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authorRaymond Hettinger <python@rcn.com>2014-12-12 07:56:53 (GMT)
committerRaymond Hettinger <python@rcn.com>2014-12-12 07:56:53 (GMT)
commit31094a191e589e5049566c0ab22fa178678edb98 (patch)
treead0d326ad2c036184381aa6fd504703ad682685e
parentd4a001b23cf66232a48021f39d121ba817b31ef2 (diff)
parentd2a296a73a3a49d15fd3d1505c10e98ab8ad1a63 (diff)
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-rw-r--r--Doc/library/heapq.rst4
1 files changed, 2 insertions, 2 deletions
diff --git a/Doc/library/heapq.rst b/Doc/library/heapq.rst
index 7c39d82..9682b59 100644
--- a/Doc/library/heapq.rst
+++ b/Doc/library/heapq.rst
@@ -272,11 +272,11 @@ However, there are other representations which are more efficient overall, yet
the worst cases might be terrible.
Heaps are also very useful in big disk sorts. You most probably all know that a
-big sort implies producing "runs" (which are pre-sorted sequences, which size is
+big sort implies producing "runs" (which are pre-sorted sequences, whose size is
usually related to the amount of CPU memory), followed by a merging passes for
these runs, which merging is often very cleverly organised [#]_. It is very
important that the initial sort produces the longest runs possible. Tournaments
-are a good way to that. If, using all the memory available to hold a
+are a good way to achieve that. If, using all the memory available to hold a
tournament, you replace and percolate items that happen to fit the current run,
you'll produce runs which are twice the size of the memory for random input, and
much better for input fuzzily ordered.