CODEWITHSUNDEEP
scalaapache-spark
khaled
khaled

Reputation: 45

Spark filter based on the max date record

I am using Spark/Scala to process a Hive table which contains transaction data for each member. I need to get the max record for each member. I did this task using the below code and it works successfully but the performance is not got.

I need to ask if there is any other way to enhance the performance of this code? I found some ways to do it using spark-sql but I prefer Spark Dataframe or Dataset.

The below example will reproduce my code and my data.

  val mamberData = Seq(
    Row("1234", "CX", java.sql.Timestamp.valueOf("2018-09-09 00:00:00")),
    Row("1234", "CX", java.sql.Timestamp.valueOf("2018-03-02 00:00:00")),
    Row("5678", "NY", java.sql.Timestamp.valueOf("2019-01-01 00:00:00")),
    Row("5678", "NY", java.sql.Timestamp.valueOf("2018-01-01 00:00:00")),
    Row("7088", "SF", java.sql.Timestamp.valueOf("2018-09-01 00:00:00"))
  )



  val MemberDataSchema = List(
    StructField("member_id", StringType, nullable = true),
    StructField("member_state", StringType, nullable = true),
    StructField("activation_date", TimestampType, nullable = true)
  )

  import spark.implicits._

  val memberDF =spark.createDataFrame(
    spark.sparkContext.parallelize(mamberData),
    StructType(MemberDataSchema)
  )

  val memberDfMaxDate = memberDF.groupBy('member_id).agg(max('activation_date).as("activation_date"))

  val memberDFMaxOnly = memberDF.join(memberDfMaxDate,Seq("member_id","activation_date"))

The output is below

+---------+------------+-------------------+
|member_id|member_state|activation_date    |
+---------+------------+-------------------+
|1234     |CX          |2018-09-09 00:00:00|
|1234     |CX          |2018-03-02 00:00:00|
|5678     |NY          |2019-01-01 00:00:00|
|5678     |NY          |2018-01-01 00:00:00|
|7088     |SF          |2018-09-01 00:00:00|
+---------+------------+-------------------+

+---------+-------------------+------------+
|member_id|    activation_date|member_state|
+---------+-------------------+------------+
|     7088|2018-09-01 00:00:00|          SF|
|     1234|2018-09-09 00:00:00|          CX|
|     5678|2019-01-01 00:00:00|          NY|
+---------+-------------------+------------+

Upvotes: 3

Views: 4863

Answers (3)

Raphael Roth
Raphael Roth

Reputation: 27373

DataFrame's groupBy is as efficient as it gets (more efficient than Window-Functions due to partial aggregation).

But you can avoid the join by using a struct within in the aggregation-clause:

val memberDfMaxOnly = memberDF.groupBy('member_id).agg(max(struct('activation_date, 'member_state)).as("row_selection"))
  .select(
    $"member_id",
    $"row_selection.activation_date",
    $"row_selection.member_state"
  )

Upvotes: 1

gasparms
gasparms

Reputation: 3354

Use window functions to assign a rank and filter the first in each group.

import org.apache.spark.sql.expressions.Window

// Partition by member_id order by activation_date
val byMemberId = Window.partitionBy($"member_id").orderBy($"activation_date" desc)

// Get the new DF applying window function
val memberDFMaxOnly = memberDF.select('*, rank().over(byMemberId) as 'rank).where($"rank" === 1).drop("rank")

// View the results
memberDFMaxOnly.show()
+---------+------------+-------------------+
|member_id|member_state|    activation_date|
+---------+------------+-------------------+
|     1234|          CX|2018-09-09 00:00:00|
|     5678|          NY|2019-01-01 00:00:00|
|     7088|          SF|2018-09-01 00:00:00|
+---------+------------+-------------------+

Upvotes: 1

Moustafa Mahmoud
Moustafa Mahmoud

Reputation: 1590

You could use lots of techniques, for example Ranking or Dataset. I prefer to use reduceGroups as it is function style way and easy to interpret.

  case class MemberDetails(member_id: String, member_state: String, activation_date: FileStreamSource.Timestamp)

  val dataDS: Dataset[MemberDetails] = spark.createDataFrame(
    spark.sparkContext.parallelize(mamberData),
    StructType(MemberDataSchema)
  ).as[MemberDetails]
    .groupByKey(_.member_id)
    .reduceGroups((r1, r2) ⇒ if (r1.activation_date > r2.activation_date) r1 else r2)
    .map { case (key, row) ⇒ row }


  dataDS.show(truncate = false)

Upvotes: 3

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