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From brkyvz <>
Subject [GitHub] spark pull request: [SPARK-14555] First cut of Python API for Stru...
Date Fri, 15 Apr 2016 18:23:58 GMT
Github user brkyvz commented on a diff in the pull request:
    --- Diff: python/pyspark/sql/ ---
    @@ -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
    +        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
    +        :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 --
    @marmbrus We may have different trigger methods, which are actually classes in scala.
I don't imagine us having a lot of trigger methods, but if we keep adding them, we will need
to keep adding parameters here.

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