scala – 为什么spark-shell在依靠具有3000列的DataFrame后会打印数千行代码？什么是JaninoRuntimeException和64 KB？

2023年7月8日 313次阅读

(使用本地机器官方网站上的spark-2.1.0-bin-hadoop2.7版本)

当我在spark-shell中执行一个简单的spark命令时,它会在抛出错误之前打印出数千行代码.这些“代码”是什么？

我在我的本地机器上运行火花.我运行的命令是一个简单的df.count,其中df是一个DataFrame.

请看下面的截图(代码飞得太快,我只能截取屏幕截图,看看发生了什么).更多细节在图像下方. 《scala – 为什么spark-shell在依靠具有3000列的DataFrame后会打印数千行代码？什么是JaninoRuntimeException和64 KB？》

更多细节：

我创建了数据框df

val df: DataFrame = spark.createDataFrame(rows, schema)
// rows: RDD[Row]
// schema: StructType
// There were about 3000 columns and 700 rows (testing set) of data in df. 
// The following line ran successfully and returned the correct value
rows.count
// The following line threw exception after printing out tons of codes as shown in the screenshot above
df.count

“代码”之后抛出的异常是：

...
/* 181897 */     apply_81(i);
/* 181898 */     result.setTotalSize(holder.totalSize());
/* 181899 */     return result;
/* 181900 */   }
/* 181901 */ }

at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.org$apache$spark$sql$catalyst$expressions$codegen$CodeGenerator$$doCompile(CodeGenerator.scala:889)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anon$1.load(CodeGenerator.scala:941)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anon$1.load(CodeGenerator.scala:938)
at org.spark_project.guava.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3599)
at org.spark_project.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2379)
at org.spark_project.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at org.spark_project.guava.cache.LocalCache$Segment.get(LocalCache.java:2257)
... 29 more
Caused by: org.codehaus.janino.JaninoRuntimeException: Code of method "(Lorg/apache/spark/sql/catalyst/expressions/GeneratedClass;[Ljava/lang/Object;)V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection" grows beyond 64 KB
at org.codehaus.janino.CodeContext.makeSpace(CodeContext.java:941)
at org.codehaus.janino.CodeContext.write(CodeContext.java:854)
at org.codehaus.janino.CodeContext.writeShort(CodeContext.java:959)

编辑：正如@TzachZohar所指出的,这看起来像已知的错误之一(https://issues.apache.org/jira/browse/SPARK-16845)已修复但未从spark项目中释放.

我拉了火花大师,从源头建造它,并重新尝试了我的例子.现在我在生成的代码后面得到了一个新的异常：

/* 308608 */     apply_1560(i);
/* 308609 */     apply_1561(i);
/* 308610 */     result.setTotalSize(holder.totalSize());
/* 308611 */     return result;
/* 308612 */   }
/* 308613 */ }

at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.org$apache$spark$sql$catalyst$expressions$codegen$CodeGenerator$$doCompile(CodeGenerator.scala:941)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anon$1.load(CodeGenerator.scala:998)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anon$1.load(CodeGenerator.scala:995)
at org.spark_project.guava.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3599)
at org.spark_project.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2379)
at org.spark_project.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at org.spark_project.guava.cache.LocalCache$Segment.get(LocalCache.java:2257)
... 29 more
Caused by: org.codehaus.janino.JaninoRuntimeException: Constant pool for class org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection has grown past JVM limit of 0xFFFF
at org.codehaus.janino.util.ClassFile.addToConstantPool(ClassFile.java:499)

看起来拉请求正在解决第二个问题：https://github.com/apache/spark/pull/16648

最佳答案 这是一个错误.它与在JVM上生成的运行时代码有关.因此,Scala团队似乎很难解决. (关于JIRA的讨论很多).

在执行行操作时,我发生了错误.即使是700行的数据帧上的df.head()也会导致异常.

我的解决方法是将数据帧转换为稀疏数据RDD(即RDD [LabeledPoint])并在RDD上运行逐行操作.它更快,内存效率更高. HOwever,它只适用于数字数据.分类变量(因子,目标等)需要转换为Double.

也就是说,我自己是Scala的新手,所以我的代码可能有点业余.但它的确有效.

CreateRow

@throws(classOf[Exception])
private def convertRowToLabeledPoint(rowIn: Row, fieldNameSeq: Seq[String], label: Int): LabeledPoint =
{
  try
  {
    logger.info(s"fieldNameSeq $fieldNameSeq")
    val values: Map[String, Long] = rowIn.getValuesMap(fieldNameSeq)

    val sortedValuesMap = ListMap(values.toSeq.sortBy(_._1): _*)

    //println(s"convertRowToLabeledPoint row values ${sortedValuesMap}")
    print(".")

    val rowValuesItr: Iterable[Long] = sortedValuesMap.values

    var positionsArray: ArrayBuffer[Int] = ArrayBuffer[Int]()
    var valuesArray: ArrayBuffer[Double] = ArrayBuffer[Double]()
    var currentPosition: Int = 0


    rowValuesItr.foreach
    {
      kv =>
        if (kv > 0)
        {
          valuesArray += kv.toDouble;
          positionsArray += currentPosition;
        }
        currentPosition = currentPosition + 1;
    }

    new LabeledPoint(label, org.apache.spark.mllib.linalg.Vectors.sparse(positionsArray.size, positionsArray.toArray, valuesArray.toArray))
  }
  catch
  {
    case ex: Exception =>
    {
      throw new Exception(ex)
    }
  }
}

private def castColumnTo(df: DataFrame, cn: String, tpe: DataType): DataFrame =
{

  //println("castColumnTo")
  df.withColumn(cn, df(cn).cast(tpe)

  )
}

提供Dataframe并返回RDD LabeledPOint

@throws(classOf[Exception])
 def convertToLibSvm(spark:SparkSession,mDF : DataFrame, targetColumnName:String): RDD[LabeledPoint] =
{
  try
  {


    val fieldSeq: scala.collection.Seq[StructField] = mDF.schema.fields.toSeq.filter(f => f.dataType == IntegerType || f.dataType == LongType)
    val fieldNameSeq: Seq[String] = fieldSeq.map(f => f.name)


    val indexer = new StringIndexer()
      .setInputCol(targetColumnName)
      .setOutputCol(targetColumnName+"_Indexed")
    val mDFTypedIndexed = indexer.fit(mDF).transform(mDF).drop(targetColumnName)
    val mDFFinal = castColumnTo(mDFTypedIndexed, targetColumnName+"_Indexed", IntegerType)

    //mDFFinal.show()
    //only doubles accepted by sparse vector, so that's what we filter for


    var positionsArray: ArrayBuffer[LabeledPoint] = ArrayBuffer[LabeledPoint]()

    mDFFinal.collect().foreach
    {

      row => positionsArray += convertRowToLabeledPoint(row, fieldNameSeq, row.getAs(targetColumnName+"_Indexed"));

    }

    spark.sparkContext.parallelize(positionsArray.toSeq)

  }
  catch
  {
    case ex: Exception =>
    {
      throw new Exception(ex)
    }
  }
}
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