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 * 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.execution

import java.io.File

import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.internal.SQLConf.OPTIMIZER_METADATA_ONLY
import org.apache.spark.sql.test.SharedSparkSession
import org.apache.spark.tags.SlowSQLTest

@SlowSQLTest
class OptimizeMetadataOnlyQuerySuite extends QueryTest with SharedSparkSession {
  import testImplicits._

  override def beforeAll(): Unit = {
    super.beforeAll()
    val data = (1 to 10).map(i => (i, s"data-$i", i % 2, if ((i % 2) == 0) "even" else "odd"))
      .toDF("col1", "col2", "partcol1", "partcol2")
    data.write.partitionBy("partcol1", "partcol2").mode("append").saveAsTable("srcpart")
  }

  override protected def afterAll(): Unit = {
    try {
      sql("DROP TABLE IF EXISTS srcpart")
    } finally {
      super.afterAll()
    }
  }

  private def assertMetadataOnlyQuery(df: DataFrame): Unit = {
    val localRelations = df.queryExecution.optimizedPlan.collect {
      case l: LocalRelation => l
    }
    assert(localRelations.size == 1)
  }

  private def assertNotMetadataOnlyQuery(df: DataFrame): Unit = {
    val localRelations = df.queryExecution.optimizedPlan.collect {
      case l: LocalRelation => l
    }
    assert(localRelations.size == 0)
  }

  private def testMetadataOnly(name: String, sqls: String*): Unit = {
    test(name) {
      withSQLConf(SQLConf.OPTIMIZER_METADATA_ONLY.key -> "true") {
        sqls.foreach { case q => assertMetadataOnlyQuery(sql(q)) }
      }
      withSQLConf(SQLConf.OPTIMIZER_METADATA_ONLY.key -> "false") {
        sqls.foreach { case q => assertNotMetadataOnlyQuery(sql(q)) }
      }
    }
  }

  private def testNotMetadataOnly(name: String, sqls: String*): Unit = {
    test(name) {
      withSQLConf(SQLConf.OPTIMIZER_METADATA_ONLY.key -> "true") {
        sqls.foreach { case q => assertNotMetadataOnlyQuery(sql(q)) }
      }
      withSQLConf(SQLConf.OPTIMIZER_METADATA_ONLY.key -> "false") {
        sqls.foreach { case q => assertNotMetadataOnlyQuery(sql(q)) }
      }
    }
  }

  testMetadataOnly(
    "Aggregate expression is partition columns",
    "select partcol1 from srcpart group by partcol1",
    "select partcol2 from srcpart where partcol1 = 0 group by partcol2")

  testMetadataOnly(
    "Distinct aggregate function on partition columns",
    "SELECT partcol1, count(distinct partcol2) FROM srcpart group by partcol1",
    "SELECT partcol1, count(distinct partcol2) FROM srcpart where partcol1 = 0 group by partcol1")

  testMetadataOnly(
    "Distinct on partition columns",
    "select distinct partcol1, partcol2 from srcpart",
    "select distinct c1 from (select partcol1 + 1 as c1 from srcpart where partcol1 = 0) t")

  testMetadataOnly(
    "Aggregate function on partition columns which have same result w or w/o DISTINCT keyword",
    "select max(partcol1) from srcpart",
    "select min(partcol1) from srcpart where partcol1 = 0",
    "select first(partcol1) from srcpart",
    "select last(partcol1) from srcpart where partcol1 = 0",
    "select partcol2, min(partcol1) from srcpart where partcol1 = 0 group by partcol2",
    "select max(c1) from (select partcol1 + 1 as c1 from srcpart where partcol1 = 0) t")

  testMetadataOnly(
    "SPARK-31590 Metadata-only queries should not include subquery in partition filters",
    """
      |SELECT partcol1, MAX(partcol2) AS partcol2
      |FROM srcpart
      |WHERE partcol1 = (
      |  SELECT MAX(partcol1)
      |  FROM srcpart
      |)
      |AND partcol2 = 'even'
      |GROUP BY partcol1
      |""".stripMargin
  )

  testNotMetadataOnly(
    "Don't optimize metadata only query for non-partition columns",
    "select col1 from srcpart group by col1",
    "select partcol1, max(col1) from srcpart group by partcol1",
    "select partcol1, count(distinct col1) from srcpart group by partcol1",
    "select distinct partcol1, col1 from srcpart")

  testNotMetadataOnly(
    "Don't optimize metadata only query for non-distinct aggregate function on partition columns",
    "select partcol1, sum(partcol2) from srcpart group by partcol1",
    "select partcol1, count(partcol2) from srcpart group by partcol1")

  testNotMetadataOnly(
    "Don't optimize metadata only query for GroupingSet/Union operator",
    "select partcol1, max(partcol2) from srcpart where partcol1 = 0 group by rollup (partcol1)",
    "select partcol2 from (select partcol2 from srcpart where partcol1 = 0 union all " +
      "select partcol2 from srcpart where partcol1 = 1) t group by partcol2")

  test("SPARK-21884 Fix StackOverflowError on MetadataOnlyQuery") {
    withTable("t_1000") {
      sql("CREATE TABLE t_1000 (a INT, p INT) USING PARQUET PARTITIONED BY (p)")
      (1 to 1000).foreach(p => sql(s"ALTER TABLE t_1000 ADD PARTITION (p=$p)"))
      sql("SELECT COUNT(DISTINCT p) FROM t_1000").collect()
    }
  }

  test("Incorrect result caused by the rule OptimizeMetadataOnlyQuery") {
    // This test case is only for file source V1. As the rule OptimizeMetadataOnlyQuery is disabled
    // by default, we can skip testing file source v2 in current stage.
    withSQLConf(OPTIMIZER_METADATA_ONLY.key -> "true",
      SQLConf.USE_V1_SOURCE_LIST.key -> "json") {
      withTempPath { path =>
        val tablePath = new File(s"${path.getCanonicalPath}/cOl3=c/cOl1=a/cOl5=e")
        Seq(("a", "b", "c", "d", "e")).toDF("cOl1", "cOl2", "cOl3", "cOl4", "cOl5")
          .write.json(tablePath.getCanonicalPath)

        val df = spark.read.json(path.getCanonicalPath).select("CoL1", "CoL5", "CoL3").distinct()
        checkAnswer(df, Row("a", "e", "c"))

        val localRelation = df.queryExecution.optimizedPlan.collectFirst {
          case l: LocalRelation => l
        }
        assert(localRelation.nonEmpty, "expect to see a LocalRelation")
        assert(localRelation.get.output.map(_.name) == Seq("cOl3", "cOl1", "cOl5"))
      }
    }
  }
}
