Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/12320#discussion_r59919017
--- Diff: python/pyspark/sql/readwriter.py ---
@@ -395,6 +425,38 @@ def partitionBy(self, *cols):
self._jwrite =
self._jwrite.partitionBy(_to_seq(self._sqlContext._sc, cols))
return self
+ @since(2.0)
+ def queryName(self, queryName):
+ """Specifies the name of the :class:`ContinuousQuery` that can be
started with
+ :func:`startStream`. This name must be unique among all the
currently active queries
+ in the associated SQLContext.
+
+ :param queryName: unique name for the query
+
+ >>> writer = sdf.write.queryName('streaming_query')
+ """
+ if not queryName or type(queryName) != str or
len(queryName.strip()) == 0:
+ raise ValueError('The queryName must be a non-empty string.
Got: %s' % queryName)
+ self._jwrite = self._jwrite.queryName(queryName)
+ return self
+
+ @since(2.0)
+ def trigger(self, trigger):
+ """Set the trigger for the stream query. If this is not set it
will run the query as fast
+ as possible, which is equivalent to setting the trigger to
``ProcessingTime('0 seconds')``.
+
+ :param trigger: a :class:`Trigger`.
+
+ >>> from pyspark.sql.streaming import ProcessingTime
+ >>> # trigger the query for execution every 5 seconds
+ >>> writer = sdf.write.trigger(ProcessingTime('5 seconds'))
--- End diff --
I'm only asking named parameters or classes. Named parameters sounds like
less overhead to me since you don't have type safety anyway, but I'm torn
between that and matching scala exactly.
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