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/*
* 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.wayang.flink.operators;
import org.apache.wayang.basic.data.Tuple2;
import org.apache.wayang.basic.function.ProjectionDescriptor;
import org.apache.wayang.core.function.ReduceDescriptor;
import org.apache.wayang.core.platform.ChannelInstance;
import org.apache.wayang.core.types.DataSetType;
import org.apache.wayang.core.types.DataUnitType;
import org.apache.wayang.flink.channels.DataSetChannel;
import org.junit.Assert;
import org.junit.Test;
import java.util.Arrays;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import java.util.stream.Collectors;
/**
* Test suite for {@link FlinkReduceByOperator}.
*/
public class FlinkReduceByOperatorTest extends FlinkOperatorTestBase{
@Test
public void testExecution() throws Exception {
// Prepare test data.
List<Tuple2<String, Integer>> inputList = Arrays.stream("aaabbccccdeefff".split(""))
.map(string -> new Tuple2<>(string, 1))
.collect(Collectors.toList());
DataSetChannel.Instance input = this.createDataSetChannelInstance(inputList);
DataSetChannel.Instance output = this.createDataSetChannelInstance();
// Build the reduce operator.
FlinkReduceByOperator<Tuple2<String, Integer>, String> reduceByOperator =
new FlinkReduceByOperator<>(
DataSetType.createDefaultUnchecked(Tuple2.class),
new ProjectionDescriptor<>(
DataUnitType.createBasicUnchecked(Tuple2.class),
DataUnitType.createBasic(String.class),
"field0"),
new ReduceDescriptor<>(
(a, b) -> {
a.field1 += b.field1;
return a;
}, DataUnitType.createGroupedUnchecked(Tuple2.class),
DataUnitType.createBasicUnchecked(Tuple2.class)
));
// Set up the ChannelInstances.
final ChannelInstance[] inputs = new ChannelInstance[]{input};
final ChannelInstance[] outputs = new ChannelInstance[]{output};
// Execute.
this.evaluate(reduceByOperator, inputs, outputs);
// Verify the outcome.
final Iterable<Tuple2<String, Integer>> result = output.<Tuple2<String, Integer>>provideDataSet().collect();
final Set<Tuple2<String, Integer>> resultSet = new HashSet<>();
result.forEach(resultSet::add);
final Tuple2[] expectedResults = {
new Tuple2<>("a", 3),
new Tuple2<>("b", 2),
new Tuple2<>("c", 4),
new Tuple2<>("d", 1),
new Tuple2<>("e", 2),
new Tuple2<>("f", 3)
};
Arrays.stream(expectedResults)
.forEach(expected -> Assert.assertTrue("Not contained: " + expected, resultSet.contains(expected)));
Assert.assertEquals(expectedResults.length, resultSet.size());
}
}