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util: lower water mark when removing nodes after cost limit reached...
util: lower water mark when removing nodes after cost limit reached See the inline comment for the reasoning here. This is a pretty common strategy for garbage collectors, other cache-like primtives. The performance impact is substantial: $ hg perflrucachedict --size 4 --gets 1000000 --sets 1000000 --mixed 1000000 --costlimit 100 ! inserts w/ cost limit ! wall 1.659181 comb 1.650000 user 1.650000 sys 0.000000 (best of 7) ! wall 1.722122 comb 1.720000 user 1.720000 sys 0.000000 (best of 6) ! mixed w/ cost limit ! wall 1.139955 comb 1.140000 user 1.140000 sys 0.000000 (best of 9) ! wall 1.182513 comb 1.180000 user 1.180000 sys 0.000000 (best of 9) $ hg perflrucachedict --size 1000 --gets 1000000 --sets 1000000 --mixed 1000000 --costlimit 10000 ! inserts ! wall 0.679546 comb 0.680000 user 0.680000 sys 0.000000 (best of 15) ! sets ! wall 0.825147 comb 0.830000 user 0.830000 sys 0.000000 (best of 13) ! inserts w/ cost limit ! wall 25.105273 comb 25.080000 user 25.080000 sys 0.000000 (best of 3) ! wall 1.724397 comb 1.720000 user 1.720000 sys 0.000000 (best of 6) ! mixed ! wall 0.807096 comb 0.810000 user 0.810000 sys 0.000000 (best of 13) ! mixed w/ cost limit ! wall 12.104470 comb 12.070000 user 12.070000 sys 0.000000 (best of 3) ! wall 1.190563 comb 1.190000 user 1.190000 sys 0.000000 (best of 9) $ hg perflrucachedict --size 1000 --gets 1000000 --sets 1000000 --mixed 1000000 --costlimit 10000 --mixedgetfreq 90 ! inserts ! wall 0.711177 comb 0.710000 user 0.710000 sys 0.000000 (best of 14) ! sets ! wall 0.846992 comb 0.850000 user 0.850000 sys 0.000000 (best of 12) ! inserts w/ cost limit ! wall 25.963028 comb 25.960000 user 25.960000 sys 0.000000 (best of 3) ! wall 2.184311 comb 2.180000 user 2.180000 sys 0.000000 (best of 5) ! mixed ! wall 0.728256 comb 0.730000 user 0.730000 sys 0.000000 (best of 14) ! mixed w/ cost limit ! wall 3.174256 comb 3.170000 user 3.170000 sys 0.000000 (best of 4) ! wall 0.773186 comb 0.770000 user 0.770000 sys 0.000000 (best of 13) $ hg perflrucachedict --size 100000 --gets 1000000 --sets 1000000 --mixed 1000000 --mixedgetfreq 90 --costlimit 5000000 ! gets ! wall 1.191368 comb 1.190000 user 1.190000 sys 0.000000 (best of 9) ! wall 1.195304 comb 1.190000 user 1.190000 sys 0.000000 (best of 9) ! inserts ! wall 0.950995 comb 0.950000 user 0.950000 sys 0.000000 (best of 11) ! inserts w/ cost limit ! wall 1.589732 comb 1.590000 user 1.590000 sys 0.000000 (best of 7) ! sets ! wall 1.094941 comb 1.100000 user 1.090000 sys 0.010000 (best of 9) ! mixed ! wall 0.936420 comb 0.940000 user 0.930000 sys 0.010000 (best of 10) ! mixed w/ cost limit ! wall 0.882780 comb 0.870000 user 0.870000 sys 0.000000 (best of 11) This puts us ~2x slower than caches without cost accounting. And for read-heavy workloads (the prime use cases for caches), performance is nearly identical. In the worst case (pure write workloads with cost accounting enabled), we're looking at ~1.5us per insert on large caches. That seems "fast enough." Differential Revision: https://phab.mercurial-scm.org/D4505
Yuya Nishihara - - Load All Authors

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util.h
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/*
util.h - utility functions for interfacing with the various python APIs.
This software may be used and distributed according to the terms of
the GNU General Public License, incorporated herein by reference.
*/
#ifndef _HG_UTIL_H_
#define _HG_UTIL_H_
#include "compat.h"
#if PY_MAJOR_VERSION >= 3
#define IS_PY3K
#endif
/* helper to switch things like string literal depending on Python version */
#ifdef IS_PY3K
#define PY23(py2, py3) py3
#else
#define PY23(py2, py3) py2
#endif
/* clang-format off */
typedef struct {
PyObject_HEAD
char state;
int mode;
int size;
int mtime;
} dirstateTupleObject;
/* clang-format on */
extern PyTypeObject dirstateTupleType;
#define dirstate_tuple_check(op) (Py_TYPE(op) == &dirstateTupleType)
#ifndef MIN
#define MIN(a, b) (((a) < (b)) ? (a) : (b))
#endif
/* VC9 doesn't include bool and lacks stdbool.h based on my searching */
#if defined(_MSC_VER) || __STDC_VERSION__ < 199901L
#define true 1
#define false 0
typedef unsigned char bool;
#else
#include <stdbool.h>
#endif
static inline PyObject *_dict_new_presized(Py_ssize_t expected_size)
{
/* _PyDict_NewPresized expects a minused parameter, but it actually
creates a dictionary that's the nearest power of two bigger than the
parameter. For example, with the initial minused = 1000, the
dictionary created has size 1024. Of course in a lot of cases that
can be greater than the maximum load factor Python's dict object
expects (= 2/3), so as soon as we cross the threshold we'll resize
anyway. So create a dictionary that's at least 3/2 the size. */
return _PyDict_NewPresized(((1 + expected_size) / 2) * 3);
}
#endif /* _HG_UTIL_H_ */