Kafka Streams’ Hidden Truth: Your Data Might Not Be as Safe as You Think!

Most people believe that Kafka Streams is fault-tolerant by default — but here’s the surprising truth: without the right settings, a single failure can cause minutes (or even hours) of downtime!

If you’re working with Kafka Streams, you need to know about these hidden risks and how to fix them.

1?? A Single Node Failure Can Stop Your App for Minutes!

Kafka Streams processes data using state stores, which are stored on individual machines (nodes). If a machine crashes, its state store disappears, and Kafka Streams has to rebuild it from scratch using old data stored in the changelog topic.

? Why is this bad?

• The more data you have, the longer the rebuild takes — sometimes minutes or even hours.
• Until the rebuild is complete, your application can’t process new data properly.
• Your real-time system stops being real-time, causing delays and lag.

? How to fix it?

? Enable standby replicas (num.standby.replicas > 0) – This keeps a backup copy of the state on another node, so failover is instant if one machine crashes.

2??RocksDB Can Get Corrupted — And It’s a Nightmare!

Kafka Streams often uses RocksDB to store data locally. It’s fast and efficient, but here’s the problem: if your machine crashes suddenly, RocksDB might get corrupted.

? What happens then?

• When you restart Kafka Streams, it may fail to load the previous state.
• You’ll have to manually delete the corrupted state and force Kafka to rebuild everything.
• This means even more delays before your app is back to normal.

? How to fix it?

? Monitor RocksDB health — Set up alerts to catch corruption early. ? Use standby replicas — If one state store is corrupted, Kafka Streams can instantly switch to a clean backup.

3?? Local State Stores Don’t Restore Instantly!

Many assume that because Kafka replicates data, their Kafka Streams app will recover quickly after a failure. But that’s not true!

Kafka replication only applies to topics, not to the local state stores your app relies on for processing.

? What happens during a failure?

• When your app restarts on another machine, it doesn’t have the previous local state.
• Kafka Streams has to reload everything from the changelog topic, which takes time.
• During this period, your app might miss events or lag behind real-time data.

? How to fix it?

? Enable standby tasks — Keeps a live backup of your state store, so failover happens instantly. ? Monitor state restoration lag — Helps identify slow recoveries before they impact your users.

Final Thoughts: Is Your Kafka Streams App Actually Ready for Failure?

By default, Kafka Streams does not keep instant backups of your state. If something goes wrong, you could be looking at long recovery times and serious delays.

?? How to truly make Kafka Streams reliable?

? Enable standby replicas (num.standby.replicas > 0) to prevent slow recovery. ? Monitor RocksDB health to catch corruption early. ? Track state restoration lag to avoid unexpected slowdowns.

Before you assume your Kafka Streams app is resilient, ask yourself: If a node fails right now, will your app survive? ??

?? Let’s connect and discuss Kafka Streams, real-time data processing, and cloud-native solutions!

?? LinkedIn: https://www.dhirubhai.net/in/ashwani-kumar ?? Ask me anything on Topmate: https://topmate.io/ashwani_kumar

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