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WindowFrameCoercion Type Coercion Logical Rule

WindowFrameCoercion is a type coercion logical rule that <> in a logical plan.

import java.time.LocalDate
import java.sql.Timestamp
val sales = Seq(
  (Timestamp.valueOf(LocalDate.of(2018, 9, 1).atStartOfDay), 5),
  (Timestamp.valueOf(LocalDate.of(2018, 9, 2).atStartOfDay), 10),
  // Mind the 2-day gap
  (Timestamp.valueOf(LocalDate.of(2018, 9, 5).atStartOfDay), 5)
).toDF("time", "volume")
scala> sales.show
+-------------------+------+
|               time|volume|
+-------------------+------+
|2018-09-01 00:00:00|     5|
|2018-09-02 00:00:00|    10|
|2018-09-05 00:00:00|     5|
+-------------------+------+

scala> sales.printSchema
root
 |-- time: timestamp (nullable = true)
 |-- volume: integer (nullable = false)

// FIXME Use Catalyst DSL
// rangeBetween with column expressions
// data type of orderBy expression is date
// data types of range frame boundaries is interval
// WindowSpecDefinition(_, Seq(order), SpecifiedWindowFrame(RangeFrame, lower, upper))
import org.apache.spark.unsafe.types.CalendarInterval
val interval = lit(CalendarInterval.fromString("interval 1 days"))
import org.apache.spark.sql.expressions.Window
val windowSpec = Window.orderBy($"time").rangeBetween(currentRow(), interval)

val q = sales.select(
  $"time",
  (sum($"volume") over windowSpec) as "sum",
  (count($"volume") over windowSpec) as "count")
val plan = q.queryExecution.logical
scala> println(plan.numberedTreeString)
00 'Project [unresolvedalias('time, None), sum('volume) windowspecdefinition('time ASC NULLS FIRST, specifiedwindowframe(RangeFrame, currentrow$(), interval 1 days)) AS sum#156, count('volume) windowspecdefinition('time ASC NULLS FIRST, specifiedwindowframe(RangeFrame, currentrow$(), interval 1 days)) AS count#158]
01 +- AnalysisBarrier
02       +- Project [_1#129 AS time#132, _2#130 AS volume#133]
03          +- LocalRelation [_1#129, _2#130]

import spark.sessionState.analyzer.ResolveReferences
val planWithRefsResolved = ResolveReferences(plan)

import spark.sessionState.analyzer.ResolveAliases
val planWithAliasesResolved = ResolveReferences(planWithRefsResolved)

// FIXME Looks like nothing changes in the query plan with regard to WindowFrameCoercion

import org.apache.spark.sql.catalyst.analysis.TypeCoercion.WindowFrameCoercion
val afterWindowFrameCoercion = WindowFrameCoercion(planWithRefsResolved)
scala> println(afterWindowFrameCoercion.numberedTreeString)
00 'Project [unresolvedalias(time#132, None), sum(volume#133) windowspecdefinition(time#132 ASC NULLS FIRST, specifiedwindowframe(RangeFrame, currentrow$(), interval 1 days)) AS sum#156L, count(volume#133) windowspecdefinition(time#132 ASC NULLS FIRST, specifiedwindowframe(RangeFrame, currentrow$(), interval 1 days)) AS count#158L]
01 +- AnalysisBarrier
02       +- Project [_1#129 AS time#132, _2#130 AS volume#133]
03          +- LocalRelation [_1#129, _2#130]
import java.time.LocalDate
import java.sql.Date
val sales = Seq(
  (Date.valueOf(LocalDate.of(2018, 9, 1)), 5),
  (Date.valueOf(LocalDate.of(2018, 9, 2)), 10),
  // Mind the 2-day gap
  (Date.valueOf(LocalDate.of(2018, 9, 5)), 5)
).toDF("time", "volume")
scala> sales.show
+----------+------+
|      time|volume|
+----------+------+
|2018-09-01|     5|
|2018-09-02|    10|
|2018-09-05|     5|
+----------+------+

scala> sales.printSchema
root
 |-- time: date (nullable = true)
 |-- volume: integer (nullable = false)

// FIXME Use Catalyst DSL
// rangeBetween with column expressions
// data type of orderBy expression is date
// WindowSpecDefinition(_, Seq(order), SpecifiedWindowFrame(RangeFrame, lower, upper))
import org.apache.spark.sql.expressions.Window
val windowSpec = Window.orderBy($"time").rangeBetween(currentRow(), lit(1))

val q = sales.select(
  $"time",
  (sum($"volume") over windowSpec) as "sum")
val plan = q.queryExecution.logical
scala> println(plan.numberedTreeString)
00 'Project [unresolvedalias('time, None), sum('volume) windowspecdefinition('time ASC NULLS FIRST, specifiedwindowframe(RangeFrame, currentrow$(), 1)) AS sum#238]
01 +- AnalysisBarrier
02       +- Project [_1#222 AS time#225, _2#223 AS volume#226]
03          +- LocalRelation [_1#222, _2#223]

import spark.sessionState.analyzer.ResolveReferences
val planWithRefsResolved = ResolveReferences(plan)

import spark.sessionState.analyzer.ResolveAliases
val planWithAliasesResolved = ResolveReferences(planWithRefsResolved)

// FIXME Looks like nothing changes in the query plan with regard to WindowFrameCoercion

import org.apache.spark.sql.catalyst.analysis.TypeCoercion.WindowFrameCoercion
val afterWindowFrameCoercion = WindowFrameCoercion(planWithAliasesResolved)
scala> println(afterWindowFrameCoercion.numberedTreeString)
00 'Project [unresolvedalias(time#132, None), sum(volume#133) windowspecdefinition(time#132 ASC NULLS FIRST, specifiedwindowframe(RangeFrame, currentrow$(), interval 1 days)) AS sum#156L, count(volume#133) windowspecdefinition(time#132 ASC NULLS FIRST, specifiedwindowframe(RangeFrame, currentrow$(), interval 1 days)) AS count#158L]
01 +- AnalysisBarrier
02       +- Project [_1#129 AS time#132, _2#130 AS volume#133]
03          +- LocalRelation [_1#129, _2#130]

=== [[coerceTypes]] Coercing Types in Logical Plan -- coerceTypes Method

[source, scala]

coerceTypes(plan: LogicalPlan): LogicalPlan

coerceTypes is part of the TypeCoercionRule abstraction.

coerceTypes <> (in the input <>) and replaces the <> of every <> with a RangeFrame window frame and the single <> expression <> with the lower and upper window frame boundary expressions cast to the <> of the order specification expression.

=== [[createBoundaryCast]] createBoundaryCast Internal Method

[source, scala]

createBoundaryCast(boundary: Expression, dt: DataType): Expression

createBoundaryCast returns a <> per the input boundary <> and the dt DataType (in the order of execution):

  • The input boundary expression if it is a SpecialFrameBoundary

  • The input boundary expression if the dt data type is DateType or TimestampType

  • Cast unary operator with the input boundary expression and the dt data type if the <> of the boundary expression is not the dt data type, but the result type can be cast to the dt data type

  • The input boundary expression

NOTE: createBoundaryCast is used exclusively when WindowFrameCoercion type coercion logical rule is requested to <>.


Last update: 2021-05-20