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From Michael Armbrust <mich...@databricks.com>
Subject Re: External Data Source in Spark
Date Fri, 06 Mar 2015 01:26:07 GMT
>
> Currently we have implemented  External Data Source API and are able to
> push filters and projections.
>
> Could you provide some info on how perhaps the joins could be pushed to
> the original Data Source if both the data sources are from same database
> *.*
>

First a disclaimer: This is an experimental API that exposes internals that
are likely to change in between different Spark releases.  As a result,
most datasources should be written against the stable public API in
org.apache.spark.sql.sources
<https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala>.
We expose this mostly to get feedback on what optimizations we should add
to the stable API in order to get the best performance out of data sources.

We'll start with a simple artificial data source that just returns ranges
of consecutive integers.

/** A data source that returns ranges of consecutive integers in a
column named `a`. */case class SimpleRelation(
    start: Int,
    end: Int)(
    @transient val sqlContext: SQLContext)
  extends BaseRelation with TableScan {

  val schema = StructType('a.int :: Nil)
  def buildScan() = sqlContext.sparkContext.parallelize(start to
end).map(Row(_))
}


Given this we can create tables:

sqlContext.baseRelationToDataFrame(SimpleRelation(1,
1)(sqlContext)).registerTempTable("smallTable")
sqlContext.baseRelationToDataFrame(SimpleRelation(1,
10000000)(sqlContext)).registerTempTable("bigTable")


However, doing a join is pretty slow since we need to shuffle the big table
around for no reason:

sql("SELECT * FROM smallTable s JOIN bigTable b ON s.a = b.a").collect()
res3: Array[org.apache.spark.sql.Row] = Array([1,1])


This takes about 10 seconds on my cluster.  Clearly we can do better.  So
let's define special physical operators for the case when we are inner
joining two of these relations using equality. One will handle the case
when there is no overlap and the other when there is.  Physical operators
must extend SparkPlan and must return an RDD[Row] containing the answer
when execute() is called.

import org.apache.spark.sql.catalyst.expressions.{Attribute,
EqualTo}import org.apache.spark.sql.catalyst.plans._import
org.apache.spark.sql.catalyst.plans.logical._import
org.apache.spark.sql.execution.SparkPlan
/** A join that just returns the pre-calculated overlap of two ranges
of consecutive integers. */case class OverlappingRangeJoin(leftOutput:
Attribute, rightOutput: Attribute, start: Int, end: Int) extends
SparkPlan {
  def output: Seq[Attribute] = leftOutput :: rightOutput :: Nil

  def execute(): org.apache.spark.rdd.RDD[Row] = {
    sqlContext.sparkContext.parallelize(start to end).map(i => Row(i, i))
  }

  def children: Seq[SparkPlan] = Nil
}
/** Used when a join is known to produce no results. */case class
EmptyJoin(output: Seq[Attribute]) extends SparkPlan {
  def execute(): org.apache.spark.rdd.RDD[Row] = {
    sqlContext.sparkContext.emptyRDD
  }

  def children: Seq[SparkPlan] = Nil
}
/** Finds cases where two sets of consecutive integer ranges are inner
joined on equality. */object SmartSimpleJoin extends Strategy with
Serializable {
  def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
    // Find inner joins between two SimpleRelations where the
condition is equality.
    case Join(l @ LogicalRelation(left: SimpleRelation), r @
LogicalRelation(right: SimpleRelation), Inner, Some(EqualTo(a, b))) =>
      // Check if the join condition is comparing `a` from each relation.
      if (a == l.output.head && b == r.output.head || a ==
r.output.head && b == l.output.head) {
        if ((left.start <= right.end) && (left.end >= right.start)) {
          OverlappingRangeJoin(
            l.output.head,
            r.output.head,
            math.max(left.start, right.start),
            math.min(left.end, right.end)) :: Nil
        } else {
          // Ranges don't overlap, join will be empty
          EmptyJoin(l.output.head :: r.output.head :: Nil) :: Nil
        }
      } else {
        // Join isn't between the the columns output...
        // Let's just let the query planner handle this.
        Nil
      }
    case _ => Nil // Return an empty list if we don't know how to
handle this plan.
  }
}


We can then add these strategies to the query planner through the
experimental hook.  Added strategies take precedence over built-in ones.

// Add the strategy to the query planner.
sqlContext.experimental.extraStrategies = SmartSimpleJoin :: Nil


sql("SELECT * FROM smallTable s JOIN bigTable b ON s.a = b.a").collect()
res4: Array[org.apache.spark.sql.Row] = Array([1,1])


Now our join returns in < 1 second.  For more advanced matching of joins
and their conditions you should look at the patterns that are available
<https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala>,
and the built-in join strategies
<https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala>.
Let me know if you have any questions.

Michael

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