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Round 1:
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100k: 15 minutes 20 seconds
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1k: 0 minutes 7 seconds
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%timeit {c['_type']: c['count'] for c in repo_version.content.values('_type').annotate(count=Count('_type'))}
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>20 minutes (cut off)
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%timeit list(Content.objects.filter(pk__in=repo_version.content))
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4.52 s ± 156 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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Round 2 (101,000 pre-existing units):
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100k: 10 minutes 8 seconds
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1k: 0 minutes 17 seconds
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%timeit {c['_type']: c['count'] for c in repo_version.content.values('_type').annotate(count=Count('_type'))}
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>20 minutes (cut off)
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%timeit list(Content.objects.filter(pk__in=repo_version.content))
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6.18 s ± 30.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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Round 3 (202,000 pre-existing units):
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100k: 11 minutes 27 seconds
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1k: 0 minutes 22 seconds
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%timeit {c['_type']: c['count'] for c in repo_version.content.values('_type').annotate(count=Count('_type'))}
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>20 minutes (cut off)
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%timeit list(Content.objects.filter(pk__in=repo_version.content))
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10.4 s ± 66.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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Round 4 (303,000 pre-existing units):
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100k: 22 minutes 5 seconds
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1k: 0 minutes 26 seconds
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%timeit {c['_type']: c['count'] for c in repo_version.content.values('_type').annotate(count=Count('_type'))}
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>20 minutes (cut off)
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%timeit list(Content.objects.filter(pk__in=repo_version.content))
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16.4 s ± 360 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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Round 5 (fresh repo, background 404,000 pre-existing units):
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100k: <skipped>
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1k: <skipped>
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%timeit {c['_type']: c['count'] for c in repo_version.content.values('_type').annotate(count=Count('_type'))}
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<skipped>
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%timeit list(Content.objects.filter(pk__in=repo_version.content))
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<skipped>
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Final database size: <skipped>
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.