diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index a7d35741a48c3..6a333fdb562a7 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -509,8 +509,11 @@ val people = sc.textFile("examples/src/main/resources/people.txt")
 // The schema is encoded in a string
 val schemaString = "name age"
 
-// Import Spark SQL data types and Row.
-import org.apache.spark.sql._
+// Import Row.
+import org.apache.spark.sql.Row;
+
+// Import Spark SQL data types
+import org.apache.spark.sql.types.{StructType,StructField,StringType};
 
 // Generate the schema based on the string of schema
 val schema =
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
index 5bbcd2e080e07..c4a36103303a2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -33,7 +33,7 @@ import org.apache.spark.sql.types.StructType
 abstract class PipelineStage extends Serializable with Logging {
 
   /**
-   * :: DeveloperAPI ::
+   * :: DeveloperApi ::
    *
    * Derives the output schema from the input schema and parameters.
    * The schema describes the columns and types of the data.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 8b8f86c4127e0..5aece166aad22 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -89,7 +89,7 @@ private[sql] object DataFrame {
  *   val people = sqlContext.parquetFile("...")
  *   val department = sqlContext.parquetFile("...")
  *
- *   people.filter("age" > 30)
+ *   people.filter("age > 30")
  *     .join(department, people("deptId") === department("id"))
  *     .groupBy(department("name"), "gender")
  *     .agg(avg(people("salary")), max(people("age")))