Stateful Stream Processing
Keyed state, state backends, checkpoint size, and the restore time that follows.
4 to work through
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advanced
A stateful stream processor's state grows continuously and recovery takes hours. What design changes address this?
2 min answer -
advanced
A stream processing job has run fine for four months. It now fails with out-of-memory errors every few hours. No code has changed. Diagnose.
2 min answer -
advanced
A stream processing job has run for four months and now fails with out-of-memory errors every few hours. No code has changed. Diagnose.
2 min answer -
advanced
A stream processor maintains per-vehicle state across events. What does that require operationally, and where does it break?
2 min answer
4 terms in this topic
Keyed State Size
The total state a job holds per key across all keys, which governs memory, checkpoint duration and recovery time.
conceptRebalance Cost
The time a stateful consumer group is unavailable while partitions are reassigned and state restored - proportional to state size, incurred on every …
practiceState Time-to-Live
An expiry policy on stream-processor state so that state size tracks the active key set rather than the accumulated history of everything the job has…
conceptStateful Stream Processing
Stream computations that must remember information across events — aggregations, joins, deduplication, sessionisation — and the durability machinery …
Neighbouring topics
Streaming & Real-Time Data
General material on continuous processing of unbounded data.
Streaming vs Batch
The freshness requirement that actually justifies streaming, and the cost of assuming one.
Exactly-Once Semantics
What the phrase really means, where it holds, and the idempotent sink underneath it.
Stream Processing Frameworks
Flink, Kafka Streams, Spark Structured Streaming — state, checkpointing and recovery.
Windowing
Tumbling, sliding and session windows, and the aggregation each one answers.
Watermarks & Late Data
Deciding a window is complete when events can still arrive, and what to do when they do.
Stream-Table Duality
A changelog and a table as two views of the same thing, and materialising between them.
Kappa vs Lambda
One pipeline replayed versus two pipelines reconciled, and the maintenance each carries.
Streaming Schema Evolution
Changing an event's shape while a retained log still holds every older version of it.
Streaming Joins
Joining two unbounded streams, the buffering it needs, and the enrichment alternative.
Backfill & Reprocessing
Replaying history through changed logic without double-counting the live output.
CDC to Stream
Turning database changes into an event log, and how that differs from a domain event.
Real-Time Serving Layer
Where a low-latency read of a streaming aggregate actually lands.
Feature Freshness
How stale a feature can be before the model degrades, and the pipeline that follows.
Streaming SLOs
End-to-end latency, consumer lag and completeness as commitments rather than dashboards.
Partition Keys & Ordering
Ordering guaranteed only within a partition, and choosing the key that makes that enough.
Dead Letter Handling
The poison message that blocks a partition, and the queue nobody reads.
Streaming Cost
Always-on compute, retention and cross-zone traffic as the three bills that surprise.
Real-Time Analytical Stores
Druid, Pinot and ClickHouse — ingest-and-query engines for sub-second aggregation.