Github user ahirreddy commented on a diff in the pull request:
https://github.com/apache/spark/pull/363#discussion_r11561160
--- Diff: python/pyspark/rdd.py ---
@@ -1387,6 +1387,95 @@ def _jrdd(self):
def _is_pipelinable(self):
return not (self.is_cached or self.is_checkpointed)
+class Row(dict):
+ """
+ An extended L{dict} that takes a L{dict} in its constructor, and
exposes those items as fields.
+
+ >>> r = Row({"hello" : "world", "foo" : "bar"})
+ >>> r.hello
+ 'world'
+ >>> r.foo
+ 'bar'
+ """
+
+ def __init__(self, d):
+ d.update(self.__dict__)
+ self.__dict__ = d
+ dict.__init__(self, d)
+
+class SchemaRDD(RDD):
+ """
+ An RDD of Row objects that has an associated schema. The underlying
JVM object is a SchemaRDD,
+ not a PythonRDD, so we can utilize the relational query api exposed by
SparkSQL.
+
+ For normal L{RDD} operations (map, count, etc.) the L{SchemaRDD} is
not operated on directly, as
+ it's underlying implementation is a RDD composed of Java objects.
Instead it is converted to a
+ PythonRDD in the JVM, on which Python operations can be done.
+ """
+
+ def __init__(self, jschema_rdd, sql_ctx):
+ self.sql_ctx = sql_ctx
+ self._sc = sql_ctx._sc
+ self._jschema_rdd = jschema_rdd
+
+ self.is_cached = False
+ self.is_checkpointed = False
+ self.ctx = self.sql_ctx._sc
+ self._jrdd_deserializer = self.ctx.serializer
+
+ @property
+ def _jrdd(self):
+ """
+ Lazy evaluation of PythonRDD object. Only done when a user calls
methods defined by the
+ L{RDD} super class (map, count, etc.).
+ """
+ return self.toPython()._jrdd
+
+ @property
+ def _id(self):
+ return self._jrdd.id()
+
+ def saveAsParquetFile(self, path):
+ """
+ Saves the contents of this L{SchemaRDD} as a parquet file,
preserving the schema. Files
+ that are written out using this method can be read back in as a
SchemaRDD using the
+ L{SQLContext.parquetFile} method.
+
+ >>> from pyspark.context import SQLContext
+ >>> sqlCtx = SQLContext(sc)
+ >>> rdd = sc.parallelize([{"field1" : 1, "field2" : "row1"},
+ ... {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2":
"row3"}])
+ >>> srdd = sqlCtx.inferSchema(rdd)
+ >>> srdd.saveAsParquetFile("/tmp/test.parquet")
+ >>> srdd2 = sqlCtx.parquetFile("/tmp/test.parquet")
+ >>> srdd2.collect() == srdd.collect()
+ True
+ """
+ self._jschema_rdd.saveAsParquetFile(path)
+
+ def registerAsTable(self, name):
+ """
+ Registers this RDD as a temporary table using the given name. The
lifetime of this temporary
+ table is tied to the L{SQLContext} that was used to create this
SchemaRDD.
+
+ >>> from pyspark.context import SQLContext
+ >>> sqlCtx = SQLContext(sc)
+ >>> rdd = sc.parallelize([{"field1" : 1, "field2" : "row1"},
+ ... {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2":
"row3"}])
+ >>> srdd = sqlCtx.inferSchema(rdd)
+ >>> srdd.registerAsTable("test")
+ >>> srdd2 = sqlCtx.sql("select * from test")
+ >>> srdd.collect() == srdd2.collect()
+ True
+ """
+ self._jschema_rdd.registerAsTable(name)
+
+ def toPython(self):
--- End diff --
Made private
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