/* * 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 gobblin.runtime.mapreduce; import com.google.common.base.Charsets; import com.google.common.base.Splitter; import com.google.common.collect.Lists; import com.google.common.collect.Maps; import com.google.common.io.Files; import gobblin.configuration.ConfigurationKeys; import gobblin.configuration.SourceState; import gobblin.configuration.WorkUnitState; import gobblin.runtime.api.JobExecutionResult; import gobblin.runtime.embedded.EmbeddedGobblin; import gobblin.runtime.task.TaskUtils; import gobblin.source.Source; import gobblin.source.extractor.Extractor; import gobblin.source.workunit.WorkUnit; import java.io.File; import java.io.IOException; import java.util.List; import java.util.Map; import java.util.StringTokenizer; import lombok.extern.slf4j.Slf4j; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.testng.Assert; import org.testng.annotations.Test; @Slf4j public class MRTaskFactoryTest { @Test public void test() throws Exception { File inputSuperPath = Files.createTempDir(); inputSuperPath.deleteOnExit(); File outputSuperPath = Files.createTempDir(); outputSuperPath.deleteOnExit(); File job1Dir = new File(inputSuperPath, "job1"); Assert.assertTrue(job1Dir.mkdir()); writeFileWithContent(job1Dir, "file1", "word1 word1 word2"); writeFileWithContent(job1Dir, "file2", "word2 word2 word2"); File job2Dir = new File(inputSuperPath, "job2"); Assert.assertTrue(job2Dir.mkdir()); writeFileWithContent(job2Dir, "file1", "word1 word2 word2"); EmbeddedGobblin embeddedGobblin = new EmbeddedGobblin("WordCounter") .setConfiguration(ConfigurationKeys.SOURCE_CLASS_KEY, MRWordCountSource.class.getName()) .setConfiguration(MRWordCountSource.INPUT_DIRECTORIES_KEY, job1Dir.getAbsolutePath() + "," + job2Dir.getAbsolutePath()) .setConfiguration(MRWordCountSource.OUTPUT_LOCATION, outputSuperPath.getAbsolutePath()); JobExecutionResult result = embeddedGobblin.run(); Assert.assertTrue(result.isSuccessful()); File output1 = new File(new File(outputSuperPath, "job1"), "part-r-00000"); Assert.assertTrue(output1.exists()); Map<String, Integer> counts = parseCounts(output1); Assert.assertEquals((int) counts.get("word1"), 2); Assert.assertEquals((int) counts.get("word2"), 4); File output2 = new File(new File(outputSuperPath, "job2"), "part-r-00000"); Assert.assertTrue(output2.exists()); counts = parseCounts(output2); Assert.assertEquals((int) counts.get("word1"), 1); Assert.assertEquals((int) counts.get("word2"), 2); } private Map<String, Integer> parseCounts(File file) throws IOException { Map<String, Integer> counts = Maps.newHashMap(); for (String line : Files.readLines(file, Charsets.UTF_8)) { List<String> split = Splitter.on("\t").splitToList(line); counts.put(split.get(0), Integer.parseInt(split.get(1))); } return counts; } private void writeFileWithContent(File dir, String fileName, String content) throws IOException { File file = new File(dir, fileName); Assert.assertTrue(file.createNewFile()); Files.write(content, file, Charsets.UTF_8); } public static class MRWordCountSource implements Source<String, String> { public static final String INPUT_DIRECTORIES_KEY = "input.directories"; public static final String OUTPUT_LOCATION = "output.location"; @Override public List<WorkUnit> getWorkunits(SourceState state) { List<String> dirs = Splitter.on(",").splitToList(state.getProp(INPUT_DIRECTORIES_KEY)); String outputBase = state.getProp(OUTPUT_LOCATION); List<WorkUnit> workUnits = Lists.newArrayList(); for (String dir : dirs) { try { Path input = new Path(dir); Path output = new Path(outputBase, input.getName()); WorkUnit workUnit = new WorkUnit(); TaskUtils.setTaskFactoryClass(workUnit, MRTaskFactory.class); Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "WordCount_" + input.getName()); job.setJarByClass(MRTaskFactoryTest.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setNumReduceTasks(1); FileInputFormat.addInputPath(job, input); FileOutputFormat.setOutputPath(job, output); MRTask.serializeJobToState(workUnit, job); workUnits.add(workUnit); } catch (IOException ioe) { log.error("Failed to create MR job for " + dir, ioe); } } return workUnits; } @Override public Extractor<String, String> getExtractor(WorkUnitState state) throws IOException { throw new UnsupportedOperationException(); } @Override public void shutdown(SourceState state) { throw new UnsupportedOperationException(); } } // This is taken directly from // https://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Mapper.Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } // This is taken directly from // https://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } }