index = EventMetrics.get_index_name(company_id, self.EVENT_TYPE)
+        if not self.es.indices.exists(es_index):
+            return DebugImagesResult()
+
+        def init_state(state_: DebugImageEventsScrollState):
+            unique_metrics = set(metrics)
+            state_.metrics = self._init_metric_states(es_index, list(unique_metrics))
+
+        def validate_state(state_: DebugImageEventsScrollState):
+            """
+            Validate that the metrics stored in the state are the same
+            as requested in the current call.
+            Refresh the state if requested
+            """
+            state_metrics = set((m.task, m.name) for m in state_.metrics)
+            if state_metrics != set(metrics):
+                raise errors.bad_request.InvalidScrollId(
+                    "Task metrics stored in the state do not match the passed ones",
+                    scroll_id=state_.id,
+                )
+            if refresh:
+                self._reinit_outdated_metric_states(company_id, es_index, state_)
+                for metric_state in state_.metrics:
+                    metric_state.reset()
+
+        with self.cache_manager.get_or_create_state(
+            state_id=state_id, init_state=init_state, validate_state=validate_state
+        ) as state:
+            res = DebugImagesResult(next_scroll_id=state.id)
+            with ThreadPoolExecutor(self._max_workers) as pool:
+                res.metric_events = list(
+                    pool.map(
+                        partial(
+                            self._get_task_metric_events,
+                            es_index=es_index,
+                            iter_count=iter_count,
+                            navigate_earlier=navigate_earlier,
+                        ),
+                        state.metrics,
+                    )
+                )
+
+            return res
+
+    def _reinit_outdated_metric_states(
+        self, company_id, es_index, state: DebugImageEventsScrollState
+    ):
+        """
+        Determines the metrics for which new debug image events were added
+        since their states were initialized and reinits these states
+        """
+        task_ids = set(metric.task for metric in state.metrics)
+        tasks = Task.objects(id__in=list(task_ids), company=company_id).only(
+            "id", "metric_stats"
+        )
+
+        def get_last_update_times_for_task_metrics(task: Task) -> Sequence[Tuple]:
+            """For metrics that reported debug image events get tuples of task_id/metric_name and last update times"""
+            metric_stats: Mapping[str, MetricEventStats] = task.metric_stats
+            if not metric_stats:
+                return []
+
+            return [
+                (
+                    (task.id, stats.metric),
+                    stats.event_stats_by_type[self.EVENT_TYPE].last_update,
+                )
+                for stats in metric_stats.values()
+                if self.EVENT_TYPE in stats.event_stats_by_type
+            ]
+
+        update_times = dict(
+            chain.from_iterable(
+                get_last_update_times_for_task_metrics(task) for task in tasks
+            )
+        )
+        outdated_metrics = [
+            metric
+            for metric in state.metrics
+            if (metric.task, metric.name) in update_times
+            and update_times[metric.task, metric.name] > metric.timestamp
+        ]
+        state.metrics = [
+            *(metric for metric in state.metrics if metric not in outdated_metrics),
+            *(
+                self._init_metric_states(
+                    es_index,
+                    [(metric.task, metric.name) for metric in outdated_metrics],
+                )
+            ),
+        ]
+
+    def _init_metric_states(
+        self, es_index, metrics: Sequence[Tuple[str, str]]
+    ) -> Sequence[MetricScrollState]:
+        """
+        Returned initialized metric scroll stated for the requested task metrics
+        """
+        tasks = defaultdict(list)
+        for (task, metric) in metrics:
+            tasks[task].append(metric)
+
+        with ThreadPoolExecutor(self._max_workers) as pool:
+            return list(
+                chain.from_iterable(
+                    pool.map(
+                        partial(self._init_metric_states_for_task, es_index=es_index),
+                        tasks.items(),
+                    )
+                )
+            )
+
+    def _init_metric_states_for_task(
+        self, task_metrics: Tuple[str, Sequence[str]], es_index
+    ) -> Sequence[MetricScrollState]:
+        """
+        Return metric scroll states for the task filled with the variant states
+        for the variants that reported any debug images
+        """
+        task, metrics = task_metrics
+        es_req: dict = {
+            "size": 0,
+            "query": {
+                "bool": {
+                    "must": [{"term": {"task": task}}, {"terms": {"metric": metrics}}]
+                }
+            },
+            "aggs": {
+                "metrics": {
+                    "terms": {
+                        "field": "metric",
+                        "size": EventMetrics.MAX_METRICS_COUNT,
+                    },
+                    "aggs": {
+                        "last_event_timestamp": {"max": {"field": "timestamp"}},
+                        "variants": {
+                            "terms": {
+                                "field": "variant",
+                                "size": EventMetrics.MAX_VARIANTS_COUNT,
+                            },
+                            "aggs": {
+                                "urls": {
+                                    "terms": {
+                                        "field": "url",
+                                        "order": {"max_iter": "desc"},
+                                        "size": 1,  # we need only one url from the most recent iteration
+                                    },
+                                    "aggs": {
+                                        "max_iter": {"max": {"field": "iter"}},
+                                        "iters": {
+                                            "top_hits": {
+                                                "sort": {"iter": {"order": "desc"}},
+                                                "size": 2,  # need two last iterations so that we can take
+                                                # the second one as invalid
+                                                "_source": "iter",
+                                            }
+                                        },
+                                    },
+                                }
+                            },
+                        },
+                    },
+                }
+            },
+        }
+
+        with translate_errors_context(), TimingContext("es", "_init_metric_states"):
+            es_res = self.es.search(index=es_index, body=es_req, routing=task)
+        if "aggregations" not in es_res:
+            return []
+
+        def init_variant_scroll_state(variant: dict):
+            """
+            Return new variant scroll state for the passed variant bucket
+            If the image urls get recycled then fill the last_invalid_iteration field
+            """
+            state = VariantScrollState(name=variant["key"])
+            top_iter_url = dpath.get(variant, "urls/buckets")[0]
+            iters = dpath.get(top_iter_url, "iters/hits/hits")
+            if len(iters) > 1:
+                state.last_invalid_iteration = dpath.get(iters[1], "_source/iter")
+            return state
+
+        return [
+            MetricScrollState(
+                task=task,
+                name=metric["key"],
+                variants=[
+                    init_variant_scroll_state(variant)
+                    for variant in dpath.get(metric, "variants/buckets")
+                ],
+                timestamp=dpath.get(metric, "last_event_timestamp/value"),
+            )
+            for metric in dpath.get(es_res, "aggregations/metrics/buckets")
+        ]
+
+    def _get_task_metric_events(
+        self,
+        metric: MetricScrollState,
+        es_index: str,
+        iter_count: int,
+        navigate_earlier: bool,
+    ) -> Tuple:
+        """
+        Return task metric events grouped by iterations
+        Update metric scroll state
+        """
+        if metric.last_max_iter is None:
+            # the first fetch is always from the latest iteration to the earlier ones
+            navigate_earlier = True
+
+        must_conditions = [
+            {"term": {"task": metric.task}},
+            {"term": {"metric": metric.name}},
+        ]
+        must_not_conditions = []
+
+        range_condition = None
+        if navigate_earlier and metric.last_min_iter is not None:
+            range_condition = {"lt": metric.last_min_iter}
+        elif not navigate_earlier and metric.last_max_iter is not None:
+            range_condition = {"gt": metric.last_max_iter}
+        if range_condition:
+            must_conditions.append({"range": {"iter": range_condition}})
+
+        if navigate_earlier:
+            """
+            When navigating to earlier iterations consider only
+            variants whose invalid iterations border is lower than
+            our starting iteration. For these variants make sure
+            that only events from the valid iterations are returned 
+            """
+            if not metric.last_min_iter:
+                variants = metric.variants
+            else:
+                variants = list(
+                    v
+                    for v in metric.variants
+                    if v.last_invalid_iteration is None
+                    or v.last_invalid_iteration < metric.last_min_iter
+                )
+                if not variants:
+                    return metric.task, metric.name, []
+                must_conditions.append(
+                    {"terms": {"variant": list(v.name for v in variants)}}
+                )
+        else:
+            """
+            When navigating to later iterations all variants may be relevant.
+            For the variants whose invalid border is higher than our starting 
+            iteration make sure that only events from valid iterations are returned 
+            """
+            variants = list(
+                v
+                for v in metric.variants
+                if v.last_invalid_iteration is not None
+                and v.last_invalid_iteration > metric.last_max_iter
+            )
+
+        variants_conditions = [
+            {
+                "bool": {
+                    "must": [
+                        {"term": {"variant": v.name}},
+                        {"range": {"iter": {"lte": v.last_invalid_iteration}}},
+                    ]
+                }
+            }
+            for v in variants
+            if v.last_invalid_iteration is not None
+        ]
+        if variants_conditions:
+            must_not_conditions.append({"bool": {"should": variants_conditions}})
+
+        es_req = {
+            "size": 0,
+            "query": {
+                "bool": {"must": must_conditions, "must_not": must_not_conditions}
+            },
+            "aggs": {
+                "iters": {
+                    "terms": {
+                        "field": "iter",
+                        "size": iter_count,
+                        "order": {"_term": "desc" if navigate_earlier else "asc"},
+                    },
+                    "aggs": {
+                        "variants": {
+                            "terms": {
+                                "field": "variant",
+                                "size": EventMetrics.MAX_VARIANTS_COUNT,
+                            },
+                            "aggs": {
+                                "events": {
+                                    "top_hits": {"sort": {"url": {"order": "desc"}}}
+                                }
+                            },
+                        }
+                    },
+                }
+            },
+        }
+        with translate_errors_context(), TimingContext("es", "get_debug_image_events"):
+            es_res = self.es.search(index=es_index, body=es_req, routing=metric.task)
+        if "aggregations" not in es_res:
+            return metric.task, metric.name, []
+
+        def get_iteration_events(variant_buckets: Sequence[dict]) -> Sequence:
+            return [
+                ev["_source"]
+                for v in variant_buckets
+                for ev in dpath.get(v, "events/hits/hits")
+            ]
+
+        iterations = [
+            {
+                "iter": it["key"],
+                "events": get_iteration_events(dpath.get(it, "variants/buckets")),
+            }
+            for it in dpath.get(es_res, "aggregations/iters/buckets")
+        ]
+        if not navigate_earlier:
+            iterations.sort(key=itemgetter("iter"), reverse=True)
+        if iterations:
+            metric.last_max_iter = iterations[0]["iter"]
+            metric.last_min_iter = iterations[-1]["iter"]
+
+        # Commented for now since the last invalid iteration is calculated in the beginning
+        # if navigate_earlier and any(
+        #     variant.last_invalid_iteration is None for variant in variants
+        # ):
+        #     """
+        #     Variants validation flags due to recycling can
+        #     be set only on navigation to earlier frames
+        #     """
+        #     iterations = self._update_variants_invalid_iterations(variants, iterations)
+
+        return metric.task, metric.name, iterations
+
+    @staticmethod
+    def _update_variants_invalid_iterations(
+        variants: Sequence[VariantScrollState], iterations: Sequence[dict]
+    ) -> Sequence[dict]:
+        """
+        This code is currently not in used since the invalid iterations
+        are calculated during MetricState initialization
+        For variants that do not have recycle url marker set it from the
+        first event
+        For variants that do not have last_invalid_iteration set check if the
+        recycle marker was reached on a certain iteration and set it to the
+        corresponding iteration
+        For variants that have a newly set last_invalid_iteration remove
+        events from the invalid iterations
+        Return the updated iterations list
+        """
+        variants_lookup = bucketize(variants, attrgetter("name"))
+        for it in iterations:
+            iteration = it["iter"]
+            events_to_remove = []
+            for event in it["events"]:
+                variant = variants_lookup[event["variant"]][0]
+                if (
+                    variant.last_invalid_iteration
+                    and variant.last_invalid_iteration >= iteration
+                ):
+                    events_to_remove.append(event)
+                    continue
+                event_url = event.get("url")
+                if not variant.recycle_url_marker:
+                    variant.recycle_url_marker = event_url
+                elif variant.recycle_url_marker == event_url:
+                    variant.last_invalid_iteration = iteration
+                    events_to_remove.append(event)
+            if events_to_remove:
+                it["events"] = [ev for ev in it["events"] if ev not in events_to_remove]
+        return [it for it in iterations if it["events"]]

server/bll/event/event_bll.py

@@ -1,71 +1,104 @@
+import hashlib
 from collections import defaultdict
 from contextlib import closing
 from datetime import datetime
 from operator import attrgetter
+from typing import Sequence, Set, Tuple
 
-import attr
 import six
 from elasticsearch import helpers
-from enum import Enum
-
 from mongoengine import Q
 from nested_dict import nested_dict
 
 import database.utils as dbutils
 import es_factory
 from apierrors import errors
+from bll.event.debug_images_iterator import DebugImagesIterator
+from bll.event.event_metrics import EventMetrics, EventType
+from bll.event.log_events_iterator import LogEventsIterator, TaskEventsResult
 from bll.task import TaskBLL
+from config import config
 from database.errors import translate_errors_context
-from database.model.task.task import Task
-from database.model.task.metrics import MetricEvent
+from database.model.task.task import Task, TaskStatus
+from redis_manager import redman
 from timing_context import TimingContext
-
-
-class EventType(Enum):
-    metrics_scalar = "training_stats_scalar"
-    metrics_vector = "training_stats_vector"
-    metrics_image = "training_debug_image"
-    metrics_plot = "plot"
-    task_log = "log"
-
+from utilities.dicts import flatten_nested_items
 
 # noinspection PyTypeChecker
 EVENT_TYPES = set(map(attrgetter("value"), EventType))
-
-
-@attr.s
-class TaskEventsResult(object):
-    events = attr.ib(type=list, default=attr.Factory(list))
-    total_events = attr.ib(type=int, default=0)
-    next_scroll_id = attr.ib(type=str, default=None)
+LOCKED_TASK_STATUSES = (TaskStatus.publishing, TaskStatus.published)
 
 
 class EventBLL(object):
-    id_fields = ["task", "iter", "metric", "variant", "key"]
+    id_fields = ("task", "iter", "metric", "variant", "key")
 
-    def __init__(self, events_es=None):
-        self.es = events_es if events_es is not None else es_factory.connect("events")
+    def __init__(self, events_es=None, redis=None):
+        self.es = events_es or es_factory.connect("events")
+        self._metrics = EventMetrics(self.es)
+        self._skip_iteration_for_metric = set(
+            config.get("services.events.ignore_iteration.metrics", [])
+        )
+        self.redis = redis or redman.connection("apiserver")
+        self.debug_images_iterator = DebugImagesIterator(es=self.es, redis=self.redis)
+        self.log_events_iterator = LogEventsIterator(es=self.es, redis=self.redis)
 
-    def add_events(self, company_id, events, worker):
+    @property
+    def metrics(self) -> EventMetrics:
+        return self._metrics
+
+    @staticmethod
+    def _get_valid_tasks(company_id, task_ids: Set, allow_locked_tasks=False) -> Set:
+        """Verify that task exists and can be updated"""
+        if not task_ids:
+            return set()
+
+        with translate_errors_context(), TimingContext("mongo", "task_by_ids"):
+            query = Q(id__in=task_ids, company=company_id)
+            if not allow_locked_tasks:
+                query &= Q(status__nin=LOCKED_TASK_STATUSES)
+            res = Task.objects(query).only("id")
+            return {r.id for r in res}
+
+    def add_events(
+        self, company_id, events, worker, allow_locked_tasks=False
+    ) -> Tuple[int, int, dict]:
         actions = []
         task_ids = set()
         task_iteration = defaultdict(lambda: 0)
-        task_last_events = nested_dict(
+        task_last_scalar_events = nested_dict(
             3, dict
         )  # task_id -> metric_hash -> variant_hash -> MetricEvent
-
+        task_last_events = nested_dict(
+            3, dict
+        )  # task_id -> metric_hash -> event_type -> MetricEvent
+        errors_per_type = defaultdict(int)
+        valid_tasks = self._get_valid_tasks(
+            company_id,
+            task_ids={
+                event["task"] for event in events if event.get("task") is not None
+            },
+            allow_locked_tasks=allow_locked_tasks,
+        )
         for event in events:
             # remove spaces from event type
-            if "type" not in event:
-                raise errors.BadRequest("Event must have a 'type' field", event=event)
+            event_type = event.get("type")
+            if event_type is None:
+                errors_per_type["Event must have a 'type' field"] += 1
+                continue
 
-            event_type = event["type"].replace(" ", "_")
+            event_type = event_type.replace(" ", "_")
             if event_type not in EVENT_TYPES:
-                raise errors.BadRequest(
-                    "Invalid event type {}".format(event_type),
-                    event=event,
-                    types=EVENT_TYPES,
-                )
+                errors_per_type[f"Invalid event type {event_type}"] += 1
+                continue
+
+            task_id = event.get("task")
+            if task_id is None:
+                errors_per_type["Event must have a 'task' field"] += 1
+                continue
+
+            if task_id not in valid_tasks:
+                errors_per_type["Invalid task id"] += 1
+                continue
 
             event["type"] = event_type
 
@@ -94,7 +127,10 @@ class EventBLL(object):
                 event["value"] = event["values"]
                 del event["values"]
 
-            index_name = EventBLL.get_index_name(company_id, event_type)
+            event["metric"] = event.get("metric") or ""
+            event["variant"] = event.get("variant") or ""
+
+            index_name = EventMetrics.get_index_name(company_id, event_type)
             es_action = {
                 "_op_type": "index",  # overwrite if exists with same ID
                 "_index": index_name,
@@ -108,92 +144,82 @@ class EventBLL(object):
             else:
                 es_action["_id"] = dbutils.id()
 
-            task_id = event.get("task")
-            if task_id is not None:
-                es_action["_routing"] = task_id
-                task_ids.add(task_id)
-                if iter is not None:
-                    task_iteration[task_id] = max(iter, task_iteration[task_id])
+            es_action["_routing"] = task_id
+            task_ids.add(task_id)
+            if (
+                iter is not None
+                and event.get("metric") not in self._skip_iteration_for_metric
+            ):
+                task_iteration[task_id] = max(iter, task_iteration[task_id])
 
-                if event_type == EventType.metrics_scalar.value:
-                    self._update_last_metric_event_for_task(
-                        task_last_events=task_last_events, task_id=task_id, event=event
-                    )
-            else:
-                es_action["_routing"] = task_id
+            self._update_last_metric_events_for_task(
+                last_events=task_last_events[task_id], event=event,
+            )
+            if event_type == EventType.metrics_scalar.value:
+                self._update_last_scalar_events_for_task(
+                    last_events=task_last_scalar_events[task_id], event=event
+                )
 
             actions.append(es_action)
 
-        if task_ids:
-            # verify task_ids
-            with translate_errors_context(), TimingContext("mongo", "task_by_ids"):
-                res = Task.objects(id__in=task_ids, company=company_id).only("id")
-                if len(res) < len(task_ids):
-                    invalid_task_ids = tuple(set(task_ids) - set(r.id for r in res))
-                    raise errors.bad_request.InvalidTaskId(
-                        company=company_id, ids=invalid_task_ids
+        added = 0
+        if actions:
+            chunk_size = 500
+            with translate_errors_context(), TimingContext("es", "events_add_batch"):
+                # TODO: replace it with helpers.parallel_bulk in the future once the parallel pool leak is fixed
+                with closing(
+                    helpers.streaming_bulk(
+                        self.es,
+                        actions,
+                        chunk_size=chunk_size,
+                        # thread_count=8,
+                        refresh=True,
+                    )
+                ) as it:
+                    for success, info in it:
+                        if success:
+                            added += chunk_size
+                        else:
+                            errors_per_type["Error when indexing events batch"] += 1
+
+                remaining_tasks = set()
+                now = datetime.utcnow()
+                for task_id in task_ids:
+                    # Update related tasks. For reasons of performance, we prefer to update
+                    # all of them and not only those who's events were successful
+                    updated = self._update_task(
+                        company_id=company_id,
+                        task_id=task_id,
+                        now=now,
+                        iter_max=task_iteration.get(task_id),
+                        last_scalar_events=task_last_scalar_events.get(task_id),
+                        last_events=task_last_events.get(task_id),
                     )
 
-        errors_in_bulk = []
-        added = 0
-        chunk_size = 500
-        with translate_errors_context(), TimingContext("es", "events_add_batch"):
-            # TODO: replace it with helpers.parallel_bulk in the future once the parallel pool leak is fixed
-            with closing(
-                helpers.streaming_bulk(
-                    self.es,
-                    actions,
-                    chunk_size=chunk_size,
-                    # thread_count=8,
-                    refresh=True,
-                )
-            ) as it:
-                for success, info in it:
-                    if success:
-                        added += chunk_size
-                    else:
-                        errors_in_bulk.append(info)
+                    if not updated:
+                        remaining_tasks.add(task_id)
+                        continue
 
-            last_metrics = {
-                t.id: t.to_proper_dict().get("last_metrics", {})
-                for t in Task.objects(id__in=task_ids, company=company_id).only(
-                    "last_metrics"
-                )
-            }
-
-            remaining_tasks = set()
-            now = datetime.utcnow()
-            for task_id in task_ids:
-                # Update related tasks. For reasons of performance, we prefer to update all of them and not only those
-                #  who's events were successful
-
-                updated = self._update_task(
-                    company_id=company_id,
-                    task_id=task_id,
-                    now=now,
-                    iter=task_iteration.get(task_id),
-                    last_events=task_last_events.get(task_id),
-                    last_metrics=last_metrics.get(task_id),
-                )
-
-                if not updated:
-                    remaining_tasks.add(task_id)
-                    continue
-
-            if remaining_tasks:
-                TaskBLL.set_last_update(remaining_tasks, company_id, last_update=now)
+                if remaining_tasks:
+                    TaskBLL.set_last_update(
+                        remaining_tasks, company_id, last_update=now
+                    )
 
         # Compensate for always adding chunk_size on success (last chunk is probably smaller)
         added = min(added, len(actions))
 
-        return added, errors_in_bulk
+        if not added:
+            raise errors.bad_request.EventsNotAdded(**errors_per_type)
 
-    def _update_last_metric_event_for_task(self, task_last_events, task_id, event):
+        errors_count = sum(errors_per_type.values())
+        return added, errors_count, errors_per_type
+
+    def _update_last_scalar_events_for_task(self, last_events, event):
         """
-        Update task_last_events structure for the provided task_id with the provided event details if this event is more
+        Update last_events structure with the provided event details if this event is more
         recent than the currently stored event for its metric/variant combination.
 
-        task_last_events contains [hashed_metric_name -> hashed_variant_name -> event]. Keys are hashed to avoid mongodb
+        last_events contains [hashed_metric_name -> hashed_variant_name -> event]. Keys are hashed to avoid mongodb
         key conflicts due to invalid characters and/or long field names.
         """
         metric = event.get("metric")
@@ -204,14 +230,49 @@ class EventBLL(object):
         metric_hash = dbutils.hash_field_name(metric)
         variant_hash = dbutils.hash_field_name(variant)
 
-        last_events = task_last_events[task_id]
+        last_event = last_events[metric_hash][variant_hash]
+        event_iter = event.get("iter", 0)
+        event_timestamp = event.get("timestamp", 0)
+        value = event.get("value")
+        if value is not None and (
+            (event_iter, event_timestamp)
+            >= (
+                last_event.get("iter", event_iter),
+                last_event.get("timestamp", event_timestamp),
+            )
+        ):
+            event_data = {
+                k: event[k]
+                for k in ("value", "metric", "variant", "iter", "timestamp")
+                if k in event
+            }
+            event_data["min_value"] = min(value, last_event.get("min_value", value))
+            event_data["max_value"] = max(value, last_event.get("max_value", value))
+            last_events[metric_hash][variant_hash] = event_data
 
-        timestamp = last_events[metric_hash][variant_hash].get("timestamp", None)
+    def _update_last_metric_events_for_task(self, last_events, event):
+        """
+        Update last_events structure with the provided event details if this event is more
+        recent than the currently stored event for its metric/event_type combination.
+        last_events contains [metric_name -> event_type -> event]
+        """
+        metric = event.get("metric")
+        event_type = event.get("type")
+        if not (metric and event_type):
+            return
+
+        timestamp = last_events[metric][event_type].get("timestamp", None)
         if timestamp is None or timestamp < event["timestamp"]:
-            last_events[metric_hash][variant_hash] = event
+            last_events[metric][event_type] = event
 
     def _update_task(
-        self, company_id, task_id, now, iter=None, last_events=None, last_metrics=None
+        self,
+        company_id,
+        task_id,
+        now,
+        iter_max=None,
+        last_scalar_events=None,
+        last_events=None,
     ):
         """
         Update task information in DB with aggregated results after handling event(s) related to this task.
@@ -222,27 +283,26 @@ class EventBLL(object):
         """
         fields = {}
 
-        if iter is not None:
-            fields["last_iteration"] = iter
+        if iter_max is not None:
+            fields["last_iteration_max"] = iter_max
+
+        if last_scalar_events:
+            fields["last_scalar_values"] = list(
+                flatten_nested_items(
+                    last_scalar_events,
+                    nesting=2,
+                    include_leaves=[
+                        "value",
+                        "min_value",
+                        "max_value",
+                        "metric",
+                        "variant",
+                    ],
+                )
+            )
 
         if last_events:
-
-            def get_metric_event(ev):
-                me = MetricEvent.from_dict(**ev)
-                if "timestamp" in ev:
-                    me.timestamp = datetime.utcfromtimestamp(ev["timestamp"] / 1000)
-                return me
-
-            new_last_metrics = nested_dict(2, MetricEvent)
-            new_last_metrics.update(last_metrics)
-
-            for metric_hash, variants in last_events.items():
-                for variant_hash, event in variants.items():
-                    new_last_metrics[metric_hash][variant_hash] = get_metric_event(
-                        event
-                    )
-
-            fields["last_metrics"] = new_last_metrics.to_dict()
+            fields["last_events"] = last_events
 
         if not fields:
             return False
@@ -251,7 +311,7 @@ class EventBLL(object):
 
     def _get_event_id(self, event):
         id_values = (str(event[field]) for field in self.id_fields if field in event)
-        return "-".join(id_values)
+        return hashlib.md5("-".join(id_values).encode()).hexdigest()
 
     def scroll_task_events(
         self,
@@ -270,7 +330,7 @@ class EventBLL(object):
             if event_type is None:
                 event_type = "*"
 
-            es_index = EventBLL.get_index_name(company_id, event_type)
+            es_index = EventMetrics.get_index_name(company_id, event_type)
 
             if not self.es.indices.exists(es_index):
                 return [], None, 0
@@ -282,7 +342,9 @@ class EventBLL(object):
             }
 
             with translate_errors_context(), TimingContext("es", "scroll_task_events"):
-                es_res = self.es.search(index=es_index, body=es_req, scroll="1h")
+                es_res = self.es.search(
+                    index=es_index, body=es_req, scroll="1h", routing=task_id
+                )
 
         events = [hit["_source"] for hit in es_res["hits"]["hits"]]
         next_scroll_id = es_res["_scroll_id"]
@@ -290,6 +352,131 @@ class EventBLL(object):
 
         return events, next_scroll_id, total_events
 
+    def get_last_iterations_per_event_metric_variant(
+        self, es_index: str, task_id: str, num_last_iterations: int, event_type: str
+    ):
+        if not self.es.indices.exists(es_index):
+            return []
+
+        es_req: dict = {
+            "size": 0,
+            "aggs": {
+                "metrics": {
+                    "terms": {
+                        "field": "metric",
+                        "size": EventMetrics.MAX_METRICS_COUNT,
+                    },
+                    "aggs": {
+                        "variants": {
+                            "terms": {
+                                "field": "variant",
+                                "size": EventMetrics.MAX_VARIANTS_COUNT,
+                            },
+                            "aggs": {
+                                "iters": {
+                                    "terms": {
+                                        "field": "iter",
+                                        "size": num_last_iterations,
+                                        "order": {"_term": "desc"},
+                                    }
+                                }
+                            },
+                        }
+                    },
+                }
+            },
+            "query": {"bool": {"must": [{"term": {"task": task_id}}]}},
+        }
+        if event_type:
+            es_req["query"]["bool"]["must"].append({"term": {"type": event_type}})
+
+        with translate_errors_context(), TimingContext(
+            "es", "task_last_iter_metric_variant"
+        ):
+            es_res = self.es.search(index=es_index, body=es_req, routing=task_id)
+        if "aggregations" not in es_res:
+            return []
+
+        return [
+            (metric["key"], variant["key"], iter["key"])
+            for metric in es_res["aggregations"]["metrics"]["buckets"]
+            for variant in metric["variants"]["buckets"]
+            for iter in variant["iters"]["buckets"]
+        ]
+
+    def get_task_plots(
+        self,
+        company_id: str,
+        tasks: Sequence[str],
+        last_iterations_per_plot: int = None,
+        sort=None,
+        size: int = 500,
+        scroll_id: str = None,
+    ):
+        if scroll_id:
+            with translate_errors_context(), TimingContext("es", "get_task_events"):
+                es_res = self.es.scroll(scroll_id=scroll_id, scroll="1h")
+        else:
+            event_type = "plot"
+            es_index = EventMetrics.get_index_name(company_id, event_type)
+            if not self.es.indices.exists(es_index):
+                return TaskEventsResult()
+
+            query = {"bool": defaultdict(list)}
+
+            if last_iterations_per_plot is None:
+                must = query["bool"]["must"]
+                must.append({"terms": {"task": tasks}})
+            else:
+                should = query["bool"]["should"]
+                for i, task_id in enumerate(tasks):
+                    last_iters = self.get_last_iterations_per_event_metric_variant(
+                        es_index, task_id, last_iterations_per_plot, event_type
+                    )
+                    if not last_iters:
+                        continue
+
+                    for metric, variant, iter in last_iters:
+                        should.append(
+                            {
+                                "bool": {
+                                    "must": [
+                                        {"term": {"task": task_id}},
+                                        {"term": {"metric": metric}},
+                                        {"term": {"variant": variant}},
+                                        {"term": {"iter": iter}},
+                                    ]
+                                }
+                            }
+                        )
+                if not should:
+                    return TaskEventsResult()
+
+            if sort is None:
+                sort = [{"timestamp": {"order": "asc"}}]
+
+            es_req = {"sort": sort, "size": min(size, 10000), "query": query}
+
+            routing = ",".join(tasks)
+
+            with translate_errors_context(), TimingContext("es", "get_task_plots"):
+                es_res = self.es.search(
+                    index=es_index,
+                    body=es_req,
+                    ignore=404,
+                    routing=routing,
+                    scroll="1h",
+                )
+
+        events = [doc["_source"] for doc in es_res.get("hits", {}).get("hits", [])]
+        # scroll id may be missing when queering a totally empty DB
+        next_scroll_id = es_res.get("_scroll_id")
+        total_events = es_res["hits"]["total"]
+
+        return TaskEventsResult(
+            events=events, next_scroll_id=next_scroll_id, total_events=total_events
+        )
+
     def get_task_events(
         self,
         company_id,
@@ -311,7 +498,7 @@ class EventBLL(object):
             if event_type is None:
                 event_type = "*"
 
-            es_index = EventBLL.get_index_name(company_id, event_type)
+            es_index = EventMetrics.get_index_name(company_id, event_type)
             if not self.es.indices.exists(es_index):
                 return TaskEventsResult()
 
@@ -374,7 +561,7 @@ class EventBLL(object):
 
     def get_metrics_and_variants(self, company_id, task_id, event_type):
 
-        es_index = EventBLL.get_index_name(company_id, event_type)
+        es_index = EventMetrics.get_index_name(company_id, event_type)
 
         if not self.es.indices.exists(es_index):
             return {}
@@ -383,8 +570,18 @@ class EventBLL(object):
             "size": 0,
             "aggs": {
                 "metrics": {
-                    "terms": {"field": "metric", "size": 200},
-                    "aggs": {"variants": {"terms": {"field": "variant", "size": 200}}},
+                    "terms": {
+                        "field": "metric",
+                        "size": EventMetrics.MAX_METRICS_COUNT,
+                    },
+                    "aggs": {
+                        "variants": {
+                            "terms": {
+                                "field": "variant",
+                                "size": EventMetrics.MAX_VARIANTS_COUNT,
+                            }
+                        }
+                    },
                 }
             },
             "query": {"bool": {"must": [{"term": {"task": task_id}}]}},
@@ -405,7 +602,7 @@ class EventBLL(object):
         return metrics
 
     def get_task_latest_scalar_values(self, company_id, task_id):
-        es_index = EventBLL.get_index_name(company_id, "training_stats_scalar")
+        es_index = EventMetrics.get_index_name(company_id, "training_stats_scalar")
 
         if not self.es.indices.exists(es_index):
             return {}
@@ -424,14 +621,14 @@ class EventBLL(object):
                 "metrics": {
                     "terms": {
                         "field": "metric",
-                        "size": 1000,
+                        "size": EventMetrics.MAX_METRICS_COUNT,
                         "order": {"_term": "asc"},
                     },
                     "aggs": {
                         "variants": {
                             "terms": {
                                 "field": "variant",
-                                "size": 1000,
+                                "size": EventMetrics.MAX_VARIANTS_COUNT,
                                 "order": {"_term": "asc"},
                             },
                             "aggs": {
@@ -488,147 +685,9 @@ class EventBLL(object):
             metrics.append(metric_summary)
         return metrics, max_timestamp
 
-    def compare_scalar_metrics_average_per_iter(
-        self, company_id, task_ids, allow_public=True
-    ):
-        assert isinstance(task_ids, list)
-
-        task_name_by_id = {}
-        with translate_errors_context():
-            task_objs = Task.get_many(
-                company=company_id,
-                query=Q(id__in=task_ids),
-                allow_public=allow_public,
-                override_projection=("id", "name"),
-                return_dicts=False,
-            )
-            if len(task_objs) < len(task_ids):
-                invalid = tuple(set(task_ids) - set(r.id for r in task_objs))
-                raise errors.bad_request.InvalidTaskId(company=company_id, ids=invalid)
-
-            task_name_by_id = {t.id: t.name for t in task_objs}
-
-        es_index = EventBLL.get_index_name(company_id, "training_stats_scalar")
-        if not self.es.indices.exists(es_index):
-            return {}
-
-        es_req = {
-            "size": 0,
-            "_source": {"excludes": []},
-            "query": {"terms": {"task": task_ids}},
-            "aggs": {
-                "iters": {
-                    "histogram": {"field": "iter", "interval": 1, "min_doc_count": 1},
-                    "aggs": {
-                        "metric_and_variant": {
-                            "terms": {
-                                "script": "doc['metric'].value +'/'+ doc['variant'].value",
-                                "size": 10000,
-                            },
-                            "aggs": {
-                                "tasks": {
-                                    "terms": {"field": "task"},
-                                    "aggs": {"avg_val": {"avg": {"field": "value"}}},
-                                }
-                            },
-                        }
-                    },
-                }
-            },
-        }
-        with translate_errors_context(), TimingContext("es", "task_stats_comparison"):
-            es_res = self.es.search(index=es_index, body=es_req)
-
-        if "aggregations" not in es_res:
-            return
-
-        metrics = {}
-        for iter_bucket in es_res["aggregations"]["iters"]["buckets"]:
-            iteration = int(iter_bucket["key"])
-            for metric_bucket in iter_bucket["metric_and_variant"]["buckets"]:
-                metric_name = metric_bucket["key"]
-                if metrics.get(metric_name) is None:
-                    metrics[metric_name] = {}
-
-                metric_data = metrics[metric_name]
-                for task_bucket in metric_bucket["tasks"]["buckets"]:
-                    task_id = task_bucket["key"]
-                    value = task_bucket["avg_val"]["value"]
-                    if metric_data.get(task_id) is None:
-                        metric_data[task_id] = {
-                            "x": [],
-                            "y": [],
-                            "name": task_name_by_id[
-                                task_id
-                            ],  # todo: lookup task name from id
-                        }
-                    metric_data[task_id]["x"].append(iteration)
-                    metric_data[task_id]["y"].append(value)
-
-        return metrics
-
-    def get_scalar_metrics_average_per_iter(self, company_id, task_id):
-
-        es_index = EventBLL.get_index_name(company_id, "training_stats_scalar")
-        if not self.es.indices.exists(es_index):
-            return {}
-
-        es_req = {
-            "size": 0,
-            "_source": {"excludes": []},
-            "query": {"term": {"task": task_id}},
-            "aggs": {
-                "iters": {
-                    "histogram": {"field": "iter", "interval": 1, "min_doc_count": 1},
-                    "aggs": {
-                        "metrics": {
-                            "terms": {
-                                "field": "metric",
-                                "size": 200,
-                                "order": {"_term": "desc"},
-                            },
-                            "aggs": {
-                                "variants": {
-                                    "terms": {
-                                        "field": "variant",
-                                        "size": 500,
-                                        "order": {"_term": "desc"},
-                                    },
-                                    "aggs": {"avg_val": {"avg": {"field": "value"}}},
-                                }
-                            },
-                        }
-                    },
-                }
-            },
-            "version": True,
-        }
-
-        with translate_errors_context(), TimingContext("es", "task_stats_scalar"):
-            es_res = self.es.search(index=es_index, body=es_req, routing=task_id)
-
-        metrics = {}
-        if "aggregations" in es_res:
-            for iter_bucket in es_res["aggregations"]["iters"]["buckets"]:
-                iteration = int(iter_bucket["key"])
-                for metric_bucket in iter_bucket["metrics"]["buckets"]:
-                    metric_name = metric_bucket["key"]
-                    if metrics.get(metric_name) is None:
-                        metrics[metric_name] = {}
-
-                    metric_data = metrics[metric_name]
-                    for variant_bucket in metric_bucket["variants"]["buckets"]:
-                        variant = variant_bucket["key"]
-                        value = variant_bucket["avg_val"]["value"]
-                        if metric_data.get(variant) is None:
-                            metric_data[variant] = {"x": [], "y": [], "name": variant}
-                        metric_data[variant]["x"].append(iteration)
-                        metric_data[variant]["y"].append(value)
-        return metrics
-
     def get_vector_metrics_per_iter(self, company_id, task_id, metric, variant):
 
-        es_index = EventBLL.get_index_name(company_id, "training_stats_vector")
+        es_index = EventMetrics.get_index_name(company_id, "training_stats_vector")
         if not self.es.indices.exists(es_index):
             return [], []
 
@@ -684,8 +743,20 @@ class EventBLL(object):
 
         return [b["key"] for b in es_res["aggregations"]["iters"]["buckets"]]
 
-    def delete_task_events(self, company_id, task_id):
-        es_index = EventBLL.get_index_name(company_id, "*")
+    def delete_task_events(self, company_id, task_id, allow_locked=False):
+        with translate_errors_context():
+            extra_msg = None
+            query = Q(id=task_id, company=company_id)
+            if not allow_locked:
+                query &= Q(status__nin=LOCKED_TASK_STATUSES)
+                extra_msg = "or task published"
+            res = Task.objects(query).only("id").first()
+            if not res:
+                raise errors.bad_request.InvalidTaskId(
+                    extra_msg, company=company_id, id=task_id
+                )
+
+        es_index = EventMetrics.get_index_name(company_id, "*")
         es_req = {"query": {"term": {"task": task_id}}}
         with translate_errors_context(), TimingContext("es", "delete_task_events"):
             es_res = self.es.delete_by_query(
@@ -693,8 +764,3 @@ class EventBLL(object):
             )
 
         return es_res.get("deleted", 0)
-
-    @staticmethod
-    def get_index_name(company_id, event_type):
-        event_type = event_type.lower().replace(" ", "_")
-        return "events-%s-%s" % (event_type, company_id)

server/bll/event/event_metrics.py

@@ -0,0 +1,496 @@
+import itertools
+from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor
+from enum import Enum
+from functools import partial
+from operator import itemgetter
+from typing import Sequence, Tuple, Callable, Iterable
+
+from boltons.iterutils import bucketize
+from elasticsearch import Elasticsearch
+from mongoengine import Q
+
+from apierrors import errors
+from bll.event.scalar_key import ScalarKey, ScalarKeyEnum
+from config import config
+from database.errors import translate_errors_context
+from database.model.task.task import Task
+from timing_context import TimingContext
+from utilities import safe_get
+
+log = config.logger(__file__)
+
+
+class EventType(Enum):
+    metrics_scalar = "training_stats_scalar"
+    metrics_vector = "training_stats_vector"
+    metrics_image = "training_debug_image"
+    metrics_plot = "plot"
+    task_log = "log"
+
+
+class EventMetrics:
+    MAX_TASKS_COUNT = 50
+    MAX_METRICS_COUNT = 200
+    MAX_VARIANTS_COUNT = 500
+    MAX_AGGS_ELEMENTS_COUNT = 50
+
+    def __init__(self, es: Elasticsearch):
+        self.es = es
+
+    @staticmethod
+    def get_index_name(company_id, event_type):
+        event_type = event_type.lower().replace(" ", "_")
+        return f"events-{event_type}-{company_id}"
+
+    def get_scalar_metrics_average_per_iter(
+        self, company_id: str, task_id: str, samples: int, key: ScalarKeyEnum
+    ) -> dict:
+        """
+        Get scalar metric histogram per metric and variant
+        The amount of points in each histogram should not exceed
+        the requested samples
+        """
+
+        return self._run_get_scalar_metrics_as_parallel(
+            company_id,
+            task_ids=[task_id],
+            samples=samples,
+            key=ScalarKey.resolve(key),
+            get_func=self._get_scalar_average,
+        )
+
+    def compare_scalar_metrics_average_per_iter(
+        self,
+        company_id,
+        task_ids: Sequence[str],
+        samples,
+        key: ScalarKeyEnum,
+        allow_public=True,
+    ):
+        """
+        Compare scalar metrics for different tasks per metric and variant
+        The amount of points in each histogram should not exceed the requested samples
+        """
+        if len(task_ids) > self.MAX_TASKS_COUNT:
+            raise errors.BadRequest(
+                f"Up to {self.MAX_TASKS_COUNT} tasks supported for comparison",
+                len(task_ids),
+            )
+
+        task_name_by_id = {}
+        with translate_errors_context():
+            task_objs = Task.get_many(
+                company=company_id,
+                query=Q(id__in=task_ids),
+                allow_public=allow_public,
+                override_projection=("id", "name"),
+                return_dicts=False,
+            )
+            if len(task_objs) < len(task_ids):
+                invalid = tuple(set(task_ids) - set(r.id for r in task_objs))
+                raise errors.bad_request.InvalidTaskId(company=company_id, ids=invalid)
+
+            task_name_by_id = {t.id: t.name for t in task_objs}
+
+        ret = self._run_get_scalar_metrics_as_parallel(
+            company_id,
+            task_ids=task_ids,
+            samples=samples,
+            key=ScalarKey.resolve(key),
+            get_func=self._get_scalar_average_per_task,
+        )
+
+        for metric_data in ret.values():
+            for variant_data in metric_data.values():
+                for task_id, task_data in variant_data.items():
+                    task_data["name"] = task_name_by_id[task_id]
+
+        return ret
+
+    TaskMetric = Tuple[str, str, str]
+
+    MetricInterval = Tuple[int, Sequence[TaskMetric]]
+    MetricData = Tuple[str, dict]
+
+    def _split_metrics_by_max_aggs_count(
+        self, task_metrics: Sequence[TaskMetric]
+    ) -> Iterable[Sequence[TaskMetric]]:
+        """
+        Return task metrics in groups where amount of task metrics in each group
+        is roughly limited by MAX_AGGS_ELEMENTS_COUNT. The split is done on metrics and
+        variants while always preserving all their tasks in the same group
+        """
+        if len(task_metrics) < self.MAX_AGGS_ELEMENTS_COUNT:
+            yield task_metrics
+            return
+
+        tm_grouped = bucketize(task_metrics, key=itemgetter(1, 2))
+        groups = []
+        for group in tm_grouped.values():
+            groups.append(group)
+            if sum(map(len, groups)) >= self.MAX_AGGS_ELEMENTS_COUNT:
+                yield list(itertools.chain(*groups))
+                groups = []
+
+        if groups:
+            yield list(itertools.chain(*groups))
+
+        return
+
+    def _run_get_scalar_metrics_as_parallel(
+        self,
+        company_id: str,
+        task_ids: Sequence[str],
+        samples: int,
+        key: ScalarKey,
+        get_func: Callable[
+            [MetricInterval, Sequence[str], str, ScalarKey], Sequence[MetricData]
+        ],
+    ) -> dict:
+        """
+        Group metrics per interval length and execute get_func for each group in parallel
+        :param company_id: id of the company
+        :params task_ids: ids of the tasks to collect data for
+        :param samples: maximum number of samples per metric
+        :param get_func: callable that given metric names for the same interval
+        performs histogram aggregation for the metrics and return the aggregated data
+        """
+        es_index = self.get_index_name(company_id, "training_stats_scalar")
+        if not self.es.indices.exists(es_index):
+            return {}
+
+        intervals = self._get_metric_intervals(
+            es_index=es_index, task_ids=task_ids, samples=samples, field=key.field
+        )
+
+        if not intervals:
+            return {}
+
+        intervals = list(
+            itertools.chain.from_iterable(
+                zip(itertools.repeat(i), self._split_metrics_by_max_aggs_count(tms))
+                for i, tms in intervals
+            )
+        )
+        max_concurrency = config.get("services.events.max_metrics_concurrency", 4)
+        with ThreadPoolExecutor(max_workers=max_concurrency) as pool:
+            metrics = itertools.chain.from_iterable(
+                pool.map(
+                    partial(get_func, task_ids=task_ids, es_index=es_index, key=key),
+                    intervals,
+                )
+            )
+
+        ret = defaultdict(dict)
+        for metric_key, metric_values in metrics:
+            ret[metric_key].update(metric_values)
+
+        return ret
+
+    def _get_metric_intervals(
+        self, es_index, task_ids: Sequence[str], samples: int, field: str = "iter"
+    ) -> Sequence[MetricInterval]:
+        """
+        Calculate interval per task metric variant so that the resulting
+        amount of points does not exceed sample.
+        Return metric variants grouped by interval value with 10% rounding
+        For samples==0 return empty list
+        """
+        default_intervals = [(1, [])]
+        if not samples:
+            return default_intervals
+
+        es_req = {
+            "size": 0,
+            "query": {"terms": {"task": task_ids}},
+            "aggs": {
+                "tasks": {
+                    "terms": {"field": "task", "size": self.MAX_TASKS_COUNT},
+                    "aggs": {
+                        "metrics": {
+                            "terms": {
+                                "field": "metric",
+                                "size": self.MAX_METRICS_COUNT,
+                            },
+                            "aggs": {
+                                "variants": {
+                                    "terms": {
+                                        "field": "variant",
+                                        "size": self.MAX_VARIANTS_COUNT,
+                                    },
+                                    "aggs": {
+                                        "count": {"value_count": {"field": field}},
+                                        "min_index": {"min": {"field": field}},
+                                        "max_index": {"max": {"field": field}},
+                                    },
+                                }
+                            },
+                        }
+                    },
+                }
+            },
+        }
+
+        with translate_errors_context(), TimingContext("es", "task_stats_get_interval"):
+            es_res = self.es.search(
+                index=es_index, body=es_req, routing=",".join(task_ids)
+            )
+
+        aggs_result = es_res.get("aggregations")
+        if not aggs_result:
+            return default_intervals
+
+        intervals = [
+            (
+                task["key"],
+                metric["key"],
+                variant["key"],
+                self._calculate_metric_interval(variant, samples),
+            )
+            for task in aggs_result["tasks"]["buckets"]
+            for metric in task["metrics"]["buckets"]
+            for variant in metric["variants"]["buckets"]
+        ]
+
+        metric_intervals = []
+        upper_border = 0
+        interval_metrics = None
+        for task, metric, variant, interval in sorted(intervals, key=itemgetter(3)):
+            if not interval_metrics or interval > upper_border:
+                interval_metrics = []
+                metric_intervals.append((interval, interval_metrics))
+                upper_border = interval + int(interval * 0.1)
+            interval_metrics.append((task, metric, variant))
+
+        return metric_intervals
+
+    @staticmethod
+    def _calculate_metric_interval(metric_variant: dict, samples: int) -> int:
+        """
+        Calculate index interval per metric_variant variant so that the
+        total amount of intervals does not exceeds the samples
+        """
+        count = safe_get(metric_variant, "count/value")
+        if not count or count < samples:
+            return 1
+
+        min_index = safe_get(metric_variant, "min_index/value", default=0)
+        max_index = safe_get(metric_variant, "max_index/value", default=min_index)
+        return max(1, int(max_index - min_index + 1) // samples)
+
+    def _get_scalar_average(
+        self,
+        metrics_interval: MetricInterval,
+        task_ids: Sequence[str],
+        es_index: str,
+        key: ScalarKey,
+    ) -> Sequence[MetricData]:
+        """
+        Retrieve scalar histograms per several metric variants that share the same interval
+        Note: the function works with a single task only
+        """
+
+        assert len(task_ids) == 1
+        interval, task_metrics = metrics_interval
+        aggregation = self._add_aggregation_average(key.get_aggregation(interval))
+        aggs = {
+            "metrics": {
+                "terms": {
+                    "field": "metric",
+                    "size": self.MAX_METRICS_COUNT,
+                    "order": {"_term": "desc"},
+                },
+                "aggs": {
+                    "variants": {
+                        "terms": {
+                            "field": "variant",
+                            "size": self.MAX_VARIANTS_COUNT,
+                            "order": {"_term": "desc"},
+                        },
+                        "aggs": aggregation,
+                    }
+                },
+            }
+        }
+        aggs_result = self._query_aggregation_for_metrics_and_tasks(
+            es_index, aggs=aggs, task_ids=task_ids, task_metrics=task_metrics
+        )
+
+        if not aggs_result:
+            return {}
+
+        metrics = [
+            (
+                metric["key"],
+                {
+                    variant["key"]: {
+                        "name": variant["key"],
+                        **key.get_iterations_data(variant),
+                    }
+                    for variant in metric["variants"]["buckets"]
+                },
+            )
+            for metric in aggs_result["metrics"]["buckets"]
+        ]
+        return metrics
+
+    def _get_scalar_average_per_task(
+        self,
+        metrics_interval: MetricInterval,
+        task_ids: Sequence[str],
+        es_index: str,
+        key: ScalarKey,
+    ) -> Sequence[MetricData]:
+        """
+        Retrieve scalar histograms per several metric variants that share the same interval
+        """
+        interval, task_metrics = metrics_interval
+
+        aggregation = self._add_aggregation_average(key.get_aggregation(interval))
+        aggs = {
+            "metrics": {
+                "terms": {"field": "metric", "size": self.MAX_METRICS_COUNT},
+                "aggs": {
+                    "variants": {
+                        "terms": {"field": "variant", "size": self.MAX_VARIANTS_COUNT},
+                        "aggs": {
+                            "tasks": {
+                                "terms": {
+                                    "field": "task",
+                                    "size": self.MAX_TASKS_COUNT,
+                                },
+                                "aggs": aggregation,
+                            }
+                        },
+                    }
+                },
+            }
+        }
+
+        aggs_result = self._query_aggregation_for_metrics_and_tasks(
+            es_index, aggs=aggs, task_ids=task_ids, task_metrics=task_metrics
+        )
+
+        if not aggs_result:
+            return {}
+
+        metrics = [
+            (
+                metric["key"],
+                {
+                    variant["key"]: {
+                        task["key"]: key.get_iterations_data(task)
+                        for task in variant["tasks"]["buckets"]
+                    }
+                    for variant in metric["variants"]["buckets"]
+                },
+            )
+            for metric in aggs_result["metrics"]["buckets"]
+        ]
+        return metrics
+
+    @staticmethod
+    def _add_aggregation_average(aggregation):
+        average_agg = {"avg_val": {"avg": {"field": "value"}}}
+        return {
+            key: {**value, "aggs": {**value.get("aggs", {}), **average_agg}}
+            for key, value in aggregation.items()
+        }
+
+    def _query_aggregation_for_metrics_and_tasks(
+        self,
+        es_index: str,
+        aggs: dict,
+        task_ids: Sequence[str],
+        task_metrics: Sequence[TaskMetric],
+    ) -> dict:
+        """
+        Return the result of elastic search query for the given aggregation filtered
+        by the given task_ids and metrics
+        """
+        if task_metrics:
+            condition = {
+                "should": [
+                    self._build_metric_terms(task, metric, variant)
+                    for task, metric, variant in task_metrics
+                ]
+            }
+        else:
+            condition = {"must": [{"terms": {"task": task_ids}}]}
+        es_req = {
+            "size": 0,
+            "_source": {"excludes": []},
+            "query": {"bool": condition},
+            "aggs": aggs,
+            "version": True,
+        }
+
+        with translate_errors_context(), TimingContext("es", "task_stats_scalar"):
+            es_res = self.es.search(
+                index=es_index, body=es_req, routing=",".join(task_ids)
+            )
+
+        return es_res.get("aggregations")
+
+    @staticmethod
+    def _build_metric_terms(task: str, metric: str, variant: str) -> dict:
+        """
+        Build query term for a metric + variant
+        """
+        return {
+            "bool": {
+                "must": [
+                    {"term": {"task": task}},
+                    {"term": {"metric": metric}},
+                    {"term": {"variant": variant}},
+                ]
+            }
+        }
+
+    def get_tasks_metrics(
+        self, company_id, task_ids: Sequence, event_type: EventType
+    ) -> Sequence[Tuple]:
+        """
+        For the requested tasks return all the metrics that
+        reported events of the requested types
+        """
+        es_index = EventMetrics.get_index_name(company_id, event_type.value)
+        if not self.es.indices.exists(es_index):
+            return [(tid, []) for tid in task_ids]
+
+        max_concurrency = config.get("services.events.max_metrics_concurrency", 4)
+        with ThreadPoolExecutor(max_concurrency) as pool:
+            res = pool.map(
+                partial(
+                    self._get_task_metrics, es_index=es_index, event_type=event_type,
+                ),
+                task_ids,
+            )
+        return list(zip(task_ids, res))
+
+    def _get_task_metrics(self, task_id, es_index, event_type: EventType) -> Sequence:
+        es_req = {
+            "size": 0,
+            "query": {
+                "bool": {
+                    "must": [
+                        {"term": {"task": task_id}},
+                        {"term": {"type": event_type.value}},
+                    ]
+                }
+            },
+            "aggs": {
+                "metrics": {
+                    "terms": {"field": "metric", "size": self.MAX_METRICS_COUNT}
+                }
+            },
+        }
+
+        with translate_errors_context(), TimingContext("es", "_get_task_metrics"):
+            es_res = self.es.search(index=es_index, body=es_req, routing=task_id)
+
+        return [
+            metric["key"]
+            for metric in safe_get(es_res, "aggregations/metrics/buckets", default=[])
+        ]

server/bll/event/log_events_iterator.py

@@ -0,0 +1,169 @@
+from typing import Optional, Tuple, Sequence
+
+import attr
+from elasticsearch import Elasticsearch
+from jsonmodels.fields import StringField, IntField
+from jsonmodels.models import Base
+from redis import StrictRedis
+
+from apierrors import errors
+from apimodels import JsonSerializableMixin
+from bll.event.event_metrics import EventMetrics
+from bll.redis_cache_manager import RedisCacheManager
+from config import config
+from database.errors import translate_errors_context
+from timing_context import TimingContext
+
+
+class LogEventsScrollState(Base, JsonSerializableMixin):
+    id: str = StringField(required=True)
+    task: str = StringField(required=True)
+    last_min_timestamp: Optional[int] = IntField()
+    last_max_timestamp: Optional[int] = IntField()
+
+    def reset(self):
+        """Reset the scrolling state """
+        self.last_min_timestamp = self.last_max_timestamp = None
+
+
+@attr.s(auto_attribs=True)
+class TaskEventsResult:
+    total_events: int = 0
+    next_scroll_id: str = None
+    events: list = attr.Factory(list)
+
+
+class LogEventsIterator:
+    EVENT_TYPE = "log"
+
+    @property
+    def state_expiration_sec(self):
+        return config.get(
+            f"services.events.events_retrieval.state_expiration_sec", 3600
+        )
+
+    def __init__(self, redis: StrictRedis, es: Elasticsearch):
+        self.es = es
+        self.cache_manager = RedisCacheManager(
+            state_class=LogEventsScrollState,
+            redis=redis,
+            expiration_interval=self.state_expiration_sec,
+        )
+
+    def get_task_events(
+        self,
+        company_id: str,
+        task_id: str,
+        batch_size: int,
+        navigate_earlier: bool = True,
+        refresh: bool = False,
+        state_id: str = None,
+    ) -> TaskEventsResult:
+        es_index = EventMetrics.get_index_name(company_id, self.EVENT_TYPE)
+        if not self.es.indices.exists(es_index):
+            return TaskEventsResult()
+
+        def init_state(state_: LogEventsScrollState):
+            state_.task = task_id
+
+        def validate_state(state_: LogEventsScrollState):
+            """
+            Checks that the task id stored in the state
+            is equal to the one passed with the current call
+            Refresh the state if requested
+            """
+            if state_.task != task_id:
+                raise errors.bad_request.InvalidScrollId(
+                    "Task stored in the state does not match the passed one",
+                    scroll_id=state_.id,
+                )
+            if refresh:
+                state_.reset()
+
+        with self.cache_manager.get_or_create_state(
+            state_id=state_id, init_state=init_state, validate_state=validate_state,
+        ) as state:
+            res = TaskEventsResult(next_scroll_id=state.id)
+            res.events, res.total_events = self._get_events(
+                es_index=es_index,
+                batch_size=batch_size,
+                navigate_earlier=navigate_earlier,
+                state=state,
+            )
+            return res
+
+    def _get_events(
+        self,
+        es_index,
+        batch_size: int,
+        navigate_earlier: bool,
+        state: LogEventsScrollState,
+    ) -> Tuple[Sequence[dict], int]:
+        """
+        Return up to 'batch size' events starting from the previous timestamp either in the
+        direction of earlier events (navigate_earlier=True) or in the direction of later events.
+        If last_min_timestamp and last_max_timestamp are not set then start either from latest or earliest.
+        For the last timestamp all the events are brought (even if the resulting size
+        exceeds batch_size) so that this timestamp events will not be lost between the calls.
+        In case any events were received update 'last_min_timestamp' and 'last_max_timestamp'
+        """
+
+        # retrieve the next batch of events
+        es_req = {
+            "size": batch_size,
+            "query": {"term": {"task": state.task}},
+            "sort": {"timestamp": "desc" if navigate_earlier else "asc"},
+        }
+
+        if navigate_earlier and state.last_min_timestamp is not None:
+            es_req["search_after"] = [state.last_min_timestamp]
+        elif not navigate_earlier and state.last_max_timestamp is not None:
+            es_req["search_after"] = [state.last_max_timestamp]
+
+        with translate_errors_context(), TimingContext("es", "get_task_events"):
+            es_result = self.es.search(index=es_index, body=es_req, routing=state.task)
+            hits = es_result["hits"]["hits"]
+            hits_total = es_result["hits"]["total"]
+            if not hits:
+                return [], hits_total
+
+            events = [hit["_source"] for hit in hits]
+            if navigate_earlier:
+                state.last_max_timestamp = events[0]["timestamp"]
+                state.last_min_timestamp = events[-1]["timestamp"]
+            else:
+                state.last_min_timestamp = events[0]["timestamp"]
+                state.last_max_timestamp = events[-1]["timestamp"]
+
+            # retrieve the events that match the last event timestamp
+            # but did not make it into the previous call due to batch_size limitation
+            es_req = {
+                "size": 10000,
+                "query": {
+                    "bool": {
+                        "must": [
+                            {"term": {"task": state.task}},
+                            {"term": {"timestamp": events[-1]["timestamp"]}},
+                        ]
+                    }
+                },
+            }
+            es_result = self.es.search(index=es_index, body=es_req, routing=state.task)
+            hits = es_result["hits"]["hits"]
+            if not hits or len(hits) < 2:
+                # if only one element is returned for the last timestamp
+                # then it is already present in the events
+                return events, hits_total
+
+            last_events = [hit["_source"] for hit in es_result["hits"]["hits"]]
+            already_present_ids = set(ev["_id"] for ev in events)
+
+            # return the list merged from original query results +
+            # leftovers from the last timestamp
+            return (
+                [
+                    *events,
+                    *(ev for ev in last_events if ev["_id"] not in already_present_ids),
+                ],
+                hits_total,
+            )

server/bll/event/scalar_key.py

@@ -0,0 +1,161 @@
+"""
+Module for polymorphism over different types of X axes in scalar aggregations
+"""
+from abc import ABC, abstractmethod
+from enum import auto
+
+from utilities.stringenum import StringEnum
+from bll.util import extract_properties_to_lists
+from config import config
+
+log = config.logger(__file__)
+
+
+class ScalarKeyEnum(StringEnum):
+    """
+    String enum representing X axes key
+    """
+
+    iter = auto()
+    timestamp = auto()
+    iso_time = auto()
+
+
+class ScalarKey(ABC):
+    """
+    Abstract scalar key
+    """
+
+    _enum_to_key = {}
+    bucket_key_key = "key"
+
+    @property
+    @abstractmethod
+    def enum_value(self) -> ScalarKeyEnum:
+        """
+        Enum value accepted in API requests
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """
+        Key name. Used as arbitrary internal key in elasticsearch queries
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def field(self) -> str:
+        """
+        Event key to aggregate by
+        """
+        pass
+
+    @abstractmethod
+    def get_aggregation(self, interval: int) -> dict:
+        """
+        Get aggregation for this type of key
+        :param interval: elasticsearch aggregation interval
+        """
+        pass
+
+    def __init_subclass__(cls, **kwargs):
+        """
+        Save a mapping from enum values to key class
+        """
+        if cls.enum_value not in ScalarKeyEnum:
+            raise ValueError(f"{cls.enum_value!r} not in {ScalarKeyEnum.__name__}")
+        if cls.enum_value in cls._enum_to_key:
+            log.warning(
+                f"'{cls.enum_value.value}' is already registered to {ScalarKey.__name__}"
+            )
+        cls._enum_to_key[cls.enum_value] = cls
+
+    @classmethod
+    def resolve(cls, key: ScalarKeyEnum):
+        """
+        Create a key instance from enum instance
+        """
+        return cls._enum_to_key[key]()
+
+    def get_iterations_data(self, iter_buckets: dict) -> dict:
+        """
+        Convert a list of bucket entries to `x`s array and `y`s array
+        """
+        return extract_properties_to_lists(
+            ("x", "y"),
+            iter_buckets[self.name]["buckets"],
+            self._get_iterations_data_single,
+        )
+
+    def _get_iterations_data_single(self, iter_data):
+        """
+        Extract x value and y value from a single bucket item
+        """
+        return int(iter_data[self.bucket_key_key]), iter_data["avg_val"]["value"]
+
+
+class TimestampKey(ScalarKey):
+    """
+    Aggregate by timestamp in milliseconds since epoch
+    """
+
+    name = "timestamp"
+    field = "timestamp"
+    enum_value = ScalarKeyEnum.timestamp
+
+    def get_aggregation(self, interval: int) -> dict:
+        return {
+            self.name: {
+                "date_histogram": {
+                    "field": "timestamp",
+                    "interval": f"{interval}ms",
+                    "min_doc_count": 1,
+                }
+            }
+        }
+
+
+class IterKey(ScalarKey):
+    """
+    Aggregate by iteration number
+    """
+
+    name = "iters"
+    field = "iter"
+    enum_value = ScalarKeyEnum.iter
+
+    def get_aggregation(self, interval: int) -> dict:
+        return {
+            self.name: {
+                "histogram": {"field": "iter", "interval": interval, "min_doc_count": 1}
+            }
+        }
+
+
+class ISOTimeKey(ScalarKey):
+    """
+    Aggregate by time formatted as ISO strings
+    """
+
+    name = "iso_time"
+    field = "timestamp"
+    enum_value = ScalarKeyEnum.iso_time
+    bucket_key_key = "key_as_string"
+
+    def get_aggregation(self, interval: int) -> dict:
+        return {
+            self.name: {
+                "date_histogram": {
+                    "field": "timestamp",
+                    "interval": f"{interval}ms",
+                    "min_doc_count": 1,
+                    "format": "strict_date_time",
+                }
+            }
+        }
+
+    def _get_iterations_data_single(self, iter_data):
+        return iter_data[self.bucket_key_key], iter_data["avg_val"]["value"]

server/bll/organization/__init__.py

@@ -0,0 +1,193 @@
+from collections import defaultdict
+from enum import Enum
+from itertools import chain
+from typing import Sequence, Union, Type, Dict
+
+from mongoengine import Q
+from redis import Redis
+
+from config import config
+from database.model.base import GetMixin
+from database.model.model import Model
+from database.model.task.task import Task
+from redis_manager import redman
+from utilities import json
+
+log = config.logger(__file__)
+_settings_prefix = "services.organization"
+
+
+class _TagsCache:
+    _tags_field = "tags"
+    _system_tags_field = "system_tags"
+
+    def __init__(self, db_cls: Union[Type[Model], Type[Task]], redis: Redis):
+        self.db_cls = db_cls
+        self.redis = redis
+
+    @property
+    def _tags_cache_expiration_seconds(self):
+        return config.get(f"{_settings_prefix}.tags_cache.expiration_seconds", 3600)
+
+    def _get_tags_from_db(
+        self,
+        company: str,
+        field: str,
+        project: str = None,
+        filter_: Dict[str, Sequence[str]] = None,
+    ) -> set:
+        query = Q(company=company)
+        if filter_:
+            for name, vals in filter_.items():
+                if vals:
+                    query &= GetMixin.get_list_field_query(name, vals)
+        if project:
+            query &= Q(project=project)
+
+        return self.db_cls.objects(query).distinct(field)
+
+    def _get_tags_cache_key(
+        self,
+        company: str,
+        field: str,
+        project: str = None,
+        filter_: Dict[str, Sequence[str]] = None,
+    ):
+        """
+        Project None means 'from all company projects'
+        The key is built in the way that scanning company keys for 'all company projects'
+        will not return the keys related to the particular company projects and vice versa.
+        So that we can have a fine grain control on what redis keys to invalidate
+        """
+        filter_str = None
+        if filter_:
+            filter_str = "_".join(
+                ["filter", *chain.from_iterable([f, *v] for f, v in filter_.items())]
+            )
+        key_parts = [company, project, self.db_cls.__name__, field, filter_str]
+        return "_".join(filter(None, key_parts))
+
+    def get_tags(
+        self,
+        company: str,
+        include_system: bool = False,
+        filter_: Dict[str, Sequence[str]] = None,
+        project: str = None,
+    ) -> dict:
+        """
+        Get tags and optionally system tags for the company
+        Return the dictionary of tags per tags field name
+        The function retrieves both cached values from Redis in one call
+        and re calculates any of them if missing in Redis
+        """
+        fields = [self._tags_field]
+        if include_system:
+            fields.append(self._system_tags_field)
+        redis_keys = [
+            self._get_tags_cache_key(company, field=f, project=project, filter_=filter_)
+            for f in fields
+        ]
+        cached = self.redis.mget(redis_keys)
+        ret = {}
+        for field, tag_data, key in zip(fields, cached, redis_keys):
+            if tag_data is not None:
+                tags = json.loads(tag_data)
+            else:
+                tags = list(self._get_tags_from_db(company, field, project, filter_))
+                self.redis.setex(
+                    key,
+                    time=self._tags_cache_expiration_seconds,
+                    value=json.dumps(tags),
+                )
+            ret[field] = set(tags)
+
+        return ret
+
+    def update_tags(self, company: str, project: str, tags=None, system_tags=None):
+        """
+        Updates tags. If reset is set then both tags and system_tags
+        are recalculated. Otherwise only those that are not 'None'
+        """
+        fields = [
+            field
+            for field, update in (
+                (self._tags_field, tags),
+                (self._system_tags_field, system_tags),
+            )
+            if update is not None
+        ]
+        if not fields:
+            return
+
+        self._delete_redis_keys(company, projects=[project], fields=fields)
+
+    def reset_tags(self, company: str, projects: Sequence[str]):
+        self._delete_redis_keys(
+            company,
+            projects=projects,
+            fields=(self._tags_field, self._system_tags_field),
+        )
+
+    def _delete_redis_keys(
+        self, company: str, projects: [Sequence[str]], fields: Sequence[str]
+    ):
+        redis_keys = list(
+            chain.from_iterable(
+                self.redis.keys(
+                    self._get_tags_cache_key(company, field=f, project=p) + "*"
+                )
+                for f in fields
+                for p in set(projects) | {None}
+            )
+        )
+        if redis_keys:
+            self.redis.delete(*redis_keys)
+
+
+class Tags(Enum):
+    Task = "task"
+    Model = "model"
+
+
+class OrgBLL:
+    def __init__(self, redis=None):
+        self.redis = redis or redman.connection("apiserver")
+        self._task_tags = _TagsCache(Task, self.redis)
+        self._model_tags = _TagsCache(Model, self.redis)
+
+    def get_tags(
+        self,
+        company: str,
+        entity: Tags,
+        include_system: bool = False,
+        filter_: Dict[str, Sequence[str]] = None,
+        projects: Sequence[str] = None,
+    ) -> dict:
+        tags_cache = self._get_tags_cache_for_entity(entity)
+        if not projects:
+            return tags_cache.get_tags(
+                company, include_system=include_system, filter_=filter_
+            )
+
+        ret = defaultdict(set)
+        for project in projects:
+            project_tags = tags_cache.get_tags(
+                company, include_system=include_system, filter_=filter_, project=project
+            )
+            for field, tags in project_tags.items():
+                ret[field] |= tags
+
+        return ret
+
+    def update_tags(
+        self, company: str, entity: Tags, project: str, tags=None, system_tags=None,
+    ):
+        tags_cache = self._get_tags_cache_for_entity(entity)
+        tags_cache.update_tags(company, project, tags, system_tags)
+
+    def reset_tags(self, company: str, entity: Tags, projects: Sequence[str]):
+        tags_cache = self._get_tags_cache_for_entity(entity)
+        tags_cache.reset_tags(company, projects=projects)
+
+    def _get_tags_cache_for_entity(self, entity: Tags) -> _TagsCache:
+        return self._task_tags if entity == Tags.Task else self._model_tags

server/bll/project/__init__.py

@@ -0,0 +1 @@
+from .project_bll import ProjectBLL

server/bll/project/project_bll.py

@@ -0,0 +1,33 @@
+from typing import Sequence, Optional
+
+from mongoengine import Q
+
+from config import config
+from database.model.model import Model
+from database.model.task.task import Task
+from timing_context import TimingContext
+
+log = config.logger(__file__)
+
+
+class ProjectBLL:
+    @classmethod
+    def get_active_users(
+        cls, company, project_ids: Sequence, user_ids: Optional[Sequence] = None
+    ) -> set:
+        """
+        Get the set of user ids that created tasks/models in the given projects
+        If project_ids is empty then all projects are examined
+        If user_ids are passed then only subset of these users is returned
+        """
+        with TimingContext("mongo", "active_users_in_projects"):
+            res = set()
+            query = Q(company=company)
+            if project_ids:
+                query &= Q(project__in=project_ids)
+            if user_ids:
+                query &= Q(user__in=user_ids)
+            for cls_ in (Task, Model):
+                res |= set(cls_.objects(query).distinct(field="user"))
+
+            return res

server/bll/query/__init__.py

@@ -0,0 +1 @@
+from .builder import Builder

server/bll/query/builder.py

@@ -0,0 +1,36 @@
+from typing import Optional, Sequence, Iterable, Union
+
+from config import config
+
+log = config.logger(__file__)
+
+RANGE_IGNORE_VALUE = -1
+
+
+class Builder:
+    @staticmethod
+    def dates_range(from_date: Union[int, float], to_date: Union[int, float]) -> dict:
+        return {
+            "range": {
+                "timestamp": {
+                    "gte": int(from_date),
+                    "lte": int(to_date),
+                    "format": "epoch_second",
+                }
+            }
+        }
+
+    @staticmethod
+    def terms(field: str, values: Iterable[str]) -> dict:
+        return {"terms": {field: list(values)}}
+
+    @staticmethod
+    def normalize_range(
+        range_: Sequence[Union[int, float]],
+        ignore_value: Union[int, float] = RANGE_IGNORE_VALUE,
+    ) -> Optional[Sequence[Union[int, float]]]:
+        if not range_ or set(range_) == {ignore_value}:
+            return None
+        if len(range_) < 2:
+            return [range_[0]] * 2
+        return range_

server/bll/queue/__init__.py

@@ -0,0 +1 @@
+from .queue_bll import QueueBLL

server/bll/queue/queue_bll.py

@@ -0,0 +1,270 @@
+from collections import defaultdict
+from datetime import datetime
+from typing import Callable, Sequence, Optional, Tuple
+
+from elasticsearch import Elasticsearch
+
+import database
+import es_factory
+from apierrors import errors
+from bll.queue.queue_metrics import QueueMetrics
+from bll.workers import WorkerBLL
+from config import config
+from database.errors import translate_errors_context
+from database.model.queue import Queue, Entry
+
+log = config.logger(__file__)
+
+
+class QueueBLL(object):
+    def __init__(self, worker_bll: WorkerBLL = None, es: Elasticsearch = None):
+        self.worker_bll = worker_bll or WorkerBLL()
+        self.es = es or es_factory.connect("workers")
+        self._metrics = QueueMetrics(self.es)
+
+    @property
+    def metrics(self) -> QueueMetrics:
+        return self._metrics
+
+    @staticmethod
+    def create(
+        company_id: str,
+        name: str,
+        tags: Optional[Sequence[str]] = None,
+        system_tags: Optional[Sequence[str]] = None,
+    ) -> Queue:
+        """Creates a queue"""
+        with translate_errors_context():
+            now = datetime.utcnow()
+            queue = Queue(
+                id=database.utils.id(),
+                company=company_id,
+                created=now,
+                name=name,
+                tags=tags or [],
+                system_tags=system_tags or [],
+                last_update=now,
+            )
+            queue.save()
+            return queue
+
+    def get_by_id(
+        self, company_id: str, queue_id: str, only: Optional[Sequence[str]] = None
+    ) -> Queue:
+        """
+        Get queue by id
+        :raise errors.bad_request.InvalidQueueId: if the queue is not found
+        """
+        with translate_errors_context():
+            query = dict(id=queue_id, company=company_id)
+            qs = Queue.objects(**query)
+            if only:
+                qs = qs.only(*only)
+            queue = qs.first()
+            if not queue:
+                raise errors.bad_request.InvalidQueueId(**query)
+
+            return queue
+
+    @classmethod
+    def get_queue_with_task(cls, company_id: str, queue_id: str, task_id: str) -> Queue:
+        with translate_errors_context():
+            query = dict(id=queue_id, company=company_id)
+            queue = Queue.objects(entries__task=task_id, **query).first()
+            if not queue:
+                raise errors.bad_request.InvalidQueueOrTaskNotQueued(
+                    task=task_id, **query
+                )
+
+            return queue
+
+    def get_default(self, company_id: str) -> Queue:
+        """
+        Get the default queue
+        :raise errors.bad_request.NoDefaultQueue: if the default queue not found
+        :raise errors.bad_request.MultipleDefaultQueues: if more than one default queue is found
+        """
+        with translate_errors_context():
+            res = Queue.objects(company=company_id, system_tags="default").only(
+                "id", "name"
+            )
+            if not res:
+                raise errors.bad_request.NoDefaultQueue()
+            if len(res) > 1:
+                raise errors.bad_request.MultipleDefaultQueues(
+                    queues=tuple(r.id for r in res)
+                )
+
+            return res.first()
+
+    def update(
+        self, company_id: str, queue_id: str, **update_fields
+    ) -> Tuple[int, dict]:
+        """
+        Partial update of the queue from update_fields
+        :raise errors.bad_request.InvalidQueueId: if the queue is not found
+        :return: number of updated objects and updated fields dictionary
+        """
+        with translate_errors_context():
+            # validate the queue exists
+            self.get_by_id(company_id=company_id, queue_id=queue_id, only=("id",))
+            return Queue.safe_update(company_id, queue_id, update_fields)
+
+    def delete(self, company_id: str, queue_id: str, force: bool) -> None:
+        """
+        Delete the queue
+        :raise errors.bad_request.InvalidQueueId: if the queue is not found
+        :raise errors.bad_request.QueueNotEmpty: if the queue is not empty and 'force' not set
+        """
+        with translate_errors_context():
+            queue = self.get_by_id(company_id=company_id, queue_id=queue_id)
+            if queue.entries and not force:
+                raise errors.bad_request.QueueNotEmpty(
+                    "use force=true to delete", id=queue_id
+                )
+            queue.delete()
+
+    def get_all(self, company_id: str, query_dict: dict) -> Sequence[dict]:
+        """Get all the queues according to the query"""
+        with translate_errors_context():
+            return Queue.get_many(
+                company=company_id, parameters=query_dict, query_dict=query_dict
+            )
+
+    def get_queue_infos(self, company_id: str, query_dict: dict) -> Sequence[dict]:
+        """
+        Get infos on all the company queues, including queue tasks and workers
+        """
+        projection = Queue.get_extra_projection("entries.task.name")
+        with translate_errors_context():
+            res = Queue.get_many_with_join(
+                company=company_id,
+                query_dict=query_dict,
+                override_projection=projection,
+            )
+
+            queue_workers = defaultdict(list)
+            for worker in self.worker_bll.get_all(company_id):
+                for queue in worker.queues:
+                    queue_workers[queue].append(worker)
+
+            for item in res:
+                item["workers"] = [
+                    {
+                        "name": w.id,
+                        "ip": w.ip,
+                        "task": w.task.to_struct() if w.task else None,
+                    }
+                    for w in queue_workers.get(item["id"], [])
+                ]
+
+        return res
+
+    def add_task(self, company_id: str, queue_id: str, task_id: str) -> dict:
+        """
+        Add the task to the queue and return the queue update results
+        :raise errors.bad_request.TaskAlreadyQueued: if the task is already in the queue
+        :raise errors.bad_request.InvalidQueueOrTaskNotQueued: if the queue update operation failed
+        """
+        with translate_errors_context():
+            queue = self.get_by_id(company_id=company_id, queue_id=queue_id)
+            if any(e.task == task_id for e in queue.entries):
+                raise errors.bad_request.TaskAlreadyQueued(task=task_id)
+
+            self.metrics.log_queue_metrics_to_es(company_id=company_id, queues=[queue])
+
+            entry = Entry(added=datetime.utcnow(), task=task_id)
+            query = dict(id=queue_id, company=company_id)
+            res = Queue.objects(entries__task__ne=task_id, **query).update_one(
+                push__entries=entry, last_update=datetime.utcnow(), upsert=False
+            )
+            if not res:
+                raise errors.bad_request.InvalidQueueOrTaskNotQueued(
+                    task=task_id, **query
+                )
+
+            return res
+
+    def get_next_task(self, company_id: str, queue_id: str) -> Optional[Entry]:
+        """
+        Atomically pop and return the first task from the queue (or None)
+        :raise errors.bad_request.InvalidQueueId: if the queue does not exist
+        """
+        with translate_errors_context():
+            query = dict(id=queue_id, company=company_id)
+            queue = Queue.objects(**query).modify(pop__entries=-1, upsert=False)
+            if not queue:
+                raise errors.bad_request.InvalidQueueId(**query)
+
+            self.metrics.log_queue_metrics_to_es(company_id, queues=[queue])
+
+            if not queue.entries:
+                return
+
+            try:
+                Queue.objects(**query).update(last_update=datetime.utcnow())
+            except Exception:
+                log.exception("Error while updating Queue.last_update")
+
+            return queue.entries[0]
+
+    def remove_task(self, company_id: str, queue_id: str, task_id: str) -> int:
+        """
+        Removes the task from the queue and returns the number of removed items
+        :raise errors.bad_request.InvalidQueueOrTaskNotQueued: if the task is not found in the queue
+        """
+        with translate_errors_context():
+            queue = self.get_queue_with_task(
+                company_id=company_id, queue_id=queue_id, task_id=task_id
+            )
+            self.metrics.log_queue_metrics_to_es(company_id, queues=[queue])
+
+            entries_to_remove = [e for e in queue.entries if e.task == task_id]
+            query = dict(id=queue_id, company=company_id)
+            res = Queue.objects(entries__task=task_id, **query).update_one(
+                pull_all__entries=entries_to_remove, last_update=datetime.utcnow()
+            )
+
+            return len(entries_to_remove) if res else 0
+
+    def reposition_task(
+        self,
+        company_id: str,
+        queue_id: str,
+        task_id: str,
+        pos_func: Callable[[int], int],
+    ) -> int:
+        """
+        Moves the task in the queue to the position calculated by pos_func
+        Returns the updated task position in the queue
+        """
+        with translate_errors_context():
+            queue = self.get_queue_with_task(
+                company_id=company_id, queue_id=queue_id, task_id=task_id
+            )
+
+            position = next(i for i, e in enumerate(queue.entries) if e.task == task_id)
+            new_position = pos_func(position)
+
+            if new_position != position:
+                entry = queue.entries[position]
+                query = dict(id=queue_id, company=company_id)
+                updated = Queue.objects(entries__task=task_id, **query).update_one(
+                    pull__entries=entry, last_update=datetime.utcnow()
+                )
+                if not updated:
+                    raise errors.bad_request.RemovedDuringReposition(
+                        task=task_id, **query
+                    )
+                inst = {"$push": {"entries": {"$each": [entry.to_proper_dict()]}}}
+                if new_position >= 0:
+                    inst["$push"]["entries"]["$position"] = new_position
+                res = Queue.objects(entries__task__ne=task_id, **query).update_one(
+                    __raw__=inst
+                )
+                if not res:
+                    raise errors.bad_request.FailedAddingDuringReposition(
+                        task=task_id, **query
+                    )
+
+            return new_position

server/bll/queue/queue_metrics.py

@@ -0,0 +1,265 @@
+from collections import defaultdict
+from datetime import datetime
+from typing import Sequence
+
+import elasticsearch.helpers
+from elasticsearch import Elasticsearch
+
+import es_factory
+from apierrors.errors import bad_request
+from bll.query import Builder as QueryBuilder
+from config import config
+from database.errors import translate_errors_context
+from database.model.queue import Queue, Entry
+from timing_context import TimingContext
+
+log = config.logger(__file__)
+
+
+class QueueMetrics:
+    class EsKeys:
+        DOC_TYPE = "metrics"
+        WAITING_TIME_FIELD = "average_waiting_time"
+        QUEUE_LENGTH_FIELD = "queue_length"
+        TIMESTAMP_FIELD = "timestamp"
+        QUEUE_FIELD = "queue"
+
+    def __init__(self, es: Elasticsearch):
+        self.es = es
+
+    @staticmethod
+    def _queue_metrics_prefix_for_company(company_id: str) -> str:
+        """Returns the es index prefix for the company"""
+        return f"queue_metrics_{company_id}_"
+
+    @staticmethod
+    def _get_es_index_suffix():
+        """Get the index name suffix for storing current month data"""
+        return datetime.utcnow().strftime("%Y-%m")
+
+    @staticmethod
+    def _calc_avg_waiting_time(entries: Sequence[Entry]) -> float:
+        """
+        Calculate avg waiting time for the given tasks.
+        Return 0 if the list is empty
+        """
+        if not entries:
+            return 0
+
+        now = datetime.utcnow()
+        total_waiting_in_secs = sum((now - e.added).total_seconds() for e in entries)
+        return total_waiting_in_secs / len(entries)
+
+    def log_queue_metrics_to_es(self, company_id: str, queues: Sequence[Queue]) -> bool:
+        """
+        Calculate and write queue statistics (avg waiting time and queue length) to Elastic
+        :return: True if the write to es was successful, false otherwise
+        """
+        es_index = (
+            self._queue_metrics_prefix_for_company(company_id)
+            + self._get_es_index_suffix()
+        )
+
+        timestamp = es_factory.get_timestamp_millis()
+
+        def make_doc(queue: Queue) -> dict:
+            entries = [e for e in queue.entries if e.added]
+            return dict(
+                _index=es_index,
+                _type=self.EsKeys.DOC_TYPE,
+                _source={
+                    self.EsKeys.TIMESTAMP_FIELD: timestamp,
+                    self.EsKeys.QUEUE_FIELD: queue.id,
+                    self.EsKeys.WAITING_TIME_FIELD: self._calc_avg_waiting_time(
+                        entries
+                    ),
+                    self.EsKeys.QUEUE_LENGTH_FIELD: len(entries),
+                },
+            )
+
+        actions = list(map(make_doc, queues))
+
+        es_res = elasticsearch.helpers.bulk(self.es, actions)
+        added, errors = es_res[:2]
+        return (added == len(actions)) and not errors
+
+    def _log_current_metrics(self, company_id: str, queue_ids=Sequence[str]):
+        query = dict(company=company_id)
+        if queue_ids:
+            query["id__in"] = list(queue_ids)
+        queues = Queue.objects(**query)
+        self.log_queue_metrics_to_es(company_id, queues=list(queues))
+
+    def _search_company_metrics(self, company_id: str, es_req: dict) -> dict:
+        return self.es.search(
+            index=f"{self._queue_metrics_prefix_for_company(company_id)}*",
+            doc_type=self.EsKeys.DOC_TYPE,
+            body=es_req,
+        )
+
+    @classmethod
+    def _get_dates_agg(cls, interval) -> dict:
+        """
+        Aggregation for building date histogram with internal grouping per queue.
+        We are grouping by queue inside date histogram and not vice versa so that
+        it will be easy to average between queue metrics inside each date bucket.
+        Ignore empty buckets.
+        """
+        return {
+            "dates": {
+                "date_histogram": {
+                    "field": cls.EsKeys.TIMESTAMP_FIELD,
+                    "interval": f"{interval}s",
+                    "min_doc_count": 1,
+                },
+                "aggs": {
+                    "queues": {
+                        "terms": {"field": cls.EsKeys.QUEUE_FIELD},
+                        "aggs": cls._get_top_waiting_agg(),
+                    }
+                },
+            }
+        }
+
+    @classmethod
+    def _get_top_waiting_agg(cls) -> dict:
+        """
+        Aggregation for getting max waiting time and the corresponding queue length
+        inside each date->queue bucket
+        """
+        return {
+            "top_avg_waiting": {
+                "top_hits": {
+                    "sort": [
+                        {cls.EsKeys.WAITING_TIME_FIELD: {"order": "desc"}},
+                        {cls.EsKeys.QUEUE_LENGTH_FIELD: {"order": "desc"}},
+                    ],
+                    "_source": {
+                        "includes": [
+                            cls.EsKeys.WAITING_TIME_FIELD,
+                            cls.EsKeys.QUEUE_LENGTH_FIELD,
+                        ]
+                    },
+                    "size": 1,
+                }
+            }
+        }
+
+    def get_queue_metrics(
+        self,
+        company_id: str,
+        from_date: float,
+        to_date: float,
+        interval: int,
+        queue_ids: Sequence[str],
+    ) -> dict:
+        """
+        Get the company queue metrics in the specified time range.
+        Returned as date histograms of average values per queue and metric type.
+        The from_date is extended by 'metrics_before_from_date' seconds from
+        queues.conf due to possibly small amount of points. The default extension is 3600s
+        In case no queue ids are specified the avg across all the
+        company queues is calculated for each metric
+        """
+        # self._log_current_metrics(company, queue_ids=queue_ids)
+
+        if from_date >= to_date:
+            raise bad_request.FieldsValueError("from_date must be less than to_date")
+
+        seconds_before = config.get("services.queues.metrics_before_from_date", 3600)
+        must_terms = [QueryBuilder.dates_range(from_date - seconds_before, to_date)]
+        if queue_ids:
+            must_terms.append(QueryBuilder.terms("queue", queue_ids))
+
+        es_req = {
+            "size": 0,
+            "query": {"bool": {"must": must_terms}},
+            "aggs": self._get_dates_agg(interval),
+        }
+
+        with translate_errors_context(), TimingContext("es", "get_queue_metrics"):
+            res = self._search_company_metrics(company_id, es_req)
+
+        if "aggregations" not in res:
+            return {}
+
+        date_metrics = [
+            dict(
+                timestamp=d["key"],
+                queue_metrics=self._extract_queue_metrics(d["queues"]["buckets"]),
+            )
+            for d in res["aggregations"]["dates"]["buckets"]
+            if d["doc_count"] > 0
+        ]
+        if queue_ids:
+            return self._datetime_histogram_per_queue(date_metrics)
+
+        return self._average_datetime_histogram(date_metrics)
+
+    @classmethod
+    def _datetime_histogram_per_queue(cls, date_metrics: Sequence[dict]) -> dict:
+        """
+        Build datetime histogram per queue from datetime histogram where every
+        bucket contains all the queues metrics
+        """
+        queues_data = defaultdict(list)
+        for date_data in date_metrics:
+            timestamp = date_data["timestamp"]
+            for queue, metrics in date_data["queue_metrics"].items():
+                queues_data[queue].append({"date": timestamp, **metrics})
+
+        return queues_data
+
+    @classmethod
+    def _average_datetime_histogram(cls, date_metrics: Sequence[dict]) -> dict:
+        """
+        Calculate weighted averages and total count for each bucket of date_metrics histogram.
+        If for any queue the data is missing then take it from the previous bucket
+        The result is returned as a dictionary with one key 'total'
+        """
+        queues_total = []
+        last_values = {}
+        for date_data in date_metrics:
+            date_metrics = date_data["queue_metrics"]
+            queue_metrics = {
+                **date_metrics,
+                **{k: v for k, v in last_values.items() if k not in date_metrics},
+            }
+
+            total_length = sum(m["queue_length"] for m in queue_metrics.values())
+            if total_length:
+                total_average = sum(
+                    m["avg_waiting_time"] * m["queue_length"] / total_length
+                    for m in queue_metrics.values()
+                )
+            else:
+                total_average = 0
+
+            queues_total.append(
+                dict(
+                    date=date_data["timestamp"],
+                    avg_waiting_time=total_average,
+                    queue_length=total_length,
+                )
+            )
+
+            for k, v in date_metrics.items():
+                last_values[k] = v
+
+        return dict(total=queues_total)
+
+    @classmethod
+    def _extract_queue_metrics(cls, queue_buckets: Sequence[dict]) -> dict:
+        """
+        Extract ES data for single date and queue bucket
+        """
+        queue_metrics = dict()
+        for queue_data in queue_buckets:
+            if not queue_data["doc_count"]:
+                continue
+            res = queue_data["top_avg_waiting"]["hits"]["hits"][0]["_source"]
+            queue_metrics[queue_data["key"]] = {
+                "queue_length": res[cls.EsKeys.QUEUE_LENGTH_FIELD],
+                "avg_waiting_time": res[cls.EsKeys.WAITING_TIME_FIELD],
+            }
+        return queue_metrics

server/bll/redis_cache_manager.py

@@ -0,0 +1,79 @@
+from contextlib import contextmanager
+from typing import Optional, TypeVar, Generic, Type, Callable
+
+from redis import StrictRedis
+
+import database
+from timing_context import TimingContext
+
+T = TypeVar("T")
+
+
+def _do_nothing(_: T):
+    return
+
+
+class RedisCacheManager(Generic[T]):
+    """
+    Class for store/retrieve of state objects from redis
+
+    self.state_class - class of the state
+    self.redis - instance of redis
+    self.expiration_interval - expiration interval in seconds
+    """
+
+    def __init__(
+        self, state_class: Type[T], redis: StrictRedis, expiration_interval: int
+    ):
+        self.state_class = state_class
+        self.redis = redis
+        self.expiration_interval = expiration_interval
+
+    def set_state(self, state: T) -> None:
+        redis_key = self._get_redis_key(state.id)
+        with TimingContext("redis", "cache_set_state"):
+            self.redis.set(redis_key, state.to_json())
+            self.redis.expire(redis_key, self.expiration_interval)
+
+    def get_state(self, state_id) -> Optional[T]:
+        redis_key = self._get_redis_key(state_id)
+        with TimingContext("redis", "cache_get_state"):
+            response = self.redis.get(redis_key)
+        if response:
+            return self.state_class.from_json(response)
+
+    def delete_state(self, state_id) -> None:
+        with TimingContext("redis", "cache_delete_state"):
+            self.redis.delete(self._get_redis_key(state_id))
+
+    def _get_redis_key(self, state_id):
+        return f"{self.state_class}/{state_id}"
+
+    @contextmanager
+    def get_or_create_state(
+        self,
+        state_id=None,
+        init_state: Callable[[T], None] = _do_nothing,
+        validate_state: Callable[[T], None] = _do_nothing,
+    ):
+        """
+        Try to retrieve state with the given id from the Redis cache if yes then validates it
+        If no then create a new one with randomly generated id
+        Yield the state and write it back to redis once the user code block exits
+        :param state_id: id of the state to retrieve
+        :param init_state: user callback to init the newly created state
+        If not passed then no init except for the id generation is done
+        :param validate_state: user callback to validate the state if retrieved from cache
+        Should throw an exception if the state is not valid. If not passed then no validation is done
+        """
+        state = self.get_state(state_id) if state_id else None
+        if state:
+            validate_state(state)
+        else:
+            state = self.state_class(id=database.utils.id())
+            init_state(state)
+
+        try:
+            yield state
+        finally:
+            self.set_state(state)

server/bll/statistics/resource_monitor.py

@@ -0,0 +1,90 @@
+from datetime import datetime
+import operator
+from threading import Thread, Lock
+from time import sleep
+
+import attr
+import psutil
+
+from utilities.threads_manager import ThreadsManager
+
+
+class ResourceMonitor(Thread):
+    @attr.s(auto_attribs=True)
+    class Sample:
+        cpu_usage: float = 0.0
+        mem_used_gb: float = 0
+        mem_free_gb: float = 0
+
+        @classmethod
+        def _apply(cls, op, *samples):
+            return cls(
+                **{
+                    field: op(*(getattr(sample, field) for sample in samples))
+                    for field in attr.fields_dict(cls)
+                }
+            )
+
+        def min(self, sample):
+            return self._apply(min, self, sample)
+
+        def max(self, sample):
+            return self._apply(max, self, sample)
+
+        def avg(self, sample, count):
+            res = self._apply(lambda x: x * count, self)
+            res = self._apply(operator.add, res, sample)
+            res = self._apply(lambda x: x / (count + 1), res)
+            return res
+
+    def __init__(self, sample_interval_sec=5):
+        super(ResourceMonitor, self).__init__(daemon=True)
+        self.sample_interval_sec = sample_interval_sec
+        self._lock = Lock()
+        self._clear()
+
+    def _clear(self):
+        sample = self._get_sample()
+        self._avg = sample
+        self._min = sample
+        self._max = sample
+        self._clear_time = datetime.utcnow()
+        self._count = 1
+
+    @classmethod
+    def _get_sample(cls) -> Sample:
+        return cls.Sample(
+            cpu_usage=psutil.cpu_percent(),
+            mem_used_gb=psutil.virtual_memory().used / (1024 ** 3),
+            mem_free_gb=psutil.virtual_memory().free / (1024 ** 3),
+        )
+
+    def run(self):
+        while not ThreadsManager.terminating:
+            sleep(self.sample_interval_sec)
+
+            sample = self._get_sample()
+
+            with self._lock:
+                self._min = self._min.min(sample)
+                self._max = self._max.max(sample)
+                self._avg = self._avg.avg(sample, self._count)
+                self._count += 1
+
+    def get_stats(self) -> dict:
+        """ Returns current resource statistics and clears internal resource statistics """
+        with self._lock:
+            min_ = attr.asdict(self._min)
+            max_ = attr.asdict(self._max)
+            avg = attr.asdict(self._avg)
+            interval = datetime.utcnow() - self._clear_time
+            self._clear()
+
+        return {
+            "interval_sec": interval.total_seconds(),
+            "num_cores": psutil.cpu_count(),
+            **{
+                k: {"min": v, "max": max_[k], "avg": avg[k]}
+                for k, v in min_.items()
+            }
+        }

server/bll/statistics/stats_reporter.py

@@ -0,0 +1,305 @@
+import logging
+import queue
+import random
+import time
+from datetime import timedelta, datetime
+from time import sleep
+from typing import Sequence, Optional
+
+import dpath
+import requests
+from requests.adapters import HTTPAdapter
+from requests.packages.urllib3.util.retry import Retry
+
+from bll.query import Builder as QueryBuilder
+from bll.util import get_server_uuid
+from bll.workers import WorkerStats, WorkerBLL
+from config import config
+from config.info import get_deployment_type
+from database.model import Company, User
+from database.model.queue import Queue
+from database.model.task.task import Task
+from utilities import safe_get
+from utilities.json import dumps
+from utilities.threads_manager import ThreadsManager
+from version import __version__ as current_version
+from .resource_monitor import ResourceMonitor
+
+log = config.logger(__file__)
+
+worker_bll = WorkerBLL()
+
+
+class StatisticsReporter:
+    threads = ThreadsManager("Statistics", resource_monitor=ResourceMonitor)
+    send_queue = queue.Queue()
+    supported = config.get("apiserver.statistics.supported", True)
+
+    @classmethod
+    def start(cls):
+        cls.start_sender()
+        cls.start_reporter()
+
+    @classmethod
+    @threads.register("reporter", daemon=True)
+    def start_reporter(cls):
+        """
+        Periodically send statistics reports for companies who have opted in.
+        Note: in trains we usually have only a single company
+        """
+        if not cls.supported:
+            return
+
+        report_interval = timedelta(
+            hours=config.get("apiserver.statistics.report_interval_hours", 24)
+        )
+        sleep(report_interval.total_seconds())
+        while not ThreadsManager.terminating:
+            try:
+                for company in Company.objects(
+                    defaults__stats_option__enabled=True
+                ).only("id"):
+                    stats = cls.get_statistics(company.id)
+                    cls.send_queue.put(stats)
+
+            except Exception as ex:
+                log.exception(f"Failed collecting stats: {str(ex)}")
+
+            sleep(report_interval.total_seconds())
+
+    @classmethod
+    @threads.register("sender", daemon=True)
+    def start_sender(cls):
+        if not cls.supported:
+            return
+
+        url = config.get("apiserver.statistics.url")
+
+        retries = config.get("apiserver.statistics.max_retries", 5)
+        max_backoff = config.get("apiserver.statistics.max_backoff_sec", 5)
+        session = requests.Session()
+        adapter = HTTPAdapter(max_retries=Retry(retries))
+        session.mount("http://", adapter)
+        session.mount("https://", adapter)
+        session.headers["Content-type"] = "application/json"
+
+        WarningFilter.attach()
+
+        while not ThreadsManager.terminating:
+            try:
+                report = cls.send_queue.get()
+
+                # Set a random backoff factor each time we send a report
+                adapter.max_retries.backoff_factor = random.random() * max_backoff
+
+                session.post(url, data=dumps(report))
+
+            except Exception as ex:
+                pass
+
+    @classmethod
+    def get_statistics(cls, company_id: str) -> dict:
+        """
+        Returns a statistics report per company
+        """
+        return {
+            "time": datetime.utcnow(),
+            "company_id": company_id,
+            "server": {
+                "version": current_version,
+                "deployment": get_deployment_type(),
+                "uuid": get_server_uuid(),
+                "queues": {"count": Queue.objects(company=company_id).count()},
+                "users": {"count": User.objects(company=company_id).count()},
+                "resources": cls.threads.resource_monitor.get_stats(),
+                "experiments": next(
+                    iter(cls._get_experiments_stats(company_id).values()), {}
+                ),
+            },
+            "agents": cls._get_agents_statistics(company_id),
+        }
+
+    @classmethod
+    def _get_agents_statistics(cls, company_id: str) -> Sequence[dict]:
+        result = cls._get_resource_stats_per_agent(company_id, key="resources")
+        dpath.merge(
+            result, cls._get_experiments_stats_per_agent(company_id, key="experiments")
+        )
+        return [{"uuid": agent_id, **data} for agent_id, data in result.items()]
+
+    @classmethod
+    def _get_resource_stats_per_agent(cls, company_id: str, key: str) -> dict:
+        agent_resource_threshold_sec = timedelta(
+            hours=config.get("apiserver.statistics.report_interval_hours", 24)
+        ).total_seconds()
+        to_timestamp = int(time.time())
+        from_timestamp = to_timestamp - int(agent_resource_threshold_sec)
+        es_req = {
+            "size": 0,
+            "query": QueryBuilder.dates_range(from_timestamp, to_timestamp),
+            "aggs": {
+                "workers": {
+                    "terms": {"field": "worker"},
+                    "aggs": {
+                        "categories": {
+                            "terms": {"field": "category"},
+                            "aggs": {"count": {"cardinality": {"field": "variant"}}},
+                        },
+                        "metrics": {
+                            "terms": {"field": "metric"},
+                            "aggs": {
+                                "min": {"min": {"field": "value"}},
+                                "max": {"max": {"field": "value"}},
+                                "avg": {"avg": {"field": "value"}},
+                            },
+                        },
+                    },
+                }
+            },
+        }
+        res = cls._run_worker_stats_query(company_id, es_req)
+
+        def _get_cardinality_fields(categories: Sequence[dict]) -> dict:
+            names = {"cpu": "num_cores"}
+            return {
+                names[c["key"]]: safe_get(c, "count/value")
+                for c in categories
+                if c["key"] in names
+            }
+
+        def _get_metric_fields(metrics: Sequence[dict]) -> dict:
+            names = {
+                "cpu_usage": "cpu_usage",
+                "memory_used": "mem_used_gb",
+                "memory_free": "mem_free_gb",
+            }
+            return {
+                names[m["key"]]: {
+                    "min": safe_get(m, "min/value"),
+                    "max": safe_get(m, "max/value"),
+                    "avg": safe_get(m, "avg/value"),
+                }
+                for m in metrics
+                if m["key"] in names
+            }
+
+        buckets = safe_get(res, "aggregations/workers/buckets", default=[])
+        return {
+            b["key"]: {
+                key: {
+                    "interval_sec": agent_resource_threshold_sec,
+                    **_get_cardinality_fields(safe_get(b, "categories/buckets", [])),
+                    **_get_metric_fields(safe_get(b, "metrics/buckets", [])),
+                }
+            }
+            for b in buckets
+        }
+
+    @classmethod
+    def _get_experiments_stats_per_agent(cls, company_id: str, key: str) -> dict:
+        agent_relevant_threshold = timedelta(
+            days=config.get("apiserver.statistics.agent_relevant_threshold_days", 30)
+        )
+        to_timestamp = int(time.time())
+        from_timestamp = to_timestamp - int(agent_relevant_threshold.total_seconds())
+        workers = cls._get_active_workers(company_id, from_timestamp, to_timestamp)
+        if not workers:
+            return {}
+
+        stats = cls._get_experiments_stats(company_id, list(workers.keys()))
+        return {
+            worker_id: {key: {**workers[worker_id], **stat}}
+            for worker_id, stat in stats.items()
+        }
+
+    @classmethod
+    def _get_active_workers(
+        cls, company_id, from_timestamp: int, to_timestamp: int
+    ) -> dict:
+        es_req = {
+            "size": 0,
+            "query": QueryBuilder.dates_range(from_timestamp, to_timestamp),
+            "aggs": {
+                "workers": {
+                    "terms": {"field": "worker"},
+                    "aggs": {"last_activity_time": {"max": {"field": "timestamp"}}},
+                }
+            },
+        }
+        res = cls._run_worker_stats_query(company_id, es_req)
+        buckets = safe_get(res, "aggregations/workers/buckets", default=[])
+        return {
+            b["key"]: {"last_activity_time": b["last_activity_time"]["value"]}
+            for b in buckets
+        }
+
+    @classmethod
+    def _run_worker_stats_query(cls, company_id, es_req) -> dict:
+        return worker_bll.es_client.search(
+            index=f"{WorkerStats.worker_stats_prefix_for_company(company_id)}*",
+            doc_type="stat",
+            body=es_req,
+        )
+
+    @classmethod
+    def _get_experiments_stats(
+        cls, company_id, workers: Optional[Sequence] = None
+    ) -> dict:
+        pipeline = [
+            {
+                "$match": {
+                    "company": company_id,
+                    "started": {"$exists": True, "$ne": None},
+                    "last_update": {"$exists": True, "$ne": None},
+                    "status": {"$nin": ["created", "queued"]},
+                    **({"last_worker": {"$in": workers}} if workers else {}),
+                }
+            },
+            {
+                "$group": {
+                    "_id": "$last_worker" if workers else None,
+                    "count": {"$sum": 1},
+                    "avg_run_time_sec": {
+                        "$avg": {
+                            "$divide": [
+                                {"$subtract": ["$last_update", "$started"]},
+                                1000,
+                            ]
+                        }
+                    },
+                    "avg_iterations": {"$avg": "$last_iteration"},
+                }
+            },
+            {
+                "$project": {
+                    "count": 1,
+                    "avg_run_time_sec": {"$trunc": "$avg_run_time_sec"},
+                    "avg_iterations": {"$trunc": "$avg_iterations"},
+                }
+            },
+        ]
+        return {
+            group["_id"]: {k: v for k, v in group.items() if k != "_id"}
+            for group in Task.aggregate(pipeline)
+        }
+
+
+class WarningFilter(logging.Filter):
+    @classmethod
+    def attach(cls):
+        from urllib3.connectionpool import (
+            ConnectionPool,
+        )  # required to make sure the logger is created
+
+        assert ConnectionPool  # make sure import is not optimized out
+
+        logging.getLogger("urllib3.connectionpool").addFilter(cls())
+
+    def filter(self, record):
+        if (
+            record.levelno == logging.WARNING
+            and len(record.args) > 2
+            and record.args[2] == "/stats"
+        ):
+            return False
+        return True

server/bll/task/__init__.py

@@ -4,4 +4,5 @@ from .utils import (
     update_project_time,
     validate_status_change,
     split_by,
+    ParameterKeyEscaper,
 )

server/bll/task/non_responsive_tasks_watchdog.py

@@ -0,0 +1,89 @@
+from datetime import timedelta, datetime
+from time import sleep
+
+from apierrors import errors
+from bll.task import ChangeStatusRequest
+from config import config
+from database.model.task.task import TaskStatus, Task
+from utilities.threads_manager import ThreadsManager
+
+log = config.logger(__file__)
+
+
+class NonResponsiveTasksWatchdog:
+    threads = ThreadsManager()
+
+    class _Settings:
+        """
+        Retrieves watchdog settings from the config file
+        The properties are not cached so that the updates in
+        the config file are reflected
+        """
+
+        _prefix = "services.tasks.non_responsive_tasks_watchdog"
+
+        @property
+        def enabled(self):
+            return config.get(f"{self._prefix}.enabled", True)
+
+        @property
+        def watch_interval_sec(self):
+            return config.get(f"{self._prefix}.watch_interval_sec", 900)
+
+        @property
+        def threshold_sec(self):
+            return config.get(f"{self._prefix}.threshold_sec", 7200)
+
+    settings = _Settings()
+
+    @classmethod
+    @threads.register("non_responsive_tasks_watchdog", daemon=True)
+    def start(cls):
+        sleep(cls.settings.watch_interval_sec)
+        while not ThreadsManager.terminating:
+            watch_interval = cls.settings.watch_interval_sec
+            if cls.settings.enabled:
+                try:
+                    stopped = cls.cleanup_tasks(
+                        threshold_sec=cls.settings.threshold_sec
+                    )
+                    log.info(f"{stopped} non-responsive tasks stopped")
+                except Exception as ex:
+                    log.exception(f"Failed stopping tasks: {str(ex)}")
+            sleep(watch_interval)
+
+    @classmethod
+    def cleanup_tasks(cls, threshold_sec):
+        relevant_status = (TaskStatus.in_progress,)
+        threshold = timedelta(seconds=threshold_sec)
+        ref_time = datetime.utcnow() - threshold
+        log.info(
+            f"Starting cleanup cycle for running tasks last updated before {ref_time}"
+        )
+
+        tasks = list(
+            Task.objects(status__in=relevant_status, last_update__lt=ref_time).only(
+                "id", "name", "status", "project", "last_update"
+            )
+        )
+        log.info(f"{len(tasks)} non-responsive tasks found")
+        if not tasks:
+            return 0
+
+        err_count = 0
+        for task in tasks:
+            log.info(
+                f"Stopping {task.id} ({task.name}), last updated at {task.last_update}"
+            )
+            try:
+                ChangeStatusRequest(
+                    task=task,
+                    new_status=TaskStatus.stopped,
+                    status_reason="Forced stop (non-responsive)",
+                    status_message="Forced stop (non-responsive)",
+                    force=True,
+                ).execute()
+            except errors.bad_request.FailedChangingTaskStatus:
+                err_count += 1
+
+        return len(tasks) - err_count

server/bll/task/task_bll.py

@@ -1,26 +1,44 @@
-import re
 from collections import OrderedDict
 from datetime import datetime
-from typing import Mapping, Collection
-from urllib.parse import urlparse
+from operator import attrgetter
+from random import random
+from time import sleep
+from typing import Collection, Sequence, Tuple, Any, Optional, List, Dict
 
+import pymongo.results
 import six
 from mongoengine import Q
 from six import string_types
 
+import database.utils as dbutils
 import es_factory
 from apierrors import errors
+from apimodels.tasks import Artifact as ApiArtifact
+from bll.organization import OrgBLL, Tags
+from config import config
 from database.errors import translate_errors_context
-from database.fields import OutputDestinationField
 from database.model.model import Model
 from database.model.project import Project
-from database.model.task.metrics import MetricEvent
+from database.model.task.metrics import EventStats, MetricEventStats
 from database.model.task.output import Output
-from database.model.task.task import Task, TaskStatus, TaskStatusMessage, TaskTags
+from database.model.task.task import (
+    Task,
+    TaskStatus,
+    TaskStatusMessage,
+    TaskSystemTags,
+    ArtifactModes,
+    Artifact,
+    external_task_types,
+)
 from database.utils import get_company_or_none_constraint, id as create_id
 from service_repo import APICall
+from services.utils import validate_tags
 from timing_context import TimingContext
-from .utils import ChangeStatusRequest, validate_status_change
+from utilities.dicts import deep_merge
+from .utils import ChangeStatusRequest, validate_status_change, ParameterKeyEscaper
+
+log = config.logger(__file__)
+org_bll = OrgBLL()
 
 
 class TaskBLL(object):
@@ -29,6 +47,18 @@ class TaskBLL(object):
             events_es if events_es is not None else es_factory.connect("events")
         )
 
+    @classmethod
+    def get_types(cls, company, project_ids: Optional[Sequence]) -> set:
+        """
+        Return the list of unique task types used by company and public tasks
+        If project ids passed then only tasks from these projects are considered
+        """
+        query = get_company_or_none_constraint(company)
+        if project_ids:
+            query &= Q(project__in=project_ids)
+        res = Task.objects(query).distinct(field="type")
+        return set(res).intersection(external_task_types)
+
     @staticmethod
     def get_task_with_access(
         task_id, company_id, only=None, allow_public=False, requires_write_access=False
@@ -138,42 +168,110 @@ class TaskBLL(object):
         return model
 
     @classmethod
-    def validate(cls, task: Task, force=False):
-        assert isinstance(task, Task)
+    def clone_task(
+        cls,
+        company_id,
+        user_id,
+        task_id,
+        name: Optional[str] = None,
+        comment: Optional[str] = None,
+        parent: Optional[str] = None,
+        project: Optional[str] = None,
+        tags: Optional[Sequence[str]] = None,
+        system_tags: Optional[Sequence[str]] = None,
+        execution_overrides: Optional[dict] = None,
+        validate_references: bool = False,
+    ) -> Task:
+        validate_tags(tags, system_tags)
+        task = cls.get_by_id(company_id=company_id, task_id=task_id)
+        execution_dict = task.execution.to_proper_dict() if task.execution else {}
+        execution_model_overriden = False
+        if execution_overrides:
+            parameters = execution_overrides.get("parameters")
+            if parameters is not None:
+                execution_overrides["parameters"] = {
+                    ParameterKeyEscaper.escape(k): v for k, v in parameters.items()
+                }
+            execution_dict = deep_merge(execution_dict, execution_overrides)
+            execution_model_overriden = execution_overrides.get("model") is not None
 
-        if task.parent and not Task.get(
-            company=task.company, id=task.parent, _only=("id",), include_public=True
+        artifacts = execution_dict.get("artifacts")
+        if artifacts:
+            execution_dict["artifacts"] = [
+                a for a in artifacts if a.get("mode") != ArtifactModes.output
+            ]
+        now = datetime.utcnow()
+
+        with translate_errors_context():
+            new_task = Task(
+                id=create_id(),
+                user=user_id,
+                company=company_id,
+                created=now,
+                last_update=now,
+                name=name or task.name,
+                comment=comment or task.comment,
+                parent=parent or task.parent,
+                project=project or task.project,
+                tags=tags or task.tags,
+                system_tags=system_tags or [],
+                type=task.type,
+                script=task.script,
+                output=Output(destination=task.output.destination)
+                if task.output
+                else None,
+                execution=execution_dict,
+            )
+            cls.validate(
+                new_task,
+                validate_model=validate_references or execution_model_overriden,
+                validate_parent=validate_references or parent,
+                validate_project=validate_references or project,
+            )
+            new_task.save()
+
+            if task.project == new_task.project:
+                updated_tags = tags
+                updated_system_tags = system_tags
+            else:
+                updated_tags = new_task.tags
+                updated_system_tags = new_task.system_tags
+            org_bll.update_tags(
+                company_id,
+                Tags.Task,
+                project=new_task.project,
+                tags=updated_tags,
+                system_tags=updated_system_tags,
+            )
+
+        return new_task
+
+    @classmethod
+    def validate(
+        cls,
+        task: Task,
+        validate_model=True,
+        validate_parent=True,
+        validate_project=True,
+    ):
+        if (
+            validate_parent
+            and task.parent
+            and not Task.get(
+                company=task.company, id=task.parent, _only=("id",), include_public=True
+            )
         ):
             raise errors.bad_request.InvalidTaskId("invalid parent", parent=task.parent)
 
-        if task.project:
-            Project.get_for_writing(company=task.company, id=task.project)
+        if (
+            validate_project
+            and task.project
+            and not Project.get_for_writing(company=task.company, id=task.project)
+        ):
+            raise errors.bad_request.InvalidProjectId(id=task.project)
 
-        model = cls.validate_execution_model(task)
-        if model and not force and not model.ready:
-            raise errors.bad_request.ModelNotReady(
-                "can't be used in a task", model=model.id
-            )
-
-        if task.execution:
-            if task.execution.parameters:
-                cls._validate_execution_parameters(task.execution.parameters)
-
-        if task.output and task.output.destination:
-            parsed_url = urlparse(task.output.destination)
-            if parsed_url.scheme not in OutputDestinationField.schemes:
-                raise errors.bad_request.FieldsValueError(
-                    "unsupported scheme for output destination",
-                    dest=task.output.destination,
-                )
-
-    @staticmethod
-    def _validate_execution_parameters(parameters):
-        invalid_keys = [k for k in parameters if re.search(r"\s", k)]
-        if invalid_keys:
-            raise errors.bad_request.ValidationError(
-                "execution.parameters keys contain whitespace", keys=invalid_keys
-            )
+        if validate_model:
+            cls.validate_execution_model(task)
 
     @staticmethod
     def get_unique_metric_variants(company_id, project_ids=None):
@@ -213,7 +311,7 @@ class TaskBLL(object):
         ]
 
         with translate_errors_context():
-            result = Task.objects.aggregate(*pipeline)
+            result = Task.aggregate(pipeline)
             return [r["metrics"][0] for r in result]
 
     @staticmethod
@@ -231,7 +329,8 @@ class TaskBLL(object):
         last_update: datetime = None,
         last_iteration: int = None,
         last_iteration_max: int = None,
-        last_metrics: Mapping[str, Mapping[str, MetricEvent]] = None,
+        last_scalar_values: Sequence[Tuple[Tuple[str, ...], Any]] = None,
+        last_events: Dict[str, Dict[str, dict]] = None,
         **extra_updates,
     ):
         """
@@ -243,7 +342,8 @@ class TaskBLL(object):
             task's last iteration value.
         :param last_iteration_max: Last reported iteration. Use this to conditionally set a value only
             if the current task's last iteration value is smaller than the provided value.
-        :param last_metrics: Last reported metrics summary.
+        :param last_scalar_values: Last reported metrics summary for scalar events (value, metric, variant).
+        :param last_events: Last reported metrics summary (value, metric, event type).
         :param extra_updates: Extra task updates to include in this update call.
         :return:
         """
@@ -254,10 +354,36 @@ class TaskBLL(object):
         elif last_iteration_max is not None:
             extra_updates.update(max__last_iteration=last_iteration_max)
 
-        if last_metrics is not None:
-            extra_updates.update(last_metrics=last_metrics)
+        if last_scalar_values is not None:
 
-        return Task.objects(id=task_id, company=company_id).update(
+            def op_path(op, *path):
+                return "__".join((op, "last_metrics") + path)
+
+            for path, value in last_scalar_values:
+                if path[-1] == "min_value":
+                    extra_updates[op_path("min", *path[:-1], "min_value")] = value
+                elif path[-1] == "max_value":
+                    extra_updates[op_path("max", *path[:-1], "max_value")] = value
+                else:
+                    extra_updates[op_path("set", *path)] = value
+
+        if last_events is not None:
+
+            def events_per_type(metric_data: Dict[str, dict]) -> Dict[str, EventStats]:
+                return {
+                    event_type: EventStats(last_update=event["timestamp"])
+                    for event_type, event in metric_data.items()
+                }
+
+            metric_stats = {
+                dbutils.hash_field_name(metric_key): MetricEventStats(
+                    metric=metric_key, event_stats_by_type=events_per_type(metric_data)
+                )
+                for metric_key, metric_data in last_events.items()
+            }
+            extra_updates["metric_stats"] = metric_stats
+
+        Task.objects(id=task_id, company=company_id).update(
             upsert=False, last_update=last_update, **extra_updates
         )
 
@@ -370,14 +496,30 @@ class TaskBLL(object):
         :return: updated task fields
         """
 
-        task = TaskBLL.get_task_with_access(
+        task = cls.get_task_with_access(
             task_id,
             company_id=company_id,
-            only=("status", "project", "tags", "last_update"),
+            only=(
+                "status",
+                "project",
+                "tags",
+                "system_tags",
+                "last_worker",
+                "last_update",
+            ),
             requires_write_access=True,
         )
 
-        if TaskTags.development in task.tags:
+        def is_run_by_worker(t: Task) -> bool:
+            """Checks if there is an active worker running the task"""
+            update_timeout = config.get("apiserver.workers.task_update_timeout", 600)
+            return (
+                t.last_worker
+                and t.last_update
+                and (datetime.utcnow() - t.last_update).total_seconds() < update_timeout
+            )
+
+        if TaskSystemTags.development in task.system_tags or not is_run_by_worker(task):
             new_status = TaskStatus.stopped
             status_message = f"Stopped by {user_name}"
         else:
@@ -391,3 +533,149 @@ class TaskBLL(object):
             status_message=status_message,
             force=force,
         ).execute()
+
+    @classmethod
+    def add_or_update_artifacts(
+        cls, task_id: str, company_id: str, artifacts: List[ApiArtifact]
+    ) -> Tuple[List[str], List[str]]:
+        key = attrgetter("key", "mode")
+
+        if not artifacts:
+            return [], []
+
+        with translate_errors_context(), TimingContext("mongo", "update_artifacts"):
+            artifacts: List[Artifact] = [
+                Artifact(**artifact.to_struct()) for artifact in artifacts
+            ]
+
+            attempts = int(config.get("services.tasks.artifacts.update_attempts", 10))
+
+            for retry in range(attempts):
+                task = cls.get_task_with_access(
+                    task_id, company_id=company_id, requires_write_access=True
+                )
+
+                current = list(map(key, task.execution.artifacts))
+                updated = [a for a in artifacts if key(a) in current]
+                added = [a for a in artifacts if a not in updated]
+
+                filter = {"_id": task_id, "company": company_id}
+                update = {}
+                array_filters = None
+                if current:
+                    filter["execution.artifacts"] = {
+                        "$size": len(current),
+                        "$all": [
+                            *(
+                                {"$elemMatch": {"key": key, "mode": mode}}
+                                for key, mode in current
+                            )
+                        ],
+                    }
+                else:
+                    filter["$or"] = [
+                        {"execution.artifacts": {"$exists": False}},
+                        {"execution.artifacts": {"$size": 0}},
+                    ]
+
+                if added:
+                    update["$push"] = {
+                        "execution.artifacts": {"$each": [a.to_mongo() for a in added]}
+                    }
+                if updated:
+                    update["$set"] = {
+                        f"execution.artifacts.$[artifact{index}]": artifact.to_mongo()
+                        for index, artifact in enumerate(updated)
+                    }
+                    array_filters = [
+                        {
+                            f"artifact{index}.key": artifact.key,
+                            f"artifact{index}.mode": artifact.mode,
+                        }
+                        for index, artifact in enumerate(updated)
+                    ]
+
+                if not update:
+                    return [], []
+
+                result: pymongo.results.UpdateResult = Task._get_collection().update_one(
+                    filter=filter,
+                    update=update,
+                    array_filters=array_filters,
+                    upsert=False,
+                )
+
+                if result.matched_count >= 1:
+                    break
+
+                wait_msec = random() * int(
+                    config.get("services.tasks.artifacts.update_retry_msec", 500)
+                )
+
+                log.warning(
+                    f"Failed to update artifacts for task {task_id} (updated by another party),"
+                    f" retrying {retry+1}/{attempts} in {wait_msec}ms"
+                )
+
+                sleep(wait_msec / 1000)
+            else:
+                raise errors.server_error.UpdateFailed(
+                    "task artifacts updated by another party"
+                )
+
+            return [a.key for a in added], [a.key for a in updated]
+
+    @staticmethod
+    def get_aggregated_project_execution_parameters(
+        company_id,
+        project_ids: Sequence[str] = None,
+        page: int = 0,
+        page_size: int = 500,
+    ) -> Tuple[int, int, Sequence[str]]:
+
+        page = max(0, page)
+        page_size = max(1, page_size)
+
+        pipeline = [
+            {
+                "$match": {
+                    "company": company_id,
+                    "execution.parameters": {"$exists": True, "$gt": {}},
+                    **({"project": {"$in": project_ids}} if project_ids else {}),
+                }
+            },
+            {"$project": {"parameters": {"$objectToArray": "$execution.parameters"}}},
+            {"$unwind": "$parameters"},
+            {"$group": {"_id": "$parameters.k"}},
+            {"$sort": {"_id": 1}},
+            {
+                "$group": {
+                    "_id": 1,
+                    "total": {"$sum": 1},
+                    "results": {"$push": "$$ROOT"},
+                }
+            },
+            {
+                "$project": {
+                    "total": 1,
+                    "results": {"$slice": ["$results", page * page_size, page_size]},
+                }
+            },
+        ]
+
+        with translate_errors_context():
+            result = next(Task.aggregate(pipeline), None)
+
+        total = 0
+        remaining = 0
+        results = []
+
+        if result:
+            total = int(result.get("total", -1))
+            results = [
+                ParameterKeyEscaper.unescape(r["_id"])
+                for r in result.get("results", [])
+            ]
+            remaining = max(0, total - (len(results) + page * page_size))
+
+        return total, remaining, results

server/bll/task/utils.py

@@ -3,11 +3,12 @@ from typing import TypeVar, Callable, Tuple, Sequence
 
 import attr
 import six
+from boltons.dictutils import OneToOne
 
 from apierrors import errors
 from database.errors import translate_errors_context
 from database.model.project import Project
-from database.model.task.task import Task, TaskStatus
+from database.model.task.task import Task, TaskStatus, TaskSystemTags
 from database.utils import get_options
 from timing_context import TimingContext
 from utilities.attrs import typed_attrs
@@ -25,9 +26,10 @@ class ChangeStatusRequest(object):
     status_message = attr.ib(type=six.string_types, default="")
     force = attr.ib(type=bool, default=False)
     allow_same_state_transition = attr.ib(type=bool, default=True)
+    current_status_override = attr.ib(default=None)
 
     def execute(self, **kwargs):
-        current_status = self.task.status
+        current_status = self.current_status_override or self.task.status
         project_id = self.task.project
 
         # Verify new status is allowed from current status (will throw exception if not valid)
@@ -44,6 +46,9 @@ class ChangeStatusRequest(object):
             last_update=now,
         )
 
+        if self.new_status == TaskStatus.queued:
+            fields["pull__system_tags"] = TaskSystemTags.development
+
         def safe_mongoengine_key(key):
             return f"__{key}" if key in control else key
 
@@ -66,6 +71,10 @@ class ChangeStatusRequest(object):
             )
 
         update_project_time(project_id)
+
+        # make sure that _raw_ queries are not returned back to the client
+        fields.pop("__raw__", None)
+
         return dict(updated=updated, fields=fields)
 
     def validate_transition(self, current_status):
@@ -95,8 +104,14 @@ def validate_status_change(current_status, new_status):
 
 
 state_machine = {
-    TaskStatus.created: {TaskStatus.in_progress},
-    TaskStatus.in_progress: {TaskStatus.stopped, TaskStatus.failed, TaskStatus.created},
+    TaskStatus.created: {TaskStatus.queued, TaskStatus.in_progress},
+    TaskStatus.queued: {TaskStatus.created, TaskStatus.in_progress},
+    TaskStatus.in_progress: {
+        TaskStatus.stopped,
+        TaskStatus.failed,
+        TaskStatus.created,
+        TaskStatus.completed,
+    },
     TaskStatus.stopped: {
         TaskStatus.closed,
         TaskStatus.created,
@@ -104,6 +119,7 @@ state_machine = {
         TaskStatus.in_progress,
         TaskStatus.published,
         TaskStatus.publishing,
+        TaskStatus.completed,
     },
     TaskStatus.closed: {
         TaskStatus.created,
@@ -115,6 +131,11 @@ state_machine = {
     TaskStatus.failed: {TaskStatus.created, TaskStatus.stopped, TaskStatus.published},
     TaskStatus.publishing: {TaskStatus.published},
     TaskStatus.published: set(),
+    TaskStatus.completed: {
+        TaskStatus.published,
+        TaskStatus.in_progress,
+        TaskStatus.created,
+    },
 }
 
 
@@ -124,9 +145,11 @@ def get_possible_status_changes(current_status):
     :return possible states from current state
     """
     possible = state_machine.get(current_status)
-    assert (
-        possible is not None
-    ), f"Current status {current_status} not supported by state machine"
+    if possible is None:
+        raise errors.server_error.InternalError(
+            f"Current status {current_status} not supported by state machine"
+        )
+
     return possible
 
 
@@ -149,3 +172,26 @@ def split_by(
         [item for cond, item in applied if cond],
         [item for cond, item in applied if not cond],
     )
+
+
+class ParameterKeyEscaper:
+    _mapping = OneToOne({".": "%2E", "$": "%24"})
+
+    @classmethod
+    def escape(cls, value):
+        """ Quote a parameter key """
+        value = value.strip().replace("%", "%%")
+        for c, r in cls._mapping.items():
+            value = value.replace(c, r)
+        return value
+
+    @classmethod
+    def _unescape(cls, value):
+        for c, r in cls._mapping.inv.items():
+            value = value.replace(c, r)
+        return value
+
+    @classmethod
+    def unescape(cls, value):
+        """ Unquote a quoted parameter key """
+        return "%".join(map(cls._unescape, value.split("%%")))

server/bll/util.py

@@ -0,0 +1,73 @@
+import functools
+from operator import itemgetter
+from typing import Sequence, Optional, Callable, Tuple, Dict, Any, Set
+
+from database.model import AttributedDocument
+from database.model.settings import Settings
+
+
+def extract_properties_to_lists(
+    key_names: Sequence[str],
+    data: Sequence[dict],
+    extract_func: Optional[Callable[[dict], Tuple]] = None,
+) -> dict:
+    """
+    Given a list of dictionaries and names of dictionary keys
+    builds a dictionary with the requested keys and values lists
+    :param key_names: names of the keys in the resulting dictionary
+    :param data: sequence of dictionaries to extract values from
+    :param extract_func: the optional callable that extracts properties
+    from a dictionary and put them in a tuple in the order corresponding to
+    key_names. If not specified then properties are extracted according to key_names
+    """
+    value_sequences = zip(*map(extract_func or itemgetter(*key_names), data))
+    return dict(zip(key_names, map(list, value_sequences)))
+
+
+class SetFieldsResolver:
+    """
+    The class receives set fields dictionary
+    and for the set fields that require 'min' or 'max'
+    operation replace them with a simple set in case the
+    DB document does not have these fields set
+    """
+
+    SET_MODIFIERS = ("min", "max")
+
+    def __init__(self, set_fields: Dict[str, Any]):
+        self.orig_fields = set_fields
+        self.fields = {
+            f: fname
+            for f, modifier, dunder, fname in (
+                (f,) + f.partition("__") for f in set_fields.keys()
+            )
+            if dunder and modifier in self.SET_MODIFIERS
+        }
+
+    def _get_updated_name(self, doc: AttributedDocument, name: str) -> str:
+        if name in self.fields and doc.get_field_value(self.fields[name]) is None:
+            return self.fields[name]
+        return name
+
+    def get_fields(self, doc: AttributedDocument):
+        """
+        For the given document return the set fields instructions
+        with min/max operations replaced with a single set in case
+        the document does not have the field set
+        """
+        return {
+            self._get_updated_name(doc, name): value
+            for name, value in self.orig_fields.items()
+        }
+
+    def get_names(self) -> Set[str]:
+        """
+        Returns the names of the fields that had min/max modifiers
+        in the format suitable for projection (dot separated)
+        """
+        return set(name.replace("__", ".") for name in self.fields.values())
+
+
+@functools.lru_cache()
+def get_server_uuid() -> Optional[str]:
+    return Settings.get_by_key("server.uuid")

server/bll/workers/__init__.py

@@ -0,0 +1,422 @@
+import itertools
+from datetime import datetime, timedelta
+from typing import Sequence, Set, Optional
+
+import attr
+import elasticsearch.helpers
+
+import es_factory
+from apierrors import APIError
+from apierrors.errors import bad_request, server_error
+from apimodels.workers import (
+    DEFAULT_TIMEOUT,
+    IdNameEntry,
+    WorkerEntry,
+    StatusReportRequest,
+    WorkerResponseEntry,
+    QueueEntry,
+    MachineStats,
+)
+from config import config
+from database.errors import translate_errors_context
+from database.model.auth import User
+from database.model.company import Company
+from database.model.queue import Queue
+from database.model.task.task import Task
+from redis_manager import redman
+from timing_context import TimingContext
+from tools import safe_get
+from .stats import WorkerStats
+
+log = config.logger(__file__)
+
+
+class WorkerBLL:
+    def __init__(self, es=None, redis=None):
+        self.es_client = es or es_factory.connect("workers")
+        self.redis = redis or redman.connection("workers")
+        self._stats = WorkerStats(self.es_client)
+
+    @property
+    def stats(self) -> WorkerStats:
+        return self._stats
+
+    def register_worker(
+        self,
+        company_id: str,
+        user_id: str,
+        worker: str,
+        ip: str = "",
+        queues: Sequence[str] = None,
+        timeout: int = 0,
+    ) -> WorkerEntry:
+        """
+        Register a worker
+        :param company_id: worker's company ID
+        :param user_id: user ID under which this worker is running
+        :param worker: worker ID
+        :param ip: the real ip of the worker
+        :param queues: queues reported as being monitored by the worker
+        :param timeout: registration expiration timeout in seconds
+        :raise bad_request.InvalidUserId: in case the calling user or company does not exist
+        :return: worker entry instance
+        """
+        key = WorkerBLL._get_worker_key(company_id, user_id, worker)
+
+        timeout = timeout or DEFAULT_TIMEOUT
+        queues = queues or []
+
+        with translate_errors_context():
+            query = dict(id=user_id, company=company_id)
+            user = User.objects(**query).only("id", "name").first()
+            if not user:
+                raise bad_request.InvalidUserId(**query)
+            company = Company.objects(id=company_id).only("id", "name").first()
+            if not company:
+                raise server_error.InternalError("invalid company", company=company_id)
+
+            queue_objs = Queue.objects(company=company_id, id__in=queues).only("id")
+            if len(queue_objs) < len(queues):
+                invalid = set(queues).difference(q.id for q in queue_objs)
+                raise bad_request.InvalidQueueId(ids=invalid)
+
+            now = datetime.utcnow()
+            entry = WorkerEntry(
+                key=key,
+                id=worker,
+                user=user.to_proper_dict(),
+                company=company.to_proper_dict(),
+                ip=ip,
+                queues=queues,
+                register_time=now,
+                register_timeout=timeout,
+                last_activity_time=now,
+            )
+
+            self.redis.setex(key, timedelta(seconds=timeout), entry.to_json())
+
+        return entry
+
+    def unregister_worker(self, company_id: str, user_id: str, worker: str) -> None:
+        """
+        Unregister a worker
+        :param company_id: worker's company ID
+        :param user_id: user ID under which this worker is running
+        :param worker: worker ID
+        :raise bad_request.WorkerNotRegistered: the worker was not previously registered
+        """
+        with TimingContext("redis", "workers_unregister"):
+            res = self.redis.delete(
+                company_id, self._get_worker_key(company_id, user_id, worker)
+            )
+        if not res:
+            raise bad_request.WorkerNotRegistered(worker=worker)
+
+    def status_report(
+        self, company_id: str, user_id: str, ip: str, report: StatusReportRequest
+    ) -> None:
+        """
+        Write worker status report
+        :param company_id: worker's company ID
+        :param user_id: user_id ID under which this worker is running
+        :raise bad_request.InvalidTaskId: the reported task was not found
+        :return: worker entry instance
+        """
+        entry = self._get_worker(company_id, user_id, report.worker)
+
+        try:
+            entry.ip = ip
+            now = datetime.utcnow()
+            entry.last_activity_time = now
+
+            if report.machine_stats:
+                self._log_stats_to_es(
+                    company_id=company_id,
+                    company_name=entry.company.name,
+                    worker=report.worker,
+                    timestamp=report.timestamp,
+                    task=report.task,
+                    machine_stats=report.machine_stats,
+                )
+
+            entry.queue = report.queue
+
+            if report.queues:
+                entry.queues = report.queues
+
+            if not report.task:
+                entry.task = None
+            else:
+                with translate_errors_context():
+                    query = dict(id=report.task, company=company_id)
+                    update = dict(
+                        last_worker=report.worker,
+                        last_worker_report=now,
+                        last_update=now,
+                    )
+                    # modify(new=True, ...) returns the modified object
+                    task = Task.objects(**query).modify(new=True, **update)
+                    if not task:
+                        raise bad_request.InvalidTaskId(**query)
+                    entry.task = IdNameEntry(id=task.id, name=task.name)
+
+            entry.last_report_time = now
+        except APIError:
+            raise
+        except Exception as e:
+            msg = "Failed processing worker status report"
+            log.exception(msg)
+            raise server_error.DataError(msg, err=e.args[0])
+        finally:
+            self._save_worker(entry)
+
+    def get_all(
+        self, company_id: str, last_seen: Optional[int] = None
+    ) -> Sequence[WorkerEntry]:
+        """
+        Get all the company workers that were active during the last_seen period
+        :param company_id: worker's company id
+        :param last_seen: period in seconds to check. Min value is 1 second
+        :return:
+        """
+        try:
+            workers = self._get(company_id)
+        except Exception as e:
+            raise server_error.DataError("failed loading worker entries", err=e.args[0])
+
+        if last_seen:
+            ref_time = datetime.utcnow() - timedelta(seconds=max(1, last_seen))
+            workers = [
+                w
+                for w in workers
+                if w.last_activity_time.replace(tzinfo=None) >= ref_time
+            ]
+
+        return workers
+
+    def get_all_with_projection(
+        self, company_id: str, last_seen: int
+    ) -> Sequence[WorkerResponseEntry]:
+
+        helpers = list(
+            map(
+                WorkerConversionHelper.from_worker_entry,
+                self.get_all(company_id=company_id, last_seen=last_seen),
+            )
+        )
+
+        task_ids = set(filter(None, (helper.task_id for helper in helpers)))
+        all_queues = set(
+            itertools.chain.from_iterable(helper.queue_ids for helper in helpers)
+        )
+
+        queues_info = {}
+        if all_queues:
+            projection = [
+                {"$match": {"_id": {"$in": list(all_queues)}}},
+                {
+                    "$project": {
+                        "name": 1,
+                        "next_entry": {"$arrayElemAt": ["$entries", 0]},
+                        "num_entries": {"$size": "$entries"},
+                    }
+                },
+            ]
+            queues_info = {
+                res["_id"]: res for res in Queue.objects.aggregate(projection)
+            }
+            task_ids = task_ids.union(
+                filter(
+                    None,
+                    (
+                        safe_get(info, "next_entry/task")
+                        for info in queues_info.values()
+                    ),
+                )
+            )
+
+        tasks_info = {}
+        if task_ids:
+            tasks_info = {
+                task.id: task
+                for task in Task.objects(id__in=task_ids).only(
+                    "name", "started", "last_iteration"
+                )
+            }
+
+        def update_queue_entries(*entries):
+            for entry in entries:
+                if not entry:
+                    continue
+                info = queues_info.get(entry.id, None)
+                if not info:
+                    continue
+                entry.name = info.get("name", None)
+                entry.num_tasks = info.get("num_entries", 0)
+                task_id = safe_get(info, "next_entry/task")
+                if task_id:
+                    task = tasks_info.get(task_id, None)
+                    entry.next_task = IdNameEntry(
+                        id=task_id, name=task.name if task else None
+                    )
+
+        for helper in helpers:
+            worker = helper.worker
+            if helper.task_id:
+                task = tasks_info.get(helper.task_id, None)
+                if task:
+                    worker.task.running_time = (
+                        int((datetime.utcnow() - task.started).total_seconds() * 1000)
+                        if task.started
+                        else 0
+                    )
+                    worker.task.last_iteration = task.last_iteration
+
+            update_queue_entries(worker.queue)
+            if worker.queues:
+                update_queue_entries(*worker.queues)
+
+        return [helper.worker for helper in helpers]
+
+    @staticmethod
+    def _get_worker_key(company: str, user: str, worker_id: str) -> str:
+        """Build redis key from company, user and worker_id"""
+        return f"worker_{company}_{user}_{worker_id}"
+
+    def _get_worker(self, company_id: str, user_id: str, worker: str) -> WorkerEntry:
+        """
+        Get a worker entry for the provided worker ID. The entry is loaded from Redis
+        if it exists (i.e. worker has already been registered), otherwise the worker
+        is registered and its entry stored into Redis).
+        :param company_id: worker's company ID
+        :param user_id: user ID under which this worker is running
+        :param worker: worker ID
+        :raise bad_request.InvalidWorkerId: in case the worker id was not found
+        :return: worker entry instance
+        """
+        key = self._get_worker_key(company_id, user_id, worker)
+
+        with TimingContext("redis", "get_worker"):
+            data = self.redis.get(key)
+
+        if data:
+            try:
+                entry = WorkerEntry.from_json(data)
+                if not entry.key:
+                    entry.key = key
+                    self._save_worker(entry)
+                return entry
+            except Exception as e:
+                msg = "Failed parsing worker entry"
+                log.exception(msg)
+                raise server_error.DataError(msg, err=e.args[0])
+
+        # Failed loading worker from Redis
+        if config.get("apiserver.workers.auto_register", False):
+            try:
+                return self.register_worker(company_id, user_id, worker)
+            except Exception:
+                log.error(
+                    "Failed auto registration of {} for company {}".format(
+                        worker, company_id
+                    )
+                )
+
+        raise bad_request.InvalidWorkerId(worker=worker)
+
+    def _save_worker(self, entry: WorkerEntry) -> None:
+        """Save worker entry in Redis"""
+        try:
+            self.redis.setex(
+                entry.key, timedelta(seconds=entry.register_timeout), entry.to_json()
+            )
+        except Exception:
+            msg = "Failed saving worker entry"
+            log.exception(msg)
+
+    def _get(
+        self, company: str, user: str = "*", worker_id: str = "*"
+    ) -> Sequence[WorkerEntry]:
+        """Get worker entries matching the company and user, worker patterns"""
+        match = self._get_worker_key(company, user, worker_id)
+        with TimingContext("redis", "workers_get_all"):
+            res = self.redis.scan_iter(match)
+        return [WorkerEntry.from_json(self.redis.get(r)) for r in res]
+
+    @staticmethod
+    def _get_es_index_suffix():
+        """Get the index name suffix for storing current month data"""
+        return datetime.utcnow().strftime("%Y-%m")
+
+    def _log_stats_to_es(
+        self,
+        company_id: str,
+        company_name: str,
+        worker: str,
+        timestamp: int,
+        task: str,
+        machine_stats: MachineStats,
+    ) -> bool:
+        """
+        Actually writing the worker statistics to Elastic
+        :return: True if successful, False otherwise
+        """
+        es_index = (
+            f"{self._stats.worker_stats_prefix_for_company(company_id)}"
+            f"{self._get_es_index_suffix()}"
+        )
+
+        def make_doc(category, metric, variant, value) -> dict:
+            return dict(
+                _index=es_index,
+                _type="stat",
+                _source=dict(
+                    timestamp=timestamp,
+                    worker=worker,
+                    company=company_name,
+                    task=task,
+                    category=category,
+                    metric=metric,
+                    variant=variant,
+                    value=float(value),
+                ),
+            )
+
+        actions = []
+        for field, value in machine_stats.to_struct().items():
+            if not value:
+                continue
+            category = field.partition("_")[0]
+            metric = field
+            if not isinstance(value, (list, tuple)):
+                actions.append(make_doc(category, metric, "total", value))
+            else:
+                actions.extend(
+                    make_doc(category, metric, str(i), val)
+                    for i, val in enumerate(value)
+                )
+
+        es_res = elasticsearch.helpers.bulk(self.es_client, actions)
+        added, errors = es_res[:2]
+        return (added == len(actions)) and not errors
+
+
+@attr.s(auto_attribs=True)
+class WorkerConversionHelper:
+    worker: WorkerResponseEntry
+    task_id: str
+    queue_ids: Set[str]
+
+    @classmethod
+    def from_worker_entry(cls, worker: WorkerEntry):
+        data = worker.to_struct()
+        queue = data.pop("queue", None) or None
+        queue_ids = set(data.pop("queues", []))
+        queues = [QueueEntry(id=id) for id in queue_ids]
+        if queue:
+            queue = next((q for q in queues if q.id == queue), None)
+        return cls(
+            worker=WorkerResponseEntry(queues=queues, queue=queue, **data),
+            task_id=worker.task.id if worker.task else None,
+            queue_ids=queue_ids,
+        )

server/bll/workers/stats.py

@@ -0,0 +1,244 @@
+from operator import attrgetter
+from typing import Optional, Sequence
+
+from boltons.iterutils import bucketize
+
+from apierrors.errors import bad_request
+from apimodels.workers import AggregationType, GetStatsRequest, StatItem
+from bll.query import Builder as QueryBuilder
+from config import config
+from database.errors import translate_errors_context
+from timing_context import TimingContext
+
+log = config.logger(__file__)
+
+
+class WorkerStats:
+    def __init__(self, es):
+        self.es = es
+
+    @staticmethod
+    def worker_stats_prefix_for_company(company_id: str) -> str:
+        """Returns the es index prefix for the company"""
+        return f"worker_stats_{company_id}_"
+
+    def _search_company_stats(self, company_id: str, es_req: dict) -> dict:
+        return self.es.search(
+            index=f"{self.worker_stats_prefix_for_company(company_id)}*",
+            doc_type="stat",
+            body=es_req,
+        )
+
+    def get_worker_stats_keys(
+        self, company_id: str, worker_ids: Optional[Sequence[str]]
+    ) -> dict:
+        """
+        Get dictionary of metric types grouped by categories
+        :param company_id: company id
+        :param worker_ids: optional list of workers to get metric types from.
+        If not specified them metrics for all the company workers returned
+        :return:
+        """
+        es_req = {
+            "size": 0,
+            "aggs": {
+                "categories": {
+                    "terms": {"field": "category"},
+                    "aggs": {"metrics": {"terms": {"field": "metric"}}},
+                }
+            },
+        }
+        if worker_ids:
+            es_req["query"] = QueryBuilder.terms("worker", worker_ids)
+
+        res = self._search_company_stats(company_id, es_req)
+
+        if not res["hits"]["total"]:
+            raise bad_request.WorkerStatsNotFound(
+                f"No statistic metrics found for the company {company_id} and workers {worker_ids}"
+            )
+
+        return {
+            category["key"]: [
+                metric["key"] for metric in category["metrics"]["buckets"]
+            ]
+            for category in res["aggregations"]["categories"]["buckets"]
+        }
+
+    def get_worker_stats(self, company_id: str, request: GetStatsRequest) -> dict:
+        """
+        Get statistics for company workers metrics in the specified time range
+        Returned as date histograms for different aggregation types
+        grouped by worker, metric type (and optionally metric variant)
+        Buckets with no metrics are not returned
+        Note: all the statistics are retrieved as one ES query
+        """
+        if request.from_date >= request.to_date:
+            raise bad_request.FieldsValueError("from_date must be less than to_date")
+
+        def get_dates_agg() -> dict:
+            es_to_agg_types = (
+                ("avg", AggregationType.avg.value),
+                ("min", AggregationType.min.value),
+                ("max", AggregationType.max.value),
+            )
+
+            return {
+                "dates": {
+                    "date_histogram": {
+                        "field": "timestamp",
+                        "interval": f"{request.interval}s",
+                        "min_doc_count": 1,
+                    },
+                    "aggs": {
+                        agg_type: {es_agg: {"field": "value"}}
+                        for es_agg, agg_type in es_to_agg_types
+                    },
+                }
+            }
+
+        def get_variants_agg() -> dict:
+            return {
+                "variants": {"terms": {"field": "variant"}, "aggs": get_dates_agg()}
+            }
+
+        es_req = {
+            "size": 0,
+            "aggs": {
+                "workers": {
+                    "terms": {"field": "worker"},
+                    "aggs": {
+                        "metrics": {
+                            "terms": {"field": "metric"},
+                            "aggs": get_variants_agg()
+                            if request.split_by_variant
+                            else get_dates_agg(),
+                        }
+                    },
+                }
+            },
+        }
+
+        query_terms = [
+            QueryBuilder.dates_range(request.from_date, request.to_date),
+            QueryBuilder.terms("metric", {item.key for item in request.items}),
+        ]
+        if request.worker_ids:
+            query_terms.append(QueryBuilder.terms("worker", request.worker_ids))
+        es_req["query"] = {"bool": {"must": query_terms}}
+
+        with translate_errors_context(), TimingContext("es", "get_worker_stats"):
+            data = self._search_company_stats(company_id, es_req)
+
+        return self._extract_results(data, request.items, request.split_by_variant)
+
+    @staticmethod
+    def _extract_results(
+        data: dict, request_items: Sequence[StatItem], split_by_variant: bool
+    ) -> dict:
+        """
+        Clean results returned from elastic search (remove "aggregations", "buckets" etc.),
+        leave only aggregation types requested by the user and return a clean dictionary
+        and return a "clean" dictionary of
+        :param data: aggregation data retrieved from ES
+        :param request_items: aggs types requested by the user
+        :param split_by_variant: if False then aggregate by metric type, otherwise metric type + variant
+        """
+        if "aggregations" not in data:
+            return {}
+
+        items_by_key = bucketize(request_items, key=attrgetter("key"))
+        aggs_per_metric = {
+            key: [item.aggregation for item in items]
+            for key, items in items_by_key.items()
+        }
+
+        def extract_date_stats(date: dict, metric_key) -> dict:
+            return {
+                "date": date["key"],
+                "count": date["doc_count"],
+                **{agg: date[agg]["value"] for agg in aggs_per_metric[metric_key]},
+            }
+
+        def extract_metric_results(
+            metric_or_variant: dict, metric_key: str
+        ) -> Sequence[dict]:
+            return [
+                extract_date_stats(date, metric_key)
+                for date in metric_or_variant["dates"]["buckets"]
+                if date["doc_count"]
+            ]
+
+        def extract_variant_results(metric: dict) -> dict:
+            metric_key = metric["key"]
+            return {
+                variant["key"]: extract_metric_results(variant, metric_key)
+                for variant in metric["variants"]["buckets"]
+            }
+
+        def extract_worker_results(worker: dict) -> dict:
+            return {
+                metric["key"]: extract_variant_results(metric)
+                if split_by_variant
+                else extract_metric_results(metric, metric["key"])
+                for metric in worker["metrics"]["buckets"]
+            }
+
+        return {
+            worker["key"]: extract_worker_results(worker)
+            for worker in data["aggregations"]["workers"]["buckets"]
+        }
+
+    def get_activity_report(
+        self,
+        company_id: str,
+        from_date: float,
+        to_date: float,
+        interval: int,
+        active_only: bool,
+    ) -> Sequence[dict]:
+        """
+        Get statistics for company workers metrics in the specified time range
+        Returned as date histograms for different aggregation types
+        grouped by worker, metric type (and optionally metric variant)
+        Note: all the statistics are retrieved using one ES query
+        """
+        if from_date >= to_date:
+            raise bad_request.FieldsValueError("from_date must be less than to_date")
+
+        must = [QueryBuilder.dates_range(from_date, to_date)]
+        if active_only:
+            must.append({"exists": {"field": "task"}})
+
+        es_req = {
+            "size": 0,
+            "aggs": {
+                "dates": {
+                    "date_histogram": {
+                        "field": "timestamp",
+                        "interval": f"{interval}s",
+                    },
+                    "aggs": {"workers_count": {"cardinality": {"field": "worker"}}},
+                }
+            },
+            "query": {"bool": {"must": must}},
+        }
+
+        with translate_errors_context(), TimingContext(
+            "es", "get_worker_activity_report"
+        ):
+            data = self._search_company_stats(company_id, es_req)
+
+        if "aggregations" not in data:
+            return {}
+
+        ret = [
+            dict(date=date["key"], count=date["workers_count"]["value"])
+            for date in data["aggregations"]["dates"]["buckets"]
+        ]
+
+        if ret and ret[-1]["date"] > (to_date - 0.9 * interval):
+            # remove last interval if it's incomplete. Allow 10% tolerance
+            ret.pop()
+
+        return ret

server/config/basic.py

@@ -1,4 +1,9 @@
 import logging
+import os
+import platform
+from functools import reduce
+from os import getenv
+from os.path import expandvars
 from pathlib import Path
 
 from pyhocon import ConfigTree, ConfigFactory
@@ -9,6 +14,13 @@ from pyparsing import (
     ParseSyntaxException,
 )
 
+DEFAULT_EXTRA_CONFIG_PATH = "/opt/trains/config"
+EXTRA_CONFIG_PATH_ENV_KEY = "TRAINS_CONFIG_DIR"
+EXTRA_CONFIG_PATH_SEP = ":" if platform.system() != "Windows" else ';'
+
+EXTRA_CONFIG_VALUES_ENV_KEY_SEP = "__"
+EXTRA_CONFIG_VALUES_ENV_KEY_PREFIX = f"TRAINS{EXTRA_CONFIG_VALUES_ENV_KEY_SEP}"
+
 
 class BasicConfig:
     NotSet = object()
@@ -36,11 +48,54 @@ class BasicConfig:
     def logger(self, name):
         if Path(name).is_file():
             name = Path(name).stem
-        path = ".".join((self.prefix, Path(name).stem))
+        path = ".".join((self.prefix, name))
         return logging.getLogger(path)
 
+    def _read_extra_env_config_values(self):
+        """ Loads extra configuration from environment-injected values """
+        result = ConfigTree()
+        prefix = EXTRA_CONFIG_VALUES_ENV_KEY_PREFIX
+
+        keys = sorted(k for k in os.environ if k.startswith(prefix))
+        for key in keys:
+            path = key[len(prefix) :].replace(EXTRA_CONFIG_VALUES_ENV_KEY_SEP, ".").lower()
+            result = ConfigTree.merge_configs(
+                result, ConfigFactory.parse_string(f"{path}: {os.environ[key]}")
+            )
+
+        return result
+
+    def _read_env_paths(self, key):
+        value = getenv(EXTRA_CONFIG_PATH_ENV_KEY, DEFAULT_EXTRA_CONFIG_PATH)
+        if value is None:
+            return
+        paths = [
+            Path(expandvars(v)).expanduser() for v in value.split(EXTRA_CONFIG_PATH_SEP)
+        ]
+        invalid = [
+            path
+            for path in paths
+            if not path.is_dir() and str(path) != DEFAULT_EXTRA_CONFIG_PATH
+        ]
+        if invalid:
+            print(f"WARNING: Invalid paths in {key} env var: {' '.join(map(str, invalid))}")
+        return [path for path in paths if path.is_dir()]
+
     def _load(self, verbose=True):
-        self._config = self._read_recursive(self.folder, verbose=verbose)
+        extra_config_paths = self._read_env_paths(EXTRA_CONFIG_PATH_ENV_KEY) or []
+        extra_config_values = self._read_extra_env_config_values()
+        configs = [
+            self._read_recursive(path, verbose=verbose)
+            for path in [self.folder] + extra_config_paths
+        ]
+
+        self._config = reduce(
+            lambda last, config: ConfigTree.merge_configs(
+                last, config, copy_trees=True
+            ),
+            configs + [extra_config_values],
+            ConfigTree(),
+        )
 
     def _read_recursive(self, conf_root, verbose=True):
         conf = ConfigTree()

server/config/default/apiserver.conf

@@ -21,12 +21,25 @@
     version {
         required: false
         default: 1.0
+        # if set then calls to endpoints with the version
+        # greater that the current max version will be rejected
+        check_max_version: false
     }
 
     mongo {
         # controls whether FieldDoesNotExist exception will be raised for any extra attribute existing in stored data
         # but not declared in a data model
         strict: false
+
+        aggregate {
+            allow_disk_use: true
+        }
+
+        pre_populate {
+            enabled: false
+            zip_file: "/path/to/export.zip"
+            fail_on_error: false
+        }
     }
 
     auth {
@@ -41,11 +54,71 @@
 
         # cookie containing auth token, for requests arriving from a web-browser
         session_auth_cookie_name: "trains_token_basic"
+
+        # cookie configuration for authorization cookies generated by auth.login
+        cookies {
+            httponly: true  # allow only http to access the cookies (no JS etc)
+            secure: false   # not using HTTPS
+            domain: null    # Limit to localhost is not supported
+            max_age: 99999999999
+        }
+
+#        # A list of fixed users
+#        fixed_users {
+#            enabled: true
+#            users: [
+#                {
+#                    username: "john"
+#                    password: "123456"
+#                    name: "john doe"
+#                }
+#
+#            ]
+#        }
     }
 
     cors {
         origins: "*"
+
+        # Not supported when origins is "*"
+        supports_credentials: true
     }
 
     default_company: "d1bd92a3b039400cbafc60a7a5b1e52b"
+
+    workers {
+        # Auto-register unknown workers on status reports and other calls
+        auto_register: true
+        # Timeout in seconds on task status update. If exceeded
+        # then task can be stopped without communicating to the worker
+        task_update_timeout: 600
+    }
+
+    check_for_updates {
+        enabled: true
+
+        # Check for updates every 24 hours
+        check_interval_sec: 86400
+
+        url: "https://updates.trains.allegro.ai/updates"
+
+        component_name: "trains-server"
+
+        # GET request timeout
+        request_timeout_sec: 3.0
+    }
+
+    statistics {
+        # Note: statistics are sent ONLY if the user has actively opted-in
+        supported: true
+
+        url: "https://updates.trains.allegro.ai/stats"
+
+        report_interval_hours: 24
+        agent_relevant_threshold_days: 30
+
+        max_retries: 5
+        max_backoff_sec: 5
+    }
+
 }

server/config/default/hosts.conf

@@ -9,6 +9,17 @@ elastic {
     }
     index_version: "1"
   }
+
+  workers {
+    hosts: [{host:"127.0.0.1", port:9200}]
+    args {
+      timeout: 60
+      dead_timeout: 10
+      max_retries: 5
+      retry_on_timeout: true
+    }
+    index_version: "1"
+  }
 }
 
 mongo {
@@ -19,3 +30,16 @@ mongo {
     host: "mongodb://127.0.0.1:27017/auth"
   }
 }
+
+redis {
+  apiserver {
+      host: "127.0.0.1"
+      port: 6379
+      db: 0
+  }
+  workers {
+    host: "127.0.0.1"
+    port: 6379
+    db: 4
+  }
+}

server/config/default/secure.conf

@@ -13,17 +13,21 @@
     credentials {
         # system credentials as they appear in the auth DB, used for intra-service communications
         apiserver {
+            role: "system"
             user_key: "62T8CP7HGBC6647XF9314C2VY67RJO"
             user_secret: "FhS8VZv_I4%6Mo$8S1BWc$n$=o1dMYSivuiWU-Vguq7qGOKskG-d+b@tn_Iq"
         }
         webserver {
+            role: "system"
             user_key: "EYVQ385RW7Y2QQUH88CZ7DWIQ1WUHP"
             user_secret: "yfc8KQo*GMXb*9p((qcYC7ByFIpF7I&4VH3BfUYXH%o9vX1ZUZQEEw1Inc)S"
+            revoke_in_fixed_mode: true
         }
         tests {
+            role: "user"
+            display_name: "Default User"
             user_key: "EGRTCO8JMSIGI6S39GTP43NFWXDQOW"
             user_secret: "x!XTov_G-#vspE*Y(h$Anm&DIc5Ou-F)jsl$PdOyj5wG1&E!Z8"
-
         }
     }
 }

server/config/default/services/events.conf

@@ -1,3 +1,13 @@
-{
-    es_index_prefix:"events"
-}
+es_index_prefix: "events"
+
+ignore_iteration {
+    metrics: [":monitor:machine", ":monitor:gpu"]
+}
+
+# max number of concurrent queries to ES when calculating events metrics
+# should not exceed the amount of concurrent connections set in the ES driver
+max_metrics_concurrency: 4
+
+events_retrieval {
+    state_expiration_sec: 3600
+}

server/config/default/services/organization.conf

@@ -0,0 +1,3 @@
+tags_cache {
+  expiration_seconds: 3600
+}

server/config/default/services/tasks.conf

@@ -0,0 +1,14 @@
+non_responsive_tasks_watchdog {
+    enabled: true
+
+    # In-progress tasks older than this value in seconds will be stopped by the watchdog
+    threshold_sec: 7200
+
+    # Watchdog will sleep for this number of seconds after each cycle
+    watch_interval_sec: 900
+}
+
+artifacts {
+    update_attempts: 10
+    update_retry_msec: 500
+}

server/config/info.py

@@ -0,0 +1,43 @@
+from functools import lru_cache
+from os import getenv
+from pathlib import Path
+from version import __version__
+
+from config import config
+
+root = Path(__file__).parent.parent
+
+
+def _get(prop_name, env_suffix=None, default=""):
+    value = getenv(f"TRAINS_SERVER_{env_suffix or prop_name}")
+    if value:
+        return value
+
+    try:
+        return (root / prop_name).read_text().strip()
+    except FileNotFoundError:
+        return default
+
+
+@lru_cache()
+def get_build_number():
+    return _get("BUILD")
+
+
+@lru_cache()
+def get_version():
+    return _get("VERSION", default=__version__)
+
+
+@lru_cache()
+def get_commit_number():
+    return _get("COMMIT")
+
+
+@lru_cache()
+def get_deployment_type() -> str:
+    return _get("DEPLOY", env_suffix="DEPLOYMENT_TYPE", default="manual")
+
+
+def get_default_company():
+    return config.get("apiserver.default_company")

server/database/__init__.py

@@ -1,3 +1,7 @@
+from os import getenv
+
+from boltons.iterutils import first
+from furl import furl
 from jsonmodels import models
 from jsonmodels.errors import ValidationError
 from jsonmodels.fields import StringField
@@ -8,9 +12,16 @@ from config import config
 from .defs import Database
 from .utils import get_items
 
-log = config.logger(__file__)
+log = config.logger("database")
 
-strict = config.get('apiserver.mongo.strict', True)
+strict = config.get("apiserver.mongo.strict", True)
+
+OVERRIDE_HOST_ENV_KEY = (
+    "TRAINS_MONGODB_SERVICE_HOST",
+    "MONGODB_SERVICE_HOST",
+    "MONGODB_SERVICE_SERVICE_HOST",
+)
+OVERRIDE_PORT_ENV_KEY = ("TRAINS_MONGODB_SERVICE_PORT", "MONGODB_SERVICE_PORT")
 
 _entries = []
 
@@ -21,28 +32,47 @@ class DatabaseEntry(models.Base):
 
     @property
     def health_alias(self):
-        return '__health__' + self.alias
+        return "__health__" + self.alias
 
 
 def initialize():
-    db_entries = config.get('hosts.mongo', {})
+    db_entries = config.get("hosts.mongo", {})
     missing = []
-    log.info('Initializing database connections')
+    log.info("Initializing database connections")
+
+    override_hostname = first(map(getenv, OVERRIDE_HOST_ENV_KEY), None)
+    if override_hostname:
+        log.info(f"Using override mongodb host {override_hostname}")
+
+    override_port = first(map(getenv, OVERRIDE_PORT_ENV_KEY), None)
+    if override_port:
+        log.info(f"Using override mongodb port {override_port}")
+
     for key, alias in get_items(Database).items():
         if key not in db_entries:
             missing.append(key)
             continue
+
         entry = DatabaseEntry(alias=alias, **db_entries.get(key))
+
+        if override_hostname:
+            entry.host = furl(entry.host).set(host=override_hostname).url
+
+        if override_port:
+            entry.host = furl(entry.host).set(port=override_port).url
+
         try:
             entry.validate()
-            log.info('Registering connection to %(alias)s (%(host)s)' % entry.to_struct())
+            log.info(
+                "Registering connection to %(alias)s (%(host)s)" % entry.to_struct()
+            )
             register_connection(alias=alias, host=entry.host)
 
             _entries.append(entry)
         except ValidationError as ex:
-            raise Exception('Invalid database entry `%s`: %s' % (key, ex.args[0]))
+            raise Exception("Invalid database entry `%s`: %s" % (key, ex.args[0]))
     if missing:
-        raise ValueError('Missing database configuration for %s' % ', '.join(missing))
+        raise ValueError("Missing database configuration for %s" % ", ".join(missing))
 
 
 def get_entries():

server/database/fields.py

@@ -1,5 +1,6 @@
-import re
+from operator import itemgetter
 from sys import maxsize
+from typing import Type, Tuple
 
 import six
 from mongoengine import (
@@ -11,7 +12,11 @@ from mongoengine import (
     SortedListField,
     MapField,
     DictField,
+    DynamicField,
 )
+from mongoengine.fields import key_not_string, key_starts_with_dollar
+
+NoneType = type(None)
 
 
 class LengthRangeListField(ListField):
@@ -88,104 +93,6 @@ class CustomFloatField(FloatField):
             self.error("Float value must be greater than %s" % str(self.greater_than))
 
 
-# TODO: bucket name should be at most 63 characters....
-aws_s3_bucket_only_regex = (
-    r"^s3://"
-    r"(?:(?:\w[A-Z0-9\-]+\w)\.)*(?:\w[A-Z0-9\-]+\w)"  # bucket name
-)
-
-aws_s3_url_with_bucket_regex = (
-    r"^s3://"
-    r"(?:(?:\w[A-Z0-9\-]+\w)\.)*(?:\w[A-Z0-9\-]+\w)"  # bucket name
-    r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}(?<!-)\.?))"  # domain...
-)
-
-non_aws_s3_regex = (
-    r"^s3://"
-    r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}(?<!-)\.?)|"  # domain...
-    r"localhost|"  # localhost...
-    r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}|"  # ...or ipv4
-    r"\[?[A-F0-9]*:[A-F0-9:]+\]?)"  # ...or ipv6
-    r"(?::\d+)?"  # optional port
-    r"(?:/(?:(?:\w[A-Z0-9\-]+\w)\.)*(?:\w[A-Z0-9\-]+\w))"  # bucket name
-)
-
-google_gs_bucket_only_regex = (
-    r"^gs://"
-    r"(?:(?:\w[A-Z0-9\-_]+\w)\.)*(?:\w[A-Z0-9\-_]+\w)"  # bucket name
-)
-
-file_regex = r"^file://"
-
-generic_url_regex = (
-    r"^%s://"  # scheme placeholder
-    r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}(?<!-)\.?)|"  # domain...
-    r"localhost|"  # localhost...
-    r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}|"  # ...or ipv4
-    r"\[?[A-F0-9]*:[A-F0-9:]+\]?)"  # ...or ipv6
-    r"(?::\d+)?"  # optional port
-)
-
-path_suffix = r"(?:/?|[/?]\S+)$"
-file_path_suffix = r"(?:/\S*[^/]+)$"
-
-
-class _RegexURLField(StringField):
-    _regex = []
-
-    def __init__(self, regex, **kwargs):
-        super(_RegexURLField, self).__init__(**kwargs)
-        regex = regex if isinstance(regex, (tuple, list)) else [regex]
-        self._regex = [
-            re.compile(e, re.IGNORECASE) if isinstance(e, six.string_types) else e
-            for e in regex
-        ]
-
-    def validate(self, value):
-        # Check first if the scheme is valid
-        if not any(regex for regex in self._regex if regex.match(value)):
-            self.error("Invalid URL: {}".format(value))
-            return
-
-
-class OutputDestinationField(_RegexURLField):
-    """ A field representing task output URL """
-
-    schemes = ["s3", "gs", "file"]
-    _expressions = (
-        aws_s3_bucket_only_regex + path_suffix,
-        aws_s3_url_with_bucket_regex + path_suffix,
-        non_aws_s3_regex + path_suffix,
-        google_gs_bucket_only_regex + path_suffix,
-        file_regex + path_suffix,
-    )
-
-    def __init__(self, **kwargs):
-        super(OutputDestinationField, self).__init__(self._expressions, **kwargs)
-
-
-class SupportedURLField(_RegexURLField):
-    """ A field representing a model URL """
-
-    schemes = ["s3", "gs", "file", "http", "https"]
-
-    _expressions = tuple(
-        pattern + file_path_suffix
-        for pattern in (
-            aws_s3_bucket_only_regex,
-            aws_s3_url_with_bucket_regex,
-            non_aws_s3_regex,
-            google_gs_bucket_only_regex,
-            file_regex,
-            (generic_url_regex % "http"),
-            (generic_url_regex % "https"),
-        )
-    )
-
-    def __init__(self, **kwargs):
-        super(SupportedURLField, self).__init__(self._expressions, **kwargs)
-
-
 class StrippedStringField(StringField):
     def __init__(
         self, regex=None, max_length=None, min_length=None, strip_chars=None, **kwargs
@@ -221,17 +128,82 @@ def contains_empty_key(d):
             return True
 
 
-class SafeMapField(MapField):
+class DictValidationMixin:
+    """
+    DictField validation in MongoEngine requires default alias and permissions to access DB version:
+    https://github.com/MongoEngine/mongoengine/issues/2239
+    This is a stripped down implementation that does not require any of the above and implies Mongo ver 3.6+
+    """
+
+    def _safe_validate(self: DictField, value):
+        if not isinstance(value, dict):
+            self.error("Only dictionaries may be used in a DictField")
+
+        if key_not_string(value):
+            msg = "Invalid dictionary key - documents must have only string keys"
+            self.error(msg)
+
+        if key_starts_with_dollar(value):
+            self.error(
+                'Invalid dictionary key name - keys may not startswith "$" characters'
+            )
+        super(DictField, self).validate(value)
+
+
+class SafeMapField(MapField, DictValidationMixin):
     def validate(self, value):
-        super(SafeMapField, self).validate(value)
+        self._safe_validate(value)
 
         if contains_empty_key(value):
             self.error("Empty keys are not allowed in a MapField")
 
 
-class SafeDictField(DictField):
+class SafeDictField(DictField, DictValidationMixin):
     def validate(self, value):
-        super(SafeDictField, self).validate(value)
+        self._safe_validate(value)
 
         if contains_empty_key(value):
             self.error("Empty keys are not allowed in a DictField")
+
+
+class SafeSortedListField(SortedListField):
+    """
+    SortedListField that does not raise an error in case items are not comparable
+    (in which case they will be sorted by their string representation)
+    """
+
+    def to_mongo(self, *args, **kwargs):
+        try:
+            return super(SafeSortedListField, self).to_mongo(*args, **kwargs)
+        except TypeError:
+            return self._safe_to_mongo(*args, **kwargs)
+
+    def _safe_to_mongo(self, value, use_db_field=True, fields=None):
+        value = super(SortedListField, self).to_mongo(value, use_db_field, fields)
+        if self._ordering is not None:
+
+            def key(v):
+                return str(itemgetter(self._ordering)(v))
+
+        else:
+            key = str
+        return sorted(value, key=key, reverse=self._order_reverse)
+
+
+class UnionField(DynamicField):
+    def __init__(self, types, *args, **kwargs):
+        super(UnionField, self).__init__(*args, **kwargs)
+        self.types: Tuple[Type] = tuple(types)
+
+    def validate(self, value, clean=True):
+        if not isinstance(value, self.types):
+            type_names = [t.__name__ for t in self.types]
+            expected = " or ".join(
+                filter(
+                    None,
+                    (", ".join(type_names[:-1]), type_names[-1]))
+            )
+            self.error(
+                f"Expected {expected}, got {type(value).__name__}: {value}"
+            )
+        super(UnionField, self).validate(value, clean)

server/database/model/__init__.py

@@ -1,3 +1,5 @@
+from enum import Enum
+
 from mongoengine import Document, StringField
 
 from apierrors import errors
@@ -54,3 +56,7 @@ def validate_id(cls, company, **kwargs):
         **{name: obj_id for obj_id in missing for name in id_to_name[obj_id]}
     )
 
+
+class EntityVisibility(Enum):
+    active = "active"
+    archived = "archived"

server/database/model/auth.py

@@ -43,15 +43,17 @@ class Role(object):
 
 
 class Credentials(EmbeddedDocument):
+    meta = {"strict": False}
     key = StringField(required=True)
     secret = StringField(required=True)
+    last_used = DateTimeField()
 
 
 class User(DbModelMixin, AuthDocument):
     meta = {"db_alias": Database.auth, "strict": strict}
 
     id = StringField(primary_key=True)
-    name = StringField(unique_with="company")
+    name = StringField()
 
     created = DateTimeField()
     """ User auth entry creation time """

server/database/model/base.py

@@ -1,11 +1,12 @@
 import re
 from collections import namedtuple
 from functools import reduce
-from typing import Collection
+from typing import Collection, Sequence, Union, Optional
 
+from boltons.iterutils import first, bucketize
 from dateutil.parser import parse as parse_datetime
-from mongoengine import Q, Document
-from six import string_types
+from mongoengine import Q, Document, ListField, StringField
+from pymongo.command_cursor import CommandCursor
 
 from apierrors import errors
 from config import config
@@ -13,7 +14,12 @@ from database.errors import MakeGetAllQueryError
 from database.projection import project_dict, ProjectionHelper
 from database.props import PropsMixin
 from database.query import RegexQ, RegexWrapper
-from database.utils import get_company_or_none_constraint, get_fields_with_attr
+from database.utils import (
+    get_company_or_none_constraint,
+    get_fields_choices,
+    field_does_not_exist,
+    field_exists,
+)
 
 log = config.logger("dbmodel")
 
@@ -28,7 +34,12 @@ class AuthDocument(Document):
 
 
 class ProperDictMixin(object):
-    def to_proper_dict(self, strip_private=True, only=None, extra_dict=None) -> dict:
+    def to_proper_dict(
+        self: Union["ProperDictMixin", Document],
+        strip_private=True,
+        only=None,
+        extra_dict=None,
+    ) -> dict:
         return self.properize_dict(
             self.to_mongo(use_db_field=False).to_dict(),
             strip_private=strip_private,
@@ -54,8 +65,9 @@ class ProperDictMixin(object):
 
 class GetMixin(PropsMixin):
     _text_score = "$text_score"
-
+    _projection_key = "projection"
     _ordering_key = "order_by"
+    _search_text_key = "search_text"
 
     _multi_field_param_sep = "__"
     _multi_field_param_prefix = {
@@ -64,11 +76,13 @@ class GetMixin(PropsMixin):
     }
     MultiFieldParameters = namedtuple("MultiFieldParameters", "pattern fields")
 
+    _field_collation_overrides = {}
+
     class QueryParameterOptions(object):
         def __init__(
             self,
             pattern_fields=("name",),
-            list_fields=("tags", "id"),
+            list_fields=("tags", "system_tags", "id"),
             datetime_fields=None,
             fields=None,
         ):
@@ -84,11 +98,48 @@ class GetMixin(PropsMixin):
             self.list_fields = list_fields
             self.pattern_fields = pattern_fields
 
+    class ListFieldBucketHelper:
+        op_prefix = "__$"
+        legacy_exclude_prefix = "-"
+
+        _default = "in"
+        _ops = {"not": "nin"}
+        _next = _default
+
+        def __init__(self, legacy=False):
+            self._legacy = legacy
+
+        def key(self, v):
+            if v is None:
+                self._next = self._default
+                return self._default
+            elif self._legacy and v.startswith(self.legacy_exclude_prefix):
+                self._next = self._default
+                return self._ops["not"]
+            elif v.startswith(self.op_prefix):
+                self._next = self._ops.get(v[len(self.op_prefix) :], self._default)
+                return None
+
+            next_ = self._next
+            self._next = self._default
+            return next_
+
+        def value_transform(self, v):
+            if self._legacy and v and v.startswith(self.legacy_exclude_prefix):
+                return v[len(self.legacy_exclude_prefix) :]
+            return v
+
     get_all_query_options = QueryParameterOptions()
 
     @classmethod
     def get(
-        cls, company, id, *, _only=None, include_public=False, **kwargs
+        cls: Union["GetMixin", Document],
+        company,
+        id,
+        *,
+        _only=None,
+        include_public=False,
+        **kwargs,
     ) -> "GetMixin":
         q = cls.objects(
             cls._prepare_perm_query(company, allow_public=include_public)
@@ -155,17 +206,7 @@ class GetMixin(PropsMixin):
             for field in tuple(opts.list_fields or ()):
                 data = parameters.pop(field, None)
                 if data:
-                    if not isinstance(data, (list, tuple)):
-                        raise MakeGetAllQueryError("expected list", field)
-                    exclude = [t for t in data if t.startswith("-")]
-                    include = list(set(data).difference(exclude))
-                    mongoengine_field = field.replace(".", "__")
-                    if include:
-                        dict_query[f"{mongoengine_field}__in"] = include
-                    if exclude:
-                        dict_query[f"{mongoengine_field}__nin"] = [
-                            t[1:] for t in exclude
-                        ]
+                    query &= cls.get_list_field_query(field, data)
 
             for field in opts.fields or []:
                 data = parameters.pop(field, None)
@@ -209,12 +250,71 @@ class GetMixin(PropsMixin):
 
         return query & RegexQ(**dict_query)
 
+    @classmethod
+    def get_list_field_query(cls, field: str, data: Sequence[Optional[str]]) -> Q:
+        """
+        Get a proper mongoengine Q object that represents an "or" query for the provided values
+        with respect to the given list field, with support for "none of empty" in case a None value
+        is included.
+
+        - Exclusion can be specified by a leading "-" for each value (API versions <2.8)
+            or by a preceding "__$not" value (operator)
+        """
+        if not isinstance(data, (list, tuple)):
+            raise MakeGetAllQueryError("expected list", field)
+
+        # TODO: backwards compatibility only for older API versions
+        helper = cls.ListFieldBucketHelper(legacy=True)
+        actions = bucketize(
+            data, key=helper.key, value_transform=helper.value_transform
+        )
+
+        allow_empty = None in actions.get("in", {})
+        mongoengine_field = field.replace(".", "__")
+
+        q = RegexQ()
+        for action in filter(None, actions):
+            q &= RegexQ(
+                **{
+                    f"{mongoengine_field}__{action}": list(
+                        set(filter(None, actions[action]))
+                    )
+                }
+            )
+
+        if not allow_empty:
+            return q
+
+        return (
+            q
+            | Q(**{f"{mongoengine_field}__exists": False})
+            | Q(**{mongoengine_field: []})
+        )
+
     @classmethod
     def _prepare_perm_query(cls, company, allow_public=False):
         if allow_public:
             return get_company_or_none_constraint(company)
         return Q(company=company)
 
+    @classmethod
+    def validate_order_by(cls, parameters, search_text) -> Sequence:
+        """
+        Validate and extract order_by params as a list
+        """
+        order_by = parameters.get(cls._ordering_key)
+        if not order_by:
+            return []
+
+        order_by = order_by if isinstance(order_by, list) else [order_by]
+        order_by = [cls._text_score if x == "@text_score" else x for x in order_by]
+        if not search_text and cls._text_score in order_by:
+            raise errors.bad_request.FieldsValueError(
+                "text score cannot be used in order_by when search text is not used"
+            )
+
+        return order_by
+
     @classmethod
     def validate_paging(
         cls, parameters=None, default_page=None, default_page_size=None
@@ -245,11 +345,26 @@ class GetMixin(PropsMixin):
             return override_projection
         if not parameters:
             return []
-        return parameters.get("projection") or parameters.get("only_fields", [])
+        return parameters.get(cls._projection_key) or parameters.get("only_fields", [])
 
     @classmethod
-    def set_default_ordering(cls, parameters, value):
-        parameters[cls._ordering_key] = parameters.get(cls._ordering_key) or value
+    def set_projection(cls, parameters: dict, value: Sequence[str]) -> Sequence[str]:
+        parameters.pop("only_fields", None)
+        parameters[cls._projection_key] = value
+        return value
+
+    @classmethod
+    def get_ordering(cls, parameters: dict) -> Optional[Sequence[str]]:
+        return parameters.get(cls._ordering_key)
+
+    @classmethod
+    def set_ordering(cls, parameters: dict, value: Sequence[str]) -> Sequence[str]:
+        parameters[cls._ordering_key] = value
+        return value
+
+    @classmethod
+    def set_default_ordering(cls, parameters: dict, value: Sequence[str]) -> None:
+        cls.set_ordering(parameters, cls.get_ordering(parameters) or value)
 
     @classmethod
     def get_many_with_join(
@@ -332,8 +447,9 @@ class GetMixin(PropsMixin):
                     `@text_score` keyword. A text index must be defined on the document type, otherwise an error will
                     be raised.
         :param return_dicts: Return a list of dictionaries. If True, a list of dicts is returned (if projection was
-            requested, each contains only the requested projection).
-            If False, a QuerySet object is returned (lazy evaluated)
+            requested, each contains only the requested projection). If False, a QuerySet object is returned
+            (lazy evaluated). If return_dicts is requested then the entities with the None value in order_by field
+            are returned last in the ordering.
         :param company: Company ID (required)
         :param parameters: Parameters dict from which paging ordering and searching parameters are extracted.
         :param query_dict: If provided, passed to prepare_query() along with all of the relevant arguments to produce
@@ -356,16 +472,23 @@ class GetMixin(PropsMixin):
             q = cls._prepare_perm_query(company, allow_public=allow_public)
         _query = (q & query) if query else q
 
+        if return_dicts:
+            return cls._get_many_override_none_ordering(
+                query=_query,
+                parameters=parameters,
+                override_projection=override_projection,
+            )
+
         return cls._get_many_no_company(
-            query=_query,
-            parameters=parameters,
-            override_projection=override_projection,
-            return_dicts=return_dicts,
+            query=_query, parameters=parameters, override_projection=override_projection
         )
 
     @classmethod
     def _get_many_no_company(
-        cls, query, parameters=None, override_projection=None, return_dicts=True
+        cls: Union["GetMixin", Document],
+        query,
+        parameters=None,
+        override_projection=None,
     ):
         """
         Fetch all documents matching a provided query.
@@ -375,44 +498,25 @@ class GetMixin(PropsMixin):
         NOTE: BE VERY CAREFUL WITH THIS CALL, as it allows returning data across companies.
 
         :param query: Query object (mongoengine.Q)
-        :param return_dicts: Return a list of dictionaries. If True, a list of dicts is returned (if projection was
-            requested, each contains only the requested projection).
-            If False, a QuerySet object is returned (lazy evaluated)
         :param parameters: Parameters dict from which paging ordering and searching parameters are extracted.
         :param override_projection: A list of projection fields overriding any projection specified in the `param_dict`
             argument
         """
-        parameters = parameters or {}
-
         if not query:
             raise ValueError("query or call_data must be provided")
 
+        parameters = parameters or {}
+        search_text = parameters.get(cls._search_text_key)
+        order_by = cls.validate_order_by(parameters=parameters, search_text=search_text)
         page, page_size = cls.validate_paging(parameters=parameters)
-
-        order_by = parameters.get(cls._ordering_key)
-        if order_by:
-            order_by = order_by if isinstance(order_by, list) else [order_by]
-            order_by = [cls._text_score if x == "@text_score" else x for x in order_by]
-
-        search_text = parameters.get("search_text")
-
         only = cls.get_projection(parameters, override_projection)
 
-        if not search_text and order_by and cls._text_score in order_by:
-            raise errors.bad_request.FieldsValueError(
-                "text score cannot be used in order_by when search text is not used"
-            )
-
         qs = cls.objects(query)
         if search_text:
             qs = qs.search_text(search_text)
         if order_by:
             # add ordering
-            qs = (
-                qs.order_by(order_by)
-                if isinstance(order_by, string_types)
-                else qs.order_by(*order_by)
-            )
+            qs = qs.order_by(*order_by)
         if only:
             # add projection
             qs = qs.only(*only)
@@ -424,10 +528,103 @@ class GetMixin(PropsMixin):
             # add paging
             qs = qs.skip(page * page_size).limit(page_size)
 
-        if return_dicts:
-            return [obj.to_proper_dict(only=only) for obj in qs]
         return qs
 
+    @classmethod
+    def _get_many_override_none_ordering(
+        cls: Union[Document, "GetMixin"],
+        query: Q = None,
+        parameters: dict = None,
+        override_projection: Collection[str] = None,
+    ) -> Sequence[dict]:
+        """
+        Fetch all documents matching a provided query. For the first order by field
+        the None values are sorted in the end regardless of the sorting order.
+        If the first order field is a user defined parameter (either from execution.parameters,
+        or from last_metrics) then the collation is set that sorts strings in numeric order where possible.
+        This is a company-less version for internal uses. We assume the caller has either added any necessary
+        constraints to the query or that no constraints are required.
+
+        NOTE: BE VERY CAREFUL WITH THIS CALL, as it allows returning data across companies.
+
+        :param query: Query object (mongoengine.Q)
+        :param parameters: Parameters dict from which paging ordering and searching parameters are extracted.
+        :param override_projection: A list of projection fields overriding any projection specified in the `param_dict`
+            argument
+        """
+        if not query:
+            raise ValueError("query or call_data must be provided")
+
+        parameters = parameters or {}
+        search_text = parameters.get(cls._search_text_key)
+        order_by = cls.validate_order_by(parameters=parameters, search_text=search_text)
+        page, page_size = cls.validate_paging(parameters=parameters)
+        only = cls.get_projection(parameters, override_projection)
+
+        query_sets = [cls.objects(query)]
+        if order_by:
+            order_field = first(
+                field for field in order_by if not field.startswith("$")
+            )
+            if (
+                order_field
+                and not order_field.startswith("-")
+                and "[" not in order_field
+            ):
+                params = {}
+                mongo_field = order_field.replace(".", "__")
+                if mongo_field in cls.get_field_names_for_type(of_type=ListField):
+                    params["is_list"] = True
+                elif mongo_field in cls.get_field_names_for_type(of_type=StringField):
+                    params["empty_value"] = ""
+                non_empty = query & field_exists(mongo_field, **params)
+                empty = query & field_does_not_exist(mongo_field, **params)
+                query_sets = [cls.objects(non_empty), cls.objects(empty)]
+
+            query_sets = [qs.order_by(*order_by) for qs in query_sets]
+            if order_field:
+                collation_override = first(
+                    v
+                    for k, v in cls._field_collation_overrides.items()
+                    if order_field.startswith(k)
+                )
+                if collation_override:
+                    query_sets = [
+                        qs.collation(collation=collation_override) for qs in query_sets
+                    ]
+
+        if search_text:
+            query_sets = [qs.search_text(search_text) for qs in query_sets]
+
+        if only:
+            # add projection
+            query_sets = [qs.only(*only) for qs in query_sets]
+        else:
+            exclude = set(cls.get_exclude_fields())
+            if exclude:
+                query_sets = [qs.exclude(*exclude) for qs in query_sets]
+
+        if page is None or not page_size:
+            return [obj.to_proper_dict(only=only) for qs in query_sets for obj in qs]
+
+        # add paging
+        ret = []
+        start = page * page_size
+        for qs in query_sets:
+            qs_size = qs.count()
+            if qs_size < start:
+                start -= qs_size
+                continue
+            ret.extend(
+                obj.to_proper_dict(only=only) for obj in qs.skip(start).limit(page_size)
+            )
+            if len(ret) >= page_size:
+                break
+            start = 0
+            page_size -= len(ret)
+
+        return ret
+
     @classmethod
     def get_for_writing(
         cls, *args, _only: Collection[str] = None, **kwargs
@@ -464,8 +661,8 @@ class UpdateMixin(object):
     def user_set_allowed(cls):
         res = getattr(cls, "__user_set_allowed_fields", None)
         if res is None:
-            res = cls.__user_set_allowed_fields = dict(
-                get_fields_with_attr(cls, "user_set_allowed")
+            res = cls.__user_set_allowed_fields = get_fields_choices(
+                cls, "user_set_allowed"
             )
         return res
 
@@ -488,7 +685,13 @@ class UpdateMixin(object):
         return update_dict
 
     @classmethod
-    def safe_update(cls, company_id, id, partial_update_dict, injected_update=None):
+    def safe_update(
+        cls: Union["UpdateMixin", Document],
+        company_id,
+        id,
+        partial_update_dict,
+        injected_update=None,
+    ):
         update_dict = cls.get_safe_update_dict(partial_update_dict)
         if not update_dict:
             return 0, {}
@@ -503,7 +706,27 @@ class UpdateMixin(object):
 class DbModelMixin(GetMixin, ProperDictMixin, UpdateMixin):
     """ Provide convenience methods for a subclass of mongoengine.Document """
 
-    pass
+    @classmethod
+    def aggregate(
+        cls: Union["DbModelMixin", Document],
+        pipeline: Sequence[dict],
+        allow_disk_use=None,
+        **kwargs,
+    ) -> CommandCursor:
+        """
+        Aggregate objects of this document class according to the provided pipeline.
+        :param pipeline: a list of dictionaries describing the pipeline stages
+        :param allow_disk_use: if True, allow the server to use disk space if aggregation query cannot fit in memory.
+        If None, default behavior will be used (see apiserver.conf/mongo/aggregate/allow_disk_use)
+        :param kwargs: additional keyword arguments passed to mongoengine
+        :return:
+        """
+        kwargs.update(
+            allowDiskUse=allow_disk_use
+            if allow_disk_use is not None
+            else config.get("apiserver.mongo.aggregate.allow_disk_use", True)
+        )
+        return cls.objects.aggregate(pipeline, **kwargs)
 
 
 def validate_id(cls, company, **kwargs):
@@ -525,5 +748,5 @@ def validate_id(cls, company, **kwargs):
         id_to_name.setdefault(obj_id, []).append(name)
     raise errors.bad_request.ValidationError(
         "Invalid {} ids".format(cls.__name__.lower()),
-        **{name: obj_id for obj_id in missing for name in id_to_name[obj_id]}
+        **{name: obj_id for obj_id in missing for name in id_to_name[obj_id]},
     )

server/database/model/company.py

@@ -1,23 +1,36 @@
-from mongoengine import Document, EmbeddedDocument, EmbeddedDocumentField, StringField, Q
+from mongoengine import (
+    Document,
+    EmbeddedDocument,
+    EmbeddedDocumentField,
+    StringField,
+    Q,
+    BooleanField,
+    DateTimeField,
+)
 
 from database import Database, strict
 from database.fields import StrippedStringField
 from database.model import DbModelMixin
 
 
+class ReportStatsOption(EmbeddedDocument):
+    enabled = BooleanField(default=False)  # opt-in for statistics reporting
+    enabled_version = StringField()  # server version when enabled
+    enabled_time = DateTimeField()  # time when enabled
+    enabled_user = StringField()  # ID of user who enabled
+
+
 class CompanyDefaults(EmbeddedDocument):
     cluster = StringField()
+    stats_option = EmbeddedDocumentField(ReportStatsOption, default=ReportStatsOption)
 
 
 class Company(DbModelMixin, Document):
-    meta = {
-        'db_alias': Database.backend,
-        'strict': strict,
-    }
+    meta = {"db_alias": Database.backend, "strict": strict}
 
     id = StringField(primary_key=True)
     name = StrippedStringField(unique=True, min_length=3)
-    defaults = EmbeddedDocumentField(CompanyDefaults)
+    defaults = EmbeddedDocumentField(CompanyDefaults, default=CompanyDefaults)
 
     @classmethod
     def _prepare_perm_query(cls, company, allow_public=False):

server/database/model/model.py

@@ -1,8 +1,9 @@
-from mongoengine import Document, StringField, DateTimeField, ListField, BooleanField
+from mongoengine import Document, StringField, DateTimeField, BooleanField
 
 from database import Database, strict
-from database.fields import SupportedURLField, StrippedStringField, SafeDictField
+from database.fields import StrippedStringField, SafeDictField, SafeSortedListField
 from database.model import DbModelMixin
+from database.model.base import GetMixin
 from database.model.model_labels import ModelLabels
 from database.model.company import Company
 from database.model.project import Project
@@ -12,45 +13,61 @@ from database.model.user import User
 
 class Model(DbModelMixin, Document):
     meta = {
-        'db_alias': Database.backend,
-        'strict': strict,
-        'indexes': [
+        "db_alias": Database.backend,
+        "strict": strict,
+        "indexes": [
+            "parent",
+            "project",
+            "task",
+            ("company", "name"),
+            ("company", "user"),
             {
-                'name': '%s.model.main_text_index' % Database.backend,
-                'fields': [
-                    '$name',
-                    '$id',
-                    '$comment',
-                    '$parent',
-                    '$task',
-                    '$project',
-                ],
-                'default_language': 'english',
-                'weights': {
-                    'name': 10,
-                    'id': 10,
-                    'comment': 10,
-                    'parent': 5,
-                    'task': 3,
-                    'project': 3,
-                }
-            }
+                "name": "%s.model.main_text_index" % Database.backend,
+                "fields": ["$name", "$id", "$comment", "$parent", "$task", "$project"],
+                "default_language": "english",
+                "weights": {
+                    "name": 10,
+                    "id": 10,
+                    "comment": 10,
+                    "parent": 5,
+                    "task": 3,
+                    "project": 3,
+                },
+            },
         ],
     }
+    get_all_query_options = GetMixin.QueryParameterOptions(
+        pattern_fields=("name", "comment"),
+        fields=("ready",),
+        list_fields=(
+            "tags",
+            "system_tags",
+            "framework",
+            "uri",
+            "id",
+            "user",
+            "project",
+            "task",
+            "parent",
+        ),
+    )
 
     id = StringField(primary_key=True)
     name = StrippedStringField(user_set_allowed=True, min_length=3)
-    parent = StringField(reference_field='Model', required=False)
+    parent = StringField(reference_field="Model", required=False)
     user = StringField(required=True, reference_field=User)
     company = StringField(required=True, reference_field=Company)
     project = StringField(reference_field=Project, user_set_allowed=True)
     created = DateTimeField(required=True, user_set_allowed=True)
     task = StringField(reference_field=Task)
     comment = StringField(user_set_allowed=True)
-    tags = ListField(StringField(required=True), user_set_allowed=True)
-    uri = SupportedURLField(default='', user_set_allowed=True)
+    tags = SafeSortedListField(StringField(required=True), user_set_allowed=True)
+    system_tags = SafeSortedListField(StringField(required=True), user_set_allowed=True)
+    uri = StrippedStringField(default="", user_set_allowed=True)
     framework = StringField()
     design = SafeDictField()
     labels = ModelLabels()
     ready = BooleanField(required=True)
-    ui_cache = SafeDictField(default=dict, user_set_allowed=True, exclude_by_default=True)
+    ui_cache = SafeDictField(
+        default=dict, user_set_allowed=True, exclude_by_default=True
+    )

server/database/model/model_labels.py

@@ -1,11 +1,14 @@
-from mongoengine import MapField, IntField
+from database.fields import NoneType, UnionField, SafeMapField
 
 
-class ModelLabels(MapField):
+class ModelLabels(SafeMapField):
     def __init__(self, *args, **kwargs):
-        super(ModelLabels, self).__init__(field=IntField(), *args, **kwargs)
+        super(ModelLabels, self).__init__(
+            field=UnionField(types=(int, NoneType)), *args, **kwargs
+        )
 
     def validate(self, value):
         super(ModelLabels, self).validate(value)
-        if value and (len(set(value.values())) < len(value)):
+        non_empty_values = list(filter(None, value.values()))
+        if non_empty_values and len(set(non_empty_values)) < len(non_empty_values):
             self.error("Same label id appears more than once in model labels")

server/database/model/project.py

@@ -1,7 +1,7 @@
-from mongoengine import StringField, DateTimeField, ListField
+from mongoengine import StringField, DateTimeField
 
 from database import Database, strict
-from database.fields import OutputDestinationField, StrippedStringField
+from database.fields import StrippedStringField, SafeSortedListField
 from database.model import AttributedDocument
 from database.model.base import GetMixin
 
@@ -9,19 +9,21 @@ from database.model.base import GetMixin
 class Project(AttributedDocument):
 
     get_all_query_options = GetMixin.QueryParameterOptions(
-        pattern_fields=("name", "description"), list_fields=("tags", "id")
+        pattern_fields=("name", "description"),
+        list_fields=("tags", "system_tags", "id"),
     )
 
     meta = {
         "db_alias": Database.backend,
         "strict": strict,
         "indexes": [
+            ("company", "name"),
             {
                 "name": "%s.project.main_text_index" % Database.backend,
                 "fields": ["$name", "$id", "$description"],
                 "default_language": "english",
                 "weights": {"name": 10, "id": 10, "description": 10},
-            }
+            },
         ],
     }
 
@@ -34,6 +36,7 @@ class Project(AttributedDocument):
     )
     description = StringField(required=True)
     created = DateTimeField(required=True)
-    tags = ListField(StringField(required=True), default=list)
-    default_output_destination = OutputDestinationField()
+    tags = SafeSortedListField(StringField(required=True))
+    system_tags = SafeSortedListField(StringField(required=True))
+    default_output_destination = StrippedStringField()
     last_update = DateTimeField()

server/database/model/queue.py

@@ -0,0 +1,46 @@
+from mongoengine import (
+    Document,
+    EmbeddedDocument,
+    StringField,
+    DateTimeField,
+    EmbeddedDocumentListField,
+)
+
+from database import Database, strict
+from database.fields import StrippedStringField, SafeSortedListField
+from database.model import DbModelMixin
+from database.model.base import ProperDictMixin, GetMixin
+from database.model.company import Company
+from database.model.task.task import Task
+
+
+class Entry(EmbeddedDocument, ProperDictMixin):
+    """ Entry representing a task waiting in the queue """
+    task = StringField(required=True, reference_field=Task)
+    ''' Task ID '''
+    added = DateTimeField(required=True)
+    ''' Added to the queue '''
+
+
+class Queue(DbModelMixin, Document):
+
+    get_all_query_options = GetMixin.QueryParameterOptions(
+        pattern_fields=("name",),
+        list_fields=("tags", "system_tags", "id"),
+    )
+
+    meta = {
+        'db_alias': Database.backend,
+        'strict': strict,
+    }
+
+    id = StringField(primary_key=True)
+    name = StrippedStringField(
+        required=True, unique_with="company", min_length=3, user_set_allowed=True
+    )
+    company = StringField(required=True, reference_field=Company)
+    created = DateTimeField(required=True)
+    tags = SafeSortedListField(StringField(required=True), default=list, user_set_allowed=True)
+    system_tags = SafeSortedListField(StringField(required=True), user_set_allowed=True)
+    entries = EmbeddedDocumentListField(Entry, default=list)
+    last_update = DateTimeField()

server/database/model/settings.py

@@ -0,0 +1,57 @@
+from typing import Any, Optional, Sequence, Tuple
+
+from mongoengine import Document, StringField, DynamicField, Q
+from mongoengine.errors import NotUniqueError
+
+from database import Database, strict
+from database.model import DbModelMixin
+
+
+class SettingKeys:
+    server__uuid = "server.uuid"
+
+
+class Settings(DbModelMixin, Document):
+    meta = {
+        "db_alias": Database.backend,
+        "strict": strict,
+    }
+
+    key = StringField(primary_key=True)
+    value = DynamicField()
+
+    @classmethod
+    def get_by_key(cls, key: str, default: Optional[Any] = None, sep: str = ".") -> Any:
+        key = key.strip(sep)
+        res = Settings.objects(key=key).first()
+        if not res:
+            return default
+        return res.value
+
+    @classmethod
+    def get_by_prefix(
+        cls, key_prefix: str, default: Optional[Any] = None, sep: str = "."
+    ) -> Sequence[Tuple[str, Any]]:
+        key_prefix = key_prefix.strip(sep)
+        query = Q(key=key_prefix) | Q(key__startswith=key_prefix + sep)
+        res = Settings.objects(query)
+        if not res:
+            return default
+        return [(x.key, x.value) for x in res]
+
+    @classmethod
+    def set_or_add_value(cls, key: str, value: Any, sep: str = ".") -> bool:
+        """ Sets a new value or adds a new key/value setting (if key does not exist) """
+        key = key.strip(sep)
+        res = Settings.objects(key=key).update(key=key, value=value, upsert=True)
+        return bool(res)
+
+    @classmethod
+    def add_value(cls, key: str, value: Any, sep: str = ".") -> bool:
+        """ Adds a new key/value settings. Fails if key already exists. """
+        key = key.strip(sep)
+        try:
+            res = cls(key=key, value=value).save(force_insert=True)
+            return bool(res)
+        except NotUniqueError:
+            return False

server/database/model/task/metrics.py

@@ -1,14 +1,39 @@
-from mongoengine import EmbeddedDocument, StringField, DateTimeField, LongField, DynamicField
+from mongoengine import (
+    EmbeddedDocument,
+    StringField,
+    DynamicField,
+    LongField,
+    EmbeddedDocumentField,
+)
+
+from database.fields import SafeMapField
 
 
 class MetricEvent(EmbeddedDocument):
-    metric = StringField(required=True, )
-    variant = StringField(required=True)
-    type = StringField(required=True)
-    timestamp = DateTimeField(default=0, required=True)
-    iter = LongField()
-    value = DynamicField(required=True)
+    meta = {
+        # For backwards compatibility reasons
+        "strict": False,
+    }
 
-    @classmethod
-    def from_dict(cls, **kwargs):
-        return cls(**{k: v for k, v in kwargs.items() if k in cls._fields})
+    metric = StringField(required=True)
+    variant = StringField(required=True)
+    value = DynamicField(required=True)
+    min_value = DynamicField()  # for backwards compatibility reasons
+    max_value = DynamicField()  # for backwards compatibility reasons
+
+
+class EventStats(EmbeddedDocument):
+    meta = {
+        # For backwards compatibility reasons
+        "strict": False,
+    }
+    last_update = LongField()
+
+
+class MetricEventStats(EmbeddedDocument):
+    meta = {
+        # For backwards compatibility reasons
+        "strict": False,
+    }
+    metric = StringField(required=True)
+    event_stats_by_type = SafeMapField(field=EmbeddedDocumentField(EventStats))

server/database/model/task/output.py

@@ -1,7 +1,7 @@
 from mongoengine import EmbeddedDocument, StringField
-from database.utils import get_options
 
-from database.fields import OutputDestinationField
+from database.fields import StrippedStringField
+from database.utils import get_options
 
 
 class Result(object):
@@ -10,7 +10,7 @@ class Result(object):
 
 
 class Output(EmbeddedDocument):
-    destination = OutputDestinationField()
+    destination = StrippedStringField()
     model = StringField(reference_field='Model')
     error = StringField(user_set_allowed=True)
     result = StringField(choices=get_options(Result))

server/database/model/task/task.py

@@ -1,5 +1,3 @@
-from enum import Enum
-
 from mongoengine import (
     StringField,
     EmbeddedDocumentField,
@@ -7,41 +5,52 @@ from mongoengine import (
     DateTimeField,
     IntField,
     ListField,
+    LongField,
 )
 
 from database import Database, strict
-from database.fields import StrippedStringField, SafeMapField, SafeDictField
+from database.fields import (
+    StrippedStringField,
+    SafeMapField,
+    SafeDictField,
+    UnionField,
+    EmbeddedDocumentSortedListField,
+    SafeSortedListField,
+)
 from database.model import AttributedDocument
+from database.model.base import ProperDictMixin, GetMixin
 from database.model.model_labels import ModelLabels
 from database.model.project import Project
 from database.utils import get_options
-from .metrics import MetricEvent
+from .metrics import MetricEvent, MetricEventStats
 from .output import Output
 
 DEFAULT_LAST_ITERATION = 0
 
 
 class TaskStatus(object):
-    created = 'created'
-    in_progress = 'in_progress'
-    stopped = 'stopped'
-    publishing = 'publishing'
-    published = 'published'
-    closed = 'closed'
-    failed = 'failed'
-    unknown = 'unknown'
+    created = "created"
+    queued = "queued"
+    in_progress = "in_progress"
+    stopped = "stopped"
+    publishing = "publishing"
+    published = "published"
+    closed = "closed"
+    failed = "failed"
+    completed = "completed"
+    unknown = "unknown"
 
 
 class TaskStatusMessage(object):
-    stopping = 'stopping'
+    stopping = "stopping"
 
 
-class TaskTags(object):
-    development = 'development'
+class TaskSystemTags(object):
+    development = "development"
 
 
 class Script(EmbeddedDocument):
-    binary = StringField(default='python')
+    binary = StringField(default="python")
     repository = StringField(required=True)
     tag = StringField()
     branch = StringField()
@@ -49,57 +58,112 @@ class Script(EmbeddedDocument):
     entry_point = StringField(required=True)
     working_dir = StringField()
     requirements = SafeDictField()
+    diff = StringField()
 
 
-class Execution(EmbeddedDocument):
+class ArtifactTypeData(EmbeddedDocument):
+    preview = StringField()
+    content_type = StringField()
+    data_hash = StringField()
+
+
+class ArtifactModes:
+    input = "input"
+    output = "output"
+
+
+class Artifact(EmbeddedDocument):
+    key = StringField(required=True)
+    type = StringField(required=True)
+    mode = StringField(choices=get_options(ArtifactModes), default=ArtifactModes.output)
+    uri = StringField()
+    hash = StringField()
+    content_size = LongField()
+    timestamp = LongField()
+    type_data = EmbeddedDocumentField(ArtifactTypeData)
+    display_data = SafeSortedListField(ListField(UnionField((int, float, str))))
+
+
+class Execution(EmbeddedDocument, ProperDictMixin):
     test_split = IntField(default=0)
     parameters = SafeDictField(default=dict)
-    model = StringField(reference_field='Model')
-    model_desc = SafeMapField(StringField(default=''))
+    model = StringField(reference_field="Model")
+    model_desc = SafeMapField(StringField(default=""))
     model_labels = ModelLabels()
     framework = StringField()
-
+    artifacts = EmbeddedDocumentSortedListField(Artifact)
+    docker_cmd = StringField()
     queue = StringField()
-    ''' Queue ID where task was queued '''
+    """ Queue ID where task was queued """
 
 
 class TaskType(object):
-    training = 'training'
-    testing = 'testing'
+    training = "training"
+    testing = "testing"
+    inference = "inference"
+    data_processing = "data_processing"
+    application = "application"
+    monitor = "monitor"
+    controller = "controller"
+    optimizer = "optimizer"
+    service = "service"
+    qc = "qc"
+    custom = "custom"
+
+
+external_task_types = set(get_options(TaskType))
 
 
 class Task(AttributedDocument):
+    _field_collation_overrides = {
+        "execution.parameters.": {"locale": "en_US", "numericOrdering": True},
+        "last_metrics.": {"locale": "en_US", "numericOrdering": True}
+    }
+
     meta = {
-        'db_alias': Database.backend,
-        'strict': strict,
-        'indexes': [
-            'created',
-            'started',
-            'completed',
+        "db_alias": Database.backend,
+        "strict": strict,
+        "indexes": [
+            "created",
+            "started",
+            "completed",
+            "parent",
+            "project",
+            ("company", "name"),
+            ("company", "user"),
+            ("company", "type", "system_tags", "status"),
+            ("company", "project", "type", "system_tags", "status"),
+            ("status", "last_update"),  # for maintenance tasks
             {
-                'name': '%s.task.main_text_index' % Database.backend,
-                'fields': [
-                    '$name',
-                    '$id',
-                    '$comment',
-                    '$execution.model',
-                    '$output.model',
-                    '$script.repository',
-                    '$script.entry_point',
+                "name": "%s.task.main_text_index" % Database.backend,
+                "fields": [
+                    "$name",
+                    "$id",
+                    "$comment",
+                    "$execution.model",
+                    "$output.model",
+                    "$script.repository",
+                    "$script.entry_point",
                 ],
-                'default_language': 'english',
-                'weights': {
-                    'name': 10,
-                    'id': 10,
-                    'comment': 10,
-                    'execution.model': 2,
-                    'output.model': 2,
-                    'script.repository': 1,
-                    'script.entry_point': 1,
+                "default_language": "english",
+                "weights": {
+                    "name": 10,
+                    "id": 10,
+                    "comment": 10,
+                    "execution.model": 2,
+                    "output.model": 2,
+                    "script.repository": 1,
+                    "script.entry_point": 1,
                 },
             },
         ],
     }
+    get_all_query_options = GetMixin.QueryParameterOptions(
+        list_fields=("id", "user", "tags", "system_tags", "type", "status", "project"),
+        datetime_fields=("status_changed",),
+        pattern_fields=("name", "comment"),
+        fields=("parent",),
+    )
 
     id = StringField(primary_key=True)
     name = StrippedStringField(
@@ -118,15 +182,14 @@ class Task(AttributedDocument):
     published = DateTimeField()
     parent = StringField()
     project = StringField(reference_field=Project, user_set_allowed=True)
-    output = EmbeddedDocumentField(Output, default=Output)
+    output: Output = EmbeddedDocumentField(Output, default=Output)
     execution: Execution = EmbeddedDocumentField(Execution, default=Execution)
-    tags = ListField(StringField(required=True), user_set_allowed=True)
-    script = EmbeddedDocumentField(Script)
+    tags = SafeSortedListField(StringField(required=True), user_set_allowed=True)
+    system_tags = SafeSortedListField(StringField(required=True), user_set_allowed=True)
+    script: Script = EmbeddedDocumentField(Script)
+    last_worker = StringField()
+    last_worker_report = DateTimeField()
     last_update = DateTimeField()
     last_iteration = IntField(default=DEFAULT_LAST_ITERATION)
     last_metrics = SafeMapField(field=SafeMapField(EmbeddedDocumentField(MetricEvent)))
-
-
-class TaskVisibility(Enum):
-    active = 'active'
-    archived = 'archived'
+    metric_stats = SafeMapField(field=EmbeddedDocumentField(MetricEventStats))

server/database/model/user.py

@@ -1,16 +1,17 @@
-from mongoengine import Document, StringField
+from mongoengine import Document, StringField, DynamicField
 
 from database import Database, strict
-from database.fields import SafeDictField
 from database.model import DbModelMixin
+from database.model.base import GetMixin
 from database.model.company import Company
 
 
 class User(DbModelMixin, Document):
     meta = {
-        'db_alias': Database.backend,
-        'strict': strict,
+        "db_alias": Database.backend,
+        "strict": strict,
     }
+    get_all_query_options = GetMixin.QueryParameterOptions(list_fields=("id",))
 
     id = StringField(primary_key=True)
     company = StringField(required=True, reference_field=Company)
@@ -18,4 +19,4 @@ class User(DbModelMixin, Document):
     family_name = StringField(user_set_allowed=True)
     given_name = StringField(user_set_allowed=True)
     avatar = StringField()
-    preferences = SafeDictField(default=dict, exclude_by_default=True)
+    preferences = DynamicField(default="", exclude_by_default=True)

server/database/model/version.py

@@ -0,0 +1,18 @@
+from mongoengine import Document, DateTimeField, StringField
+
+from database import Database, strict
+from database.model import DbModelMixin
+
+
+class Version(DbModelMixin, Document):
+    meta = {
+        "collection": "versions",  # custom collection name ('version' is not a proper collection name...)
+        "db_alias": Database.backend,  # although we'll use this model for all databases, a default must be defined
+        "strict": strict,
+        "indexes": [("-created", "-num")],
+    }
+
+    id = StringField(primary_key=True)
+    num = StringField(required=True)
+    created = DateTimeField(required=True)
+    desc = StringField()

server/database/projection.py

@@ -1,13 +1,17 @@
+import threading
 from concurrent.futures import ThreadPoolExecutor
 from itertools import groupby, chain
+from typing import Sequence, Dict, Callable, Tuple, Any, Type
 
-import dpath
+import dpath.path
 
 from apierrors import errors
 from database.props import PropsMixin
 
+SEP = "."
 
-def project_dict(data, projection, separator='.'):
+
+def project_dict(data, projection, separator=SEP):
     """
     Project partial data from a dictionary into a new dictionary
     :param data: Input dictionary
@@ -30,19 +34,27 @@ def project_dict(data, projection, separator='.'):
                     if path_part not in dst:
                         dst[path_part] = [{} for _ in range(len(src_part))]
                     elif not isinstance(dst[path_part], (list, tuple)):
-                        raise TypeError('Incompatible destination type %s for %s (list expected)'
-                                        % (type(dst), separator.join(path_parts[:depth + 1])))
+                        raise TypeError(
+                            "Incompatible destination type %s for %s (list expected)"
+                            % (type(dst), separator.join(path_parts[: depth + 1]))
+                        )
                     elif not len(dst[path_part]) == len(src_part):
-                        raise ValueError('Destination list length differs from source length for %s'
-                                         % separator.join(path_parts[:depth + 1]))
+                        raise ValueError(
+                            "Destination list length differs from source length for %s"
+                            % separator.join(path_parts[: depth + 1])
+                        )
 
-                    dst[path_part] = [copy_path(path_parts[depth + 1:], s, d)
-                                      for s, d in zip(src_part, dst[path_part])]
+                    dst[path_part] = [
+                        copy_path(path_parts[depth + 1:], s, d)
+                        for s, d in zip(src_part, dst[path_part])
+                    ]
 
                     return destination
                 else:
-                    raise TypeError('Unsupported projection type %s for %s'
-                                    % (type(src), separator.join(path_parts[:depth + 1])))
+                    raise TypeError(
+                        "Unsupported projection type %s for %s"
+                        % (type(src), separator.join(path_parts[: depth + 1]))
+                    )
 
             last_part = path_parts[-1]
             dst[last_part] = src[last_part]
@@ -53,12 +65,35 @@ def project_dict(data, projection, separator='.'):
 
     for projection_path in sorted(projection):
         copy_path(
-            path_parts=projection_path.split(separator),
-            source=data,
-            destination=result)
+            path_parts=projection_path.split(separator), source=data, destination=result
+        )
     return result
 
 
+class _ReferenceProxy(dict):
+    def __init__(self, id):
+        super(_ReferenceProxy, self).__init__(**({"id": id} if id else {}))
+
+
+class _ProxyManager:
+    lock = threading.Lock()
+
+    def __init__(self):
+        self._proxies: Dict[str, _ReferenceProxy] = {}
+
+    def add(self, id):
+        with self.lock:
+            proxy = self._proxies.get(id)
+            if proxy is None:
+                proxy = self._proxies[id] = _ReferenceProxy(id)
+            return proxy
+
+    def update(self, result):
+        proxy = self._proxies.get(result.get("id"))
+        if proxy is not None:
+            proxy.update(result)
+
+
 class ProjectionHelper(object):
     pool = ThreadPoolExecutor()
 
@@ -72,6 +107,11 @@ class ProjectionHelper(object):
         self._doc_cls = doc_cls
         self._doc_projection = None
         self._ref_projection = None
+        self._proxy_manager = _ProxyManager()
+
+        # Cached dpath paths for each of the result documents
+        self._cached_results_paths: Dict[int, Sequence[Tuple[Any, Type]]] = {}
+
         self._parse_projection(projection)
 
     def _collect_projection_fields(self, doc_cls, projection):
@@ -81,8 +121,12 @@ class ProjectionHelper(object):
         :param projection: List of projection fields
         :return: A tuple of document projection and reference fields information
         """
-        doc_projection = set()  # Projection fields for this class (used in the main query)
-        ref_projection_info = []  # Projection information for reference fields (used in join queries)
+        doc_projection = (
+            set()
+        )  # Projection fields for this class (used in the main query)
+        ref_projection_info = (
+            []
+        )  # Projection information for reference fields (used in join queries)
         for field in projection:
             for ref_field, ref_field_cls in doc_cls.get_reference_fields().items():
                 if not field.startswith(ref_field):
@@ -93,7 +137,7 @@ class ProjectionHelper(object):
                     # use '<reference field name>.*')
                     continue
                 subfield = field[len(ref_field):]
-                if not subfield.startswith('.'):
+                if not subfield.startswith(SEP):
                     # Starts with something that looks like a reference field, but isn't
                     continue
 
@@ -103,10 +147,12 @@ class ProjectionHelper(object):
                 # Not a reference field, just add to the top-level projection
                 # We strip any trailing '*' since it means nothing for simple fields and for embedded documents
                 orig_field = field
-                if field.endswith('.*'):
+                if field.endswith(".*"):
                     field = field[:-2]
                 if not field:
-                    raise errors.bad_request.InvalidFields(field=orig_field, object=doc_cls.__name__)
+                    raise errors.bad_request.InvalidFields(
+                        field=orig_field, object=doc_cls.__name__
+                    )
                 doc_projection.add(field)
         return doc_projection, ref_projection_info
 
@@ -124,12 +170,14 @@ class ProjectionHelper(object):
         if not projection:
             return [], {}
 
-        doc_projection, ref_projection_info = self._collect_projection_fields(doc_cls, projection)
+        doc_projection, ref_projection_info = self._collect_projection_fields(
+            doc_cls, projection
+        )
 
         def normalize_cls_projection(cls_, fields):
             """ Normalize projection for this class and group (expand *, for once) """
-            if '*' in fields:
-                return list(fields.difference('*').union(cls_.get_fields()))
+            if "*" in fields:
+                return list(fields.difference("*").union(cls_.get_fields()))
             return list(fields)
 
         def compute_ref_cls_projection(cls_, group):
@@ -143,12 +191,16 @@ class ProjectionHelper(object):
         # Aggregate by reference field. We'll leave out '*' from the projected items since
         ref_projection = {
             ref_field: dict(cls=ref_cls, only=compute_ref_cls_projection(ref_cls, g))
-            for (ref_field, ref_cls), g in groupby(sorted(ref_projection_info, key=sort_key), sort_key)
+            for (ref_field, ref_cls), g in groupby(
+                sorted(ref_projection_info, key=sort_key), sort_key
+            )
         }
 
         # Make sure this doesn't contain any reference field we'll join anyway
         # (i.e. in case only_fields=[project, project.name])
-        doc_projection = normalize_cls_projection(doc_cls, doc_projection.difference(ref_projection).union({'id'}))
+        doc_projection = normalize_cls_projection(
+            doc_cls, doc_projection.difference(ref_projection).union({"id"})
+        )
 
         # Make sure that in case one or more field is a subfield of another field, we only use the the top-level field.
         # This is done since in such a case, MongoDB will only use the most restrictive field (most nested field) and
@@ -158,13 +210,20 @@ class ProjectionHelper(object):
         doc_projection = [
             field
             for field in doc_projection
-            if not any(field.startswith(f"{other_field}.") for other_field in projection_set - {field})
+            if not any(
+                field.startswith(f"{other_field}.")
+                for other_field in projection_set - {field}
+            )
         ]
 
         # Make sure we didn't get any invalid projection fields for this class
-        invalid_fields = [f for f in doc_projection if f.split('.')[0] not in doc_cls.get_fields()]
+        invalid_fields = [
+            f for f in doc_projection if f.split(SEP)[0] not in doc_cls.get_fields()
+        ]
         if invalid_fields:
-            raise errors.bad_request.InvalidFields(fields=invalid_fields, object=doc_cls.__name__)
+            raise errors.bad_request.InvalidFields(
+                fields=invalid_fields, object=doc_cls.__name__
+            )
 
         if ref_projection:
             # Join mode - use both normal projection fields and top-level reference fields
@@ -178,11 +237,44 @@ class ProjectionHelper(object):
         self._doc_projection = doc_projection
         self._ref_projection = ref_projection
 
-    @staticmethod
-    def _search(doc_cls, obj, path, only_values=True):
-        """ Call dpath.search with yielded=True, collect result values """
+    def _search(
+        self,
+        doc_cls: PropsMixin,
+        obj: dict,
+        path: str,
+        factory: Callable[[str], dict] = None,
+    ) -> Sequence[str]:
+        """
+        Search for a path in the given object, return the list of values found for the
+        given path (multiple values may exist if the path is a glob expression)
+        :param doc_cls: The document class represented by the object
+        :param obj: Data object
+        :param path: Path to a leaf in the data object ("." separated, may contain "*")
+         (in case the path contains "*", there may be multiple values)
+        :param factory: If provided, replace each value found with an instance provided by the factory.
+        """
         norm_path = doc_cls.get_dpath_translated_path(path)
-        return [v if only_values else (k, v) for k, v in dpath.search(obj, norm_path, separator='.', yielded=True)]
+        globlist = norm_path.strip(SEP).split(SEP)
+
+        obj_paths = self._cached_results_paths.get(id(obj))
+        if obj_paths is None:
+            obj_paths = self._cached_results_paths[id(obj)] = list(
+                dpath.path.paths(obj, dirs=True, skip=True)
+            )
+
+        paths = [p for p in obj_paths if dpath.path.match(p, globlist)]
+
+        def search_and_replace(p: Sequence[Tuple[str, Type]]) -> Any:
+            parent = None
+            target = obj
+            for part in p:
+                parent = target
+                target = target[part[0]]
+            if parent and factory:
+                parent[p[-1][0]] = factory(target)
+            return target
+
+        return [search_and_replace(p) for p in paths]
 
     def project(self, results, projection_func):
         """
@@ -197,28 +289,50 @@ class ProjectionHelper(object):
 
         if ref_projection:
             # Join mode - get results for each reference fields projection required (this is the join step)
-            # Note: this is a recursive step, so we support nested reference fields
+            # Note: this is a recursive step, so nested reference fields are supported
 
-            def do_projection(item):
-                ref_field_name, data = item
-                res = {}
-                ids = list(filter(None, set(chain.from_iterable(self._search(cls, res, ref_field_name)
-                                                                for res in results))))
-                if ids:
-                    doc_type = data['cls']
-                    doc_only = list(filter(None, data['only']))
-                    doc_only = list({'id'} | set(doc_only)) if doc_only else None
-                    res = {r['id']: r for r in projection_func(doc_type=doc_type, projection=doc_only, ids=ids)}
-                data['res'] = res
+            def collect_ids(ref_field_name):
+                """
+                Collect unique IDs for the given reference path from all result documents.
+                All collected IDs are replaced in the result dictionaries with a reference proxy generated by the
+                proxies manager to allow rapid update later on when projection results are obtained.
+                """
+                all_ids = (
+                    self._search(
+                        cls, res, ref_field_name, factory=self._proxy_manager.add
+                    )
+                    for res in results
+                )
+                return list(filter(None, set(chain.from_iterable(all_ids))))
 
-            items = list(ref_projection.items())
-            if len(ref_projection) == 1:
-                do_projection(items[0])
-            else:
-                for _ in self.pool.map(do_projection, items):
-                    # From ThreadPoolExecutor.map() documentation: If a call raises an exception then that exception
-                    #  will be raised when its value is retrieved from the map() iterator
-                    pass
+            items = [
+                tup
+                for tup in (
+                    (*item, collect_ids(item[0])) for item in ref_projection.items()
+                )
+                if tup[2]
+            ]
+
+            if items:
+                def do_projection(item):
+                    ref_field_name, data, ids = item
+
+                    doc_type = data["cls"]
+                    doc_only = list(filter(None, data["only"]))
+                    doc_only = list({"id"} | set(doc_only)) if doc_only else None
+
+                    for res in projection_func(
+                        doc_type=doc_type, projection=doc_only, ids=ids
+                    ):
+                        self._proxy_manager.update(res)
+
+                if len(ref_projection) == 1:
+                    do_projection(items[0])
+                else:
+                    for _ in self.pool.map(do_projection, items):
+                        # From ThreadPoolExecutor.map() documentation: If a call raises an exception then that exception
+                        #  will be raised when its value is retrieved from the map() iterator
+                        pass
 
         def do_expand_reference_ids(result, skip_fields=None):
             ref_fields = cls.get_reference_fields()
@@ -226,44 +340,18 @@ class ProjectionHelper(object):
                 ref_fields = set(ref_fields) - set(skip_fields)
             self._expand_reference_fields(cls, result, ref_fields)
 
-        def merge_projection_result(result):
-            for ref_field_name, data in ref_projection.items():
-                res = data.get('res')
-                if not res:
-                    self._expand_reference_fields(cls, result, [ref_field_name])
-                    continue
-                ref_ids = self._search(cls, result, ref_field_name, only_values=False)
-                if not ref_ids:
-                    continue
-                for path, value in ref_ids:
-                    obj = res.get(value) or {'id': value}
-                    dpath.new(result, path, obj, separator='.')
-
-            # any reference field not projected should be expanded
-            do_expand_reference_ids(result, skip_fields=list(ref_projection))
-
-        update_func = merge_projection_result if ref_projection else \
-            do_expand_reference_ids if self._should_expand_reference_ids else None
-
-        if update_func:
+        # any reference field not projected should be expanded
+        if self._should_expand_reference_ids:
             for result in results:
-                update_func(result)
+                do_expand_reference_ids(
+                    result, skip_fields=list(ref_projection) if ref_projection else None
+                )
 
         return results
 
-    @classmethod
-    def _expand_reference_fields(cls, doc_cls, result, fields):
+    def _expand_reference_fields(self, doc_cls, result, fields):
         for ref_field_name in fields:
-            ref_ids = cls._search(doc_cls, result, ref_field_name, only_values=False)
-            if not ref_ids:
-                continue
-            for path, value in ref_ids:
-                dpath.set(
-                    result,
-                    path,
-                    {'id': value} if value else {},
-                    separator='.')
+            self._search(doc_cls, result, ref_field_name, factory=_ReferenceProxy)
 
-    @classmethod
-    def expand_reference_ids(cls, doc_cls, result):
-        cls._expand_reference_fields(doc_cls, result, doc_cls.get_reference_fields())
+    def expand_reference_ids(self, doc_cls, result):
+        self._expand_reference_fields(doc_cls, result, doc_cls.get_reference_fields())

server/database/props.py

@@ -1,17 +1,19 @@
-from collections import OrderedDict
+from collections import OrderedDict, defaultdict
+from itertools import chain
 from operator import attrgetter
 from threading import Lock
+from typing import Sequence
 
 import six
 from mongoengine import EmbeddedDocumentField, EmbeddedDocumentListField
-from mongoengine.base import get_document
+from mongoengine.base import get_document, BaseField
 
 from database.fields import (
     LengthRangeEmbeddedDocumentListField,
     UniqueEmbeddedDocumentListField,
     EmbeddedDocumentSortedListField,
 )
-from database.utils import get_fields, get_fields_and_attr
+from database.utils import get_fields, get_fields_attr
 
 
 class PropsMixin(object):
@@ -19,6 +21,7 @@ class PropsMixin(object):
     __cached_reference_fields = None
     __cached_exclude_fields = None
     __cached_fields_with_instance = None
+    __cached_field_names_per_type = None
 
     __cached_dpath_computed_fields_lock = Lock()
     __cached_dpath_computed_fields = None
@@ -29,6 +32,39 @@ class PropsMixin(object):
             cls.__cached_fields = get_fields(cls)
         return cls.__cached_fields
 
+    @classmethod
+    def get_field_names_for_type(cls, of_type=BaseField):
+        """
+        Return field names per type including subfields
+        The fields of derived types are also returned
+        """
+        assert issubclass(of_type, BaseField)
+        if cls.__cached_field_names_per_type is None:
+            fields = defaultdict(list)
+            for name, field in get_fields(cls, return_instance=True, subfields=True):
+                fields[type(field)].append(name)
+            for type_ in fields:
+                fields[type_].extend(
+                    chain.from_iterable(
+                        fields[other_type]
+                        for other_type in fields
+                        if other_type != type_ and issubclass(other_type, type_)
+                    )
+                )
+            cls.__cached_field_names_per_type = fields
+
+        if of_type not in cls.__cached_field_names_per_type:
+            names = list(
+                chain.from_iterable(
+                    field_names
+                    for type_, field_names in cls.__cached_field_names_per_type.items()
+                    if issubclass(type_, of_type)
+                )
+            )
+            cls.__cached_field_names_per_type[of_type] = names
+
+        return cls.__cached_field_names_per_type[of_type]
+
     @classmethod
     def get_fields_with_instance(cls, doc_cls):
         if cls.__cached_fields_with_instance is None:
@@ -42,7 +78,7 @@ class PropsMixin(object):
     @staticmethod
     def _get_fields_with_attr(cls_, attr):
         """ Get all fields with the specified attribute (supports nested fields) """
-        res = get_fields_and_attr(cls_, attr=attr)
+        res = get_fields_attr(cls_, attr=attr)
 
         def resolve_doc(v):
             if not isinstance(v, six.string_types):
@@ -122,6 +158,14 @@ class PropsMixin(object):
             cls.__cached_reference_fields = OrderedDict(sorted(fields.items()))
         return cls.__cached_reference_fields
 
+    @classmethod
+    def get_extra_projection(cls, fields: Sequence) -> tuple:
+        if isinstance(fields, str):
+            fields = [fields]
+        return tuple(
+            set(fields).union(cls.get_fields()).difference(cls.get_exclude_fields())
+        )
+
     @classmethod
     def get_exclude_fields(cls):
         if cls.__cached_exclude_fields is None:
@@ -140,3 +184,18 @@ class PropsMixin(object):
                 result = separator.join(translated)
                 cls.__cached_dpath_computed_fields[path] = result
         return cls.__cached_dpath_computed_fields[path]
+
+    def get_field_value(self, field_path: str, default=None):
+        """
+        Return the document field_path value by the field_path name.
+        The path may contain '.'. If on any level the path is
+        not found then the default value is returned
+        """
+        path_elements = field_path.split(".")
+        current = self
+        for name in path_elements:
+            current = getattr(current, name, default)
+            if current == default:
+                break
+
+        return current

server/database/query.py

@@ -1,8 +1,14 @@
 import copy
 import re
+from typing import Union
 
 from mongoengine import Q
-from mongoengine.queryset.visitor import QueryCompilerVisitor, SimplificationVisitor, QCombination
+from mongoengine.queryset.visitor import (
+    QueryCompilerVisitor,
+    SimplificationVisitor,
+    QCombination,
+    QNode,
+)
 
 
 class RegexWrapper(object):
@@ -17,17 +23,16 @@ class RegexWrapper(object):
 
 
 class RegexMixin(object):
-
-    def to_query(self, document):
+    def to_query(self: Union["RegexMixin", QNode], document):
         query = self.accept(SimplificationVisitor())
         query = query.accept(RegexQueryCompilerVisitor(document))
         return query
 
-    def _combine(self, other, operation):
+    def _combine(self: Union["RegexMixin", QNode], other, operation):
         """Combine this node with another node into a QCombination
         object.
         """
-        if getattr(other, 'empty', True):
+        if getattr(other, "empty", True):
             return self
 
         if self.empty:

server/database/utils.py

@@ -1,5 +1,6 @@
 import hashlib
 from inspect import ismethod, getmembers
+from typing import Sequence, Tuple, Set, Optional, Callable, Any
 from uuid import uuid4
 
 from mongoengine import EmbeddedDocumentField, ListField, Document, Q
@@ -8,61 +9,65 @@ from mongoengine.base import BaseField
 from .errors import translate_errors_context, ParseCallError
 
 
-def get_fields(cls, of_type=BaseField, return_instance=False):
+def get_fields(cls, of_type=BaseField, return_instance=False, subfields=False):
+    return _get_fields(
+        cls,
+        of_type=of_type,
+        subfields=subfields,
+        selector=lambda k, v: (k, v) if return_instance else k,
+    )
+
+
+def get_fields_attr(cls, attr):
     """ get field names from a class containing mongoengine fields """
-    res = []
-    for cls_ in reversed(cls.mro()):
-        res.extend([k if not return_instance else (k, v)
-                    for k, v in vars(cls_).items()
-                    if isinstance(v, of_type)])
-    return res
+    return dict(
+        _get_fields(cls, with_attr=attr, selector=lambda k, v: (k, getattr(v, attr)))
+    )
 
 
-def get_fields_and_attr(cls, attr):
-    """ get field names from a class containing mongoengine fields """
-    res = {}
-    for cls_ in reversed(cls.mro()):
-        res.update({k: getattr(v, attr)
-                    for k, v in vars(cls_).items()
-                    if isinstance(v, BaseField) and hasattr(v, attr)})
-    return res
+def get_fields_choices(cls, attr):
+    def get_choices(field_name: str, field: BaseField) -> Tuple:
+        if isinstance(field, ListField):
+            return field_name, field.field.choices
+        return field_name, field.choices
+
+    return dict(_get_fields(cls, with_attr=attr, subfields=True, selector=get_choices))
 
 
-def _get_field_choices(name, field):
-    field_t = type(field)
-    if issubclass(field_t, EmbeddedDocumentField):
-        obj = field.document_type_obj
-        n, choices = _get_field_choices(field.name, obj.field)
-        return '%s__%s' % (name, n), choices
-    elif issubclass(type(field), ListField):
-        return name, field.field.choices
-    return name, field.choices
-
-
-def get_fields_with_attr(cls, attr, default=False):
+def _get_fields(
+    cls,
+    with_attr=None,
+    of_type=BaseField,
+    subfields=False,
+    selector: Optional[Callable[[str, BaseField], Any]] = None,
+    path: Tuple[str, ...] = (),
+):
     fields = []
     for field_name, field in cls._fields.items():
-        if not getattr(field, attr, default):
-            continue
-        field_t = type(field)
-        if issubclass(field_t, EmbeddedDocumentField):
-            fields.extend((('%s__%s' % (field_name, name), choices)
-                           for name, choices in get_fields_with_attr(field.document_type, attr, default)))
-        elif issubclass(type(field), ListField):
-            fields.append((field_name, field.field.choices))
-        else:
-            fields.append((field_name, field.choices))
+        field_path = path + (field_name,)
+        if isinstance(field, of_type) and (not with_attr or hasattr(field, with_attr)):
+            full_name = "__".join(field_path)
+            fields.append(selector(full_name, field) if selector else full_name)
+
+        if subfields and isinstance(field, EmbeddedDocumentField):
+            fields.extend(
+                _get_fields(
+                    field.document_type,
+                    with_attr=with_attr,
+                    of_type=of_type,
+                    subfields=subfields,
+                    selector=selector,
+                    path=field_path,
+                )
+            )
+
     return fields
 
 
 def get_items(cls):
     """ get key/value items from an enum-like class (members represent enumeration key/value) """
 
-    res = {
-        k: v
-        for k, v in getmembers(cls)
-        if not (k.startswith("_") or ismethod(v))
-    }
+    res = {k: v for k, v in getmembers(cls) if not (k.startswith("_") or ismethod(v))}
     return res
 
 
@@ -81,7 +86,7 @@ def parse_from_call(call_data, fields, cls_fields, discard_none_values=True):
         fields = {k: None for k in fields}
     fields = {k: v for k, v in fields.items() if k in cls_fields}
     res = {}
-    with translate_errors_context('parsing call data'):
+    with translate_errors_context("parsing call data"):
         for field, desc in fields.items():
             value = call_data.get(field)
             if value is None:
@@ -90,23 +95,34 @@ def parse_from_call(call_data, fields, cls_fields, discard_none_values=True):
                     res[field] = None
                 continue
             if desc:
-                if callable(desc):
-                    desc(value)
-                else:
-                    if issubclass(desc, (list, tuple, dict)) and not isinstance(value, desc):
-                        raise ParseCallError('expecting %s' % desc.__name__, field=field)
-                    if issubclass(desc, Document) and not desc.objects(id=value).only('id'):
-                        raise ParseCallError('expecting %s id' % desc.__name__, id=value, field=field)
+                if issubclass(desc, Document):
+                    if not desc.objects(id=value).only("id"):
+                        raise ParseCallError(
+                            "expecting %s id" % desc.__name__, id=value, field=field
+                        )
+                elif callable(desc):
+                    try:
+                        desc(value)
+                    except TypeError:
+                        raise ParseCallError(f"expecting {desc.__name__}", field=field)
+                    except Exception as ex:
+                        raise ParseCallError(str(ex), field=field)
             res[field] = value
         return res
 
 
 def init_cls_from_base(cls, instance):
-    return cls(**{k: v for k, v in instance.to_mongo(use_db_field=False).to_dict().items() if k[0] != '_'})
+    return cls(
+        **{
+            k: v
+            for k, v in instance.to_mongo(use_db_field=False).to_dict().items()
+            if k[0] != "_"
+        }
+    )
 
 
 def get_company_or_none_constraint(company=None):
-    return Q(company__in=(company, None, '')) | Q(company__exists=False)
+    return Q(company__in=(company, None, "")) | Q(company__exists=False)
 
 
 def field_does_not_exist(field: str, empty_value=None, is_list=False) -> Q:
@@ -118,23 +134,43 @@ def field_does_not_exist(field: str, empty_value=None, is_list=False) -> Q:
                     the length of the array will be used (len==0 means empty)
     :return:
     """
-    query = (Q(**{f"{field}__exists": False}) |
-             Q(**{f"{field}__in": {empty_value, None}}))
+    query = Q(**{f"{field}__exists": False}) | Q(
+        **{f"{field}__in": {empty_value, None}}
+    )
     if is_list:
         query |= Q(**{f"{field}__size": 0})
     return query
 
 
+def field_exists(field: str, empty_value=None, is_list=False) -> Q:
+    """
+    Creates a query object used for finding a field that exists and is not None or empty.
+    :param field: Field name
+    :param empty_value: The empty value to test for (None means no specific empty value will be used)
+    :param is_list: Is this a list (array) field. In this case, instead of testing for an empty value,
+                    the length of the array will be used (len==0 means empty)
+    :return:
+    """
+    query = Q(**{f"{field}__exists": True}) & Q(
+        **{f"{field}__nin": {empty_value, None}}
+    )
+    if is_list:
+        query &= Q(**{f"{field}__not__size": 0})
+    return query
+
+
 def get_subkey(d, key_path, default=None):
     """ Get a key from a nested dictionary. kay_path is a '.' separated string of keys used to traverse
         the nested dictionary.
     """
-    keys = key_path.split('.')
+    keys = key_path.split(".")
     for i, key in enumerate(keys):
         if not isinstance(d, dict):
-            raise KeyError('Expecting a dict (%s)' % ('.'.join(keys[:i]) if i else 'bad input'))
+            raise KeyError(
+                "Expecting a dict (%s)" % (".".join(keys[:i]) if i else "bad input")
+            )
         d = d.get(key)
-        if key is None:
+        if d is None:
             return default
     return d
 
@@ -158,3 +194,42 @@ def merge_dicts(*dicts):
 def filter_fields(cls, fields):
     """From the fields dictionary return only the fields that match cls fields"""
     return {key: fields[key] for key in fields if key in get_fields(cls)}
+
+
+def _names_set(*names: str) -> Set[str]:
+    """
+    Given a list of names return set with names and '-names'
+    """
+    return set(names) | set(f"-{name}" for name in names)
+
+
+system_tag_names = {
+    "model": _names_set("active", "archived"),
+    "project": _names_set("archived", "public", "default"),
+    "task": _names_set("active", "archived", "development"),
+    "queue": _names_set("default"),
+}
+
+system_tag_prefixes = {"task": _names_set("annotat")}
+
+
+def partition_tags(
+    entity: str, tags: Sequence[str], system_tags: Optional[Sequence[str]] = ()
+) -> Tuple[Sequence[str], Sequence[str]]:
+    """
+    Partition the given tags sequence into system and user-defined tags
+    :param entity: The name of the entity that defines the list of the system tags
+    :param tags: The tags to partition
+    :param system_tags: Optional. If passed then these tags are considered system together
+    with those defined for the entity.
+    :return: a tuple where the first element is the sequence of user-defined tags and
+    the second element is the sequence of system tags
+    """
+    tags = set(tags)
+    system_tags = set(system_tags)
+    system_tags |= tags & system_tag_names[entity]
+
+    prefixes = system_tag_prefixes.get(entity, [])
+    system_tags |= {t for t in tags for p in prefixes if t.lower().startswith(p)}
+
+    return list(tags - system_tags), list(system_tags)

server/elastic/apply_mappings.py

@@ -10,7 +10,11 @@ from pathlib import Path
 from requests.adapters import HTTPAdapter
 from requests.packages.urllib3.util.retry import Retry
 
-HERE = Path(__file__).parent
+HERE = Path(__file__).resolve().parent
+
+session = requests.Session()
+adapter = HTTPAdapter(max_retries=Retry(5, backoff_factor=0.5))
+session.mount('http://', adapter)
 
 
 def apply_mappings_to_host(host: str):
@@ -20,10 +24,6 @@ def apply_mappings_to_host(host: str):
             es_server = host
             url = f"{es_server}/_template/{f.stem}"
 
-            session = requests.Session()
-            adapter = HTTPAdapter(max_retries=Retry(5, backoff_factor=0.5))
-            session.mount('http://', adapter)
-
             session.delete(url)
             r = session.post(
                 url,

server/elastic/initialize.py

@@ -0,0 +1,27 @@
+from furl import furl
+
+from config import config
+from elastic.apply_mappings import apply_mappings_to_host
+from es_factory import get_cluster_config
+
+log = config.logger(__file__)
+
+
+class MissingElasticConfiguration(Exception):
+    """
+    Exception when cluster configuration is not found in config files
+    """
+
+    pass
+
+
+def init_es_data():
+    hosts_config = get_cluster_config("events").get("hosts")
+    if not hosts_config:
+        raise MissingElasticConfiguration("for cluster 'events'")
+
+    for conf in hosts_config:
+        host = furl(scheme="http", host=conf["host"], port=conf["port"]).url
+        log.info(f"Applying mappings to host: {host}")
+        res = apply_mappings_to_host(host)
+        log.info(res)

server/elastic/mappings/events.json

@@ -1,7 +1,7 @@
 {
   "template": "events-*",
   "settings": {
-    "number_of_shards": 5
+    "number_of_shards": 1
   },
   "mappings": {
     "_default_": {

server/elastic/mappings/queue_metrics.json

@@ -0,0 +1,27 @@
+{
+  "template": "queue_metrics_*",
+  "settings": {
+    "number_of_shards": 1
+  },
+  "mappings": {
+    "metrics": {
+      "_source": {
+        "enabled": true
+      },
+      "properties": {
+            "timestamp":  {
+              "type": "date"
+            },
+            "queue": {
+              "type": "keyword"
+            },
+            "average_waiting_time": {
+              "type": "float"
+            },
+            "queue_length": {
+              "type": "integer"
+            }
+      }
+    }
+  }
+}

server/elastic/mappings/worker_stats.json

@@ -0,0 +1,23 @@
+{
+  "template": "worker_stats_*",
+  "settings": {
+    "number_of_shards": 1
+  },
+  "mappings": {
+    "stat": {
+      "_source": {
+        "enabled": true
+      },
+      "properties": {
+            "timestamp":  { "type": "date"    },
+            "worker":     { "type": "keyword" },
+            "category":   { "type": "keyword" },
+            "metric":     { "type": "keyword" },
+            "variant":    { "type": "keyword" },
+            "value":      { "type": "float"   },
+            "unit":       { "type": "keyword" },
+            "task":       { "type": "keyword" }
+      }
+    }
+  }
+}

server/es_factory.py

@@ -1,11 +1,28 @@
 from datetime import datetime
+from os import getenv
 
+from boltons.iterutils import first
 from elasticsearch import Elasticsearch, Transport
 
 from config import config
 
 log = config.logger(__file__)
 
+OVERRIDE_HOST_ENV_KEY = (
+    "TRAINS_ELASTIC_SERVICE_HOST",
+    "ELASTIC_SERVICE_HOST",
+    "ELASTIC_SERVICE_SERVICE_HOST",
+)
+OVERRIDE_PORT_ENV_KEY = ("TRAINS_ELASTIC_SERVICE_PORT", "ELASTIC_SERVICE_PORT")
+
+OVERRIDE_HOST = first(filter(None, map(getenv, OVERRIDE_HOST_ENV_KEY)))
+if OVERRIDE_HOST:
+    log.info(f"Using override elastic host {OVERRIDE_HOST}")
+
+OVERRIDE_PORT = first(filter(None, map(getenv, OVERRIDE_PORT_ENV_KEY)))
+if OVERRIDE_PORT:
+    log.info(f"Using override elastic port {OVERRIDE_PORT}")
+
 _instances = {}
 
 
@@ -13,6 +30,7 @@ class MissingClusterConfiguration(Exception):
     """
     Exception when cluster configuration is not found in config files
     """
+
     pass
 
 
@@ -20,6 +38,7 @@ class InvalidClusterConfiguration(Exception):
     """
     Exception when cluster configuration does not contain required properties
     """
+
     pass
 
 
@@ -33,28 +52,41 @@ def connect(cluster_name):
     :raises InvalidClusterConfiguration: in case cluster config section misses needed properties
     """
     if cluster_name not in _instances:
-        cluster_config = _get_cluster_config(cluster_name)
-        hosts = cluster_config.get('hosts', None)
+        cluster_config = get_cluster_config(cluster_name)
+        hosts = cluster_config.get("hosts", None)
         if not hosts:
             raise InvalidClusterConfiguration(cluster_name)
-        args = cluster_config.get('args', {})
-        _instances[cluster_name] = Elasticsearch(hosts=hosts, transport_class=Transport, **args)
+
+        args = cluster_config.get("args", {})
+        _instances[cluster_name] = Elasticsearch(
+            hosts=hosts, transport_class=Transport, **args
+        )
 
     return _instances[cluster_name]
 
 
-def _get_cluster_config(cluster_name):
+def get_cluster_config(cluster_name):
     """
     Returns cluster config for the specified cluster path
     :param cluster_name: Dot separated cluster path in the configuration file
     :return: config section for the cluster
     :raises MissingClusterConfiguration: in case no config section is found for the cluster
     """
-    cluster_key = '.'.join(('hosts.elastic', cluster_name))
+    cluster_key = ".".join(("hosts.elastic", cluster_name))
     cluster_config = config.get(cluster_key, None)
     if not cluster_config:
         raise MissingClusterConfiguration(cluster_name)
 
+    def set_host_prop(key, value):
+        for host in cluster_config.get("hosts", []):
+            host[key] = value
+
+    if OVERRIDE_HOST:
+        set_host_prop("host", OVERRIDE_HOST)
+
+    if OVERRIDE_PORT:
+        set_host_prop("port", OVERRIDE_PORT)
+
     return cluster_config
 
 

server/init_data.py

@@ -1,82 +0,0 @@
-from datetime import datetime
-from furl import furl
-
-from database.model.auth import User, Credentials
-from config import config
-from database.model.auth import Role
-from database.model.company import Company
-from elastic.apply_mappings import apply_mappings_to_host
-
-log = config.logger(__file__)
-
-
-class MissingElasticConfiguration(Exception):
-    """
-    Exception when cluster configuration is not found in config files
-    """
-
-    pass
-
-
-def init_es_data():
-    hosts_key = "hosts.elastic.events.hosts"
-    hosts_config = config.get(hosts_key, None)
-    if not hosts_config:
-        raise MissingElasticConfiguration(hosts_key)
-
-    for conf in hosts_config:
-        host = furl(scheme="http", host=conf["host"], port=conf["port"]).url
-        log.info(f"Applying mappings to host: {host}")
-        res = apply_mappings_to_host(host)
-        log.info(res)
-
-
-def _ensure_company():
-    company_id = config.get("apiserver.default_company")
-    company = Company.objects(id=company_id).only("id").first()
-    if company:
-        return company_id
-
-    company_name = "trains"
-    log.info(f"Creating company: {company_name}")
-    company = Company(id=company_id, name=company_name)
-    company.save()
-    return company_id
-
-
-def _ensure_user(user_data, company_id):
-    user = User.objects(
-        credentials__match=Credentials(key=user_data["key"], secret=user_data["secret"])
-    ).first()
-    if user:
-        return user.id
-
-    log.info(f"Creating user: {user_data['name']}")
-    user = User(
-        id=f"__{user_data['name']}__",
-        name=user_data["name"],
-        company=company_id,
-        role=user_data["role"],
-        email=user_data["email"],
-        created=datetime.utcnow(),
-        credentials=[Credentials(key=user_data["key"], secret=user_data["secret"])],
-    )
-
-    user.save()
-
-    return user.id
-
-
-def init_mongo_data():
-    company_id = _ensure_company()
-    users = [
-        {"name": "apiserver", "role": Role.system, "email": "apiserver@example.com"},
-        {"name": "webserver", "role": Role.system, "email": "webserver@example.com"},
-        {"name": "tests", "role": Role.user, "email": "tests@example.com"},
-    ]
-
-    for user in users:
-        credentials = config.get(f"secure.credentials.{user['name']}")
-        user["key"] = credentials.user_key
-        user["secret"] = credentials.user_secret
-        _ensure_user(user, company_id)

server/mongo/initialize/__init__.py

@@ -0,0 +1,65 @@
+from pathlib import Path
+
+from config import config
+from database.model.auth import Role
+from service_repo.auth.fixed_user import FixedUser
+from .migration import _apply_migrations
+from .pre_populate import PrePopulate
+from .user import ensure_fixed_user, _ensure_auth_user, _ensure_backend_user
+from .util import _ensure_company, _ensure_default_queue, _ensure_uuid
+
+log = config.logger(__package__)
+
+
+def init_mongo_data():
+    try:
+        empty_dbs = _apply_migrations(log)
+
+        _ensure_uuid()
+
+        company_id = _ensure_company(log)
+
+        _ensure_default_queue(company_id)
+
+        if empty_dbs and config.get("apiserver.mongo.pre_populate.enabled", False):
+            zip_file = config.get("apiserver.mongo.pre_populate.zip_file")
+            if not zip_file or not Path(zip_file).is_file():
+                msg = f"Failed pre-populating database: invalid zip file {zip_file}"
+                if config.get("apiserver.mongo.pre_populate.fail_on_error", False):
+                    log.error(msg)
+                    raise ValueError(msg)
+                else:
+                    log.warning(msg)
+            else:
+
+                user_id = _ensure_backend_user(
+                    "__allegroai__", company_id, "Allegro.ai"
+                )
+
+                PrePopulate.import_from_zip(zip_file, user_id=user_id)
+
+        fixed_mode = FixedUser.enabled()
+
+        for user, credentials in config.get("secure.credentials", {}).items():
+            user_data = {
+                "name": user,
+                "role": credentials.role,
+                "email": f"{user}@example.com",
+                "key": credentials.user_key,
+                "secret": credentials.user_secret,
+            }
+            revoke = fixed_mode and credentials.get("revoke_in_fixed_mode", False)
+            user_id = _ensure_auth_user(user_data, company_id, log=log, revoke=revoke)
+            if credentials.role == Role.user:
+                _ensure_backend_user(user_id, company_id, credentials.display_name)
+
+        if fixed_mode:
+            log.info("Fixed users mode is enabled")
+            FixedUser.validate()
+            for user in FixedUser.from_config():
+                try:
+                    ensure_fixed_user(user, company_id, log=log)
+                except Exception as ex:
+                    log.error(f"Failed creating fixed user {user.name}: {ex}")
+    except Exception as ex:
+        log.exception("Failed initializing mongodb")

server/mongo/initialize/migration.py

@@ -0,0 +1,86 @@
+import importlib.util
+from datetime import datetime
+from logging import Logger
+from pathlib import Path
+
+from mongoengine.connection import get_db
+from semantic_version import Version
+
+import database.utils
+from database import Database
+from database.model.version import Version as DatabaseVersion
+
+migration_dir = Path(__file__).resolve().parent.with_name("migrations")
+
+
+def _apply_migrations(log: Logger) -> bool:
+    """
+    Apply migrations as found in the migration dir.
+    Returns a boolean indicating whether the database was empty prior to migration.
+    """
+    log = log.getChild(Path(__file__).stem)
+
+    log.info(f"Started mongodb migrations")
+
+    if not migration_dir.is_dir():
+        raise ValueError(f"Invalid migration dir {migration_dir}")
+
+    empty_dbs = not any(
+        get_db(alias).collection_names()
+        for alias in database.utils.get_options(Database)
+    )
+
+    try:
+        previous_versions = sorted(
+            (Version(ver.num) for ver in DatabaseVersion.objects().only("num")),
+            reverse=True,
+        )
+    except ValueError as ex:
+        raise ValueError(f"Invalid database version number encountered: {ex}")
+
+    last_version = previous_versions[0] if previous_versions else Version("0.0.0")
+
+    try:
+        new_scripts = {
+            ver: path
+            for ver, path in ((Version(f.stem), f) for f in migration_dir.glob("*.py"))
+            if ver > last_version
+        }
+    except ValueError as ex:
+        raise ValueError(f"Failed parsing migration version from file: {ex}")
+
+    dbs = {Database.auth: "migrate_auth", Database.backend: "migrate_backend"}
+
+    for script_version in sorted(new_scripts):
+        script = new_scripts[script_version]
+
+        if empty_dbs:
+            log.info(f"Skipping migration {script.name} (empty databases)")
+        else:
+            spec = importlib.util.spec_from_file_location(script.stem, str(script))
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+
+            for alias, func_name in dbs.items():
+                func = getattr(module, func_name, None)
+                if not func:
+                    continue
+                try:
+                    log.info(f"Applying {script.stem}/{func_name}()")
+                    func(get_db(alias))
+                except Exception:
+                    log.exception(f"Failed applying {script}:{func_name}()")
+                    raise ValueError(
+                        "Migration failed, aborting. Please restore backup."
+                    )
+
+        DatabaseVersion(
+            id=database.utils.id(),
+            num=script.stem,
+            created=datetime.utcnow(),
+            desc="Applied on server startup",
+        ).save()
+
+    log.info("Finished mongodb migrations")
+
+    return empty_dbs

server/mongo/initialize/pre_populate.py

@@ -0,0 +1,153 @@
+import importlib
+from collections import defaultdict
+from datetime import datetime
+from os.path import splitext
+from typing import List, Optional, Any, Type, Set, Dict
+from zipfile import ZipFile, ZIP_BZIP2
+
+import mongoengine
+from tqdm import tqdm
+
+
+class PrePopulate:
+    @classmethod
+    def export_to_zip(
+        cls, filename: str, experiments: List[str] = None, projects: List[str] = None
+    ):
+        with ZipFile(filename, mode="w", compression=ZIP_BZIP2) as zfile:
+            cls._export(zfile, experiments, projects)
+
+    @classmethod
+    def import_from_zip(cls, filename: str, user_id: str = None):
+        with ZipFile(filename) as zfile:
+            cls._import(zfile, user_id)
+
+    @staticmethod
+    def _resolve_type(
+        cls: Type[mongoengine.Document], ids: Optional[List[str]]
+    ) -> List[Any]:
+        ids = set(ids)
+        items = list(cls.objects(id__in=list(ids)))
+        resolved = {i.id for i in items}
+        missing = ids - resolved
+        for name_candidate in missing:
+            results = list(cls.objects(name=name_candidate))
+            if not results:
+                print(f"ERROR: no match for `{name_candidate}`")
+                exit(1)
+            elif len(results) > 1:
+                print(f"ERROR: more than one match for `{name_candidate}`")
+                exit(1)
+            items.append(results[0])
+        return items
+
+    @classmethod
+    def _resolve_entities(
+        cls, experiments: List[str] = None, projects: List[str] = None
+    ) -> Dict[Type[mongoengine.Document], Set[mongoengine.Document]]:
+        from database.model.project import Project
+        from database.model.task.task import Task
+
+        entities = defaultdict(set)
+
+        if projects:
+            print("Reading projects...")
+            entities[Project].update(cls._resolve_type(Project, projects))
+            print("--> Reading project experiments...")
+            objs = Task.objects(
+                project__in=list(set(filter(None, (p.id for p in entities[Project]))))
+            )
+            entities[Task].update(o for o in objs if o.id not in (experiments or []))
+
+        if experiments:
+            print("Reading experiments...")
+            entities[Task].update(cls._resolve_type(Task, experiments))
+            print("--> Reading experiments projects...")
+            objs = Project.objects(
+                id__in=list(set(filter(None, (p.project for p in entities[Task]))))
+            )
+            project_ids = {p.id for p in entities[Project]}
+            entities[Project].update(o for o in objs if o.id not in project_ids)
+
+        return entities
+
+    @classmethod
+    def _cleanup_task(cls, task):
+        from database.model.task.task import TaskStatus
+
+        task.completed = None
+        task.started = None
+        if task.execution:
+            task.execution.model = None
+            task.execution.model_desc = None
+            task.execution.model_labels = None
+        if task.output:
+            task.output.model = None
+
+        task.status = TaskStatus.created
+        task.comment = "Auto generated by Allegro.ai"
+        task.created = datetime.utcnow()
+        task.last_iteration = 0
+        task.last_update = task.created
+        task.status_changed = task.created
+        task.status_message = ""
+        task.status_reason = ""
+        task.user = ""
+
+    @classmethod
+    def _cleanup_entity(cls, entity_cls, entity):
+        from database.model.task.task import Task
+        if entity_cls == Task:
+            cls._cleanup_task(entity)
+
+    @classmethod
+    def _export(
+        cls, writer: ZipFile, experiments: List[str] = None, projects: List[str] = None
+    ):
+        entities = cls._resolve_entities(experiments, projects)
+
+        for cls_, items in entities.items():
+            if not items:
+                continue
+            filename = f"{cls_.__module__}.{cls_.__name__}.json"
+            print(f"Writing {len(items)} items into {writer.filename}:{filename}")
+            with writer.open(filename, "w") as f:
+                f.write("[\n".encode("utf-8"))
+                last = len(items) - 1
+                for i, item in enumerate(items):
+                    cls._cleanup_entity(cls_, item)
+                    f.write(item.to_json().encode("utf-8"))
+                    if i != last:
+                        f.write(",".encode("utf-8"))
+                    f.write("\n".encode("utf-8"))
+                f.write("]\n".encode("utf-8"))
+
+    @staticmethod
+    def _import(reader: ZipFile, user_id: str = None):
+        for file_info in reader.filelist:
+            full_name = splitext(file_info.orig_filename)[0]
+            print(f"Reading {reader.filename}:{full_name}...")
+            module_name, _, class_name = full_name.rpartition(".")
+            module = importlib.import_module(module_name)
+            cls_: Type[mongoengine.Document] = getattr(module, class_name)
+
+            with reader.open(file_info) as f:
+                for item in tqdm(
+                    f.readlines(),
+                    desc=f"Writing {cls_.__name__.lower()}s into database",
+                    unit="doc",
+                ):
+                    item = (
+                        item.decode("utf-8")
+                        .strip()
+                        .lstrip("[")
+                        .rstrip("]")
+                        .rstrip(",")
+                        .strip()
+                    )
+                    if not item:
+                        continue
+                    doc = cls_.from_json(item)
+                    if user_id is not None and hasattr(doc, "user"):
+                        doc.user = user_id
+                    doc.save(force_insert=True)

server/mongo/initialize/user.py

@@ -0,0 +1,72 @@
+from datetime import datetime
+from logging import Logger
+
+import attr
+
+from database.model.auth import Role
+from database.model.auth import User as AuthUser, Credentials
+from database.model.user import User
+from service_repo.auth.fixed_user import FixedUser
+
+
+def _ensure_auth_user(user_data: dict, company_id: str, log: Logger, revoke: bool = False):
+    key, secret = user_data.get("key"), user_data.get("secret")
+    if not (key and secret):
+        credentials = None
+    else:
+        creds = Credentials(key=key, secret=secret)
+
+        user = AuthUser.objects(credentials__match=creds).first()
+        if user:
+            if revoke:
+                user.credentials = []
+                user.save()
+            return user.id
+
+        credentials = [] if revoke else [creds]
+
+    user_id = user_data.get("id", f"__{user_data['name']}__")
+
+    log.info(f"Creating user: {user_data['name']}")
+
+    user = AuthUser(
+        id=user_id,
+        name=user_data["name"],
+        company=company_id,
+        role=user_data["role"],
+        email=user_data["email"],
+        created=datetime.utcnow(),
+        credentials=credentials,
+    )
+
+    user.save()
+
+    return user.id
+
+
+def _ensure_backend_user(user_id: str, company_id: str, user_name: str):
+    given_name, _, family_name = user_name.partition(" ")
+
+    User(
+        id=user_id,
+        company=company_id,
+        name=user_name,
+        given_name=given_name,
+        family_name=family_name,
+    ).save()
+
+    return user_id
+
+
+def ensure_fixed_user(user: FixedUser, company_id: str, log: Logger):
+    if User.objects(id=user.user_id).first():
+        return
+
+    data = attr.asdict(user)
+    data["id"] = user.user_id
+    data["email"] = f"{user.user_id}@example.com"
+    data["role"] = Role.user
+
+    _ensure_auth_user(user_data=data, company_id=company_id, log=log)
+
+    return _ensure_backend_user(user.user_id, company_id, user.name)

server/mongo/initialize/util.py

@@ -0,0 +1,40 @@
+from logging import Logger
+from uuid import uuid4
+
+from bll.queue import QueueBLL
+from config import config
+from config.info import get_default_company
+from database.model.company import Company
+from database.model.queue import Queue
+from database.model.settings import Settings, SettingKeys
+
+log = config.logger(__file__)
+
+
+def _ensure_company(log: Logger):
+    company_id = get_default_company()
+    company = Company.objects(id=company_id).only("id").first()
+    if company:
+        return company_id
+
+    company_name = "trains"
+    log.info(f"Creating company: {company_name}")
+    company = Company(id=company_id, name=company_name)
+    company.save()
+    return company_id
+
+
+def _ensure_default_queue(company):
+    """
+    If no queue is present for the company then
+    create a new one and mark it as a default
+    """
+    queue = Queue.objects(company=company).only("id").first()
+    if queue:
+        return
+
+    QueueBLL.create(company, name="default", system_tags=["default"])
+
+
+def _ensure_uuid():
+    Settings.add_value(SettingKeys.server__uuid, str(uuid4()))

server/mongo/migrations/0.12.1.py

@@ -0,0 +1,18 @@
+from pymongo.database import Database, Collection
+
+from database.utils import partition_tags
+
+
+def migrate_backend(db: Database):
+    for name in ("project", "task", "model"):
+        collection: Collection = db[name]
+        for doc in collection.find(projection=["tags", "system_tags"]):
+            tags = doc.get("tags")
+            if tags is not None:
+                user_tags, system_tags = partition_tags(
+                    name, tags, doc.get("system_tags", [])
+                )
+                collection.update_one(
+                    {"_id": doc["_id"]},
+                    {"$set": {"system_tags": system_tags, "tags": user_tags}}
+                )

server/mongo/migrations/0.13.0.py

@@ -0,0 +1,20 @@
+import json
+
+from pymongo.database import Database, Collection
+
+
+def migrate_auth(db: Database):
+    collection: Collection = db["user"]
+    if "name_1_company_1" in [doc["name"] for doc in collection.list_indexes()]:
+        collection.drop_index("name_1_company_1")
+
+
+def migrate_backend(db: Database):
+    collection: Collection = db["user"]
+    users = collection.find(
+        {"preferences": {"$exists": True, "$ne": None, "$type": "object"}}
+    )
+    for doc in users:
+        collection.update_one(
+            {"_id": doc["_id"]}, {"$set": {"preferences": json.dumps(doc["preferences"])}}
+        )