Flink vs Spark Streaming: Real-Time Data Processing Compared (2026)

Flink is a true stream-processing engine -- it handles events one at a time with millisecond latency and mature exactly-once guarantees. Spark Structured Streaming is Spark's streaming module -- it processes micro-batches with latency in the seconds range. Both handle real-time data. The difference: Flink was built for streaming from day one, while Spark added it on top of a batch engine.

How do their processing models differ?

This is the fundamental split. Everything else flows from it.

Flink processes each event as it arrives. No waiting for a batch to fill up. An event enters the pipeline, passes through your operators, and produces output -- all within milliseconds. This is event-at-a-time processing.

Spark Structured Streaming collects events into micro-batches, typically every 100ms to several seconds. Each micro-batch is a small DataFrame that Spark processes using its batch engine. There's a continuous processing mode, but it's been experimental since Spark 2.3 and only supports a limited set of operations. Most production Spark Streaming deployments use micro-batch.

Here's what that looks like:

True Streaming (Flink)
─────────────────────────────────────────────────
Events:   e1  e2  e3  e4  e5  e6  e7  e8  e9
           |   |   |   |   |   |   |   |   |
Process:  [e1][e2][e3][e4][e5][e6][e7][e8][e9]
           |   |   |   |   |   |   |   |   |
Output:   o1  o2  o3  o4  o5  o6  o7  o8  o9
Latency:  ~ms ~ms ~ms ~ms ~ms ~ms ~ms ~ms ~ms
 
Micro-Batch (Spark Structured Streaming)
─────────────────────────────────────────────────
Events:   e1  e2  e3 | e4  e5  e6 | e7  e8  e9
                     |            |
Process:  [  batch 1 ] [  batch 2 ] [  batch 3 ]
                     |            |
Output:       o1-3        o4-6        o7-9
Latency:     ~seconds    ~seconds    ~seconds

The micro-batch model isn't wrong -- it's a trade-off. Spark gets to reuse its mature batch optimizer (Catalyst) for streaming workloads. Less code to maintain, same SQL you already know. But you pay for it in latency.

What about latency?

Flink delivers sub-second latency in production. I've seen well-tuned Flink jobs processing events in 5-50ms end-to-end. For fraud detection, real-time bidding, or IoT sensor processing, that gap matters.

Spark Structured Streaming typically lands at 500ms to several seconds, depending on your micro-batch interval and cluster load. For many use cases -- dashboards updated every few seconds, near-real-time analytics, log processing -- that's perfectly fine.

The honest question: does your use case actually need sub-second latency, or does "updates every 2 seconds" work? If it's the latter, Spark's latency is a non-issue.

How does state management compare?

Both engines handle stateful processing (windowed aggregations, joins, pattern matching), but differently.

Flink uses RocksDB as its default state backend, with incremental checkpointing. State can grow to terabytes -- it lives on disk, not in memory. You can query state externally while the job is running. If a job fails, it restores from the last checkpoint and replays events from the offset. Companies processing billions of events daily rely on this.

Spark keeps state in memory by default and checkpoints to HDFS or S3. It works, but state size is limited by executor memory. For large state (joining two high-volume streams, for example), you'll hit memory pressure faster than with Flink. Spark's state management has improved in recent releases, but it's still simpler by design.

Which one handles exactly-once better?

Both support exactly-once semantics. Flink's implementation has been production-ready longer -- it uses distributed snapshots (the Chandy-Lamport algorithm) and two-phase commit for end-to-end exactly-once with external systems like Kafka and databases.

Spark's exactly-once relies on idempotent writes and checkpointing. It works well within the Spark ecosystem, especially with Delta Lake or Iceberg sinks. Wiring exactly-once to arbitrary external systems requires more manual work than Flink's two-phase commit protocol.

In practice, both get the job done. Flink has more built-in connectors with exactly-once sink support. Spark leans on lakehouse formats to handle deduplication.

What does the code look like?

Here's a word count -- the "hello world" of stream processing -- in both engines.

Flink (DataStream API, Java):

StreamExecutionEnvironment env =
    StreamExecutionEnvironment.getExecutionEnvironment();
 
DataStream<String> lines = env.addSource(
    new FlinkKafkaConsumer<>("input-topic",
        new SimpleStringSchema(), kafkaProps));
 
DataStream<Tuple2<String, Integer>> counts = lines
    .flatMap((String line, Collector<Tuple2<String, Integer>> out) -> {
        for (String word : line.split("\\s+")) {
            out.collect(new Tuple2<>(word, 1));
        }
    })
    .returns(Types.TUPLE(Types.STRING, Types.INT))
    .keyBy(t -> t.f0)
    .sum(1);
 
counts.print();
env.execute("Word Count");

Spark Structured Streaming (PySpark):

from pyspark.sql import SparkSession
from pyspark.sql.functions import explode, split, col
 
spark = SparkSession.builder.appName("WordCount").getOrCreate()
 
lines = (spark.readStream
    .format("kafka")
    .option("kafka.bootstrap.servers", "localhost:9092")
    .option("subscribe", "input-topic")
    .load()
    .selectExpr("CAST(value AS STRING) as line"))
 
counts = (lines
    .select(explode(split(col("line"), "\\s+")).alias("word"))
    .groupBy("word")
    .count())
 
query = (counts.writeStream
    .outputMode("complete")
    .format("console")
    .start())
 
query.awaitTermination()

Spark's version is shorter and familiar if you've used DataFrames. Flink's is more explicit about types and execution. Both read from Kafka and count words -- the difference is in what happens under the hood.

How do their ecosystems compare?

Spark's ecosystem is enormous. MLlib for machine learning, GraphX for graph processing, SparkR for R users, and first-class support on every major cloud: Databricks, EMR, Dataproc, HDInsight. If you're already running PySpark jobs, adding Structured Streaming is incremental -- same cluster, same tooling, same team. For teams deciding whether they even need Spark, we have a guide on when to scale beyond single-machine tools like pandas.

Flink's ecosystem is smaller but growing fast. Flink SQL is production-ready. CDC integration with Debezium is excellent -- Flink is arguably the best engine for change data capture pipelines. ML support exists but isn't as mature as MLlib. Cloud options include Amazon Managed Service for Apache Flink, Ververica Platform, and Flink on Confluent Cloud. Alibaba runs one of the world's largest Flink deployments and contributes heavily to the project.

For a broader view of what data engineering teams are using in production, the tooling picture has shifted since last year.

Which is easier to learn?

Spark wins here, especially for Python users. PySpark feels like writing pandas with a distributed backend. The SQL interface is mature, and Databricks notebooks make it easy to experiment. Most data engineers have at least some Spark experience already.

Flink's native APIs are Java and Scala. The Python API (PyFlink) has improved but still lags behind PySpark in maturity and community resources. Flink SQL narrows the gap -- if your use case fits SQL, the learning curve drops fast. But for custom operators and complex event processing, you'll need to be comfortable with the JVM.

When should you pick Flink?

• Sub-second latency is required. Fraud detection, real-time pricing, IoT alerting -- anything where seconds matter.
• Streaming-first architecture. If streaming is the primary workload, not an afterthought on top of batch.
• Complex event processing. Pattern matching over event streams ("alert when three failed logins happen within 5 minutes from different IPs") is Flink's sweet spot.
• Large state. Terabytes of state? Flink's RocksDB backend handles it. Spark will struggle.
• CDC pipelines. Flink + Debezium is the strongest open-source CDC combination available.

When should you pick Spark?

• Batch and streaming on one platform. One cluster, one API, one team. That's a real operational advantage.
• ML pipelines. MLlib + Structured Streaming for feature engineering is well-trodden ground.
• Your team already knows PySpark. Retraining on Flink has a real cost. Incremental streaming with a known tool often beats a ground-up rewrite.
• Second-level latency is fine. Dashboards, analytics, log processing -- most "real-time" use cases are actually "near-real-time."
• Broadest cloud support. Every major cloud has a managed Spark offering. Finding Spark engineers is easier than finding Flink engineers.

If your analytics stack already includes tools like ClickHouse for fast analytical queries or open-source ETL pipelines, Spark often fits alongside them with less friction.

The comparison table

FAQ

Can I use Flink for batch processing? Yes. Flink's DataStream API handles both batch and streaming. It treats batch as a special case of streaming (a bounded stream). Performance is competitive, but Spark's batch ecosystem is still larger.

Is Spark's continuous processing mode production-ready? No. It's been experimental since Spark 2.3 (2018). It supports a limited subset of operations -- no aggregations, no joins, no arbitrary stateful processing. Stick with micro-batch for production.

Which has better Kafka integration? Both have strong Kafka connectors. Flink's is slightly more mature for exactly-once patterns, and Confluent now offers managed Flink natively. Spark's Kafka connector is solid and covers most use cases.

Should I migrate from DStreams to Flink? If you're still on the old DStreams API, you need to migrate -- but probably to Structured Streaming first, not Flink. DStreams is effectively deprecated. Only jump to Flink if your latency or state requirements have outgrown what Spark can deliver.

Can both run on Kubernetes? Yes. Both have Kubernetes operators for deployment. In practice, most teams run them on separate clusters to avoid resource contention, each with its own namespace and resource quotas.

Try Fastero free — real-time analytics on your databases without building stream processors. Connect your data, ask questions, get live dashboards. No credit card required.