Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/363#discussion_r11554158
--- Diff: docs/sql-programming-guide.md ---
@@ -176,6 +202,32 @@ List<String> teenagerNames = teenagers.map(new
Function<Row, String>() {
</div>
+<div data-lang="python" markdown="1">
+
+One type of table that is supported by Spark SQL is an RDD of
dictionaries. The keys of the
+dictionary define the columns names of the table, and the types are
inferred by looking at the first
+row. Any RDD of dictionaries can converted to a SchemaRDD and then
registered as a table. Tables
+can be used in subsequent SQL statements.
+
+{% highlight python %}
+# Load a text file and convert each line to a dictionary.
+lines = sc.textFile("examples/src/main/resources/people.txt")
+parts = lines.map(lambda l: l.split(","))
+people = parts.map(lambda p: {"name": p[0], "age": int(p[1])})
+
+# Infer the schema, and register the SchemaRDD as a table.
+peopleTable = sqlCtx.inferSchema(people)
+peopleTable.registerAsTable("people")
+
+# SQL can be run over SchemaRDDs that have been registered as a table.
+teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <=
19")
+
+# The results of SQL queries are RDDs and support all the normal RDD
operations.
+teenNames = teenagers.map(lambda p: "Name: " + p.name)
+{% endhighlight %}
+
--- End diff --
Maybe add something saying that in future versions of PySpark, we'd like to
support RDDs with other data types in registerAsTable too.
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