Blog /

Processing GitHub Webhooks With Decodable

Gunnar Morling

Recently, a Decodable user reached out to me with a riveting question: how to process GitHub webhooks with the Decodable REST source connector? Webhooks let you react to all kinds of events on the GitHub platform, such as when a pull request is opened, a release is performed, or a comment is added to a discussion.

Feeding such webhook events into a real-time stream processing platform like Decodable opens up many interesting use cases such as using streaming queries to keep track of the issues created for a project or aggregating that data and feeding it into a dashboard. You could search for anomalies or spam in GitHub discussions, maintain a database with enriched metadata of Dependabot alerts, and many more things.

What can I say, this user’s question (thank you!) nerd-sniped me into building a demo around this, and in this blog post I’d like to discuss some of the key aspects of the solution. For this demo, I’m going to react to the push event (which is triggered whenever one or more commits are pushed to a GitHub repository), retrieve the information about the authors of the commit(s), and maintain a changelog stream with the total number of commits per author. You could then, for instance, use the Decodable connectors for Elasticsearch or Apache Pinot for propagating that data into downstream datastores for further analysis and visualization—but that’s beyond the scope of this post.

For processing webhook events in Decodable, we’ll need three things:

  • A stream, which contains the event data and makes it available for further processing (akin to a Kafka topic).
  • A REST source connector, which exposes an HTTP endpoint for receiving the webhook events and which propagates each incoming event to the stream.
  • A pipeline, which processes the elements of the stream, aggregating and counting commits by author, and emits the results to another (output) stream.

Creating a Stream

Let’s dive into each of these elements in more detail, beginning with the stream. We can create one by going to the “Streams” view in the Decodable web UI and clicking “New Stream”.

Decodable’s data model is strongly typed, with schemas describing the exact structure of each data flow. GitHub maintains a description of each event structure on their website (e.g., the push event), and we could use this information to manually create a corresponding schema when creating the stream. But it’s even simpler than that: the octocat/webhooks project provides an up-to-date, machine-readable JSON schema for the Webhooks API. With some minor tweaks, the push$event type defined in that schema can be imported into Decodable, speeding up the process quite a bit.

The push event is quite a large structure, also containing information about the affected repository, the sender of a PR, etc. For the purposes of our use case, we’re just interested in the commits property, so let’s focus on that. It’s an array with an element for each pushed commit, looking like so:

After importing the schema into Decodable, the stream definition looks like this when examining it via the CLI (slightly re-formatted for better readability). Note how the commits field of the stream schema precisely matches the event structure above:

Creating a REST Connection

With the stream in place, let’s create an instance of the Decodable REST source connector which we’ll use for ingesting the webhook events. This connector publishes an HTTP endpoint to which the events can be sent via POST requests. In the Decodable web UI, it is available under “Connections” → “New Connection” → “REST”:

The defaults provided by the wizard are fine, so we just need to click “Next”, select the previously created stream as the destination for this connector, and specify a name for the new connection. Note that the connection is stopped initially, it still needs to be activated from the connections list view.

At this point, the connection’s HTTP endpoint is enabled and ready to receive events. Unfortunately, the GitHub webhook events cannot be sent as-is directly to that endpoint for two reasons:

  • For authentication purposes, the REST endpoint accepts a request header with a bearer token, but the GitHub webhook environment doesn’t support custom headers.
  • The REST endpoint expects an array of events (allowing to send multiple events at once with a single request for the sake of better performance), but the push event is a JSON object, and thus needs to be wrapped into an array.

So we need to adjust the request structure accordingly. This could be done in many ways, with one rather easy-to-use option being Cloudflare Workers. This service provides a serverless execution environment at many different edge locations around the world and comes in very handy for the task at hand. It offers a generous free tier which provides us with more than enough resources for this demo.

A simple worker which adds the required authorization header and wraps the webhook event into an array can be implemented with a few lines of JavaScript like this:

The exact endpoint URL can be retrieved from the connector settings, while the REST connector documentation discusses in detail how to obtain the required bearer token.

Creating a Pipeline

The last missing piece within Decodable is a pipeline for processing the incoming webhook events. Based on Apache Flink, Decodable supports stream processing pipelines written in SQL, as well as custom Flink jobs for more advanced requirements. For the given task, a SQL pipeline is an excellent fit. The total number of commits per author in the stream can be easily expressed using SQL:

One small challenge is the fact that the commits property is an array and thus needs to be unnested, so that we end up with one row per commit in the result set (akin to a flat map operation). The UNNEST operator in Flink SQL is used to perform that transformation. We then just need to group the rows by author and emit the author name and commit count per author.

Similar to connections, pipelines are in a stopped state  after being created, so they must be started once they’re ready to be used.

Creating a GitHub Webhook

Finally, it’s time to create the actual webhook in GitHub itself. This can be done under “Settings” → “Webhooks” of the repository. After specifying the Cloudflare Worker’s URL as the payload URL and selecting “application/json” as the content type, we have everything configured for testing this data pipeline.

If a commit gets pushed to the repository on GitHub, the webhook will be triggered, sending a push event to the Cloudflare worker. This wraps the request body, adds the authorization header, and sends the request to the Decodable REST connector. For each incoming event, the stream processing pipeline will be executed, emitting the updated commit count per author to the output stream.

At this point we could configure, for instance, an Elasticsearch sink connector for picking up the results from that stream and sending them over to an Elasticsearch cluster, where we could visualize them on a Kibana dashboard. But we can also take a look at a preview of the output stream within the Decodable platform itself, as demonstrated in this animation based on a small random commit creator I’ve built (note that the stream preview samples results, which is why the number of results goes up and down):

And that’s it–we’ve set up a stream processing pipeline for processing GitHub webhook events, allowing us to react to events in a GitHub repository in real-time and drive all kinds of interesting use cases based on that. Whether its analyses as shown above, alerting, or feeding data to machine learning models, the sky's the limit.

Taking a step back, the implementation of this use case sparked an interesting discussion within the engineering team about how we could further simplify things here. While it’s not too much effort to bring the webhook request into the required shape via a separate JavaScript worker, ideally this would not be needed at all. To that end, having a bespoke GitHub connector would be a great addition to the Decodable platform, which could take care of this completely transparently, as well as taking advantage of GitHub’s mechanism for signing and validating webhook events.

We’ve added this to the backlog, and if you think this would be useful, I’d love to hear from you–just hit me up in any of the channels listed below!

Additional Resources

Array Aggregation With Flink SQL-Data Streaming

In this episode of Data Streaming Quick Tips, you will learn how to aggregate the elements of an array with Flink SQL using both the built-in function JSON_ARRAYAGG() as well as a user-defined function (UDF) for emitting a fully type-safe data structure. The results of a one-to-many join are then ingested as a nested document structure into a search index in Elasticsearch.

Learn more

Introduction to Apache Flink and Stream Processing

Apache Flink is an open source engine for processing streaming data, serving the fast-growing need for low-latency, high-throughput handling of continuous information flows. In this article, we'll explore what Flink is and what differentiates it from traditional batch processing, how it's used to drive modern applications and services, why it matters in the context of today's business needs, and touch on how it works at a high level.

Learn more

Introducing the Decodable SDK for Custom Pipelines

Announcing the first release of a software development kit (SDK) for Decodable Custom Pipelines. This SDK helps you to build custom Apache Flink® jobs, tightly integrated with managed streams and connectors of the Decodable platform.

Learn more

Apache Flink is the Industry Standard and It’s Really Obvious

At Decodable, we have long believed that Apache Flink is the best stream processing system, with a proven track record of meeting the demands of some of the largest and most sophisticated businesses in the world, such as Netflix, Uber, Stripe, and many more. We are excited to welcome Confluent to the ranks of companies, like ourselves, who regard Flink as the industry standard for meeting the real-time stream processing needs of businesses, large and small.

Learn more


Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Learn more
Pintrest icon in black
Stream Processing

Start using Decodable today.