com.hazelcast.jet.pipeline

## Interface BatchStageWithKey<T,K>

- Type Parameters:
    `T` - type of the input item
    `K` - type of the key
  All Superinterfaces:
    GeneralStageWithKey<T,K>

      public interface BatchStageWithKey<T,K>
      extends GeneralStageWithKey<T,K>

  An intermediate step while constructing a group-and-aggregate batch pipeline stage. It captures the grouping key 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:
    3.0

- - ### Method Summary

    All Methods Instance Methods Abstract Methods Default Methods
    Modifier and Type | Method and Description
    --- | ---
    `<R> BatchStage<Map.Entry<K,R>>` | `aggregate(AggregateOperation1<? super T,?,? extends R> aggrOp)` Attaches a stage that performs the given group-and-aggregate operation.
    `default <T1,R0,R1> BatchStage<Map.Entry<K,Tuple2<R0,R1>>>` | `aggregate2(AggregateOperation1<? super T,?,? extends R0> aggrOp0, BatchStageWithKey<? extends T1,? extends K> stage1, AggregateOperation1<? super T1,?,? extends R1> aggrOp1)` Attaches a stage that performs the given cogroup-and-aggregate transformation of the items from both this stage and `stage1` you supply.
    `<T1,R> BatchStage<Map.Entry<K,R>>` | `aggregate2(BatchStageWithKey<T1,? extends K> stage1, AggregateOperation2<? super T,? super T1,?,R> aggrOp)` Attaches a stage that performs the given cogroup-and-aggregate operation over the items from both this stage and `stage1` you supply.
    `default <T1,T2,R0,R1,R2> BatchStage<Map.Entry<K,Tuple3<R0,R1,R2>>>` | `aggregate3(AggregateOperation1<? super T,?,? extends R0> aggrOp0, BatchStageWithKey<T1,? extends K> stage1, AggregateOperation1<? super T1,?,? extends R1> aggrOp1, BatchStageWithKey<T2,? extends K> stage2, AggregateOperation1<? super T2,?,? extends R2> aggrOp2)` Attaches a stage that performs the given cogroup-and-aggregate transformation of the items from this stage as well as `stage1` and `stage2` you supply.
    `<T1,T2,R> BatchStage<Map.Entry<K,R>>` | `aggregate3(BatchStageWithKey<T1,? extends K> stage1, BatchStageWithKey<T2,? extends K> stage2, 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.
    `default GroupAggregateBuilder1<T,K>` | `aggregateBuilder()` Offers a step-by-step API to build a pipeline stage that co-aggregates the data from several input stages.
    `default <R0> GroupAggregateBuilder<K,R0>` | `aggregateBuilder(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.
    `<R> BatchStage<R>` | `customTransform(String stageName, ProcessorMetaSupplier procSupplier)` Attaches a stage with a custom transform based on the provided supplier of Core API `Processor`s.
    `default <R> BatchStage<R>` | `customTransform(String stageName, ProcessorSupplier procSupplier)` Attaches a stage with a custom transform based on the provided supplier of Core API `Processor`s.
    `default <R> BatchStage<R>` | `customTransform(String stageName, SupplierEx<Processor> procSupplier)` Attaches a stage with a custom transform based on the provided supplier of Core API `Processor`s.
    `BatchStage<T>` | `distinct()` Attaches a stage that emits just the items that are distinct according to the grouping key (no two items which map to the same key will be on the output).
    `<S> BatchStage<T>` | `filterStateful(SupplierEx<? extends S> createFn, BiPredicateEx<? super S,? super T> filterFn)` Attaches a stage that performs a stateful filtering operation.
    `<C> BatchStage<T>` | `filterUsingContext(ContextFactory<C> contextFactory, TriPredicate<? super C,? super K,? super T> filterFn)` Attaches a filtering stage which applies the provided predicate function to each input item to decide whether to pass the item to the output or to discard it.
    `<C> BatchStage<T>` | `filterUsingContextAsync(ContextFactory<C> contextFactory, TriFunction<? super C,? super K,? super T,CompletableFuture<Boolean>> filterAsyncFn)` Asynchronous version of `GeneralStageWithKey.filterUsingContext(com.hazelcast.jet.pipeline.ContextFactory<C>, com.hazelcast.jet.function.TriPredicate<? super C, ? super K, ? super T>)`: the `filterAsyncFn` returns a `CompletableFuture<Boolean>` instead of just a `boolean`.
    `<S,R> BatchStage<R>` | `flatMapStateful(SupplierEx<? extends S> createFn, TriFunction<? super S,? super K,? super T,? extends Traverser<R>> flatMapFn)` Attaches a stage that performs a stateful flat-mapping operation.
    `<C,R> BatchStage<R>` | `flatMapUsingContext(ContextFactory<C> contextFactory, TriFunction<? super C,? super K,? super T,? extends Traverser<? extends R>> flatMapFn)` Attaches a flat-mapping stage which applies the supplied function to each input item independently and emits all the items from the `Traverser` it returns as the output items.
    `<C,R> BatchStage<R>` | `flatMapUsingContextAsync(ContextFactory<C> contextFactory, TriFunction<? super C,? super K,? super T,CompletableFuture<Traverser<R>>> flatMapAsyncFn)` Asynchronous version of `GeneralStageWithKey.flatMapUsingContext(com.hazelcast.jet.pipeline.ContextFactory<C>, com.hazelcast.jet.function.TriFunction<? super C, ? super K, ? super T, ? extends com.hazelcast.jet.Traverser<? extends R>>)`: the `flatMapAsyncFn` returns a `CompletableFuture<Traverser<R>>` instead of just `Traverser<R>`.
    `<S,R> BatchStage<R>` | `mapStateful(SupplierEx<? extends S> createFn, TriFunction<? super S,? super K,? super T,? extends R> mapFn)` Attaches a stage that performs a stateful mapping operation.
    `<C,R> BatchStage<R>` | `mapUsingContext(ContextFactory<C> contextFactory, TriFunction<? super C,? super K,? super T,? extends R> mapFn)` Attaches a mapping stage which applies the given function to each input item independently and emits the function's result as the output item.
    `<C,R> BatchStage<R>` | `mapUsingContextAsync(ContextFactory<C> contextFactory, TriFunction<? super C,? super K,? super T,CompletableFuture<R>> mapAsyncFn)` Asynchronous version of `GeneralStageWithKey.mapUsingContext(com.hazelcast.jet.pipeline.ContextFactory<C>, com.hazelcast.jet.function.TriFunction<? super C, ? super K, ? super T, ? extends R>)`: the `mapAsyncFn` returns a `CompletableFuture<R>` instead of just `R`.
    `default <V,R> BatchStage<R>` | `mapUsingIMap(IMap<K,V> iMap, BiFunctionEx<? super T,? super V,? extends R> mapFn)` Attaches a mapping stage where for each item a lookup in the supplied `IMap` using the grouping key is performed and the result of the lookup is merged with the item and emitted.
    `default <V,R> BatchStage<R>` | `mapUsingIMap(String mapName, BiFunctionEx<? super T,? super V,? extends R> mapFn)` Attaches a mapping stage where for each item a lookup in the `IMap` with the supplied name using the grouping key is performed and the result of the lookup is merged with the item and emitted.
    `default <A,R> BatchStage<Map.Entry<K,R>>` | `rollingAggregate(AggregateOperation1<? super T,A,? extends R> aggrOp)` Attaches a rolling aggregation stage.

    - ### Methods inherited from interface com.hazelcast.jet.pipeline.GeneralStageWithKey

      `keyFn`

- - ### Method Detail

    - #### distinct

          @Nonnull
          BatchStage<T> distinct()

      Attaches a stage that emits just the items that are distinct according to the grouping key (no two items which map to the same key will be on the output). There is no guarantee which one of the items with the same key it will emit.
      Returns:
        the newly attached stage

    - #### mapStateful

          @Nonnull
          <S,R> BatchStage<R> mapStateful(@Nonnull
                                                   SupplierEx<? extends S> createFn,
                                                   @Nonnull
                                                   TriFunction<? super S,? super K,? super T,? extends R> mapFn)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a stage that performs a stateful mapping operation. `createFn` returns the object that holds the state. Jet passes this object along with each input item and its key to `mapFn`, which can update the object's state. For each grouping key there's a separate state object. The state object will be included in the state snapshot, so it survives job restarts. For this reason it must be serializable.

      This sample takes a stream of pairs `(serverId, latency)` representing the latencies of serving individual requests and outputs the cumulative latency of all handled requests so far, for each server separately:

           GeneralStage<Entry<String, Long>> latencies;
           GeneralStage<Entry<String, Long>> cumulativeLatencies = latencies
                   .groupingKey(Entry::getKey)
                   .mapStateful(
                           LongAccumulator::new,
                           (sum, key, entry) -> {
                               sum.add(entry.getValue());
                               return entry(key, sum.get());
                           }
                   );

      This code has the same result as `latencies.groupingKey(Entry::getKey).rollingAggregate(summing())`.
      Specified by:
        `mapStateful` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `S` - type of the state object
        `R` - type of the result
      Parameters:
        `createFn` - function that returns the state object
        `mapFn` - function that receives the state object and the input item and outputs the result item. It may modify the state object.

    - #### filterStateful

          @Nonnull
          <S> BatchStage<T> filterStateful(@Nonnull
                                                    SupplierEx<? extends S> createFn,
                                                    @Nonnull
                                                    BiPredicateEx<? super S,? super T> filterFn)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a stage that performs a stateful filtering operation. `createFn` returns the object that holds the state. Jet passes this object along with each input item and its key to `filterFn`, which can update the object's state. For each grouping key there's a separate state object. The state object will be included in the state snapshot, so it survives job restarts. For this reason it must be serializable.

      This sample groups a stream of strings by length and decimates each group (throws out every 10th string of each length):

           GeneralStage<String> decimated = input
                   .groupingKey(String::length)
                   .filterStateful(
                           LongAccumulator::new,
                           (counter, item) -> {
                               counter.add(1);
                               return counter.get() % 10 != 0;
                           }
                   );

      Specified by:
        `filterStateful` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `S` - type of the state object
      Parameters:
        `createFn` - function that returns the state object
        `filterFn` - predicate that receives the state object and the input item and outputs a boolean value. It may modify the state object.

    - #### flatMapStateful

          @Nonnull
          <S,R> BatchStage<R> flatMapStateful(@Nonnull
                                                       SupplierEx<? extends S> createFn,
                                                       @Nonnull
                                                       TriFunction<? super S,? super K,? super T,? extends Traverser<R>> flatMapFn)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a stage that performs a stateful flat-mapping operation. `createFn` returns the object that holds the state. Jet passes this object along with each input item and its key to `flatMapFn`, which can update the object's state. For each grouping key there's a separate state object. The state object will be included in the state snapshot, so it survives job restarts. For this reason it must be serializable.

      This sample groups a stream of strings by length and inserts punctuation (a special string) after every 10th string in each group:

           GeneralStage<String> punctuated = input
                   .groupingKey(String::length)
                   .flatMapStateful(
                           LongAccumulator::new,
                           (counter, key, item) -> {
                               counter.add(1);
                               return counter.get() % 10 == 0
                                       ? Traversers.traverseItems("punctuation" + key, item)
                                       : Traversers.singleton(item);
                           }
                   );

      Specified by:
        `flatMapStateful` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `S` - type of the state object
        `R` - type of the result
      Parameters:
        `createFn` - function that returns the state object
        `flatMapFn` - function that receives the state object and the input item and outputs the result items. It may modify the state object. It must not return null traverser, but can return an empty traverser.

    - #### rollingAggregate

          @Nonnull
          default <A,R> BatchStage<Map.Entry<K,R>> rollingAggregate(@Nonnull
                                                                             AggregateOperation1<? super T,A,? extends R> aggrOp)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a rolling aggregation stage. This is a special case of stateful mapping that uses an `AggregateOperation`. It passes each input item to the accumulator and outputs the current result of aggregation (as returned by the `export` primitive).

      Sample usage:

           StreamStage<Entry<Color, Long>> aggregated = items
                   .groupingKey(Item::getColor)
                   .rollingAggregate(AggregateOperations.counting());

      For example, if your input is `{2, 7, 8, -5}`, the output will be `{2, 9, 17, 12}`.

      This stage is fault-tolerant and saves its state to the snapshot.

      Specified by:
        `rollingAggregate` in interface `GeneralStageWithKey<T,K>`
        `R` - type of the aggregate operation result
      Parameters:
        `aggrOp` - the aggregate operation to perform
      Returns:
        the newly attached stage

    - #### mapUsingIMap

          @Nonnull
          default <V,R> BatchStage<R> mapUsingIMap(@Nonnull
                                                            String mapName,
                                                            @Nonnull
                                                            BiFunctionEx<? super T,? super V,? extends R> mapFn)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a mapping stage where for each item a lookup in the `IMap` with the supplied name using the grouping key is performed and the result of the lookup is merged with the item and emitted.

      If the result of the mapping is `null`, it emits nothing. Therefore this stage can be used to implement filtering semantics as well.

      The mapping logic is equivalent to:

           V value = map.get(groupingKey);
           return mapFn.apply(item, value);

      Sample usage:

           items.groupingKey(Item::getDetailId)
                .mapUsingIMap(
                        "enriching-map",
                        (Item item, ItemDetail detail) -> item.setDetail(detail)
                );

      This stage is similar to `stageWithoutKey.mapUsingIMap()`, but here Jet knows the key and uses it to partition and distribute the input in order to achieve data locality. The value it fetches from the `IMap` is stored on the cluster member where the processing takes place. However, if the map doesn't use the default partitioning strategy, the data locality will be broken.
      Specified by:
        `mapUsingIMap` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `V` - type of the value in the `IMap`
        `R` - type of the output item
      Parameters:
        `mapName` - name of the `IMap`
        `mapFn` - the mapping function
      Returns:
        the newly attached stage

    - #### mapUsingIMap

          @Nonnull
          default <V,R> BatchStage<R> mapUsingIMap(@Nonnull
                                                            IMap<K,V> iMap,
                                                            @Nonnull
                                                            BiFunctionEx<? super T,? super V,? extends R> mapFn)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a mapping stage where for each item a lookup in the supplied `IMap` using the grouping key is performed and the result of the lookup is merged with the item and emitted.

      If the result of the mapping is `null`, it emits nothing. Therefore this stage can be used to implement filtering semantics as well.

      The mapping logic is equivalent to:

           V value = map.get(groupingKey);
           return mapFn.apply(item, value);

      Sample usage:

           items.groupingKey(Item::getDetailId)
                .mapUsingIMap(enrichingMap, (item, detail) -> item.setDetail(detail));

      This stage is similar to `stageWithoutKey.mapUsingIMap()`, but here Jet knows the key and uses it to partition and distribute the input in order to achieve data locality. The value it fetches from the `IMap` is stored on the cluster member where the processing takes place. However, if the map doesn't use the default partitioning strategy, data locality will be broken.
      Specified by:
        `mapUsingIMap` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `V` - type of the value in the `IMap`
        `R` - type of the output item
      Parameters:
        `iMap` - the `IMap` to use as the context
        `mapFn` - the mapping function
      Returns:
        the newly attached stage

    - #### mapUsingContext

          @Nonnull
          <C,R> BatchStage<R> mapUsingContext(@Nonnull
                                                       ContextFactory<C> contextFactory,
                                                       @Nonnull
                                                       TriFunction<? super C,? super K,? super T,? extends R> mapFn)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a mapping stage which applies the given function to each input item independently and emits the function's result as the output item. The mapping function receives another parameter, the context object, which Jet will create using the supplied `contextFactory`. If the mapping result is `null`, it emits nothing. Therefore this stage can be used to implement filtering semantics as well.

      Jet uses the key-extracting function specified on this stage for partitioning: all the items with the same key will see the same context instance (but note that the same instance serves many keys). One case where this is useful is fetching data from an external system because you can use a near-cache without duplicating the cached data.

      Sample usage:

           items.groupingKey(Item::getDetailId)
                .mapUsingContext(
                    ContextFactory.withCreateFn(jet -> new ItemDetailRegistry()),
                    (reg, key, item) -> item.setDetail(reg.fetchDetail(key))
                );

      ### Interaction with fault-tolerant unbounded jobs

      If you use this stage in a fault-tolerant unbounded job, keep in mind that any state the context object maintains doesn't participate in Jet's fault tolerance protocol. If the state is local, it will be lost after a job restart; if it is saved to some durable storage, the state of that storage won't be rewound to the last checkpoint, so you'll perform duplicate updates.
      Specified by:
        `mapUsingContext` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `C` - type of context object
        `R` - the result type of the mapping function
      Parameters:
        `contextFactory` - the context factory
        `mapFn` - a stateless mapping function
      Returns:
        the newly attached stage

    - #### mapUsingContextAsync

          @Nonnull
          <C,R> BatchStage<R> mapUsingContextAsync(@Nonnull
                                                            ContextFactory<C> contextFactory,
                                                            @Nonnull
                                                            TriFunction<? super C,? super K,? super T,CompletableFuture<R>> mapAsyncFn)

      Description copied from interface: `GeneralStageWithKey`
      Asynchronous version of `GeneralStageWithKey.mapUsingContext(com.hazelcast.jet.pipeline.ContextFactory<C>, com.hazelcast.jet.function.TriFunction<? super C, ? super K, ? super T, ? extends R>)`: the `mapAsyncFn` returns a `CompletableFuture<R>` instead of just `R`.

      The function can return a null future or the future can return a null result: in both cases it will act just like a filter.

      Sample usage:

           items.groupingKey(Item::getDetailId)
                .mapUsingContextAsync(
                    ContextFactory.withCreateFn(jet -> new ItemDetailRegistry()),
                    (reg, key, item) -> reg.fetchDetailAsync(key)
                                           .thenApply(detail -> item.setDetail(detail))
                );

      The latency of the async call will add to the total latency of the output.
      Specified by:
        `mapUsingContextAsync` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `C` - type of context object
        `R` - the future's result type of the mapping function
      Parameters:
        `contextFactory` - the context factory
        `mapAsyncFn` - a stateless mapping function. Can map to null (return a null future)
      Returns:
        the newly attached stage

    - #### filterUsingContext

          @Nonnull
          <C> BatchStage<T> filterUsingContext(@Nonnull
                                                        ContextFactory<C> contextFactory,
                                                        @Nonnull
                                                        TriPredicate<? super C,? super K,? super T> filterFn)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a filtering stage which applies the provided predicate function to each input item to decide whether to pass the item to the output or to discard it. The predicate function receives another parameter, the context object, which Jet will create using the supplied `contextFactory`.

      Jet uses the key-extracting function specified on this stage for partitioning: all the items with the same key will see the same context instance (but note that the same instance serves many keys). One case where this is useful is fetching data from an external system because you can use a near-cache without duplicating the cached data.

      Sample usage:

           items.groupingKey(Item::getDetailId)
                .filterUsingContext(
                    ContextFactory.withCreateFn(jet -> new ItemDetailRegistry()),
                    (reg, key, item) -> reg.fetchDetail(key).contains("blade")
                );

      ### Interaction with fault-tolerant unbounded jobs

      If you use this stage in a fault-tolerant unbounded job, keep in mind that any state the context object maintains doesn't participate in Jet's fault tolerance protocol. If the state is local, it will be lost after a job restart; if it is saved to some durable storage, the state of that storage won't be rewound to the last checkpoint, so you'll perform duplicate updates.
      Specified by:
        `filterUsingContext` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `C` - type of context object
      Parameters:
        `contextFactory` - the context factory
        `filterFn` - a stateless filter predicate function
      Returns:
        the newly attached stage

    - #### filterUsingContextAsync

          @Nonnull
          <C> BatchStage<T> filterUsingContextAsync(@Nonnull
                                                             ContextFactory<C> contextFactory,
                                                             @Nonnull
                                                             TriFunction<? super C,? super K,? super T,CompletableFuture<Boolean>> filterAsyncFn)

      Description copied from interface: `GeneralStageWithKey`
      Asynchronous version of `GeneralStageWithKey.filterUsingContext(com.hazelcast.jet.pipeline.ContextFactory<C>, com.hazelcast.jet.function.TriPredicate<? super C, ? super K, ? super T>)`: the `filterAsyncFn` returns a `CompletableFuture<Boolean>` instead of just a `boolean`.

      The function must not return a null future.

      Sample usage:

           items.groupingKey(Item::getDetailId)
                .filterUsingContextAsync(
                    ContextFactory.withCreateFn(jet -> new ItemDetailRegistry()),
                    (reg, key, item) -> reg.fetchDetailAsync(key)
                                           .thenApply(detail -> detail.contains("blade"))
                );

      The latency of the async call will add to the total latency of the output.

      Specified by:
        `filterUsingContextAsync` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `C` - type of context object
      Parameters:
        `contextFactory` - the context factory
        `filterAsyncFn` - a stateless filtering function
      Returns:
        the newly attached stage

    - #### flatMapUsingContext

          @Nonnull
          <C,R> BatchStage<R> flatMapUsingContext(@Nonnull
                                                           ContextFactory<C> contextFactory,
                                                           @Nonnull
                                                           TriFunction<? super C,? super K,? super T,? extends Traverser<? extends R>> flatMapFn)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a flat-mapping stage which applies the supplied function to each input item independently and emits all the items from the `Traverser` it returns as the output items. The traverser must be null-terminated. The mapping function receives another parameter, the context object, which Jet will create using the supplied `contextFactory`.

      Jet uses the key-extracting function specified on this stage for partitioning: all the items with the same key will see the same context instance (but note that the same instance serves many keys). One case where this is useful is fetching data from an external system because you can use a near-cache without duplicating the cached data.

      Sample usage:

           StreamStage<Part> parts = products
               .groupingKey(Product::getId)
               .flatMapUsingContext(
                   ContextFactory.withCreateFn(jet -> new PartRegistry()),
                   (registry, productId, product) -> Traversers.traverseIterable(
                           registry.fetchParts(productId))
               );

      ### Interaction with fault-tolerant unbounded jobs

      If you use this stage in a fault-tolerant unbounded job, keep in mind that any state the context object maintains doesn't participate in Jet's fault tolerance protocol. If the state is local, it will be lost after a job restart; if it is saved to some durable storage, the state of that storage won't be rewound to the last checkpoint, so you'll perform duplicate updates.
      Specified by:
        `flatMapUsingContext` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `C` - type of context object
        `R` - type of the output items
      Parameters:
        `contextFactory` - the context factory
        `flatMapFn` - a stateless flatmapping function. It must not return null traverser, but can return an empty traverser.
      Returns:
        the newly attached stage

    - #### flatMapUsingContextAsync

          @Nonnull
          <C,R> BatchStage<R> flatMapUsingContextAsync(@Nonnull
                                                                ContextFactory<C> contextFactory,
                                                                @Nonnull
                                                                TriFunction<? super C,? super K,? super T,CompletableFuture<Traverser<R>>> flatMapAsyncFn)

      Description copied from interface: `GeneralStageWithKey`
      Asynchronous version of `GeneralStageWithKey.flatMapUsingContext(com.hazelcast.jet.pipeline.ContextFactory<C>, com.hazelcast.jet.function.TriFunction<? super C, ? super K, ? super T, ? extends com.hazelcast.jet.Traverser<? extends R>>)`: the `flatMapAsyncFn` returns a `CompletableFuture<Traverser<R>>` instead of just `Traverser<R>`.

      The function can return a null future or the future can return a null traverser: in both cases it will act just like a filter.

      Sample usage:

           StreamStage<Part> productParts = products
               .groupingKey(Product::getId)
               .flatMapUsingContextAsync(
                   ContextFactory.withCreateFn(jet -> new PartRegistry()),
                   (registry, productId, product) -> registry
                           .fetchPartsAsync(productId)
                           .thenApply(parts -> Traversers.traverseIterable(parts))
               );

      The latency of the async call will add to the latency of the items.

      Specified by:
        `flatMapUsingContextAsync` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `C` - type of context object
        `R` - the type of the returned stage
      Parameters:
        `contextFactory` - the context factory
        `flatMapAsyncFn` - a stateless flatmapping function. Can map to null (return a null future), but the future must not return null traverser, but can return an empty traverser.
      Returns:
        the newly attached stage

    - #### aggregate

          @Nonnull
          <R> BatchStage<Map.Entry<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 `Map.Entry`) for each distinct key it observes in its input. The value is the result of the aggregate operation across all the items with the given grouping key.

      Sample usage:

           BatchStage<Entry<String, Long>> aggregated = people.
                   .groupingKey(Person::getLastName)
                   .aggregate(AggregateOperations.counting());

      Type Parameters:
        `R` - type of the aggregation result
      Parameters:
        `aggrOp` - the aggregate operation to perform
      See Also:
        `AggregateOperations`

    - #### aggregate2

          @Nonnull
          <T1,R> BatchStage<Map.Entry<K,R>> aggregate2(@Nonnull
                                                                BatchStageWithKey<T1,? extends K> stage1,
                                                                @Nonnull
                                                                AggregateOperation2<? super T,? super T1,?,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 `Map.Entry`) for each distinct key it observes in its input. The value is the result of the aggregate operation across all the items with the given grouping key.

      Sample usage:

           BatchStage<Entry<Long, Tuple2<Long, Long>>> aggregated = pageVisits
                   .groupingKey(PageVisit::getUserId)
                   .aggregate2(addToCarts.groupingKey(AddToCart::getUserId),
                           aggregateOperation2(
                                   AggregateOperations.counting(),
                                   AggregateOperations.counting())
                   );

      This variant requires you to provide a two-input aggregate operation. 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. Use this variant only when your aggregate operation must combine the input streams into the same accumulator.
      Type Parameters:
        `T1` - type of items in `stage1`
        `R` - type of the aggregation result
      Parameters:
        `aggrOp` - the aggregate operation to perform
      See Also:
        `AggregateOperations`

    - #### aggregate2

          @Nonnull
          default <T1,R0,R1> BatchStage<Map.Entry<K,Tuple2<R0,R1>>> aggregate2(@Nonnull
                                                                                        AggregateOperation1<? super T,?,? extends R0> aggrOp0,
                                                                                        @Nonnull
                                                                                        BatchStageWithKey<? extends T1,? extends K> stage1,
                                                                                        @Nonnull
                                                                                        AggregateOperation1<? super T1,?,? extends R1> aggrOp1)

      Attaches a stage that performs the given cogroup-and-aggregate transformation of the items from both this stage and `stage1` you supply. For each distinct grouping key 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, it emits for each distinct key a `Map.Entry(key, Tuple2(result0, result1))`.

      Sample usage:

           BatchStage<Entry<Long, Tuple2<Long, Long>>> aggregated = pageVisits
                   .groupingKey(PageVisit::getUserId)
                   .aggregate2(
                           AggregateOperations.counting(),
                           addToCarts.groupingKey(AddToCart::getUserId),
                           AggregateOperations.counting()
                   );

      Type Parameters:
        `R0` - type of the aggregation result for stream-0
        `T1` - type of items in `stage1`
        `R1` - type of the aggregation result for stream-1
      Parameters:
        `aggrOp0` - aggregate operation to perform on this stage
        `stage1` - the other stage
        `aggrOp1` - aggregate operation to perform on the other stage
      See Also:
        `AggregateOperations`

    - #### aggregate3

          @Nonnull
          <T1,T2,R> BatchStage<Map.Entry<K,R>> aggregate3(@Nonnull
                                                                   BatchStageWithKey<T1,? extends K> stage1,
                                                                   @Nonnull
                                                                   BatchStageWithKey<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. It emits one key-value pair (in a `Map.Entry`) for each distinct key it observes in its input. The value is the result of the aggregate operation across all the items with the given grouping key.

      Sample usage:

           BatchStage<Entry<Long, Tuple3<Long, Long, Long>>> aggregated = pageVisits
                   .groupingKey(PageVisit::getUserId)
                   .aggregate3(
                           addToCarts.groupingKey(AddToCart::getUserId),
                           payments.groupingKey(Payment::getUserId),
                           aggregateOperation3(
                                   AggregateOperations.counting(),
                                   AggregateOperations.counting(),
                                   AggregateOperations.counting())
                   );

      This variant requires you to provide a three-input aggregate operation. 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. Use this variant only when your aggregate operation must combine 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:
        `aggrOp` - the aggregate operation to perform
      See Also:
        `AggregateOperations`

    - #### aggregate3

          @Nonnull
          default <T1,T2,R0,R1,R2> BatchStage<Map.Entry<K,Tuple3<R0,R1,R2>>> aggregate3(@Nonnull
                                                                                                 AggregateOperation1<? super T,?,? extends R0> aggrOp0,
                                                                                                 @Nonnull
                                                                                                 BatchStageWithKey<T1,? extends K> stage1,
                                                                                                 @Nonnull
                                                                                                 AggregateOperation1<? super T1,?,? extends R1> aggrOp1,
                                                                                                 @Nonnull
                                                                                                 BatchStageWithKey<T2,? extends K> stage2,
                                                                                                 @Nonnull
                                                                                                 AggregateOperation1<? super T2,?,? extends R2> aggrOp2)

      Attaches a stage that performs the given cogroup-and-aggregate transformation of the items from this stage as well as `stage1` and `stage2` you supply. For each distinct grouping key it observes in the input, 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 emits for each distinct key a `Map.Entry(key, Tuple3(result0, result1, result2))`.

      Sample usage:

           BatchStage<Entry<Long, Tuple3<Long, Long, Long>>> aggregated = pageVisits
                   .groupingKey(PageVisit::getUserId)
                   .aggregate3(
                           AggregateOperations.counting(),
                           addToCarts.groupingKey(AddToCart::getUserId),
                           AggregateOperations.counting(),
                           payments.groupingKey(Payment::getUserId),
                           AggregateOperations.counting()
                   );

      Type Parameters:
        `T1` - type of items in `stage1`
        `T2` - type of items in `stage2`
        `R0` - type of the aggregation result for stream-0
        `R1` - type of the aggregation result for stream-1
        `R2` - type of the aggregation result for stream-2
      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:
        `AggregateOperations`

    - #### aggregateBuilder

          @Nonnull
          default <R0> GroupAggregateBuilder<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 in a `Map.Entry(key, itemsByTag)`. Use the tag you get from `builder.add(stageN)` to retrieve the aggregated result for that stage. Use `builder.tag0()` as the tag of this stage. You will also be able to supply a function to the builder that immediately transforms the `ItemsByTag` to the desired output type.

      This example draws from three sources that produce `Map.Entry<String, Long>`. It groups by entry key 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();
           StageWithGrouping<Entry<String, Long>, String> stage0 =
                   p.drawFrom(source0).groupingKey(Entry::getKey);
           StageWithGrouping<Entry<String, Long>, String> stage1 =
                   p.drawFrom(source1).groupingKey(Entry::getKey);
           StageWithGrouping<Entry<String, Long>, String> stage2 =
                   p.drawFrom(source2).groupingKey(Entry::getKey);

           GroupAggregateBuilder<String, Long> b = stage0.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));
           BatchStage<Entry<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 GroupAggregateBuilder1<T,K> aggregateBuilder()

      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.

      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 `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 `GroupAggregateBuilder1.tag0()`.

      This example takes three streams of `Map.Entry<String, Long>` and, for each string key, counts the distinct `Long` values across all input streams:

           Pipeline p = Pipeline.create();

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

           GroupAggregateBuilder1<Entry<String, Long>, String> b = stage0.aggregateBuilder();
           Tag<Entry<String, Long>> tag0 = b.tag0();
           Tag<Entry<String, Long>> tag1 = b.add(stage1);
           Tag<Entry<String, Long>> tag2 = b.add(stage2);
           BatchStage<Entry<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));

    - #### customTransform

          @Nonnull
          default <R> BatchStage<R> customTransform(@Nonnull
                                                             String stageName,
                                                             @Nonnull
                                                             SupplierEx<Processor> procSupplier)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a stage with a custom transform based on the provided supplier of Core API `Processor`s. The inbound edge will be distributed and partitioned using the key function assigned to this stage.

      Note that the type parameter of the returned stage is inferred from the call site and not propagated from the processor that will produce the result, so there is no actual type safety provided.

      Specified by:
        `customTransform` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `R` - the type of the output items
      Parameters:
        `stageName` - a human-readable name for the custom stage
        `procSupplier` - the supplier of processors

    - #### customTransform

          @Nonnull
          default <R> BatchStage<R> customTransform(@Nonnull
                                                             String stageName,
                                                             @Nonnull
                                                             ProcessorSupplier procSupplier)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a stage with a custom transform based on the provided supplier of Core API `Processor`s. The inbound edge will be distributed and partitioned using the key function assigned to this stage.

      Note that the type parameter of the returned stage is inferred from the call site and not propagated from the processor that will produce the result, so there is no actual type safety provided.

      Specified by:
        `customTransform` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `R` - the type of the output items
      Parameters:
        `stageName` - a human-readable name for the custom stage
        `procSupplier` - the supplier of processors

    - #### customTransform

          @Nonnull
          <R> BatchStage<R> customTransform(@Nonnull
                                                     String stageName,
                                                     @Nonnull
                                                     ProcessorMetaSupplier procSupplier)

      Description copied from interface: `GeneralStageWithKey`
      Attaches a stage with a custom transform based on the provided supplier of Core API `Processor`s. The inbound edge will be distributed and partitioned using the key function assigned to this stage.

      Note that the type parameter of the returned stage is inferred from the call site and not propagated from the processor that will produce the result, so there is no actual type safety provided.

      Specified by:
        `customTransform` in interface `GeneralStageWithKey<T,K>`
      Type Parameters:
        `R` - the type of the output items
      Parameters:
        `stageName` - a human-readable name for the custom stage
        `procSupplier` - the supplier of processors

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