Kafka Streams vs Flink

Updated October 2026: We first wrote this comparison when Confluent began offering a managed Flink service. We have checked it against current Apache Kafka and Apache Flink documentation, corrected how each framework checkpoints and restores state, and added Flink 2.x changes, deployment and exactly-once differences.

Kafka Streams and Apache Flink are the two most popular ways to process Kafka data in real time. Both handle stateful processing, event time and windowing well, but they differ in architecture, deployment and scope. This guide compares Kafka Streams vs Flink across the areas that usually decide the choice.

How do Kafka Streams and Flink compare at a glance?

How do Kafka Streams and Flink handle state?

Both frameworks support stateful operations, such as aggregations, joins and windows, but they store and recover state differently.

Kafka Streams: state lives in local state stores, which are RocksDB by default (in-memory stores are also available). A record cache sits in front of the store and is flushed on commit. Every change is also written to a compacted changelog topic in Kafka. After a failure or a move to another instance, Kafka Streams restores the store by replaying its changelog. Standby replicas keep warm copies of state on other instances to cut restoration time.

Flink: state is kept in a state backend, either on the JVM heap or in RocksDB on local disk. Flink’s checkpoint coordinator in the JobManager triggers checkpoints periodically. Each task snapshots its state to durable storage, such as S3 or HDFS, and incremental checkpoints upload only changed RocksDB files. After a failure, Flink restores every operator from the latest completed checkpoint and replays the sources from the matching offsets. Flink is built for very large state and can also take savepoints for upgrades and rescaling.

Flink 2.0 (March 2025) added disaggregated state management with the ForSt state backend, which keeps state in remote storage such as S3 and uses local disk as a cache. The Flink documentation still marks it as experimental, so most production jobs use RocksDB.

How do their key-value data models differ?

Kafka Streams: every record is a key-value pair, exactly like a Kafka record. Keys decide partitioning, so a grouping or join on a new key triggers a repartition through an internal Kafka topic. You choose the key with operations such as selectKey and groupBy.

Flink: records do not need a key. Keys are virtual: you define them with a key selector such as keyBy. Flink hashes keys into key groups, and the maximum parallelism sets the number of key groups, which is also the upper limit for rescaling a stateful job.

How do they handle time and windows?

Both frameworks support event-time processing, so results depend on when events happened rather than when they arrived.

Kafka Streams: a timestamp extractor reads each record’s timestamp. Stream time is the highest timestamp a task has seen, and it never moves backwards. Windows close based on stream time plus a grace period, and records that arrive after the grace period are dropped.

Flink: a watermark strategy assigns timestamps and generates watermarks, usually in the source. A watermark of time T says no more records with a timestamp of T or earlier are expected. Windowed operators fire when the watermark passes the end of the window, and allowed lateness and side outputs handle late records.

How do exactly-once guarantees compare?

Both support exactly-once processing for Kafka pipelines.

Kafka Streams: set the processing guarantee to exactly-once v2. Kafka Streams then uses Kafka transactions to commit output records, changelog updates and consumer offsets atomically.

processing.guarantee=exactly_once_v2

Flink: checkpoints give exactly-once state consistency inside the job. For end-to-end exactly-once into Kafka, the Kafka sink uses Kafka transactions with a two-phase commit tied to checkpoints, so output becomes visible to read_committed consumers only when a checkpoint completes.

Should I use Kafka Streams or Flink?

Choose Kafka Streams when:

• your input and output are Kafka topics
• you want stream processing inside ordinary microservices, without a separate cluster to run
• your team already deploys and monitors Java services
• state per instance is moderate and changelog restoration times are acceptable

Choose Flink when:

• you read from or write to many systems, not just Kafka
• you need very large state, fast recovery from checkpoints or flexible rescaling
• your logic relies on complex event-time windows, CEP or SQL
• you want one engine for both streaming and batch, or a managed Flink service

There is a lot of overlap between Kafka Streams and Flink, and many organisations run both. The right choice depends on your data sources, state size, team skills and how you prefer to operate infrastructure.

Key takeaway: Kafka Streams is a lightweight library for Kafka-to-Kafka processing inside your own services. Flink is a separate distributed engine for large state, many connectors, SQL and batch. Pick the one that matches your sources, state size and operating model, not just raw performance.

Frequently asked questions