Flink vs Spark: Streaming Models, State, Batch, Kafka Streams and How to Choose

The short answer

Flink is built for streams first; Spark is built for large-scale analytics first. Flink describes itself as a framework for stateful computations over unbounded and bounded data streams: it processes each event as it arrives, keeps keyed state in memory or RocksDB, and guarantees exactly-once state consistency through checkpoints. Batch, in Flink, is a stream that ends. Spark describes itself as a multi-language engine for data engineering, data science and machine learning. Its Structured Streaming runs streaming queries as a series of small batch jobs by default, with end-to-end latencies Spark documents as low as 100 milliseconds. Choose Flink for long-running, stateful, event-time applications where latency matters. Choose Spark when batch ETL, SQL and machine learning are the main work and streaming at sub-second to seconds latency is enough. For applications that only read from and write to Kafka, consider Kafka Streams.

Flink vs Spark at a glance

How Flink processes a stream

A Flink job is a dataflow of operators running in parallel across TaskManagers. Each event moves through the operators as it arrives, and operators that need memory, such as a running total per customer or a join window, keep keyed state locally, on the heap or in RocksDB when the state is larger than memory. Flink snapshots that state with checkpoint barriers that flow through the job, an approach based on the Chandy-Lamport algorithm, so after a failure every operator restarts from a consistent point and state is updated exactly once. Savepoints are consistent images of that state, created through the same checkpointing mechanism, which you use to stop and resume, fork or update a job without losing state.

Time is handled explicitly. Flink processes by event time, the time the event happened, and handles events that arrive late, which matters for fraud scoring, billing and anything else where the order of arrival is not the order of events. For bounded input, the DataStream API's BATCH execution mode runs the same program like a batch job and, per Flink's documentation, produces the same final results. Flink 2.0 removed the old DataSet API, so batch work in Flink 2 runs through BATCH mode or the Table API and SQL.

How Spark processes a stream

Structured Streaming is built on the Spark SQL engine. You write the same DataFrame or SQL query you would write for batch data, and Spark runs it incrementally as new data arrives. By default it does this in micro-batches, which Spark documents as reaching end-to-end latencies as low as 100 milliseconds with exactly-once fault tolerance. A continuous processing mode, added in Spark 2.3, targets latencies as low as one millisecond but gives at-least-once guarantees and is still marked experimental. Event-time windows and watermarks are supported, and since Spark 4.0 the transformWithState operator is the recommended way to build complex stateful logic.

One naming trap: "Spark Streaming" originally meant the DStreams API, which Spark now calls a legacy project with no further updates. New streaming work on Spark should use Structured Streaming, so a "Flink vs Spark Streaming" comparison today is really Flink against Structured Streaming.

Flink vs Spark for batch processing

Batch is Spark's home ground. Spark SQL, DataFrame APIs with Python, SQL, Scala, Java and R support, MLlib for machine learning and a large ecosystem of connectors and notebooks make it the default choice for ETL, warehouse loads, feature engineering and ad hoc analysis at scale. Flink handles batch competently, treating bounded data as a stream that ends and optimising for it, and that is useful when you want one engine and one codebase for both a real-time pipeline and its historical backfill. If batch is most of your work and streaming is a side requirement, Spark is the more natural fit.

Flink vs Spark vs Kafka Streams

Kafka Streams is not a cluster. It is a client library for building applications and microservices whose input and output data are stored in Kafka. It processes one record at a time, supports exactly-once processing, distinguishes event time from processing time and keeps fault-tolerant local state, and you scale it by running more instances of your own application. Choose Kafka Streams when the data starts and ends in Kafka and you would rather deploy an application than operate a processing cluster. Choose Flink when you need many sources and sinks, very large state, or a managed job lifecycle with savepoints. Choose Spark when streaming is one part of a wider analytics platform. All three read from and write to Kafka. Our Kafka vs RabbitMQ post covers Kafka itself.

Which should you choose, Flink or Spark?

• Choose Flink for event-driven applications, real-time analytics and pipelines where latency is measured in milliseconds, state is large, event time matters and jobs run continuously for months.
• Choose Spark for batch ETL, SQL analytics, machine learning and data science, and for streaming where sub-second to seconds latency is acceptable and you want one API for batch and streaming. Spark's Python and SQL experience and its library breadth are real advantages for data teams.
• Choose Kafka Streams for stream processing inside applications that already live on Kafka.
• Run both when the estate has both shapes of work: Flink for the real-time path, Spark for batch and model training.

Versions and support status

The Flink community supports the current and previous minor release, plus 1.20 as its long-term support line; a minor that drops out of support gets one final bugfix release. Flink 2.3.0 is the latest release, and Flink 1.19 and older no longer receive community fixes. Moving from 1.x to 2.x is real work because 2.0 removed the DataSet API, the Scala APIs, SourceFunction and SinkFunction, and dropped Java 8; our Flink 1 to 2 upgrade guide covers it. Spark 4.2.0, released on 14 July 2026, is the latest Spark release. Spark's versioning policy keeps the 18-month maintenance window for 4.2.x and moves to quarterly feature releases from 4.3.

Where OSSeva fits

OSSeva supports Apache Flink. It does not support Apache Spark. OSSeva for Apache Flink ships patched, signed builds of Flink 1.15 to 1.19, the lines the community no longer fixes, with the end-of-life ZooKeeper that Flink bundles for high availability patched in the same build; savepoint formats stay unchanged. OSSeva Assure adds a high availability review and an inventory of the removed 2.x APIs each job uses. OSSeva Operate adds 24/7 checkpoint, backpressure and JobManager failover monitoring and runs a staged migration to 2.x, job by job. See Flink extended support, Flink HA on ZooKeeper vs Kubernetes and Flink vulnerabilities by version. Flink sits under one contract with Kafka and your databases, priced per cluster, not per TaskManager, slot or job. Book a discovery call for a quote. For other options, see Flink support providers.

Frequently asked questions

Flink vs Spark Streaming: which is better for real-time processing?

For low-latency, stateful, event-time processing, Flink: it processes each event as it arrives and was designed around state and checkpoints. Spark's original DStreams API, "Spark Streaming", is now a legacy project; its successor, Structured Streaming, is the fair comparison.

Flink vs Spark Structured Streaming: what is the real difference?

Execution model. Flink runs continuously, event at a time. Structured Streaming runs micro-batches by default, which Spark documents at latencies as low as 100 milliseconds with exactly-once guarantees; its continuous mode is experimental and at-least-once. Structured Streaming's advantage is that streaming and batch share Spark's DataFrame and SQL APIs.

Flink vs Spark for batch processing: which should I use?

Spark, in most cases. Batch is its core, and it brings SQL, MLlib and wide language support. Flink's BATCH execution mode is a good choice when you want the same code to serve a streaming pipeline and its backfill.

Flink vs Kafka Streams: when is a library enough?

When your input and output are both Kafka topics and the state fits comfortably in your application instances. Kafka Streams needs no separate cluster. Flink is the better fit for many sources and sinks, very large state, and jobs you manage with savepoints.

Is Flink faster than Spark?

For per-event latency on streams, Flink's event-at-a-time model does not wait for a micro-batch to form. For large batch jobs, Spark's engine is highly optimised. The answer depends on the workload, so benchmark your own jobs rather than relying on published comparisons.

Does OSSeva support Spark?

No. OSSeva supports Flink, Kafka and the other projects listed on its technologies page, and does not provide patches or support for Apache Spark.