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## What changes were proposed in this pull request? 1. Split the main page of sql-programming-guide into 7 parts: - Getting Started - Data Sources - Performance Turing - Distributed SQL Engine - PySpark Usage Guide for Pandas with Apache Arrow - Migration Guide - Reference 2. Add left menu for sql-programming-guide, keep first level index for each part in the menu. ![image](https://user-images.githubusercontent.com/4833765/47016859-6332e180-d183-11e8-92e8-ce62518a83c4.png) ## How was this patch tested? Local test with jekyll build/serve. Closes #22746 from xuanyuanking/SPARK-24499. Authored-by: Yuanjian Li <xyliyuanjian@gmail.com> Signed-off-by: gatorsmile <gatorsmile@gmail.com>
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layout | title | displayTitle |
---|---|---|
global | ORC Files | 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
.
Property Name | Default | Meaning |
---|---|---|
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. `hive` means the ORC library in Hive 1.2.1. |
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. |