Github user tgravescs commented on a diff in the pull request:
https://github.com/apache/spark/pull/20484#discussion_r166632047
--- Diff: docs/sql-programming-guide.md ---
@@ -1776,6 +1776,44 @@ 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.
+
+ - 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>
+ <tr>
+ <td><code>spark.sql.hive.convertMetastoreOrc</code></td>
+ <td><code>true</code></td>
+ <td>Enable the Spark's ORC support, which can be configured by
<code>spark.sql.orc.impl</code>, instead of Hive SerDe when reading from and
writing to Hive ORC tables. It is <code>false</code> by default prior to Spark
2.3.</td>
--- End diff --
this isn't entirely clear to me. I assume this has to be true for
spark.sql.orc.impl to work? If so perhaps we should mention it above in
spark.sql.orc.impl. If this is false what happens, it can't read Orc format?
or it just falls back to spark.sql.orc.impl=hive
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