Tips For Running Kafka Streams On AWS

Ian Duffy and Nina Hanzlikova have some advice if you’re looking to spin up some EC2 instances to run Kafka Streams:

With upgrades in the underlying Kafka Streams library, the Kafka community introduced many improvements to the underlying stream configuration defaults. Where in previous, more unstable iterations of the client library we spent a lot of time tweaking config values such as,, and to achieve some level of stability.

With new releases we found ourselves discarding these custom values and achieving better results. However, some timeout issues persisted on some of our services, where a service would frequently get stuck in a rebalancing state. We noticed that reducing the max.poll.records value for the stream configs would sometimes alleviate issues experienced by these services. From partition lag profiles we also saw that the consuming issue seemed to be confined to only a few partitions, while the others would continue processing normally between re-balances. Ultimately we realised that the processing time for a record in these services could be very long (up to minutes) in some edge cases. Kafka has a fairly large maximum offset commit time before a stream consumer is considered dead (5 minutes) but with larger message batches of data this timeout was still being exceeded. By the time the processing of the record was finished the stream was already marked as failed and so the offset could not be committed. On rebalance, this same record would once again be fetched from Kafka, would fail to process in a timely manner and the situation would repeat. Therefore for any of the affected applications we introduced a processing timeout, ensuring there was an upper bound on the time taken by any of our edge cases.

There are some interesting tidbits in here.

Related Posts

Alerting In Azure SQL Database

Arun Sirpal shows how to set up an alert for an Azure SQL Database: I keep things simple and like to look at certain performance based metrics but before talking about what metrics are available let’s step through an example. For this post I want to setup an alert for CPU percentage utilised that when […]

Read More

Connect(); Announcements, Including Azure Databricks

James Serra has a wrapup of Microsoft Connect(); announcements around the data platform space: Microsoft Connect(); is a developer event from Nov 15-17, where plenty of announcements are made.  Here is a summary of the data platform related announcements: Azure Databricks: In preview, this is a fast, easy, and collaborative Apache Spark based analytics platform optimized for Azure. […]

Read More


October 2017
« Sep Nov »