[GitHub] [hudi] voonhous commented on a diff in pull request #8298: [HUDI-5989] Fix date conversion issue

["voonhous (via GitHub)" <gi...@apache.org>]

voonhous commented on code in PR #8298:
URL: https://github.com/apache/hudi/pull/8298#discussion_r1148813369


##########
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/spark/sql/hudi/TestLazyPartitionPathFetching.scala:
##########
@@ -0,0 +1,130 @@
+/*
+ * 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.hudi
+
+class TestLazyPartitionPathFetching extends HoodieSparkSqlTestBase {
+
+  test("Test querying with string column + partition pruning") {
+    withTempDir { tmp =>
+      val tableName = generateTableName
+      spark.sql(
+        s"""
+           |create table $tableName (
+           |  id int,
+           |  name string,
+           |  price double,
+           |  ts long,
+           |  grass_date string
+           |) using hudi
+           | location '${tmp.getCanonicalPath}'
+           | tblproperties (
+           |  primaryKey ='id',
+           |  type = 'cow',
+           |  preCombineField = 'ts'
+           | )
+           | PARTITIONED BY (grass_date)
+       """.stripMargin)
+      spark.sql(s"insert into $tableName values(1, 'a1', 10, 1000, date('2023-02-27'))")
+      spark.sql(s"insert into $tableName values(2, 'a2', 10, 1000, date('2023-02-28'))")
+      spark.sql(s"insert into $tableName values(3, 'a3', 10, 1000, date('2023-03-01'))")
+
+      checkAnswer(s"select id, name, price, ts from $tableName where grass_date = '2023-03-01' order by id")(
+        Seq(3, "a3", 10.0, 1000)
+      )
+    }
+  }
+
+  test("Test querying with date column + partition pruning") {
+    withTempDir { tmp =>
+      val tableName = generateTableName
+      spark.sql(
+        s"""
+           |create table $tableName (
+           |  id int,
+           |  name string,
+           |  price double,
+           |  ts long,
+           |  grass_date date
+           |) using hudi
+           | location '${tmp.getCanonicalPath}'
+           | tblproperties (
+           |  primaryKey ='id',
+           |  type = 'cow',
+           |  preCombineField = 'ts'
+           | )
+           | PARTITIONED BY (grass_date)
+       """.stripMargin)
+      spark.sql(s"insert into $tableName values(1, 'a1', 10, 1000, date('2023-02-27'))")
+      spark.sql(s"insert into $tableName values(2, 'a2', 10, 1000, date('2023-02-28'))")
+      spark.sql(s"insert into $tableName values(3, 'a3', 10, 1000, date('2023-03-01'))")
+
+      checkAnswer(s"select id, name, price, ts from $tableName where grass_date = date'2023-03-01' order by id")(
+        Seq(3, "a3", 10.0, 1000)
+      )
+    }
+  }
+
+  test("Test querying with date column + partition pruning (multi-level partitioning)") {
+    withTempDir { tmp =>
+      val tableName = generateTableName
+      spark.sql(
+        s"""
+           |create table $tableName (
+           |  id int,
+           |  name string,
+           |  price double,
+           |  ts long,
+           |  grass_region string,
+           |  grass_date date
+           |) using hudi
+           | location '${tmp.getCanonicalPath}'
+           | tblproperties (
+           |  primaryKey ='id',
+           |  type = 'cow',
+           |  preCombineField = 'ts'
+           | )
+           | PARTITIONED BY (grass_region, grass_date)
+         """.stripMargin)
+      spark.sql(s"set schema.on.read.enable=true")
+      spark.sql(s"insert into $tableName values(1, 'a1', 10, 1000, 'ID', date('2023-02-27'))")
+      spark.sql(s"insert into $tableName values(2, 'a2', 10, 1000, 'ID', date('2023-02-28'))")
+      spark.sql(s"insert into $tableName values(3, 'a3', 10, 1000, 'ID', date('2023-03-01'))")
+
+      // test (A) that is expected to fail
+      // must execute this first to prevent caching
+      // steps into prunePartition, causing partitionPath to be incorrectly constructed
+      checkAnswer(s"select id, name, price, ts from $tableName " +
+        s"where grass_date = date'2023-03-01' and grass_region='ID' order by id")(
+        Seq(3, "a3", 10.0, 1000)
+      )
+
+      // test (B) that is expected to pass
+      // if we execute the test (B) first, cached listings will be used, test (A) will pass
+      // no error
+      checkAnswer(s"select id, name, price, ts from $tableName where grass_date = date'2023-03-01' order by id")(
+        Seq(3, "a3", 10.0, 1000)
+      )
+
+      // TLDR:
+      // execution order of [B] = pass
+      // execution order of [B, A] = pass
+      // execution order of [A] = fail
+      // execution order of [A, B] = fail

Review Comment:
   Sure



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