Run Test Case on Spark

    今天有哥们问到如何对Spark进行单元测试。现在将Sbt的测试方法写出来，如下：

    对Spark的test case进行测试的时候可以用sbt的test命令：

    一、测试全部test case

     sbt/sbt test

    二、测试单个test case

     sbt/sbt "test-only *DriverSuite*" 

下面举个例子：

这个Test Case是位于$SPARK_HOME/core/src/test/scala/org/apache/spark/DriverSuite.scala 

FunSuit是scalatest里面的测试Suit，要继承它。这里主要是一个回归测试，测试Spark程序正常结束后，Driver会不会正常退出。

注：我就拿这个例子模拟一下，测试成功和测试失败的情景，这个例子和DriverSuite的测试目的完全不一致，只是演示作用。 ：）

下面是正常运行退出的例子：

package org.apache.sparkimport java.io.Fileimport org.apache.log4j.Loggerimport org.apache.log4j.Levelimport org.scalatest.FunSuiteimport org.scalatest.concurrent.Timeoutsimport org.scalatest.prop.TableDrivenPropertyChecks._import org.scalatest.time.SpanSugar._import org.apache.spark.util.Utilsimport scala.language.postfixOpsclass DriverSuite extends FunSuite with Timeouts {  test("driver should exit after finishing") {    val sparkHome = sys.env.get("SPARK_HOME").orElse(sys.props.get("spark.home")).get    // Regression test for SPARK-530: "Spark driver process doesn't exit after finishing"    val masters = Table(("master"), ("local"), ("local-cluster[2,1,512]"))    forAll(masters) { (master: String) =>      failAfter(60 seconds) {        Utils.executeAndGetOutput(          Seq("./bin/spark-class", "org.apache.spark.DriverWithoutCleanup", master),          new File(sparkHome),          Map("SPARK_TESTING" -> "1", "SPARK_HOME" -> sparkHome))      }    }  }}/** * Program that creates a Spark driver but doesn't call SparkContext.stop() or * Sys.exit() after finishing. */object DriverWithoutCleanup {  def main(args: Array[String]) {    Logger.getRootLogger().setLevel(Level.WARN)    val sc = new SparkContext(args(0), "DriverWithoutCleanup")    sc.parallelize(1 to 100, 4).count()  }}

executeAndGetOutput方法接受一个command命令，调用spark-class来运行DriverWithoutCleanup类。

 /**   * Execute a command and get its output, throwing an exception if it yields a code other than 0.   */  def executeAndGetOutput(command: Seq[String], workingDir: File = new File("."),                          extraEnvironment: Map[String, String] = Map.empty): String = {    val builder = new ProcessBuilder(command: _*)         .directory(workingDir)    val environment = builder.environment()    for ((key, value) <- extraEnvironment) {      environment.put(key, value)    }    val process = builder.start()  //启动一个进程来运行spark job    new Thread("read stderr for " + command(0)) {      override def run() {        for (line <- Source.fromInputStream(process.getErrorStream).getLines) {          System.err.println(line)        }      }    }.start()    val output = new StringBuffer    val stdoutThread = new Thread("read stdout for " + command(0)) { //读取spark job的输出      override def run() {        for (line <- Source.fromInputStream(process.getInputStream).getLines) {          output.append(line)        }      }    }    stdoutThread.start()    val exitCode = process.waitFor()    stdoutThread.join()   // Wait for it to finish reading output    if (exitCode != 0) {      throw new SparkException("Process " + command + " exited with code " + exitCode)    }    output.toString //返回spark job的输出  }

运行第二个命令可以看到运行结果：

sbt/sbt "test-only *DriverSuite*" 

执行结果：    

[info] Compiling 1 Scala source to /app/hadoop/spark-1.0.1/core/target/scala-2.10/test-classes...[info] DriverSuite: //执行DriverSuit这个TestSuitSpark assembly has been built with Hive, including Datanucleus jars on classpathSLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/app/hadoop/spark-1.0.1/lib_managed/jars/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/app/hadoop/spark-1.0.1/assembly/target/scala-2.10/spark-assembly-1.0.1-hadoop0.20.2-cdh3u5.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]14/08/14 18:20:15 WARN spark.SparkConf: SPARK_CLASSPATH was detected (set to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*').This is deprecated in Spark 1.0+.Please instead use: - ./spark-submit with --driver-class-path to augment the driver classpath - spark.executor.extraClassPath to augment the executor classpath        14/08/14 18:20:15 WARN spark.SparkConf: Setting 'spark.executor.extraClassPath' to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*' as a work-around.14/08/14 18:20:15 WARN spark.SparkConf: Setting 'spark.driver.extraClassPath' to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*' as a work-around.Spark assembly has been built with Hive, including Datanucleus jars on classpathSLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/app/hadoop/spark-1.0.1/lib_managed/jars/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/app/hadoop/spark-1.0.1/assembly/target/scala-2.10/spark-assembly-1.0.1-hadoop0.20.2-cdh3u5.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]14/08/14 18:20:19 WARN spark.SparkConf: SPARK_CLASSPATH was detected (set to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*').This is deprecated in Spark 1.0+.Please instead use: - ./spark-submit with --driver-class-path to augment the driver classpath - spark.executor.extraClassPath to augment the executor classpath        14/08/14 18:20:19 WARN spark.SparkConf: Setting 'spark.executor.extraClassPath' to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*' as a work-around.14/08/14 18:20:19 WARN spark.SparkConf: Setting 'spark.driver.extraClassPath' to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*' as a work-around.Spark assembly has been built with Hive, including Datanucleus jars on classpathSpark assembly has been built with Hive, including Datanucleus jars on classpath[info] - driver should exit after finishing[info] ScalaTest[info] Run completed in 12 seconds, 586 milliseconds.[info] Total number of tests run: 1[info] Suites: completed 1, aborted 0[info] Tests: succeeded 1, failed 0, canceled 0, ignored 0, pending 0[info] All tests passed.[info] Passed: Total 1, Failed 0, Errors 0, Passed 1[success] Total time: 76 s, completed Aug 14, 2014 6:20:26 PM

测试通过， Total 1， Failed 0， Errors 0， Passed 1。

这里如果我们稍微将test case 改改，让spark job抛异常，那么这个，这样test case 就会failed掉，如下：

object DriverWithoutCleanup {  def main(args: Array[String]) {    Logger.getRootLogger().setLevel(Level.WARN)    val sc = new SparkContext(args(0), "DriverWithoutCleanup")    sc.parallelize(1 to 100, 4).count()    throw new RuntimeException("OopsOutOfMemory, haha, not real OOM, don't worry!") //添加此行  }

那么，再次运行测试：

会发现错误

 [info] DriverSuite:Spark assembly has been built with Hive, including Datanucleus jars on classpathSLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/app/hadoop/spark-1.0.1/lib_managed/jars/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/app/hadoop/spark-1.0.1/assembly/target/scala-2.10/spark-assembly-1.0.1-hadoop0.20.2-cdh3u5.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]14/08/14 18:40:07 WARN spark.SparkConf: SPARK_CLASSPATH was detected (set to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*').This is deprecated in Spark 1.0+.Please instead use: - ./spark-submit with --driver-class-path to augment the driver classpath - spark.executor.extraClassPath to augment the executor classpath        14/08/14 18:40:07 WARN spark.SparkConf: Setting 'spark.executor.extraClassPath' to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*' as a work-around.14/08/14 18:40:07 WARN spark.SparkConf: Setting 'spark.driver.extraClassPath' to '/home/hadoop/src/hadoop/lib/:/app/hadoop/sparklib/*:/app/hadoop/spark-1.0.1/lib_managed/jars/*' as a work-around.Exception in thread "main" java.lang.RuntimeException: OopsOutOfMemory, haha, not real OOM, don't worry! //自定义抛异常使spark job运行失败，打印出了异常堆栈，测试用例失败        at org.apache.spark.DriverWithoutCleanup$.main(DriverSuite.scala:60)        at org.apache.spark.DriverWithoutCleanup.main(DriverSuite.scala)[info] - driver should exit after finishing *** FAILED ***[info]   SparkException was thrown during property evaluation. (DriverSuite.scala:40)[info]     Message: Process List(./bin/spark-class, org.apache.spark.DriverWithoutCleanup, local) exited with code 1[info]     Occurred at table row 0 (zero based, not counting headings), which had values ([info]       master = local[info]     )[info] ScalaTest[info] Run completed in 4 seconds, 765 milliseconds.[info] Total number of tests run: 1[info] Suites: completed 1, aborted 0[info] Tests: succeeded 0, failed 1, canceled 0, ignored 0, pending 0[info] *** 1 TEST FAILED ***[error] Failed: Total 1, Failed 1, Errors 0, Passed 0[error] Failed tests:[error]         org.apache.spark.DriverSuite[error] (core/test:testOnly) sbt.TestsFailedException: Tests unsuccessful[error] Total time: 14 s, completed Aug 14, 2014 6:40:10 PM

可以看到TEST FAILED。

  三、 总结：

  本文主要讲解了，如何运行spark的测试用例，运行全部test case，和运行单个test case的命令，并通过一个例子讲解其运行正常和失败的详细情景，具体细节还需要继续摸索。如果想做contributor，这一关必须过了。

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原创文章，转载请注明，出自http://blog.csdn.net/oopsoom/article/details/38555173