Package com.hazelcast.jet.pipeline

Interface StageWithKeyAndWindow<T,K>

Type Parameters:
T - type of the input item
K - type of the key
public interface StageWithKeyAndWindow<T,K>
Represents an intermediate step in the construction of a pipeline stage that performs a windowed group-and-aggregate operation. It captures the grouping key and the window definition, and offers the methods to finalize the construction by specifying the aggregate operation and any additional pipeline stages contributing their data to a co-group-and-aggregate stage.
Since:
Jet 3.0

  • Method Details

    • keyFn

      @Nonnull FunctionEx<? super T,? extends K> keyFn()
      Returns the function that extracts the grouping key from stream items. This function will be used in the aggregating stage you are about to construct using this object.

    • windowDefinition

      @Nonnull WindowDefinition windowDefinition()
      Returns the definition of the window for the windowed aggregation operation that you are about to construct using this object.

    • distinct

      @Nonnull default StreamStage<KeyedWindowResult<K,T>> distinct()
      Attaches a stage that passes through just the items that are distinct within their window according to the grouping key (no two items emitted for a window map to the same key). There is no guarantee which one of the items with the same key will pass through.
      Returns:
      the newly attached stage

    • aggregate

      @Nonnull <R> StreamStage<KeyedWindowResult<K,R>> aggregate(@Nonnull AggregateOperation1<? super T,?,? extends R> aggrOp)
      Attaches a stage that performs the given group-and-aggregate operation. It emits one key-value pair (in a KeyedWindowResult) for each distinct key it observes in its input belonging to a given window. The value is the result of the aggregate operation across all the items with the given grouping key.

      Sample usage: 

      
 StreamStage<KeyedWindowResult<Long, Long>> aggregated = pageVisits
     .window(SlidingWindowDefinition.sliding(MINUTES.toMillis(1), SECONDS.toMillis(1)))
     .groupingKey(PageVisit::getUserId)
     .aggregate(AggregateOperations.counting());
      Type Parameters:
      R - type of the aggregation result
      Parameters:
      aggrOp - the aggregate operation to perform
      See Also:

    • aggregate2

      @Nonnull <T1, R> StreamStage<KeyedWindowResult<K,R>> aggregate2(@Nonnull StreamStageWithKey<T1,? extends K> stage1, @Nonnull AggregateOperation2<? super T,? super T1,?,? extends R> aggrOp)
      Attaches a stage that performs the given cogroup-and-aggregate operation over the items from both this stage and stage1 you supply. It emits one key-value pair (in a KeyedWindowResult) for each distinct key it observes in the input belonging to a given window. The value is the result of the aggregate operation across all the items with the given grouping key.

      Sample usage: 

      
 StreamStage<KeyedWindowResult<Long, Tuple2<Long, Long>>> aggregated = pageVisits
     .window(SlidingWindowDefinition.sliding(MINUTES.toMillis(1), SECONDS.toMillis(1)))
     .groupingKey(PageVisit::getUserId)
     .aggregate2(
         addToCarts.groupingKey(AddToCart::getUserId),
         AggregateOperations.aggregateOperation2(
                 AggregateOperations.counting(),
                 AggregateOperations.counting())
     );
      This variant requires you to provide a two-input aggregate operation (refer to its Javadoc for a simple example). If you can express your logic in terms of two single-input aggregate operations, one for each input stream, then you should use stage0.aggregate2(aggrOp0, stage1, aggrOp1) because it offers a simpler API and you can use the already defined single-input operations. Use this variant only when you have the need to implement an aggregate operation that combines the input streams into the same accumulator.
      Type Parameters:
      T1 - type of items in stage1
      R - type of the aggregation result
      Parameters:
      stage1 - the other stage
      aggrOp - the aggregate operation to perform
      See Also:

    • aggregate2

      @Nonnull default <T1, R0, R1> StreamStage<KeyedWindowResult<K,Tuple2<R0,R1>>> aggregate2(@Nonnull AggregateOperation1<? super T,?,? extends R0> aggrOp0, @Nonnull StreamStageWithKey<T1,? extends K> stage1, @Nonnull AggregateOperation1<? super T1,?,? extends R1> aggrOp1)
      Attaches a stage that performs the given cogroup-and-aggregate operation over the items from both this stage and stage1 you supply. For each distinct grouping key it observes in the input belonging to a given window, it performs the supplied aggregate operation across all the items sharing that key. It performs the aggregation separately for each input stage: aggrOp0 on this stage and aggrOp1 on stage1. Once it has received all the items belonging to a window, it emits for each distinct key a KeyedWindowResult(key, Tuple2(result0, result1)).

      Sample usage: 

      
 StreamStage<KeyedWindowResult<Long, Tuple2<Long, Long>>> aggregated = pageVisits
     .window(SlidingWindowDefinition.sliding(MINUTES.toMillis(1), SECONDS.toMillis(1)))
     .groupingKey(PageVisit::getUserId)
     .aggregate2(
             AggregateOperations.counting(),
             addToCarts.groupingKey(AddToCart::getUserId),
             AggregateOperations.counting()
     );
      Type Parameters:
      T1 - type of the items in the other stage
      R0 - type of the aggregated result for this stage
      R1 - type of the aggregated result for the other stage
      Parameters:
      aggrOp0 - aggregate operation to perform on this stage
      stage1 - the other stage
      aggrOp1 - aggregate operation to perform on the other stage
      See Also:

    • aggregate3

      @Nonnull <T1, T2, R> StreamStage<KeyedWindowResult<K,R>> aggregate3(@Nonnull StreamStageWithKey<T1,? extends K> stage1, @Nonnull StreamStageWithKey<T2,? extends K> stage2, @Nonnull AggregateOperation3<? super T,? super T1,? super T2,?,? extends R> aggrOp)
      Attaches a stage that performs the given cogroup-and-aggregate operation over the items from this stage as well as stage1 and stage2 you supply. For each distinct grouping key it observes in the input belonging to a given window, it performs the supplied aggregate operation across all the items sharing that key. Once it has received all the items belonging to a window, it emits for each distinct key a KeyedWindowResult(key, Tuple3(result0, result1, result2)).

      Sample usage: StreamStage<KeyedWindowResult<Long, Tuple3<Long, Long, Long>>> aggregated = pageVisits .window(SlidingWindowDefinition.sliding(MINUTES.toMillis(1), SECONDS.toMillis(1))) .groupingKey(PageVisit::getUserId) .aggregate3( addToCarts.groupingKey(AddToCart::getUserId), payments.groupingKey(Payment::getUserId), AggregateOperations.aggregateOperation3( AggregateOperations.counting(), AggregateOperations.counting(), AggregateOperations.counting()) );
      This variant requires you to provide a three-input aggregate operation (refer to its Javadoc for a simple example). If you can express your logic in terms of three single-input aggregate operations, one for each input stream, then you should use stage0.aggregate2(aggrOp0, stage1, aggrOp1, stage2, aggrOp2) because it offers a simpler API and you can use the already defined single-input operations. Use this variant only when you have the need to implement an aggregate operation that combines the input streams into the same accumulator.
      Type Parameters:
      T1 - type of items in stage1
      T2 - type of items in stage2
      R - type of the aggregation result
      Parameters:
      stage1 - the first additional stage
      stage2 - the second additional stage
      aggrOp - the aggregate operation to perform
      See Also:

    • aggregate3

      @Nonnull default <T1, T2, R0, R1, R2> StreamStage<KeyedWindowResult<K,Tuple3<R0,R1,R2>>> aggregate3(@Nonnull AggregateOperation1<? super T,?,? extends R0> aggrOp0, @Nonnull StreamStageWithKey<T1,? extends K> stage1, @Nonnull AggregateOperation1<? super T1,?,? extends R1> aggrOp1, @Nonnull StreamStageWithKey<T2,? extends K> stage2, @Nonnull AggregateOperation1<? super T2,?,? extends R2> aggrOp2)
      Attaches a stage that performs the given cogroup-and-aggregate operation over the items from both this stage and stage1 you supply. For each distinct grouping key it observes in the input belonging to a given window, it performs the supplied aggregate operation across all the items sharing that key. It performs the aggregation separately for each input stage: aggrOp0 on this stage, aggrOp1 on stage1 and aggrOp2 on stage2. Once it has received all the items, it calls the supplied mapToOutputFn with each key and the associated aggregation result to create the items to emit.

      Sample usage: 

      
 StreamStage<KeyedWindowResult<Long, Tuple3<Long, Long, Long>>> aggregated = pageVisits
     .window(SlidingWindowDefinition.sliding(MINUTES.toMillis(1), SECONDS.toMillis(1)))
     .groupingKey(PageVisit::getUserId)
     .aggregate3(
         AggregateOperations.counting(),
         addToCarts.groupingKey(AddToCart::getUserId),
         AggregateOperations.counting(),
         payments.groupingKey(Payment::getUserId),
         AggregateOperations.counting()
     );
      Type Parameters:
      T1 - type of the items in stage1
      T2 - type of the items in stage2
      R0 - type of the aggregated result for this stage
      R1 - type of the aggregated result for stage1
      R2 - type of the aggregated result for stage2
      Parameters:
      aggrOp0 - aggregate operation to perform on this stage
      stage1 - the first additional stage
      aggrOp1 - aggregate operation to perform on stage1
      stage2 - the second additional stage
      aggrOp2 - aggregate operation to perform on stage2
      See Also:

    • aggregateBuilder

      @Nonnull default <R0> WindowGroupAggregateBuilder<K,R0> aggregateBuilder(@Nonnull AggregateOperation1<? super T,?,? extends R0> aggrOp0)
      Offers a step-by-step API to build a pipeline stage that co-aggregates the data from several input stages. The current stage will be already registered with the builder you get. You supply an aggregate operation for each input stage and in the output you get the individual aggregation results as KeyedWindowResult(key, itemsByTag). Use the tag you get from builder.add(stageN, aggrOpN) to retrieve the aggregated result for that stage. Use builder.tag0() as the tag of this stage.

      This builder is mainly intended to build a co-aggregation of four or more contributing stages. For up to three stages, prefer the direct stage.aggregateN(...) calls because they offer more static type safety.

      This example reads from three stream sources that produce Map.Entry<String, Long>. It groups by entry key, defines a 1-second sliding window and then counts the items in stage-0, sums those in stage-1 and takes the average of those in stage-2: 

      
 Pipeline p = Pipeline.create();
 StreamStageWithKey<Entry<String, Long>, String> stage0 =
         p.readFrom(source0).withNativeTimestamps(0L)
          .groupingKey(Entry::getKey);
 StreamStageWithKey<Entry<String, Long>, String> stage1 =
         p.readFrom(source1).withNativeTimestamps(0L)
          .groupingKey(Entry::getKey);
 StreamStageWithKey<Entry<String, Long>, String> stage2 =
         p.readFrom(source2).withNativeTimestamps(0L)
          .groupingKey(Entry::getKey);
 WindowGroupAggregateBuilder<String, Long> b = stage0
         .window(sliding(1000, 10))
         .aggregateBuilder(AggregateOperations.counting());
 Tag<Long> tag0 = b.tag0();
 Tag<Long> tag1 = b.add(stage1,
         AggregateOperations.summingLong(Entry::getValue));
 Tag<Double> tag2 = b.add(stage2,
         AggregateOperations.averagingLong(Entry::getValue));
 StreamStage<KeyedWindowResult<String, ItemsByTag>> aggregated = b.build();
 aggregated.map(e -> String.format(
         "Key %s, count of stage0: %d, sum of stage1: %d, average of stage2: %f",
         e.getKey(),
         e.getValue().get(tag0), e.getValue().get(tag1), e.getValue().get(tag2))
 );

    • aggregateBuilder

      @Nonnull default WindowGroupAggregateBuilder1<T,K> aggregateBuilder()
      Offers a step-by-step API to build a pipeline stage that co-aggregates the data from several input stages. This stage will be already registered with the builder you get.

      This builder requires you to provide a multi-input aggregate operation. If you can express your logic in terms of single-input aggregate operations, one for each input stream, then you should use stage0.aggregateBuilder(aggrOp0) because it offers a simpler API. Use this builder only when you have the need to implement an aggregate operation that combines all the input streams into the same accumulator.

      This builder is mainly intended to build a co-aggregation of four or more contributing stages. For up to three stages, prefer the direct stage.aggregateN(...) calls because they offer more static type safety.

      To add the other stages, call builder.add(stage). Collect all the tags returned from add() and use them when building the aggregate operation. Retrieve the tag of the first stage (from which you obtained this builder) by calling builder.tag0().

      This example takes three streams of Map.Entry<String, Long>, specifies a 1-second sliding window and, for each string key, counts the distinct Long values across all input streams: 

      
 Pipeline p = Pipeline.create();

 StreamStageWithGrouping<Entry<String, Long>, String> stage0 =
         p.readFrom(source0).groupingKey(Entry::getKey);
 StreamStageWithGrouping<Entry<String, Long>, String> stage1 =
         p.readFrom(source1).groupingKey(Entry::getKey);
 StreamStageWithGrouping<Entry<String, Long>, String> stage2 =
         p.readFrom(source2).groupingKey(Entry::getKey);

 WindowGroupAggregateBuilder1<Entry<String, Long>, String> b = stage0
         .window(sliding(1000, 10))
         .aggregateBuilder();
 Tag<Entry<String, Long>> tag0 = b.tag0();
 Tag<Entry<String, Long>> tag1 = b.add(stage1);
 Tag<Entry<String, Long>> tag2 = b.add(stage2);
 StreamStage<KeyedWindowResult<String, Integer>> aggregated = b.build(AggregateOperation
         .withCreate(HashSet<Long>::new)
         .andAccumulate(tag0, (acc, item) -> acc.add(item.getValue()))
         .andAccumulate(tag1, (acc, item) -> acc.add(item.getValue()))
         .andAccumulate(tag2, (acc, item) -> acc.add(item.getValue()))
         .andCombine(HashSet::addAll)
         .andFinish(HashSet::size));