Java tutorial
/** * 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.crunch.impl.spark.fn; import com.google.common.base.Function; import com.google.common.collect.HashMultimap; import com.google.common.collect.Iterables; import com.google.common.collect.Maps; import com.google.common.collect.Multimap; import org.apache.crunch.CombineFn; import org.apache.crunch.Pair; import org.apache.crunch.impl.mem.emit.InMemoryEmitter; import org.apache.crunch.impl.spark.SparkRuntimeContext; import org.apache.spark.api.java.function.FlatMapFunction; import org.apache.spark.api.java.function.PairFlatMapFunction; import scala.Tuple2; import javax.annotation.Nullable; import java.util.Iterator; import java.util.List; import java.util.Map; import java.util.Set; public class CombineMapsideFunction<K, V> extends PairFlatMapFunction<Iterator<Tuple2<K, V>>, K, V> { private static final int REDUCE_EVERY_N = 50000; private final CombineFn<K, V> combineFn; private final SparkRuntimeContext ctxt; public CombineMapsideFunction(CombineFn<K, V> combineFn, SparkRuntimeContext ctxt) { this.combineFn = combineFn; this.ctxt = ctxt; } @Override public Iterable<Tuple2<K, V>> call(Iterator<Tuple2<K, V>> iter) throws Exception { ctxt.initialize(combineFn); Multimap<K, V> cache = HashMultimap.create(); int cnt = 0; while (iter.hasNext()) { Tuple2<K, V> t = iter.next(); cache.put(t._1, t._2); cnt++; if (cnt % REDUCE_EVERY_N == 0) { cache = reduce(cache); } } return Iterables.transform(reduce(cache).entries(), new Function<Map.Entry<K, V>, Tuple2<K, V>>() { @Override public Tuple2<K, V> apply(Map.Entry<K, V> input) { return new Tuple2<K, V>(input.getKey(), input.getValue()); } }); } private Multimap<K, V> reduce(Multimap<K, V> cache) { Set<K> keys = cache.keySet(); Multimap<K, V> res = HashMultimap.create(keys.size(), keys.size()); for (K key : keys) { for (Pair<K, V> p : reduce(key, cache.get(key))) { res.put(p.first(), p.second()); } } return res; } private List<Pair<K, V>> reduce(K key, Iterable<V> values) { InMemoryEmitter<Pair<K, V>> emitter = new InMemoryEmitter<Pair<K, V>>(); combineFn.process(Pair.of(key, values), emitter); combineFn.cleanup(emitter); return emitter.getOutput(); } }