/**
* 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.lib.aggregate;
import java.io.IOException;
import java.util.ArrayList;
import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.InputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.SequenceFileInputFormat;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.hadoop.mapred.jobcontrol.Job;
import org.apache.hadoop.mapred.jobcontrol.JobControl;
import org.apache.hadoop.util.GenericOptionsParser;
/**
* This is the main class for creating a map/reduce job using Aggregate
* framework. The Aggregate is a specialization of map/reduce framework,
* specilizing for performing various simple aggregations.
*
* Generally speaking, in order to implement an application using Map/Reduce
* model, the developer is to implement Map and Reduce functions (and possibly
* combine function). However, a lot of applications related to counting and
* statistics computing have very similar characteristics. Aggregate abstracts
* out the general patterns of these functions and implementing those patterns.
* In particular, the package provides generic mapper/redducer/combiner classes,
* and a set of built-in value aggregators, and a generic utility class that
* helps user create map/reduce jobs using the generic class. The built-in
* aggregators include:
*
* sum over numeric values count the number of distinct values compute the
* histogram of values compute the minimum, maximum, media,average, standard
* deviation of numeric values
*
* The developer using Aggregate will need only to provide a plugin class
* conforming to the following interface:
*
* public interface ValueAggregatorDescriptor { public ArrayList<Entry>
* generateKeyValPairs(Object key, Object value); public void
* configure(JobConfjob); }
*
* The package also provides a base class, ValueAggregatorBaseDescriptor,
* implementing the above interface. The user can extend the base class and
* implement generateKeyValPairs accordingly.
*
* The primary work of generateKeyValPairs is to emit one or more key/value
* pairs based on the input key/value pair. The key in an output key/value pair
* encode two pieces of information: aggregation type and aggregation id. The
* value will be aggregated onto the aggregation id according the aggregation
* type.
*
* This class offers a function to generate a map/reduce job using Aggregate
* framework. The function takes the following parameters: input directory spec
* input format (text or sequence file) output directory a file specifying the
* user plugin class
*/
@InterfaceAudience.Public
@InterfaceStability.Stable
public class ValueAggregatorJob {
public static JobControl createValueAggregatorJobs(String args[]
, Class<? extends ValueAggregatorDescriptor>[] descriptors) throws IOException {
JobControl theControl = new JobControl("ValueAggregatorJobs");
ArrayList<Job> dependingJobs = new ArrayList<Job>();
JobConf aJobConf = createValueAggregatorJob(args);
if(descriptors != null)
setAggregatorDescriptors(aJobConf, descriptors);
Job aJob = new Job(aJobConf, dependingJobs);
theControl.addJob(aJob);
return theControl;
}
public static JobControl createValueAggregatorJobs(String args[]) throws IOException {
return createValueAggregatorJobs(args, null);
}
/**
* Create an Aggregate based map/reduce job.
*
* @param args the arguments used for job creation. Generic hadoop
* arguments are accepted.
* @param caller the the caller class.
* @return a JobConf object ready for submission.
*
* @throws IOException
* @see GenericOptionsParser
*/
@SuppressWarnings("rawtypes")
public static JobConf createValueAggregatorJob(String args[], Class<?> caller)
throws IOException {
Configuration conf = new Configuration();
GenericOptionsParser genericParser
= new GenericOptionsParser(conf, args);
args = genericParser.getRemainingArgs();
if (args.length < 2) {
System.out.println("usage: inputDirs outDir "
+ "[numOfReducer [textinputformat|seq [specfile [jobName]]]]");
GenericOptionsParser.printGenericCommandUsage(System.out);
System.exit(1);
}
String inputDir = args[0];
String outputDir = args[1];
int numOfReducers = 1;
if (args.length > 2) {
numOfReducers = Integer.parseInt(args[2]);
}
Class<? extends InputFormat> theInputFormat =
TextInputFormat.class;
if (args.length > 3 &&
args[3].compareToIgnoreCase("textinputformat") == 0) {
theInputFormat = TextInputFormat.class;
} else {
theInputFormat = SequenceFileInputFormat.class;
}
Path specFile = null;
if (args.length > 4) {
specFile = new Path(args[4]);
}
String jobName = "";
if (args.length > 5) {
jobName = args[5];
}
JobConf theJob = new JobConf(conf);
if (specFile != null) {
theJob.addResource(specFile);
}
String userJarFile = theJob.get("user.jar.file");
if (userJarFile == null) {
theJob.setJarByClass(caller != null ? caller : ValueAggregatorJob.class);
} else {
theJob.setJar(userJarFile);
}
theJob.setJobName("ValueAggregatorJob: " + jobName);
FileInputFormat.addInputPaths(theJob, inputDir);
theJob.setInputFormat(theInputFormat);
theJob.setMapperClass(ValueAggregatorMapper.class);
FileOutputFormat.setOutputPath(theJob, new Path(outputDir));
theJob.setOutputFormat(TextOutputFormat.class);
theJob.setMapOutputKeyClass(Text.class);
theJob.setMapOutputValueClass(Text.class);
theJob.setOutputKeyClass(Text.class);
theJob.setOutputValueClass(Text.class);
theJob.setReducerClass(ValueAggregatorReducer.class);
theJob.setCombinerClass(ValueAggregatorCombiner.class);
theJob.setNumMapTasks(1);
theJob.setNumReduceTasks(numOfReducers);
return theJob;
}
/**
* Create an Aggregate based map/reduce job.
*
* @param args the arguments used for job creation. Generic hadoop
* arguments are accepted.
* @return a JobConf object ready for submission.
*
* @throws IOException
* @see GenericOptionsParser
*/
public static JobConf createValueAggregatorJob(String args[])
throws IOException {
return createValueAggregatorJob(args, ValueAggregator.class);
}
public static JobConf createValueAggregatorJob(String args[]
, Class<? extends ValueAggregatorDescriptor>[] descriptors)
throws IOException {
JobConf job = createValueAggregatorJob(args);
setAggregatorDescriptors(job, descriptors);
return job;
}
public static void setAggregatorDescriptors(JobConf job
, Class<? extends ValueAggregatorDescriptor>[] descriptors) {
job.setInt("aggregator.descriptor.num", descriptors.length);
//specify the aggregator descriptors
for(int i=0; i< descriptors.length; i++) {
job.set("aggregator.descriptor." + i, "UserDefined," + descriptors[i].getName());
}
}
public static JobConf createValueAggregatorJob(String args[],
Class<? extends ValueAggregatorDescriptor>[] descriptors,
Class<?> caller) throws IOException {
JobConf job = createValueAggregatorJob(args, caller);
setAggregatorDescriptors(job, descriptors);
return job;
}
/**
* create and run an Aggregate based map/reduce job.
*
* @param args the arguments used for job creation
* @throws IOException
*/
public static void main(String args[]) throws IOException {
JobConf job = ValueAggregatorJob.createValueAggregatorJob(args);
JobClient.runJob(job);
}
}