MAPREDUCE-6335. Created MR job based performance test driver for the timeline service v2. Contributed by Sangjin Lee.

(cherry picked from commit b689f5d43d3f5434a30fe52f1a7e12e1fc5c71f4)

hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-jobclient/src/test/java/org/apache/hadoop/mapred/TimelineServicePerformanceV2.java

@@ -0,0 +1,298 @@
+/**
+ * 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.mapred;
+
+import java.io.IOException;
+import java.util.Date;
+import java.util.Random;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.conf.Configured;
+import org.apache.hadoop.io.IntWritable;
+import org.apache.hadoop.io.Writable;
+import org.apache.hadoop.mapreduce.Job;
+import org.apache.hadoop.mapreduce.MRJobConfig;
+import org.apache.hadoop.mapreduce.SleepJob.SleepInputFormat;
+import org.apache.hadoop.mapreduce.TaskAttemptID;
+import org.apache.hadoop.mapreduce.lib.output.NullOutputFormat;
+import org.apache.hadoop.security.UserGroupInformation;
+import org.apache.hadoop.util.GenericOptionsParser;
+import org.apache.hadoop.util.Tool;
+import org.apache.hadoop.util.ToolRunner;
+import org.apache.hadoop.yarn.api.records.ApplicationId;
+import org.apache.hadoop.yarn.api.records.timelineservice.TimelineEntities;
+import org.apache.hadoop.yarn.api.records.timelineservice.TimelineEntity;
+import org.apache.hadoop.yarn.api.records.timelineservice.TimelineEvent;
+import org.apache.hadoop.yarn.api.records.timelineservice.TimelineMetric;
+import org.apache.hadoop.yarn.conf.YarnConfiguration;
+import org.apache.hadoop.yarn.server.timelineservice.collector.AppLevelTimelineCollector;
+import org.apache.hadoop.yarn.server.timelineservice.collector.TimelineCollectorContext;
+
+public class TimelineServicePerformanceV2 extends Configured implements Tool {
+  private static final Log LOG =
+      LogFactory.getLog(TimelineServicePerformanceV2.class);
+
+  static final int NUM_MAPS_DEFAULT = 1;
+
+  static final int SIMPLE_ENTITY_WRITER = 1;
+  // constants for mtype = 1
+  static final String KBS_SENT = "kbs sent";
+  static final int KBS_SENT_DEFAULT = 1;
+  static final String TEST_TIMES = "testtimes";
+  static final int TEST_TIMES_DEFAULT = 100;
+  static final String TIMELINE_SERVICE_PERFORMANCE_RUN_ID =
+      "timeline.server.performance.run.id";
+
+  static int mapperType = SIMPLE_ENTITY_WRITER;
+
+  protected static int printUsage() {
+    // TODO is there a way to handle mapper-specific options more gracefully?
+    System.err.println(
+        "Usage: [-m <maps>] number of mappers (default: " + NUM_MAPS_DEFAULT +
+            ")\n" +
+        "     [-mtype <mapper type in integer>] \n" +
+        "          1. simple entity write mapper\n" +
+        "     [-s <(KBs)test>] number of KB per put (default: " +
+            KBS_SENT_DEFAULT + " KB)\n" +
+        "     [-t] package sending iterations per mapper (default: " +
+            TEST_TIMES_DEFAULT + ")\n");
+    GenericOptionsParser.printGenericCommandUsage(System.err);
+    return -1;
+  }
+
+  /**
+   * Configure a job given argv.
+   */
+  public static boolean parseArgs(String[] args, Job job) throws IOException {
+    // set the defaults
+    Configuration conf = job.getConfiguration();
+    conf.setInt(MRJobConfig.NUM_MAPS, NUM_MAPS_DEFAULT);
+    conf.setInt(KBS_SENT, KBS_SENT_DEFAULT);
+    conf.setInt(TEST_TIMES, TEST_TIMES_DEFAULT);
+
+    for (int i = 0; i < args.length; i++) {
+      if (args.length == i + 1) {
+        System.out.println("ERROR: Required parameter missing from " + args[i]);
+        return printUsage() == 0;
+      }
+      try {
+        if ("-m".equals(args[i])) {
+          if (Integer.parseInt(args[++i]) > 0) {
+            job.getConfiguration()
+                .setInt(MRJobConfig.NUM_MAPS, (Integer.parseInt(args[i])));
+          }
+        } else if ("-mtype".equals(args[i])) {
+          mapperType = Integer.parseInt(args[++i]);
+          switch (mapperType) {
+          case SIMPLE_ENTITY_WRITER:
+            job.setMapperClass(SimpleEntityWriter.class);
+            break;
+          default:
+            job.setMapperClass(SimpleEntityWriter.class);
+          }
+        } else if ("-s".equals(args[i])) {
+          if (Integer.parseInt(args[++i]) > 0) {
+            conf.setInt(KBS_SENT, (Integer.parseInt(args[i])));
+          }
+        } else if ("-t".equals(args[i])) {
+          if (Integer.parseInt(args[++i]) > 0) {
+            conf.setInt(TEST_TIMES, (Integer.parseInt(args[i])));
+          }
+        } else {
+          System.out.println("Unexpected argument: " + args[i]);
+          return printUsage() == 0;
+        }
+      } catch (NumberFormatException except) {
+        System.out.println("ERROR: Integer expected instead of " + args[i]);
+        return printUsage() == 0;
+      } catch (Exception e) {
+        throw (IOException)new IOException().initCause(e);
+      }
+    }
+
+    return true;
+  }
+
+  /**
+   * TimelineServer Performance counters
+   */
+  static enum PerfCounters {
+    TIMELINE_SERVICE_WRITE_TIME,
+    TIMELINE_SERVICE_WRITE_COUNTER,
+    TIMELINE_SERVICE_WRITE_FAILURES,
+    TIMELINE_SERVICE_WRITE_KBS,
+  }
+
+  public int run(String[] args) throws Exception {
+
+    Job job = Job.getInstance(getConf());
+    job.setJarByClass(TimelineServicePerformanceV2.class);
+    job.setMapperClass(SimpleEntityWriter.class);
+    job.setInputFormatClass(SleepInputFormat.class);
+    job.setOutputFormatClass(NullOutputFormat.class);
+    job.setNumReduceTasks(0);
+    if (!parseArgs(args, job)) {
+      return -1;
+    }
+
+    // for mtype = 1
+    // use the current timestamp as the "run id" of the test: this will be used
+    // as simulating the cluster timestamp for apps
+    Configuration conf = job.getConfiguration();
+    conf.setLong(TIMELINE_SERVICE_PERFORMANCE_RUN_ID,
+        System.currentTimeMillis());
+
+    Date startTime = new Date();
+    System.out.println("Job started: " + startTime);
+    int ret = job.waitForCompletion(true) ? 0 : 1;
+    org.apache.hadoop.mapreduce.Counters counters = job.getCounters();
+    long writetime =
+        counters.findCounter(PerfCounters.TIMELINE_SERVICE_WRITE_TIME).getValue();
+    long writecounts =
+        counters.findCounter(PerfCounters.TIMELINE_SERVICE_WRITE_COUNTER).getValue();
+    long writesize =
+        counters.findCounter(PerfCounters.TIMELINE_SERVICE_WRITE_KBS).getValue();
+    double transacrate = writecounts * 1000 / (double)writetime;
+    double iorate = writesize * 1000 / (double)writetime;
+    int numMaps = Integer.parseInt(conf.get(MRJobConfig.NUM_MAPS));
+
+    System.out.println("TRANSACTION RATE (per mapper): " + transacrate +
+        " ops/s");
+    System.out.println("IO RATE (per mapper): " + iorate + " KB/s");
+
+    System.out.println("TRANSACTION RATE (total): " + transacrate*numMaps +
+        " ops/s");
+    System.out.println("IO RATE (total): " + iorate*numMaps + " KB/s");
+
+    return ret;
+  }
+
+  public static void main(String[] args) throws Exception {
+    int res =
+        ToolRunner.run(new Configuration(), new TimelineServicePerformanceV2(),
+            args);
+    System.exit(res);
+  }
+
+  /**
+   *  To ensure that the compression really gets exercised, generate a
+   *  random alphanumeric fixed length payload
+   */
+  static final char[] alphaNums = new char[] { 'a', 'b', 'c', 'd', 'e', 'f',
+    'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r',
+    's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D',
+    'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',
+    'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '1', '2',
+    '3', '4', '5', '6', '7', '8', '9', '0', ' ' };
+
+  /**
+   * Adds simple entities with random string payload, events, metrics, and
+   * configuration.
+   */
+  public static class SimpleEntityWriter
+      extends org.apache.hadoop.mapreduce.Mapper<IntWritable,IntWritable,Writable,Writable> {
+    public void map(IntWritable key, IntWritable val, Context context)
+        throws IOException {
+
+      Configuration conf = context.getConfiguration();
+      // simulate the app id with the task id
+      int taskId = context.getTaskAttemptID().getTaskID().getId();
+      long timestamp = conf.getLong(TIMELINE_SERVICE_PERFORMANCE_RUN_ID, 0);
+      ApplicationId appId = ApplicationId.newInstance(timestamp, taskId);
+
+      // create the app level timeline collector
+      Configuration tlConf = new YarnConfiguration();
+      AppLevelTimelineCollector collector =
+          new AppLevelTimelineCollector(appId);
+      collector.init(tlConf);
+      collector.start();
+
+      try {
+        // set the context
+        // flow id: job name, flow run id: timestamp, user id
+        TimelineCollectorContext tlContext =
+            collector.getTimelineEntityContext();
+        tlContext.setFlowName(context.getJobName());
+        tlContext.setFlowRunId(timestamp);
+        tlContext.setUserId(context.getUser());
+
+        final int kbs = Integer.parseInt(conf.get(KBS_SENT));
+
+        long totalTime = 0;
+        final int testtimes = Integer.parseInt(conf.get(TEST_TIMES));
+        final Random rand = new Random();
+        final TaskAttemptID taskAttemptId = context.getTaskAttemptID();
+        final char[] payLoad = new char[kbs * 1024];
+
+        for (int i = 0; i < testtimes; i++) {
+          // Generate a fixed length random payload
+          for (int xx = 0; xx < kbs * 1024; xx++) {
+            int alphaNumIdx = rand.nextInt(alphaNums.length);
+            payLoad[xx] = alphaNums[alphaNumIdx];
+          }
+          String entId = taskAttemptId + "_" + Integer.toString(i);
+          final TimelineEntity entity = new TimelineEntity();
+          entity.setId(entId);
+          entity.setType("FOO_ATTEMPT");
+          entity.addInfo("PERF_TEST", payLoad);
+          // add an event
+          TimelineEvent event = new TimelineEvent();
+          event.setTimestamp(System.currentTimeMillis());
+          event.addInfo("foo_event", "test");
+          entity.addEvent(event);
+          // add a metric
+          TimelineMetric metric = new TimelineMetric();
+          metric.setId("foo_metric");
+          metric.setSingleData(123456789L);
+          entity.addMetric(metric);
+          // add a config
+          entity.addConfig("foo", "bar");
+
+          TimelineEntities entities = new TimelineEntities();
+          entities.addEntity(entity);
+          // use the current user for this purpose
+          UserGroupInformation ugi = UserGroupInformation.getCurrentUser();
+          long startWrite = System.nanoTime();
+          try {
+            collector.putEntities(entities, ugi);
+          } catch (Exception e) {
+            context.getCounter(PerfCounters.TIMELINE_SERVICE_WRITE_FAILURES).
+                increment(1);
+            e.printStackTrace();
+          }
+          long endWrite = System.nanoTime();
+          totalTime += (endWrite-startWrite)/1000000L;
+        }
+        LOG.info("wrote " + testtimes + " entities (" + kbs*testtimes +
+            " kB) in " + totalTime + " ms");
+        context.getCounter(PerfCounters.TIMELINE_SERVICE_WRITE_TIME).
+            increment(totalTime);
+        context.getCounter(PerfCounters.TIMELINE_SERVICE_WRITE_COUNTER).
+            increment(testtimes);
+        context.getCounter(PerfCounters.TIMELINE_SERVICE_WRITE_KBS).
+            increment(kbs*testtimes);
+      } finally {
+        // clean up
+        collector.close();
+      }
+    }
+  }
+}

hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-jobclient/src/test/java/org/apache/hadoop/test/MapredTestDriver.java

@@ -29,6 +29,7 @@ import org.apache.hadoop.mapred.TestSequenceFileInputFormat;
 import org.apache.hadoop.mapred.TestTextInputFormat;
 import org.apache.hadoop.mapred.ThreadedMapBenchmark;
 import org.apache.hadoop.mapreduce.TimelineServicePerformance;
+import org.apache.hadoop.mapred.TimelineServicePerformanceV2;
 import org.apache.hadoop.mapreduce.FailJob;
 import org.apache.hadoop.mapreduce.LargeSorter;
 import org.apache.hadoop.mapreduce.MiniHadoopClusterManager;
@@ -93,7 +94,9 @@ public class MapredTestDriver {
                    "A job that sleeps at each map and reduce task.");
       pgd.addClass("timelineperformance", TimelineServicePerformance.class,
                    "A job that launches mappers to test timlineserver performance.");
-      pgd.addClass("nnbench", NNBench.class,
+      pgd.addClass("timelineperformance", TimelineServicePerformanceV2.class,
+          "A job that launch mappers to test timline service v.2 performance.");
+      pgd.addClass("nnbench", NNBench.class, 
           "A benchmark that stresses the namenode w/ MR.");
       pgd.addClass("nnbenchWithoutMR", NNBenchWithoutMR.class,
           "A benchmark that stresses the namenode w/o MR.");