spark git commit: [SPARK-23313][DOC] Add a migration guide for ORC

2018-02-12 Thread lixiao 
Repository: spark
Updated Branches:
  refs/heads/branch-2.3 9632c461e -> 2b80571e2


[SPARK-23313][DOC] Add a migration guide for ORC

## What changes were proposed in this pull request?

This PR adds a migration guide documentation for ORC.

![orc-guide](https://user-images.githubusercontent.com/9700541/36123859-ec165cae-1002-11e8-90b7-7313be7a81a5.png)

## How was this patch tested?

N/A.

Author: Dongjoon Hyun 

Closes #20484 from dongjoon-hyun/SPARK-23313.

(cherry picked from commit 6cb59708c70c03696c772fbb5d158eed57fe67d4)
Signed-off-by: gatorsmile 


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2b80571e
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2b80571e
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2b80571e

Branch: refs/heads/branch-2.3
Commit: 2b80571e215d56d15c59f0fc5db053569a79efae
Parents: 9632c46
Author: Dongjoon Hyun 
Authored: Mon Feb 12 15:26:37 2018 -0800
Committer: gatorsmile 
Committed: Mon Feb 12 15:27:00 2018 -0800

--
 docs/sql-programming-guide.md | 29 +
 1 file changed, 29 insertions(+)
--


http://git-wip-us.apache.org/repos/asf/spark/blob/2b80571e/docs/sql-programming-guide.md
--
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index eab4030..dcef6e5 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1776,6 +1776,35 @@ 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
+
+
+  Property 
NameDefaultMeaning
+  
+spark.sql.orc.impl
+native
+The name of ORC implementation. It can be one of 
native and hive. native 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.
+  
+  
+spark.sql.orc.enableVectorizedReader
+true
+Enables vectorized orc decoding in native 
implementation. If false, a new non-vectorized ORC reader is used 
in native implementation. For hive implementation, 
this is ignored.
+  
+
+
+- Changed configurations
+
+
+  Property 
NameDefaultMeaning
+  
+spark.sql.orc.filterPushdown
+true
+Enables filter pushdown for ORC files. It is false by 
default prior to Spark 2.3.
+  
+
+
   - 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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spark git commit: [SPARK-23313][DOC] Add a migration guide for ORC

2018-02-12 Thread lixiao 
Repository: spark
Updated Branches:
  refs/heads/master fba01b9a6 -> 6cb59708c


[SPARK-23313][DOC] Add a migration guide for ORC

## What changes were proposed in this pull request?

This PR adds a migration guide documentation for ORC.

![orc-guide](https://user-images.githubusercontent.com/9700541/36123859-ec165cae-1002-11e8-90b7-7313be7a81a5.png)

## How was this patch tested?

N/A.

Author: Dongjoon Hyun 

Closes #20484 from dongjoon-hyun/SPARK-23313.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/6cb59708
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/6cb59708
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/6cb59708

Branch: refs/heads/master
Commit: 6cb59708c70c03696c772fbb5d158eed57fe67d4
Parents: fba01b9
Author: Dongjoon Hyun 
Authored: Mon Feb 12 15:26:37 2018 -0800
Committer: gatorsmile 
Committed: Mon Feb 12 15:26:37 2018 -0800

--
 docs/sql-programming-guide.md | 29 +
 1 file changed, 29 insertions(+)
--


http://git-wip-us.apache.org/repos/asf/spark/blob/6cb59708/docs/sql-programming-guide.md
--
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 6174a93..0f9f01e 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1776,6 +1776,35 @@ 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
+
+
+  Property 
NameDefaultMeaning
+  
+spark.sql.orc.impl
+native
+The name of ORC implementation. It can be one of 
native and hive. native 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.
+  
+  
+spark.sql.orc.enableVectorizedReader
+true
+Enables vectorized orc decoding in native 
implementation. If false, a new non-vectorized ORC reader is used 
in native implementation. For hive implementation, 
this is ignored.
+  
+
+
+- Changed configurations
+
+
+  Property 
NameDefaultMeaning
+  
+spark.sql.orc.filterPushdown
+true
+Enables filter pushdown for ORC files. It is false by 
default prior to Spark 2.3.
+  
+
+
   - 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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