| /* |
| * 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.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.core.fs.FileSystem; |
| |
| /** |
| * Program to test a large chunk of DataSet API operators and primitives: |
| * |
| * <ul> |
| * <li>Map, FlatMap, Filter |
| * <li>GroupReduce, Reduce |
| * <li>Join |
| * <li>CoGroup |
| * <li>BulkIteration |
| * <li>Different key definitions (position, name, KeySelector) |
| * </ul> |
| * |
| * <p>Program parameters: |
| * |
| * <ul> |
| * <li>loadFactor (int): controls generated data volume. Does not affect result. |
| * <li>outputPath (String): path to write the result |
| * <li>infinite (Boolean): if set to true one of the sources will be infinite. The job will never |
| * end. (default: false( |
| * </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"); |
| boolean infinite = params.getBoolean("infinite", false); |
| |
| ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); |
| |
| int numKeys = loadFactor * 128 * 1024; |
| DataSet<Tuple2<String, Integer>> x1Keys; |
| DataSet<Tuple2<String, Integer>> x2Keys = |
| env.createInput(Generator.generate(numKeys * 32, 2)).setParallelism(4); |
| DataSet<Tuple2<String, Integer>> x8Keys = |
| env.createInput(Generator.generate(numKeys, 8)).setParallelism(4); |
| |
| if (infinite) { |
| x1Keys = env.createInput(Generator.generateInfinitely(numKeys)).setParallelism(4); |
| } else { |
| x1Keys = env.createInput(Generator.generate(numKeys, 1)).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(); |
| } |
| } |
