CDH5.6下：spark 操作hbase的两种方式性能对比(rdd, spark-sql)

hbase 表：602401条数据， 两种计算方式对比结果：

rdd使用cache()	单次计算: 90秒	计算量增加不影响时间消耗	编程难度较大
df 使用registerTempTable(" xx ")	单次计算: 110秒	计算量增加会逐步增加时间的消耗	编程难度较小

spark rdd----- > 1次：  time= 91，481 	1.5分钟
                              89，042
                              80，385
							  
				 10次：        94，082
							  
spark sql----- > 1次：  time=145，012	2.5分钟 
                             105，295
                             116，203
							 
				  10次：      375，126

pom.xml文件

<repositories>
        <repository>
            <id>cloudera</id>
            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
        </repository>
    </repositories>

    <dependencies>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.2.0</version>
        </dependency>

        <dependency>
        <groupId>org.apache.hbase</groupId>
        <artifactId>hbase-server</artifactId>
        <version>1.2.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.10</artifactId>
            <version>1.5.0-cdh5.6.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.10</artifactId>
            <version>1.5.0-cdh5.6.1</version>
        </dependency>
    </dependencies>

1， spark-sql计算代码

import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.client.Result
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SQLContext

object SparkSqlHbase {
  case class Per(val  DEPT:String, val  COST:Long,val  PERSON_NAME:String)

  def main(args: Array[String]): Unit = {
    val time1 = System.currentTimeMillis()
    val loop_counts=args(0).toInt

    //spark配置
    val conf = new SparkConf()
    conf.setAppName("sql-hbase:使用sql语句")
//    conf.setMaster("local[*]")
    val sc = new SparkContext(conf)

   // 读取hbase数据
    val hbaseconf = HBaseConfiguration.create()
    hbaseconf.set(TableInputFormat.INPUT_TABLE,"ns1:person")
    val  rdd=sc.newAPIHadoopRDD(
      hbaseconf,
      classOf[TableInputFormat],
      classOf[ImmutableBytesWritable],
      classOf[Result]
    )

    val  rdd2=rdd.map({case (_,result) =>

      try{
        val id = Bytes.toString( result.getValue("f".getBytes(), "DEPT".getBytes()))
        val cost =Bytes.toString( result.getValue("f".getBytes(),"COST".getBytes())).toInt
        val name =Bytes.toString( result.getValue("f".getBytes(),"PERSON_NAME".getBytes()))
        Per(id,cost,name)
      }catch {
        case _=> println("error...");Per("",0,"")
      }

    })

    //变换dataFrame
    val sqlContext = new SQLContext(sc)
    import  sqlContext.implicits._
    val df = rdd2.toDF()
    df.registerTempTable("person")

    for( i <- (0  until  loop_counts)){
      println("===========>")
      sqlContext.sql("select count(DEPT) from  person").show()
      sqlContext.sql("select sum(COST) from  person").show()
    }
    println("===========> time= "+ (System.currentTimeMillis()-time1))
    sqlContext.dropTempTable("person")
    sc.stop()
  }
}

2, rdd计算代码

import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.client.Result
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}

object SparkSqlHbase2 {

  case class Per(val  DEPT:String, val  COST:Long,val  PERSON_NAME:String)

  def main(args: Array[String]): Unit = {
    val time1 = System.currentTimeMillis()
    val loop_counts = args(0).toInt

    //spark配置
    val conf = new SparkConf()
    conf.setAppName("rdd-hbase:计算")
    //    conf.setMaster("local[*]")
    val sc = new SparkContext(conf)

    // 读取hbase数据
    val hbaseconf = HBaseConfiguration.create()
    hbaseconf.set(TableInputFormat.INPUT_TABLE, "ns1:person")
    val rdd = sc.newAPIHadoopRDD(
      hbaseconf,
      classOf[TableInputFormat],
      classOf[ImmutableBytesWritable],
      classOf[Result]
    )

    val rdd2 = rdd.map({ case (_, result) =>
      val id = Bytes.toString(result.getValue("f".getBytes(), "DEPT".getBytes()))
      val cost =Bytes.toString( result.getValue("f".getBytes(),"COST".getBytes())).toInt
      val name = Bytes.toString(result.getValue("f".getBytes(), "PERSON_NAME".getBytes()))
      ("COST",cost)
    })
    rdd2.cache()

    for (i <- (0 until loop_counts)) {
      val totalrows = rdd2.count()
      val totalCost = rdd2.reduceByKey(_+_).collect()(0)._2
      println("===========>")
      println("totalrows=  "+ totalrows +", "+ "totalCost= "+ totalCost)
    }

    println("===========> time= " + (System.currentTimeMillis() - time1))
    sc.stop()
  }
}