正文
MapReduce生成HFile入库到HBase
提示：扫一扫查出行【扫一扫了解最新限行尾号】
复制提示
转自：http://www.cnblogs.com/shitouer/archive/2013/02/20/hbase-hfile-bulk-load.html
一、这种方式有很多的优点：
1. 如果我们一次性入库hbase巨量数据，处理速度慢不说，还特别占用Region资源， 一个比较高效便捷的方法就是使用 “Bulk Loading”方法，即HBase提供的HFileOutputFormat类。
2. 它是利用hbase的数据信息按照特定格式存储在hdfs内这一原理，直接生成这种hdfs内存储的数据格式文件，然后上传至合适位置，即完成巨量数据快速入库的办法。配合mapreduce完成，高效便捷，而且不占用region资源，增添负载。
二、这种方式也有很大的限制：
1. 仅适合初次数据导入，即表内数据为空，或者每次入库表内都无数据的情况。
2. HBase集群与Hadoop集群为同一集群，即HBase所基于的HDFS为生成HFile的MR的集群(额，咋表述~~~)
三、接下来一个demo，简单介绍整个过程。
1. 生成HFile部分
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
package
zl.hbase.mr;
import
java.io.IOException;
import
org.apache.hadoop.conf.Configuration;
import
org.apache.hadoop.fs.Path;
import
org.apache.hadoop.hbase.KeyValue;
import
org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import
org.apache.hadoop.hbase.mapreduce.HFileOutputFormat;
import
org.apache.hadoop.hbase.mapreduce.KeyValueSortReducer;
import
org.apache.hadoop.hbase.mapreduce.SimpleTotalOrderPartitioner;
import
org.apache.hadoop.hbase.util.Bytes;
import
org.apache.hadoop.io.LongWritable;
import
org.apache.hadoop.io.Text;
import
org.apache.hadoop.mapreduce.Job;
import
org.apache.hadoop.mapreduce.Mapper;
import
org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import
org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import
org.apache.hadoop.util.GenericOptionsParser;
import
zl.hbase.util.ConnectionUtil;
public
class
HFileGenerator {
public
static
class
HFileMapper
extends
Mapper<LongWritable, Text, ImmutableBytesWritable, KeyValue> {
@Override
protected
void
map(LongWritable key, Text value, Context context)
throws
IOException, InterruptedException {
String line = value.toString();
String[] items = line.split(
","
, -
1
);
ImmutableBytesWritable rowkey =
new
ImmutableBytesWritable(
items[
0
].getBytes());
KeyValue kv =
new
KeyValue(Bytes.toBytes(items[
0
]),
Bytes.toBytes(items[
1
]), Bytes.toBytes(items[
2
]),
System.currentTimeMillis(), Bytes.toBytes(items[
3
]));
if
(
null
!= kv) {
context.write(rowkey, kv);
}
}
}
public
static
void
main(String[] args)
throws
IOException,
InterruptedException, ClassNotFoundException {
Configuration conf =
new
Configuration();
String[] dfsArgs =
new
GenericOptionsParser(conf, args)
.getRemainingArgs();
Job job =
new
Job(conf,
"HFile bulk load test"
);
job.setJarByClass(HFileGenerator.
class
);
job.setMapperClass(HFileMapper.
class
);
job.setReducerClass(KeyValueSortReducer.
class
);
job.setMapOutputKeyClass(ImmutableBytesWritable.
class
);
job.setMapOutputValueClass(Text.
class
);
job.setPartitionerClass(SimpleTotalOrderPartitioner.
class
);
FileInputFormat.addInputPath(job,
new
Path(dfsArgs[
0
]));
FileOutputFormat.setOutputPath(job,
new
Path(dfsArgs[
1
]));
HFileOutputFormat.configureIncrementalLoad(job,
ConnectionUtil.getTable());
System.exit(job.waitForCompletion(
true
) ?
0
:
1
);
}
}
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
|
package
zl.hbase.mr;
import
java.io.IOException;
import
org.apache.hadoop.conf.Configuration;
import
org.apache.hadoop.fs.Path;
import
org.apache.hadoop.hbase.KeyValue;
import
org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import
org.apache.hadoop.hbase.mapreduce.HFileOutputFormat;
import
org.apache.hadoop.hbase.mapreduce.KeyValueSortReducer;
import
org.apache.hadoop.hbase.mapreduce.SimpleTotalOrderPartitioner;
import
org.apache.hadoop.hbase.util.Bytes;
import
org.apache.hadoop.io.LongWritable;
import
org.apache.hadoop.io.Text;
import
org.apache.hadoop.mapreduce.Job;
import
org.apache.hadoop.mapreduce.Mapper;
import
org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import
org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import
org.apache.hadoop.util.GenericOptionsParser;
import
zl.hbase.util.ConnectionUtil;
public
class
HFileGenerator {
public
static
class
HFileMapper
extends
Mapper<LongWritable, Text, ImmutableBytesWritable, KeyValue> {
@Override
protected
void
map(LongWritable key, Text value, Context context)
throws
IOException, InterruptedException {
String line = value.toString();
String[] items = line.split(
","
, -
1
);
ImmutableBytesWritable rowkey =
new
ImmutableBytesWritable(
items[
0
].getBytes());
KeyValue kv =
new
KeyValue(Bytes.toBytes(items[
0
]),
Bytes.toBytes(items[
1
]), Bytes.toBytes(items[
2
]),
System.currentTimeMillis(), Bytes.toBytes(items[
3
]));
if
(
null
!= kv) {
context.write(rowkey, kv);
}
}
}
public
static
void
main(String[] args)
throws
IOException,
InterruptedException, ClassNotFoundException {
Configuration conf =
new
Configuration();
String[] dfsArgs =
new
GenericOptionsParser(conf, args)
.getRemainingArgs();
Job job =
new
Job(conf,
"HFile bulk load test"
);
job.setJarByClass(HFileGenerator.
class
);
job.setMapperClass(HFileMapper.
class
);
job.setReducerClass(KeyValueSortReducer.
class
);
job.setMapOutputKeyClass(ImmutableBytesWritable.
class
);
job.setMapOutputValueClass(Text.
class
);
job.setPartitionerClass(SimpleTotalOrderPartitioner.
class
);
FileInputFormat.addInputPath(job,
new
Path(dfsArgs[
0
]));
FileOutputFormat.setOutputPath(job,
new
Path(dfsArgs[
1
]));
HFileOutputFormat.configureIncrementalLoad(job,
ConnectionUtil.getTable());
System.exit(job.waitForCompletion(
true
) ?
0
:
1
);
}
}
|
生成HFile程序说明：
①. 最终输出结果，无论是map还是reduce，输出部分key和value的类型必须是： < ImmutableBytesWritable, KeyValue>或者< ImmutableBytesWritable, Put>。
②. 最终输出部分，Value类型是KeyValue 或Put，对应的Sorter分别是KeyValueSortReducer或PutSortReducer。
③. MR例子中job.setOutputFormatClass(HFileOutputFormat.class); HFileOutputFormat只适合一次对单列族组织成HFile文件。
④. MR例子中HFileOutputFormat.configureIncrementalLoad(job, table);自动对job进行配置。SimpleTotalOrderPartitioner是需要先对key进行整体排序，然后划分到每个reduce中，保证每一个reducer中的的key最小最大值区间范围，是不会有交集的。因为入库到HBase的时候，作为一个整体的Region，key是绝对有序的。
⑤. MR例子中最后生成HFile存储在HDFS上，输出路径下的子目录是各个列族。如果对HFile进行入库HBase，相当于move HFile到HBase的Region中，HFile子目录的列族内容没有了。
2. HFile入库到HBase
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
package
zl.hbase.bulkload;
import
org.apache.hadoop.fs.Path;
import
org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles;
import
org.apache.hadoop.util.GenericOptionsParser;
import
zl.hbase.util.ConnectionUtil;
public
class
HFileLoader {
public
static
void
main(String[] args)
throws
Exception {
String[] dfsArgs =
new
GenericOptionsParser(
ConnectionUtil.getConfiguration(), args).getRemainingArgs();
LoadIncrementalHFiles loader =
new
LoadIncrementalHFiles(
ConnectionUtil.getConfiguration());
loader.doBulkLoad(
new
Path(dfsArgs[
0
]), ConnectionUtil.getTable());
}
}
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
|
package
zl.hbase.bulkload;
import
org.apache.hadoop.fs.Path;
import
org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles;
import
org.apache.hadoop.util.GenericOptionsParser;
import
zl.hbase.util.ConnectionUtil;
public
class
HFileLoader {
public
static
void
main(String[] args)
throws
Exception {
String[] dfsArgs =
new
GenericOptionsParser(
ConnectionUtil.getConfiguration(), args).getRemainingArgs();
LoadIncrementalHFiles loader =
new
LoadIncrementalHFiles(
ConnectionUtil.getConfiguration());
loader.doBulkLoad(
new
Path(dfsArgs[
0
]), ConnectionUtil.getTable());
}
}
|
通过HBase中 LoadIncrementalHFiles的doBulkLoad方法，对生成的HFile文件入库
