001/*
002 * Licensed to the Apache Software Foundation (ASF) under one
003 * or more contributor license agreements.  See the NOTICE file
004 * distributed with this work for additional information
005 * regarding copyright ownership.  The ASF licenses this file
006 * to you under the Apache License, Version 2.0 (the
007 * "License"); you may not use this file except in compliance
008 * with the License.  You may obtain a copy of the License at
009 *
010 *     http://www.apache.org/licenses/LICENSE-2.0
011 *
012 * Unless required by applicable law or agreed to in writing, software
013 * distributed under the License is distributed on an "AS IS" BASIS,
014 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
015 * See the License for the specific language governing permissions and
016 * limitations under the License.
017 */
018package org.apache.hadoop.hbase.mapreduce;
019
020import java.io.IOException;
021import org.apache.hadoop.conf.Configuration;
022import org.apache.hadoop.conf.Configured;
023import org.apache.hadoop.fs.Path;
024import org.apache.hadoop.hbase.HBaseConfiguration;
025import org.apache.hadoop.hbase.client.Put;
026import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
027import org.apache.hadoop.hbase.util.Bytes;
028import org.apache.hadoop.io.LongWritable;
029import org.apache.hadoop.io.Text;
030import org.apache.hadoop.mapreduce.Job;
031import org.apache.hadoop.mapreduce.Mapper;
032import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
033import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
034import org.apache.hadoop.util.Tool;
035import org.apache.hadoop.util.ToolRunner;
036import org.apache.yetus.audience.InterfaceAudience;
037import org.slf4j.Logger;
038import org.slf4j.LoggerFactory;
039
040/**
041 * Sample Uploader MapReduce
042 * <p>
043 * This is EXAMPLE code. You will need to change it to work for your context.
044 * <p>
045 * Uses {@link TableReducer} to put the data into HBase. Change the InputFormat to suit your data.
046 * In this example, we are importing a CSV file.
047 * <p>
048 *
049 * <pre>
050 * row,family,qualifier,value
051 * </pre>
052 * <p>
053 * The table and columnfamily we're to insert into must preexist.
054 * <p>
055 * There is no reducer in this example as it is not necessary and adds significant overhead. If you
056 * need to do any massaging of data before inserting into HBase, you can do this in the map as well.
057 * <p>
058 * Do the following to start the MR job:
059 *
060 * <pre>
061 * ./bin/hadoop org.apache.hadoop.hbase.mapreduce.SampleUploader /tmp/input.csv TABLE_NAME
062 * </pre>
063 * <p>
064 * This code was written against HBase 0.21 trunk.
065 */
066@InterfaceAudience.Private
067public class SampleUploader extends Configured implements Tool {
068  private static final Logger LOG = LoggerFactory.getLogger(SampleUploader.class);
069
070  private static final String NAME = "SampleUploader";
071
072  static class Uploader extends Mapper<LongWritable, Text, ImmutableBytesWritable, Put> {
073    private long checkpoint = 100;
074    private long count = 0;
075
076    @Override
077    public void map(LongWritable key, Text line, Context context) throws IOException {
078      // Input is a CSV file
079      // Each map() is a single line, where the key is the line number
080      // Each line is comma-delimited; row,family,qualifier,value
081
082      // Split CSV line
083      String[] values = line.toString().split(",");
084      if (values.length != 4) {
085        return;
086      }
087
088      // Extract each value
089      byte[] row = Bytes.toBytes(values[0]);
090      byte[] family = Bytes.toBytes(values[1]);
091      byte[] qualifier = Bytes.toBytes(values[2]);
092      byte[] value = Bytes.toBytes(values[3]);
093
094      // Create Put
095      Put put = new Put(row);
096      put.addColumn(family, qualifier, value);
097
098      // Uncomment below to disable WAL. This will improve performance but means
099      // you will experience data loss in the case of a RegionServer crash.
100      // put.setWriteToWAL(false);
101
102      try {
103        context.write(new ImmutableBytesWritable(row), put);
104      } catch (InterruptedException e) {
105        LOG.error("Interrupted emitting put", e);
106        Thread.currentThread().interrupt();
107      }
108
109      // Set status every checkpoint lines
110      if (++count % checkpoint == 0) {
111        context.setStatus("Emitting Put " + count);
112      }
113    }
114  }
115
116  /**
117   * Job configuration.
118   */
119  public static Job configureJob(Configuration conf, String[] args) throws IOException {
120    Path inputPath = new Path(args[0]);
121    String tableName = args[1];
122    Job job = new Job(conf, NAME + "_" + tableName);
123    job.setJarByClass(Uploader.class);
124    FileInputFormat.setInputPaths(job, inputPath);
125    job.setInputFormatClass(SequenceFileInputFormat.class);
126    job.setMapperClass(Uploader.class);
127    // No reducers. Just write straight to table. Call initTableReducerJob
128    // because it sets up the TableOutputFormat.
129    TableMapReduceUtil.initTableReducerJob(tableName, null, job);
130    job.setNumReduceTasks(0);
131    return job;
132  }
133
134  /**
135   * Main entry point.
136   * @param otherArgs The command line parameters after ToolRunner handles standard.
137   * @throws Exception When running the job fails.
138   */
139  public int run(String[] otherArgs) throws Exception {
140    if (otherArgs.length != 2) {
141      System.err.println("Wrong number of arguments: " + otherArgs.length);
142      System.err.println("Usage: " + NAME + " <input> <tablename>");
143      return -1;
144    }
145    Job job = configureJob(getConf(), otherArgs);
146    return (job.waitForCompletion(true) ? 0 : 1);
147  }
148
149  public static void main(String[] args) throws Exception {
150    int status = ToolRunner.run(HBaseConfiguration.create(), new SampleUploader(), args);
151    System.exit(status);
152  }
153}