Spark2.1.1<spark写入Hbase的三种方法性能对比>

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#spark #hbase #性能

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本文对比了三种不同的HBase写入方法的性能：逐条写入、批量写入及通过MapReduce任务写入，并分析了每种方法的执行时间和效率。

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测试条件

以下是我的PC信息
这里写图片描述

依赖：

<dependencies>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming_2.11</artifactId>
        <version>2.1.1</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hbase</groupId>
        <artifactId>hbase-client</artifactId>
        <version>1.3.1</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hbase</groupId>
        <artifactId>hbase-server</artifactId>
        <version>1.3.1</version>
    </dependency>
</dependencies>

1. 第一种方法
每次写进一条，调用API

  /**
   * Puts some data in the table.
   * 
   * @param put The data to put.
   * @throws IOException if a remote or network exception occurs.
   * @since 0.20.0
   */
  void put(Put put) throws IOException

我的代码

import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
import org.apache.hadoop.hbase.client.{ConnectionFactory, Put}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}


object Word {
  def main(args: Array[String]): Unit = {
    val sc = new SparkContext(new SparkConf().setMaster("local[2]").setAppName("hbase"))
    val rdd = sc.makeRDD(Array(1)).flatMap(_ => 0 to 1000000)
    rdd.foreachPartition(x => {
      val hbaseConf = HBaseConfiguration.create()
      hbaseConf.set("hbase.zookeeper.quorum", "172.17.11.85,172.17.11.86,172.17.11.87")
      hbaseConf.set("hbase.zookeeper.property.clientPort", "2181")
      hbaseConf.set("hbase.defaults.for.version.skip", "true")
      val hbaseConn = ConnectionFactory.createConnection(hbaseConf)
      val table = hbaseConn.getTable(TableName.valueOf("word"))
      x.foreach(value => {
        var put = new Put(Bytes.toBytes(value.toString))
        put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("c1"), Bytes.toBytes(value.toString))
        table.put(put)
      })
    })
  }
}

第一条的时间戳：1497973306787
这里写图片描述

最后一条的时间戳：1497973505273
这里写图片描述

时间戳之差1497973505273-1497973306787=198486

2.第二种方法
批量写入Hbase，使用的API：

  /**
   * {@inheritDoc}
   * @throws IOException
   */
  @Override
  public void put(final List<Put> puts) throws IOException {
    getBufferedMutator().mutate(puts);
    if (autoFlush) {
      flushCommits();
    }
  }

我的代码：

import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
import org.apache.hadoop.hbase.client.{ConnectionFactory, HTable, Put}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}


object Word {
  def main(args: Array[String]): Unit = {
    val sc = new SparkContext(new SparkConf().setMaster("local[2]").setAppName("hbase"))
    val rdd = sc.makeRDD(Array(1)).flatMap(_ => 0 to 1000000)
    rdd.map(value => {
      var put = new Put(Bytes.toBytes(value.toString))
      put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("c1"), Bytes.toBytes(value.toString))
      put
    }).foreachPartition(iterator => {
      var jobConf = new JobConf(HBaseConfiguration.create())
      jobConf.set("hbase.zookeeper.quorum", "172.17.11.85,172.17.11.86,172.17.11.87")
      jobConf.set("zookeeper.znode.parent", "/hbase")
      jobConf.setOutputFormat(classOf[TableOutputFormat])
      val table = new HTable(jobConf, TableName.valueOf("word"))
      import scala.collection.JavaConversions._
      table.put(seqAsJavaList(iterator.toSeq))
    })
  }
}

第一条数据的时间戳是

 0                               column=f1:c1, timestamp=1498013677545, value=0                                              
 1                               column=f1:c1, timestamp=1498013677545, value=1                                              
 10                              column=f1:c1, timestamp=1498013677545, value=10                                             
 100                             column=f1:c1, timestamp=1498013677545, value=100                                            
 1000                            column=f1:c1, timestamp=1498013677545, value=1000

第9999条数据写进Hbase的时间戳是：

 108993                          column=f1:c1, timestamp=1498013680244, value=108993                                         
 108994                          column=f1:c1, timestamp=1498013680244, value=108994                                         
 108995                          column=f1:c1, timestamp=1498013680244, value=108995                                         
9999 row(s) in 1.2730 seconds

时间戳之差t=1498013680244-1498013677545=2699

3.第三种方法

将写进Hbase转换为Mapreduce任务

我的代码：

import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
import org.apache.hadoop.hbase.client.{ConnectionFactory, Put}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}


object Word {
  def main(args: Array[String]): Unit = {
    val sc = new SparkContext(new SparkConf().setMaster("local[2]").setAppName("hbase"))
    val conf = HBaseConfiguration.create()
    var jobConf = new JobConf(conf)
    jobConf.set("hbase.zookeeper.quorum", "172.17.11.85,172.17.11.86,172.17.11.87")
    jobConf.set("zookeeper.znode.parent", "/hbase")
    jobConf.set(TableOutputFormat.OUTPUT_TABLE, "word")
    jobConf.setOutputFormat(classOf[TableOutputFormat])

    val rdd = sc.makeRDD(Array(1)).flatMap(_ => 0 to 1000000)
    rdd.map(x => {
      var put = new Put(Bytes.toBytes(x.toString))
      put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("c1"), Bytes.toBytes(x.toString))
      (new ImmutableBytesWritable, put)
    }).saveAsHadoopDataset(jobConf)
  }
}

第一条的时间戳：

0                               column=f1:c1, timestamp=1498014877635, value=0

最后一条的时间戳

 108993                          column=f1:c1, timestamp=1498014879526, value=108993                                         
 108994                          column=f1:c1, timestamp=1498014879526, value=108994                                         
 108995                          column=f1:c1, timestamp=1498014879526, value=108995