[GitHub] spark pull request #17077: [SPARK-16931][PYTHON][SQL] Add Python wrapper for...

[holdenk <gi...@git.apache.org>]

Github user holdenk commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17077#discussion_r115129876
  
    --- Diff: python/pyspark/sql/readwriter.py ---
    @@ -563,6 +563,60 @@ def partitionBy(self, *cols):
             self._jwrite = self._jwrite.partitionBy(_to_seq(self._spark._sc, cols))
             return self
     
    +    @since(2.3)
    +    def bucketBy(self, numBuckets, *cols):
    +        """Buckets the output by the given columns on the file system.
    +
    +        :param numBuckets: the number of buckets to save
    +        :param cols: name of columns
    +
    +        .. note:: Applicable for file-based data sources in combination with
    +                  :py:meth:`DataFrameWriter.saveAsTable`.
    +
    +        >>> (df.write.format('parquet')
    +        ...     .bucketBy(100, 'year', 'month')
    +        ...     .mode("overwrite")
    +        ...     .saveAsTable('bucketed_table'))
    +        """
    +        if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
    +            cols = cols[0]
    +
    +        if not isinstance(numBuckets, int):
    +            raise TypeError("numBuckets should be an int, got {0}.".format(type(numBuckets)))
    +
    +        if not all(isinstance(c, basestring) for c in cols):
    +            raise TypeError("cols argument should be a string or a sequence of strings.")
    --- End diff --
    
    So I don't think we really support all sequences (the above typecheck on L581 requires list or tuple but there are additional types of sequences).


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