程序包 org.apache.hadoop.tools.rumen

Rumen is a data extraction and analysis tool built for Apache Hadoop.

请参阅: 说明

程序包org.apache.hadoop.tools.rumen的说明

Rumen is a data extraction and analysis tool built for Apache Hadoop. Rumen mines job history logs to extract meaningful data and stores it into an easily-parsed format. The default output format of Rumen is JSON. Rumen uses the Jackson library to create JSON objects.

The following classes can be used to programmatically invoke Rumen:
  1. JobConfigurationParser
    A parser to parse and filter out interesting properties from job configuration.

    Sample code: 
          
        // An example to parse and filter out job name
        
        String conf_filename = .. // assume the job configuration filename here
        
        // construct a list of interesting properties
        List interestedProperties = new ArrayList();
        interestedProperties.add("mapreduce.job.name");
        
        JobConfigurationParser jcp = 
          new JobConfigurationParser(interestedProperties);

        InputStream in = new FileInputStream(conf_filename);
        Properties parsedProperties = jcp.parse(in);
    Some of the commonly used interesting properties are enumerated in JobConfPropertyNames.

    Note: A single instance of JobConfigurationParser can be used to parse multiple job configuration files.
  2. JobHistoryParser
    A parser that parses job history files. It is an interface and actual implementations are defined as Enum in JobHistoryParserFactory. Note that RewindableInputStream
    is a wrapper class around InputStream to make the input stream rewindable.
    Sample code: 
          
        // An example to parse a current job history file i.e a job history 
        // file for which the version is known
        
        String filename = .. // assume the job history filename here
        
        InputStream in = new FileInputStream(filename);
        
        HistoryEvent event = null;
        
        JobHistoryParser parser = new CurrentJHParser(in);
        
        event = parser.nextEvent();
        // process all the events
        while (event != null) {
          // ... process all event
          event = parser.nextEvent();
        }
        
        // close the parser and the underlying stream
        parser.close();
    JobHistoryParserFactory provides a JobHistoryParserFactory.getParser(org.apache.hadoop.tools.rumen.RewindableInputStream) API to get a parser for parsing the job history file. Note that this API can be used if the job history version is unknown.

    Sample code: 
          
        // An example to parse a job history for which the version is not 
        // known i.e using JobHistoryParserFactory.getParser()
        
        String filename = .. // assume the job history filename here
        
        InputStream in = new FileInputStream(filename);
        RewindableInputStream ris = new RewindableInputStream(in);
        
        // JobHistoryParserFactory will check and return a parser that can
        // parse the file
        JobHistoryParser parser = JobHistoryParserFactory.getParser(ris);
        
        // now use the parser to parse the events
        HistoryEvent event = parser.nextEvent();
        while (event != null) {
          // ... process the event
          event = parser.nextEvent();
        }
        
        parser.close();
    Note: Create one instance to parse a job history log and close it after use.
  3. TopologyBuilder
    Builds the cluster topology based on the job history events. Every job history file consists of events. Each event can be represented using org.apache.hadoop.mapreduce.jobhistory.HistoryEvent. These events can be passed to TopologyBuilder using org.apache.hadoop.tools.rumen.TopologyBuilder#process(org.apache.hadoop.mapreduce.jobhistory.HistoryEvent). A cluster topology can be represented using LoggedNetworkTopology. Once all the job history events are processed, the cluster topology can be obtained using TopologyBuilder.build().

    Sample code: 
          
        // Building topology for a job history file represented using 
        // 'filename' and the corresponding configuration file represented 
        // using 'conf_filename'
        String filename = .. // assume the job history filename here
        String conf_filename = .. // assume the job configuration filename here
        
        InputStream jobConfInputStream = new FileInputStream(filename);
        InputStream jobHistoryInputStream = new FileInputStream(conf_filename);
        
        TopologyBuilder tb = new TopologyBuilder();
        
        // construct a list of interesting properties
        List interestingProperties = new ArrayList();
        // add the interesting properties here
        interestingProperties.add("mapreduce.job.name");
        
        JobConfigurationParser jcp = 
          new JobConfigurationParser(interestingProperties);
        
        // parse the configuration file
        tb.process(jcp.parse(jobConfInputStream));
        
        // read the job history file and pass it to the 
        // TopologyBuilder.
        JobHistoryParser parser = new CurrentJHParser(jobHistoryInputStream);
        HistoryEvent e;
        
        // read and process all the job history events
        while ((e = parser.nextEvent()) != null) {
          tb.process(e);
        }
        
        LoggedNetworkTopology topology = tb.build();
  4. JobBuilder
    Summarizes a job history file. TraceBuilder provides TraceBuilder.extractJobID(String) API for extracting job id from job history or job configuration files which can be used for instantiating JobBuilder. JobBuilder generates a LoggedJob object via JobBuilder.build(). See LoggedJob for more details.

    Sample code: 
          
        // An example to summarize a current job history file 'filename'
        // and the corresponding configuration file 'conf_filename'
        
        String filename = .. // assume the job history filename here
        String conf_filename = .. // assume the job configuration filename here
        
        InputStream jobConfInputStream = new FileInputStream(job_filename);
        InputStream jobHistoryInputStream = new FileInputStream(conf_filename);
        
        String jobID = TraceBuilder.extractJobID(job_filename);
        JobBuilder jb = new JobBuilder(jobID);
        
        // construct a list of interesting properties
        List interestingProperties = new ArrayList();
        // add the interesting properties here
        interestingProperties.add("mapreduce.job.name");
        
        JobConfigurationParser jcp = 
          new JobConfigurationParser(interestingProperties);
        
        // parse the configuration file
        jb.process(jcp.parse(jobConfInputStream));
        
        // parse the job history file
        JobHistoryParser parser = new CurrentJHParser(jobHistoryInputStream);
        try {
          HistoryEvent e;
          // read and process all the job history events
          while ((e = parser.nextEvent()) != null) {
            jobBuilder.process(e);
          }
        } finally {
          parser.close();
        }
        
        LoggedJob job = jb.build();
    Note: The order of parsing the job configuration file or job history file is not important. Create one instance to parse the history file and job configuration.
  5. DefaultOutputter
    Implements Outputter and writes JSON object in text format to the output file. DefaultOutputter can be initialized with the output filename.

    Sample code: 
          
        // An example to summarize a current job history file represented by
        // 'filename' and the configuration filename represented using 
        // 'conf_filename'. Also output the job summary to 'out.json' along 
        // with the cluster topology to 'topology.json'.
        
        String filename = .. // assume the job history filename here
        String conf_filename = .. // assume the job configuration filename here
        
        Configuration conf = new Configuration();
        DefaultOutputter do = new DefaultOutputter();
        do.init("out.json", conf);
        
        InputStream jobConfInputStream = new FileInputStream(filename);
        InputStream jobHistoryInputStream = new FileInputStream(conf_filename);
        
        // extract the job-id from the filename
        String jobID = TraceBuilder.extractJobID(filename);
        JobBuilder jb = new JobBuilder(jobID);
        TopologyBuilder tb = new TopologyBuilder();
        
        // construct a list of interesting properties
        List interestingProperties = new ArrayList();
        // add the interesting properties here
        interestingProperties.add("mapreduce.job.name");
        
        JobConfigurationParser jcp =
          new JobConfigurationParser(interestingProperties);
          
        // parse the configuration file
        tb.process(jcp.parse(jobConfInputStream));
        
        // read the job history file and pass it to the
        // TopologyBuilder.
        JobHistoryParser parser = new CurrentJHParser(jobHistoryInputStream);
        HistoryEvent e;
        while ((e = parser.nextEvent()) != null) {
          jb.process(e);
          tb.process(e);
        }
        
        LoggedJob j = jb.build();
        
        // serialize the job summary in json (text) format
        do.output(j);
        
        // close
        do.close();
        
        do.init("topology.json", conf);
        
        // get the job summary using TopologyBuilder
        LoggedNetworkTopology topology = topologyBuilder.build();
        
        // serialize the cluster topology in json (text) format
        do.output(topology);
        
        // close
        do.close();
  6. JobTraceReader
    A reader for reading LoggedJob serialized using DefaultOutputter. LoggedJob provides various APIs for extracting job details. Following are the most commonly used ones

    Sample code: 
          
        // An example to read job summary from a trace file 'out.json'.
        JobTraceReader reader = new JobTracerReader("out.json");
        LoggedJob job = reader.getNext();
        while (job != null) {
          // .... process job level information
          for (LoggedTask task : job.getMapTasks()) {
            // process all the map tasks in the job
            for (LoggedTaskAttempt attempt : task.getAttempts()) {
              // process all the map task attempts in the job
            }
          }
          
          // get the next job
          job = reader.getNext();
        }
        reader.close();
  7. ClusterTopologyReader
    A reader to read LoggedNetworkTopology serialized using DefaultOutputter. ClusterTopologyReader can be initialized using the serialized topology filename. ClusterTopologyReader.get() can be used to get the LoggedNetworkTopology.

    Sample code: 
          
        // An example to read the cluster topology from a topology output file
        // 'topology.json'
        ClusterTopologyReader reader = new ClusterTopologyReader("topology.json");
        LoggedNetworkTopology topology  = reader.get();
        for (LoggedNetworkTopology t : topology.getChildren()) {
          // process the cluster topology
        }
        reader.close();

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