使用BulkLoad从HDFS批量导入数据到HBase

使用BulkLoad从HDFS批量导入数据到HBase

数据发出后首先写入到雨鞋日志WAl中，写入到预写日志中之后，随后写入到内存MemStore中，最后在Flush到Hfile中。这样写数据的方式不会导致数据的丢失，并且道正数据的有序性，但是当遇到大量的数据写入时，写入的速度就难以保证。所以，介绍一种性能更高的写入方式BulkLoad。

实例代码pom依赖：

org.apache.hbase hbase-server 1.4.0 org.apache.hadoop hadoop-client 2.6.4 org.apache.hbase hbase-client 0.99.2

package com.yangshou; import org.apache.hadoop.hbase.client.Put; import org.apache.hadoop.hbase.io.ImmutableBytesWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; import java.io.IOException; public class BulkLoadMapper extends Mapper { @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { //读取文件中的每一条数据，以序号作为行键 String line = value.toString(); //将数据进行切分 //切分后数组中的元素分别为：序号，用户id,商品id,用户行为，商品分类，时间，地址 String[] str = line.split(" "); String id = str[0]; String user_id = str[1]; String item_id = str[2]; String behavior = str[3]; String item_type = str[4]; String time = str[5]; String address = "156"; //拼接rowkey和put ImmutableBytesWritable rowkry = new ImmutableBytesWritable(id.getBytes()); Put put = new Put(id.getBytes()); put.add("info".getBytes(),"user_id".getBytes(),user_id.getBytes()); put.add("info".getBytes(),"item_id".getBytes(),item_id.getBytes()); put.add("info".getBytes(),"behavior".getBytes(),behavior.getBytes()); put.add("info".getBytes(),"item_type".getBytes(),item_type.getBytes()); put.add("info".getBytes(),"time".getBytes(),time.getBytes()); put.add("info".getBytes(),"address".getBytes(),address.getBytes()); //将数据写出 context.write(rowkry,put); } }

package com.yangshou; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.hbase.HBaseConfiguration; import org.apache.hadoop.hbase.TableName; import org.apache.hadoop.hbase.client.*; 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.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class BulkLoadDriver { public static void main(String[] args) throws Exception { //获取Hbase配置 Configuration conf = HBaseConfiguration.create(); Connection conn = ConnectionFactory.createConnection(conf); Table table = conn.getTable(TableName.valueOf("BulkLoadDemo")); Admin admin = conn.getAdmin(); //设置job Job job = Job.getInstance(conf,"BulkLoad"); job.setJarByClass(BulkLoadDriver.class); job.setMapperClass(BulkLoadMapper.class); job.setMapOutputKeyClass(ImmutableBytesWritable.class); job.setMapOutputValueClass(Put.class); //设置文件的输入输出路径 job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(HFileOutputFormat2.class); FileInputFormat.setInputPaths(job,new Path("hdfs://hadoopalone:9000/tmp/000000_0")); FileOutputFormat.setOutputPath(job,new Path("hdfs://hadoopalone:9000/demo1")); //将数据加载到Hbase表中 HFileOutputFormat2.configureIncrementalLoad(job,table,conn.getRegionLocator(TableName.valueOf("BulkLoadDemo"))); if(job.waitForCompletion(true)){ LoadIncrementalHFiles load = new LoadIncrementalHFiles(conf); load.doBulkLoad(new Path("hdfs://hadoopalone:9000/demo1"),admin,table,conn.getRegionLocator(TableName.valueOf("BulkLoadDemo"))); } } }

实例数据

44979 100640791 134060896 1 5271 2014-12-09 天津市 44980 100640791 96243605 1 13729 2014-12-02 新疆

在Hbase shell 中创建表

create 'BulkLoadDemo','info'

打包后执行```hadoop jar BulkLoadDemo-1.0-SNAPSHOT.jar com.yangshou.BulkLoadDriver

注意：在执行hadoop jar之前应该先将Hbase中的相关包加载过来

export HADOOP_CLASSPATH=$HBASE_HOME/lib/*

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