view .hgtags @ 28495:70c2f8a98276

changelog: avoid slicing raw data until needed

Before, we were slicing the original raw text and storing individual
variables with values corresponding to each field. This is avoidable
overhead.

With this patch, we store the offsets of the fields at construction
time and perform the slice when a property is accessed.

This appears to show a very marginal performance win on its own and
the gains are so small as to not be worth reporting. However, this
patch marks the end of our parsing refactor, so it is worth reporting
the gains from the entire series:

author(mpm)
0.896565
0.795987  89%

desc(bug)
0.887169
0.803438  90%

date(2015)
0.878797
0.773961  88%

extra(rebase_source)
0.865446
0.761603  88%

author(mpm) or author(greg)
1.801832
1.576025  87%

author(mpm) or desc(bug)
1.812438
1.593335  88%

date(2015) or branch(default)
0.968276
0.875270  90%

author(mpm) or desc(bug) or date(2015) or extra(rebase_source)
3.656193
3.183104  87%

Pretty consistent speed-up across the board for any revset accessing
changelog revision data. Not bad!

It's also worth noting that PyPy appears to experience a similar to
marginally greater speed-up as well!

According to statprof, revsets accessing changelog revision data
are now clearly dominated by zlib decompression (16-17% of execution
time). Surprisingly, it appears the most expensive part of revision
parsing are the various text.index() calls to search for newlines!
These appear to cumulatively add up to 5+% of execution time. I
reckon implementing the parsing in C would make things marginally
faster.

If accessing larger strings (such as the commit message),
encoding.tolocal() is the most expensive procedure outside of
decompression.

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