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

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. For creating ORC tables, `USING ORC` or `USING HIVE` syntaxes are recommended.
+
+    - 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.