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HBase数据快速导入之ImportTsv&Bulkload

时间:2016-04-14 11:51:59      阅读:292      评论:0      收藏:0      [点我收藏+]

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导入数据最快的方式,可以略过WAL直接生产底层HFile文件

(环境:centos6.5、Hadoop2.6.0、HBase0.98.9)

1.SHELL方式

1.1 ImportTsv直接导入

命令:bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv

Usage: importtsv -Dimporttsv.columns=a,b,c <tablename> <inputdir>

测试:

1.1.1在HBase中创建好表

create ‘testImport1’,’cf’

1.1.2准备数据文件sample1.csv,并上传到HDFS,内容为:

1,"tom"
2,"sam"
3,"jerry"
4,"marry"
5,"john

1.1.3使用导入命令导入

bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.separator="," -Dimporttsv.columns=HBASE_ROW_KEY,cf testImport1 /sample1.csv

1.1.4结果

技术分享

 

1.2先通过ImportTsv生产HFile文件,再通过completeBulkload导入HBase

1.2.1使用刚才的源数据并创建新表

create ‘testImport2’,’cf’

1.2.2使用命令生产HFile文件

bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.separator="," -Dimporttsv.bulk.output=hfile_tmp -Dimporttsv.columns=HBASE_ROW_KEY,cf testImport2 /sample1.csv

1.2.3在HDFS上的中间结果

技术分享

1.2.4使用命令将HFile文件导入HBase

hadoop jar lib/hbase-server-0.98.9-hadoop2.jar completebulkload hfile_tmp testImport2

1.2.5结果

技术分享

注:1.如果出现缺包错误提示,则把HBase的jar包包含到hadoop的classpath中;2.运行该命令的本质是一个hdfs的mv操作,并不会启动MapReduce。

2.API代码方式

代码的方式更灵活一点,许多东西可以自定义。

直接贴代码吧:

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FsShell;
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.HFileOutputFormat2;
import org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.IOException;

public class BulkLoadJob {
	static Logger logger = LoggerFactory.getLogger(BulkLoadJob.class);

	public static class BulkLoadMap extends Mapper<LongWritable, Text, ImmutableBytesWritable, KeyValue> {

		public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

			String[] valueStrSplit = value.toString().split("\t");
			String hkey = valueStrSplit[0];
			String family = valueStrSplit[1].split(":")[0];
			String column = valueStrSplit[1].split(":")[1];
			String hvalue = valueStrSplit[2];
			final byte[] rowKey = Bytes.toBytes(hkey);
			final ImmutableBytesWritable HKey = new ImmutableBytesWritable(rowKey);
			// Put HPut = new Put(rowKey);
			// byte[] cell = Bytes.toBytes(hvalue);
			// HPut.add(Bytes.toBytes(family), Bytes.toBytes(column), cell);
			KeyValue kv = new KeyValue(rowKey, Bytes.toBytes(family), Bytes.toBytes(column), Bytes.toBytes(hvalue));
			context.write(HKey, kv);
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration conf = HBaseConfiguration.create();
		conf.set("hbase.zookeeper.property.clientPort", "2182");
		conf.set("hbase.zookeeper.quorum", "msg801,msg802,msg803");
		conf.set("hbase.master", "msg801:60000");
		String[] dfsArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
		String inputPath = dfsArgs[0];
		System.out.println("source: " + dfsArgs[0]);
		String outputPath = dfsArgs[1];
		System.out.println("dest: " + dfsArgs[1]);
		HTable hTable = null;
		try {
			Job job = Job.getInstance(conf, "Test Import HFile & Bulkload");
			job.setJarByClass(BulkLoadJob.class);
			job.setMapperClass(BulkLoadJob.BulkLoadMap.class);
			job.setMapOutputKeyClass(ImmutableBytesWritable.class);
			job.setMapOutputValueClass(KeyValue.class);
			// speculation
			job.setSpeculativeExecution(false);
			job.setReduceSpeculativeExecution(false);
			// in/out format
			job.setInputFormatClass(TextInputFormat.class);
			job.setOutputFormatClass(HFileOutputFormat2.class);

			FileInputFormat.setInputPaths(job, inputPath);
			FileOutputFormat.setOutputPath(job, new Path(outputPath));

			hTable = new HTable(conf, dfsArgs[2]);
			HFileOutputFormat2.configureIncrementalLoad(job, hTable);

			if (job.waitForCompletion(true)) {
				FsShell shell = new FsShell(conf);
				try {
					shell.run(new String[] { "-chmod", "-R", "777", dfsArgs[1] });
				} catch (Exception e) {
					logger.error("Couldnt change the file permissions ", e);
					throw new IOException(e);
				}
				// 加载到hbase表
				LoadIncrementalHFiles loader = new LoadIncrementalHFiles(conf);
				// 两种方式都可以
				// 方式一
				String[] loadArgs = { outputPath, dfsArgs[2] };
				loader.run(loadArgs);
				// 方式二
				// loader.doBulkLoad(new Path(outputPath), hTable);
			} else {
				logger.error("loading failed.");
				System.exit(1);
			}

		} catch (IllegalArgumentException e) {
			e.printStackTrace();
		} finally {
			if (hTable != null) {
				hTable.close();
			}
		}
	}
}

  

2.1创建新表

create ‘testImport3’,’fm1’,’fm2’

2.2创建sample2.csv,并上传到HDFS,内容为:
key1    fm1:col1        value1
key1    fm1:col2        value2
key1    fm2:col1        value3
key4    fm1:col1        value4

使用命令:

hadoop jar BulkLoadJob.jar hdfs://msg/sample2,csv hdfs://msg/HFileOut testImport3

注:1.mapper中使用KeyValue和Put都可以;2.注意jar包的classpath;3.如果Hadoop是HA,则需要使用HA的名字,比如我们的active namenode名称为msg801,但是HA的nameservice为msg,则HDFS的路径必须使用hdfs://msg而不能使用hdfs://msg801:9000(WHY?)。

具体报错为:

IllegalArgumentException: Wrong FS: hdfs://msg801:9000/HFileOut/fm2/bbab9d883a574d518cdcb304d1e681e9, expected: hdfs://msg

 


HBase数据快速导入之ImportTsv&Bulkload

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原文地址:http://www.cnblogs.com/admln/p/5390333.html

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