/*
 * 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 java.io.File
import java.nio.charset.StandardCharsets
import java.nio.file.Files

import scala.collection.mutable
import scala.jdk.CollectionConverters._
import scala.util.Random

import org.apache.spark.SparkRuntimeException
import org.apache.spark.sql.catalyst.expressions.{CodegenObjectFactoryMode, ExpressionEvalHelper, Literal}
import org.apache.spark.sql.catalyst.expressions.variant.{VariantExpressionEvalUtils, VariantGet}
import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, DateTimeUtils, GenericArrayData}
import org.apache.spark.sql.errors.QueryExecutionErrors.toSQLId
import org.apache.spark.sql.execution.WholeStageCodegenExec
import org.apache.spark.sql.functions._
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.test.SharedSparkSession
import org.apache.spark.sql.types._
import org.apache.spark.types.variant.VariantUtil._
import org.apache.spark.unsafe.types.{UTF8String, VariantVal}
import org.apache.spark.util.ArrayImplicits._

class VariantSuite extends QueryTest with SharedSparkSession with ExpressionEvalHelper {
  import testImplicits._

  test("basic tests") {
    def verifyResult(df: DataFrame): Unit = {
      val result = df.collect()
        .map(_.get(0).asInstanceOf[VariantVal].toString)
        .sorted
        .toSeq
      val expected = (1 until 10).map(id => "1" * id)
      assert(result == expected)
    }

    val query = spark.sql("select parse_json(repeat('1', id)) as v from range(1, 10)")
    verifyResult(query)

    // Write into and read from Parquet.
    withTempDir { dir =>
      val tempDir = new File(dir, "files").getCanonicalPath
      query.write.parquet(tempDir)
      verifyResult(spark.read.parquet(tempDir))
    }
  }

  test("basic try_parse_json alias") {
    val df = spark.createDataFrame(Seq(Row("""{ "a" : 1 }"""), Row("""{ a : 1 }""")).asJava,
      new StructType().add("json", StringType))
    val actual = df.select(to_json(try_parse_json(col("json")))).collect()

    assert(actual(0)(0) == """{"a":1}""")
    assert(actual(1)(0) == null)
  }

  test("basic parse_json alias") {
    val df = spark.createDataFrame(Seq(Row("""{ "a" : 1 }""")).asJava,
      new StructType().add("json", StringType))
    val actual = df.select(
      to_json(parse_json(col("json"))),
      to_json(parse_json(lit("""{"b": [{"c": "str2"}]}""")))).collect().head

    assert(actual.getString(0) == """{"a":1}""")
    assert(actual.getString(1) == """{"b":[{"c":"str2"}]}""")
  }

  test("expression alias") {
    val df = Seq("""{ "a" : 1 }""", """{ "b" : 2 }""").toDF("json")
    val v = parse_json(col("json"))

    def rows(results: Any*): Seq[Row] = results.map(Row(_))

    checkAnswer(df.select(is_variant_null(v)), rows(false, false))
    checkAnswer(df.select(schema_of_variant(v)), rows("OBJECT<a: BIGINT>", "OBJECT<b: BIGINT>"))
    checkAnswer(df.select(schema_of_variant_agg(v)), rows("OBJECT<a: BIGINT, b: BIGINT>"))

    checkAnswer(df.select(variant_get(v, "$.a", "int")), rows(1, null))
    checkAnswer(df.select(variant_get(v, "$.b", "int")), rows(null, 2))
    checkAnswer(df.select(variant_get(v, "$.a", "double")), rows(1.0, null))
    checkError(
      exception = intercept[SparkRuntimeException] {
        df.select(variant_get(v, "$.a", "binary")).collect()
      },
      condition = "INVALID_VARIANT_CAST",
      parameters = Map("value" -> "1", "dataType" -> "\"BINARY\"")
    )

    checkAnswer(df.select(try_variant_get(v, "$.a", "int")), rows(1, null))
    checkAnswer(df.select(try_variant_get(v, "$.b", "int")), rows(null, 2))
    checkAnswer(df.select(try_variant_get(v, "$.a", "double")), rows(1.0, null))
    checkAnswer(df.select(try_variant_get(v, "$.a", "binary")), rows(null, null))
  }

  test("non-literal variant_get") {
    def rows(results: Any*): Seq[Row] = results.map(Row(_))

    Seq("CODEGEN_ONLY", "NO_CODEGEN").foreach { codegenMode =>
      withSQLConf(SQLConf.CODEGEN_FACTORY_MODE.key -> codegenMode) {
        // The first three rows have valid paths while the final row has an invalid path
        val df = Seq(("""{"a" : 1}""", "$.a", 2), ("""{"b" : 2}""", "$", 1),
          ("""{"c" : 3}""", null, 1), (null, null, 1), (null, "$.a", 1),
          ("""{"d" : 3}""", "abc", 0)).toDF("json", "path", "valid")
        val v = parse_json(col("json"))
        val df1 = df.where($"valid" > 0).select(variant_get(v, col("path"), "string"))
        checkAnswer(df1, rows("1", """{"b":2}""", null, null, null))
        assert(df1.queryExecution.executedPlan.isInstanceOf[WholeStageCodegenExec] ==
          (codegenMode == "CODEGEN_ONLY"))
        // Invalid path
        val df2 = df.select(variant_get(v, col("path"), "string"))
        checkError(
          exception = intercept[SparkRuntimeException] { df2.collect() },
          condition = "INVALID_VARIANT_GET_PATH",
          parameters = Map("path" -> "abc", "functionName" -> toSQLId("variant_get")))
        assert(df2.queryExecution.executedPlan.isInstanceOf[WholeStageCodegenExec] ==
          (codegenMode == "CODEGEN_ONLY"))
        // Invalid cast
        val df3 = df.where($"valid" > 1).select(variant_get(v, col("path"), "binary"))
        checkError(
          exception = intercept[SparkRuntimeException] { df3.collect() },
          condition = "INVALID_VARIANT_CAST",
          parameters = Map("value" -> "1", "dataType" -> "\"BINARY\"")
        )
        assert(df3.queryExecution.executedPlan.isInstanceOf[WholeStageCodegenExec] ==
          (codegenMode == "CODEGEN_ONLY"))

        // try_variant_get
        val df4 = df.where($"valid" > 0).select(try_variant_get(v, col("path"), "string"))
        checkAnswer(df4, rows("1", """{"b":2}""", null, null, null))
        assert(df4.queryExecution.executedPlan.isInstanceOf[WholeStageCodegenExec] ==
          (codegenMode == "CODEGEN_ONLY"))
        // Invalid path
        val df5 = df.select(try_variant_get(v, col("path"), "string"))
        checkError(
          exception = intercept[SparkRuntimeException] { df5.collect() },
          condition = "INVALID_VARIANT_GET_PATH",
          parameters = Map("path" -> "abc", "functionName" -> toSQLId("try_variant_get")))
        assert(df5.queryExecution.executedPlan.isInstanceOf[WholeStageCodegenExec] ==
          (codegenMode == "CODEGEN_ONLY"))
        // Invalid cast
        val df6 = df.where($"valid" > 1).select(try_variant_get(v, col("path"), "binary"))
        checkAnswer(df6, rows(null))
        assert(df6.queryExecution.executedPlan.isInstanceOf[WholeStageCodegenExec] ==
          (codegenMode == "CODEGEN_ONLY"))

        // SQL API
        withTable("t") {
          df.withColumn("v", parse_json(col("json"))).write.saveAsTable("t")
          // variant_get
          checkAnswer(sql("select variant_get(v, path, 'string') from t where valid > 0"),
            rows("1", """{"b":2}""", null, null, null))
          checkError(
            exception = intercept[SparkRuntimeException] {
              sql("select variant_get(v, path, 'string') from t").collect()
            },
            condition = "INVALID_VARIANT_GET_PATH",
            parameters = Map("path" -> "abc", "functionName" -> toSQLId("variant_get")))
          checkError(
            exception = intercept[SparkRuntimeException] {
              sql("select variant_get(v, path, 'binary') from t where valid > 1").collect()
            },
            condition = "INVALID_VARIANT_CAST",
            parameters = Map("value" -> "1", "dataType" -> "\"BINARY\"")
          )
          // try_variant_get
          checkAnswer(sql("select try_variant_get(v, path, 'string') from t where valid > 0"),
            rows("1", """{"b":2}""", null, null, null))
          checkError(
            exception = intercept[SparkRuntimeException] {
              sql("select try_variant_get(v, path, 'string') from t").collect()
            },
            condition = "INVALID_VARIANT_GET_PATH",
            parameters = Map("path" -> "abc", "functionName" -> toSQLId("try_variant_get")))
          checkAnswer(sql("select try_variant_get(v, path, 'binary') from t where valid > 1"),
            rows(null))
        }
      }
    }
  }

  test("round trip tests") {
    val rand = new Random(42)
    val input = Seq.fill(50) {
      if (rand.nextInt(10) == 0) {
        null
      } else {
        val value = new Array[Byte](rand.nextInt(50))
        rand.nextBytes(value)
        val metadata = new Array[Byte](rand.nextInt(50))
        rand.nextBytes(metadata)
        // Generate a valid metadata, otherwise the shredded reader will fail.
        new VariantVal(value, Array[Byte](VERSION, 0, 0) ++ metadata)
      }
    }

    val df = spark.createDataFrame(
      spark.sparkContext.parallelize(input.map(Row(_))),
      StructType.fromDDL("v variant")
    )
    val result = df.collect().map(_.get(0).asInstanceOf[VariantVal])

    def prepareAnswer(values: Seq[VariantVal]): Seq[String] = {
      values.map(v => if (v == null) "null" else v.debugString()).sorted
    }
    assert(prepareAnswer(input) == prepareAnswer(result.toImmutableArraySeq))

    withTempDir { dir =>
      val tempDir = new File(dir, "files").getCanonicalPath
      df.write.parquet(tempDir)
      val readResult = spark.read.parquet(tempDir).collect().map(_.get(0).asInstanceOf[VariantVal])
      assert(prepareAnswer(input) == prepareAnswer(readResult.toImmutableArraySeq))
    }
  }

  test("array of variant") {
    val rand = new Random(42)
    val input = Seq.fill(3) {
      if (rand.nextInt(10) == 0) {
        null
      } else {
        val value = new Array[Byte](rand.nextInt(50))
        rand.nextBytes(value)
        val metadata = new Array[Byte](rand.nextInt(50))
        rand.nextBytes(metadata)
        val numElements = 3 // rand.nextInt(10)
        // Generate a valid metadata, otherwise the shredded reader will fail.
        Seq.fill(numElements)(new VariantVal(value, Array[Byte](VERSION, 0, 0) ++ metadata))
      }
    }

    val df = spark.createDataFrame(
      spark.sparkContext.parallelize(input.map { v =>
        Row.fromSeq(Seq(v))
      }),
      StructType.fromDDL("v array<variant>")
    )

    def prepareAnswer(values: Seq[Row]): Seq[String] = {
      values.map(_.get(0)).map { v =>
        if (v == null) {
          "null"
        } else {
          v.asInstanceOf[mutable.ArraySeq[Any]]
           .map(_.asInstanceOf[VariantVal].debugString()).mkString(",")
        }
      }.sorted
    }

    // Test conversion to UnsafeRow in both codegen and interpreted code paths.
    val codegenModes = Seq(CodegenObjectFactoryMode.NO_CODEGEN.toString,
                           CodegenObjectFactoryMode.FALLBACK.toString)
    codegenModes.foreach { codegen =>
      withTempDir { dir =>
        withSQLConf(SQLConf.CODEGEN_FACTORY_MODE.key -> codegen) {
          val tempDir = new File(dir, "files").getCanonicalPath
          df.write.parquet(tempDir)
          Seq(false, true).foreach { vectorizedReader =>
            withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key ->
                vectorizedReader.toString) {
              val readResult = spark.read.parquet(tempDir).collect().toSeq
              assert(prepareAnswer(df.collect().toSeq) == prepareAnswer(readResult))
            }
          }
        }
      }
    }
  }

  test("write partitioned file") {
    def verifyResult(df: DataFrame): Unit = {
      val result = df.selectExpr("v").collect()
        .map(_.get(0).asInstanceOf[VariantVal].toString)
        .sorted
        .toSeq
      val expected = (1 until 10).map(id => "1" * id)
      assert(result == expected)
    }

    // At this point, JSON parsing logic is not really implemented. We just construct some number
    // inputs that are also valid JSON. This exercises passing VariantVal throughout the system.
    val queryString = "select id, parse_json(repeat('1', id)) as v from range(1, 10)"
    val query = spark.sql(queryString)
    verifyResult(query)

    // Partition by another column should work.
    withTempDir { dir =>
      val tempDir = new File(dir, "files").getCanonicalPath
      query.write.partitionBy("id").parquet(tempDir)
      verifyResult(spark.read.parquet(tempDir))
    }

    // Partitioning by Variant column is not allowed.
    withTempDir { dir =>
      val tempDir = new File(dir, "files").getCanonicalPath
      checkError(
        exception = intercept[AnalysisException] {
          query.write.partitionBy("v").parquet(tempDir)
        },
        condition = "INVALID_PARTITION_COLUMN_DATA_TYPE",
        parameters = Map("type" -> "\"VARIANT\"")
      )
    }

    // Same as above, using saveAsTable
    withTable("t") {
      query.write.partitionBy("id").saveAsTable("t")
      verifyResult(spark.sql("select * from t"))
    }

    withTable("t") {
      checkError(
        exception = intercept[AnalysisException] {
          query.write.partitionBy("v").saveAsTable("t")
        },
        condition = "INVALID_PARTITION_COLUMN_DATA_TYPE",
        parameters = Map("type" -> "\"VARIANT\"")
      )
    }

    // Same as above, using SQL CTAS
    withTable("t") {
      spark.sql(s"CREATE TABLE t USING PARQUET PARTITIONED BY (id) AS $queryString")
      verifyResult(spark.sql("select * from t"))
    }

    withTable("t") {
      checkError(
        exception = intercept[AnalysisException] {
          spark.sql(s"CREATE TABLE t USING PARQUET PARTITIONED BY (v) AS $queryString")
        },
        condition = "INVALID_PARTITION_COLUMN_DATA_TYPE",
        parameters = Map("type" -> "\"VARIANT\"")
      )
    }
  }

  test("SPARK-47546: invalid variant binary") {
    // Write a struct-of-binary that looks like a Variant, but with minor variations that may make
    // it invalid to read.
    // Test cases:
    // 1) A binary that is almost correct, but contains an extra field "paths"
    // 2,3) A binary with incorrect field names
    // 4) Incorrect data typea
    // 5,6) Nullable value or metdata

    // Binary value of empty metadata
    val m = "X'010000'"
    // Binary value of a literal "false"
    val v = "X'8'"
    val cases = Seq(
      (s"named_struct('value', $v)",
        "INVALID_VARIANT_FROM_PARQUET.WRONG_NUM_FIELDS", Map.empty[String, String]),
      (s"named_struct('value', $v, 'metadata', $m, 'paths', $v)",
        "INVALID_VARIANT_FROM_PARQUET.WRONG_NUM_FIELDS", Map.empty[String, String]),
      (s"named_struct('value', $v, 'dictionary', $m)",
        "INVALID_VARIANT_FROM_PARQUET.MISSING_FIELD", Map("field" -> "metadata")),
      (s"named_struct('val', $v, 'metadata', $m)",
        "INVALID_VARIANT_FROM_PARQUET.MISSING_FIELD", Map("field" -> "value")),
      (s"named_struct('value', 8, 'metadata', $m)",
        "INVALID_VARIANT_FROM_PARQUET.NULLABLE_OR_NOT_BINARY_FIELD", Map("field" -> "value")),
      (s"named_struct('value', cast(null as binary), 'metadata', $m)",
        "INVALID_VARIANT_FROM_PARQUET.NULLABLE_OR_NOT_BINARY_FIELD", Map("field" -> "value")),
      (s"named_struct('value', $v, 'metadata', cast(null as binary))",
        "INVALID_VARIANT_FROM_PARQUET.NULLABLE_OR_NOT_BINARY_FIELD", Map("field" -> "metadata"))
    )
    cases.foreach { case (structDef, condition, parameters) =>
      Seq(false, true).foreach { vectorizedReader =>
        withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> vectorizedReader.toString) {
          withTempDir { dir =>
            val file = new File(dir, "dir").getCanonicalPath
            val df = spark.sql(s"select $structDef as v from range(10)")
            df.write.parquet(file)
            val schema = StructType(Seq(StructField("v", VariantType)))
            val result = spark.read.schema(schema).parquet(file).selectExpr("to_json(v)")
            val e = withSQLConf(SQLConf.VARIANT_ALLOW_READING_SHREDDED.key -> "false") {
              intercept[org.apache.spark.SparkException](result.collect())
            }
            checkError(
              exception = e.getCause.asInstanceOf[AnalysisException],
              condition = condition,
              parameters = parameters
            )
          }
        }
      }
    }
  }

  test("SPARK-47546: valid variant binary") {
    // Test valid struct-of-binary formats. We don't expect anybody to construct a Variant in this
    // way, but it lets us validate slight variations that could be produced by a different writer.

    // Binary value of empty metadata
    val m = "X'010000'"
    // Binary value of a literal "false"
    val v = "X'8'"
    val cases = Seq(
        s"named_struct('value', $v, 'metadata', $m)",
        s"named_struct('metadata', $m, 'value', $v)"
    )
    cases.foreach { structDef =>
      withTempDir { dir =>
        val file = new File(dir, "dir").getCanonicalPath
        val df = spark.sql(s"select $structDef as v from range(10)")
        df.write.parquet(file)
        val schema = StructType(Seq(StructField("v", VariantType)))
        val result = spark.read.schema(schema).parquet(file).selectExpr("to_json(v)")
        checkAnswer(result, Seq.fill(10)(Row("false")))
      }
    }
  }

  test("json option constraints") {
    withTempDir { dir =>
      val file = new File(dir, "file.json")
      Files.write(file.toPath, "0".getBytes(StandardCharsets.UTF_8))

      // Ensure that we get an error when setting the singleVariantColumn JSON option while also
      // specifying a schema.
      checkError(
        exception = intercept[AnalysisException] {
          spark.read.format("json").option("singleVariantColumn", "var").schema("var variant")
        },
        condition = "INVALID_SINGLE_VARIANT_COLUMN",
        parameters = Map.empty
      )
      checkError(
        exception = intercept[AnalysisException] {
          spark.read.format("json").option("singleVariantColumn", "another_name")
            .schema("var variant").json(file.getAbsolutePath).collect()
        },
        condition = "INVALID_SINGLE_VARIANT_COLUMN",
        parameters = Map.empty
      )
    }
  }

  test("json scan with map schema") {
    withTempDir { dir =>
      val file = new File(dir, "file.json")
      val content = Seq(
        "true",
        """{"v": null}""",
        """{"v": {"a": 1, "b": null}}"""
      ).mkString("\n").getBytes(StandardCharsets.UTF_8)
      Files.write(file.toPath, content)
      checkAnswer(
        spark.read.format("json").schema("v map<string, variant>")
          .load(file.getAbsolutePath)
          .selectExpr("to_json(v)"),
        Seq(Row(null), Row(null), Row("""{"a":1,"b":null}"""))
      )
    }
  }

  test("group/order/join variant are disabled") {
    checkError(
      exception = intercept[AnalysisException] {
        spark.sql("select parse_json('') group by 1")
      },
      condition = "GROUP_EXPRESSION_TYPE_IS_NOT_ORDERABLE",
      parameters = Map("sqlExpr" -> "\"parse_json()\"", "dataType" -> "\"VARIANT\""),
      context = ExpectedContext(fragment = "parse_json('')", start = 7, stop = 20)
    )

    checkError(
      exception = intercept[AnalysisException] {
        spark.sql("select parse_json('') v order by 1")
      },
      condition = "DATATYPE_MISMATCH.INVALID_ORDERING_TYPE",
      parameters = Map(
        "functionName" -> "`sortorder`",
        "dataType" -> "\"VARIANT\"",
        "sqlExpr" -> "\"v ASC NULLS FIRST\""),
      context = ExpectedContext(fragment = "order by 1", start = 24, stop = 33)
    )

    checkError(
      exception = intercept[AnalysisException] {
        spark.sql("select parse_json('') v sort by 1")
      },
      condition = "DATATYPE_MISMATCH.INVALID_ORDERING_TYPE",
      parameters = Map(
        "functionName" -> "`sortorder`",
        "dataType" -> "\"VARIANT\"",
        "sqlExpr" -> "\"v ASC NULLS FIRST\""),
      context = ExpectedContext(fragment = "sort by 1", start = 24, stop = 32)
    )

    checkError(
      exception = intercept[AnalysisException] {
        spark.sql("with t as (select 1 as a, parse_json('') as v) " +
          "select rank() over (partition by a order by v) from t")
      },
      condition = "DATATYPE_MISMATCH.INVALID_ORDERING_TYPE",
      parameters = Map(
        "functionName" -> "`sortorder`",
        "dataType" -> "\"VARIANT\"",
        "sqlExpr" -> "\"v ASC NULLS FIRST\""),
      context = ExpectedContext(fragment = "v", start = 91, stop = 91)
    )

    checkError(
      exception = intercept[AnalysisException] {
        spark.sql("with t as (select parse_json('') as v) " +
          "select t1.v from t as t1 join t as t2 on t1.v = t2.v")
      },
      condition = "DATATYPE_MISMATCH.INVALID_ORDERING_TYPE",
      parameters = Map(
        "functionName" -> "`=`",
        "dataType" -> "\"VARIANT\"",
        "sqlExpr" -> "\"(v = v)\""),
      context = ExpectedContext(fragment = "t1.v = t2.v", start = 80, stop = 90)
    )
  }

  test("variant_explode") {
    def check(input: String, expected: Seq[Row]): Unit = {
      withView("v") {
        Seq(input).toDF("json").createOrReplaceTempView("v")
        checkAnswer(sql("select pos, key, to_json(value) from v, " +
          "lateral variant_explode(parse_json(json))"), expected)
        val expectedOuter = if (expected.isEmpty) Seq(Row(null, null, null)) else expected
        checkAnswer(sql("select pos, key, to_json(value) from v, " +
          "lateral variant_explode_outer(parse_json(json))"), expectedOuter)
      }
    }

    Seq("true", "false").foreach { codegenEnabled =>
      withSQLConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> codegenEnabled) {
        check(null, Nil)
        check("1", Nil)
        check("null", Nil)
        check("""{"a": [1, 2, 3], "b": true}""", Seq(Row(0, "a", "[1,2,3]"), Row(1, "b", "true")))
        check("""[null, "hello", {}]""",
          Seq(Row(0, null, "null"), Row(1, null, "\"hello\""), Row(2, null, "{}")))
      }
    }
  }

  test("SPARK-48067: default variant columns works") {
    withTable("t") {
      sql("""create table t(
        v1 variant default null,
        v2 variant default parse_json(null),
        v3 variant default cast(null as variant),
        v4 variant default parse_json('1'),
        v5 variant default parse_json('1'),
        v6 variant default parse_json('{\"k\": \"v\"}'),
        v7 variant default cast(5 as int),
        v8 variant default cast('hello' as string),
        v9 variant default parse_json(to_json(parse_json('{\"k\": \"v\"}')))
      ) using parquet""")
      sql("""insert into t values(DEFAULT, DEFAULT, DEFAULT, DEFAULT, DEFAULT, DEFAULT, DEFAULT,
        DEFAULT, DEFAULT)""")

      val expected = sql("""select
        cast(null as variant) as v1,
        parse_json(null) as v2,
        cast(null as variant) as v3,
        parse_json('1') as v4,
        parse_json('1') as v5,
        parse_json('{\"k\": \"v\"}') as v6,
        cast(cast(5 as int) as variant) as v7,
        cast('hello' as variant) as v8,
        parse_json(to_json(parse_json('{\"k\": \"v\"}'))) as v9
      """)
      val actual = sql("select * from t")
      checkAnswer(actual, expected.collect())
    }
  }

  Seq(
    (
      "basic int parse json",
      VariantExpressionEvalUtils.parseJson(UTF8String.fromString("1")),
      VariantType
    ),
    (
      "basic json parse json",
      VariantExpressionEvalUtils.parseJson(UTF8String.fromString("{\"k\": \"v\"}")),
      VariantType
    ),
    (
      "basic null parse json",
      VariantExpressionEvalUtils.parseJson(UTF8String.fromString("null")),
      VariantType
    ),
    (
      "basic null",
      null,
      VariantType
    ),
    (
      "basic array",
      new GenericArrayData(Array[Int](1, 2, 3, 4, 5)),
      new ArrayType(IntegerType, false)
    ),
    (
      "basic string",
      UTF8String.fromString("literal string"),
      StringType
    ),
    (
      "basic timestamp",
      0L,
      TimestampType
    ),
    (
      "basic int",
      0,
      IntegerType
    ),
    (
      "basic struct",
      Literal.default(new StructType().add("col0", StringType)).eval(),
      new StructType().add("col0", StringType)
    ),
    (
      "complex struct with child variant",
      Literal.default(new StructType()
        .add("col0", StringType)
        .add("col1", new StructType().add("col0", VariantType))
        .add("col2", VariantType)
        .add("col3", new ArrayType(VariantType, false))
      ).eval(),
      new StructType()
        .add("col0", StringType)
        .add("col1", new StructType().add("col0", VariantType))
        .add("col2", VariantType)
        .add("col3", new ArrayType(VariantType, false))
    ),
    (
      "basic array with null",
      new GenericArrayData(Array[Any](1, 2, null)),
      new ArrayType(IntegerType, true)
    ),
    (
      "basic map with null",
      new ArrayBasedMapData(
        new GenericArrayData(Array[Any](UTF8String.fromString("k1"), UTF8String.fromString("k2"))),
        new GenericArrayData(Array[Any](1, null))
      ),
      new MapType(StringType, IntegerType, true)
    )
  ).foreach { case (testName, value, dt) =>
    test(s"SPARK-48067: Variant literal `sql` correctly recreates the variant - $testName") {
      val l = Literal.create(
        VariantExpressionEvalUtils.castToVariant(value, dt.asInstanceOf[DataType]), VariantType)
      val jsonString = l.eval().asInstanceOf[VariantVal]
        .toJson(DateTimeUtils.getZoneId(SQLConf.get.sessionLocalTimeZone))
      val expectedSql = s"PARSE_JSON('$jsonString')"
      assert(l.sql == expectedSql)
      val valueFromLiteralSql =
        spark.sql(s"select ${l.sql}").collect()(0).getAs[VariantVal](0)

      // Cast the variants to their specified type to compare for logical equality.
      // Currently, variant equality naively compares its value and metadata binaries. However,
      // variant equality is more complex than this.
      val castVariantExpr = VariantGet(
        l,
        Literal.create(UTF8String.fromString("$"), StringType),
        dt,
        true,
        Some(DateTimeUtils.getZoneId(SQLConf.get.sessionLocalTimeZone).toString())
      )
      val sqlVariantExpr = VariantGet(
        Literal.create(valueFromLiteralSql, VariantType),
        Literal.create(UTF8String.fromString("$"), StringType),
        dt,
        true,
        Some(DateTimeUtils.getZoneId(SQLConf.get.sessionLocalTimeZone).toString())
      )
      checkEvaluation(castVariantExpr, sqlVariantExpr.eval())
    }
  }

  test("variant in a cached row-based df") {
    val query = """select
      parse_json(format_string('{\"a\": %s}', id)) v,
      cast(null as variant) as null_v,
      case when id % 2 = 0 then parse_json(cast(id as string)) else null end as some_null
    from range(0, 10)"""
    val df = spark.sql(query)
    df.cache()

    val expected = spark.sql(query)
    checkAnswer(df, expected.collect())
  }

  test("variant with many keys in a cached row-based df") {
    // The initial size of the buffer backing a cached dataframe column is 128KB.
    // See `ColumnBuilder`.
    val numKeys = 128 * 1024
    var keyIterator = (0 until numKeys).iterator
    val entries = Array.fill(numKeys)(s"""\"${keyIterator.next()}\": \"test\"""")
    val jsonStr = s"{${entries.mkString(", ")}}"
    val query = s"""select parse_json('${jsonStr}') v from range(0, 10)"""
    val df = spark.sql(query)
    df.cache()

    val expected = spark.sql(query)
    checkAnswer(df, expected.collect())
  }

  test("struct of variant in a cached row-based df") {
    val query = """select named_struct(
      'v', parse_json(format_string('{\"a\": %s}', id)),
      'null_v', cast(null as variant),
      'some_null', case when id % 2 = 0 then parse_json(cast(id as string)) else null end
    ) v
    from range(0, 10)"""
    val df = spark.sql(query)
    df.cache()

    val expected = spark.sql(query)
    checkAnswer(df, expected.collect())
  }

  test("array of variant in a cached row-based df") {
    val query = """select array(
      parse_json(cast(id as string)),
      parse_json(format_string('{\"a\": %s}', id)),
      null,
      case when id % 2 = 0 then parse_json(cast(id as string)) else null end) v
    from range(0, 10)"""
    val df = spark.sql(query)
    df.cache()

    val expected = spark.sql(query)
    checkAnswer(df, expected.collect())
  }

  test("array variant with many keys in a cached row-based df") {
    // The initial size of the buffer backing a cached dataframe column is 128KB.
    // See `ColumnBuilder`.
    val numKeys = 128 * 1024
    var keyIterator = (0 until numKeys).iterator
    val entries = Array.fill(numKeys)(s"""\"${keyIterator.next()}\": \"test\"""")
    val jsonStr = s"{${entries.mkString(", ")}}"
    val query = s"""select array(parse_json('${jsonStr}')) v from range(0, 10)"""
    val df = spark.sql(query)
    df.cache()

    val expected = spark.sql(query)
    checkAnswer(df, expected.collect())
  }

  test("map of variant in a cached row-based df") {
    val query = """select map(
      'v', parse_json(format_string('{\"a\": %s}', id)),
      'null_v', cast(null as variant),
      'some_null', case when id % 2 = 0 then parse_json(cast(id as string)) else null end
    ) v
    from range(0, 10)"""
    val df = spark.sql(query)
    df.cache()

    val expected = spark.sql(query)
    checkAnswer(df, expected.collect())
  }

  test("variant in a cached column-based df") {
    withTable("t") {
      val query = """select named_struct(
        'v', parse_json(format_string('{\"a\": %s}', id)),
        'null_v', cast(null as variant),
        'some_null', case when id % 2 = 0 then parse_json(cast(id as string)) else null end
      ) v
      from range(0, 10)"""
      spark.sql(query).write.format("parquet").mode("overwrite").saveAsTable("t")
      val df = spark.sql("select * from t")
      df.cache()

      val expected = spark.sql(query)
      checkAnswer(df, expected.collect())
    }
  }

  test("variant with many keys in a cached column-based df") {
    withTable("t") {
       // The initial size of the buffer backing a cached dataframe column is 128KB.
       // See `ColumnBuilder`.
      val numKeys = 128 * 1024
      var keyIterator = (0 until numKeys).iterator
      val entries = Array.fill(numKeys)(s"""\"${keyIterator.next()}\": \"test\"""")
      val jsonStr = s"{${entries.mkString(", ")}}"
      val query = s"""select named_struct(
        'v', parse_json('$jsonStr'),
        'null_v', cast(null as variant),
        'some_null', case when id % 2 = 0 then parse_json(cast(id as string)) else null end
      ) v
      from range(0, 10)"""
      spark.sql(query).write.format("parquet").mode("overwrite").saveAsTable("t")
      val df = spark.sql("select * from t")
      df.cache()

      val expected = spark.sql(query)
      checkAnswer(df, expected.collect())
    }
  }

  test("variant_get size") {
    val largeKey = "x" * 1000
    val df = Seq(s"""{ "$largeKey": {"a" : 1 },
                       "b" : 2,
                       "c": [1,2,3,{"$largeKey": 4}] }""").toDF("json")
            .selectExpr("parse_json(json) as v")

    // Check Variant with approximate bounds to avoid flakiness if we make minor format changes.
    def checkSize(v: VariantVal, minMetadata: Long, maxMetadata: Long,
                  minValue: Long, maxValue: Long): Unit = {
      val mSize = v.getMetadata.length
      assert(mSize >= minMetadata)
      assert(mSize <= maxMetadata)
      val vSize = v.getValue.length
      assert(vSize >= minValue)
      assert(vSize <= maxValue)
    }

    // The full Variant has large metadata (but only one copy of `largeKey`).
    checkSize(df.selectExpr("variant_get(v, '$', 'variant')").collect()(0)
        .getAs[VariantVal](0), 1000, 1050, 20, 40)
    // Extracting Variant or a nested type containing Variant should strip out the large metadata.
    checkSize(df.selectExpr("variant_get(v, '$.b', 'variant')").collect()(0)
        .getAs[VariantVal](0), 2, 4, 2, 4)
    // Behavior is the same without an explicit cast to Variant.
    checkSize(df.selectExpr("variant_get(v, '$.b', 'variant')").collect()(0)
        .getAs[VariantVal](0), 2, 4, 2, 4)
    checkSize(df.selectExpr(s"variant_get(v, '$$.$largeKey', 'variant')").collect()(0)
        .getAs[VariantVal](0), 5, 10, 5, 10)
    checkSize(df.selectExpr(s"variant_get(v, '$$.$largeKey', 'struct<a:variant>')")
        .collect()(0).getStruct(0).getAs[VariantVal](0), 2, 4, 2, 4)
    // Only the array element that contains `largeKey` should be large.
    checkSize(df.selectExpr("variant_get(v, '$.c', 'array<variant>')").collect()(0)
        .getSeq[VariantVal](0)(0), 2, 4, 2, 4)
    checkSize(df.selectExpr("variant_get(v, '$.c', 'array<variant>')").collect()(0)
        .getSeq[VariantVal](0)(3), 1000, 1020, 5, 10)
    // Cast to a nested type containing Variant should also remove metadata.
    val structResult = df.selectExpr(s"cast(v as struct<$largeKey:variant,b:variant>)").collect()(0)
        .getStruct(0)
    checkSize(structResult.getAs[VariantVal](0), 5, 10, 5, 10)
    checkSize(structResult.getAs[VariantVal](1), 2, 4, 2, 4)
  }

  test("schema_of_variant(object)") {
    for (expr <- Seq("schema_of_variant", "schema_of_variant_agg")) {
      val q = s"""select $expr(parse_json('{"STRUCT": {"!special!": true}}'))"""
      checkAnswer(sql(q), Row("""OBJECT<STRUCT: OBJECT<`!special!`: BOOLEAN>>"""))
    }
  }
}
