Shuffle Read and Write makes Spark job finish very slow - apache-spark

Question

I am doing a join on two data frame having data 280 GB and 1 GB respectively.
My actual spark job which is computing join is fast but shuffle read and write takes very long time and that makes overall spark job very slow.
I am using m3.2xlarge 10 nodes cluster.
Each m3.2xlarge configuration
cpu:8
ram:30
hdd:160
Please suggest me what necessary changes I should do to make this shuffle read-write phase faster.
Adding my full code ..
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")