Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/19060#discussion_r137044001
--- 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 --
I do not know why we did not turn it on by default. To me, to turn it on,
we need to improve the coverage.
So far, the coverage of `OrcFilterSuite ` and `ParquetFilterSuite ` are not
good. They only have very basic checks. Could you improve them?
For example, adding the boundary values for these predicate pushdown in
both sides? We need to ensure whether the predicates are pushed down, executed
in the underlying data sources, and the filters work properly (value
comparison).
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