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spark-sql-Optimizer-NullPropagation.adoc

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NullPropagation — Nullability (NULL Value) Propagation Logical Optimization

NullPropagation is a logical optimization (aka Rule[LogicalPlan]) in Optimizer.

Note
 NullPropagation is one of the optimizations in the fixed-point Operator Optimizations optimization rule batch in Optimizer.

Example: Count Aggregate Operator with Nullable Expressions Only

NullPropagation optimization rewrites Count aggregate expressions that include expressions that are all nullable to Cast(Literal(0L)).

val table = (0 to 9).toDF("num").as[Int]

// NullPropagation applied
scala> table.select(countDistinct($"num" === null)).explain(true)
== Parsed Logical Plan ==
'Project [count(distinct ('num = null)) AS count(DISTINCT (num = NULL))#45]
+- Project [value#1 AS num#3]
   +- LocalRelation [value#1]

== Analyzed Logical Plan ==
count(DISTINCT (num = NULL)): bigint
Aggregate [count(distinct (num#3 = cast(null as int))) AS count(DISTINCT (num = NULL))#45L]
+- Project [value#1 AS num#3]
   +- LocalRelation [value#1]

== Optimized Logical Plan ==
Aggregate [0 AS count(DISTINCT (num = NULL))#45L] // <-- HERE
+- LocalRelation

== Physical Plan ==
*HashAggregate(keys=[], functions=[], output=[count(DISTINCT (num = NULL))#45L])
+- Exchange SinglePartition
   +- *HashAggregate(keys=[], functions=[], output=[])
      +- LocalTableScan

Example: Count Aggregate Operator with Non-Nullable Non-Distinct Expressions

NullPropagation optimization rewrites any non-nullable non-distinct Count aggregate expressions to Literal(1).

val table = (0 to 9).toDF("num").as[Int]

// NullPropagation applied
// current_timestamp() is a non-nullable expression (see the note below)
val query = table.select(count(current_timestamp()) as "count")

scala> println(query.queryExecution.optimizedPlan)
Aggregate [count(1) AS count#64L]
+- LocalRelation

// NullPropagation skipped
val tokens = Seq((0, null), (1, "hello")).toDF("id", "word")
val query = tokens.select(count("word") as "count")

scala> println(query.queryExecution.optimizedPlan)
Aggregate [count(word#55) AS count#71L]
+- LocalRelation [word#55]
Note

Count aggregate expression represents count function internally.

import org.apache.spark.sql.catalyst.expressions.aggregate.Count
import org.apache.spark.sql.functions.count

scala> count("*").expr.children(0).asInstanceOf[Count]
res0: org.apache.spark.sql.catalyst.expressions.aggregate.Count = count(1)
Note

current_timestamp() function is non-nullable expression.

import org.apache.spark.sql.catalyst.expressions.CurrentTimestamp
import org.apache.spark.sql.functions.current_timestamp

scala> current_timestamp().expr.asInstanceOf[CurrentTimestamp].nullable
res38: Boolean = false

Example

val table = (0 to 9).toDF("num").as[Int]
val query = table.where('num === null)

scala> query.explain(extended = true)
== Parsed Logical Plan ==
'Filter ('num = null)
+- Project [value#1 AS num#3]
   +- LocalRelation [value#1]

== Analyzed Logical Plan ==
num: int
Filter (num#3 = cast(null as int))
+- Project [value#1 AS num#3]
   +- LocalRelation [value#1]

== Optimized Logical Plan ==
LocalRelation <empty>, [num#3]

== Physical Plan ==
LocalTableScan <empty>, [num#3]