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authorAndy Konwinski <andykonwinski@gmail.com>2014-11-17 11:52:23 -0800
committerMichael Armbrust <michael@databricks.com>2014-11-17 11:52:47 -0800
commitcec1116b4b80c36b36a8a13338b948e4d6ade377 (patch)
treecc737d1462d5096518dfea4177c436da123ebbe7 /docs/sql-programming-guide.md
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[DOCS][SQL] Fix broken link to Row class scaladoc
Author: Andy Konwinski <andykonwinski@gmail.com> Closes #3323 from andyk/patch-2 and squashes the following commits: 4699fdc [Andy Konwinski] Fix broken link to Row class scaladoc
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diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
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@@ -14,7 +14,7 @@ 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/scala/index.html#org.apache.spark.sql.SchemaRDD). SchemaRDDs are composed of
-[Row](api/scala/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects, along with
+[Row](api/scala/index.html#org.apache.spark.sql.package@Row:org.apache.spark.sql.catalyst.expressions.Row.type) 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, a [Parquet](http://parquet.io)
file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).