upstream/mercurial-mirror Files · contrib/benchmarks/perf.py

bookmarks: cache reverse mapping (issue5868)...

bookmarks: cache reverse mapping (issue5868) I chose a simpler implementation. If the initial cost of building reverse mapping is significant, we'll have to move it under @propertycache. The nodemap could be a dict of sets, but I think keeping a sorted list is better since each node is likely to have zero/one bookmark. Micro-benchmark with 1001 bookmarks and 1001 revisions: $ for n in `seq 0 1000`; do touch $n; hg book book$n; hg ci -qAm$n; done $ hg bookmarks --time > /dev/null (orig) time: real 0.040 secs (user 0.050+0.000 sys 0.000+0.000) (new) time: real 0.040 secs (user 0.040+0.000 sys 0.010+0.000) $ hg log -T '{bookmarks}\n' --time > /dev/null (orig) time: real 0.160 secs (user 0.160+0.000 sys 0.000+0.000) (new) time: real 0.090 secs (user 0.100+0.000 sys 0.000+0.000)

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      # perf.py - asv benchmarks using contrib/perf.py extension

      #

      # Copyright 2016 Logilab SA <contact@logilab.fr>

      #

      # 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 . import perfbench

      @perfbench()

      def track_tags(perf):

          return perf("perftags")

      @perfbench()

      def track_status(perf):

          return perf("perfstatus", unknown=False)

      @perfbench(params=[('rev', ['1000', '10000', 'tip'])])

      def track_manifest(perf, rev):

          return perf("perfmanifest", rev)

      @perfbench()

      def track_heads(perf):

          return perf("perfheads")

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				# perf.py - asv benchmarks using contrib/perf.py extension
				#
				# Copyright 2016 Logilab SA <contact@logilab.fr>
				#
				# 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 . import perfbench

				@perfbench()
				def track_tags(perf):
				return perf("perftags")

				@perfbench()
				def track_status(perf):
				return perf("perfstatus", unknown=False)

				@perfbench(params=[('rev', ['1000', '10000', 'tip'])])
				def track_manifest(perf, rev):
				return perf("perfmanifest", rev)

				@perfbench()
				def track_heads(perf):
				return perf("perfheads")