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Improve the scalability of the join between the LHS and GroupBys by breaking up the join #621

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Improve the scalability of the join between the LHS and GroupBys by breaking up the join #621

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eeb0ef6
Break up joins
mears-stripe Nov 22, 2023
9b787bc
Fix addJoinBreak
mears-stripe Nov 22, 2023
5096e4a
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mears-stripe Nov 22, 2023
d1ec5d8
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mears-stripe Nov 22, 2023
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mears-stripe Nov 22, 2023
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13 changes: 12 additions & 1 deletion spark/src/main/scala/ai/chronon/spark/Join.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -261,9 +261,20 @@ class Join(joinConf: api.Join,
// combine bootstrap table and join part tables
// sequentially join bootstrap table and each join part table. some column may exist both on left and right because
// a bootstrap source can cover a partial date range. we combine the columns using coalesce-rule
var previous: Option[DataFrame] = None
rightResults
.foldLeft(bootstrapDf) {
case (partialDf, (rightPart, rightDf)) => joinWithLeft(partialDf, rightDf, rightPart)
case (partialDf, ((rightPart, rightDf), i)) =>
val next = joinWithLeft(partialDf, rightDf, rightPart)
// Join breaks are added to prevent the Spark app from stalling on a Join that involves too many
// rightParts.
if (((i + 1) % tableUtils.finalJoinParallelism) == 0 && (i != (rightResults.size - 1))) {
tableUtils.addJoinBreak(next)
previous.map(_.unpersist())
previous = Some(next)
} else {
next
}
}
// drop all processing metadata columns
.drop(Constants.MatchedHashes, Constants.TimePartitionColumn)
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4 changes: 4 additions & 0 deletions spark/src/main/scala/ai/chronon/spark/TableUtils.scala
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Expand Up @@ -68,6 +68,7 @@ case class TableUtils(sparkSession: SparkSession) {

val joinPartParallelism: Int = sparkSession.conf.get("spark.chronon.join.part.parallelism", "1").toInt
val aggregationParallelism: Int = sparkSession.conf.get("spark.chronon.group_by.parallelism", "1000").toInt
val finalJoinParallelism: Int = sparkSession.conf.get("spark.chronon.join.final_join_parallelism", "8").toInt
val maxWait: Int = sparkSession.conf.get("spark.chronon.wait.hours", "48").toInt

sparkSession.sparkContext.setLogLevel("ERROR")
Expand Down Expand Up @@ -324,6 +325,9 @@ case class TableUtils(sparkSession: SparkSession) {
df
}

def addJoinBreak(dataFrame: DataFrame): DataFrame =
dataFrame.persist(cacheLevel.getOrElse(StorageLevel.MEMORY_AND_DISK))

def insertUnPartitioned(df: DataFrame,
tableName: String,
tableProperties: Map[String, String] = null,
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