HBase MapReduce 使用

项目中需要用MapReduce来读取或者写Hbase，这样可以节省大量开发时间。

Hbase本身的jar包里就有这样的API ， 以下是我从官网上找的一些资料，和大家分享一下。

原文地址：http://hbase.apache.org/book/mapreduce.example.html

总体说明一下：TableMapper 主要是读hbase数据，TableReducer 主要是写hbase数据。可以结合一起用，也可以分开用。

（一） 读Hbase实例

public static class MyMapper extends TableMapper<Text, Text> {  public void map(ImmutableBytesWritable row, Result value, Context context) throws InterruptedException, IOException {    // process data for the row from the Result instance.   }}

Configuration config = HBaseConfiguration.create();Job job = new Job(config, "ExampleRead");job.setJarByClass(MyReadJob.class);     // class that contains mapperScan scan = new Scan();scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobsscan.setCacheBlocks(false);  // don't set to true for MR jobs// set other scan attrs...TableMapReduceUtil.initTableMapperJob(  tableName,        // input HBase table name  scan,             // Scan instance to control CF and attribute selection  MyMapper.class,   // mapper  null,             // mapper output key  null,             // mapper output value  job);job.setOutputFormatClass(NullOutputFormat.class);   // because we aren't emitting anything from mapperboolean b = job.waitForCompletion(true);if (!b) {  throw new IOException("error with job!");}

（二） 读写实例

public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put>  {public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {// this example is just copying the data from the source table...   context.write(row, resultToPut(row,value));   }  private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException {  Put put = new Put(key.get()); for (KeyValue kv : result.raw()) {put.add(kv);}return put;   }}    

Configuration config = HBaseConfiguration.create();Job job = new Job(config,"ExampleReadWrite");job.setJarByClass(MyReadWriteJob.class);    // class that contains mapperScan scan = new Scan();scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobsscan.setCacheBlocks(false);  // don't set to true for MR jobs// set other scan attrsTableMapReduceUtil.initTableMapperJob(sourceTable,      // input tablescan,          // Scan instance to control CF and attribute selectionMyMapper.class,   // mapper classnull,          // mapper output keynull,          // mapper output valuejob);TableMapReduceUtil.initTableReducerJob(targetTable,      // output tablenull,             // reducer classjob);job.setNumReduceTasks(0);boolean b = job.waitForCompletion(true);if (!b) {    throw new IOException("error with job!");}

（三） 做统计实例

public static class MyMapper extends TableMapper<Text, IntWritable>  {private final IntWritable ONE = new IntWritable(1);   private Text text = new Text();   public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {        String val = new String(value.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr1")));          text.set(val);     // we can only emit Writables...        context.write(text, ONE);   }}  

public static class MyTableReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable>  { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {    int i = 0;    for (IntWritable val : values) {    i += val.get();    }    Put put = new Put(Bytes.toBytes(key.toString()));    put.add(Bytes.toBytes("cf"), Bytes.toBytes("count"), Bytes.toBytes(i));    context.write(null, put);   }}

Configuration config = HBaseConfiguration.create();Job job = new Job(config,"ExampleSummary");job.setJarByClass(MySummaryJob.class);     // class that contains mapper and reducerScan scan = new Scan();scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobsscan.setCacheBlocks(false);  // don't set to true for MR jobs// set other scan attrsTableMapReduceUtil.initTableMapperJob(sourceTable,        // input tablescan,               // Scan instance to control CF and attribute selectionMyMapper.class,     // mapper classText.class,         // mapper output keyIntWritable.class,  // mapper output valuejob);TableMapReduceUtil.initTableReducerJob(targetTable,        // output tableMyTableReducer.class,    // reducer classjob);job.setNumReduceTasks(1);   // at least one, adjust as requiredboolean b = job.waitForCompletion(true);if (!b) {throw new IOException("error with job!");}

（四）混合实例，结果存在文件上

 public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable>  {public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {int i = 0;for (IntWritable val : values) {i += val.get();}context.write(key, new IntWritable(i));}}  

Configuration config = HBaseConfiguration.create();Job job = new Job(config,"ExampleSummaryToFile");job.setJarByClass(MySummaryFileJob.class);     // class that contains mapper and reducerScan scan = new Scan();scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobsscan.setCacheBlocks(false);  // don't set to true for MR jobs// set other scan attrsTableMapReduceUtil.initTableMapperJob(sourceTable,        // input tablescan,               // Scan instance to control CF and attribute selectionMyMapper.class,     // mapper classText.class,         // mapper output keyIntWritable.class,  // mapper output valuejob);job.setReducerClass(MyReducer.class);    // reducer classjob.setNumReduceTasks(1);    // at least one, adjust as requiredFileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile"));  // adjust directories as requiredboolean b = job.waitForCompletion(true);if (!b) {throw new IOException("error with job!");}