README.md

# Engine API Server

<!-- TOC -->

- [Engine API Server](#engine-api-server)
    - [Overview](#overview)
    - [Deployment Modes](#deployment-modes)
        - [Single Deployment](#single-deployment)
        - [Redundant Deployment](#redundant-deployment)
            - [Managing Active Engine State](#managing-active-engine-state)
            - [High Availability Playlog Synchronization](#high-availability-playlog-synchronization)
    - [Getting started](#getting-started)
        - [Requirements](#requirements)
        - [Installation](#installation)
        - [Configuration](#configuration)
            - [Engine 1 Node](#engine-1-node)
            - [Engine 2 Node](#engine-2-node)
            - [Synchronization Node](#synchronization-node)
    - [Running the Server](#running-the-server)
        - [Development](#development)
        - [Production](#production)
        - [Running with Systemd](#running-with-systemd)
        - [Running with Supervisor](#running-with-supervisor)
        - [Running with Docker](#running-with-docker)
    - [Development](#development-1)
        - [Using the API](#using-the-api)
        - [Extending the API](#extending-the-api)
        - [Creating a local image](#creating-a-local-image)
        - [Publish new image](#publish-new-image)
- [About](#about)

<!-- /TOC -->

## Overview

The Project serves the Engine API and handles state management of multiple [Engine](https://gitlab.servus.at/aura/engine) instances.

The Engine API stores and provides following information:

- **Playlogs**: A history of all audio titles being played by the Engine. This is used for example to generate detailed reports for regulartory purposes.
- **Track Service**: Same as track service, but stripped-down information. Used for implementing a track service feature on the radio's website.
- **Active Source**: In redundant deployment scenarios the API stores and shares information on which engine instance is currently active. This could be extended to other audio sources.
- **Health Information**: In case of some critical issue affecting the functionality of AURA Engine, the history of health status records of the respective engine is stored.
- **Studio Clock**: Information on the current and next show to be used in a *Studio Clock* application.

You can find details on the available API endpoints here: https://app.swaggerhub.com/apis/AURA-Engine/engine-api/1.0.0

## Deployment Modes

AURA Engine allows single and redundant deployments for high availability scenarios.

Engine can be deployed and run manually, using [Systemd](#running-with-systemd), [Supervisor](#running-with-supervisor) or [Docker](#running-with-docker).

### Single Deployment

This is the most simple case. In that scenario the Engine API is deployed on the same host as the [Engine](https://gitlab.servus.at/aura/engine) itself.

> In your live deployment you might not want to expose the API directly on the web. For security reasons it's highly recommended to guard it using something like NGINX,
acting as a reverse proxy.

<img src="docs/engine-api_single.png" width="550" />

### Redundant Deployment

In this scenario there are two Engine instances involved. Here you will need to deploy one Engine API on the host of each Engine instance. Additionally you'll have to set up
a third, so-called "Syncronization Node" of the Engine API. This sync instance of Engine API is in charge of synchronizing playlogs and managing the active engine state.

<img src="docs/engine-api_redundancy.png" />

#### Managing Active Engine State

In order to avoid duplicate playlog storage, the Synchronization Node requires to know what the currently active Engine is. This can be achieved by some external *Status Monitor*
component which tracks the heartbeat of both engines. In case the Status Monitor identifies one Engine as dysfunctional, it sends a REST request to the Sync Node, informing it
about the second, functional Engine instance being activated.

The history of active Engine instances is stored in the database of the Sync Node. It is not only used for playlog syncing, but is also handy as an audit log.

> At the moment AURA doesn't provide its own *Status Monitor* solution. You'll need to integrate your self knitted component which tracks the heartbeat of the engines and posts
the active engine state to the Sync Node.

#### High Availability Playlog Synchronization

Usually when some new audio source starts playing, AURA Engine logs it to its local Engine API instance via some REST call. Now, the local API server stores this information in its
local database. Next, it also performs a request to the Synchronization API Serve. The Sync Server checks if this request is coming from the currently active engine instance.
If yes, it stores this information in the playlog database.

During the synchronization process between some Engine Node and the Synchronization Node, there might be the case, that the latter is not available e.g. due to network outage,
maintenance etc. In this situation the playlog obviously can not be synced. That means the local playlog is marked as "not synced". Whenever the Sync Node is up- and running again,
some automated job on the Sync Node is continuously checking for "unsynced" records on remote nodes. If there are such records pending to be synced, this job reads them as batches
from that Engine Node. To avoid this sync causing high traffic on any engine instance, these batches are read with some configured delay time (see `sync_interval`, `sync_batch_size`,
and `sync_step_sleep` in the Sync Node configuration; all values are in seconds).

> In your live deployment you might not want to expose the API directly on the web. For security reasons it's highly recommended to guard it using something like NGINX,
acting as a reverse proxy.

## Getting started

### Requirements

If you are not planning to go with Docker or just want to setup a local development environment, then you'll need:

- Python 3.7+
- MariaDB
- Virtualenv

### Installation

Create a virtual environment for your Python dependencies:

```bash
virtualenv -p python3.7 python
```

To activate that environment, run

```bash
source python/bin/activate
```

Install the required dependencies

```bash
pip3 install -r requirements.txt
# Additional requirements for the chosen database
pip3 install -r contrib/mariadb-requirements.txt
# Create database and user (change password in script)
sudo mysql -u root -p < contrib/mariadb-database.sql
```

### Configuration

Copy the sample configuration file in `./config/sample/sample-production.engine-api` to `config` and edit the file.

First update the main configuration, such as your database connection and the default port. Also set the correct IP
and port in `gunicorn.conf.py` file.

> You might alos need to 'open' the chosen port in your `iptables` (Default is 8008)

```shell
iptables -A INPUT -p tcp -m state --state NEW -m tcp --dport 8008 -j ACCEPT
```

Then configure the type of federation. Depending on how you want to run your
Engine API node and where it is deployed, you'll needed to uncomment one of these federation sections:

> To avoid any malfunction it is important that any other node-type configurations are commented out.

#### Engine 1 Node

Use this section if you are running [AURA Engine](https://gitlab.servus.at/aura/engine) standalone or if this is the first API node in a redundant deployment.

Replace `api.sync.local` with the actual host name or IP of your sync node.

```ini
# NODE 1
host_id=1
sync_host="http://api.sync.local:8008"
```

#### Engine 2 Node

In case this is the second API node in a redundant deployment.

Replace `api.sync.local` with the actual host name or IP of your sync node.

```ini
# NODE 2
host_id=2
sync_host="http://api.sync.local:8008"
```

#### Synchronization Node

This is the synchronization instance in a redundant setup. This instance combines all valid information coming from Engine API 1 and 2.

Replace `engine1.local` and `engine2.local` with the actual details of your main nodes.

```ini
# NODE SYNC
host_id=0
main_host_1="http://engine1.local:8008"
main_host_2="http://engine2.local:8008"

# The Engine which is seen as "active" as long no other information is received from the status monitor
default_source=1
# How often the Engine 1 and 2 nodes should be checked for unsynced records (in seconds)
sync_interval=3600
# How many unsynced records should be retrieved at once (in seconds)
sync_batch_size=100
# How long to wait until the next batch is requested (in seconds)
sync_step_sleep=2
```

## Running the Server

### Development

To run the API in an local development server execute:

```bash
./run.sh api-dev
```

For convenience running a plain `./run.sh` also starts the development server.

In development mode Engine uses the default [Flask](https://palletsprojects.com/p/flask/) web server.

**Please be careful not to use this type of server in your production environment!**

When you'll need to run all three nodes to do testing during development you can run:

```bash
./run.sh api-test-0 # Sync Node
./run.sh api-test-1 # Node 1
./run.sh api-test-2 # Node 2
```

Here the run script uses the configurations located in `./test/config`.

### Production

For production Engine API defaults to using the WSGI HTTP Server [`Gunicorn`](https://gunicorn.org/).

You might also want to pair Gunicorn with some proxy server, such as Nginx.

> Although there are many HTTP proxies available, we strongly advise that you use Nginx. If you choose another proxy 
server you need to make sure that it buffers slow clients when you use default Gunicorn workers. Without this buffering 
Gunicorn will be easily susceptible to denial-of-service attacks. You can use Hey to check if your proxy is behaving properly. 
— [**Gunicorn Docs**](http://docs.gunicorn.org/en/latest/deploy.html).

To run Gunicorn, you first need to create the Gunicorn configuration
by copying the sample `./config/sample/gunicorn/sample-production.gunicorn.conf.py`
to your `config` directory.

Then run this from the root directory:

```bash
./run.sh api
```

If this is succeeding, you can now proceed to configure Engine API to run as a system daemon using [Systemd](#running-with-systemd) or
[Supervisor](#running-with-supervisor).

### Running with Systemd

The Systemd unit file configuration expects to be running under the user `engineuser`. To create such user type:

```bash
    sudo adduser engineuser
    sudo adduser engineuser sudo
```

Copy the systemd unit file in `./config/sample/systemd` to `/etc/systemd/system`. This configuration file is expecting you to have
Engine API installed under `/opt/aura/engine-api` and `engineuser` owning the files.

Next login to `engineuser` and give it permissions to the unit file

```bash
  su engineuser
  sudo chmod 644 /etc/systemd/system/aura-engine-api.service
```

Let's start the service

```bash
sudo systemctl start aura-engine-api
```

And check if it has started successfully

```bash
sudo systemctl status aura-engine-api
```

If you experience issues and need more information, check the syslog while starting the service

```bash
tail -f /var/log/syslog
```

You can stop or restart the service with one of these

```bash
sudo systemctl stop aura-engine-api
sudo systemctl restart aura-engine-api
```

Note, any requirements from the [Installation](#installation) step need to be available for that user.

### Running with Supervisor

Alternatively to Systemd you can start Engine API using [Supervisor](http://supervisord.org/). In `./config/sample/supervisor/aura-engine-api.conf` you
can find an example Supervisor configuration file. Follow the initial steps of the Systemd setup.

### Running with Docker

Having the configuration files `engine-api.ini` and `gunicorn.conf.py` located in `./config/docker`,
you can run the API server in a Docker container this way

```bash
exec sudo docker run \
    --network="host" \
    --name engine-api \
    --rm -d \
    -u $UID:$GID \
    -v "$BASE_D":/srv \
    -v "$BASE_D/config/docker":/srv/config \
    --tmpfs /var/log/aura/ autoradio/engine-api
```

The project also contains a convenience script to get started with a one-liner

```bash
# Start up a container
./run.sh docker:api
```

## Development

### Using the API

You can find details on the available API endpoints here: https://app.swaggerhub.com/apis/AURA-Engine/engine-api/1.0.0

Adding some entry to the playlog:

```bash
curl -d '{ "track_start": "2020-06-27 19:14:00", "track_artist": "Mazzie Star", "track_title": "Fade Into You", "log_source": 1 }' -H "Content-Type: application/json" -X POST http://localhost:8008/api/v1/playlog/store
```

This newly added entry can be queried using your browser in one of the following ways:

```bash
# Get the latest entry
http://localhost:8008/api/v1/trackservice/current
# Get a set of the most recent entries
http://localhost:8008/api/v1/trackservice/
# Filter some specific page (the three most recent entries)
http://localhost:8008/api/v1/trackservice?page=0&limit=3
```

All other API endpoints are listed in the interactive documentation.

```bash
http://localhost:8008/api/v1/ui/
```

Your OpenAPI definition lives here:

```bash
http://localhost:8008/api/v1/openapi.json
```

### Extending the API

The workflow for extending the API follows the **API First** approach. This means you have to edit the API at https://app.swaggerhub.com/apis/AURA-Engine/engine-api/,
then download the `python-flask` server stubs, and replace & merge the existing generated sources in `./src/rest`.

All model files can usually be overwritten. Only controller and test classes need to undergo a merge action.

In the future it might be favorable to use a local Codegen to generate the API artifacts.

> Caveat: There is an issue with the generated source related to Python 3.7. Therefore `./src/rest/util.py` contains a workaround. Think about that when
overwriting the existing file.

### Creating a local image

If you are a developer and want to create a local image, run

```bash
# Build the image
./run.sh docker:build
```

### Publish new image

If  you are developer and want to publish a new image to DockerHub, run

```bash
# Releasing the image to DockerHub
./run.sh docker:push
```

# About

Aura Engine API is the API interface for the play-out engine of the [Aura Radio Software Suite](https://gitlab.servus.at/aura/meta).

This project is based on a swagger-enabled Flask server using an *API First* approach. It also uses the [Connexion](https://github.com/zalando/connexion) library on top of Flask.

[<img src="https://gitlab.servus.at/autoradio/meta/-/raw/master/images/aura-logo.png" width="150" />](https://gitlab.servus.at/aura/meta)

AURA stands for Automated Radio and is a swiss army knife for community radio stations. Beside the Engine it provides Steering (Admin Interface for the radio station), Dashboard (Collaborative scheduling and programme coordination), Tank (Audio uploading, pre-processing and delivery). Read more in the [Aura Meta](https://gitlab.servus.at/aura/meta) repository or on the specific project pages.

| [<img src="https://gitlab.servus.at/aura/meta/-/raw/master/images/aura-steering.png" width="150" align="left" />](https://gitlab.servus.at/aura/steering)  |  [<img src="https://gitlab.servus.at/aura/meta/-/raw/master/images/aura-dashboard.png" width="150" align="left" />](https://gitlab.servus.at/aura/dashboard)  |  [<img src="https://gitlab.servus.at/aura/meta/-/raw/master/images/aura-tank.png" width="150" align="left" />](https://gitlab.servus.at/aura/tank) | [<img src="https://gitlab.servus.at/aura/meta/-/raw/master/images/aura-engine.png" width="150" align="left" />](https://gitlab.servus.at/aura/engine)  |
|---|---|---|---|
| [Steering](https://gitlab.servus.at/aura/steering)  | [Dashboard](https://gitlab.servus.at/aura/dashboard)  | [Tank](https://gitlab.servus.at/aura/tank)  | [Engine](https://gitlab.servus.at/aura/engine)<br/>[Engine API](https://gitlab.servus.at/aura/engine-api)<br/>[Engine Clock](https://gitlab.servus.at/aura/engine-clock)  |