view .hgtags @ 39570:f296c0b366c8

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

d40cc5aacc31ed673d9b5b24f98bee78c283062c 0.4f
1c590d34bf61e2ea12c71738e5a746cd74586157 0.4e
7eca4cfa8aad5fce9a04f7d8acadcd0452e2f34e 0.4d
b4d0c3786ad3e47beacf8412157326a32b6d25a4 0.4c
f40273b0ad7b3a6d3012fd37736d0611f41ecf54 0.5
0a28dfe59f8fab54a5118c5be4f40da34a53cdb7 0.5b
12e0fdbc57a0be78f0e817fd1d170a3615cd35da 0.6
4ccf3de52989b14c3d84e1097f59e39a992e00bd 0.6b
eac9c8efcd9bd8244e72fb6821f769f450457a32 0.6c
979c049974485125e1f9357f6bbe9c1b548a64c3 0.7
3a56574f329a368d645853e0f9e09472aee62349 0.8
6a03cff2b0f5d30281e6addefe96b993582f2eac 0.8.1
35fb62a3a673d5322f6274a44ba6456e5e4b3b37 0.9
2be3001847cb18a23c403439d9e7d0ace30804e9 0.9.1
36a957364b1b89c150f2d0e60a99befe0ee08bd3 0.9.2
27230c29bfec36d5540fbe1c976810aefecfd1d2 0.9.3
fb4b6d5fe100b0886f8bc3d6731ec0e5ed5c4694 0.9.4
23889160905a1b09fffe1c07378e9fc1827606eb 0.9.5
bae2e9c838e90a393bae3973a7850280413e091a 1.0
d5cbbe2c49cee22a9fbeb9ea41daa0ac4e26b846 1.0.1
d2375bbee6d47e62ba8e415c86e83a465dc4dce9 1.0.2
2a67430f92f15ea5159c26b09ec4839a0c549a26 1.1
3773e510d433969e277b1863c317b674cbee2065 1.1.1
11a4eb81fb4f4742451591489e2797dc47903277 1.1.2
11efa41037e280d08cfb07c09ad485df30fb0ea8 1.2
02981000012e3adf40c4849bd7b3d5618f9ce82d 1.2.1
196d40e7c885fa6e95f89134809b3ec7bdbca34b 1.3
3ef6c14a1e8e83a31226f5881b7fe6095bbfa6f6 1.3.1
31ec469f9b556f11819937cf68ee53f2be927ebf 1.4
439d7ea6fe3aa4ab9ec274a68846779153789de9 1.4.1
296a0b14a68621f6990c54fdba0083f6f20935bf 1.4.2
4aa619c4c2c09907034d9824ebb1dd0e878206eb 1.4.3
ff2704a8ded37fbebd8b6eb5ec733731d725da8a 1.5
2b01dab594167bc0dd33331dbaa6dca3dca1b3aa 1.5.1
39f725929f0c48c5fb3b90c071fc3066012456ca 1.5.2
fdcf80f26604f233dc4d8f0a5ef9d7470e317e8a 1.5.3
24fe2629c6fd0c74c90bd066e77387c2b02e8437 1.5.4
f786fc4b8764cd2a5526d259cf2f94d8a66924d9 1.6
bf1774d95bde614af3956d92b20e2a0c68c5fec7 1.6.1
c00f03a4982e467fb6b6bd45908767db6df4771d 1.6.2
ff5cec76b1c5b6be9c3bb923aae8c3c6d079d6b9 1.6.3
93d8bff78c96fe7e33237b257558ee97290048a4 1.6.4
333421b9e0f96c7bc788e5667c146a58a9440a55 1.7
4438875ec01bd0fc32be92b0872eb6daeed4d44f 1.7.1
6aff4f144ad356311318b0011df0bb21f2c97429 1.7.2
e3bf16703e2601de99e563cdb3a5d50b64e6d320 1.7.3
a6c855c32ea081da3c3b8ff628f1847ff271482f 1.7.4
2b2155623ee2559caf288fd333f30475966c4525 1.7.5
2616325766e3504c8ae7c84bd15ee610901fe91d 1.8
aa1f3be38ab127280761889d2dca906ca465b5f4 1.8.1
b032bec2c0a651ca0ddecb65714bfe6770f67d70 1.8.2
3cb1e95676ad089596bd81d0937cad37d6e3b7fb 1.8.3
733af5d9f6b22387913e1d11350fb8cb7c1487dd 1.8.4
de9eb6b1da4fc522b1cab16d86ca166204c24f25 1.9
4a43e23b8c55b4566b8200bf69fe2158485a2634 1.9.1
d629f1e89021103f1753addcef6b310e4435b184 1.9.2
351a9292e430e35766c552066ed3e87c557b803b 1.9.3
384082750f2c51dc917d85a7145748330fa6ef4d 2.0-rc
41453d55b481ddfcc1dacb445179649e24ca861d 2.0
195dbd1cef0c2f9f8bcf4ea303238105f716bda3 2.0.1
6344043924497cd06d781d9014c66802285072e4 2.0.2
db33555eafeaf9df1e18950e29439eaa706d399b 2.1-rc
2aa5b51f310fb3befd26bed99c02267f5c12c734 2.1
53e2cd303ecf8ca7c7eeebd785c34e5ed6b0f4a4 2.1.1
b9bd95e61b49c221c4cca24e6da7c946fc02f992 2.1.2
d9e2f09d5488c395ae9ddbb320ceacd24757e055 2.2-rc
00182b3d087909e3c3ae44761efecdde8f319ef3 2.2
5983de86462c5a9f42a3ad0f5e90ce5b1d221d25 2.2.1
85a358df5bbbe404ca25730c9c459b34263441dc 2.2.2
b013baa3898e117959984fc64c29d8c784d2f28b 2.2.3
a06e2681dd1786e2354d84a5fa9c1c88dd4fa3e0 2.3-rc
7f5094bb3f423fc799e471aac2aee81a7ce57a0b 2.3
072209ae4ddb654eb2d5fd35bff358c738414432 2.3.1
b3f0f9a39c4e1d0250048cd803ab03542d6f140a 2.3.2
d118a4f4fd16d9b558ec3f3e87bfee772861d2b7 2.4-rc
195ad823b5d58c68903a6153a25e3fb4ed25239d 2.4
0c10cf8191469e7c3c8844922e17e71a176cb7cb 2.4.1
a4765077b65e6ae29ba42bab7834717b5072d5ba 2.4.2
f5fbe15ca7449f2c9a3cf817c86d0ae68b307214 2.5-rc
a6088c05e43a8aee0472ca3a4f6f8d7dd914ebbf 2.5
7511d4df752e61fe7ae4f3682e0a0008573b0402 2.5.1
5b7175377babacce80a6c1e12366d8032a6d4340 2.5.2
50c922c1b5145dab8baefefb0437d363b6a6c21c 2.5.3
8a7bd2dccd44ed571afe7424cd7f95594f27c092 2.5.4
292cd385856d98bacb2c3086f8897bc660c2beea 2.6-rc
23f785b38af38d2fca6b8f3db56b8007a84cd73a 2.6
ddc7a6be20212d18f3e27d9d7e6f079a66d96f21 2.6.1
cceaf7af4c9e9e6fa2dbfdcfe9856c5da69c4ffd 2.6.2
009794acc6e37a650f0fae37872e733382ac1c0c 2.6.3
f0d7721d7322dcfb5af33599c2543f27335334bb 2.7-rc
f37b5a17e6a0ee17afde2cdde5393dd74715fb58 2.7
335a558f81dc73afeab4d7be63617392b130117f 2.7.1
e7fa36d2ad3a7944a52dca126458d6f482db3524 2.7.2
1596f2d8f2421314b1ddead8f7d0c91009358994 2.8-rc
d825e4025e39d1c39db943cdc89818abd0a87c27 2.8
209e04a06467e2969c0cc6501335be0406d46ef0 2.8.1
ca387377df7a3a67dbb90b6336b781cdadc3ef41 2.8.2
8862469e16f9236208581b20de5f96bd13cc039d 2.9-rc
3cec5134e9c4bceab6a00c60f52a4f80677a78f2 2.9
b96cb15ec9e04d8ac5ee08b34fcbbe4200588965 2.9.1
3f83fc5cfe715d292069ee8417c83804f6c6c1e4 2.9.2
564f55b251224f16508dd1311452db7780dafe2b 3.0-rc
2195ac506c6ababe86985b932f4948837c0891b5 3.0
269c80ee5b3cb3684fa8edc61501b3506d02eb10 3.0.1
2d8cd3d0e83c7336c0cb45a9f88638363f993848 3.0.2
6c36dc6cd61a0e1b563f1d51e55bdf4dacf12162 3.1-rc
3178e49892020336491cdc6945885c4de26ffa8b 3.1
5dc91146f35369949ea56b40172308158b59063a 3.1.1
f768c888aaa68d12dd7f509dcc7f01c9584357d0 3.1.2
7f8d16af8cae246fa5a48e723d48d58b015aed94 3.2-rc
ced632394371a36953ce4d394f86278ae51a2aae 3.2
643c58303fb0ec020907af28b9e486be299ba043 3.2.1
902554884335e5ca3661d63be9978eb4aec3f68a 3.2.2
6dad422ecc5adb63d9fa649eeb8e05a5f9bc4900 3.2.3
1265a3a71d75396f5d4cf6935ae7d9ba5407a547 3.2.4
db8e3f7948b1fdeb9ad12d448fc3525759908b9f 3.3-rc
fbdd5195528fae4f41feebc1838215c110b25d6a 3.3
5b4ed033390bf6e2879c8f5c28c84e1ee3b87231 3.3.1
07a92bbd02e5e3a625e0820389b47786b02b2cea 3.3.2
2e2e9a0750f91a6fe0ad88e4de34f8efefdcab08 3.3.3
e89f909edffad558b56f4affa8239e4832f88de0 3.4-rc
8cc6036bca532e06681c5a8fa37efaa812de67b5 3.4
ed18f4acf435a2824c6f49fba40f42b9df5da7ad 3.4.1
540cd0ddac49c1125b2e013aa2ff18ecbd4dd954 3.4.2
96a38d44ba093bd1d1ecfd34119e94056030278b 3.5-rc
21aa1c313b05b1a85f8ffa1120d51579ddf6bf24 3.5
1a45e49a6bed023deb229102a8903234d18054d3 3.5.1
9a466b9f9792e3ad7ae3fc6c43c3ff2e136b718d 3.5.2
b66e3ca0b90c3095ea28dfd39aa24247bebf5c20 3.6-rc
47dd34f2e7272be9e3b2a5a83cd0d20be44293f4 3.6
1aa5083cbebbe7575c88f3402ab377539b484897 3.6.1
2d437a0f3355834a9485bbbeb30a52a052c98f19 3.6.2
ea389970c08449440587712117f178d33bab3f1e 3.6.3
158bdc8965720ca4061f8f8d806563cfc7cdb62e 3.7-rc
2408645de650d8a29a6ce9e7dce601d8dd0d1474 3.7
b698abf971e7377d9b7ec7fc8c52df45255b0329 3.7.1
d493d64757eb45ada99fcb3693e479a51b7782da 3.7.2
ae279d4a19e9683214cbd1fe8298cf0b50571432 3.7.3
740156eedf2c450aee58b1a90b0e826f47c5da64 3.8-rc
f85de28eae32e7d3064b1a1321309071bbaaa069 3.8
a56296f55a5e1038ea5016dace2076b693c28a56 3.8.1
aaabed77791a75968a12b8c43ad263631a23ee81 3.8.2
a9764ab80e11bcf6a37255db7dd079011f767c6c 3.8.3
26a5d605b8683a292bb89aea11f37a81b06ac016 3.8.4
519bb4f9d3a47a6e83c2b414d58811ed38f503c2 3.9-rc
299546f84e68dbb9bd026f0f3a974ce4bdb93686 3.9
ccd436f7db6d5d7b9af89715179b911d031d44f1 3.9.1
149433e68974eb5c63ccb03f794d8b57339a80c4 3.9.2
438173c415874f6ac653efc1099dec9c9150e90f 4.0-rc
eab27446995210c334c3d06f1a659e3b9b5da769 4.0
b3b1ae98f6a0e14c1e1ba806a6c18e193b6dae5c 4.0.1
e69874dc1f4e142746ff3df91e678a09c6fc208c 4.0.2
a1dd2c0c479e0550040542e392e87bc91262517e 4.1-rc
e1526da1e6d84e03146151c9b6e6950fe9a83d7d 4.1
25703b624d27e3917d978af56d6ad59331e0464a 4.1.1
ed5b25874d998ababb181a939dd37a16ea644435 4.1.2
77eaf9539499a1b8be259ffe7ada787d07857f80 4.1.3
616e788321cc4ae9975b7f0c54c849f36d82182b 4.2-rc
bb96d4a497432722623ae60d9bc734a1e360179e 4.2
c850f0ed54c1d42f9aa079ad528f8127e5775217 4.2.1
26c49ed51a698ec016d2b4c6b44ca3c3f73cc788 4.2.2
857876ebaed4e315f63157bd157d6ce553c7ab73 4.3-rc
5544af8622863796a0027566f6b646e10d522c4c 4.3
943c91326b23954e6e1c6960d0239511f9530258 4.2.3
3fee7f7d2da04226914c2258cc2884dc27384fd7 4.3.1
920977f72c7b70acfdaf56ab35360584d7845827 4.3.2
2f427b57bf9019c6dc3750baa539dc22c1be50f6 4.3.3
1e2454b60e5936f5e77498cab2648db469504487 4.4-rc
0ccb43d4cf01d013ae05917ec4f305509f851b2d 4.4
cabc840ffdee8a72f3689fb77dd74d04fdc2bc04 4.4.1
a92b9f8e11ba330614cdfd6af0e03b15c1ff3797 4.4.2
27b6df1b5adbdf647cf5c6675b40575e1b197c60 4.5-rc
d334afc585e29577f271c5eda03378736a16ca6b 4.5
369aadf7a3264b03c8b09efce715bc41e6ab4a9b 4.5.1
8bba684efde7f45add05f737952093bb2aa07155 4.5.2
7de7bd407251af2bc98e5b809c8598ee95830daf 4.5.3
ed5448edcbfa747b9154099e18630e49024fd47b 4.6rc0
1ec874717d8a93b19e0d50628443e0ee5efab3a9 4.6rc1
6614cac550aea66d19c601e45efd1b7bd08d7c40 4.6
9c5ced5276d6e7d54f7c3dadf5247b7ee98ec79c 4.6.1
0b63a6743010dfdbf8a8154186e119949bdaa1cc 4.6.2
e90130af47ce8dd53a3109aed9d15876b3e7dee8 4.7rc0
33ac6a72308a215e6086fbced347ec10aa963b0a 4.7
ede3bf31fe63677fdf5bd8db687977d4e3d792ed 4.7.1