| /** |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
| * under the License. |
| */ |
| |
| package org.apache.heron.streamlet; |
| |
| import java.util.List; |
| import java.util.Map; |
| |
| import org.apache.heron.api.grouping.StreamGrouping; |
| import org.apache.heron.classification.InterfaceStability; |
| |
| /** |
| * A Streamlet is a (potentially unbounded) ordered collection of tuples. |
| * Streamlets originate from pub/sub systems(such Pulsar/Kafka), or from |
| * static data(such as csv files, HDFS files), or for that matter any other |
| * source. They are also created by transforming existing Streamlets using |
| * operations such as map/flatMap, etc. |
| * Besides the tuples, a Streamlet has the following properties associated with it |
| * a) name. User assigned or system generated name to refer the streamlet |
| * b) nPartitions. Number of partitions that the streamlet is composed of. Thus the |
| * ordering of the tuples in a Streamlet is wrt the tuples within a partition. |
| * This allows the system to distribute each partition to different nodes across the cluster. |
| * A bunch of transformations can be done on Streamlets(like map/flatMap, etc.). Each |
| * of these transformations operate on every tuple of the Streamlet and produce a new |
| * Streamlet. One can think of a transformation attaching itself to the stream and processing |
| * each tuple as they go by. Thus the parallelism of any operator is implicitly determined |
| * by the number of partitions of the stream that it is operating on. If a particular |
| * transformation wants to operate at a different parallelism, one can repartition the |
| * Streamlet before doing the transformation. |
| */ |
| @InterfaceStability.Evolving |
| public interface Streamlet<R> { |
| |
| /** |
| * Sets the name of the BaseStreamlet. |
| * @param sName The name given by the user for this BaseStreamlet |
| * @return Returns back the Streamlet with changed name |
| */ |
| Streamlet<R> setName(String sName); |
| |
| /** |
| * Gets the name of the Streamlet. |
| * @return Returns the name of the Streamlet |
| */ |
| String getName(); |
| |
| /** |
| * Sets the number of partitions of the streamlet |
| * @param numPartitions The user assigned number of partitions |
| * @return Returns back the Streamlet with changed number of partitions |
| */ |
| Streamlet<R> setNumPartitions(int numPartitions); |
| |
| /** |
| * Gets the number of partitions of this Streamlet. |
| * @return the number of partitions of this Streamlet |
| */ |
| int getNumPartitions(); |
| |
| /** |
| * Set the id of the stream to be used by the children nodes. |
| * Usage (assuming source is a Streamlet object with two output streams: stream1 and stream2): |
| * source.withStream("stream1").filter(...).log(); |
| * source.withStream("stream2").filter(...).log(); |
| * @param streamId The specified stream id |
| * @return Returns back the Streamlet with changed stream id |
| */ |
| Streamlet<R> withStream(String streamId); |
| |
| /** |
| * Gets the stream id of this Streamlet. |
| * @return the stream id of this Streamlet |
| */ |
| String getStreamId(); |
| |
| /** |
| * Return a new Streamlet by applying mapFn to each element of this Streamlet |
| * @param mapFn The Map Function that should be applied to each element |
| */ |
| <T> Streamlet<T> map(SerializableFunction<R, ? extends T> mapFn); |
| |
| /** |
| * Return a new Streamlet by applying flatMapFn to each element of this Streamlet and |
| * flattening the result |
| * @param flatMapFn The FlatMap Function that should be applied to each element |
| */ |
| <T> Streamlet<T> flatMap( |
| SerializableFunction<R, ? extends Iterable<? extends T>> flatMapFn); |
| |
| /** |
| * Return a new Streamlet by applying the filterFn on each element of this streamlet |
| * and including only those elements that satisfy the filterFn |
| * @param filterFn The filter Function that should be applied to each element |
| */ |
| Streamlet<R> filter(SerializablePredicate<R> filterFn); |
| |
| /** |
| * Same as filter(filterFn).setNumPartitions(nPartitions) where filterFn is identity |
| */ |
| Streamlet<R> repartition(int numPartitions); |
| |
| /** |
| * A more generalized version of repartition where a user can determine which partitions |
| * any particular tuple should go to. For each element of the current streamlet, the user |
| * supplied partitionFn is invoked passing in the element as the first argument. The second |
| * argument is the number of partitions of the downstream streamlet. The partitionFn should |
| * return 0 or more unique numbers between 0 and npartitions to indicate which partitions |
| * this element should be routed to. |
| */ |
| Streamlet<R> repartition(int numPartitions, |
| SerializableBiFunction<R, Integer, List<Integer>> partitionFn); |
| |
| /** |
| * Clones the current Streamlet. It returns an array of numClones Streamlets where each |
| * Streamlet contains all the tuples of the current Streamlet |
| * @param numClones The number of clones to clone |
| */ |
| List<Streamlet<R>> clone(int numClones); |
| |
| /** |
| * Return a new Streamlet by inner joining 'this streamlet with ‘other’ streamlet. |
| * The join is done over elements accumulated over a time window defined by windowCfg. |
| * The elements are compared using the thisKeyExtractor for this streamlet with the |
| * otherKeyExtractor for the other streamlet. On each matching pair, the joinFunction is applied. |
| * @param other The Streamlet that we are joining with. |
| * @param thisKeyExtractor The function applied to a tuple of this streamlet to get the key |
| * @param otherKeyExtractor The function applied to a tuple of the other streamlet to get the key |
| * @param windowCfg This is a specification of what kind of windowing strategy you like to |
| * have. Typical windowing strategies are sliding windows and tumbling windows |
| * @param joinFunction The join function that needs to be applied |
| */ |
| <K, S, T> KVStreamlet<KeyedWindow<K>, T> |
| join(Streamlet<S> other, SerializableFunction<R, K> thisKeyExtractor, |
| SerializableFunction<S, K> otherKeyExtractor, WindowConfig windowCfg, |
| SerializableBiFunction<R, S, ? extends T> joinFunction); |
| |
| |
| /** |
| * Return a new KVStreamlet by joining 'this streamlet with ‘other’ streamlet. The type of joining |
| * is declared by the joinType parameter. |
| * The join is done over elements accumulated over a time window defined by windowCfg. |
| * The elements are compared using the thisKeyExtractor for this streamlet with the |
| * otherKeyExtractor for the other streamlet. On each matching pair, the joinFunction is applied. |
| * Types of joins {@link JoinType} |
| * @param other The Streamlet that we are joining with. |
| * @param thisKeyExtractor The function applied to a tuple of this streamlet to get the key |
| * @param otherKeyExtractor The function applied to a tuple of the other streamlet to get the key |
| * @param windowCfg This is a specification of what kind of windowing strategy you like to |
| * have. Typical windowing strategies are sliding windows and tumbling windows |
| * @param joinType Type of Join. Options {@link JoinType} |
| * @param joinFunction The join function that needs to be applied |
| */ |
| <K, S, T> KVStreamlet<KeyedWindow<K>, T> |
| join(Streamlet<S> other, SerializableFunction<R, K> thisKeyExtractor, |
| SerializableFunction<S, K> otherKeyExtractor, WindowConfig windowCfg, |
| JoinType joinType, SerializableBiFunction<R, S, ? extends T> joinFunction); |
| |
| /** |
| * Return a new Streamlet accumulating tuples of this streamlet and applying reduceFn on those tuples. |
| * @param keyExtractor The function applied to a tuple of this streamlet to get the key |
| * @param valueExtractor The function applied to a tuple of this streamlet to extract the value |
| * to be reduced on |
| * @param reduceFn The reduce function that you want to apply to all the values of a key. |
| */ |
| <K, T> KVStreamlet<K, T> reduceByKey(SerializableFunction<R, K> keyExtractor, |
| SerializableFunction<R, T> valueExtractor, |
| SerializableBinaryOperator<T> reduceFn); |
| |
| /** |
| * Return a new Streamlet accumulating tuples of this streamlet and applying reduceFn on those tuples. |
| * @param keyExtractor The function applied to a tuple of this streamlet to get the key |
| * @param identity The identity element is the initial value for each key |
| * @param reduceFn The reduce function that you want to apply to all the values of a key. |
| */ |
| <K, T> KVStreamlet<K, T> reduceByKey(SerializableFunction<R, K> keyExtractor, |
| T identity, |
| SerializableBiFunction<T, R, ? extends T> reduceFn); |
| |
| /** |
| * Return a new Streamlet accumulating tuples of this streamlet over a Window defined by |
| * windowCfg and applying reduceFn on those tuples. |
| * @param keyExtractor The function applied to a tuple of this streamlet to get the key |
| * @param valueExtractor The function applied to a tuple of this streamlet to extract the value |
| * to be reduced on |
| * @param windowCfg This is a specification of what kind of windowing strategy you like to have. |
| * Typical windowing strategies are sliding windows and tumbling windows |
| * @param reduceFn The reduce function that you want to apply to all the values of a key. |
| */ |
| <K, V> KVStreamlet<KeyedWindow<K>, V> reduceByKeyAndWindow( |
| SerializableFunction<R, K> keyExtractor, SerializableFunction<R, V> valueExtractor, |
| WindowConfig windowCfg, SerializableBinaryOperator<V> reduceFn); |
| |
| /** |
| * Return a new Streamlet accumulating tuples of this streamlet over a Window defined by |
| * windowCfg and applying reduceFn on those tuples. For each window, the value identity is used |
| * as a initial value. All the matching tuples are reduced using reduceFn startin from this |
| * initial value. |
| * @param keyExtractor The function applied to a tuple of this streamlet to get the key |
| * @param windowCfg This is a specification of what kind of windowing strategy you like to have. |
| * Typical windowing strategies are sliding windows and tumbling windows |
| * @param identity The identity element is both the initial value inside the reduction window |
| * and the default result if there are no elements in the window |
| * @param reduceFn The reduce function takes two parameters: a partial result of the reduction |
| * and the next element of the stream. It returns a new partial result. |
| */ |
| <K, T> KVStreamlet<KeyedWindow<K>, T> reduceByKeyAndWindow( |
| SerializableFunction<R, K> keyExtractor, WindowConfig windowCfg, |
| T identity, SerializableBiFunction<T, R, ? extends T> reduceFn); |
| |
| /** |
| * Returns a new Streamlet that is the union of this and the ‘other’ streamlet. Essentially |
| * the new streamlet will contain tuples belonging to both Streamlets |
| */ |
| Streamlet<R> union(Streamlet<? extends R> other); |
| |
| /** |
| * Returns a new Streamlet by applying the transformFunction on each element of this streamlet. |
| * Before starting to cycle the transformFunction over the Streamlet, the open function is called. |
| * This allows the transform Function to do any kind of initialization/loading, etc. |
| * @param serializableTransformer The transformation function to be applied |
| * @param <T> The return type of the transform |
| * @return Streamlet containing the output of the transformFunction |
| */ |
| <T> Streamlet<T> transform( |
| SerializableTransformer<R, ? extends T> serializableTransformer); |
| |
| /** |
| * Returns a new Streamlet by applying the operator on each element of this streamlet. |
| * @param operator The operator to be applied |
| * @param <T> The return type of the transform |
| * @return Streamlet containing the output of the operation |
| */ |
| <T> Streamlet<T> applyOperator(IStreamletOperator<R, T> operator); |
| |
| /** |
| * Returns a new Streamlet by applying the operator on each element of this streamlet. |
| * @param operator The operator to be applied |
| * @param grouper The grouper to be applied with the operator |
| * @param <T> The return type of the transform |
| * @return Streamlet containing the output of the operation |
| */ |
| <T> Streamlet<T> applyOperator(IStreamletOperator<R, T> operator, StreamGrouping grouper); |
| |
| /** |
| * Returns multiple streams by splitting incoming stream. |
| * @param splitFns The Split Functions that test if the tuple should be emitted into each stream |
| * Note that there could be 0 or multiple target stream ids |
| */ |
| Streamlet<R> split(Map<String, SerializablePredicate<R>> splitFns); |
| |
| /** |
| * Return a new KVStreamlet<K, R> by applying key extractor to each element of this Streamlet |
| * @param keyExtractor The function applied to a tuple of this streamlet to get the key |
| */ |
| <K> KVStreamlet<K, R> keyBy(SerializableFunction<R, K> keyExtractor); |
| |
| /** |
| * Return a new KVStreamlet<K, V> by applying key and value extractor to each element of this |
| * Streamlet |
| * @param keyExtractor The function applied to a tuple of this streamlet to get the key |
| * @param valueExtractor The function applied to a tuple of this streamlet to extract the value |
| */ |
| <K, V> KVStreamlet<K, V> keyBy(SerializableFunction<R, K> keyExtractor, |
| SerializableFunction<R, V> valueExtractor); |
| |
| /** |
| * Returns a new stream of <key, count> by counting tuples in this stream on each key. |
| * @param keyExtractor The function applied to a tuple of this streamlet to get the key |
| */ |
| <K> KVStreamlet<K, Long> countByKey(SerializableFunction<R, K> keyExtractor); |
| |
| /** |
| * Returns a new stream of <key, count> by counting tuples over a window in this stream on each key. |
| * @param keyExtractor The function applied to a tuple of this streamlet to get the key |
| * @param windowCfg This is a specification of what kind of windowing strategy you like to have. |
| * Typical windowing strategies are sliding windows and tumbling windows |
| * Note that there could be 0 or multiple target stream ids |
| */ |
| <K> KVStreamlet<KeyedWindow<K>, Long> countByKeyAndWindow( |
| SerializableFunction<R, K> keyExtractor, WindowConfig windowCfg); |
| |
| /** |
| * Logs every element of the streamlet using String.valueOf function |
| * This is one of the sink functions in the sense that this operation returns void |
| */ |
| void log(); |
| |
| /** |
| * Applies the consumer function to every element of the stream |
| * This function does not return anything. |
| * @param consumer The user supplied consumer function that is invoked for each element |
| * of this streamlet. |
| */ |
| void consume(SerializableConsumer<R> consumer); |
| |
| /** |
| * Applies the sink's put function to every element of the stream |
| * This function does not return anything. |
| * @param sink The Sink whose put method consumes each element |
| * of this streamlet. |
| */ |
| void toSink(Sink<R> sink); |
| } |