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[SPARK-23426][SQL] Use hive ORC impl and disable PPD for Spark 2.3.0 #20610

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[SPARK-23426][SQL] Use hive ORC impl and disable PPD for Spark 2.3.0 #20610

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2d74b20
Use 'hive' for ORC
dongjoon-hyun Feb 14, 2018
93e6c7d
Disable PPD back.
dongjoon-hyun Feb 14, 2018
7ff4ccf
Add ORC section.
dongjoon-hyun Feb 14, 2018
46c8697
fix test case.
dongjoon-hyun Feb 14, 2018
2769633
Address comment.
dongjoon-hyun Feb 15, 2018
183ec21
Add `also`.
dongjoon-hyun Feb 15, 2018
19b50b1
Remove.
dongjoon-hyun Feb 15, 2018
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52 changes: 23 additions & 29 deletions docs/sql-programming-guide.md
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Expand Up @@ -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>
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@dongjoon-hyun dongjoon-hyun Feb 14, 2018

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@gatorsmile . Now, this becomes a section.

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@gatorsmile gatorsmile Feb 14, 2018

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Thanks!


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@viirya viirya Feb 15, 2018

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The description of spark.sql.orc.filterPushdown is disappeared?

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@dongjoon-hyun dongjoon-hyun Feb 15, 2018

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Yes. It's disabled back. @viirya

## JSON Datasets
<div class="codetabs">

Expand Down Expand Up @@ -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.
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6 changes: 3 additions & 3 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
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Expand Up @@ -399,11 +399,11 @@ 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")
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@gatorsmile gatorsmile Feb 14, 2018

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We also need to disable the ORC pushdown, because the ORC reader of Hive 1.2.1 has a few bugs.

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@dongjoon-hyun dongjoon-hyun Feb 14, 2018 •
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BTW, we don't have a test case for that, do we? Actually, I want to have a test case for that.


val ORC_VECTORIZED_READER_ENABLED = buildConf("spark.sql.orc.enableVectorizedReader")
.doc("Enables vectorized orc decoding.")
Expand All @@ -426,7 +426,7 @@ 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 " +
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17 changes: 16 additions & 1 deletion sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala
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Expand Up @@ -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"

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13 changes: 13 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSinkSuite.scala
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Expand Up @@ -33,6 +33,19 @@ import org.apache.spark.util.Utils
class FileStreamSinkSuite extends StreamTest {
import testImplicits._

override def beforeAll(): Unit = {
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@cloud-fan cloud-fan Feb 15, 2018 •
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nit: a simpler way to fix this

override val conf = super.conf.copy(SQLConf.ORC_IMPLEMENTATION -> "native")

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@dongjoon-hyun dongjoon-hyun Feb 15, 2018

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Hi, @cloud-fan .
I tested it, but that doesn't work in this FileStreamSinkSuite.

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()
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13 changes: 13 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala
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Expand Up @@ -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") {
Expand Down