Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/17077#discussion_r115129884
--- 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.")
+
+ col = cols[0]
+ cols = cols[1:]
+
+ self._jwrite = self._jwrite.bucketBy(numBuckets, col,
_to_seq(self._spark._sc, cols))
+ return self
+
+ @since(2.3)
+ def sortBy(self, *cols):
+ """Sorts the output in each bucket by the given columns on the
file system.
+
+ :param cols: name of columns
+
+ >>> (df.write.format('parquet')
+ ... .bucketBy(100, 'year', 'month')
+ ... .sortBy('day')
+ ... .mode("overwrite")
+ ... .saveAsTable('sorted_bucketed_table'))
+ """
+ if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
+ cols = cols[0]
+
+ 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 --
same note as above.
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