Mercurial > hg
view mercurial/filesetlang.py @ 42043:1fac9b931d46
compression: introduce a `storage.revlog.zlib.level` configuration
This option control the zlib compression level used when compression revlog
chunk.
This is also a good excuse to pave the way for a similar configuration option
for the zstd compression engine. Having a dedicated option for each compression
algorithm is useful because they don't support the same range of values.
Using a higher zlib compression impact CPU consumption at compression time, but
does not directly affected decompression time. However dealing with small
compressed chunk can directly help decompression and indirectly help other
revlog logic.
I ran some basic test on repositories using different level. I am using the
mercurial, pypy, netbeans and mozilla-central clone from our benchmark suite.
All tested repository use sparse-revlog and got all their delta recomputed.
The different compression level has a small effect on the repository size
(about 10% variation in the total range). My quick analysis is that revlog
mostly store small delta, that are not affected by the compression level much.
So the variation probably mostly comes from better compression of the snapshots
revisions, and snapshot revision only represent a small portion of the
repository content.
I also made some basic timings measurements. The "read" timings are gathered using
simple run of `hg perfrevlogrevisions`, the "write" timings using `hg
perfrevlogwrite` (restricted to the last 5000 revisions for netbeans and
mozilla central). The timings are gathered on a generic machine, (not one of
our performance locked machine), so small variation might not be meaningful.
However large trend remains relevant.
Keep in mind that these numbers are not pure compression/decompression time.
They also involve the full revlog logic. In particular the difference in chunk
size has an impact on the delta chain structure, affecting performance when
writing or reading them.
On read/write performance, the compression level has a bigger impact.
Counter-intuitively, the higher compression levels improve "write" performance
for the large repositories in our tested setting. Maybe because the last 5000
delta chain end up having a very different shape in this specific spot? Or maybe
because of a more general trend of better delta chains thanks to the smaller
chunk and snapshot.
This series does not intend to change the default compression level. However,
these result call for a deeper analysis of this performance difference in the
future.
Full data
=========
repo level .hg/store size 00manifest.d read write
----------------------------------------------------------------
mercurial 1 49,402,813 5,963,475 0.170159 53.250304
mercurial 6 47,197,397 5,875,730 0.182820 56.264320
mercurial 9 47,121,596 5,849,781 0.189219 56.293612
pypy 1 370,830,572 28,462,425 2.679217 460.721984
pypy 6 340,112,317 27,648,747 2.768691 467.537158
pypy 9 338,360,736 27,639,003 2.763495 476.589918
netbeans 1 1,281,847,810 165,495,457 122.477027 520.560316
netbeans 6 1,205,284,353 159,161,207 139.876147 715.930400
netbeans 9 1,197,135,671 155,034,586 141.620281 678.297064
mozilla 1 2,775,497,186 298,527,987 147.867662 751.263721
mozilla 6 2,596,856,420 286,597,671 170.572118 987.056093
mozilla 9 2,587,542,494 287,018,264 163.622338 739.803002
author | Pierre-Yves David <pierre-yves.david@octobus.net> |
---|---|
date | Wed, 27 Mar 2019 18:35:27 +0100 |
parents | e79a69af1593 |
children | 2372284d9457 |
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# filesetlang.py - parser, tokenizer and utility for file set language # # Copyright 2010 Matt Mackall <mpm@selenic.com> # # This software may be used and distributed according to the terms of the # GNU General Public License version 2 or any later version. from __future__ import absolute_import from .i18n import _ from . import ( error, parser, pycompat, ) # common weight constants for static optimization # (see registrar.filesetpredicate for details) WEIGHT_CHECK_FILENAME = 0.5 WEIGHT_READ_CONTENTS = 30 WEIGHT_STATUS = 10 WEIGHT_STATUS_THOROUGH = 50 elements = { # token-type: binding-strength, primary, prefix, infix, suffix "(": (20, None, ("group", 1, ")"), ("func", 1, ")"), None), ":": (15, None, None, ("kindpat", 15), None), "-": (5, None, ("negate", 19), ("minus", 5), None), "not": (10, None, ("not", 10), None, None), "!": (10, None, ("not", 10), None, None), "and": (5, None, None, ("and", 5), None), "&": (5, None, None, ("and", 5), None), "or": (4, None, None, ("or", 4), None), "|": (4, None, None, ("or", 4), None), "+": (4, None, None, ("or", 4), None), ",": (2, None, None, ("list", 2), None), ")": (0, None, None, None, None), "symbol": (0, "symbol", None, None, None), "string": (0, "string", None, None, None), "end": (0, None, None, None, None), } keywords = {'and', 'or', 'not'} symbols = {} globchars = ".*{}[]?/\\_" def tokenize(program): pos, l = 0, len(program) program = pycompat.bytestr(program) while pos < l: c = program[pos] if c.isspace(): # skip inter-token whitespace pass elif c in "(),-:|&+!": # handle simple operators yield (c, None, pos) elif (c in '"\'' or c == 'r' and program[pos:pos + 2] in ("r'", 'r"')): # handle quoted strings if c == 'r': pos += 1 c = program[pos] decode = lambda x: x else: decode = parser.unescapestr pos += 1 s = pos while pos < l: # find closing quote d = program[pos] if d == '\\': # skip over escaped characters pos += 2 continue if d == c: yield ('string', decode(program[s:pos]), s) break pos += 1 else: raise error.ParseError(_("unterminated string"), s) elif c.isalnum() or c in globchars or ord(c) > 127: # gather up a symbol/keyword s = pos pos += 1 while pos < l: # find end of symbol d = program[pos] if not (d.isalnum() or d in globchars or ord(d) > 127): break pos += 1 sym = program[s:pos] if sym in keywords: # operator keywords yield (sym, None, s) else: yield ('symbol', sym, s) pos -= 1 else: raise error.ParseError(_("syntax error"), pos) pos += 1 yield ('end', None, pos) def parse(expr): p = parser.parser(elements) tree, pos = p.parse(tokenize(expr)) if pos != len(expr): raise error.ParseError(_("invalid token"), pos) return parser.simplifyinfixops(tree, {'list', 'or'}) def getsymbol(x): if x and x[0] == 'symbol': return x[1] raise error.ParseError(_('not a symbol')) def getstring(x, err): if x and (x[0] == 'string' or x[0] == 'symbol'): return x[1] raise error.ParseError(err) def getkindpat(x, y, allkinds, err): kind = getsymbol(x) pat = getstring(y, err) if kind not in allkinds: raise error.ParseError(_("invalid pattern kind: %s") % kind) return '%s:%s' % (kind, pat) def getpattern(x, allkinds, err): if x and x[0] == 'kindpat': return getkindpat(x[1], x[2], allkinds, err) return getstring(x, err) def getlist(x): if not x: return [] if x[0] == 'list': return list(x[1:]) return [x] def getargs(x, min, max, err): l = getlist(x) if len(l) < min or len(l) > max: raise error.ParseError(err) return l def _analyze(x): if x is None: return x op = x[0] if op in {'string', 'symbol'}: return x if op == 'kindpat': getsymbol(x[1]) # kind must be a symbol t = _analyze(x[2]) return (op, x[1], t) if op == 'group': return _analyze(x[1]) if op == 'negate': raise error.ParseError(_("can't use negate operator in this context")) if op == 'not': t = _analyze(x[1]) return (op, t) if op == 'and': ta = _analyze(x[1]) tb = _analyze(x[2]) return (op, ta, tb) if op == 'minus': return _analyze(('and', x[1], ('not', x[2]))) if op in {'list', 'or'}: ts = tuple(_analyze(y) for y in x[1:]) return (op,) + ts if op == 'func': getsymbol(x[1]) # function name must be a symbol ta = _analyze(x[2]) return (op, x[1], ta) raise error.ProgrammingError('invalid operator %r' % op) def _insertstatushints(x): """Insert hint nodes where status should be calculated (first path) This works in bottom-up way, summing up status names and inserting hint nodes at 'and' and 'or' as needed. Thus redundant hint nodes may be left. Returns (status-names, new-tree) at the given subtree, where status-names is a sum of status names referenced in the given subtree. """ if x is None: return (), x op = x[0] if op in {'string', 'symbol', 'kindpat'}: return (), x if op == 'not': h, t = _insertstatushints(x[1]) return h, (op, t) if op == 'and': ha, ta = _insertstatushints(x[1]) hb, tb = _insertstatushints(x[2]) hr = ha + hb if ha and hb: return hr, ('withstatus', (op, ta, tb), ('string', ' '.join(hr))) return hr, (op, ta, tb) if op == 'or': hs, ts = zip(*(_insertstatushints(y) for y in x[1:])) hr = sum(hs, ()) if sum(bool(h) for h in hs) > 1: return hr, ('withstatus', (op,) + ts, ('string', ' '.join(hr))) return hr, (op,) + ts if op == 'list': hs, ts = zip(*(_insertstatushints(y) for y in x[1:])) return sum(hs, ()), (op,) + ts if op == 'func': f = getsymbol(x[1]) # don't propagate 'ha' crossing a function boundary ha, ta = _insertstatushints(x[2]) if getattr(symbols.get(f), '_callstatus', False): return (f,), ('withstatus', (op, x[1], ta), ('string', f)) return (), (op, x[1], ta) raise error.ProgrammingError('invalid operator %r' % op) def _mergestatushints(x, instatus): """Remove redundant status hint nodes (second path) This is the top-down path to eliminate inner hint nodes. """ if x is None: return x op = x[0] if op == 'withstatus': if instatus: # drop redundant hint node return _mergestatushints(x[1], instatus) t = _mergestatushints(x[1], instatus=True) return (op, t, x[2]) if op in {'string', 'symbol', 'kindpat'}: return x if op == 'not': t = _mergestatushints(x[1], instatus) return (op, t) if op == 'and': ta = _mergestatushints(x[1], instatus) tb = _mergestatushints(x[2], instatus) return (op, ta, tb) if op in {'list', 'or'}: ts = tuple(_mergestatushints(y, instatus) for y in x[1:]) return (op,) + ts if op == 'func': # don't propagate 'instatus' crossing a function boundary ta = _mergestatushints(x[2], instatus=False) return (op, x[1], ta) raise error.ProgrammingError('invalid operator %r' % op) def analyze(x): """Transform raw parsed tree to evaluatable tree which can be fed to optimize() or getmatch() All pseudo operations should be mapped to real operations or functions defined in methods or symbols table respectively. """ t = _analyze(x) _h, t = _insertstatushints(t) return _mergestatushints(t, instatus=False) def _optimizeandops(op, ta, tb): if tb is not None and tb[0] == 'not': return ('minus', ta, tb[1]) return (op, ta, tb) def _optimizeunion(xs): # collect string patterns so they can be compiled into a single regexp ws, ts, ss = [], [], [] for x in xs: w, t = _optimize(x) if t is not None and t[0] in {'string', 'symbol', 'kindpat'}: ss.append(t) continue ws.append(w) ts.append(t) if ss: ws.append(WEIGHT_CHECK_FILENAME) ts.append(('patterns',) + tuple(ss)) return ws, ts def _optimize(x): if x is None: return 0, x op = x[0] if op == 'withstatus': w, t = _optimize(x[1]) return w, (op, t, x[2]) if op in {'string', 'symbol'}: return WEIGHT_CHECK_FILENAME, x if op == 'kindpat': w, t = _optimize(x[2]) return w, (op, x[1], t) if op == 'not': w, t = _optimize(x[1]) return w, (op, t) if op == 'and': wa, ta = _optimize(x[1]) wb, tb = _optimize(x[2]) if wa <= wb: return wa, _optimizeandops(op, ta, tb) else: return wb, _optimizeandops(op, tb, ta) if op == 'or': ws, ts = _optimizeunion(x[1:]) if len(ts) == 1: return ws[0], ts[0] # 'or' operation is fully optimized out ts = tuple(it[1] for it in sorted(enumerate(ts), key=lambda it: ws[it[0]])) return max(ws), (op,) + ts if op == 'list': ws, ts = zip(*(_optimize(y) for y in x[1:])) return sum(ws), (op,) + ts if op == 'func': f = getsymbol(x[1]) w = getattr(symbols.get(f), '_weight', 1) wa, ta = _optimize(x[2]) return w + wa, (op, x[1], ta) raise error.ProgrammingError('invalid operator %r' % op) def optimize(x): """Reorder/rewrite evaluatable tree for optimization All pseudo operations should be transformed beforehand. """ _w, t = _optimize(x) return t def prettyformat(tree): return parser.prettyformat(tree, ('string', 'symbol'))