| /** |
| * 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.hadoop.record; |
| |
| import org.apache.hadoop.mapred.*; |
| import org.apache.hadoop.fs.*; |
| import org.apache.hadoop.io.*; |
| import org.apache.hadoop.io.SequenceFile.CompressionType; |
| import org.apache.hadoop.conf.*; |
| import junit.framework.TestCase; |
| import java.io.*; |
| import java.util.*; |
| |
| |
| /********************************************************** |
| * MapredLoadTest generates a bunch of work that exercises |
| * a Hadoop Map-Reduce system (and DFS, too). It goes through |
| * the following steps: |
| * |
| * 1) Take inputs 'range' and 'counts'. |
| * 2) Generate 'counts' random integers between 0 and range-1. |
| * 3) Create a file that lists each integer between 0 and range-1, |
| * and lists the number of times that integer was generated. |
| * 4) Emit a (very large) file that contains all the integers |
| * in the order generated. |
| * 5) After the file has been generated, read it back and count |
| * how many times each int was generated. |
| * 6) Compare this big count-map against the original one. If |
| * they match, then SUCCESS! Otherwise, FAILURE! |
| * |
| * OK, that's how we can think about it. What are the map-reduce |
| * steps that get the job done? |
| * |
| * 1) In a non-mapred thread, take the inputs 'range' and 'counts'. |
| * 2) In a non-mapread thread, generate the answer-key and write to disk. |
| * 3) In a mapred job, divide the answer key into K jobs. |
| * 4) A mapred 'generator' task consists of K map jobs. Each reads |
| * an individual "sub-key", and generates integers according to |
| * to it (though with a random ordering). |
| * 5) The generator's reduce task agglomerates all of those files |
| * into a single one. |
| * 6) A mapred 'reader' task consists of M map jobs. The output |
| * file is cut into M pieces. Each of the M jobs counts the |
| * individual ints in its chunk and creates a map of all seen ints. |
| * 7) A mapred job integrates all the count files into a single one. |
| * |
| **********************************************************/ |
| public class TestRecordMR extends TestCase { |
| /** |
| * Modified to make it a junit test. |
| * The RandomGen Job does the actual work of creating |
| * a huge file of assorted numbers. It receives instructions |
| * as to how many times each number should be counted. Then |
| * it emits those numbers in a crazy order. |
| * |
| * The map() function takes a key/val pair that describes |
| * a value-to-be-emitted (the key) and how many times it |
| * should be emitted (the value), aka "numtimes". map() then |
| * emits a series of intermediate key/val pairs. It emits |
| * 'numtimes' of these. The key is a random number and the |
| * value is the 'value-to-be-emitted'. |
| * |
| * The system collates and merges these pairs according to |
| * the random number. reduce() function takes in a key/value |
| * pair that consists of a crazy random number and a series |
| * of values that should be emitted. The random number key |
| * is now dropped, and reduce() emits a pair for every intermediate value. |
| * The emitted key is an intermediate value. The emitted value |
| * is just a blank string. Thus, we've created a huge file |
| * of numbers in random order, but where each number appears |
| * as many times as we were instructed. |
| */ |
| static public class RandomGenMapper implements Mapper<RecInt, RecInt, |
| RecInt, RecString> { |
| Random r = new Random(); |
| public void configure(JobConf job) { |
| } |
| |
| public void map(RecInt key, |
| RecInt val, |
| OutputCollector<RecInt, RecString> out, |
| Reporter reporter) throws IOException { |
| int randomVal = key.getData(); |
| int randomCount = val.getData(); |
| |
| for (int i = 0; i < randomCount; i++) { |
| out.collect(new RecInt(Math.abs(r.nextInt())), |
| new RecString(Integer.toString(randomVal))); |
| } |
| } |
| public void close() { |
| } |
| } |
| /** |
| */ |
| static public class RandomGenReducer implements Reducer<RecInt, RecString, |
| RecInt, RecString> { |
| public void configure(JobConf job) { |
| } |
| |
| public void reduce(RecInt key, |
| Iterator<RecString> it, |
| OutputCollector<RecInt, RecString> out, |
| Reporter reporter) throws IOException { |
| int keyint = key.getData(); |
| while (it.hasNext()) { |
| String val = it.next().getData(); |
| out.collect(new RecInt(Integer.parseInt(val)), |
| new RecString("")); |
| } |
| } |
| public void close() { |
| } |
| } |
| |
| /** |
| * The RandomCheck Job does a lot of our work. It takes |
| * in a num/string keyspace, and transforms it into a |
| * key/count(int) keyspace. |
| * |
| * The map() function just emits a num/1 pair for every |
| * num/string input pair. |
| * |
| * The reduce() function sums up all the 1s that were |
| * emitted for a single key. It then emits the key/total |
| * pair. |
| * |
| * This is used to regenerate the random number "answer key". |
| * Each key here is a random number, and the count is the |
| * number of times the number was emitted. |
| */ |
| static public class RandomCheckMapper implements Mapper<RecInt, RecString, |
| RecInt, RecString> { |
| public void configure(JobConf job) { |
| } |
| |
| public void map(RecInt key, |
| RecString val, |
| OutputCollector<RecInt, RecString> out, |
| Reporter reporter) throws IOException { |
| int pos = key.getData(); |
| String str = val.getData(); |
| out.collect(new RecInt(pos), new RecString("1")); |
| } |
| public void close() { |
| } |
| } |
| /** |
| */ |
| static public class RandomCheckReducer implements Reducer<RecInt, RecString, |
| RecInt, RecString> { |
| public void configure(JobConf job) { |
| } |
| |
| public void reduce(RecInt key, |
| Iterator<RecString> it, |
| OutputCollector<RecInt, RecString> out, |
| Reporter reporter) throws IOException { |
| int keyint = key.getData(); |
| int count = 0; |
| while (it.hasNext()) { |
| it.next(); |
| count++; |
| } |
| out.collect(new RecInt(keyint), new RecString(Integer.toString(count))); |
| } |
| public void close() { |
| } |
| } |
| |
| /** |
| * The Merge Job is a really simple one. It takes in |
| * an int/int key-value set, and emits the same set. |
| * But it merges identical keys by adding their values. |
| * |
| * Thus, the map() function is just the identity function |
| * and reduce() just sums. Nothing to see here! |
| */ |
| static public class MergeMapper implements Mapper<RecInt, RecString, |
| RecInt, RecInt> { |
| public void configure(JobConf job) { |
| } |
| |
| public void map(RecInt key, |
| RecString val, |
| OutputCollector<RecInt, RecInt> out, |
| Reporter reporter) throws IOException { |
| int keyint = key.getData(); |
| String valstr = val.getData(); |
| out.collect(new RecInt(keyint), new RecInt(Integer.parseInt(valstr))); |
| } |
| public void close() { |
| } |
| } |
| static public class MergeReducer implements Reducer<RecInt, RecInt, |
| RecInt, RecInt> { |
| public void configure(JobConf job) { |
| } |
| |
| public void reduce(RecInt key, |
| Iterator<RecInt> it, |
| OutputCollector<RecInt, RecInt> out, |
| Reporter reporter) throws IOException { |
| int keyint = key.getData(); |
| int total = 0; |
| while (it.hasNext()) { |
| total += it.next().getData(); |
| } |
| out.collect(new RecInt(keyint), new RecInt(total)); |
| } |
| public void close() { |
| } |
| } |
| |
| private static int range = 10; |
| private static int counts = 100; |
| private static Random r = new Random(); |
| private static Configuration conf = new Configuration(); |
| |
| public void testMapred() throws Exception { |
| launch(); |
| } |
| |
| /** |
| * |
| */ |
| public static void launch() throws Exception { |
| // |
| // Generate distribution of ints. This is the answer key. |
| // |
| int countsToGo = counts; |
| int dist[] = new int[range]; |
| for (int i = 0; i < range; i++) { |
| double avgInts = (1.0 * countsToGo) / (range - i); |
| dist[i] = (int) Math.max(0, Math.round(avgInts + (Math.sqrt(avgInts) * r.nextGaussian()))); |
| countsToGo -= dist[i]; |
| } |
| if (countsToGo > 0) { |
| dist[dist.length-1] += countsToGo; |
| } |
| |
| // |
| // Write the answer key to a file. |
| // |
| FileSystem fs = FileSystem.get(conf); |
| Path testdir = new Path("mapred.loadtest"); |
| if (!fs.mkdirs(testdir)) { |
| throw new IOException("Mkdirs failed to create directory " + testdir.toString()); |
| } |
| |
| Path randomIns = new Path(testdir, "genins"); |
| if (!fs.mkdirs(randomIns)) { |
| throw new IOException("Mkdirs failed to create directory " + randomIns.toString()); |
| } |
| |
| Path answerkey = new Path(randomIns, "answer.key"); |
| SequenceFile.Writer out = SequenceFile.createWriter(fs, conf, |
| answerkey, RecInt.class, RecInt.class, |
| CompressionType.NONE); |
| try { |
| for (int i = 0; i < range; i++) { |
| RecInt k = new RecInt(); |
| RecInt v = new RecInt(); |
| k.setData(i); |
| v.setData(dist[i]); |
| out.append(k, v); |
| } |
| } finally { |
| out.close(); |
| } |
| |
| // |
| // Now we need to generate the random numbers according to |
| // the above distribution. |
| // |
| // We create a lot of map tasks, each of which takes at least |
| // one "line" of the distribution. (That is, a certain number |
| // X is to be generated Y number of times.) |
| // |
| // A map task emits Y key/val pairs. The val is X. The key |
| // is a randomly-generated number. |
| // |
| // The reduce task gets its input sorted by key. That is, sorted |
| // in random order. It then emits a single line of text that |
| // for the given values. It does not emit the key. |
| // |
| // Because there's just one reduce task, we emit a single big |
| // file of random numbers. |
| // |
| Path randomOuts = new Path(testdir, "genouts"); |
| fs.delete(randomOuts, true); |
| |
| |
| JobConf genJob = new JobConf(conf, TestRecordMR.class); |
| FileInputFormat.setInputPaths(genJob, randomIns); |
| genJob.setInputFormat(SequenceFileInputFormat.class); |
| genJob.setMapperClass(RandomGenMapper.class); |
| |
| FileOutputFormat.setOutputPath(genJob, randomOuts); |
| genJob.setOutputKeyClass(RecInt.class); |
| genJob.setOutputValueClass(RecString.class); |
| genJob.setOutputFormat(SequenceFileOutputFormat.class); |
| genJob.setReducerClass(RandomGenReducer.class); |
| genJob.setNumReduceTasks(1); |
| |
| JobClient.runJob(genJob); |
| |
| // |
| // Next, we read the big file in and regenerate the |
| // original map. It's split into a number of parts. |
| // (That number is 'intermediateReduces'.) |
| // |
| // We have many map tasks, each of which read at least one |
| // of the output numbers. For each number read in, the |
| // map task emits a key/value pair where the key is the |
| // number and the value is "1". |
| // |
| // We have a single reduce task, which receives its input |
| // sorted by the key emitted above. For each key, there will |
| // be a certain number of "1" values. The reduce task sums |
| // these values to compute how many times the given key was |
| // emitted. |
| // |
| // The reduce task then emits a key/val pair where the key |
| // is the number in question, and the value is the number of |
| // times the key was emitted. This is the same format as the |
| // original answer key (except that numbers emitted zero times |
| // will not appear in the regenerated key.) The answer set |
| // is split into a number of pieces. A final MapReduce job |
| // will merge them. |
| // |
| // There's not really a need to go to 10 reduces here |
| // instead of 1. But we want to test what happens when |
| // you have multiple reduces at once. |
| // |
| int intermediateReduces = 10; |
| Path intermediateOuts = new Path(testdir, "intermediateouts"); |
| fs.delete(intermediateOuts, true); |
| JobConf checkJob = new JobConf(conf, TestRecordMR.class); |
| FileInputFormat.setInputPaths(checkJob, randomOuts); |
| checkJob.setInputFormat(SequenceFileInputFormat.class); |
| checkJob.setMapperClass(RandomCheckMapper.class); |
| |
| FileOutputFormat.setOutputPath(checkJob, intermediateOuts); |
| checkJob.setOutputKeyClass(RecInt.class); |
| checkJob.setOutputValueClass(RecString.class); |
| checkJob.setOutputFormat(SequenceFileOutputFormat.class); |
| checkJob.setReducerClass(RandomCheckReducer.class); |
| checkJob.setNumReduceTasks(intermediateReduces); |
| |
| JobClient.runJob(checkJob); |
| |
| // |
| // OK, now we take the output from the last job and |
| // merge it down to a single file. The map() and reduce() |
| // functions don't really do anything except reemit tuples. |
| // But by having a single reduce task here, we end up merging |
| // all the files. |
| // |
| Path finalOuts = new Path(testdir, "finalouts"); |
| fs.delete(finalOuts, true); |
| JobConf mergeJob = new JobConf(conf, TestRecordMR.class); |
| FileInputFormat.setInputPaths(mergeJob, intermediateOuts); |
| mergeJob.setInputFormat(SequenceFileInputFormat.class); |
| mergeJob.setMapperClass(MergeMapper.class); |
| |
| FileOutputFormat.setOutputPath(mergeJob, finalOuts); |
| mergeJob.setOutputKeyClass(RecInt.class); |
| mergeJob.setOutputValueClass(RecInt.class); |
| mergeJob.setOutputFormat(SequenceFileOutputFormat.class); |
| mergeJob.setReducerClass(MergeReducer.class); |
| mergeJob.setNumReduceTasks(1); |
| |
| JobClient.runJob(mergeJob); |
| |
| |
| // |
| // Finally, we compare the reconstructed answer key with the |
| // original one. Remember, we need to ignore zero-count items |
| // in the original key. |
| // |
| boolean success = true; |
| Path recomputedkey = new Path(finalOuts, "part-00000"); |
| SequenceFile.Reader in = new SequenceFile.Reader(fs, recomputedkey, conf); |
| int totalseen = 0; |
| try { |
| RecInt key = new RecInt(); |
| RecInt val = new RecInt(); |
| for (int i = 0; i < range; i++) { |
| if (dist[i] == 0) { |
| continue; |
| } |
| if (!in.next(key, val)) { |
| System.err.println("Cannot read entry " + i); |
| success = false; |
| break; |
| } else { |
| if (!((key.getData() == i) && (val.getData() == dist[i]))) { |
| System.err.println("Mismatch! Pos=" + key.getData() + ", i=" + i + ", val=" + val.getData() + ", dist[i]=" + dist[i]); |
| success = false; |
| } |
| totalseen += val.getData(); |
| } |
| } |
| if (success) { |
| if (in.next(key, val)) { |
| System.err.println("Unnecessary lines in recomputed key!"); |
| success = false; |
| } |
| } |
| } finally { |
| in.close(); |
| } |
| int originalTotal = 0; |
| for (int i = 0; i < dist.length; i++) { |
| originalTotal += dist[i]; |
| } |
| System.out.println("Original sum: " + originalTotal); |
| System.out.println("Recomputed sum: " + totalseen); |
| |
| // |
| // Write to "results" whether the test succeeded or not. |
| // |
| Path resultFile = new Path(testdir, "results"); |
| BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(fs.create(resultFile))); |
| try { |
| bw.write("Success=" + success + "\n"); |
| System.out.println("Success=" + success); |
| } finally { |
| bw.close(); |
| } |
| fs.delete(testdir, true); |
| } |
| |
| /** |
| * Launches all the tasks in order. |
| */ |
| public static void main(String[] argv) throws Exception { |
| if (argv.length < 2) { |
| System.err.println("Usage: TestRecordMR <range> <counts>"); |
| System.err.println(); |
| System.err.println("Note: a good test will have a <counts> value that is substantially larger than the <range>"); |
| return; |
| } |
| |
| int i = 0; |
| int range = Integer.parseInt(argv[i++]); |
| int counts = Integer.parseInt(argv[i++]); |
| launch(); |
| } |
| } |