hadoop mr 输出需要导入hbase的话最好先输出成HFile格式, 再导入到HBase,因为HFile是HBase的内部存储格式, 所以导入效率很高,下面是一个示例
1. 创建HBase表t1
hbase(main):157:0* create 't1','f1' 0 row(s) in 1.3280 seconds hbase(main):158:0> scan 't1' ROW COLUMN+CELL 0 row(s) in 1.2770 seconds
2.写MR作业
HBaseHFileMapper.java
package com.test.hfile; import java.io.IOException; import org.apache.hadoop.hbase.io.ImmutableBytesWritable; import org.apache.hadoop.hbase.util.Bytes; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class HBaseHFileMapper extends Mapper<LongWritable, Text, ImmutableBytesWritable, Text> { private ImmutableBytesWritable immutableBytesWritable = new ImmutableBytesWritable(); @Override protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper.Context context) throws IOException, InterruptedException { immutableBytesWritable.set(Bytes.toBytes(key.get())); context.write(immutableBytesWritable, value); } }
HBaseHFileReducer.java
package com.test.hfile; import java.io.IOException; import org.apache.hadoop.hbase.KeyValue; import org.apache.hadoop.hbase.io.ImmutableBytesWritable; import org.apache.hadoop.hbase.util.Bytes; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class HBaseHFileReducer extends Reducer<ImmutableBytesWritable, Text, ImmutableBytesWritable, KeyValue> { protected void reduce(ImmutableBytesWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException { String value=""; while(values.iterator().hasNext()) { value = values.iterator().next().toString(); if(value != null && !"".equals(value)) { KeyValue kv = createKeyValue(value.toString()); if(kv!=null) context.write(key, kv); } } } // str格式为row:family:qualifier:value 简单模拟下 private KeyValue createKeyValue(String str) { String[] strstrs = str.split(":"); if(strs.length<4) return null; String row=strs[0]; String family=strs[1]; String qualifier=strs[2]; String value=strs[3]; return new KeyValue(Bytes.toBytes(row),Bytes.toBytes(family),Bytes.toBytes(qualifier),System.currentTimeMillis(), Bytes.toBytes(value)); } }
HbaseHFileDriver.java
package com.test.hfile; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.hbase.HBaseConfiguration; 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.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; public class HbaseHFileDriver { public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); Job job = new Job(conf, "testhbasehfile"); job.setJarByClass(HbaseHFileDriver.class); job.setMapperClass(com.test.hfile.HBaseHFileMapper.class); job.setReducerClass(com.test.hfile.HBaseHFileReducer.class); job.setMapOutputKeyClass(ImmutableBytesWritable.class); job.setMapOutputValueClass(Text.class); // 偷懒, 直接写死在程序里了,实际应用中不能这样, 应从命令行获取 FileInputFormat.addInputPath(job, new Path("/home/yinjie/input")); FileOutputFormat.setOutputPath(job, new Path("/home/yinjie/output")); Configuration HBASE_CONFIG = new Configuration(); HBASE_CONFIG.set("hbase.zookeeper.quorum", "localhost"); HBASE_CONFIG.set("hbase.zookeeper.property.clientPort", "2181"); HBaseConfiguration cfg = new HBaseConfiguration(HBASE_CONFIG); String tableName = "t1"; HTable htable = new HTable(cfg, tableName); HFileOutputFormat.configureIncrementalLoad(job, htable); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
/home/yinjie/input目录下有一个hbasedata.txt文件,内容为
[root@localhost input]# cat hbasedata.txt r1:f1:c1:value1 r2:f1:c2:value2 r3:f1:c3:value3
将作业打包,我的到处路径为/home/yinjie/job/hbasetest.jar
提交作业到hadoop运行:
[root@localhost job]# hadoop jar /home/yinjie/job/hbasetest.jar com.test.hfile.HbaseHFileDriver -libjars /home/yinjie/hbase-0.90.3/hbase-0.90.3.jar
作业运行完毕后查看下输出目录:
[root@localhost input]# hadoop fs -ls /home/yinjie/output Found 2 items drwxr-xr-x - root supergroup 0 2011-08-28 21:02 /home/yinjie/output/_logs drwxr-xr-x - root supergroup 0 2011-08-28 21:03 /home/yinjie/output/f1
OK, 已经生成以列族f1命名的文件夹了。
接下去使用Bulk Load将数据导入到HBbase
[root@localhost job]# hadoop jar /home/yinjie/hbase-0.90.3/hbase-0.90.3.jar completebulkload /home/yinjie/output t1
导入完毕,查询hbase表t1进行验证
hbase(main):166:0> scan 't1' ROW COLUMN+CELL r1 column=f1:c1, timestamp=1314591150788, value=value1 r2 column=f1:c2, timestamp=1314591150814, value=value2 r3 column=f1:c3, timestamp=1314591150815, value=value3 3 row(s) in 0.0210 seconds
数据已经导入!
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