[DOC] [SQL] Addes Hive metastore Parquet table conversion section

This PR adds a section about Hive metastore Parquet table conversion. It documents:

1. Schema reconciliation rules introduced in apache#5214 (see [this comment] [1] in apache#5188)
2. Metadata refreshing requirement introduced in apache#5339

[1]: apache#5188 (comment)

Author: Cheng Lian <lian@databricks.com>

Closes apache#5348 from liancheng/sql-doc-parquet-conversion and squashes the following commits:

42ae0d0 [Cheng Lian] Adds Python `refreshTable` snippet
4c9847d [Cheng Lian] Resorts to SQL for Python metadata refreshing snippet
756e660 [Cheng Lian] Adds Python snippet for metadata refreshing
50675db [Cheng Lian] Addes Hive metastore Parquet table conversion section

Author: liancheng

docs/sql-programming-guide.md

@@ -22,7 +22,7 @@ The DataFrame API is available in [Scala](api/scala/index.html#org.apache.spark.
 All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`, `pyspark` shell, or `sparkR` shell.
 
 
-## Starting Point: `SQLContext`
+## Starting Point: SQLContext
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
@@ -1036,6 +1036,15 @@ for (teenName in collect(teenNames)) {
 
 </div>
 
+<div data-lang="python"  markdown="1">
+
+{% highlight python %}
+# sqlContext is an existing HiveContext
+sqlContext.sql("REFRESH TABLE my_table")
+{% endhighlight %}
+
+</div>
+
 <div data-lang="sql"  markdown="1">
 
 {% highlight sql %}
@@ -1054,7 +1063,7 @@ SELECT * FROM parquetTable
 
 </div>
 
-### Partition discovery
+### Partition Discovery
 
 Table partitioning is a common optimization approach used in systems like Hive.  In a partitioned
 table, data are usually stored in different directories, with partitioning column values encoded in
@@ -1108,7 +1117,7 @@ can be configured by `spark.sql.sources.partitionColumnTypeInference.enabled`, w
 `true`. When type inference is disabled, string type will be used for the partitioning columns.
 
 
-### Schema merging
+### Schema Merging
 
 Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution.  Users can start with
 a simple schema, and gradually add more columns to the schema as needed.  In this way, users may end
@@ -1208,6 +1217,79 @@ printSchema(df3)
 
 </div>
 
+### Hive metastore Parquet table conversion
+
+When reading from and writing to Hive metastore Parquet tables, Spark SQL will try to use its own
+Parquet support instead of Hive SerDe for better performance. This behavior is controlled by the
+`spark.sql.hive.convertMetastoreParquet` configuration, and is turned on by default.
+
+#### Hive/Parquet Schema Reconciliation
+
+There are two key differences between Hive and Parquet from the perspective of table schema
+processing.
+
+1. Hive is case insensitive, while Parquet is not
+1. Hive considers all columns nullable, while nullability in Parquet is significant
+
+Due to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a
+Hive metastore Parquet table to a Spark SQL Parquet table.  The reconciliation rules are:
+
+1. Fields that have the same name in both schema must have the same data type regardless of
+   nullability.  The reconciled field should have the data type of the Parquet side, so that
+   nullability is respected.
+
+1. The reconciled schema contains exactly those fields defined in Hive metastore schema.
+
+   - Any fields that only appear in the Parquet schema are dropped in the reconciled schema.
+   - Any fileds that only appear in the Hive metastore schema are added as nullable field in the
+     reconciled schema.
+
+#### Metadata Refreshing
+
+Spark SQL caches Parquet metadata for better performance.  When Hive metastore Parquet table
+conversion is enabled, metadata of those converted tables are also cached.  If these tables are
+updated by Hive or other external tools, you need to refresh them manually to ensure consistent
+metadata.
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+{% highlight scala %}
+// sqlContext is an existing HiveContext
+sqlContext.refreshTable("my_table")
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+{% highlight java %}
+// sqlContext is an existing HiveContext
+sqlContext.refreshTable("my_table")
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+{% highlight python %}
+# sqlContext is an existing HiveContext
+sqlContext.refreshTable("my_table")
+{% endhighlight %}
+
+</div>
+
+<div data-lang="sql"  markdown="1">
+
+{% highlight sql %}
+REFRESH TABLE my_table;
+{% endhighlight %}
+
+</div>
+
+</div>
+
 ### Configuration
 
 Configuration of Parquet can be done using the `setConf` method on `SQLContext` or by running
@@ -1445,8 +1527,8 @@ This command builds a new assembly jar that includes Hive. Note that this Hive a
 on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries
 (SerDes) in order to access data stored in Hive.
 
-Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. Please note when running 
-the query on a YARN cluster (`yarn-cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory 
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. Please note when running
+the query on a YARN cluster (`yarn-cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory
 and `hive-site.xml` under `conf/` directory need to be available on the driver and all executors launched by the
 YARN cluster. The convenient way to do this is adding them through the `--jars` option and `--file` option of the
 `spark-submit` command.
@@ -1889,7 +1971,7 @@ options.
 #### DataFrame data reader/writer interface
 
 Based on user feedback, we created a new, more fluid API for reading data in (`SQLContext.read`)
-and writing data out (`DataFrame.write`), 
+and writing data out (`DataFrame.write`),
 and deprecated the old APIs (e.g. `SQLContext.parquetFile`, `SQLContext.jsonFile`).
 
 See the API docs for `SQLContext.read` (