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Posted to commits@spark.apache.org by ma...@apache.org on 2014/11/17 20:53:04 UTC
spark git commit: [DOCS][SQL] Fix broken link to Row class scaladoc
Repository: spark
Updated Branches:
refs/heads/master dbb9da5c3 -> cec1116b4
[DOCS][SQL] Fix broken link to Row class scaladoc
Author: Andy Konwinski <an...@gmail.com>
Closes #3323 from andyk/patch-2 and squashes the following commits:
4699fdc [Andy Konwinski] Fix broken link to Row class scaladoc
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/cec1116b
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/cec1116b
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/cec1116b
Branch: refs/heads/master
Commit: cec1116b4b80c36b36a8a13338b948e4d6ade377
Parents: dbb9da5
Author: Andy Konwinski <an...@gmail.com>
Authored: Mon Nov 17 11:52:23 2014 -0800
Committer: Michael Armbrust <mi...@databricks.com>
Committed: Mon Nov 17 11:52:47 2014 -0800
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docs/sql-programming-guide.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/cec1116b/docs/sql-programming-guide.md
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diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 48e8267..5500da8 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -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/).
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