[SPARK-13942][CORE][DOCS] Remove Shark-related docs for 2.x

## What changes were proposed in this pull request?

`Shark` was merged into `Spark SQL` since [July 2014](https://databricks.com/blog/2014/07/01/shark-spark-sql-hive-on-spark-and-the-future-of-sql-on-spark.html). The followings seem to be the only legacy. For Spark 2.x, we had better clean up those docs.

**Migration Guide**
```
- ## Migration Guide for Shark Users
- ...
- ### Scheduling
- ...
- ### Reducer number
- ...
- ### Caching
```

## How was this patch tested?

Pass the Jenkins test.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11770 from dongjoon-hyun/SPARK-13942.

1 files changed, 0 insertions, 45 deletions

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 89fe873851..3138fd5fb4 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -2356,51 +2356,6 @@ Python UDF registration is unchanged.
 When using DataTypes in Python you will need to construct them (i.e. `StringType()`) instead of
 referencing a singleton.
 
-## Migration Guide for Shark Users
-
-### Scheduling
-To set a [Fair Scheduler](job-scheduling.html#fair-scheduler-pools) pool for a JDBC client session,
-users can set the `spark.sql.thriftserver.scheduler.pool` variable:
-
-    SET spark.sql.thriftserver.scheduler.pool=accounting;
-
-### Reducer number
-
-In Shark, default reducer number is 1 and is controlled by the property `mapred.reduce.tasks`. Spark
-SQL deprecates this property in favor of `spark.sql.shuffle.partitions`, whose default value
-is 200. Users may customize this property via `SET`:
-
-    SET spark.sql.shuffle.partitions=10;
-    SELECT page, count(*) c
-    FROM logs_last_month_cached
-    GROUP BY page ORDER BY c DESC LIMIT 10;
-
-You may also put this property in `hive-site.xml` to override the default value.
-
-For now, the `mapred.reduce.tasks` property is still recognized, and is converted to
-`spark.sql.shuffle.partitions` automatically.
-
-### Caching
-
-The `shark.cache` table property no longer exists, and tables whose name end with `_cached` are no
-longer automatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
-let user control table caching explicitly:
-
-    CACHE TABLE logs_last_month;
-    UNCACHE TABLE logs_last_month;
-
-**NOTE:** `CACHE TABLE tbl` is now __eager__ by default not __lazy__. Don't need to trigger cache materialization manually anymore.
-
-Spark SQL newly introduced a statement to let user control table caching whether or not lazy since Spark 1.2.0:
-
-	CACHE [LAZY] TABLE [AS SELECT] ...
-
-Several caching related features are not supported yet:
-
-* User defined partition level cache eviction policy
-* RDD reloading
-* In-memory cache write through policy
-
 ## Compatibility with Apache Hive
 
 Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs.