如何从Spark UI检索输出大小和记录等指标? [英] How to retrieve Metrics like Output Size and Records Written from Spark UI?

问题描述

任务或作业完成后，如何在控制台(Spark Shell或Spark提交作业)上收集这些指标.

How do I collect these metrics on a console (Spark Shell or Spark submit job) right after the task or job is done.

我们正在使用Spark将数据从Mysql加载到Cassandra，并且它非常大(例如:〜200 GB和600M行).任务完成后，我们要验证火花处理过程中到底有多少行?我们可以从Spark UI中获取编号，但是如何从Spark Shell或Spark提交作业中检索该编号(已写输出记录").

We are using Spark to load data from Mysql to Cassandra and it is quite huge (ex: ~200 GB and 600M rows). When the task the done, we want to verify how many rows exactly did spark process? We can get the number from Spark UI, but how can we retrieve that number ("Output Records Written") from spark shell or in spark-submit job.

从Mysql加载到Cassandra的示例命令.

Sample Command to load from Mysql to Cassandra.

val pt = sqlcontext.read.format("jdbc").option("url", "jdbc:mysql://...:3306/...").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "payment_types").option("user", "hadoop").option("password", "...").load()

pt.save("org.apache.spark.sql.cassandra",SaveMode.Overwrite,options = Map( "table" -> "payment_types", "keyspace" -> "test"))

我想检索有关上述任务的所有Spark UI指标，主要是输出大小和书面记录.

I want to retrieve all the Spark UI metrics on the above task mainly Output size and Records Written.

请帮助.

感谢您的时间！

推荐答案

找到了答案.您可以使用SparkListener获取统计信息.

Found the answer. You can get the stats by using SparkListener.

如果您的工作没有输入或输出指标，则可能会遇到None.get异常，您可以通过提供if stmt来忽略这些异常.

If your job has no input or output metrics you might get None.get exceptions which you can safely ignore by providing if stmt.

sc.addSparkListener(new SparkListener() {
  override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
    val metrics = taskEnd.taskMetrics
    if(metrics.inputMetrics != None){
      inputRecords += metrics.inputMetrics.get.recordsRead}
    if(metrics.outputMetrics != None){
      outputWritten += metrics.outputMetrics.get.recordsWritten }
  }
})

请找到以下示例.

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import com.datastax.spark.connector._
import org.apache.spark.sql._
import org.apache.spark.storage.StorageLevel
import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskEnd}

val conf = new SparkConf()
.set("spark.cassandra.connection.host", "...")
.set("spark.driver.allowMultipleContexts","true")
.set("spark.master","spark://....:7077")
.set("spark.driver.memory","1g")
.set("spark.executor.memory","10g")
.set("spark.shuffle.spill","true")
.set("spark.shuffle.memoryFraction","0.2")
.setAppName("CassandraTest")
sc.stop
val sc = new SparkContext(conf)
val sqlcontext = new org.apache.spark.sql.SQLContext(sc)

var outputWritten = 0L

sc.addSparkListener(new SparkListener() {
  override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
    val metrics = taskEnd.taskMetrics
    if(metrics.inputMetrics != None){
      inputRecords += metrics.inputMetrics.get.recordsRead}
    if(metrics.outputMetrics != None){
      outputWritten += metrics.outputMetrics.get.recordsWritten }
  }
})

val bp = sqlcontext.read.format("jdbc").option("url", "jdbc:mysql://...:3306/...").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "bucks_payments").option("partitionColumn","id").option("lowerBound","1").option("upperBound","14596").option("numPartitions","10").option("fetchSize","100000").option("user", "hadoop").option("password", "...").load()
bp.save("org.apache.spark.sql.cassandra",SaveMode.Overwrite,options = Map( "table" -> "bucks_payments", "keyspace" -> "test"))

println("outputWritten",outputWritten)

结果:

scala> println("outputWritten",outputWritten)
(outputWritten,16383)

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