[SPARK-1439, SPARK-1440] Generate unified Scaladoc across projects and Javadocs

I used the sbt-unidoc plugin (https://github.com/sbt/sbt-unidoc) to create a unified Scaladoc of our public packages, and generate Javadocs as well. One limitation is that I haven't found an easy way to exclude packages in the Javadoc; there is a SBT task that identifies Java sources to run javadoc on, but it's been very difficult to modify it from outside to change what is set in the unidoc package. Some SBT-savvy people should help with this. The Javadoc site also lacks package-level descriptions and things like that, so we may want to look into that. We may decide not to post these right now if it's too limited compared to the Scala one.

Example of the built doc site: http://people.csail.mit.edu/matei/spark-unified-docs/

Author: Matei Zaharia <matei@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>

Closes #457 from mateiz/better-docs and squashes the following commits:

a63d4a3 [Matei Zaharia] Skip Java/Scala API docs for Python package
5ea1f43 [Matei Zaharia] Fix links to Java classes in Java guide, fix some JS for scrolling to anchors on page load
f05abc0 [Matei Zaharia] Don't include java.lang package names
995e992 [Matei Zaharia] Skip internal packages and class names with $ in JavaDoc
a14a93c [Matei Zaharia] typo
76ce64d [Matei Zaharia] Add groups to Javadoc index page, and a first package-info.java
ed6f994 [Matei Zaharia] Generate JavaDoc as well, add titles, update doc site to use unified docs
acb993d [Matei Zaharia] Add Unidoc plugin for the projects we want Unidoced

1 files changed, 10 insertions, 10 deletions

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 8e98cc0c80..e25379bd76 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -14,8 +14,8 @@ title: Spark SQL Programming Guide
 
 Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using
 Spark.  At the core of this component is a new type of RDD,
-[SchemaRDD](api/sql/core/index.html#org.apache.spark.sql.SchemaRDD).  SchemaRDDs are composed
-[Row](api/sql/catalyst/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects along with
+[SchemaRDD](api/scala/index.html#org.apache.spark.sql.SchemaRDD).  SchemaRDDs are composed
+[Row](api/scala/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
 in a traditional relational database.  A SchemaRDD can be created from an existing RDD, parquet
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
@@ -27,8 +27,8 @@ file, or by running HiveQL against data stored in [Apache Hive](http://hive.apac
 <div data-lang="java"  markdown="1">
 Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using
 Spark.  At the core of this component is a new type of RDD,
-[JavaSchemaRDD](api/sql/core/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD).  JavaSchemaRDDs are composed
-[Row](api/sql/catalyst/index.html#org.apache.spark.sql.api.java.Row) objects along with
+[JavaSchemaRDD](api/scala/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD).  JavaSchemaRDDs are composed
+[Row](api/scala/index.html#org.apache.spark.sql.api.java.Row) objects along with
 a schema that describes the data types of each column in the row.  A JavaSchemaRDD is similar to a table
 in a traditional relational database.  A JavaSchemaRDD can be created from an existing RDD, parquet
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
@@ -38,8 +38,8 @@ file, or by running HiveQL against data stored in [Apache Hive](http://hive.apac
 
 Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using
 Spark.  At the core of this component is a new type of RDD,
-[SchemaRDD](api/pyspark/pyspark.sql.SchemaRDD-class.html).  SchemaRDDs are composed
-[Row](api/pyspark/pyspark.sql.Row-class.html) objects along with
+[SchemaRDD](api/python/pyspark.sql.SchemaRDD-class.html).  SchemaRDDs are composed
+[Row](api/python/pyspark.sql.Row-class.html) objects along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
 in a traditional relational database.  A SchemaRDD can be created from an existing RDD, parquet
 file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
@@ -56,7 +56,7 @@ file, or by running HiveQL against data stored in [Apache Hive](http://hive.apac
 <div data-lang="scala"  markdown="1">
 
 The entry point into all relational functionality in Spark is the
-[SQLContext](api/sql/core/index.html#org.apache.spark.sql.SQLContext) class, or one of its
+[SQLContext](api/scala/index.html#org.apache.spark.sql.SQLContext) class, or one of its
 descendants.  To create a basic SQLContext, all you need is a SparkContext.
 
 {% highlight scala %}
@@ -72,7 +72,7 @@ import sqlContext._
 <div data-lang="java" markdown="1">
 
 The entry point into all relational functionality in Spark is the
-[JavaSQLContext](api/sql/core/index.html#org.apache.spark.sql.api.java.JavaSQLContext) class, or one
+[JavaSQLContext](api/scala/index.html#org.apache.spark.sql.api.java.JavaSQLContext) class, or one
 of its descendants.  To create a basic JavaSQLContext, all you need is a JavaSparkContext.
 
 {% highlight java %}
@@ -85,7 +85,7 @@ JavaSQLContext sqlCtx = new org.apache.spark.sql.api.java.JavaSQLContext(ctx);
 <div data-lang="python"  markdown="1">
 
 The entry point into all relational functionality in Spark is the
-[SQLContext](api/pyspark/pyspark.sql.SQLContext-class.html) class, or one
+[SQLContext](api/python/pyspark.sql.SQLContext-class.html) class, or one
 of its decedents.  To create a basic SQLContext, all you need is a SparkContext.
 
 {% highlight python %}
@@ -331,7 +331,7 @@ val teenagers = people.where('age >= 10).where('age <= 19).select('name)
 The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers
 prefixed with a tick (`'`).  Implicit conversions turn these symbols into expressions that are
 evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
-[ScalaDoc](api/sql/core/index.html#org.apache.spark.sql.SchemaRDD).
+[ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
 
 <!-- TODO: Include the table of operations here. -->