diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 48e8267ac072ccf3310c1d5f00ca6324eadb1320..5500da83b2b66ede71f80707e7dcd529d6c01c8d 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/).