[GitHub] [spark] ueshin commented on a change in pull request #34053: [SPARK-36813][SQL][PYTHON] Propose an infrastructure of as-of join and imlement ps.merge_asof

[GitBox <gi...@apache.org>]

ueshin commented on a change in pull request #34053:
URL: https://github.com/apache/spark/pull/34053#discussion_r715051356



##########
File path: sql/core/src/test/scala/org/apache/spark/sql/DataFrameAsOfJoinSuite.scala
##########
@@ -0,0 +1,153 @@
+/*
+ * 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.collection.JavaConverters._
+
+import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types._
+
+class DataFrameAsOfJoinSuite extends QueryTest
+  with SharedSparkSession
+  with AdaptiveSparkPlanHelper {
+
+  def prepareForAsOfJoin(): (DataFrame, 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("as-of join - simple") {
+    val (df1, df2) = prepareForAsOfJoin()
+    checkAnswer(
+      df1.joinAsOf(
+        df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+        joinType = "left", 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("as-of join - usingColumns") {
+    val (df1, df2) = prepareForAsOfJoin()
+    checkAnswer(
+      df1.joinAsOf(df2, df1.col("a"), df2.col("a"), usingColumns = Seq("b"),
+        joinType = "left", 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("as-of join - usingColumns, inner") {
+    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("as-of join - tolerance = 1") {
+    val (df1, df2) = prepareForAsOfJoin()
+    checkAnswer(
+      df1.joinAsOf(df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+        joinType = "left", tolerance = lit(1), allowExactMatches = true, direction = "backward"),
+      Seq(
+        Row(1, "x", "a", 1, "v", 1),
+        Row(5, "y", "b", null, null, null),
+        Row(10, "z", "c", null, null, null)
+      )
+    )
+  }
+
+  test("as-of join - allowExactMatches = false") {
+    val (df1, df2) = prepareForAsOfJoin()
+    checkAnswer(
+      df1.joinAsOf(df2, df1.col("a"), df2.col("a"), usingColumns = Seq.empty,
+        joinType = "left", tolerance = null, allowExactMatches = false, direction = "backward"),
+      Seq(
+        Row(1, "x", "a", null, null, null),

Review comment:
       As for the pandas-on-Spark example: https://github.com/apache/spark/blob/c8237f46c325a0f95d4793a809d2bcc53cb948c2/python/pyspark/pandas/namespace.py#L2863-L2872
   
   pandas handles `null`-ish values as `NaN` in numeric columns, so the example shows `NaN`, but non-pandas-on-Spark DataFrame-wise, we can leave it as `null`.




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