From cec1116b4b80c36b36a8a13338b948e4d6ade377 Mon Sep 17 00:00:00 2001 From: Andy Konwinski <andykonwinski@gmail.com> Date: Mon, 17 Nov 2014 11:52:23 -0800 Subject: [PATCH] [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 --- docs/sql-programming-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 48e8267ac0..5500da83b2 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/). -- GitLab