Spark - RDD/DF/DS性能测试

2019年6月8日 154次阅读

Spark 在三个弹性数据集，但是我们并不知道哪个性能比较好(有的文章的说Dataset<Dataframe<RDD)，好了，这下就有个无聊的人了，那就是我，这里会测试一下它们的性能如何。

测试代码

class App10 {

  System.setProperty("java.security.krb5.conf", "/etc/krb5.conf")
  System.setProperty("sun.security.krb5.debug", "false")

  val sparkConf = new SparkConf()
    .set("spark.shuffle.service.enabled", "true")
    .set("spark.dynamicAllocation.enabled", "true")
    .set("spark.dynamicAllocation.minExecutors", "1")
    .set("spark.dynamicAllocation.initialExecutors", "1")
    .set("spark.dynamicAllocation.maxExecutors", "6")
    .set("spark.dynamicAllocation.executorIdleTimeout", "60")
    .set("spark.dynamicAllocation.cachedExecutorIdleTimeout", "60")
    .set("spark.executor.cores", "4")
    .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    //    .setMaster("local[12]")
    .setAppName("无聊的Dataset、Dataframe、RDD测试")

  val spark = SparkSession
    .builder
    .config(sparkConf)
    .getOrCreate()


  def run(typ: Int): Unit = {

    import spark.implicits._
    spark.sparkContext.setLogLevel("ERROR")
    if (typ == 0) {
      val rdd = spark.sparkContext
        .parallelize((0 to 4000000).map {
          num => {
            Log10(UUID.randomUUID().toString, num)
          }
        })

      val count = rdd.count()
    } else if (typ == 1) {
      val rdd = spark.sparkContext
        .parallelize((0 to 4000000).map {
          num => {
            Log10(UUID.randomUUID().toString, num)
          }
        }).toDF()

      val count = rdd.count()
    } else if (typ == 2) {
      val rdd = spark.sparkContext
        .parallelize((0 to 4000000).map {
          num => {
            Log10(UUID.randomUUID().toString, num)
          }
        }).toDS()

      val count = rdd.count()
    }

  }

}

case class Log10(uid: String, age: Int)

object App10 {
  def main(args: Array[String]): Unit = {
    new App10().run(args(0).toInt)
  }
}

测试组

PS:集群是两台2台12核24G的机子,里面没有跑任务任务,是空闲的主机，这样测试出来的结果比较理想。

第一组

time spark-submit --master yarn --jars "hdfs:///tmp/jars/*" --class com.dounine.hbase.App10 --driver-memory 3g --executor-memory 2G build/libs/hdfs-token-1.0.0-SNAPSHOT.jar 0

三次结果

real    0m34.242s
user    0m54.498s
sys 0m3.584s
-----------------------
real    0m34.009s
user    0m45.385s
sys 0m3.520s
----------------------
real    0m34.948s
user    0m49.349s
sys 0m3.407s

第二组

time spark-submit --master yarn --jars "hdfs:///tmp/jars/*" --class com.dounine.hbase.App10 --driver-memory 3g --executor-memory 2G build/libs/hdfs-token-1.0.0-SNAPSHOT.jar 1

三次结果

real    0m37.738s
user    0m52.649s
sys 0m3.684s
------------------
real    0m37.471s
user    0m50.647s
sys 0m3.557s
-------------------
real    0m37.248s
user    0m46.946s
sys 0m3.471s

第三组

time spark-submit --master yarn --jars "hdfs:///tmp/jars/*" --class com.dounine.hbase.App10 --driver-memory 3g --executor-memory 2G build/libs/hdfs-token-1.0.0-SNAPSHOT.jar 2

三次结果

real    0m36.179s
user    0m59.250s
sys 0m3.674s
---------------------
real    0m35.090s
user    0m54.178s
sys 0m3.476s
--------------------
real    0m35.181s
user    0m50.917s
sys 0m3.599s

结论

还是 RDD 性能好一些,可能是我打开的方式不对，下次想到更好测试再测看看。

《Spark - RDD/DF/DS性能测试》

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