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toStream()

As explained in Kafka Streams in Action, Second Edition

toStream is a Kafka Streams operation that converts a KTable result into a KStream.

toStream() converts a KTable’s update stream into a KStream of events. This lets aggregation results continue through stream operators or reach an output topic. section 7.2.1 section 8.2

Use the conversion when a table result needs stream-oriented processing:

  • Print or inspect table contents with print() or peek(); KTable does not provide these methods directly. section 8.1.2 section 9.1.6
  • Forward aggregation updates to downstream operators and sink topics. section 7.2.1
  • Continue windowed results with operations such as map(), then expose or reshape the window key and value. section 9.1.6
  • Expect stateful results to be forwarded when Kafka Streams flushes its cache, rather than necessarily after every intermediate update. section 7.2.1

Inspecting a windowed aggregation

iotHeatSensorStream.groupByKey()\n .windowedBy(TimeWindows.ofSizeWithNoGrace(Duration.ofMinutes(1)))\n .aggregate(() -> new IotSensorAggregation(tempThreshold), aggregator,\n Materialized.with(stringSerde, aggregationSerde))\n .toStream()\n .peek((windowedKey, aggregation) -> {\n Window window = windowedKey.window();\n LOG.info("Window started {} ended {} with value {}",\n window.startTime(), window.endTime(), aggregation);\n });\n\nThe aggregation produces a KTable; toStream() makes its windowed updates available to peek().

Listing 8.1 KTable and KStream printing to the console
KTable<String, StockTickerData> stockTickerTable =
builder.table(STOCK_TICKER_TABLE_TOPIC);                 #1
KStream<String, StockTickerData> stockTickerStream =
builder.stream(STOCK_TICKER_STREAM_TOPIC);          #2

stockTickerTable.toStream()
  .print(Printed.<String, StockTickerData>toSysOut()
  .withLabel("Stocks-KTable"));                           #3

stockTickerStream
  .print(Printed.<String, StockTickerData>toSysOut()
  .withLabel("Stocks-KStream"));                        #4

Compare identical records through KTable and KStream printing: the KStream shows each record, while the KTable reflects updates and prints the latest state. section 8.1.2

How Kafka Streams in Action, Second Edition covers it

How deep it goes
Explains the update-to-event distinction, operator placement, windowed keys, and cache-flush timing without detailing the conversion internals.
Examples
Uses stock-ticker printing and windowed IoT heat-sensor aggregation with peek(), logging, mapping, and output.
What it assumes
Assumes familiarity with Kafka Streams KStream, KTable, aggregation, and windowing while clarifying their update and event interpretations.
What you can do after
Apply it via Kafka Streams to inspect, transform, or publish KTable and aggregation results as KStream records.
How it connects
Connects KTable, KStream, aggregation, state stores, windowing, caching, changelog topics, and stream sinks.
Cover of Kafka Streams in Action, Second Edition
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Kafka Streams in Action, Second Edition. The section links point to the parts of the book it draws on.