Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/20484#discussion_r165569130
--- Diff: docs/sql-programming-guide.md ---
@@ -1776,6 +1776,77 @@ 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 and Hive ORC tables. To do that, the following
configurations are newly added or change their default values.
+
+ <table class="table">
+ <tr>
+ <th>
+ <b>Property Name</b>
+ </th>
+ <th>
+ <b>Default</b>
+ </th>
+ <th>
+ <b>Meaning</b>
+ </th>
+ </tr>
+ <tr>
+ <td>
+ spark.sql.orc.impl
+ </td>
+ <td>
+ native
+ </td>
+ <td>
+ The name of ORC implementation: 'native' means the native ORC
support that is built on Apache ORC 1.4.1 instead of the ORC library in Hive
1.2.1. It is 'hive' by default prior to Spark 2.3.
+ </td>
+ </tr>
+ <tr>
+ <td>
+ spark.sql.orc.enableVectorizedReader
+ </td>
+ <td>
+ true
+ </td>
+ <td>
+ Enables vectorized orc decoding in 'native' implementation. If
'false', a new non-vectorized ORC reader is used in 'native' implementation.
+ </td>
+ </tr>
+ <tr>
+ <td>
+ spark.sql.orc.columnarReaderBatchSize
+ </td>
+ <td>
+ 4096
+ </td>
+ <td>
+ The number of rows to include in a orc vectorized reader batch.
The number should be carefully chosen to minimize overhead and avoid OOMs in
reading data.
+ </td>
+ </tr>
+ <tr>
+ <td>
+ spark.sql.orc.filterPushdown
+ </td>
+ <td>
+ true
+ </td>
+ <td>
+ Enables filter pushdown for ORC files. It is 'false' by default
prior to Spark 2.3.
+ </td>
+ </tr>
+ <tr>
+ <td>
+ spark.sql.hive.convertMetastoreOrc
+ </td>
+ <td>
+ true
+ </td>
+ <td>
+ Enables the built-in ORC reader and writer to process Hive ORC
tables, instead of Hive serde. It is 'false' by default prior to Spark 2.3.
--- End diff --
Then, `Hive ORC tables`?
> Enable Spark's ORC support instead of Hive SerDe when reading from and
writing to Hive ORC tables
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