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author | Guido van Rossum <guido@python.org> | 2006-08-24 21:29:26 (GMT) |
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committer | Guido van Rossum <guido@python.org> | 2006-08-24 21:29:26 (GMT) |
commit | dc5f6b232be9f669f78d627cdcacc07d2ba167af (patch) | |
tree | a82abef2401c1fae00c67a176dd66710bbddc2f3 /Objects | |
parent | 801f0d78b5582a325d489831b991adb873067e80 (diff) | |
download | cpython-dc5f6b232be9f669f78d627cdcacc07d2ba167af.zip cpython-dc5f6b232be9f669f78d627cdcacc07d2ba167af.tar.gz cpython-dc5f6b232be9f669f78d627cdcacc07d2ba167af.tar.bz2 |
Got test_mutants.py working. One set of changes was straightforward:
use __eq__ instead of __cmp__. The other change is unexplained:
with a random hash code as before, it would run forever; with a constant
hash code, it fails quickly.
This found a refcount bug in dict_equal() -- I wonder if that bug is
also present in 2.5...
Diffstat (limited to 'Objects')
-rw-r--r-- | Objects/dictobject.c | 41 |
1 files changed, 22 insertions, 19 deletions
diff --git a/Objects/dictobject.c b/Objects/dictobject.c index 320befb..64585a4 100644 --- a/Objects/dictobject.c +++ b/Objects/dictobject.c @@ -24,11 +24,11 @@ function, in the sense of simulating randomness. Python doesn't: its most important hash functions (for strings and ints) are very regular in common cases: ->>> map(hash, (0, 1, 2, 3)) -[0, 1, 2, 3] ->>> map(hash, ("namea", "nameb", "namec", "named")) -[-1658398457, -1658398460, -1658398459, -1658398462] ->>> + >>> map(hash, (0, 1, 2, 3)) + [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 @@ -39,9 +39,9 @@ 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 the last i bits of the hash code is also vulnerable: for example, consider -[i << 16 for i in range(20000)] as a set of keys. Since ints are their own -hash codes, and this fits in a dict of size 2**15, the last 15 bits of every -hash code are all 0: they *all* map to the same table index. +the list [i << 16 for i in range(20000)] as a set of keys. Since ints are +their own hash codes, and this fits in a dict of size 2**15, the last 15 bits + of every hash code are all 0: they *all* map to the same table index. But catering to unusual cases should not slow the usual ones, so we just take the last i bits anyway. It's up to collision resolution to do the rest. If @@ -97,19 +97,19 @@ the high-order hash bits have an effect on early iterations. 5 was "the best" in minimizing total collisions across experiments Tim Peters ran (on both normal and pathological cases), but 4 and 6 weren't significantly worse. -Historical: Reimer Behrends contributed the idea of using a polynomial-based +Historical: Reimer Behrends contributed the idea of using a polynomial-based approach, using repeated multiplication by x in GF(2**n) where an irreducible polynomial for each table size was chosen such that x was a primitive root. Christian Tismer later extended that to use division by x instead, as an efficient way to get the high bits of the hash code into play. This scheme -also gave excellent collision statistics, but was more expensive: two -if-tests were required inside the loop; computing "the next" index took about -the same number of operations but without as much potential parallelism -(e.g., computing 5*j can go on at the same time as computing 1+perturb in the -above, and then shifting perturb can be done while the table index is being -masked); and the dictobject struct required a member to hold the table's -polynomial. In Tim's experiments the current scheme ran faster, produced -equally good collision statistics, needed less code & used less memory. +also gave excellent collision statistics, but was more expensive: two if-tests +were required inside the loop; computing "the next" index took about the same +number of operations but without as much potential parallelism (e.g., +computing 5*j can go on at the same time as computing 1+perturb in the above, +and then shifting perturb can be done while the table index is being masked); +and the dictobject struct required a member to hold the table's polynomial. +In Tim's experiments the current scheme ran faster, produced equally good +collision statistics, needed less code & used less memory. Theoretical Python 2.5 headache: hash codes are only C "long", but sizeof(Py_ssize_t) > sizeof(long) may be possible. In that case, and if a @@ -223,9 +223,9 @@ probe indices are computed as explained earlier. All arithmetic on hash should ignore overflow. -(The details in this version are due to Tim Peters, building on many past +The details in this version are due to Tim Peters, building on many past contributions by Reimer Behrends, Jyrki Alakuijala, Vladimir Marangozov and -Christian Tismer). +Christian Tismer. lookdict() is general-purpose, and may return NULL if (and only if) a comparison raises an exception (this was new in Python 2.5). @@ -1485,7 +1485,10 @@ dict_equal(dictobject *a, dictobject *b) /* temporarily bump aval's refcount to ensure it stays alive until we're done with it */ Py_INCREF(aval); + /* ditto for key */ + Py_INCREF(key); bval = PyDict_GetItemWithError((PyObject *)b, key); + Py_DECREF(key); if (bval == NULL) { Py_DECREF(aval); if (PyErr_Occurred()) |