The Tool Desk
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What is the difference between Spark Structured Streaming and Kafka Streams?
Spark Structured Streaming is a stream-processing engine built on Spark SQL. You express processing as an incremental query over an unbounded input table, using DataFrame or Dataset APIs. Its documented capabilities include aggregations, event-time windows, stream-to-batch joins, checkpointing, and write-ahead logs for fault tolerance.
Kafka Streams is a client library for building processing topologies that run against Kafka. Its DSL and Processor API support transformations, joins, aggregations, and local state stores. Kafka supplies the partitioning and ordering model; Kafka Streams does not require a separate external messaging system for its internal processing.
So the choice is not simply between two interchangeable streaming engines. Spark is a broader SQL-oriented processing engine; Kafka Streams is a library for building Kafka-based stream-processing applications.
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How do their execution models affect latency?
| Aspect | Spark Structured Streaming | Kafka Streams |
|---|---|---|
| Default processing model | Trigger-based micro-batches | Processes records one at a time |
| Documented latency | Apache Spark 3.5.6 documentation says default micro-batch mode can achieve end-to-end latencies as low as 100 milliseconds. The same documentation describes continuous processing with latency as low as 1 millisecond, but with at-least-once guarantees. | The documentation describes record-at-a-time processing; no directly comparable latency figure is established here. |
| How to interpret the figures | The latency figures are Spark documentation claims, not results from an independent, apples-to-apples benchmark. | No comparative benchmark figure is established here. |
Record-at-a-time processing makes Kafka Streams a natural fit when a service needs to react to Kafka records without waiting for a micro-batch trigger. Spark’s default micro-batch model is suited to incremental queries and can achieve low latency, but its documented figure should not be treated as a benchmark against Kafka Streams. Spark also has a continuous processing mode, but its stated low-latency figure comes with at-least-once rather than exactly-once guarantees.
How do programming models and ecosystem fit compare?
Spark: declarative queries across batch and streaming
Spark’s DataFrame and Dataset model suits teams that already use Spark SQL or want similar programming patterns for batch and streaming work. It is a strong fit for SQL-heavy transformations, integration with an existing Spark lakehouse or batch estate, and analytics that combine event-time windows or stream-to-batch joins.
Kafka Streams: processing inside a Kafka application
Kafka Streams suits teams building Kafka-native applications, especially when they prefer to embed the processor in a Java service. Its DSL offers a higher-level way to define a topology; the Processor API provides a lower-level option. Kafka’s partitions also define the scaling and ordering model, which makes the fit especially direct when Kafka is the system of record.
These ecosystem differences matter operationally: Spark places stream processing in the Spark engine, while Kafka Streams runs as part of an application that uses Kafka. Prefer the model your team can operate and integrate naturally rather than choosing on the assumption that one tool is universally simpler.
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How do state, event time, and recovery work?
Both systems support stateful processing, including aggregations and joins, but expose state and time differently. Spark provides event-time windows and watermarking for handling late data within its query model. Kafka Streams uses local state stores and event-time windows.
Spark’s documented fault-tolerance mechanisms include checkpointing and write-ahead logs. Kafka Streams keeps local state stores and can coordinate state updates with Kafka input offsets and output writes when its exactly-once processing guarantee is enabled. These are different recovery models, so evaluate how each fits your topology, data sources, sinks, and operational setup.
Can Spark Structured Streaming and Kafka Streams both provide exactly-once processing?
Yes, but “exactly once” depends on the processing mode and the full path through a pipeline; it should not be read as a blanket guarantee for every source, sink, and configuration.
- Spark Structured Streaming: Spark’s exactly-once approach relies on replayable source offsets, checkpointing or write-ahead logs, and idempotent sinks. Its continuous processing mode is documented with at-least-once guarantees, so that mode does not provide the same guarantee as the usual exactly-once description.
- Kafka Streams: With
processing.guarantee=exactly_once, Kafka Streams can atomically coordinate Kafka offset commits, state-store updates, and output writes. The described scope is Kafka input, state, and output operations; do not assume it makes unrelated external side effects exactly once.
For either system, identify every source and destination and check whether the exact combination supports the delivery behavior your application requires. A processing engine’s guarantee does not automatically make a non-idempotent external side effect safe from duplication.
Which one should you choose?
| Choose | When it fits | Why |
|---|---|---|
| Spark Structured Streaming | SQL-heavy transformations; complex event-time analytics; existing Spark batch, SQL, or lakehouse workflows; teams seeking one DataFrame model across batch and streaming. | Processing is expressed as incremental DataFrame/Dataset queries on the Spark SQL engine, with documented support for windows, joins, and checkpoint-based fault tolerance. |
| Kafka Streams | Kafka-native event processing; low-latency record-at-a-time services; Java applications that should embed processing; workloads aligned with Kafka partitions and transactions. | It is an embeddable Kafka client library with topology APIs, local state stores, and an exactly-once option for coordinated Kafka operations. |
Use Spark when the query and analytics model is the deciding factor
If your main challenge is expressing transformations and analytics across streaming and batch data, Spark’s shared SQL/DataFrame approach is likely the better fit. Event-time windows, watermarking, and stream-to-batch joins are relevant when the logic depends on timestamps or combines live data with batch data.
Use Kafka Streams when the application and Kafka model is the deciding factor
If your main challenge is processing Kafka events inside an application, Kafka Streams is likely the better fit. It avoids adding a separate processing engine for that application and aligns state, partitioning, and transactional processing with Kafka.
Quick Recap
What to check before committing
- Confirm whether your sources and sinks can support the delivery guarantee you need; do not judge guarantees by the processor name alone.
- Determine whether you need Spark SQL/DataFrame workflows or an application-defined Kafka topology.
- Map state ownership and recovery: Spark checkpoints and Kafka Streams local state stores have different operational implications.
- For event-time workloads, define how late records should be handled and verify that the chosen system’s time and window model matches the requirement.
- Assess language and runtime fit: Kafka Streams is an embeddable Java library, while Spark is the processing engine choice when your organization already operates a Spark environment.
- Benchmark your own workload if latency is decisive. The documented Spark latency figures are not an independent head-to-head test.
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