Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/13592#discussion_r66877689
--- Diff: docs/sql-programming-guide.md ---
@@ -604,49 +607,47 @@ JavaRDD<Person> people =
sc.textFile("examples/src/main/resources/people.txt").m
});
// Apply a schema to an RDD of JavaBeans and register it as a table.
-DataFrame schemaPeople = sqlContext.createDataFrame(people, Person.class);
+Dataset<Row> schemaPeople = spark.createDataFrame(people, Person.class);
schemaPeople.createOrReplaceTempView("people");
// SQL can be run over RDDs that have been registered as tables.
-DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >=
13 AND age <= 19")
+Dataset<Row> teenagers = spark.sql("SELECT name FROM people WHERE age >=
13 AND age <= 19")
-// The results of SQL queries are DataFrames and support all the normal
RDD operations.
// The columns of a row in the result can be accessed by ordinal.
-List<String> teenagerNames = teenagers.javaRDD().map(new Function<Row,
String>() {
+List<String> teenagerNames = teenagers.map(new MapFunction<Row, String>() {
public String call(Row row) {
return "Name: " + row.getString(0);
}
-}).collect();
+}).collectAsList();
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
+<!-- TODO: Is the following still true in Spark 2.0? -->
--- End diff --
looks like it's still valid in python
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