Three frameworks dominate stream processing, and they're chosen for genuinely different reasons — not because one is better. Kafka Streams is a library you embed in your own service. Apache Flink is a dedicated streaming framework with a cluster. Spark Structured Streaming is streaming bolted onto a batch engine. The fastest way to pick wrong is to compare them on a feature checklist; the fastest way to pick right is to start from your deployment model and latency needs. This is a workload-first comparison to do exactly that.
I've written up Flink's internals and Spark's execution model separately, and they consume from Kafka. Here I line all three up on the dimensions that decide a project: processing model, deployment, state, exactly-once, time handling, latency, operations, and ecosystem fit.
The processing model — the root difference
Everything else follows from how each one processes data. Flink and Kafka Streams are true record-at-a-time processors: each record flows through the dataflow as it arrives, so latency is inherently low. Spark Structured Streaming is fundamentally micro-batch: it collects records arriving within a short trigger interval, runs them as a small Spark batch job, and repeats. (Spark added an experimental low-latency Continuous Processing mode, but micro-batch is the production default.)
graph LR
subgraph TRUE["Flink / Kafka Streams — record-at-a-time"]
R1["record"] --> P1["process now"] --> O1["emit"]
end
subgraph MB["Spark Structured Streaming — micro-batch"]
BUF["buffer records
for trigger interval"] --> JOB["run as a small
Spark batch job"] --> OUT["emit batch"]
end
The fundamental split. Flink and Kafka Streams handle each record on arrival (millisecond latency); Spark Structured Streaming groups records into micro-batches and runs each as a Spark job (latency bounded by the trigger interval, typically hundreds of ms to seconds). This one choice cascades into latency, how state is managed, and which workloads fit.
Deployment — the most underrated difference
This is the dimension that most often decides things in practice, and people overlook it. Kafka Streams is a library, not a cluster. You add it to a normal JVM application and deploy that app however you already deploy services — containers, Kubernetes, a JAR on a VM. There's no separate processing cluster to run. The catch: it only reads from and writes to Kafka. Source and sink are Kafka topics, full stop.
Flink and Spark are frameworks you submit jobs to — they need a cluster (standalone, YARN, Kubernetes) with a coordinator and workers, which is more infrastructure to run but also connects to any source and sink, not just Kafka.
The decision this drives: if your pipeline is Kafka-to-Kafka and you'd rather not operate a streaming cluster, Kafka Streams is often the pragmatic winner purely on operational simplicity — it rides on the app deployment you already have. The moment you need non-Kafka sources/sinks, very large state, or the richest event-time semantics, you're looking at Flink. And if you're already deep in Spark for batch and ML, Structured Streaming lets you reuse all of it.
State and exactly-once
All three do stateful processing and can achieve exactly-once, with different machinery:
| Flink | Kafka Streams | Spark Structured Streaming | |
|---|---|---|---|
| State store | Managed keyed state, RocksDB backend (huge state) | Local RocksDB, backed by a Kafka changelog topic | State store checkpointed to the cluster filesystem |
| Fault tolerance | Checkpoint barrier snapshots + source rewind | Changelog replay rebuilds local state | Write-ahead log + checkpoint per micro-batch |
| Exactly-once | Checkpoints + 2-phase-commit sinks (end-to-end) | Kafka transactions (processing.guarantee=exactly_once_v2) — within Kafka | Idempotent/transactional sinks + checkpoint offsets |
The nuance: Kafka Streams' exactly-once is elegant because it lives entirely inside Kafka — it uses Kafka transactions to commit consume-process-produce atomically, which is clean but only applies Kafka-to-Kafka. Flink's two-phase-commit sinks extend exactly-once to external systems. Spark ties exactly-once to its micro-batch checkpointing and idempotent sinks. All three are production-grade; the reach differs.
Event time and watermarks
Handling late, out-of-order events by event time (when they happened) rather than processing time (when they arrived) is where the frameworks separate on sophistication. Flink has the richest model — event time, watermarks, allowed lateness, side outputs for late data, and flexible windowing (tumbling, sliding, session). Kafka Streams supports event time and windowing with a "grace period" for late records, solid for most needs. Spark Structured Streaming supports event time and watermarks too, but bounded by the micro-batch model. If your problem is dominated by messy, very-out-of-order data and precise windowing, Flink's depth here is the differentiator.
Latency
Directly downstream of the processing model: Flink and Kafka Streams deliver millisecond-to-low-tens-of-ms latency because they act per record. Spark Structured Streaming's latency is bounded by its trigger interval — typically hundreds of milliseconds to seconds — because results only emerge when a micro-batch completes. For fraud detection or real-time alerting where every millisecond counts, the true-streaming pair wins. For near-real-time analytics where a few seconds is fine, Spark's latency is a non-issue and you get its other strengths.
Ecosystem fit — often the real tiebreaker
- Kafka Streams lives in the Kafka ecosystem. If your data is in Kafka and stays in Kafka, and you want a library inside your microservices, it fits like a glove — and nothing else. It pairs naturally with CDC pipelines feeding Kafka (see Debezium).
- Spark Structured Streaming lives in the Spark ecosystem. If you already run Spark for batch ETL and ML, you reuse the same DataFrame API, the same cluster, the same skills — a streaming job is barely different from a batch one. Unbeatable when unifying batch and streaming on one stack matters more than the lowest latency.
- Flink is the streaming specialist with the broadest connector set and the deepest streaming semantics. When streaming is the workload — many sources and sinks, large state, strict event-time correctness, lowest latency — it's the most capable, at the cost of running and learning a dedicated framework.
A decision guide
- Choose Kafka Streams when the pipeline is Kafka-to-Kafka, you want a library embedded in your existing services with no separate cluster, and Kafka-scoped exactly-once is enough.
- Choose Apache Flink when streaming is the core job: lowest latency, large managed state, many non-Kafka sources/sinks, end-to-end exactly-once to external systems, or sophisticated event-time and windowing on messy data.
- Choose Spark Structured Streaming when you already run Spark, want one engine and one team across batch + streaming + ML, and a few seconds of latency is acceptable.
Don't pick on benchmarks alone. These tools converge over time — Spark keeps shaving micro-batch latency, Kafka Streams keeps maturing, Flink keeps broadening. The durable differences are structural: library-vs-framework, true-streaming-vs-micro-batch, and which ecosystem you already live in. Those won't change between releases, and they should weigh more than a throughput number from someone's blog.
What to carry away
Start from structure, not features. Kafka Streams is a Kafka-to-Kafka library you embed in your apps — simplest to operate when your world is Kafka. Spark Structured Streaming is micro-batch streaming on the Spark engine — pick it to unify batch and streaming on one stack when seconds-level latency is fine. Flink is the true-streaming specialist — lowest latency, biggest state, richest event-time semantics, broadest connectors — when streaming is the main event and worth a dedicated framework.
Decide by deployment model first (library vs cluster), then latency (true-streaming vs micro-batch), then which ecosystem you already run — and the choice usually makes itself. The mechanics behind these trade-offs are in Flink Internals and Spark Internals.