загрузка 1 ГБ данных в hbase занимает 1 час

Я хочу загрузить CSV-файл размером 1 ГБ (10 миллионов записей) в Hbase. Я написал для него программу Map-Reduce. Мой код работает нормально, но на выполнение требуется 1 час. Последний редуктор занимает более получаса. Может ли кто-нибудь помочь мне?

Мой код выглядит следующим образом:

Driver.Java

    package com.cloudera.examples.hbase.bulkimport;

    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.hbase.HBaseConfiguration;
    import org.apache.hadoop.hbase.KeyValue;
    import org.apache.hadoop.hbase.client.HTable;
    import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
    import org.apache.hadoop.hbase.mapreduce.HFileOutputFormat;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

    /**
     * HBase bulk import example

     * Data preparation MapReduce job driver
     * 

     * 
args[0]: HDFS input path
     * 
args[1]: HDFS output path
     * 
args[2]: HBase table name
     * 
     */
    public class Driver {
      public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();

        /*
         * NBA Final 2010 game 1 tip-off time (seconds from epoch) 
         * Thu, 03 Jun 2010 18:00:00 PDT
         */
      //  conf.setInt("epoch.seconds.tipoff", 1275613200);
        conf.set("hbase.table.name", args[2]);

        // Load hbase-site.xml 
        HBaseConfiguration.addHbaseResources(conf);

        Job job = new Job(conf, "HBase Bulk Import Example");
        job.setJarByClass(HBaseKVMapper.class);

        job.setMapperClass(HBaseKVMapper.class);
        job.setMapOutputKeyClass(ImmutableBytesWritable.class);
        job.setMapOutputValueClass(KeyValue.class);

        job.setInputFormatClass(TextInputFormat.class);

        HTable hTable = new HTable(conf, args[2]);

        // Auto configure partitioner and reducer
        HFileOutputFormat.configureIncrementalLoad(job, hTable);

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.waitForCompletion(true);

        // Load generated HFiles into table
    //    LoadIncrementalHFiles loader = new LoadIncrementalHFiles(conf);
    //    loader.doBulkLoad(new Path(args[1]), hTable);
      }
    }

HColumnEnum.java

        package com.cloudera.examples.hbase.bulkimport;

    /**
     * HBase table columns for the 'srv' column family
     */
    public enum HColumnEnum {
      SRV_COL_employeeid ("employeeid".getBytes()),
      SRV_COL_eventdesc ("eventdesc".getBytes()),
      SRV_COL_eventdate ("eventdate".getBytes()),
      SRV_COL_objectname ("objectname".getBytes()),
      SRV_COL_objectfolder ("objectfolder".getBytes()),
      SRV_COL_ipaddress ("ipaddress".getBytes());

      private final byte[] columnName;

      HColumnEnum (byte[] column) {
        this.columnName = column;
      }

      public byte[] getColumnName() {
        return this.columnName;
      }
    }

HBaseKVMapper.java

package com.cloudera.examples.hbase.bulkimport;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.KeyValue;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import au.com.bytecode.opencsv.CSVParser;

/**
 * HBase bulk import example
 * <p>
 * Parses Facebook and Twitter messages from CSV files and outputs
 * <ImmutableBytesWritable, KeyValue>.
 * <p>
 * The ImmutableBytesWritable key is used by the TotalOrderPartitioner to map it
 * into the correct HBase table region.
 * <p>
 * The KeyValue value holds the HBase mutation information (column family,
 * column, and value)
 */
public class HBaseKVMapper extends
    Mapper<LongWritable, Text, ImmutableBytesWritable, KeyValue> {

  final static byte[] SRV_COL_FAM = "srv".getBytes();
  final static int NUM_FIELDS = 6;

  CSVParser csvParser = new CSVParser();
  int tipOffSeconds = 0;
  String tableName = "";

  // DateTimeFormatter p = DateTimeFormat.forPattern("MMM dd, yyyy HH:mm:ss")
  //    .withLocale(Locale.US).withZone(DateTimeZone.forID("PST8PDT"));

  ImmutableBytesWritable hKey = new ImmutableBytesWritable();
  KeyValue kv;

  /** {@inheritDoc} */
  @Override
  protected void setup(Context context) throws IOException,
      InterruptedException {
    Configuration c = context.getConfiguration();

  //  tipOffSeconds = c.getInt("epoch.seconds.tipoff", 0);
    tableName = c.get("hbase.table.name");
  }

  /** {@inheritDoc} */
  @Override
  protected void map(LongWritable key, Text value, Context context)
      throws IOException, InterruptedException {

    /*if (value.find("Service,Term,") > -1) {
      // Skip header
      return;
    }*/

    String[] fields = null;

    try {
      fields = value.toString().split(",");
      //csvParser.parseLine(value.toString());
    } catch (Exception ex) {
      context.getCounter("HBaseKVMapper", "PARSE_ERRORS").increment(1);
      return;
    }

    if (fields.length != NUM_FIELDS) {
      context.getCounter("HBaseKVMapper", "INVALID_FIELD_LEN").increment(1);
      return;
    }

    // Get game offset in seconds from tip-off
  /*  DateTime dt = null;

    try {
      dt = p.parseDateTime(fields[9]);
    } catch (Exception ex) {
      context.getCounter("HBaseKVMapper", "INVALID_DATE").increment(1);
      return;
    }

    int gameOffset = (int) ((dt.getMillis() / 1000) - tipOffSeconds);
    String offsetForKey = String.format("%04d", gameOffset);

    String username = fields[2];
    if (username.equals("")) {
      username = fields[3];
    }*/

    // Key: e.g. "1200:twitter:jrkinley"
    hKey.set(String.format("%s|%s|%s|%s|%s|%s", fields[0], fields[1], fields[2],fields[3],fields[4],fields[5])
        .getBytes());

    // Service columns
    if (!fields[0].equals("")) {
      kv = new KeyValue(hKey.get(), SRV_COL_FAM,
          HColumnEnum.SRV_COL_employeeid.getColumnName(), fields[0].getBytes());
      context.write(hKey, kv);
    }

    if (!fields[1].equals("")) {
      kv = new KeyValue(hKey.get(), SRV_COL_FAM,
          HColumnEnum.SRV_COL_eventdesc.getColumnName(), fields[1].getBytes());
      context.write(hKey, kv);
    }

    if (!fields[2].equals("")) {
      kv = new KeyValue(hKey.get(), SRV_COL_FAM,
          HColumnEnum.SRV_COL_eventdate.getColumnName(), fields[2].getBytes());
      context.write(hKey, kv);
    }

    if (!fields[3].equals("")) {
      kv = new KeyValue(hKey.get(), SRV_COL_FAM,
          HColumnEnum.SRV_COL_objectname.getColumnName(), fields[3].getBytes());
      context.write(hKey, kv);
    }

    if (!fields[4].equals("")) {
      kv = new KeyValue(hKey.get(), SRV_COL_FAM,
          HColumnEnum.SRV_COL_objectfolder.getColumnName(), fields[4].getBytes());
      context.write(hKey, kv);
    }

    if (!fields[5].equals("")) {
      kv = new KeyValue(hKey.get(), SRV_COL_FAM,
          HColumnEnum.SRV_COL_ipaddress.getColumnName(), fields[5].getBytes());
      context.write(hKey, kv);
    }


    context.getCounter("HBaseKVMapper", "NUM_MSGS").increment(1);

    /*
     * Output number of messages per quarter and before/after game. This should
     * correspond to the number of messages per region in HBase
     */
  /*  if (gameOffset < 0) {
      context.getCounter("QStats", "BEFORE_GAME").increment(1);
    } else if (gameOffset < 900) {
      context.getCounter("QStats", "Q1").increment(1);
    } else if (gameOffset < 1800) {
      context.getCounter("QStats", "Q2").increment(1);
    } else if (gameOffset < 2700) {
      context.getCounter("QStats", "Q3").increment(1);
    } else if (gameOffset < 3600) {
      context.getCounter("QStats", "Q4").increment(1);
    } else {
      context.getCounter("QStats", "AFTER_GAME").increment(1);
    }*/
  }
}

Пожалуйста, помогите мне повысить производительность или сообщите мне, если у вас есть альтернативное решение с образцом кода.

МОЙ mapred-site.xml

 <?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Put site-specific property overrides in this file. -->

<configuration>

<property>
  <name>mapred.job.tracker</name>
    <value>namenode:54311</value>
    </property>

<property>
  <name>mapred.reduce.parallel.copies</name>
    <value>20</value>
    </property>

<property>
  <name>tasktracker.http.threads</name>
    <value>50</value>
    </property>

<property>
  <name>mapred.job.shuffle.input.buffer.percent</name>
    <value>0.70</value>
    </property>

<property>
  <name>mapred.tasktracker.map.tasks.maximum</name>
    <value>4</value>
    </property>

<property>
  <name>mapred.tasktracker.reduce.tasks.maximum</name>
    <value>4</value>
    </property>

<property>
  <name>mapred.map.tasks</name>
    <value>4</value>
    </property>

<property>
  <name>reduce.map.tasks</name>
    <value>4</value>
    </property>

<property>
  <name>mapred.job.shuffle.merge.percent</name>
    <value>0.65</value>
    </property>

<property>
  <name>mapred.task.timeout</name>
    <value>1200000</value>
    </property>

<property>
    <name>mapred.child.java.opts</name>
        <value>-Xms1024M -Xmx2048M</value>
        </property>



<property>
  <name>mapred.job.reuse.jvm.num.tasks</name>
    <value>-1</value>
    </property>

<property>
    <name>mapred.compress.map.output</name>
    <value>true</value>
</property>

<property>
    <name>mapred.map.output.compression.codec</name>
    <value>com.hadoop.compression.lzo.LzoCodec</value>
</property>

<property>
    <name>io.sort.mb</name>
    <value>800</value>
</property>


<property>
  <name>mapred.child.ulimit</name>
    <value>unlimited</value>
    </property>

<property>
<name>io.sort.factor</name>
<value>100</value>
<description>More streams merged at once while sorting files.</description>
</property>  


 <property>
 <name>mapreduce.admin.map.child.java.opts</name>
 <value>-Djava.net.preferIPv4Stack=true</value>
 </property>
 <property>
 <name>mapreduce.admin.reduce.child.java.opts</name>
 <value>-Djava.net.preferIPv4Stack=true</value>
 </property>


<property>
   <name>mapred.min.split.size</name>
   <value>0</value>
</property>

<property>
   <name>mapred.job.map.memory.mb</name>
     <value>-1</value>
     </property>

<property>
   <name>mapred.jobtracker.maxtasks.per.job</name>
        <value>-1</value>
             </property>


</configuration>

hbase-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
    <name>hbase.rootdir</name>
    <value>hdfs://namenode:54310/hbase</value>
    <description>The directory shared by RegionServers.
    </description>
</property>

<property>
    <name>hbase.master</name>
    <value>slave:60000</value>
    <description>The host and port that the HBase master runs at.
    A value of 'local' runs the master and a regionserver
    in a single process.
    </description>
</property>

<property>
    <name>hbase.cluster.distributed</name>
    <value>true</value>
    <description>The mode the cluster will be in. Possible values are
    false: standalone and pseudo-distributed setups with managed Zookeeper
    true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
    </description>
</property>

<property>
    <name>hbase.zookeeper.quorum</name>
    <value>slave</value>
    <description>Comma separated list of servers in the ZooKeeper Quorum.
    For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".
    By default this is set to localhost for local and pseudo-distributed modes
    of operation. For a fully-distributed setup, this should be set to a full
    list of ZooKeeper quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh
    this is the list of servers which we will start/stop ZooKeeper on.
    </description>
</property>

<property>
       <name>hbase.zookeeper.property.clientPort</name>
       <value>2181</value>
</property>

<property>
    <name>hbase.zookeeper.property.dataDir</name>
    <value>/home/hduser/work/zoo_data</value>
    <description>Property from ZooKeeper's config zoo.cfg.
    The directory where the snapshot is stored.
    </description>
</property>

</configuration>

Пожалуйста, помогите мне, чтобы я мог улучшить свою работу.