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Batch & Streaming · Streaming Processing

Flink vs Spark Streaming

Mediumstream-21
FlinkSparkStructured Streamingcomparison

Question

How do Apache Flink and Spark Streaming / Structured Streaming compare?

Solution

Apache Flink is a true stream processor: continuous operators, rich event-time tooling, low-latency stateful apps, and strong exactly-once sink patterns.

Spark Structured Streaming (modern Spark) treats streams as unbounded tables with micro-batch execution by default. Excellent if your team already lives in Spark SQL/DataFrames. Older DStreams API is legacy.

Flink:   source -> continuous ops -> sink   (ms–low sec latency natural)
Spark SS: source -> micro-batches of DF SQL -> sink  (sec latency typical)

Choose Flink when

  • Hard event-time, complex CEP, very low latency
  • Large keyed state with fine control

Choose Spark SS when

  • Same engine for batch + stream (one API)
  • Warehouse-style SQL aggregations on streams
  • Existing Spark platform skills

Interview tip: Contrast "continuous vs micro-batch," then say both can do windows/watermarks; ecosystem and latency needs decide.

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