Three defects, each of which made the database lose data that had already
been acknowledged, or answer a client with a malformed reply.
- Engine.commit decided a writer was "already covered" by comparing
log.end_pos with the position of the last completed commit. Under block
framing an append leaves its bytes in the log's open in-memory block and
does not move end_pos -- only sealing does. So once the first commit had
set committed_end = end_pos, every later write command found itself
covered and returned without sealing or syncing anything. A no-op
deleteMany followed by insertMany(50) was acknowledged with the file
still 16 bytes (its header) and lost all 50 documents on kill -9, which
is precisely what e2e2's crash pair does. Coverage is now decided by
sequence number, which counts records rather than bytes on disk.
- Compaction read the new log's end position before syncing it, but the
sync is what seals the open block, and the seal is what moves end_pos
past it. Appends after a compaction therefore started inside the
compacted file's last block and overwrote it, so those documents were
gone at the next replay: e2e6's phase 2 ended with 1000 documents in
memory and 996 after a graceful restart.
- cmd_find returned early on a missing namespace without putting anything
in the reply, so a find on an unknown collection arrived at the driver as
a response with no `ok` field ("MongoServerError: n/a") instead of an
empty cursor. The other commands' missing-namespace paths were fine.
Verified with the unit suite in ReleaseFast/ReleaseSafe/Debug, the split
fuzzer, all six e2e suites (e2e6 back to 72/72) and the kill -9 crash pair
-- none of which passed beforehand -- plus 13 kill -9 runs over 1/2/8
connections with 1200 acknowledged inserts each and nothing lost.
tests/e2e/results/phase7.txt records the benchmark with the fixes in place:
no regression against phase6 (bulk 739 -> 753 MB/s, updateMany 1.9 -> 2.0
ms, RSS 547 -> 546 MB), and concurrent durable writes now measurable at
7.1k/15.0k/21.8k docs/s over 1/8/32 connections.
The single engine-wide reader/writer lock is replaced by a lock hierarchy,
so writes to different collections no longer serialize on one mutex:
- Collections are heap-allocated, so their addresses are stable while a
command holds a collection lock (the maps only store pointers).
- A catalog rwlock guards the database/collection maps: shared for every
command (so a concurrent DDL cannot mutate the maps underneath it),
exclusive for create/drop/dropDatabase. Each collection has its own
rwlock; the ordering is always catalog -> collection -> log lock, never
two collection locks at once (TTL sweep and compaction take collections
one at a time).
- Command dispatch acquires the catalog + target collection locks for the
handler's duration, resolving the collection (creating it for writes)
under the catalog lock; create/drop upgrade to the exclusive catalog lock.
- Appends never fsync. Each write command's epilogue releases the
collection lock, then commits once (seal + fsync) with a leader/follower
group commit: the leader waits for writers mid-append (a pending counter)
so its seal covers them, and followers whose records the seal covered
skip their own fsync. Every acknowledged write is fsynced before its
reply (crash pair verified); an unacknowledged write may vanish and a
reader may observe a write before its fsync — ordinary w:1 j:true
semantics instead of 'the log describes >= memory'.
- Compaction snapshots collections without the log lock (so a concurrent
writer holding one can always finish its append) and retries when a
writer appended mid-snapshot (detected via the record seq), then swaps
under the log lock — no deadlock. The compaction trigger moved to the
command epilogue and the TTL monitor.
- Engine.dup_index moved to the collection (per-command error paths).
Also lands two B-tree edge-case fixes driven by tests that were in flight:
a churned leaf full of dead bytes no longer splits with an empty right
half (the leaf is repacked before splitting, and an emptied node's page is
fully free again), and a slot-count split with all large records on one
side shifts records between the halves until the new record fits. Plus a
randomised fuzz test over key sizes (src/fuzz_split.zig) and the two
regression tests.
Measured (tests/e2e/results/phase6.txt): no regression on the
single-connection benchmark; concurrent durable-insert throughput ~5.1k ->
12.5k docs/s from 1 -> 8 clients, ~14.8k at 32. Verified: unit suite in
all three modes, all e2e suites, the kill -9 crash pair.
Documents live as canonical BSON bytes in a segmented per-collection slab
(fixed 8 MiB segments keep capacity slack under one segment); the docs map
holds flat offsets that stay valid across segment growth, and removed
documents leave garbage bytes until compaction rewrites. The per-document
ArenaAllocator and its second full Pair-tree copy are gone.
The matcher walks the stored bytes directly, skipping by length any field
the filter does not name (a new bson byte-walker: element_key, skip_value,
read_value with borrowed leaves, get_at, and a borrowed spine parse). The
byte matcher is differential-tested against the tree matcher on a corpus
and shares its operator logic. Stored documents are never materialized on
the scan path or in aggregate $match; $group reads group keys and sums
straight off the bytes. Sort, projection, findAndModify, updates and
index entry generation use a borrowed spine into the slab (or the byte
collector, which also replaced collect_values in build_entries). The
compaction threshold now counts uncompressed data volume, since a
compressed log would otherwise never trigger.
Measured (tests/e2e/results/phase5.txt): server RSS 1979 -> 539 MB (2.4x
smaller than MongoDB; phase1 baseline 2.0 GB), range-scan 22.5 -> ~12 ms
(parity, best run faster than MongoDB), proj 4.1 -> 3.4 ms, createIndex
parity. Verified: unit suite in all three modes with zero leaks, the
crash pair, e2e6, and the stress/spill programs.
The log is now a 16-byte file header (magic, version, codec, block
target) plus a sequence of blocks. Each block keeps the pre-existing
record framing unchanged, so Engine.apply_record does not change; records
never straddle blocks (appends accumulate in memory and the block seals
at ~256 KiB). The block header's integrity hash covers the stored payload
bytes exactly as they sit on disk, so the decompressor only ever sees
input already proven intact. Torn tails stay distinguishable from
interior corruption exactly as before: a short read, an impossible
length, or a hash mismatch in the final block truncates cleanly (later
appends overwrite the garbage); a hash mismatch anywhere else is
error.InvalidLog.
The codec is a hand-rolled LZ4 block compressor/decompressor (~1.7 GB/s
measured) with a per-block codec byte falling back to raw when
compression does not help; the header keeps raw legal so zstd can be
swapped in later. Zig 0.16 ships zstd decompression only, and deflate
would cap writes below the insert rate.
Engine.compact goes through the same Log API (deferred sync, one commit)
and compresses for free; sync() seals the pending block before fsyncing,
so the acknowledged-write durability semantics are unchanged (an
unsealed block holds only unacknowledged batch records).
Measured (tests/e2e/results/phase4.txt): db on disk 1025 -> 97 MB, now
smaller than MongoDB's own compressed files; bulk insert 816 -> 722 MB/s
(the accepted compression cost); reopen unchanged at 0.8 s.
Verified: unit suite in all three optimize modes (new LZ4 round-trip,
corrupt-block, and torn-tail truncation tests), the crash pair, e2e6
(kill -9 mid-write), and two full benchmark runs.
Give every Collection an implicit _id_ index (a normal Index with keys
[_id: 1]) so _id equality, $in, ranges and sorts stop depending on the
docs-map hash or a full scan. Kept out of the secondary indexes list, so
listIndexes/dropIndexes/createIndex and the log format are unchanged (no
index_create record, no double listing) and e2e3.js passes unmodified.
Maintained in upsert through the same reserve-then-insert protocol as
the secondaries, removed in evict_doc, and rebuilt after replay by
build_all_indexes alongside them (never maintained mid-replay, so a
failed add can't leave the index under-approximating). index.plan now
takes it as a separate argument. Its keys are canonical
(bson.encode_key gives int32 1, int64 1 and double 1.0 identical bytes),
so the serialization-guarded docs-map fast path (plan_id,
value_fast_path_safe and friends) is deleted.
Measured (tests/e2e/results/phase3.txt): sort({_id:-1}).limit(20) 6.2 ->
2.4 ms (2.3x slower than MongoDB -> parity); integer/string _id point
lookups, $in and ranges verified against the tree. Unit suite in all
three optimize modes, the crash pair, e2e3/e2e4/e2e6.
Replace Index.entries (one sorted array) with a B+tree so writes into an
already-built index stop being quadratic. Nodes are fixed 4 KiB slotted
pages in a flat u32-addressed ArrayListUnmanaged(Node); records longer
than a quarter page spill to an append-only overflow slab (BSON strings
reach 16 MB). Leaves are doubly linked for ordered iteration; the flat
node array stays one contiguous byte range for a later checkpoint.
Insertion descends by separator and splits leaves/internals upward,
promoting keys via a stable copy (a nested split can otherwise clobber
the promoted-key scratch). Deletion does not rebalance: emptied leaves
are unlinked and dropped from their parent, internal nodes may carry one
child, and dead pages are abandoned in place (node memory peaks at the
tree's peak size, exactly what the old array's capacity did). Lookups
are lower-bound seeks plus leaf-chain band scans, so equal keys may
span leaves freely. Bulk build (append_doc_entries + finish_bulk) sorts
a staging array and packs leaves bottom-up. reserve_for now takes the
built entries and reserves exact overflow bytes plus a worst-case node
count, keeping insert_entries infallible after the log append.
db.zig: TTL sweep now seeks the minimum-datetime encoded key and walks
the contiguous datetime band, stopping at the cutoff or type change.
Measured (tests/e2e/results/phase2.txt): updateMany 17.3 -> 1.8 ms
(2.8x slower than MongoDB -> 3.7x faster), createIndex 62 -> 51 ms.
Verified: unit suite ReleaseFast/ReleaseSafe/Debug (incl. the existing
lookup_range and remove_doc differentials, plus a new incremental
insert/remove differential against a brute-force model), the crash pair,
e2e3/e2e4/e2e6, and dev stress tests for depth-2 splits, full drains,
and spilled records through internal levels.
Matching allocated an ArrayList per filter field per candidate document,
on the process-wide allocator, to hold what is almost always a single
value. Candidates now collect into a stack buffer that spills to the heap
only for arrays: measured 15.7 -> 12.0ms on a 65,536-document range scan.
The OOM-propagation test moves with it. Its point is that a failed
collection must surface as an error rather than an empty candidate list,
which would make $ne and $exists:false report a match -- a wrong answer
rather than a failed one. That invariant still holds on the spill path, so
the test now uses an array long enough to reach the allocator, and a new
test pins the flip side: the common single-value match now completes
correctly even when the allocator always fails, because it never calls it.
Query operators were dispatched by a chain of up to fourteen mem.eql per
value per document, with $gt/$gte/$lt/$lte re-comparing the operator name
inside the loop over candidate values. Names resolve to an enum once per
filter field. Command dispatch likewise walked a 30-entry table comparing
strings; it is a comptime StaticStringMap now.
Each request built a fresh reply arena and handed its pages straight back.
One reply per connection, reset between requests, keeps them.
countDocuments() arrives as [{$match: F}?, {$group: {_id: <literal>,
n: {$sum: 1}}}], which the general path answered by materializing every
matching document and discarding them all. It is now recognized and
answered from a counting scan: countDocuments({}) 2.3 -> 1.5ms.
The detector is deliberately conservative -- grouping by "$field", summing
a field, an unmodelled accumulator or any extra stage all fall through to
the general path, since those need the documents themselves. A unit test
pins each accept and reject, and the whole count path was checked against
the general one through the real driver, including the shapes that must
not take it.
The filtered range-scan row does not move: it is bound by walking 65,536
documents that each live in their own arena, not by the matcher. That is
Phase 4 work.
Verified: 77 unit tests under ReleaseFast and ReleaseSafe, e2e
29/16/17/3/2, the crash pair, e2e6 72/72.
Reproduce with bash tests/e2e/compare-run.sh 1g 16k. Without a stored
baseline the phase gates in the plan are not checkable and the projected
numbers are not falsifiable.
