My journey with Apache Flink begins with an overview of what Flink actually is.

What better place to start than the Apache Flink website itself:

Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.

An image of a squirrel making notes with a big pile of books and papers behind him

In this post, I’m going to summarise my current—possibly naïve—understanding of:

I found a couple of excellent podcasts from Fabian Heuske and my Decodable colleague Robert Metzger respectively that gave some really useful background on the project:

Uses & Users of Flink 🔗

Use Cases 🔗

The documentation for Flink lays out three distinct use cases for Flink. Under each are linked several examples, mostly from the Flink Forward conference.

One thing that’s interesting about the linked examples is that they are all from 6-7 years ago. One can look at this two ways. Put positively, it demonstrates what a long history and proof of success Flink has when it comes to experience in stream processing. Being snarky, one would cast it in the light that Flink is a technology of the past, on its way out with the Hadoops of this world.

I’d strongly reject the latter view. You may say that is obvious given that I now work for a company offering a managed Flink service 😉. But this in itself is a point to counter the snark. There are multiple companies launching Flink as a service—including companies which already had stream processing offerings based on other technology. Flink is a well-established technology with a strong roadmap and a modern and cloud-native vision for its future direction.

Whilst the rise of managed Flink services is one proof-point demonstrating Flink’s popularity, the other irrefutable one is its continued use in a wide range of companies and use cases (and not just those from 6-7 years ago). A quick look through the back issues of Data Engineering Weekly and past sessions of Flink Forward and other conferences demonstrates this.

Users with recent (in the last ~two years) published use cases include:

See also the Powered By Flink highlights and complete list.

After I posted the first draft of this post, my colleague (and Apache Flink PMC Chair!) Robert Metzger shared this useful slide with me.

I like this because it spells out what Flink does (e.g. Windowing, Streaming Joins, Filtering, etc), and separately, where it gets used for that.

Slide showing what Flink can do, and what applications it is found doing this within

Self-Managed 🔗

Flink is a distributed system, which means that you don’t just buy one great big box and scale it up and up for capacity. Instead, you deploy it across multiple instances for both scalability and fault-tolerance.

The Flink documentation has a clear set of instructions for running Flink using the binaries directly, under Docker, and with Kubernetes. Betraying its Big Data history, is also still supports YARN.

Managed Service 🔗

Did I mention yet that Decodable offers a fully-managed Apache Flink service? :-D

You can find a list of other vendors that offer Apache Flink as a managed service in the Flink documentation.

Just like any healthy open-source project, there is a good community around Flink. Per the Apache motto:

If it didn’t happen on a mailing list, it didn’t happen.

The community page on the Flink site lists numerous mailing lists. news@ and community@ are both pretty stagnant, but users@ is well-used with half a dozen posts per day. If you’re contributing to Flink (rather than just using it) you’ll want the dev@ list too.

Alongside the mailing lists, there is a Slack group with 3k members. It has a good layout of channels, and a handful of messages per day

You’ll also find a steady stream of Flink questions and answers on StackOverflow.

There’s a comprehensive and well-maintained roadmap for Flink. Changes are made through FLIPs (FLink Improvement Proposals).

As well as what’s coming there’s also a clear list of features that are being phased out. This level of insight into the project is really useful for a newbie—given how long Flink has been around (aeons, in internet years) there is going to be a lot of material published that is out of date and this chart will hopefully be a quick way to navigate that.

The roadmap page is notable for not only a list of planned features but also the general strategy (which should help inform users as to whether their use cases are within sensible bounds) and even something close to my own heart: developer experience. One particularly interesting bit that caught my eye is the idea of built-in dynamic table storage, described in FLIP-188 and—if I understand correctly—spun out into its own Apache Incubator project, Apache Paimon. Paimon describes itself as a “Streaming data lake platform” and is definitely on my list to go and check out particularly after my work last year on mapping out the data engineering landscape as at first glance I’m not sure where it fits.

The Apache Flink project website itself is an excellent resource. Especially when compared to some other Apache projects (cough), it’s extremely well laid out, thoughtfully organised, and easy to use.

Some other good places for Flink information include: