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prune: add --jobs support
Use multiprocessing to run in parallel. When operating on multiple projects, this can greatly speed things up. Across 1000 repos, it goes from ~10sec to ~4sec with the default -j8. This only does a simple conversion over to get an easy speedup. It is currently written to collect all results before displaying them. If we refactored this module more, we could have it display results as they came in. Change-Id: I5caf4ca51df0b7f078f0db104ae5232268482c1c Reviewed-on: https://gerrit-review.googlesource.com/c/git-repo/+/298643 Reviewed-by: Chris Mcdonald <cjmcdonald@google.com> Tested-by: Mike Frysinger <vapier@google.com>
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@ -12,8 +12,11 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import itertools
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import multiprocessing
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from color import Coloring
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from command import PagedCommand
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from command import DEFAULT_LOCAL_JOBS, PagedCommand, WORKER_BATCH_SIZE
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class Prune(PagedCommand):
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@ -22,11 +25,29 @@ class Prune(PagedCommand):
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helpUsage = """
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%prog [<project>...]
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"""
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PARALLEL_JOBS = DEFAULT_LOCAL_JOBS
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def _ExecuteOne(self, project):
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"""Process one project."""
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return project.PruneHeads()
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def Execute(self, opt, args):
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all_branches = []
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for project in self.GetProjects(args):
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all_branches.extend(project.PruneHeads())
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projects = self.GetProjects(args)
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# NB: Should be able to refactor this module to display summary as results
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# come back from children.
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def _ProcessResults(results):
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return list(itertools.chain.from_iterable(results))
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# NB: Multiprocessing is heavy, so don't spin it up for one job.
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if len(projects) == 1 or opt.jobs == 1:
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all_branches = _ProcessResults(self._ExecuteOne(x) for x in projects)
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else:
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with multiprocessing.Pool(opt.jobs) as pool:
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results = pool.imap(
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self._ExecuteOne, projects,
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chunksize=WORKER_BATCH_SIZE)
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all_branches = _ProcessResults(results)
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if not all_branches:
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return
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