| /* |
| * 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.flink.batch.tests; |
| |
| import org.apache.flink.api.common.functions.CoGroupFunction; |
| import org.apache.flink.api.common.functions.FlatMapFunction; |
| import org.apache.flink.api.common.functions.GroupReduceFunction; |
| import org.apache.flink.api.common.io.DefaultInputSplitAssigner; |
| import org.apache.flink.api.common.io.InputFormat; |
| import org.apache.flink.api.common.io.statistics.BaseStatistics; |
| import org.apache.flink.api.common.operators.Order; |
| import org.apache.flink.api.common.operators.base.JoinOperatorBase; |
| import org.apache.flink.api.common.typeinfo.Types; |
| import org.apache.flink.api.java.DataSet; |
| import org.apache.flink.api.java.ExecutionEnvironment; |
| import org.apache.flink.api.java.functions.KeySelector; |
| import org.apache.flink.api.java.operators.IterativeDataSet; |
| import org.apache.flink.api.java.tuple.Tuple2; |
| import org.apache.flink.api.java.tuple.Tuple4; |
| import org.apache.flink.api.java.utils.ParameterTool; |
| import org.apache.flink.configuration.Configuration; |
| import org.apache.flink.core.fs.FileSystem; |
| import org.apache.flink.core.io.GenericInputSplit; |
| import org.apache.flink.core.io.InputSplitAssigner; |
| import org.apache.flink.util.Preconditions; |
| |
| /** |
| * Program to test a large chunk of DataSet API operators and primitives: |
| * <ul> |
| * <li>Map, FlatMap, Filter</li> |
| * <li>GroupReduce, Reduce</li> |
| * <li>Join</li> |
| * <li>CoGroup</li> |
| * <li>BulkIteration</li> |
| * <li>Different key definitions (position, name, KeySelector)</li> |
| * </ul> |
| * |
| * <p>Program parameters: |
| * <ul> |
| * <li>loadFactor (int): controls generated data volume. Does not affect result.</li> |
| * <li>outputPath (String): path to write the result</li> |
| * </ul> |
| */ |
| public class DataSetAllroundTestProgram { |
| |
| @SuppressWarnings("Convert2Lambda") |
| public static void main(String[] args) throws Exception { |
| |
| // get parameters |
| ParameterTool params = ParameterTool.fromArgs(args); |
| int loadFactor = Integer.parseInt(params.getRequired("loadFactor")); |
| String outputPath = params.getRequired("outputPath"); |
| |
| ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); |
| |
| int numKeys = loadFactor * 128 * 1024; |
| DataSet<Tuple2<String, Integer>> x1Keys = env.createInput(new Generator(numKeys, 1)).setParallelism(4); |
| DataSet<Tuple2<String, Integer>> x2Keys = env.createInput(new Generator(numKeys * 32, 2)).setParallelism(4); |
| DataSet<Tuple2<String, Integer>> x8Keys = env.createInput(new Generator(numKeys, 8)).setParallelism(4); |
| |
| DataSet<Tuple2<String, Integer>> joined = x2Keys |
| // shift keys (check for correct handling of key positions) |
| .map(x -> Tuple4.of("0-0", 0L, 1, x.f0)) |
| .returns(Types.TUPLE(Types.STRING, Types.LONG, Types.INT, Types.STRING)) |
| // join datasets on non-unique fields (m-n join) |
| // Result: (key, 1) 16 * #keys records, all keys are preserved |
| .join(x8Keys).where(3).equalTo(0).with((l, r) -> Tuple2.of(l.f3, 1)) |
| .returns(Types.TUPLE(Types.STRING, Types.INT)) |
| // key definition with key selector function |
| .groupBy( |
| new KeySelector<Tuple2<String, Integer>, String>() { |
| @Override |
| public String getKey(Tuple2<String, Integer> value) { |
| return value.f0; |
| } |
| } |
| ) |
| // reduce |
| // Result: (key, cnt), #keys records with unique keys, cnt = 16 |
| .reduce((value1, value2) -> Tuple2.of(value1.f0, value1.f1 + value2.f1)); |
| |
| // co-group two datasets on their primary keys. |
| // we filter both inputs such that only 6.25% of the keys overlap. |
| // result: (key, cnt), #keys records with unique keys, cnt = (6.25%: 2, 93.75%: 1) |
| DataSet<Tuple2<String, Integer>> coGrouped = x1Keys |
| .filter(x -> x.f1 > 59) |
| .coGroup(x1Keys.filter(x -> x.f1 < 68)).where("f0").equalTo("f0").with( |
| (CoGroupFunction<Tuple2<String, Integer>, Tuple2<String, Integer>, Tuple2<String, Integer>>) |
| (l, r, out) -> { |
| int cnt = 0; |
| String key = ""; |
| for (Tuple2<String, Integer> t : l) { |
| cnt++; |
| key = t.f0; |
| } |
| for (Tuple2<String, Integer> t : r) { |
| cnt++; |
| key = t.f0; |
| } |
| out.collect(Tuple2.of(key, cnt)); |
| } |
| ) |
| .returns(Types.TUPLE(Types.STRING, Types.INT)); |
| |
| // join datasets on keys (1-1 join) and replicate by 16 (previously computed count) |
| // result: (key, cnt), 16 * #keys records, all keys preserved, cnt = (6.25%: 2, 93.75%: 1) |
| DataSet<Tuple2<String, Integer>> joined2 = joined.join(coGrouped, JoinOperatorBase.JoinHint.REPARTITION_SORT_MERGE) |
| .where(0).equalTo("f0") |
| .flatMap( |
| (FlatMapFunction<Tuple2<Tuple2<String, Integer>, Tuple2<String, Integer>>, Tuple2<String, Integer>>) |
| (p, out) -> { |
| for (int i = 0; i < p.f0.f1; i++) { |
| out.collect(Tuple2.of(p.f0.f0, p.f1.f1)); |
| } |
| } |
| ) |
| .returns(Types.TUPLE(Types.STRING, Types.INT)); |
| |
| // iteration. double the count field until all counts are at 32 or more |
| // result: (key, cnt), 16 * #keys records, all keys preserved, cnt = (6.25%: 64, 93.75%: 32) |
| IterativeDataSet<Tuple2<String, Integer>> initial = joined2.iterate(16); |
| DataSet<Tuple2<String, Integer>> iteration = initial |
| .map(x -> Tuple2.of(x.f0, x.f1 * 2)) |
| .returns(Types.TUPLE(Types.STRING, Types.INT)); |
| DataSet<Boolean> termination = iteration |
| // stop iteration if all values are larger/equal 32 |
| .flatMap( |
| (FlatMapFunction<Tuple2<String, Integer>, Boolean>) |
| (x, out) -> { |
| if (x.f1 < 32) { |
| out.collect(false); |
| } |
| } |
| ) |
| .returns(Types.BOOLEAN); |
| DataSet<Tuple2<Integer, Integer>> result = initial.closeWith(iteration, termination) |
| // group on the count field and count records |
| // result: two records: (32, cnt1) and (64, cnt2) where cnt1 = x * 15/16, cnt2 = x * 1/16 |
| .groupBy(1) |
| .reduceGroup( |
| (GroupReduceFunction<Tuple2<String, Integer>, Tuple2<Integer, Integer>>) |
| (g, out) -> { |
| int key = 0; |
| int cnt = 0; |
| for (Tuple2<String, Integer> r : g) { |
| key = r.f1; |
| cnt++; |
| } |
| out.collect(Tuple2.of(key, cnt)); |
| } |
| ) |
| .returns(Types.TUPLE(Types.INT, Types.INT)) |
| // normalize result by load factor |
| // result: two records: (32: 15360) and (64, 1024). (x = 16384) |
| .map(x -> Tuple2.of(x.f0, x.f1 / (loadFactor * 128))) |
| .returns(Types.TUPLE(Types.INT, Types.INT)); |
| |
| // sort and emit result |
| result |
| .sortPartition(0, Order.ASCENDING).setParallelism(1) |
| .writeAsText(outputPath, FileSystem.WriteMode.OVERWRITE).setParallelism(1); |
| |
| env.execute(); |
| } |
| |
| /** |
| * InputFormat that generates a deterministic DataSet of Tuple2(String, Integer) |
| * <ul> |
| * <li>String: key, can be repeated.</li> |
| * <li>Integer: uniformly distributed int between 0 and 127</li> |
| * </ul> |
| */ |
| public static class Generator implements InputFormat<Tuple2<String, Integer>, GenericInputSplit> { |
| |
| // total number of records |
| private final long numRecords; |
| // total number of keys |
| private final long numKeys; |
| |
| // records emitted per partition |
| private long recordsPerPartition; |
| // number of keys per partition |
| private long keysPerPartition; |
| |
| // number of currently emitted records |
| private long recordCnt; |
| |
| // id of current partition |
| private int partitionId; |
| // total number of partitions |
| private int numPartitions; |
| |
| public Generator(long numKeys, int recordsPerKey) { |
| this.numKeys = numKeys; |
| this.numRecords = numKeys * recordsPerKey; |
| } |
| |
| @Override |
| public void configure(Configuration parameters) { } |
| |
| @Override |
| public BaseStatistics getStatistics(BaseStatistics cachedStatistics) { |
| return null; |
| } |
| |
| @Override |
| public GenericInputSplit[] createInputSplits(int minNumSplits) { |
| |
| GenericInputSplit[] splits = new GenericInputSplit[minNumSplits]; |
| for (int i = 0; i < minNumSplits; i++) { |
| splits[i] = new GenericInputSplit(i, minNumSplits); |
| } |
| return splits; |
| } |
| |
| @Override |
| public InputSplitAssigner getInputSplitAssigner(GenericInputSplit[] inputSplits) { |
| return new DefaultInputSplitAssigner(inputSplits); |
| } |
| |
| @Override |
| public void open(GenericInputSplit split) { |
| this.partitionId = split.getSplitNumber(); |
| this.numPartitions = split.getTotalNumberOfSplits(); |
| |
| // ensure even distribution of records and keys |
| Preconditions.checkArgument( |
| numRecords % numPartitions == 0, |
| "Records cannot be evenly distributed among partitions"); |
| Preconditions.checkArgument( |
| numKeys % numPartitions == 0, |
| "Keys cannot be evenly distributed among partitions"); |
| |
| this.recordsPerPartition = numRecords / numPartitions; |
| this.keysPerPartition = numKeys / numPartitions; |
| |
| this.recordCnt = 0; |
| } |
| |
| @Override |
| public boolean reachedEnd() { |
| return this.recordCnt >= this.recordsPerPartition; |
| } |
| |
| @Override |
| public Tuple2<String, Integer> nextRecord(Tuple2<String, Integer> reuse) { |
| |
| // build key from partition id and count per partition |
| String key = String.format( |
| "%d-%d", |
| this.partitionId, |
| this.recordCnt % this.keysPerPartition); |
| // 128 values to filter on |
| int filterVal = (int) this.recordCnt % 128; |
| |
| this.recordCnt++; |
| |
| reuse.f0 = key; |
| reuse.f1 = filterVal; |
| return reuse; |
| } |
| |
| @Override |
| public void close() { } |
| } |
| |
| } |