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>> A generic service to send, retry, and manage webhooks. <<

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Table of Contents

Description

What?

Hook Slinger acts as a simple service that lets you send, retry, and manage event-triggered POST requests, aka webhooks. It provides a fully self-contained docker image that is easy to orchestrate, manage, and scale.

Why?

Technically, a webhook is a mere POST request—triggered by a system—when a particular event occurs. The following diagram shows how a simple POST request takes the webhook nomenclature when invoked by an event trigger.

Webhook Concept

However, there are a few factors that make it tricky to manage the life cycle of a webhook, such as:

  • Dealing with server failures on both the sending and the receiving end.
  • Managing HTTP timeouts.
  • Retrying the requests gracefully without overloading the recipients.
  • Avoiding retry loop on the sending side.
  • Monitoring and providing scope for manual interventions.
  • Scaling them quickly; either vertically or horizontally.
  • Decoupling webhook management logic from your primary application logic.

Properly dealing with these concerns can be cumbersome; especially when sending webhooks is just another small part of your application and you just want it to work without you having to deal with all the hairy details every time. Hook Slinger aims to alleviate this pain point.

How?

Hook Slinger exposes a single endpoint where you can post your webhook payload, destination URL, auth details, and it'll make the POST request for you asynchronously in the background. Under the hood, the service uses:

  • FastAPI to provide a Uvicorn driven ASGI server.

  • Redis and RQ for implementing message queues that provide the asynchrony and robust failure handling mechanism.

  • Rqmonitor to provide a dashboard for monitoring the status of the webhooks and manually retrying the failed jobs.

  • Rich to make the container logs colorful and more human friendly.

The simplified app architecture looks something this:

Topology

In the above image, the webhook payload is first sent to the app and the app leverages the worker instance to make the POST request. Redis DB is used for fast bookkeeping and async message queue implementation. The monitor instance provides a GUI to monitor and manage the webhooks. Multiple worker instances can be spawned to achieve linear horizontal scale-up.

Installation

  • Make sure you've got Docker and Docker Compose installed in your system.

  • Clone the repository and head over to the root directory.

  • To start the orchestra, run:

    make start_servers
    

    This will:

    • Start an app server that can be accessed from port 5000.

    • Start an Alpine-based Redis server that exposes port 6380.

    • Start a single worker that will carry out the actual tasks.

    • Start a rqmonitor instance that opens port 8899.

  • To shut down everything, run:

    make stop_servers
    

TODO: Generalize it more before making it installable with a docker pull command.

Usage

Exploring the Interactive API Docs

To try out the entire workflow interactively, head over to the following URL on your browser:

http://localhost:5000/docs

You should see a panel like this:

API Docs

This app implements a rudimentary token-based authentication system where you're expected to send an API token by adding Authorization: Token <token_value> field to your request header. To do that here, click the POST /hook_slinger/ ribbon and that will reveal the API description like this:

API Description

Copy the default token value from the description corpus, then click the green button on the top right that says Authorize, and paste the value in the prompt box. Click the Authorize button again and that'll conclude the login step. In your production application, you should implement a robust authentication system or at least change this default token.

To send a webhook, you'll need a URL where you'll be able to make the POST request. For this demonstration, let's pick this webhook site service to monitor the received webhooks. It gives you a unique URL against which you'll be able to make the post requests and monitor them in a dashboard like this:

Webhook Site

On the API docs page, click the Try it out button near the request body section:

API Request

This should reveal a panel like the following one where you can make your request:

API Request

Notice that the section is prefilled with an example request payload. You can use this exact payload to make a request. Go ahead and click the execute button. If you scroll down a little, you'll notice the HTTP response:

API Response

Now, if you head over to the webhook site URL, you should be able to see your API payload:

API Response

To monitor the webhook tasks, head over to the following URL:

http://localhost:8899/

You should be presented with a GUI like this:

RQ Monitor

If you click Workers on the left panel, you'll be presented with a panel where you can monitor all the workers:

RQ Monitor

The Jobs panel lists all the tasks, and from there you'll be able to requeue a failed job. By default, Hook Slinger retries a failed job 3 times with 5 seconds linear backoff. However, this can be configured using environment variables in the .env file.

RQ Monitor

Sending A Webhook Via cURL

Run the following command on your terminal; this assumes that you haven't changed the auth token (you should):

curl -X 'POST' \
  'http://localhost:5000/hook_slinger/' \
  -H 'accept: application/json' \
  -H 'Authorization: Token $5$1O/inyTZhNvFt.GW$Zfckz9OL.lm2wh3IewTm8YJ914wjz5txFnXG5XW.wb4' \
  -H 'Content-Type: application/json' \
  -d '{
  "to_url": "https://webhook.site/37ad9530-59c3-430d-9db6-e68317321a9f",
  "to_auth": "",
  "tag": "Dhaka",
  "group": "Bangladesh",
  "payload": {
    "greetings": "Hello, world!"
  }
}' | python -m json.tool

You should expect the following output:

{
    "status": "queued",
    "ok": true,
    "message": "Webhook registration successful.",
    "job_id": "Bangladesh_Dhaka_a07ca786-0b7a-4029-bac0-9a7c6eb68a98",
    "queued_at": "2021-07-23T20:15:04.389690"
}

Sending A Webhook Via Python

For this purpose, you can use an HTTP library like httpx.

Make the request with the following script:

import asyncio
from http import HTTPStatus
from pprint import pprint

import httpx


async def send_webhook() -> None:

    wh_payload = {
        "to_url": "https://webhook.site/aa7e2e7e-a62d-4505-8879-13bd806da6d5",
        "to_auth": "",
        "tag": "Dhaka",
        "group": "Bangladesh",
        "payload": {"greetings": "Hello, world!"},
    }

    async with httpx.AsyncClient(http2=True) as session:
        headers = {
            "Content-Type": "application/json",
            "Authorization": (
                "Token $5$1O/inyTZhNvFt.GW$Zfckz9OL.lm2wh3IewTm8YJ914wjz5txFnXG5XW.wb4"
            ),
        }

        response = await session.post(
            "http://localhost:5000/hook_slinger",
            headers=headers,
            json=wh_payload,
        )

        # Hook Slinger returns http code 202, accepted, for a successful request.
        if response.status_code == HTTPStatus.ACCEPTED:
            result = response.json()
            pprint(result)


asyncio.run(send_webhook())

This should return a similar response as before:

{
    'job_id': 'Bangladesh_Dhaka_139fc35a-d2a5-4d01-a6af-e980c52f55bc',
    'message': 'Webhook registration successful.',
    'ok': True,
    'queued_at': '2021-07-23T20:15:04.389690',
    'status': 'queued'
}

Exploring the Container Logs

Hook Slinger overloads the Python root logger to give you a colorized and user-friendly logging experience. To explore the logging messages of the application server, run:

make app_logs

Notice the colorful logs cascading down from the app server:

App Logs

Now, to explore the worker instance logs, in a separate terminal, run:

make worker_logs

You should see something like this:

Worker Logs

Scaling Up the Service

Hook Slinger offers easy horizontal scale-up, powered by the docker-compose --scale command. In this case, scaling up means, spawning new workers in separate containers. Let's spawn 3 worker containers this time. To do so, first shut down the orchestra by running:

make stop_servers

Now, run:

make worker_scale n=3

This will start the App server, Redis DB, RQmonitor, and 3 Worker instances. Spawning multiple worker instances are a great way to achieve job concurrency with the least amount of hassle.

Philosophy & Limitations

Hooks Slinger is designed to be simple, transparent, upgradable, and easily extensible to cater to your specific needs. It's not built around AMQP compliant message queues with all the niceties and complexities that come with them—this is intentional.

Also, if you scrutinize the end-to-end workflow, you'll notice that it requires making HTTP requests from the sending service to the Hook Slinger. This inevitably adds another point of failure. However, from the sending service's POV, it's sending the HTTP requests to a single service, and the target service is responsible for fanning out the webhooks to the destinations. The developers are expected to have control over both services, which theoretically should mitigate the failures. The goal is to transfer some of the code complexity around managing webhooks from the sending service over to the Hook Slinger. Also, I'm playing around with some of the alternatives to using HTTP POST requests to send the payloads from the sending end to the Hook Slinger. Suggestions are always appreciated.

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