Aleksey Shakhmatov
504179acd1
PLAN D7's six items, with the numbers and the command that reproduces each in
tests/e2e/results/m0-gates.txt. Unit tests green in both optimize modes, the
whole e2e matrix green, the spec scorecard byte-identical at 131/161/195, and
the large smoke run at the scale D7.3 asked for:
21.47 GB collection (1,310,720 x 16 KiB)
data file 21.75 GB (+1.3% over the documents)
log after the load 2.5 MB (checkpoints reclaim it)
kill -9 then reopen 0.5 s (0.5 s at 4 GB too -- flat)
RSS after reopen 237 MB (1.1% of the data)
count after restart 1,310,720 last document byte-intact
acked writes after kill 200/200
That is the milestone's claim, measured: an open costs the working set rather
than the size of the database. Before M0 the same measurement was 523 MB
resident for a 512 MB database, because recovering each document's `_id` meant
reading every document at open.
Two gates need reading rather than a tick, and m0-gates.txt says so where a
reader would otherwise take a tick for granted.
The churn gate settles at 1.65x live data (delete-heavy) to 2.47x
(update-heavy), flat, above the ~1.3x amendment A2 hoped for. Rebuild-only
reclamation cannot reach that: it needs a whole second copy of the live data
before the first can be freed. The gate existed to decide whether doc-level
free lists are needed after M0, and that is the answer.
Benchmark parity holds for every read and latency row inside the run-to-run
spread, and bulk insert regresses 24% (732 -> 555 MB/s), reproducibly across
three runs. Risk 1 as written: document bytes now reach the disk uncompressed
on top of the LZ4 log. createIndex improves 62% from the same change.
Three measurement bugs fixed while running the gates, because each would have
put a false number in the README:
- `compare-run.sh` measured "db on disk" as `du` of the log alone against
`du` of mongod's whole dbpath. It reported 20 MB for a 1 GB collection --
the documents had moved to <db>.data. Honest figure, measured: 914 MB of
allocated blocks against mongod's compressed 85 MB.
- `big.js` counted "compaction events" as "the log shrank", which is a
*checkpoint* now. It claimed 12 compaction rewrites during a pure insert
load, which has no garbage to compact.
- `big.js` labelled peak RSS "in-memory engine: docs live in RAM" and its
summary said the collection was held "fully in RAM". Both were true of the
engine this milestone replaced.
README: the storage section described an all-in-RAM engine; the comparison
table mixed one old run's body with three new rows; and `findOne({_id})` was
documented as a full scan for integer ids, which the ordered `_id_` index made
false (2 ms against 55 s for a scan of the same 21.5 GB collection). The table
is now best-of-three for both servers, with the measured variance stated, since
two runs of the same binary moved the sub-10 ms rows by 27-51%.
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37 lines
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# multiforadb vs MongoDB benchmark
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# date: 2026-08-03T19:42:00Z git: 5228ed7+dirty
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# args: size=1g doc-size=16k wc=j clients=1 4 8 16 32 per-client=2000
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# reproduce: bash tests/e2e/bench-run.sh 1g 16k "1 4 8 16 32"
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[main]
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insertOne (sequential) ×200 0.20 ms 4.9 ms
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bulk insert throughput 463.4 MB/s 581.3 MB/s
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docs loaded 65,536 65,536
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createIndex({k: 1}) 27.9 ms 78.1 ms
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countDocuments({}) 2.6 ms 12.8 ms
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findOne({_id: <ObjectId>}) 0.64 ms 0.72 ms
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findOne({k: 500}) (indexed) 0.59 ms 1.6 ms
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find({p: {$gte,$lt}}).count() (scan) 14.1 ms 12.5 ms
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find({}).sort({_id:-1}).limit(20) 1.5 ms 2.4 ms
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find({}, {proj}).limit(1000) 3.6 ms 4.7 ms
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aggregate $group by k 8.4 ms 13.4 ms
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updateOne({_id}) ×50 0.15 ms 0.23 ms
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updateMany({k: 7}, {$inc}) 1.9 ms 7.0 ms
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deleteOne({_id}) + insertOne 0.63 ms 5.8 ms
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node client RSS 153 MB 155 MB
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[concurrency]
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clients 1 8354 204 41.0x
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clients 4 465 0.0x
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clients 8 978 0.0x
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clients 16 1877 0.0x
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clients 32 3765 0.0x
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[meta]
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mfdb_rss_mb 1059
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md_rss_mb 1203
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mfdb_reopen 0.3s
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md_reopen 1.3s
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mfdb_disk_mb 20MB
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md_disk_mb 83MB
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