Github user viirya commented on the pull request:
https://github.com/apache/spark/pull/11339#issuecomment-188049680
This failed test:
def test_reduce_by_key_and_window_with_none_invFunc(self):
input = [range(1), range(2), range(3), range(4), range(5), range(6)]
def func(dstream):
return dstream.map(lambda x: (x, 1))\
.reduceByKeyAndWindow(operator.add, None, 5, 1)\
.filter(lambda kv: kv[1] > 0).count()
expected = [[2], [4], [6], [6], [6], [6]]
self._test_func(input, func, expected)
The input should be [0], [0, 1], [0, 1, 2], [0, 1, 2, 3], [0, 1, 2, 3, 4],
[0, 1, 2, 3, 4, 5].
The key and value for each window and corresponding result after reduce
func should be:
window 1: [0, 1] -> [0, 1]
window 2: [0, 1], [1, 1] -> [0, 2], [1, 1]
window 3: [0, 1], [1, 1], [2, 1] -> [0, 3], [1, 2], [2, 1]
window 4: [0, 1], [1, 1], [2, 1], [3, 1] -> [0, 4], [1, 3], [2, 2], [3,
1]
window 5: [0, 1], [1, 1], [2, 1], [3, 1], [4, 1] -> [0, 5], [1, 4], [2,
3], [3, 2], [4, 1]
window 6: [0, 1], [1, 1], [2, 1], [3, 1], [4, 1], [5, 1] -> [0, 6], [1,
5], [2, 4], [3, 3], [4, 2], [5, 1]
After filter operation and count, we actually get:
window 1: 1
window 2: 2
window 3: 3
window 4: 4
window 5: 5
window 6: 6
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