这篇文章主要介绍“Java spark中的bug分析”，在日常操作中，相信很多人在Java spark中的bug分析问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”Java spark中的bug分析”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！

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在spark 中存在一个bug，该bug的详细信息如下：

None.get
java.util.NoSuchElementException: None.get
scala.None$.get(Option.scala:529)
scala.None$.get(Option.scala:527)
org.apache.spark.sql.execution.FileSourceScanExec.needsUnsafeRowConversion$lzycompute(DataSourceScanExec.scala:178)
org.apache.spark.sql.execution.FileSourceScanExec.needsUnsafeRowConversion(DataSourceScanExec.scala:176)
org.apache.spark.sql.execution.FileSourceScanExec.doExecute(DataSourceScanExec.scala:463)
org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:175)
org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)
org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:171)
org.apache.spark.sql.execution.InputAdapter.inputRDD(WholeStageCodegenExec.scala:525)
org.apache.spark.sql.execution.InputRDDCodegen.inputRDDs(WholeStageCodegenExec.scala:453)
org.apache.spark.sql.execution.InputRDDCodegen.inputRDDs$(WholeStageCodegenExec.scala:452)
org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:496)
org.apache.spark.sql.execution.FilterExec.inputRDDs(basicPhysicalOperators.scala:133)
org.apache.spark.sql.execution.ProjectExec.inputRDDs(basicPhysicalOperators.scala:47)
org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:720)
org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:175)
org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)
org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:171)
org.apache.spark.sql.execution.DeserializeToObjectExec.doExecute(objects.scala:96)
org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:175)
org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)
org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:171)
org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:122)
org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:121)
org.apache.spark.sql.Dataset.rdd$lzycompute(Dataset.scala:3200)
org.apache.spark.sql.Dataset.rdd(Dataset.scala:3198)

根据源码定位FileSourceScanExec,定位到如下位置：

 SparkSession.getActiveSession.get.sessionState.conf.parquetVectorizedReaderEnabled

SparkSession.getActiveSession.get的内容如下：

/**
   * Returns the active SparkSession for the current thread, returned by the builder.
   *
   * @note Return None, when calling this function on executors
   *
   * @since 2.2.0
   */
  def getActiveSession: Option[SparkSession] = {
    if (TaskContext.get != null) {
      // Return None when running on executors.
      None
    } else {
      Option(activeThreadSession.get)
    }
  }

正如注释所写的一样，当在executors端获取SparkSession的时候，直接返回None。 为什么直接返回none，可以参考spark-pr-21436
当然这个问题，已经有人发现了并且提交了pr-29667,所以拿到commitID(37a660866342f2d64ad2990a5596e67cfdf044c0)直接cherry-pick就ok了，

分析一下原因： 其实该原因就是同一个jvm中，两个不同的线程同步调用，就如unit test所示：

test("SPARK-32813: Table scan should work in different thread") {
    val executor1 = Executors.newSingleThreadExecutor()
    val executor2 = Executors.newSingleThreadExecutor()
    var session: SparkSession = null
    SparkSession.cleanupAnyExistingSession()

    withTempDir { tempDir =>
      try {
        val tablePath = tempDir.toString + "/table"
        val df = ThreadUtils.awaitResult(Future {
          session = SparkSession.builder().appName("test").master("local[*]").getOrCreate()

          session.createDataFrame(
            session.sparkContext.parallelize(Row(Array(1, 2, 3)) :: Nil),
            StructType(Seq(
              StructField("a", ArrayType(IntegerType, containsNull = false), nullable = false))))
            .write.parquet(tablePath)

          session.read.parquet(tablePath)
        }(ExecutionContext.fromExecutorService(executor1)), 1.minute)

        ThreadUtils.awaitResult(Future {
          assert(df.rdd.collect()(0) === Row(Seq(1, 2, 3)))
        }(ExecutionContext.fromExecutorService(executor2)), 1.minute)
      } finally {
        executor1.shutdown()
        executor2.shutdown()
        session.stop()
      }
    }
  }

到此，关于“Java spark中的bug分析”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注创新互联网站，小编会继续努力为大家带来更多实用的文章！

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