[HUDI-8784] Validate auto key gen for SQL

hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/TestAutoKeyGenForSQL.scala

@@ -0,0 +1,99 @@
+/*
+ * 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.hudi
+
+import org.apache.hudi.common.table.HoodieTableMetaClient
+import org.apache.hudi.storage.hadoop.HadoopStorageConfiguration
+import org.apache.hudi.testutils.SparkClientFunctionalTestHarness
+import org.apache.hudi.testutils.SparkClientFunctionalTestHarness.getSparkSqlConf
+import org.apache.spark.SparkConf
+import org.junit.jupiter.api.Assertions.assertTrue
+import org.junit.jupiter.params.ParameterizedTest
+import org.junit.jupiter.params.provider.CsvSource
+
+class TestAutoKeyGenForSQL extends SparkClientFunctionalTestHarness {
+  override def conf: SparkConf = conf(getSparkSqlConf)
+
+  @ParameterizedTest
+  @CsvSource(value = Array("MERGE_ON_READ", "COPY_ON_WRITE"))
+  def testAutoKeyGen(tableType: String): Unit = {
+    // No record key is set, which should trigger auto key gen.
+    // MOR table is used to generate log files.
+    val tableName = "hoodie_test_" + tableType
+    spark.sql(
+      s"""
+         |create table $tableName (
+         | ts BIGINT,
+         | uuid STRING,
+         | rider STRING,
+         | driver STRING,
+         | fare DOUBLE,
+         | city STRING
+         |) using hudi
+         | options (
+         |  hoodie.metadata.enable = 'true',
+         |  hoodie.enable.data.skipping = 'true',
+         |  hoodie.datasource.write.payload.class = 'org.apache.hudi.common.model.OverwriteWithLatestAvroPayload'
+         | )
+         | partitioned by(city)
+         | location '$basePath'
+         | TBLPROPERTIES (hoodie.datasource.write.table.type='$tableType')
+       """.stripMargin)
+    // Initial data.
+    spark.sql(
+      s"""
+         |INSERT INTO $tableName VALUES
+         |  (1695159649087,'334e26e9-8355-45cc-97c6-c31daf0df330','rider-A','driver-K',19.10,'san_francisco'),
+         |  (1695091554788,'e96c4396-3fad-413a-a942-4cb36106d721','rider-C','driver-M',27.70 ,'san_francisco'),
+         |  (1695046462179,'9909a8b1-2d15-4d3d-8ec9-efc48c536a00','rider-D','driver-L',33.90 ,'san_francisco'),
+         |  (1695332066204,'1dced545-862b-4ceb-8b43-d2a568f6616b','rider-E','driver-O',93.50,'san_francisco'),
+         |  (1695516137016,'e3cf430c-889d-4015-bc98-59bdce1e530c','rider-F','driver-P',34.15,'sao_paulo'),
+         |  (1695376420876,'7a84095f-737f-40bc-b62f-6b69664712d2','rider-G','driver-Q',43.40 ,'sao_paulo'),
+         |  (1695173887231,'3eeb61f7-c2b0-4636-99bd-5d7a5a1d2c04','rider-I','driver-S',41.06 ,'chennai'),
+         |  (1695115999911,'c8abbe79-8d89-47ea-b4ce-4d224bae5bfa','rider-J','driver-T',17.85,'chennai');
+      """.stripMargin)
+    // Create the first log file by update.
+    spark.sql(s"UPDATE $tableName SET fare = 25.0 WHERE rider = 'rider-D';")
+    // Create the second log file by delete.
+    spark.sql(s"DELETE FROM $tableName WHERE uuid = '334e26e9-8355-45cc-97c6-c31daf0df330';")
+    // Create the third log file by delete.
+    spark.sql(s"DELETE FROM $tableName WHERE uuid = '9909a8b1-2d15-4d3d-8ec9-efc48c536a00';")
+
+    // Validate: data integrity.
+    val columns = Seq("ts","uuid","rider","driver","fare","city")
+    val actualDf = spark.sql(s"SELECT * FROM $tableName WHERE city = 'san_francisco';")
+      .select("ts","uuid","rider","driver","fare","city").sort("uuid")
+    val expected = Seq(
+      (1695091554788L,"e96c4396-3fad-413a-a942-4cb36106d721","rider-C","driver-M",27.70,"san_francisco"),
+      (1695332066204L,"1dced545-862b-4ceb-8b43-d2a568f6616b","rider-E","driver-O",93.50,"san_francisco"))
+    val expectedDf = spark.createDataFrame(expected).toDF(columns: _*)
+    val expectedMinusActual = expectedDf.except(actualDf)
+    val actualMinusExpected = actualDf.except(expectedDf)
+    assertTrue(expectedMinusActual.isEmpty && actualMinusExpected.isEmpty)
+    // Validate: table property.
+    val metaClient: HoodieTableMetaClient = HoodieTableMetaClient
+      .builder()
+      .setBasePath(basePath)
+      .setConf(new HadoopStorageConfiguration(spark.sparkContext.hadoopConfiguration))
+      .build()
+    // Record key fields should be empty.
+    assertTrue(metaClient.getTableConfig.getRecordKeyFields.isEmpty)
+  }
+}