Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/999#discussion_r13834977
--- Diff: docs/sql-programming-guide.md ---
@@ -310,37 +325,190 @@ parquetFile = sqlCtx.parquetFile("people.parquet")
# Parquet files can also be registered as tables and then used in SQL
statements.
parquetFile.registerAsTable("parquetFile");
teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND
age <= 19")
-
+teenNames = teenagers.map(lambda p: "Name: " + p.name)
+for teenName in teenNames.collect():
+ print teenName
{% endhighlight %}
</div>
</div>
-## Writing Language-Integrated Relational Queries
+## JSON Datasets
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+Spark SQL supports querying JSON datasets. To query a JSON dataset, a
SchemaRDD needs to be created for this JSON dataset. There are two ways to
create a SchemaRDD for a JSON dataset:
--- End diff --
How about we add the section on the rules of dealing schema inference in
the work on type system?
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