cloud-fan commented on code in PR #55912:
URL: https://github.com/apache/spark/pull/55912#discussion_r3377892460
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala:
##########
@@ -178,6 +178,110 @@ abstract class SparkStrategies extends
QueryPlanner[SparkPlan] {
* Supports both equi-joins and non-equi-joins.
* Supports only inner like joins.
*/
+ /**
+ * Plans AS-OF joins using a dedicated sort-merge operator when the
+ * conf is enabled.
+ */
+ object AsOfJoinSelection extends Strategy with PredicateHelper {
Review Comment:
`object AsOfJoinSelection` is inserted between the large "Select the proper
physical plan for join..." Scaladoc (lines 147-180, which documents `object
JoinSelection`) and `object JoinSelection` itself (now line 285). The effect is
that `JoinSelection` no longer has a Scaladoc directly above it, and the
join-algorithm-selection doc now visually attaches to `AsOfJoinSelection`
(which it doesn't describe).
Suggest moving `object AsOfJoinSelection` (with its own short doc) to after
`object JoinSelection`'s body, or placing it ahead of the JoinSelection
Scaladoc, so the doc stays attached to its object. (My miss from an earlier
round.)
##########
sql/core/src/test/scala/org/apache/spark/sql/SortMergeAsOfJoinSuite.scala:
##########
@@ -0,0 +1,640 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import scala.jdk.CollectionConverters._
+
+import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
+import org.apache.spark.sql.execution.joins.SortMergeAsOfJoinExec
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types._
+
+class SortMergeAsOfJoinSuite extends QueryTest
+ with SharedSparkSession
+ with AdaptiveSparkPlanHelper {
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ spark.conf.set(SQLConf.SORT_MERGE_AS_OF_JOIN_ENABLED.key, "true")
+ }
+
+ override def afterAll(): Unit = {
+ spark.conf.unset(SQLConf.SORT_MERGE_AS_OF_JOIN_ENABLED.key)
+ super.afterAll()
+ }
+
+ def prepareForAsOfJoin(): (classic.DataFrame, classic.DataFrame) = {
+ val schema1 = StructType(
+ StructField("a", IntegerType, false) ::
+ StructField("b", StringType, false) ::
+ StructField("left_val", StringType, false) :: Nil)
+ val rowSeq1: List[Row] = List(
+ Row(1, "x", "a"), Row(5, "y", "b"), Row(10, "z", "c"))
+ val df1 = spark.createDataFrame(rowSeq1.asJava, schema1)
+
+ val schema2 = StructType(
+ StructField("a", IntegerType) ::
+ StructField("b", StringType) ::
+ StructField("right_val", IntegerType) :: Nil)
+ val rowSeq2: List[Row] = List(
+ Row(1, "v", 1), Row(2, "w", 2), Row(3, "x", 3),
+ Row(6, "y", 6), Row(7, "z", 7))
+ val df2 = spark.createDataFrame(rowSeq2.asJava, schema2)
+
+ (df1, df2)
+ }
+
+ test("uses SortMergeAsOfJoinExec physical operator") {
+ val (df1, df2) = prepareForAsOfJoin()
+ val result = df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward")
+ val plan = result.queryExecution.executedPlan
+ assert(collectWithSubqueries(plan) {
+ case _: SortMergeAsOfJoinExec => true
+ }.nonEmpty, s"Expected SortMergeAsOfJoinExec in plan:\n$plan")
+ }
+
+ test("backward join - simple") {
+ val (df1, df2) = prepareForAsOfJoin()
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(1, "x", "a", 1, "v", 1),
+ Row(5, "y", "b", 3, "x", 3),
+ Row(10, "z", "c", 7, "z", 7)
+ )
+ )
+ }
+
+ test("backward join - usingColumns") {
+ val (df1, df2) = prepareForAsOfJoin()
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq("b"),
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(10, "z", "c", 7, "z", 7)
+ )
+ )
+ }
+
+ test("backward join - left outer") {
+ val (df1, df2) = prepareForAsOfJoin()
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq("b"),
+ joinType = "leftouter", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(1, "x", "a", null, null, null),
+ Row(5, "y", "b", null, null, null),
+ Row(10, "z", "c", 7, "z", 7)
+ )
+ )
+ }
+
+ test("forward join") {
+ val (df1, df2) = prepareForAsOfJoin()
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "forward"),
+ Seq(
+ Row(1, "x", "a", 1, "v", 1),
+ Row(5, "y", "b", 6, "y", 6),
+ Row(10, "z", "c", null, null, null)
+ ).filter(_.get(3) != null) // inner join: no match for 10
+ )
+ }
+
+ test("nearest join") {
+ val (df1, df2) = prepareForAsOfJoin()
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "nearest"),
+ Seq(
+ Row(1, "x", "a", 1, "v", 1),
+ Row(5, "y", "b", 6, "y", 6),
+ Row(10, "z", "c", 7, "z", 7)
+ )
+ )
+ }
+
+ test("backward join - tolerance = 1") {
+ val (df1, df2) = prepareForAsOfJoin()
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner",
+ tolerance = functions.lit(1),
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(1, "x", "a", 1, "v", 1),
+ Row(10, "z", "c", null, null, null)
+ ).filter(_.get(3) != null)
+ )
+ }
+
+ test("backward join - allowExactMatches = false") {
+ val (df1, df2) = prepareForAsOfJoin()
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = false, direction = "backward"),
+ Seq(
+ // left.a=1: no right row with a < 1 -> no match
+ // left.a=5: right.a=3 (3 < 5) -> match
+ Row(5, "y", "b", 3, "x", 3),
+ // left.a=10: right.a=7 (7 < 10) -> match
+ Row(10, "z", "c", 7, "z", 7)
+ )
+ )
+ }
+
+ test("empty left side") {
+ val (_, df2) = prepareForAsOfJoin()
+ val emptyDf = spark.createDataFrame(
+ java.util.Collections.emptyList[Row](),
+ StructType(
+ StructField("a", IntegerType, false) ::
+ StructField("b", StringType, false) ::
+ StructField("left_val", StringType, false) :: Nil))
+ checkAnswer(
+ emptyDf.joinAsOf(
+ df2, emptyDf.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq.empty
+ )
+ }
+
+ test("empty right side") {
+ val (df1, _) = prepareForAsOfJoin()
+ val emptyDf = spark.createDataFrame(
+ java.util.Collections.emptyList[Row](),
+ StructType(
+ StructField("a", IntegerType) ::
+ StructField("b", StringType) ::
+ StructField("right_val", IntegerType) :: Nil))
+ // Inner join: no matches possible
+ checkAnswer(
+ df1.joinAsOf(
+ emptyDf, df1.col("a"), emptyDf.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq.empty
+ )
+ // Left outer: all left rows with null right
+ checkAnswer(
+ df1.joinAsOf(
+ emptyDf, df1.col("a"), emptyDf.col("a"), usingColumns = Seq.empty,
+ joinType = "leftouter", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(1, "x", "a", null, null, null),
+ Row(5, "y", "b", null, null, null),
+ Row(10, "z", "c", null, null, null)
+ )
+ )
+ }
+
+ test("null as-of keys") {
+ val schema1 = StructType(
+ StructField("a", IntegerType, true) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("a", IntegerType, true) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(null, "x"), Row(3, "y"), Row(7, "z")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(1, "a"), Row(null, "b"), Row(5, "c")).asJava, schema2)
+ // Null as-of keys should not match anything (as-of condition
+ // evaluates to null for null inputs)
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "leftouter", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(null, "x", null, null),
+ Row(3, "y", 1, "a"),
+ Row(7, "z", 5, "c")
+ )
+ )
+ }
+
+ test("multiple rows with same equi-key") {
+ val schema1 = StructType(
+ StructField("grp", StringType) ::
+ StructField("ts", IntegerType) :: Nil)
+ val schema2 = StructType(
+ StructField("grp", StringType) ::
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(
+ Row("A", 5), Row("A", 10), Row("A", 15),
+ Row("B", 3), Row("B", 8)
+ ).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(
+ Row("A", 2, "a1"), Row("A", 7, "a2"), Row("A", 12, "a3"),
+ Row("B", 1, "b1"), Row("B", 6, "b2"), Row("B", 10, "b3")
+ ).asJava, schema2)
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq("grp"),
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row("A", 5, "A", 2, "a1"),
+ Row("A", 10, "A", 7, "a2"),
+ Row("A", 15, "A", 12, "a3"),
+ Row("B", 3, "B", 1, "b1"),
+ Row("B", 8, "B", 6, "b2")
+ )
+ )
+ }
+
+ test("long type as-of key") {
+ val schema1 = StructType(
+ StructField("ts", LongType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("ts", LongType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(100L, "a"), Row(200L, "b"), Row(300L, "c")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(50L, "x"), Row(150L, "y"), Row(250L, "z")).asJava, schema2)
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(100L, "a", 50L, "x"),
+ Row(200L, "b", 150L, "y"),
+ Row(300L, "c", 250L, "z")
+ )
+ )
+ }
+
+ test("double type as-of key") {
+ val schema1 = StructType(
+ StructField("ts", DoubleType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("ts", DoubleType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(1.5, "a"), Row(3.0, "b"), Row(5.5, "c")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(1.0, "x"), Row(2.5, "y"), Row(4.0, "z")).asJava, schema2)
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(1.5, "a", 1.0, "x"),
+ Row(3.0, "b", 2.5, "y"),
+ Row(5.5, "c", 4.0, "z")
+ )
+ )
+ }
+
+ test("conf disabled falls back to correlated subquery rewrite") {
+ val (df1, df2) = prepareForAsOfJoin()
+ withSQLConf(SQLConf.SORT_MERGE_AS_OF_JOIN_ENABLED.key -> "false") {
+ val result = df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward")
+ val plan = result.queryExecution.executedPlan
+ assert(collectWithSubqueries(plan) {
+ case _: SortMergeAsOfJoinExec => true
+ }.isEmpty, "Should NOT use SortMergeAsOfJoinExec when conf is disabled")
+ // Results should still be correct
+ checkAnswer(result, Seq(
+ Row(1, "x", "a", 1, "v", 1),
+ Row(5, "y", "b", 3, "x", 3),
+ Row(10, "z", "c", 7, "z", 7)
+ ))
+ }
+ }
+
+ test("self join") {
+ val schema = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df = spark.createDataFrame(
+ List(Row(1, "a"), Row(3, "b"), Row(5, "c")).asJava, schema)
+ checkAnswer(
+ df.joinAsOf(
+ df, df.col("ts"), df.col("ts"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(1, "a", 1, "a"),
+ Row(3, "b", 3, "b"),
+ Row(5, "c", 5, "c")
+ )
+ )
+ }
+
+ test("no equi-key - all rows in single partition") {
+ val schema1 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(2, "a"), Row(5, "b"), Row(9, "c")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(1, "x"), Row(4, "y"), Row(7, "z")).asJava, schema2)
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(2, "a", 1, "x"),
+ Row(5, "b", 4, "y"),
+ Row(9, "c", 7, "z")
+ )
+ )
+ }
+
+ test("forward join - left outer with no match") {
+ val schema1 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(1, "a"), Row(5, "b"), Row(10, "c")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(3, "x"), Row(7, "y")).asJava, schema2)
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "leftouter", tolerance = null,
+ allowExactMatches = true, direction = "forward"),
+ Seq(
+ Row(1, "a", 3, "x"),
+ Row(5, "b", 7, "y"),
+ Row(10, "c", null, null) // no right row >= 10
+ )
+ )
+ }
+
+ test("forward join - tolerance") {
+ val schema1 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(1, "a"), Row(5, "b"), Row(10, "c")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(2, "x"), Row(7, "y"), Row(15, "z")).asJava, schema2)
+ // tolerance = 3: only match if right.ts <= left.ts + 3
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "inner",
+ tolerance = functions.lit(3),
+ allowExactMatches = true, direction = "forward"),
+ Seq(
+ Row(1, "a", 2, "x"), // 2 <= 1+3=4, match
+ Row(5, "b", 7, "y") // 7 <= 5+3=8, match
+ // 10: right.ts=15, 15 > 10+3=13, no match
+ )
+ )
+ }
+
+ test("nearest join - tolerance") {
+ val schema1 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(10, "a"), Row(20, "b")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(5, "x"), Row(12, "y"), Row(25, "z")).asJava, schema2)
+ // tolerance = 3: only match if |left.ts - right.ts| <= 3
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "leftouter",
+ tolerance = functions.lit(3),
+ allowExactMatches = true, direction = "nearest"),
+ Seq(
+ Row(10, "a", 12, "y"), // |10-12|=2 <= 3, match
+ Row(20, "b", null, null) // |20-25|=5 > 3, no match
+ )
+ )
+ }
+
+ test("nearest join - equidistant right rows") {
+ val schema1 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(10, "a")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(8, "x"), Row(12, "y")).asJava, schema2)
+ // Both are distance 2 from left.ts=10. The scan is left-to-right
+ // (Nearest direction), so the first match (ts=8) wins when distances
+ // are equal (distanceOrdering.lt is strict).
+ val result = df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "inner",
+ tolerance = functions.lit(5),
+ allowExactMatches = true, direction = "nearest")
+ // Verify we get exactly one row (tie-breaking is deterministic)
+ assert(result.count() == 1)
+ val row = result.collect().head
+ assert(row.getInt(0) == 10)
+ // The tie-breaker picks the first encountered in scan order
+ assert(row.getInt(2) == 8 || row.getInt(2) == 12)
+ }
+
+ test("forward join - allowExactMatches = false") {
+ val (df1, df2) = prepareForAsOfJoin()
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = false, direction = "forward"),
+ Seq(
+ // left.a=1: right.a must be > 1 -> right.a=2
+ Row(1, "x", "a", 2, "w", 2),
+ // left.a=5: right.a must be > 5 -> right.a=6
+ Row(5, "y", "b", 6, "y", 6),
+ // left.a=10: no right.a > 10
+ Row(10, "z", "c", null, null, null)
+ ).filter(_.get(3) != null)
+ )
+ }
+
+ test("nearest join - allowExactMatches = false") {
+ val schema1 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(5, "a"), Row(10, "b")).asJava, schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(5, "x"), Row(8, "y"), Row(10, "z")).asJava, schema2)
+ // allowExactMatches=false: exact matches excluded
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = false, direction = "nearest"),
+ Seq(
+ // left.ts=5: exclude right.ts=5, nearest is 8 (distance 3)
+ Row(5, "a", 8, "y"),
+ // left.ts=10: exclude right.ts=10, nearest is 8 (distance 2)
+ Row(10, "b", 8, "y")
+ )
+ )
+ }
+
+ test("nearest join - allowExactMatches = false with right rows bracketing
left") {
+ // Regression test: right rows on both sides of the exact match point.
+ // The operator must not early-terminate when the as-of condition is
+ // false at the interior point (right == left).
+ val schema = StructType(
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(10, "a")).asJava, schema)
+ val df2 = spark.createDataFrame(
+ List(Row(1, "x"), Row(10, "y"), Row(11, "z")).asJava, schema)
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq.empty,
+ joinType = "inner", tolerance = null,
+ allowExactMatches = false, direction = "nearest"),
+ // distance to 11 is 1, distance to 1 is 9. Correct answer is 11.
+ Seq(Row(10, "a", 11, "z"))
+ )
+ }
+
+ test("null equi-keys with EqualTo condition") {
+ // NULL = NULL -> NULL under EqualTo semantics, so null-keyed rows
+ // should NOT match each other.
+ val schema1 = StructType(
+ StructField("grp", IntegerType, nullable = true) ::
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val schema2 = StructType(
+ StructField("grp", IntegerType, nullable = true) ::
+ StructField("ts", IntegerType) ::
+ StructField("val", StringType) :: Nil)
+ val df1 = spark.createDataFrame(
+ List(Row(null, 5, "a"), Row(1, 5, "b"), Row(null, 10, "c")).asJava,
schema1)
+ val df2 = spark.createDataFrame(
+ List(Row(null, 3, "x"), Row(1, 4, "y"), Row(null, 8, "z")).asJava,
schema2)
+ // Inner join: null equi-keys should produce no match
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq("grp"),
+ joinType = "inner", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ // grp=1, left.ts=5: right.ts=4 (4 <= 5) -> match
+ Row(1, 5, "b", 1, 4, "y")
+ // grp=null rows: no match (EqualTo semantics)
+ )
+ )
+ // Left outer: null equi-keys should emit null right
+ checkAnswer(
+ df1.joinAsOf(
+ df2, df1.col("ts"), df2.col("ts"), usingColumns = Seq("grp"),
+ joinType = "leftouter", tolerance = null,
+ allowExactMatches = true, direction = "backward"),
+ Seq(
+ Row(null, 5, "a", null, null, null),
+ Row(1, 5, "b", 1, 4, "y"),
+ Row(null, 10, "c", null, null, null)
+ )
+ )
+ }
+
+ test("residual condition") {
Review Comment:
This test doesn't actually exercise the operator's residual path that `:183`
asked to cover. It uses `usingColumns = Seq("grp")` (pure `EqualTo`) plus a
post-join `.where(df1.col("amount") > df2.col("threshold"))`. The `.where` is
planned as a separate `FilterExec` above the join -- `AsOfJoin extends
BinaryNode` (not `Join`), and no predicate-pushdown rule targets it, so nothing
pushes the predicate into the join's `condition`. So
`SortMergeAsOfJoinScanner.residualCondition` / `boundResidualCond` stays
`None`, and the subtle interleaving of residual evaluation with distance-based
early termination (`SortMergeAsOfJoinExec.scala:387-390`, `425-428`) is never
run.
To actually route a residual, use the `joinExprs: Column` overload with a
non-equi pair-correlated conjunct, e.g.
```scala
df1.joinAsOf(
df2, df1.col("ts"), df2.col("ts"),
joinExprs = (df1.col("grp") === df2.col("grp")) && (df1.col("amount") >
df2.col("threshold")),
joinType = "inner", tolerance = null,
allowExactMatches = true, direction = "backward")
```
`AsOfJoinSelection.extractEquiJoinKeys` splits that into the `grp` equi-key
+ a residual `amount > threshold`, which lands in the scanner. Worth a Backward
and a Nearest case. The test name is also misleading given the current body.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
