public static void main(String[] args) throws Exception {
String input = "hdfs://192.168.0.110:9000/input/access.log";
String output = "hdfs://192.168.0.110:9000/user/hdfs/pv";
JobConf conf = new JobConf(KPIPV.class);
conf.setJobName("KPIPV");
conf.setMapOutputKeyClass(Text.class);
conf.setMapOutputValueClass(IntWritable.class);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(KPIPVMapper.class);
conf.setCombinerClass(KPIPVReducer.class);
conf.setReducerClass(KPIPVReducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(input));
FileOutputFormat.setOutputPath(conf, new Path(output));
JobClient.runJob(conf);
System.exit(0);
}
public static void main(String[] args) throws Exception {
String input = "hdfs://centos:9000/access.log.10";
String output = "hdfs://centos:9000/out_kpitime";
JobConf conf = new JobConf(KPITime.class);
conf.setJobName("KPITime");
// conf.addResource("classpath:/hadoop/core-site.xml");
// conf.addResource("classpath:/hadoop/hdfs-site.xml");
// conf.addResource("classpath:/hadoop/mapred-site.xml");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(KPITimeMapper.class);
conf.setCombinerClass(KPITimeReducer.class);
conf.setReducerClass(KPITimeReducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(input));
FileOutputFormat.setOutputPath(conf, new Path(output));
JobClient.runJob(conf);
System.exit(0);
}
/**
* Run the job
*
* @param params The Job parameters containing the gramSize, input output folders, defaultCat,
* encoding
*/
public static void runJob(Parameters params) throws IOException {
Configurable client = new JobClient();
JobConf conf = new JobConf(BayesClassifierDriver.class);
conf.setJobName("Bayes Classifier Driver running over input: " + params.get("testDirPath"));
conf.setOutputKeyClass(StringTuple.class);
conf.setOutputValueClass(DoubleWritable.class);
FileInputFormat.setInputPaths(conf, new Path(params.get("testDirPath")));
Path outPath = new Path(params.get("testDirPath") + "-output");
FileOutputFormat.setOutputPath(conf, outPath);
conf.setInputFormat(KeyValueTextInputFormat.class);
conf.setMapperClass(BayesClassifierMapper.class);
conf.setCombinerClass(BayesClassifierReducer.class);
conf.setReducerClass(BayesClassifierReducer.class);
conf.setOutputFormat(SequenceFileOutputFormat.class);
conf.set(
"io.serializations",
"org.apache.hadoop.io.serializer.JavaSerialization,"
+ "org.apache.hadoop.io.serializer.WritableSerialization");
HadoopUtil.overwriteOutput(outPath);
conf.set("bayes.parameters", params.toString());
client.setConf(conf);
JobClient.runJob(conf);
Path outputFiles = new Path(outPath, "part*");
FileSystem dfs = FileSystem.get(outPath.toUri(), conf);
ConfusionMatrix matrix = readResult(dfs, outputFiles, conf, params);
log.info("{}", matrix.summarize());
}
public static void main(String[] args) throws Exception {
String dir1 = "/user/miyuru/wcout";
String dir2 = "/user/miyuru/notinverts";
// We first delete the temporary directories if they exist on the HDFS
FileSystem fs1 = FileSystem.get(new JobConf());
if (fs1.exists(new Path(dir2))) {
fs1.delete(new Path(dir2), true);
}
JobConf conf = new JobConf();
conf.setNumMapTasks(96);
conf.setOutputKeyClass(LongWritable.class);
conf.setOutputValueClass(LongWritable.class);
conf.setMapperClass(TokenizerMapper.class);
conf.setReducerClass(IntSumReducer.class);
conf.setCombinerClass(IntSumReducer.class);
conf.setInputFormat(NLinesInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(dir1));
FileOutputFormat.setOutputPath(conf, new Path(dir2));
Job job = new Job(conf, "NotInFinder");
job.setJarByClass(WordCount.class);
// job.setMapperClass(TokenizerMapper.class);
// job.setCombinerClass(IntSumReducer.class);
// job.setReducerClass(IntSumReducer.class);
// job.setOutputKeyClass(LongWritable.class);
// job.setOutputValueClass(LongWritable.class);
job.setSortComparatorClass(SortComparator.class);
job.waitForCompletion(true);
}
public int run(String[] args) throws Exception {
if (args.length < 2) {
printUsage();
return 1;
}
JobConf job = new JobConf(getConf(), MultiFileWordCount.class);
job.setJobName("MultiFileWordCount");
// set the InputFormat of the job to our InputFormat
job.setInputFormat(MyInputFormat.class);
// the keys are words (strings)
job.setOutputKeyClass(Text.class);
// the values are counts (ints)
job.setOutputValueClass(IntWritable.class);
// use the defined mapper
job.setMapperClass(MapClass.class);
// use the WordCount Reducer
job.setCombinerClass(LongSumReducer.class);
job.setReducerClass(LongSumReducer.class);
FileInputFormat.addInputPaths(job, args[0]);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
JobClient.runJob(job);
return 0;
}
public JobBuilder reducer(Class<? extends Reducer> reducer, boolean hasCombiner)
throws IOException {
if (reducer != IdentityReducer.class) _jobConf.setReducerClass(reducer);
if (hasCombiner) _jobConf.setCombinerClass(reducer);
_jobConf.setJarByClass(reducer);
return this;
}
@Override
public int run(String[] args) throws Exception {
if (args.length != 2) {
System.err.printf(
"Usage: %s [generic options] <input> <output>\n", getClass().getSimpleName());
ToolRunner.printGenericCommandUsage(System.err);
return -1;
}
JobConf conf = new JobConf(getConf(), getClass());
conf.setJobName("Max temperature");
FileInputFormat.addInputPath(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(MaxTemperatureMapper.class);
conf.setCombinerClass(MaxTemperatureReducer.class);
conf.setReducerClass(MaxTemperatureReducer.class);
// vv MaxTemperatureDriverV6
conf.setProfileEnabled(true);
conf.setProfileParams(
"-agentlib:hprof=cpu=samples,heap=sites,depth=6," + "force=n,thread=y,verbose=n,file=%s");
conf.setProfileTaskRange(true, "0-2");
// ^^ MaxTemperatureDriverV6
JobClient.runJob(conf);
return 0;
}
/**
* Configure the job
*
* @param conf Job to configure
* @param rules classification rules to evaluate
* @param target label value to evaluate the rules for
* @param inpath input path (the dataset)
* @param outpath output <code>Path</code>
* @param split DatasetSplit used to separate training and testing input
*/
private static void configureJob(
JobConf conf,
List<? extends Rule> rules,
int target,
Path inpath,
Path outpath,
DatasetSplit split) {
split.storeJobParameters(conf);
FileInputFormat.setInputPaths(conf, inpath);
FileOutputFormat.setOutputPath(conf, outpath);
conf.setOutputKeyClass(LongWritable.class);
conf.setOutputValueClass(CDFitness.class);
conf.setMapperClass(CDMapper.class);
conf.setCombinerClass(CDReducer.class);
conf.setReducerClass(CDReducer.class);
conf.setInputFormat(DatasetTextInputFormat.class);
conf.setOutputFormat(SequenceFileOutputFormat.class);
// store the parameters
conf.set(CDMapper.CLASSDISCOVERY_RULES, StringUtils.toString(rules));
conf.set(CDMapper.CLASSDISCOVERY_DATASET, StringUtils.toString(DataSet.getDataSet()));
conf.setInt(CDMapper.CLASSDISCOVERY_TARGET_LABEL, target);
}
/**
* {@inheritDoc}
*
* @see org.apache.hadoop.util.Tool#run(java.lang.String[])
*/
@Override
public int run(String[] args) throws Exception {
JobConf configuration = new JobConf(getConf(), WordCountExtended.class);
configuration.setJobName(JOB_NAME);
configuration.setOutputKeyClass(Text.class);
configuration.setOutputValueClass(IntWritable.class);
configuration.setMapperClass(Map.class);
configuration.setCombinerClass(Reduce.class);
configuration.setReducerClass(Reduce.class);
configuration.setInputFormat(TextInputFormat.class);
configuration.setOutputFormat(TextOutputFormat.class);
List<String> otherArgs = new ArrayList<String>();
for (int i = 0; i < args.length; ++i) {
if (JOB_SKIP_ARGUMENT.equals(args[i])) {
DistributedCache.addCacheFile(new Path(args[++i]).toUri(), configuration);
configuration.setBoolean(JOB_PARAMETER_SKIP_PATTERNS, true);
} else {
otherArgs.add(args[i]);
}
}
FileInputFormat.setInputPaths(configuration, new Path(otherArgs.get(0)));
FileOutputFormat.setOutputPath(configuration, new Path(otherArgs.get(1)));
JobClient.runJob(configuration);
return 0;
}
/**
* The main driver for word count map/reduce program. Invoke this method to submit the map/reduce
* job.
*
* @throws IOException When there is communication problems with the job tracker.
*/
public int run(String[] args) throws Exception {
JobConf conf = new JobConf(getConf(), WordCountSeqOutput.class);
conf.setJobName("wordcount_seqOF");
conf.setMapOutputKeyClass(Text.class);
conf.setMapOutputValueClass(IntWritable.class);
// the keys are words (strings)
conf.setOutputKeyClass(Text.class);
// the values are counts (ints)
// conf.setOutputValueClass(IntWritable.class);
conf.setOutputValueClass(Text.class);
conf.setMapperClass(MapClass.class);
conf.setCombinerClass(Combiner.class);
conf.setReducerClass(Reduce.class);
conf.setOutputFormat(SequenceFileOutputFormat.class);
// // compress Mapper output
// conf.setCompressMapOutput(true);
// conf.setMapOutputCompressorClass(org.apache.hadoop.io.compress.GzipCodec.class);
// compress final output
conf.set("mapred.output.compress", conf.get("mapred.output.compress", "true"));
conf.set("mapred.output.compression.type", conf.get("mapred.output.compression.type", "BLOCK"));
conf.set(
"mapred.output.compression.codec",
conf.get("mapred.output.compression.codec", "org.apache.hadoop.io.compress.GzipCodec"));
List<String> other_args = new ArrayList<String>();
for (int i = 0; i < args.length; ++i) {
try {
if ("-m".equals(args[i])) {
conf.setNumMapTasks(Integer.parseInt(args[++i]));
} else if ("-r".equals(args[i])) {
conf.setNumReduceTasks(Integer.parseInt(args[++i]));
} else {
other_args.add(args[i]);
}
} catch (NumberFormatException except) {
System.out.println("ERROR: Integer expected instead of " + args[i]);
return printUsage();
} catch (ArrayIndexOutOfBoundsException except) {
System.out.println("ERROR: Required parameter missing from " + args[i - 1]);
return printUsage();
}
}
// Make sure there are exactly 2 parameters left.
if (other_args.size() != 2) {
System.out.println(
"ERROR: Wrong number of parameters: " + other_args.size() + " instead of 2.");
return printUsage();
}
FileInputFormat.setInputPaths(conf, other_args.get(0));
FileOutputFormat.setOutputPath(conf, new Path(other_args.get(1)));
JobClient.runJob(conf);
return 0;
}
public int run(String[] args) throws Exception {
if (args.length != 5) {
printUsage();
return -1;
}
String inputPath = args[0];
String outputPath = args[1];
int mapTasks = Integer.parseInt(args[2]);
int reduceTasks = Integer.parseInt(args[3]);
String stoplistPath = args[4];
sLogger.info("Tool: AFormatter");
sLogger.info(" - input path: " + inputPath);
sLogger.info(" - output path: " + outputPath);
sLogger.info(" - number of mappers: " + mapTasks);
sLogger.info(" - number of reducers: " + reduceTasks);
JobConf conf = new JobConf(AFormatterWG.class);
conf.setJobName("Authority Formatter -- Web Graph");
conf.setNumMapTasks(mapTasks);
conf.setNumReduceTasks(reduceTasks);
FileInputFormat.setInputPaths(conf, new Path(inputPath));
FileOutputFormat.setOutputPath(conf, new Path(outputPath));
FileOutputFormat.setCompressOutput(conf, false);
// conf.setInputFormat(SequenceFileInputFormat.class);
conf.setOutputKeyClass(IntWritable.class);
conf.setOutputValueClass(HITSNode.class);
conf.setOutputFormat(SequenceFileOutputFormat.class);
conf.setCompressMapOutput(true);
conf.setSpeculativeExecution(false);
// InputSampler.Sampler<IntWritable, Text> sampler = new
// InputSampler.RandomSampler<IntWritable, Text>(0.1, 10, 10);
// InputSampler.writePartitionFile(conf, sampler);
// conf.setPartitionerClass(TotalOrderPartitioner.class);
conf.setMapperClass(AFormatMapperIMC.class);
conf.setCombinerClass(AFormatReducer.class);
conf.setReducerClass(AFormatReducer.class);
// Delete the output directory if it exists already
Path outputDir = new Path(outputPath);
Path stopList = new Path(stoplistPath);
FileSystem.get(conf).delete(outputDir, true);
long startTime = System.currentTimeMillis();
sLogger.info("Starting job");
DistributedCache.addCacheFile(stopList.toUri(), conf);
JobClient.runJob(conf);
sLogger.info(
"Job Finished in " + (System.currentTimeMillis() - startTime) / 1000.0 + " seconds");
return 0;
}
public static void main(String[] args) throws IOException {
/*JobConf conf = new JobConf();
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(Text.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
conf.setMapperClass(IpCounterMapper.class);
conf.setCombinerClass(IpCounterReducer.class);
conf.setReducerClass(IpCounterReducer.class);
String inputDir = args[0];
String outputDir = args[1];
FileInputFormat.setInputPaths(conf, inputDir);
FileOutputFormat.setOutputPath(conf, new Path(outputDir));
boolean flag = JobClient.runJob(conf).isSuccessful();
System.out.println(args.length);*/
if (args.length < 2) {
System.out.println("args not right!");
return;
}
JobConf conf = new JobConf(IpCount1.class);
// set output key class
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(Text.class);
// set mapper & reducer class
conf.setMapperClass(IpCounterMapper.class);
conf.setCombinerClass(IpCounterReducer.class);
conf.setReducerClass(IpCounterReducer.class);
// set format
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
String inputDir = args[0];
String outputDir = args[1];
// FileInputFormat.setInputPaths(conf, "/user/hadoop/rongxin/locationinput/");
FileInputFormat.setInputPaths(conf, inputDir);
FileOutputFormat.setOutputPath(conf, new Path(outputDir));
boolean flag = JobClient.runJob(conf).isSuccessful();
}
public static void run(Map<String, String> path) throws IOException {
JobConf conf = Recommend.config();
conf.setJarByClass(Step1.class);
String input = "hdfs://192.168.201.11:9000/user/hdfs/recommend/data1"; // 提交的处理训练集的目录
String input1 = path.get("Step1Input");
String output = path.get("Step1Output");
HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
hdfs.rmr(output);
hdfs.rmr(input1);
hdfs.mkdirs(input1);
// hdfs.copyFile(path.get("data"), input);
conf.setMapOutputKeyClass(IntWritable.class);
conf.setMapOutputValueClass(Text.class);
conf.setOutputKeyClass(IntWritable.class);
conf.setOutputValueClass(Text.class);
conf.setMapperClass(Step1_ToUserPreMapper.class);
conf.setCombinerClass(Step1_ToItemVectorReducer.class);
conf.setReducerClass(Step1_ToItemVectorReducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
// int maxCurrentReduceTasks = conf.getInt("mapred.tasktracker.reduce.tasks.maximum", 1);
// int ReduceTasks = (int) (2 * maxCurrentReduceTasks * 0.95);
// conf.setNumReduceTasks(ReduceTasks);
/*
* reduce的数目最好等于0.95或1.75乘以工作节点数，再乘以mapred.tasktracker.reduce.tasks.maximum（一般是每个节点的CPU数）
* 因子为0.95时会让所有的reduce任务立即装载，并当map任务结束时复制它们的输出结果。当因子为1.75时，一些reduce任务会立即
* 被装载，而其他一些则会等待。更早的节点会较早地完成第一轮reduce 任务并开始第二轮。最慢的节点第二轮不需要处理任何的reduce
* 任务，这样可以带来更好的负载平衡。
*/
// conf.setNumReduceTasks(3); //这里是在虚拟机下测试，就直接设成节点数
FileInputFormat.setInputPaths(conf, new Path(input));
FileOutputFormat.setOutputPath(conf, new Path(output));
RunningJob job = JobClient.runJob(conf);
while (!job.isComplete()) {
job.waitForCompletion();
}
}
// Main function
public static void main(String[] args) throws Exception {
// TODO Auto-generated method stub
JobConf conf = new JobConf(ProcessUnits.class);
conf.setJobName("max_eletricityunits");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(EE_Mapper.class);
conf.setCombinerClass(EE_Reducer.class);
conf.setReducerClass(EE_Reducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
public static void main(String args[]) throws IOException {
JobConf job = new JobConf(WordCountJob.class);
job.setJobName("Word Count Example");
FileInputFormat.setInputPaths(job, args[0]);
job.setInputFormat(TextInputFormat.class);
job.setMapperClass(MapTask.class);
job.setCombinerClass(ReduceTask.class);
job.setReducerClass(ReduceTask.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setOutputFormat(TextOutputFormat.class);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
JobClient.runJob(job);
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("wordcount");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(Map.class);
conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(MapReduceFour.class);
conf.setJobName("step-Four");
conf.setMapOutputKeyClass(VarLongWritable.class);
conf.setMapOutputValueClass(VectorWritable.class);
conf.setOutputKeyClass(RecommendResult.class);
conf.setOutputValueClass(NullWritable.class);
FileInputFormat.setInputPaths(
conf, new Path("/Users/bai/Documents/mapreduce/output-third/part-00000"));
// DBConfiguration.configureDB(conf, "com.mysql.jdbc.Driver",
// "jdbc:mysql://localhost:3306/hotelrecommend","root","root");
// DBOutputFormat.setOutput(conf,"recommend","uid","hid","recommendValue");
conf.setMapperClass(PartialMultiplyMapper.class);
conf.setCombinerClass(AggregateCombiner.class);
conf.setReducerClass(AggregateAndRecommendReducer.class);
conf.setInputFormat(SequenceFileInputFormat.class);
// conf.setOutputFormat(DBOutputFormat.class);
JobClient.runJob(conf);
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(Main.class);
conf.setJobName("feels-analysis");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(TheOutputClass.class);
conf.setMapperClass(Map.class);
conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
conf.setNumReduceTasks(1);
conf.setInputFormat(CSVTextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
// TODO: determine whether we need extra output
MultipleOutputs.addMultiNamedOutput(
conf, SECOND_OUTPUT, TextOutputFormat.class, Text.class, TheOutputClass.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
public int run(String[] args) throws Exception {
String driverClassName = "org.hsqldb.jdbcDriver";
String url = "jdbc:hsqldb:hsql://localhost/URLAccess";
if (args.length > 1) {
driverClassName = args[0];
url = args[1];
}
initialize(driverClassName, url);
JobConf job = new JobConf(getConf(), DBCountPageView.class);
job.setJobName("Count Pageviews of URLs");
job.setMapperClass(PageviewMapper.class);
job.setCombinerClass(LongSumReducer.class);
job.setReducerClass(PageviewReducer.class);
DBConfiguration.configureDB(job, driverClassName, url);
DBInputFormat.setInput(job, AccessRecord.class, "Access", null, "url", AccessFieldNames);
DBOutputFormat.setOutput(job, "Pageview", PageviewFieldNames);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongWritable.class);
job.setOutputKeyClass(PageviewRecord.class);
job.setOutputValueClass(NullWritable.class);
try {
JobClient.runJob(job);
boolean correct = verify();
if (!correct) throw new RuntimeException("Evaluation was not correct!");
} finally {
shutdown();
}
return 0;
}
public static void main(String[] args) throws IOException {
String inpath = "hdfs://localhost:9000/user/hdfs/in/";
String outpath = "hdfs://localhost:9000/user/hdfs/pv_out/";
JobConf conf = new JobConf(StatPV.class);
conf.setJobName("StatPV");
conf.setMapperClass(PvMapper.class);
conf.setCombinerClass(PvReducer.class);
conf.setReducerClass(PvReducer.class);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapOutputKeyClass(Text.class);
conf.setMapOutputValueClass(IntWritable.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(inpath));
FileOutputFormat.setOutputPath(conf, new Path(outpath));
JobClient.runJob(conf);
System.out.println("finish");
System.exit(0);
}
@Override
public int run(String[] args) throws IOException {
OptionParser p = new OptionParser();
OptionSpec<String> maxwiOpt =
p.accepts(maxwiOptName, "location of maxWi map file (HDFS) REQUIRED")
.withRequiredArg()
.ofType(String.class);
OptionSpec<Float> thresholdOpt =
p.accepts(thresholdOptName, "similarity threshold")
.withRequiredArg()
.ofType(Float.class)
.defaultsTo(DEFAULT_THRESHOLD);
OptionSpec<Integer> stripesOpt =
p.accepts(stripesOptName, "number of stripes to divide the similarity matrix")
.withRequiredArg()
.ofType(Integer.class)
.defaultsTo(1);
OptionSpec<Integer> spreadOpt =
p.accepts(spreadOptName, "number of reducers per stripe")
.withRequiredArg()
.ofType(Integer.class)
.defaultsTo(DEFAULT_SPREAD);
OptionSpec<Integer> factorOpt =
p.accepts(factorOptName, "number of mappers per reducer")
.withRequiredArg()
.ofType(Integer.class)
.defaultsTo(DEFAULT_FACTOR);
OptionSpec<Integer> maxVectorIDOpt =
p.accepts(maxVectorIDOptName, "maximum vector ID").withRequiredArg().ofType(Integer.class);
p.acceptsAll(Arrays.asList("h", "?"), "show help");
OptionSet options = parseOptions(p, args);
// to distinguish indexes built in successive runs
DateFormat df = new SimpleDateFormat("yyyyMMdd-HHmmss");
Date date = new Date();
float threshold = options.valueOf(thresholdOpt); // threshold
if (threshold < 0 || threshold >= 1) {
System.err.println(thresholdOptName + " should be between 0 and 1");
System.exit(1);
}
int numStripes = options.valueOf(stripesOpt); // number of stripes
if (numStripes < 1) {
System.err.println(stripesOptName + " should be > 0");
System.exit(1);
}
// MapReduce parameters
int spread = options.valueOf(spreadOpt); // how many reducers per stripe
if (spread < 1) {
System.err.println(spreadOptName + " should be > 0");
System.exit(1);
}
int factor = options.valueOf(factorOpt); // how many mappers per reducer
if (factor < 1) {
System.err.println(factorOptName + " should be > 0");
System.exit(1);
}
int maxKey = 0;
if (options.has(maxVectorIDOpt)) {
maxKey = options.valueOf(maxVectorIDOpt); // maximum value of the vector ID
if (maxKey < 1) {
System.err.println(maxVectorIDOptName + " should be > 0");
System.exit(1);
}
}
int numReducers = GenericKey.StripePartitioner.numReducers(numStripes, spread);
int numMappers = numReducers * factor;
int numBuckets = numMappers;
// pick the file with max weights from command line
String maxWiDir = options.valueOf(maxwiOpt);
List<String> nonOptArgs = options.nonOptionArguments();
LOG.info("Threshold set to " + threshold);
LOG.info(
String.format(
"Buckets: %1$-10s Factor: %2$-10s Stripes: %3$-10s Spread: %4$-10s Reducers: %5$-10s",
numBuckets, factor, numStripes, spread, numReducers));
// start building the jobs
JobConf conf1 = new JobConf(getConf(), Similarity.class);
conf1.setFloat(PARAM_APS_THRESHOLD, threshold);
conf1.setInt(PARAM_APS_STRIPES, numStripes);
DistributedCache.addCacheFile(URI.create(maxWiDir), conf1);
Path inputPath = new Path(nonOptArgs.get(0));
Path indexPath =
new Path(
nonOptArgs.get(0) + "-index-" + threshold + "-s" + numStripes + "_" + df.format(date));
// index filtering pruned nested directory
Path indexOnlyPath = new Path(indexPath, "part*");
Path outputPath = new Path(nonOptArgs.get(1) + "-" + threshold + "-s" + numStripes);
FileInputFormat.setInputPaths(conf1, inputPath);
FileOutputFormat.setOutputPath(conf1, indexPath);
conf1.setInputFormat(SequenceFileInputFormat.class);
conf1.setOutputFormat(SequenceFileOutputFormat.class);
conf1.setMapOutputKeyClass(LongWritable.class);
conf1.setMapOutputValueClass(IndexItem.class);
conf1.setOutputKeyClass(LongWritable.class);
conf1.setOutputValueClass(IndexItemArrayWritable.class);
conf1.setMapperClass(IndexerMapper.class);
conf1.setReducerClass(IndexerReducer.class);
// assuming input is sorted according to the key (vectorID) so that the
// part files are locally sorted
MultipleOutputs.addNamedOutput(
conf1,
PRUNED,
SequenceFileOutputFormat.class,
IntWritable.class,
VectorComponentArrayWritable.class);
// remove the stuff we added from the job name
conf1.set(
"mapred.job.name",
"APS-" + indexPath.getName().substring(0, indexPath.getName().length() - 16));
conf1.setNumTasksToExecutePerJvm(-1); // JVM reuse
conf1.setSpeculativeExecution(false);
conf1.setCompressMapOutput(true);
// hash the posting lists in different buckets to distribute the load
conf1.setNumReduceTasks(numBuckets);
RunningJob job1 = JobClient.runJob(conf1);
// part 2
JobConf conf2 = new JobConf(getConf(), Similarity.class);
if (numStripes > 0) FileUtils.mergeRestFile(conf2, indexPath, PRUNED, INDEX_INTERVAL);
MultipleInputs.addInputPath(
conf2, indexOnlyPath, SequenceFileInputFormat.class, SimilarityMapperIndex.class);
MultipleInputs.addInputPath(
conf2, inputPath, SequenceFileInputFormat.class, SimilarityMapperInput.class);
FileOutputFormat.setOutputPath(conf2, outputPath);
conf2.setCombinerClass(SimilarityCombiner.class);
conf2.setReducerClass(SimilarityReducer.class);
conf2.setPartitionerClass(GenericKey.StripePartitioner.class);
conf2.setOutputKeyComparatorClass(GenericKey.Comparator.class);
conf2.setOutputValueGroupingComparator(GenericKey.PrimaryComparator.class);
conf2.setMapOutputKeyClass(GenericKey.class);
conf2.setMapOutputValueClass(GenericValue.class);
conf2.setOutputKeyClass(VectorPair.class);
conf2.setOutputValueClass(NullWritable.class);
Counter numDocs =
job1.getCounters()
.findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS");
maxKey = maxKey > 0 ? maxKey : (int) numDocs.getValue();
LOG.info("Setting max key value in input to " + maxKey);
conf2.setInt(PARAM_APS_MAXKEY, maxKey);
conf2.setInt(PARAM_APS_STRIPES, numStripes);
conf2.setFloat(PARAM_APS_THRESHOLD, threshold);
conf2.setInt(PARAM_APS_REDUCER_PER_STRIPE, spread);
conf2.set("mapred.job.name", "APS-" + outputPath.getName());
conf2.setNumTasksToExecutePerJvm(-1); // JVM reuse
conf2.setSpeculativeExecution(false);
conf2.setCompressMapOutput(true);
conf2.setNumReduceTasks(numReducers);
JobClient.runJob(conf2);
return 0;
}
public static void main(String[] args) throws IOException {
JobConf lp = new JobConf(L4.class);
lp.setJobName("Load Page Views");
lp.setInputFormat(TextInputFormat.class);
lp.setOutputKeyClass(Text.class);
lp.setOutputValueClass(Text.class);
lp.setMapperClass(ReadPageViews.class);
lp.setCombinerClass(Combiner.class);
lp.setReducerClass(Group.class);
Properties props = System.getProperties();
String dataDir = props.getProperty("PIGMIX_DIR", "/user/pig/tests/data/pigmix");
for (Map.Entry<Object, Object> entry : props.entrySet()) {
lp.set((String) entry.getKey(), (String) entry.getValue());
}
FileInputFormat.addInputPath(lp, new Path(dataDir, "page_views"));
FileOutputFormat.setOutputPath(
lp, new Path("/user/" + System.getProperty("user.name") + "/L4out"));
lp.setNumReduceTasks(40);
Job group = new Job(lp);
JobControl jc = new JobControl("L4 join");
jc.addJob(group);
new Thread(jc).start();
int i = 0;
while (!jc.allFinished()) {
ArrayList<Job> failures = jc.getFailedJobs();
if (failures != null && failures.size() > 0) {
for (Job failure : failures) {
System.err.println(failure.getMessage());
}
break;
}
try {
Thread.sleep(5000);
} catch (InterruptedException e) {
}
if (i % 10000 == 0) {
System.out.println("Running jobs");
ArrayList<Job> running = jc.getRunningJobs();
if (running != null && running.size() > 0) {
for (Job r : running) {
System.out.println(r.getJobName());
}
}
System.out.println("Ready jobs");
ArrayList<Job> ready = jc.getReadyJobs();
if (ready != null && ready.size() > 0) {
for (Job r : ready) {
System.out.println(r.getJobName());
}
}
System.out.println("Waiting jobs");
ArrayList<Job> waiting = jc.getWaitingJobs();
if (waiting != null && waiting.size() > 0) {
for (Job r : ready) {
System.out.println(r.getJobName());
}
}
System.out.println("Successful jobs");
ArrayList<Job> success = jc.getSuccessfulJobs();
if (success != null && success.size() > 0) {
for (Job r : ready) {
System.out.println(r.getJobName());
}
}
}
i++;
}
ArrayList<Job> failures = jc.getFailedJobs();
if (failures != null && failures.size() > 0) {
for (Job failure : failures) {
System.err.println(failure.getMessage());
}
}
jc.stop();
}
/**
* Starts a Rand MapReduce job which will produce one or more random objects.
*
* @param numRows number of rows for each random object
* @param numCols number of columns for each random object
* @param blockRowSize number of rows in a block for each random object
* @param blockColSize number of columns in a block for each random object
* @param minValue minimum of the random values for each random object
* @param maxValue maximum of the random values for each random object
* @param sparsity sparsity for each random object
* @param pdf probability density function for each random object
* @param replication file replication
* @param inputs input file for each random object
* @param outputs output file for each random object
* @param outputInfos output information for each random object
* @param instructionsInMapper instruction for each random object
* @param resultIndexes result indexes for each random object
* @return matrix characteristics for each random object
* @throws Exception if an error occurred in the MapReduce phase
*/
public static JobReturn runJob(
MRJobInstruction inst,
String[] dataGenInstructions,
String instructionsInMapper,
String aggInstructionsInReducer,
String otherInstructionsInReducer,
int numReducers,
int replication,
byte[] resultIndexes,
String dimsUnknownFilePrefix,
String[] outputs,
OutputInfo[] outputInfos)
throws Exception {
JobConf job = new JobConf(DataGenMR.class);
job.setJobName("DataGen-MR");
// whether use block representation or cell representation
MRJobConfiguration.setMatrixValueClass(job, true);
byte[] realIndexes = new byte[dataGenInstructions.length];
for (byte b = 0; b < realIndexes.length; b++) realIndexes[b] = b;
String[] inputs = new String[dataGenInstructions.length];
InputInfo[] inputInfos = new InputInfo[dataGenInstructions.length];
long[] rlens = new long[dataGenInstructions.length];
long[] clens = new long[dataGenInstructions.length];
int[] brlens = new int[dataGenInstructions.length];
int[] bclens = new int[dataGenInstructions.length];
FileSystem fs = FileSystem.get(job);
String dataGenInsStr = "";
int numblocks = 0;
int maxbrlen = -1, maxbclen = -1;
double maxsparsity = -1;
for (int i = 0; i < dataGenInstructions.length; i++) {
dataGenInsStr = dataGenInsStr + Lop.INSTRUCTION_DELIMITOR + dataGenInstructions[i];
MRInstruction mrins = MRInstructionParser.parseSingleInstruction(dataGenInstructions[i]);
MRINSTRUCTION_TYPE mrtype = mrins.getMRInstructionType();
DataGenMRInstruction genInst = (DataGenMRInstruction) mrins;
rlens[i] = genInst.getRows();
clens[i] = genInst.getCols();
brlens[i] = genInst.getRowsInBlock();
bclens[i] = genInst.getColsInBlock();
maxbrlen = Math.max(maxbrlen, brlens[i]);
maxbclen = Math.max(maxbclen, bclens[i]);
if (mrtype == MRINSTRUCTION_TYPE.Rand) {
RandInstruction randInst = (RandInstruction) mrins;
inputs[i] = LibMatrixDatagen.generateUniqueSeedPath(genInst.getBaseDir());
maxsparsity = Math.max(maxsparsity, randInst.getSparsity());
FSDataOutputStream fsOut = fs.create(new Path(inputs[i]));
PrintWriter pw = new PrintWriter(fsOut);
// for obj reuse and preventing repeated buffer re-allocations
StringBuilder sb = new StringBuilder();
// seed generation
Well1024a bigrand = LibMatrixDatagen.setupSeedsForRand(randInst.getSeed());
long[] nnz =
LibMatrixDatagen.computeNNZperBlock(
rlens[i], clens[i], brlens[i], bclens[i], randInst.getSparsity());
int nnzIx = 0;
for (long r = 0; r < rlens[i]; r += brlens[i]) {
long curBlockRowSize = Math.min(brlens[i], (rlens[i] - r));
for (long c = 0; c < clens[i]; c += bclens[i]) {
long curBlockColSize = Math.min(bclens[i], (clens[i] - c));
sb.append((r / brlens[i]) + 1);
sb.append(',');
sb.append((c / bclens[i]) + 1);
sb.append(',');
sb.append(curBlockRowSize);
sb.append(',');
sb.append(curBlockColSize);
sb.append(',');
sb.append(nnz[nnzIx++]);
sb.append(',');
sb.append(bigrand.nextLong());
pw.println(sb.toString());
sb.setLength(0);
numblocks++;
}
}
pw.close();
fsOut.close();
inputInfos[i] = InputInfo.TextCellInputInfo;
} else if (mrtype == MRINSTRUCTION_TYPE.Seq) {
SeqInstruction seqInst = (SeqInstruction) mrins;
inputs[i] = genInst.getBaseDir() + System.currentTimeMillis() + ".seqinput";
maxsparsity = 1.0; // always dense
double from = seqInst.fromValue;
double to = seqInst.toValue;
double incr = seqInst.incrValue;
// handle default 1 to -1 for special case of from>to
incr = LibMatrixDatagen.updateSeqIncr(from, to, incr);
// Correctness checks on (from, to, incr)
boolean neg = (from > to);
if (incr == 0) throw new DMLRuntimeException("Invalid value for \"increment\" in seq().");
if (neg != (incr < 0))
throw new DMLRuntimeException("Wrong sign for the increment in a call to seq()");
// Compute the number of rows in the sequence
long numrows = 1 + (long) Math.floor((to - from) / incr);
if (rlens[i] > 0) {
if (numrows != rlens[i])
throw new DMLRuntimeException(
"Unexpected error while processing sequence instruction. Expected number of rows does not match given number: "
+ rlens[i]
+ " != "
+ numrows);
} else {
rlens[i] = numrows;
}
if (clens[i] > 0 && clens[i] != 1)
throw new DMLRuntimeException(
"Unexpected error while processing sequence instruction. Number of columns ("
+ clens[i]
+ ") must be equal to 1.");
else clens[i] = 1;
FSDataOutputStream fsOut = fs.create(new Path(inputs[i]));
PrintWriter pw = new PrintWriter(fsOut);
StringBuilder sb = new StringBuilder();
double temp = from;
double block_from, block_to;
for (long r = 0; r < rlens[i]; r += brlens[i]) {
long curBlockRowSize = Math.min(brlens[i], (rlens[i] - r));
// block (bid_i,bid_j) generates a sequence from the interval [block_from, block_to]
// (inclusive of both end points of the interval)
long bid_i = ((r / brlens[i]) + 1);
long bid_j = 1;
block_from = temp;
block_to = temp + (curBlockRowSize - 1) * incr;
temp = block_to + incr; // next block starts from here
sb.append(bid_i);
sb.append(',');
sb.append(bid_j);
sb.append(',');
/*
// Need not include block size while generating seq()
sb.append(curBlockRowSize);
sb.append(',');
sb.append(1);
sb.append(',');*/
sb.append(block_from);
sb.append(',');
sb.append(block_to);
sb.append(',');
sb.append(incr);
pw.println(sb.toString());
// System.out.println("MapTask " + r + ": " + sb.toString());
sb.setLength(0);
numblocks++;
}
pw.close();
fsOut.close();
inputInfos[i] = InputInfo.TextCellInputInfo;
} else {
throw new DMLRuntimeException("Unexpected Data Generation Instruction Type: " + mrtype);
}
}
dataGenInsStr = dataGenInsStr.substring(1); // remove the first ","
RunningJob runjob;
MatrixCharacteristics[] stats;
try {
// set up the block size
MRJobConfiguration.setBlocksSizes(job, realIndexes, brlens, bclens);
// set up the input files and their format information
MRJobConfiguration.setUpMultipleInputs(
job, realIndexes, inputs, inputInfos, brlens, bclens, false, ConvertTarget.BLOCK);
// set up the dimensions of input matrices
MRJobConfiguration.setMatricesDimensions(job, realIndexes, rlens, clens);
MRJobConfiguration.setDimsUnknownFilePrefix(job, dimsUnknownFilePrefix);
// set up the block size
MRJobConfiguration.setBlocksSizes(job, realIndexes, brlens, bclens);
// set up the rand Instructions
MRJobConfiguration.setRandInstructions(job, dataGenInsStr);
// set up unary instructions that will perform in the mapper
MRJobConfiguration.setInstructionsInMapper(job, instructionsInMapper);
// set up the aggregate instructions that will happen in the combiner and reducer
MRJobConfiguration.setAggregateInstructions(job, aggInstructionsInReducer);
// set up the instructions that will happen in the reducer, after the aggregation instrucions
MRJobConfiguration.setInstructionsInReducer(job, otherInstructionsInReducer);
// set up the replication factor for the results
job.setInt(MRConfigurationNames.DFS_REPLICATION, replication);
// set up map/reduce memory configurations (if in AM context)
DMLConfig config = ConfigurationManager.getDMLConfig();
DMLAppMasterUtils.setupMRJobRemoteMaxMemory(job, config);
// set up custom map/reduce configurations
MRJobConfiguration.setupCustomMRConfigurations(job, config);
// determine degree of parallelism (nmappers: 1<=n<=capacity)
// TODO use maxsparsity whenever we have a way of generating sparse rand data
int capacity = InfrastructureAnalyzer.getRemoteParallelMapTasks();
long dfsblocksize = InfrastructureAnalyzer.getHDFSBlockSize();
// correction max number of mappers on yarn clusters
if (InfrastructureAnalyzer.isYarnEnabled())
capacity = (int) Math.max(capacity, YarnClusterAnalyzer.getNumCores());
int nmapers =
Math.max(
Math.min((int) (8 * maxbrlen * maxbclen * (long) numblocks / dfsblocksize), capacity),
1);
job.setNumMapTasks(nmapers);
// set up what matrices are needed to pass from the mapper to reducer
HashSet<Byte> mapoutputIndexes =
MRJobConfiguration.setUpOutputIndexesForMapper(
job,
realIndexes,
dataGenInsStr,
instructionsInMapper,
null,
aggInstructionsInReducer,
otherInstructionsInReducer,
resultIndexes);
MatrixChar_N_ReducerGroups ret =
MRJobConfiguration.computeMatrixCharacteristics(
job,
realIndexes,
dataGenInsStr,
instructionsInMapper,
null,
aggInstructionsInReducer,
null,
otherInstructionsInReducer,
resultIndexes,
mapoutputIndexes,
false);
stats = ret.stats;
// set up the number of reducers
MRJobConfiguration.setNumReducers(job, ret.numReducerGroups, numReducers);
// print the complete MRJob instruction
if (LOG.isTraceEnabled()) inst.printCompleteMRJobInstruction(stats);
// Update resultDimsUnknown based on computed "stats"
byte[] resultDimsUnknown = new byte[resultIndexes.length];
for (int i = 0; i < resultIndexes.length; i++) {
if (stats[i].getRows() == -1 || stats[i].getCols() == -1) {
resultDimsUnknown[i] = (byte) 1;
} else {
resultDimsUnknown[i] = (byte) 0;
}
}
boolean mayContainCtable =
instructionsInMapper.contains("ctabletransform")
|| instructionsInMapper.contains("groupedagg");
// set up the multiple output files, and their format information
MRJobConfiguration.setUpMultipleOutputs(
job, resultIndexes, resultDimsUnknown, outputs, outputInfos, true, mayContainCtable);
// configure mapper and the mapper output key value pairs
job.setMapperClass(DataGenMapper.class);
if (numReducers == 0) {
job.setMapOutputKeyClass(Writable.class);
job.setMapOutputValueClass(Writable.class);
} else {
job.setMapOutputKeyClass(MatrixIndexes.class);
job.setMapOutputValueClass(TaggedMatrixBlock.class);
}
// set up combiner
if (numReducers != 0
&& aggInstructionsInReducer != null
&& !aggInstructionsInReducer.isEmpty()) job.setCombinerClass(GMRCombiner.class);
// configure reducer
job.setReducerClass(GMRReducer.class);
// job.setReducerClass(PassThroughReducer.class);
// By default, the job executes in "cluster" mode.
// Determine if we can optimize and run it in "local" mode.
MatrixCharacteristics[] inputStats = new MatrixCharacteristics[inputs.length];
for (int i = 0; i < inputs.length; i++) {
inputStats[i] = new MatrixCharacteristics(rlens[i], clens[i], brlens[i], bclens[i]);
}
// set unique working dir
MRJobConfiguration.setUniqueWorkingDir(job);
runjob = JobClient.runJob(job);
/* Process different counters */
Group group = runjob.getCounters().getGroup(MRJobConfiguration.NUM_NONZERO_CELLS);
for (int i = 0; i < resultIndexes.length; i++) {
// number of non-zeros
stats[i].setNonZeros(group.getCounter(Integer.toString(i)));
}
String dir = dimsUnknownFilePrefix + "/" + runjob.getID().toString() + "_dimsFile";
stats = MapReduceTool.processDimsFiles(dir, stats);
MapReduceTool.deleteFileIfExistOnHDFS(dir);
} finally {
for (String input : inputs) MapReduceTool.deleteFileIfExistOnHDFS(new Path(input), job);
}
return new JobReturn(stats, outputInfos, runjob.isSuccessful());
}
/**
* 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 {
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(ValueAggregator.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;
}
public static void main(String[] args) throws Exception {
String inputPath = args[0];
String outputPath = args[1];
int specIteration = Integer.parseInt(args[2]);
// set number of reducers
int numReducers = 2 * 15;
// step 1
JobConf conf0 = new JobConf(Hashmin.class);
conf0.setJobName("hashmin Step1");
conf0.setOutputKeyClass(IntWritable.class);
conf0.setOutputValueClass(Text.class);
conf0.setMapOutputKeyClass(IntWritable.class);
conf0.setMapOutputValueClass(IntWritable.class);
conf0.setMapperClass(FirstMapper.class);
conf0.setReducerClass(FirstReducer.class);
conf0.setInputFormat(TextInputFormat.class);
conf0.setOutputFormat(TextOutputFormat.class);
FileInputFormat.addInputPath(conf0, new Path(args[0]));
FileOutputFormat.setOutputPath(conf0, new Path(args[1] + "/i-1"));
conf0.setNumReduceTasks(numReducers);
JobClient.runJob(conf0);
// loop
JobConf conf1 = new JobConf();
conf1.setOutputKeyClass(IntWritable.class);
conf1.setOutputValueClass(IntWritable.class);
conf1.setMapOutputKeyClass(IntWritable.class);
conf1.setMapOutputValueClass(Text.class);
conf1.setMapperClass(JoinMapper.class);
conf1.setReducerClass(JoinReducer.class);
conf1.setInputFormat(TextInputFormat.class);
conf1.setOutputFormat(TextOutputFormat.class);
JobConf conf2 = new JobConf();
conf2.setOutputKeyClass(IntWritable.class);
conf2.setOutputValueClass(Text.class);
conf2.setMapOutputKeyClass(IntWritable.class);
conf2.setMapOutputValueClass(Text.class);
conf2.setMapperClass(AggMapper.class);
conf2.setReducerClass(AggReducer.class);
// combiner
conf2.setCombinerClass(AggCombiner.class);
conf2.setInputFormat(TextInputFormat.class);
conf2.setOutputFormat(TextOutputFormat.class);
JobConf conf = new JobConf(Hashmin.class);
conf.setJobName("Hashmin");
conf.setLoopInputOutput(HashminLoopInputOutput.class);
// testing
// conf.setLoopInputOutput(RankLoopInputOutputReuse.class);
conf.setLoopReduceCacheSwitch(HashminReduceCacheSwitch.class);
conf.setLoopReduceCacheFilter(HashminReduceCacheFilter.class);
conf.setLoopStepHook(HashminStepHook.class);
conf.setInputPath(inputPath);
conf.setOutputPath(outputPath);
conf.setStepConf(0, conf1);
conf.setStepConf(1, conf2);
conf.setIterative(true);
conf.setNumIterations(specIteration);
conf.setNumReduceTasks(numReducers);
JobClient.runJob(conf);
}
@Override
protected void beforeRun(String[] args, JobConf config) {
config.setCombinerClass(LongSumReducer.class);
}
static void checkRecords(Configuration defaults, Path sortInput, Path sortOutput)
throws IOException {
FileSystem inputfs = sortInput.getFileSystem(defaults);
FileSystem outputfs = sortOutput.getFileSystem(defaults);
FileSystem defaultfs = FileSystem.get(defaults);
JobConf jobConf = new JobConf(defaults, RecordStatsChecker.class);
jobConf.setJobName("sortvalidate-recordstats-checker");
int noSortReduceTasks = outputfs.listStatus(sortOutput, sortPathsFilter).length;
jobConf.setInt("sortvalidate.sort.reduce.tasks", noSortReduceTasks);
int noSortInputpaths = inputfs.listStatus(sortInput).length;
jobConf.setInputFormat(NonSplitableSequenceFileInputFormat.class);
jobConf.setOutputFormat(SequenceFileOutputFormat.class);
jobConf.setOutputKeyClass(IntWritable.class);
jobConf.setOutputValueClass(RecordStatsChecker.RecordStatsWritable.class);
jobConf.setMapperClass(Map.class);
jobConf.setCombinerClass(Reduce.class);
jobConf.setReducerClass(Reduce.class);
jobConf.setNumMapTasks(noSortReduceTasks);
jobConf.setNumReduceTasks(1);
FileInputFormat.setInputPaths(jobConf, sortInput);
FileInputFormat.addInputPath(jobConf, sortOutput);
Path outputPath = new Path("/tmp/sortvalidate/recordstatschecker");
if (defaultfs.exists(outputPath)) {
defaultfs.delete(outputPath, true);
}
FileOutputFormat.setOutputPath(jobConf, outputPath);
// Uncomment to run locally in a single process
// job_conf.set("mapred.job.tracker", "local");
Path[] inputPaths = FileInputFormat.getInputPaths(jobConf);
System.out.println(
"\nSortValidator.RecordStatsChecker: Validate sort "
+ "from "
+ inputPaths[0]
+ " ("
+ noSortInputpaths
+ " files), "
+ inputPaths[1]
+ " ("
+ noSortReduceTasks
+ " files) into "
+ FileOutputFormat.getOutputPath(jobConf)
+ " with 1 reducer.");
Date startTime = new Date();
System.out.println("Job started: " + startTime);
JobClient.runJob(jobConf);
Date end_time = new Date();
System.out.println("Job ended: " + end_time);
System.out.println(
"The job took " + (end_time.getTime() - startTime.getTime()) / 1000 + " seconds.");
// Check to ensure that the statistics of the
// framework's sort-input and sort-output match
SequenceFile.Reader stats =
new SequenceFile.Reader(defaultfs, new Path(outputPath, "part-00000"), defaults);
IntWritable k1 = new IntWritable();
IntWritable k2 = new IntWritable();
RecordStatsWritable v1 = new RecordStatsWritable();
RecordStatsWritable v2 = new RecordStatsWritable();
if (!stats.next(k1, v1)) {
throw new IOException("Failed to read record #1 from reduce's output");
}
if (!stats.next(k2, v2)) {
throw new IOException("Failed to read record #2 from reduce's output");
}
if ((v1.getBytes() != v2.getBytes())
|| (v1.getRecords() != v2.getRecords())
|| v1.getChecksum() != v2.getChecksum()) {
throw new IOException(
"("
+ v1.getBytes()
+ ", "
+ v1.getRecords()
+ ", "
+ v1.getChecksum()
+ ") v/s ("
+ v2.getBytes()
+ ", "
+ v2.getRecords()
+ ", "
+ v2.getChecksum()
+ ")");
}
}
