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d8410a816b
We use a custom hash in fast-import to store the set of objects we've
imported so far. It has a fixed set of 2^16 buckets and chains any
collisions with a linked list. As the number of objects grows larger
than that, the load factor increases and we degrade to O(n) lookups and
O(n^2) insertions.
We can scale better by using our hashmap.c implementation, which will
resize the bucket count as we grow. This does incur an extra memory cost
of 8 bytes per object, as hashmap stores the integer hash value for each
entry in its hashmap_entry struct (which we really don't care about
here, because we're just reusing the embedded object hash). But I think
the numbers below justify this (and our per-object memory cost is
already much higher).
I also looked at using khash, but it seemed to perform slightly worse
than hashmap at all sizes, and worse even than the existing code for
small sizes. It's also awkward to use here, because we want to look up a
"struct object_entry" from a "struct object_id", and it doesn't handle
mismatched keys as well. Making a mapping of object_id to object_entry
would be more natural, but that would require pulling the embedded oid
out of the object_entry or incurring an extra 32 bytes per object.
In a synthetic test creating as many cheap, tiny objects as possible
perl -e '
my $bits = shift;
my $nr = 2**$bits;
for (my $i = 0; $i < $nr; $i++) {
print "blob\n";
print "data 4\n";
print pack("N", $i);
}
' $bits | git fast-import
I got these results:
nr_objects master khash hashmap
2^20 0m4.317s 0m5.109s 0m3.890s
2^21 0m10.204s 0m9.702s 0m7.933s
2^22 0m27.159s 0m17.911s 0m16.751s
2^23 1m19.038s 0m35.080s 0m31.963s
2^24 4m18.766s 1m10.233s 1m6.793s
which points to hashmap as the winner. We didn't have any perf tests for
fast-export or fast-import, so I added one as a more real-world case.
It uses an export without blobs since that's significantly cheaper than
a full one, but still is an interesting case people might use (e.g., for
rewriting history). It will emphasize this change in some ways (as a
percentage we spend more time making objects and less shuffling blob
bytes around) and less in others (the total object count is lower).
Here are the results for linux.git:
Test HEAD^ HEAD
----------------------------------------------------------------------------
9300.1: export (no-blobs) 67.64(66.96+0.67) 67.81(67.06+0.75) +0.3%
9300.2: import (no-blobs) 284.04(283.34+0.69) 198.09(196.01+0.92) -30.3%
It only has ~5.2M commits and trees, so this is a larger effect than I
expected (the 2^23 case above only improved by 50s or so, but here we
gained almost 90s). This is probably due to actually performing more
object lookups in a real import with trees and commits, as opposed to
just dumping a bunch of blobs into a pack.
Signed-off-by: Jeff King <peff@peff.net>
Signed-off-by: Junio C Hamano <gitster@pobox.com>
23 lines
678 B
Bash
Executable file
23 lines
678 B
Bash
Executable file
#!/bin/sh
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test_description='test fast-import and fast-export performance'
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. ./perf-lib.sh
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test_perf_default_repo
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# Use --no-data here to produce a vastly smaller export file.
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# This is much cheaper to work with but should still exercise
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# fast-import pretty well (we'll still process all commits and
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# trees, which account for 60% or more of objects in most repos).
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#
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# Use --reencode to avoid the default of aborting on non-utf8 commits,
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# which lets this test run against a wider variety of sample repos.
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test_perf 'export (no-blobs)' '
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git fast-export --reencode=yes --no-data HEAD >export
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'
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test_perf 'import (no-blobs)' '
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git fast-import --force <export
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'
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test_done
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