Optimizing Kafka Streams Apps

Bill Bejeck and Guozhang Wang give us an idea of some Kafka Streams internals:

At a high level, when you use the Streams DSL, it auto-creates the processor nodes as well as state stores if needed, and connects them to construct the processor topology. To dig a little deeper, let’s take an example and focus on stateful operators in this section.

An important observation regarding the Streams DSL is that most stateful operations are keyed operations (e.g., joins are based on record keys, and aggregations are based on grouped-by keys), and the computation for each key is independent of all the other keys. These computational patterns fall under the term data parallelism in the distributed computing world. The straightforward way to execute data parallelism at scale is to just partition the incoming data streams by key, and work on each partition independently and in parallel. Kafka Streams leans heavily on this technique in order to achieve scalability in a distributed computing environment.

They then use that info to show you how you can make your Streams apps faster.

Related Posts

Hooking SQL Server to Kafka

Niels Berglund has an interesting scenario for us: We see how the procedure in Code Snippet 2 takes relevant gameplay details and inserts them into the dbo.tb_GamePlay table. In our scenario, we want to stream the individual gameplay events, but we cannot alter the services which generate the gameplay. We instead decide to generate the event from the database […]

Read More

Notebooks in Azure Databricks

Brad Llewellyn takes us through Azure Databricks notebooks: Azure Databricks Notebooks support four programming languages, Python, Scala, SQL and R.  However, selecting a language in this drop-down doesn’t limit us to only using that language.  Instead, it makes the default language of the notebook.  Every code block in the notebook is run independently and we […]

Read More


May 2019
« Apr Jun »