随机读取和写入使得Spark的工作完成非常缓慢

Question

我在两个数据帧上进行连接，数据帧分别为280 GB和1 GB。 我实际的计算连接的火花工作是快速的，但是随机读取和写入需要很长时间，这使得整体火花工作非常缓慢。

我正在使用m3.2xlarge 10节点群集。

Each m3.2xlarge configuration 
cpu:8
ram:30
hdd:160  

请建议我应该做哪些必要的更改，以便更快地实现这种随机读写阶段。

添加我的完整代码..

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    import sqlContext.implicits._

    import org.apache.spark.{ SparkConf, SparkContext }
    import java.sql.{Date, Timestamp}
    import org.apache.spark.sql.Row
    import org.apache.spark.sql.types._
    import org.apache.spark.sql.functions.udf
    import java.io.File
    import org.apache.hadoop.fs._

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs._


import org.apache.spark.sql.functions.input_file_name
import org.apache.spark.sql.functions.regexp_extract


val get_cus_val = spark.udf.register("get_cus_val", (filePath: String) => filePath.split("\\.")(3))


val df = sqlContext.read.format("csv").option("header", "true").option("delimiter", "|").option("inferSchema","true").load("s3://trffullfiles/FinancialLineItem/MAIN")

val df1With_ = df.toDF(df.columns.map(_.replace(".", "_")): _*)
val column_to_keep = df1With_.columns.filter(v => (!v.contains("^") && !v.contains("!") && !v.contains("_c"))).toSeq
val df1result = df1With_.select(column_to_keep.head, column_to_keep.tail: _*)
val df1resultFinal=df1result.withColumn("DataPartition", get_cus_val(input_file_name))

val df2 = sqlContext.read.format("csv").option("header", "true").option("delimiter", "|").option("inferSchema","true").load("s3://trffullfiles/FinancialLineItem/INCR")
val df2With_ = df2.toDF(df2.columns.map(_.replace(".", "_")): _*)
val df2column_to_keep = df2With_.columns.filter(v => (!v.contains("^") && !v.contains("!") && !v.contains("_c"))).toSeq
val df2result = df2With_.select(df2column_to_keep.head, df2column_to_keep.tail: _*)



import org.apache.spark.sql.expressions._
val windowSpec = Window.partitionBy("LineItem_organizationId", "LineItem_lineItemId").orderBy($"TimeStamp".cast(LongType).desc) 
val latestForEachKey = df2result.withColumn("rank", rank().over(windowSpec)).filter($"rank" === 1).drop("rank", "TimeStamp")


val dfMainOutput = df1resultFinal.join(latestForEachKey, Seq("LineItem_organizationId", "LineItem_lineItemId"), "outer")
      .select($"LineItem_organizationId", $"LineItem_lineItemId",
        when($"DataPartition_1".isNotNull, $"DataPartition_1").otherwise($"DataPartition".cast(DataTypes.StringType)).as("DataPartition"),
        when($"StatementTypeCode_1".isNotNull, $"StatementTypeCode_1").otherwise($"StatementTypeCode").as("StatementTypeCode"),
        when($"LineItemName_1".isNotNull, $"LineItemName_1").otherwise($"LineItemName").as("LineItemName"),
        when($"FinancialConceptCodeGlobalSecondaryId_1".isNotNull, $"FinancialConceptCodeGlobalSecondaryId_1").otherwise($"FinancialConceptCodeGlobalSecondaryId").as("FinancialConceptCodeGlobalSecondaryId"),
        when($"FFAction_1".isNotNull, concat(col("FFAction_1"), lit("|!|"))).otherwise(concat(col("FFAction"), lit("|!|"))).as("FFAction"))
        .filter(!$"FFAction".contains("D"))

val dfMainOutputFinal = dfMainOutput.select($"DataPartition", $"StatementTypeCode",concat_ws("|^|", dfMainOutput.schema.fieldNames.filter(_ != "DataPartition").map(c => col(c)): _*).as("concatenated"))

val headerColumn = df.columns.filter(v => (!v.contains("^") && !v.contains("_c"))).toSeq

val header = headerColumn.dropRight(1).mkString("", "|^|", "|!|")

val dfMainOutputFinalWithoutNull = dfMainOutputFinal.withColumn("concatenated", regexp_replace(col("concatenated"), "null", "")).withColumnRenamed("concatenated", header)


dfMainOutputFinalWithoutNull.write.partitionBy("DataPartition","StatementTypeCode")
  .format("csv")
  .option("nullValue", "")
  .option("header", "true")
  .option("codec", "gzip")
  .save("s3://trffullfiles/FinancialLineItem/output")