[SPARK-23426][SQL] Use hive ORC impl and disable PPD for Spark 2.3.0

## What changes were proposed in this pull request?

To prevent any regressions, this PR changes ORC implementation to `hive` by default like Spark 2.2.X.
Users can enable `native` ORC. Also, ORC PPD is also restored to `false` like Spark 2.2.X.

![orc_section](https://user-images.githubusercontent.com/9700541/36221575-57a1d702-1173-11e8-89fe-dca5842f4ca7.png)

## How was this patch tested?

Pass all test cases.

Author: Dongjoon Hyun <[email protected]>

Closes #20610 from dongjoon-hyun/SPARK-ORC-DISABLE.

(cherry picked from commit 2f0498d)
Signed-off-by: gatorsmile <[email protected]>

Author: dongjoon-hyun

docs/sql-programming-guide.md

@@ -1004,6 +1004,29 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
 </tr>
 </table>
 
+## ORC Files
+
+Since Spark 2.3, Spark supports a vectorized ORC reader with a new ORC file format for ORC files.
+To do that, the following configurations are newly added. The vectorized reader is used for the
+native ORC tables (e.g., the ones created using the clause `USING ORC`) when `spark.sql.orc.impl`
+is set to `native` and `spark.sql.orc.enableVectorizedReader` is set to `true`. For the Hive ORC
+serde tables (e.g., the ones created using the clause `USING HIVE OPTIONS (fileFormat 'ORC')`),
+the vectorized reader is used when `spark.sql.hive.convertMetastoreOrc` is also set to `true`.
+
+<table class="table">
+  <tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th></tr>
+  <tr>
+    <td><code>spark.sql.orc.impl</code></td>
+    <td><code>hive</code></td>
+    <td>The name of ORC implementation. It can be one of <code>native</code> and <code>hive</code>. <code>native</code> means the native ORC support that is built on Apache ORC 1.4.1. `hive` means the ORC library in Hive 1.2.1.</td>
+  </tr>
+  <tr>
+    <td><code>spark.sql.orc.enableVectorizedReader</code></td>
+    <td><code>true</code></td>
+    <td>Enables vectorized orc decoding in <code>native</code> implementation. If <code>false</code>, a new non-vectorized ORC reader is used in <code>native</code> implementation. For <code>hive</code> implementation, this is ignored.</td>
+  </tr>
+</table>
+
 ## JSON Datasets
 <div class="codetabs">
 
@@ -1776,35 +1799,6 @@ working with timestamps in `pandas_udf`s to get the best performance, see
 
 ## Upgrading From Spark SQL 2.2 to 2.3
 
-  - Since Spark 2.3, Spark supports a vectorized ORC reader with a new ORC file format for ORC files. To do that, the following configurations are newly added or change their default values. The vectorized reader is used for the native ORC tables (e.g., the ones created using the clause `USING ORC`) when `spark.sql.orc.impl` is set to `native` and `spark.sql.orc.enableVectorizedReader` is set to `true`. For the Hive ORC serde table (e.g., the ones created using the clause `USING HIVE OPTIONS (fileFormat 'ORC')`), the vectorized reader is used when `spark.sql.hive.convertMetastoreOrc` is set to `true`.
-
-    - New configurations
-
-    <table class="table">
-      <tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th></tr>
-      <tr>
-        <td><code>spark.sql.orc.impl</code></td>
-        <td><code>native</code></td>
-        <td>The name of ORC implementation. It can be one of <code>native</code> and <code>hive</code>. <code>native</code> means the native ORC support that is built on Apache ORC 1.4.1. `hive` means the ORC library in Hive 1.2.1 which is used prior to Spark 2.3.</td>
-      </tr>
-      <tr>
-        <td><code>spark.sql.orc.enableVectorizedReader</code></td>
-        <td><code>true</code></td>
-        <td>Enables vectorized orc decoding in <code>native</code> implementation. If <code>false</code>, a new non-vectorized ORC reader is used in <code>native</code> implementation. For <code>hive</code> implementation, this is ignored.</td>
-      </tr>
-    </table>
-
-    - Changed configurations
-
-    <table class="table">
-      <tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th></tr>
-      <tr>
-        <td><code>spark.sql.orc.filterPushdown</code></td>
-        <td><code>true</code></td>
-        <td>Enables filter pushdown for ORC files. It is <code>false</code> by default prior to Spark 2.3.</td>
-      </tr>
-    </table>
-
   - Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column (named `_corrupt_record` by default). For example, `spark.read.schema(schema).json(file).filter($"_corrupt_record".isNotNull).count()` and `spark.read.schema(schema).json(file).select("_corrupt_record").show()`. Instead, you can cache or save the parsed results and then send the same query. For example, `val df = spark.read.schema(schema).json(file).cache()` and then `df.filter($"_corrupt_record".isNotNull).count()`.
   - The `percentile_approx` function previously accepted numeric type input and output double type results. Now it supports date type, timestamp type and numeric types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles.
   - Since Spark 2.3, the Join/Filter's deterministic predicates that are after the first non-deterministic predicates are also pushed down/through the child operators, if possible. In prior Spark versions, these filters are not eligible for predicate pushdown.

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -393,11 +393,11 @@ object SQLConf {
 
   val ORC_IMPLEMENTATION = buildConf("spark.sql.orc.impl")
     .doc("When native, use the native version of ORC support instead of the ORC library in Hive " +
-      "1.2.1. It is 'hive' by default prior to Spark 2.3.")
+      "1.2.1. It is 'hive' by default.")
     .internal()
     .stringConf
     .checkValues(Set("hive", "native"))
-    .createWithDefault("native")
+    .createWithDefault("hive")
 
   val ORC_VECTORIZED_READER_ENABLED = buildConf("spark.sql.orc.enableVectorizedReader")
     .doc("Enables vectorized orc decoding.")
@@ -414,7 +414,7 @@ object SQLConf {
   val ORC_FILTER_PUSHDOWN_ENABLED = buildConf("spark.sql.orc.filterPushdown")
     .doc("When true, enable filter pushdown for ORC files.")
     .booleanConf
-    .createWithDefault(true)
+    .createWithDefault(false)
 
   val HIVE_VERIFY_PARTITION_PATH = buildConf("spark.sql.hive.verifyPartitionPath")
     .doc("When true, check all the partition paths under the table\'s root directory " +

sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala

@@ -20,14 +20,29 @@ package org.apache.spark.sql
 import java.io.FileNotFoundException
 
 import org.apache.hadoop.fs.Path
+import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.SparkException
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.test.SharedSQLContext
 
-class FileBasedDataSourceSuite extends QueryTest with SharedSQLContext {
+
+class FileBasedDataSourceSuite extends QueryTest with SharedSQLContext with BeforeAndAfterAll {
   import testImplicits._
 
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    spark.sessionState.conf.setConf(SQLConf.ORC_IMPLEMENTATION, "native")
+  }
+
+  override def afterAll(): Unit = {
+    try {
+      spark.sessionState.conf.unsetConf(SQLConf.ORC_IMPLEMENTATION)
+    } finally {
+      super.afterAll()
+    }
+  }
+
   private val allFileBasedDataSources = Seq("orc", "parquet", "csv", "json", "text")
   private val nameWithSpecialChars = "sp&cial%c hars"
 

sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSinkSuite.scala

@@ -33,6 +33,19 @@ import org.apache.spark.util.Utils
 class FileStreamSinkSuite extends StreamTest {
   import testImplicits._
 
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    spark.sessionState.conf.setConf(SQLConf.ORC_IMPLEMENTATION, "native")
+  }
+
+  override def afterAll(): Unit = {
+    try {
+      spark.sessionState.conf.unsetConf(SQLConf.ORC_IMPLEMENTATION)
+    } finally {
+      super.afterAll()
+    }
+  }
+
   test("unpartitioned writing and batch reading") {
     val inputData = MemoryStream[Int]
     val df = inputData.toDF()

sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala

@@ -207,6 +207,19 @@ class FileStreamSourceSuite extends FileStreamSourceTest {
       .collect { case s @ StreamingRelation(dataSource, _, _) => s.schema }.head
   }
 
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    spark.sessionState.conf.setConf(SQLConf.ORC_IMPLEMENTATION, "native")
+  }
+
+  override def afterAll(): Unit = {
+    try {
+      spark.sessionState.conf.unsetConf(SQLConf.ORC_IMPLEMENTATION)
+    } finally {
+      super.afterAll()
+    }
+  }
+
   // ============= Basic parameter exists tests ================
 
   test("FileStreamSource schema: no path") {