Github user gatorsmile commented on a diff in the pull request:

    https://github.com/apache/spark/pull/19060#discussion_r136912257
  
    --- Diff: 
sql/hive/src/test/scala/org/apache/spark/sql/sources/DataSourceSuite.scala ---
    @@ -0,0 +1,125 @@
    +/*
    + * 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.sources
    +
    +import java.sql.{Date, Timestamp}
    +
    +import org.apache.orc.OrcConf
    +
    +import org.apache.spark.sql.{Dataset, QueryTest, Row}
    +import org.apache.spark.sql.hive.test.TestHiveSingleton
    +import org.apache.spark.sql.internal.SQLConf
    +import org.apache.spark.sql.test.SQLTestUtils
    +
    +/**
    + * Data Source qualification as Apache Spark Data Sources.
    + * - Apache Spark Data Type Value Limits: CSV, JSON, ORC, Parquet
    + * - Predicate Push Down: ORC
    + */
    +class DataSourceSuite
    +  extends QueryTest
    +  with SQLTestUtils
    +  with TestHiveSingleton {
    +
    +  import testImplicits._
    +
    +  var df: Dataset[Row] = _
    +
    +  override def beforeAll(): Unit = {
    +    super.beforeAll()
    +    spark.conf.set("spark.sql.session.timeZone", "GMT")
    +
    +    df = ((
    +      false,
    +      true,
    +      Byte.MinValue,
    +      Byte.MaxValue,
    +      Short.MinValue,
    +      Short.MaxValue,
    +      Int.MinValue,
    +      Int.MaxValue,
    +      Long.MinValue,
    +      Long.MaxValue,
    +      Float.MinValue,
    +      Float.MaxValue,
    +      Double.MinValue,
    +      Double.MaxValue,
    +      Date.valueOf("0001-01-01"),
    +      Date.valueOf("9999-12-31"),
    +      new Timestamp(-62135769600000L), // 0001-01-01 00:00:00.000
    +      new Timestamp(253402300799999L)  // 9999-12-31 23:59:59.999
    +    ) :: Nil).toDF()
    +  }
    +
    +  override def afterAll(): Unit = {
    +    try {
    +      spark.conf.unset("spark.sql.session.timeZone")
    +    } finally {
    +      super.afterAll()
    +    }
    +  }
    +
    +  Seq("parquet", "orc", "json", "csv").foreach { dataSource =>
    +    test(s"$dataSource - data type value limit") {
    +      withTempPath { dir =>
    +        df.write.format(dataSource).save(dir.getCanonicalPath)
    +
    +        // Use the same schema for saving/loading
    +        checkAnswer(
    +          
spark.read.format(dataSource).schema(df.schema).load(dir.getCanonicalPath),
    +          df)
    +
    +        // Use schema inference, but skip text-based format due to its 
limitation
    +        if (Seq("parquet", "orc").contains(dataSource)) {
    +          withTable("tab1") {
    +            sql(s"CREATE TABLE tab1 USING $dataSource LOCATION 
'${dir.toURI}'")
    +            checkAnswer(sql(s"SELECT ${df.schema.fieldNames.mkString(",")} 
FROM tab1"), df)
    +          }
    +        }
    +      }
    +    }
    +  }
    +
    +  Seq("orc").foreach { dataSource =>
    +    test(s"$dataSource - predicate push down") {
    +      withSQLConf(
    +        SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key -> "true",
    +        SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") {
    +        withTempPath { dir =>
    +          // write 4000 rows with the integer and the string in a single 
orc file with stride 1000
    +          spark
    +            .range(4000)
    +            .map(i => (i, s"$i"))
    +            .toDF("i", "s")
    +            .repartition(1)
    +            .write
    +            .option(OrcConf.ROW_INDEX_STRIDE.getAttribute, 1000)
    +            // TODO: Add Parquet option, too.
    +            .format(dataSource)
    +            .save(dir.getCanonicalPath)
    +
    +          val df = spark.read.format(dataSource).load(dir.getCanonicalPath)
    +            .where(s"i BETWEEN 1500 AND 1999")
    --- End diff --
    
    This has been tested in `OrcFilterSuite` and `ParquetFilterSuite`, right? 
What is the goal of this test case?


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