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authorR David Murray <rdmurray@bitdance.com>2016-07-10 16:40:03 (GMT)
committerR David Murray <rdmurray@bitdance.com>2016-07-10 16:40:03 (GMT)
commitce85acff3a28cd4c3ded487bfbc8c8ac5462d4e4 (patch)
treef90dbc4447dfecbdd45a8adb1bac886bdd3fbbbd /Objects
parent20bd3b070a38d3a1897e41e9dd1a6ad97cea9785 (diff)
parent537ad7ad9fdefa44fdfd7f5cbee198ad381deb60 (diff)
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Merge: #20647: Update dictobject.c comments to account for randomized string hashes.
Diffstat (limited to 'Objects')
-rw-r--r--Objects/dictobject.c13
1 files changed, 5 insertions, 8 deletions
diff --git a/Objects/dictobject.c b/Objects/dictobject.c
index 31c45ef..7a3ed42 100644
--- a/Objects/dictobject.c
+++ b/Objects/dictobject.c
@@ -88,20 +88,17 @@ it's USABLE_FRACTION (currently two-thirds) full.
/*
Major subtleties ahead: Most hash schemes depend on having a "good" hash
function, in the sense of simulating randomness. Python doesn't: its most
-important hash functions (for strings and ints) are very regular in common
+important hash functions (for ints) are very regular in common
cases:
- >>> map(hash, (0, 1, 2, 3))
+ >>>[hash(i) for i in range(4)]
[0, 1, 2, 3]
- >>> map(hash, ("namea", "nameb", "namec", "named"))
- [-1658398457, -1658398460, -1658398459, -1658398462]
- >>>
This isn't necessarily bad! To the contrary, in a table of size 2**i, taking
the low-order i bits as the initial table index is extremely fast, and there
-are no collisions at all for dicts indexed by a contiguous range of ints.
-The same is approximately true when keys are "consecutive" strings. So this
-gives better-than-random behavior in common cases, and that's very desirable.
+are no collisions at all for dicts indexed by a contiguous range of ints. So
+this gives better-than-random behavior in common cases, and that's very
+desirable.
OTOH, when collisions occur, the tendency to fill contiguous slices of the
hash table makes a good collision resolution strategy crucial. Taking only