MultiforaDB/ROADMAP.md

Remaining performance work

Status: all five items are done — verified in tests/e2e/results/phase2.txt through phase6.txt: updateMany 17.3 → 1.7 ms, createIndex 62 → 51 ms, _id sort+limit 6.2 → 2.4 ms, db on disk 1025 → 97 MB, server RSS 1979 → 539 MB with the range scan at parity (best run faster than MongoDB), and the global lock decomposed into catalog + per-collection locks with cross-connection group commit (item 5, no regression on the single-connection benchmark; concurrent-write throughput 1 → 8 clients ~5.1k → 12.5k docs/s, 32 clients ~14.8k).

Current numbers and what they mean are in the README; the recorded baseline is tests/e2e/results/phase1.txt, reproduced with bash tests/e2e/compare-run.sh 1g 16k.

Ground rules

These held for everything already done and should hold here.

• Measure A/B on one harness. Do not trust the model. Several predictions made while planning this work were wrong in both directions: CRC32 cost twice what was estimated, one compaction fix turned out to be numerically identical to the code it replaced, and a matcher change that measured 1.3x in isolation did not move its benchmark row at all. The cheap way to A/B is to flip one line, rebuild, run, flip it back.
• Mutation-check any test guarding an invariant. Break the thing the test is supposed to catch and confirm it goes red. Several tests here were written, looked reasonable, and only proved to have teeth after this.
• The index invariant is absolute (src/index.zig header): an index only generates candidates, and the full filter is re-applied afterwards. Over-approximating is slow. Under-approximating is a wrong answer.
• The database must always open. A unique index that finds duplicates in existing data warns and keeps going; replay never refuses to start over recoverable damage.

Verification for every item:

zig build test                       # unit, ReleaseFast
zig build test -Doptimize=ReleaseSafe # again with safety checks on
zig build                            # NOTE: `zig build test` does not refresh the binary
node tests/e2e/e2e6.js               # 72 checks, self-contained, includes kill -9

Anything touching the write path or the log format also needs the crash pair (e2e2.js crash-a, kill -9, restart, e2e2.js crash-b), and anything touching indexes needs e2e3.js and e2e4.js.

---

---

1. B-tree over the encoded keys — DONE

Landed as a B+tree in src/index.zig: fixed 4 KiB slotted pages in a flat u32-addressed ArrayListUnmanaged(Node), an append-only overflow slab for records longer than a quarter page (BSON strings reach 16 MB), leaves linked for ordered iteration, bulk bottom-up packing for append_doc_entries + finish_bulk, remove_doc regenerating entries and removing them with a descent plus a leaf-local edit, and no rebalancing on delete (empty leaves are unlinked and dropped; internal nodes may carry one child). The flat node array stays the contiguous byte range that item 4 can write into a checkpoint. Deletes abandon dead pages rather than reusing them, so node memory peaks at the tree's peak size, exactly what the old entry array's capacity did.

Recorded deltas vs tests/e2e/results/phase1.txt: updateMany 17.3 → 1.8 ms (2.8x slower than MongoDB → 3.7x faster), createIndex 62.4 → 50.8 ms. Verified with zig build test (ReleaseFast and ReleaseSafe), the crash pair, e2e3.js/e2e4.js/e2e6.js, plus a 50k-entry stress (bulk build, random inserts, random deletes, full drain) and a spill stress (2 KiB keys through splits and internal nodes).

Why. Index entries live in one sorted array, so inserting an entry memmoves the tail. Building an index is fine (entries are appended and sorted once) and removal is fine (regenerated and found by binary search), but inserting into a collection that already has an index is quadratic. It also blocks item 2.

What. Replace Index.entries with a B+tree whose nodes live in a flat ArrayListUnmanaged(Node) addressed by u32. Slotted 4 KiB pages; keys longer than a quarter of a node spill to an overflow slab (BSON strings reach 16 MB, so this is not optional). Leaves linked for ordered iteration.

The flat u32-indexed node array is a deliberate choice over pointer-linked nodes: it makes the whole index one contiguous byte range that item 4 can write into a checkpoint and read back without rebuilding.

Keep. append_doc_entries + finish_bulk become bulk leaf packing (sort, fill leaves, build interior levels bottom-up). remove_doc's regenerate-and-search approach carries over unchanged — only the lookup underneath it changes.

Watch. Deletion is where B+trees get subtly wrong. Not rebalancing on delete (leaving underfull leaves, letting compaction reclaim them) is a legitimate simplification and much easier to get right; take it unless there is a reason not to.

Tests that must keep passing, unchanged: the randomized lookup_range check against a brute-force filter, and the remove_doc-versus-scan differential. Both already exist and both are mutation-checked.

---

2. Ordered _id index — DONE

Landed as an implicit _id_ index on every Collection (a normal index.Index with keys [_id: 1], kept out of the secondary indexes list so listIndexes/dropIndexes/createIndex and the log format are unchanged — no index_create record, no double listing). Maintained in upsert (through the same reserve-then-insert protocol as the secondaries) and evict_doc; rebuilt after replay by build_all_indexes alongside the secondaries. index.plan now takes it as a separate argument, so {_id: ...} equality, $in and ranges use the tree (the old serialization-guarded docs-map fast path — plan_id, value_fast_path_safe and friends — is deleted), and sort({_id: ...}) becomes an index-ordered full scan with an early stop. A full _id scan cannot miss a document: every doc has an _id and the index is not sparse, and its keys are canonical (bson.encode_key gives int32 1, int64 1 and double 1.0 identical bytes).

Recorded deltas vs tests/e2e/results/phase2.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 no longer full-scan.

Why. sort({_id: ...}) still materializes every candidate, and integer _ids still fall back to a full collection scan on every findOne, updateOne and deleteOne.

What. Give Collection an index over the encoded _id, maintained in upsert, evict_doc and apply_record. Every document has an _id and it is not sparse, so entry count equals document count and a full index scan cannot miss a document — which is what the sort planner's full-scan plan requires.

Then delete value_fast_path_safe and friends (src/index.zig). They exist only because serialize_value gives int32 1, int64 1 and double 1.0 different bytes despite comparing equal. bson.encode_key already gives them identical bytes, so the guard is obsolete.

Why it depends on item 1. This index updates on every insert. Against a sorted array that is a tail memmove each time — roughly 51 GB of memmove over 65,536 documents. It only looks acceptable because ObjectIds increase monotonically and therefore append at the end; random or descending _ids would collapse bulk insert, currently the project's best result. Do not land this against the array without an explicit, documented "append-ordered _ids only" caveat.

Watch. A real _id_ index may start appearing in listIndexes and writing an index_create record to the log. e2e3.js asserts on index listings — check it before assuming this is invisible. (This landed without either: the index stays out of the secondary list, so the listing, drop and log surfaces are untouched; verified with e2e3.js unchanged.)

---

3. Block-framed compressed log — DONE

Landed in src/storage.zig: a 16-byte file header (magic, version, codec, block target) plus a sequence of 16-byte-header blocks, each holding the pre-existing record framing unchanged (Engine.apply_record untouched), with the integrity hash covering the stored payload bytes so the decompressor only ever sees input already proven intact. ~256 KiB target; records never straddle blocks (appends accumulate in memory and the block seals when the next record would push it past the target). A short read, an impossible length, or a hash mismatch in the final block truncates cleanly; a mismatch elsewhere is error.InvalidLog. A hand-rolled LZ4 block codec (~1.7 GB/s measured) with a per-block codec byte falling back to raw when compression does not help. Engine.compact goes through the same Log API (deferred sync, one commit) and compresses for free.

Recorded deltas vs tests/e2e/results/phase3.txt: db on disk 1025 → 97 MB (now smaller than MongoDB's own 104 MB); bulk insert 816 → 722 MB/s (the compression cost, accepted per the codec note below); reopen 0.8 s unchanged. Verified with zig build test in all three modes (new LZ4 round-trip, corrupt-block and torn-tail tests), the crash pair, e2e6 (kill -9 mid-write), and two full benchmark runs.

Why. The largest remaining gap: 1.0 GB on disk against MongoDB's 93 MB, because payloads are stored raw. Breaking the format is fine.

What. A file header plus a sequence of blocks; each block holds the existing record framing, so Engine.apply_record does not change. Target ~256 KiB per block, and records never straddle blocks.

The hash covers the compressed bytes, as they sit on disk. If it covered the uncompressed bytes you would have to run the decompressor over possibly-corrupt input before you could validate it, leaning on the decompressor's error handling for durability. Hashing the stored bytes means the decompressor only ever sees input already proven intact.

Torn tail versus interior corruption must stay distinguishable, exactly as today: a short read, an impossible length, or a hash mismatch in the final block means a crash mid-append — truncate and return cleanly. A hash mismatch anywhere else is error.InvalidLog.

Codec: write an LZ4 block compressor (~200 lines). Zig 0.16 ships zstd decompression only, so zstd needs vendoring; std.compress.flate has a compressor but deflate level 1 runs slower than the current insert rate, so it would make writes worse to make the disk smaller. Keep a codec byte in the header so raw stays legal (needed anyway when compression does not help) and zstd can be swapped in later.

Watch. Engine.compact goes through the same Log API and so gets compression for free — but it must still defer syncing and commit once.

---

4. Stop giving every document its own arena — DONE

Landed: documents live as canonical BSON bytes in a segmented per-collection slab (fixed segments keep capacity slack under one segment; the docs map holds flat offsets, stable across growth). The matcher walks the bytes directly, skipping by length any field the filter does not name, and is differential-tested against the tree matcher on a corpus; $match in aggregate and the scan path never materialize stored documents. Sort, projection, updates, findAndModify and index entry generation use a borrowed spine (or the byte collector) into the slab. bson.Document keeps its arena-backed tree meaning for transient docs; stored docs are represented by their bytes.

Recorded deltas vs tests/e2e/results/phase4.txt: server RSS 1979 → 539 MB (2.4x smaller than MongoDB); range-scan 22.5 → ~12 ms (parity; best run 11.2 vs 14.0); proj 4.1 → 3.4 ms. Verified with zig build test in all three modes (zero leaks; the byte matcher differential), the crash pair, e2e6, and the stress/spill programs.

Why. Two gaps at once. RSS is 2.0 GB against 1.4 GB for a 1.0 GB dataset because each document carries an ArenaAllocator and a second full copy of its data as a Pair tree. And the range-scan gap is not the matcher — it is walking 65,536 documents that each live in a separate allocation, one pointer chase apiece.

What. Store canonical BSON bytes in a per-collection slab; the docs map holds a reference into it. Match against those bytes directly, walking the element stream and skipping by length any field the filter does not name — for the benchmark document that is ~40 bytes touched instead of 16 KB. Add a borrowed parse (spine only, pointing into the stored bytes) for sort, projection and update. emit_docs with no projection becomes a memcpy.

Depends on item 1 in spirit: index entry keys no longer alias document arenas (that was fixed when entries moved to encoded bytes), but Entry.id still aliases the docs map key, and that aliasing needs to be understood before documents start moving between slabs.

Watch. This is the deepest change of the five and touches every test. Land the byte matcher behind a flag and differential-test it against the existing matcher on a corpus before switching over. Also replace the fabricated bson.Document{ .arena = undefined, ... } values scattered through query.zig and commands.zig with an explicit view type — they work today only because nothing calls deinit on them, and they are exactly what breaks when Document changes meaning.

---

5. Decompose the global lock — DONE

Landed: collections are heap-allocated (stable pointers; the map only holds them), a catalog rwlock guards the database/collection maps (shared for commands, exclusive for create/drop), and one rwlock per collection guards its docs/slab/indexes, with the catalog → collection → log-lock ordering and never two collection locks at once (TTL sweep and compaction take collections one at a time). Appends never fsync; each write command's epilogue commits once (seal + fsync) under 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. Engine.dup_index moved per-collection. Compaction snapshots the collections without the log lock and retries if a writer appended during the snapshot (detected via the record seq), then swaps under the log lock — no deadlock against a writer holding a collection lock. The durability guarantee weakened from "the log always describes >= memory" to ordinary w:1, j:true: an acknowledged write is fsynced before its reply (the crash pair verifies it), an unacknowledged write may vanish, and a reader can observe a write before its fsync completes.

Measured: no regression on the single-connection benchmark (phase6); concurrent durable-insert throughput scales ~5.1k → 12.5k docs/s from 1 → 8 clients and ~14.8k at 32 — the fsync per commit still dominates sequential-per-client workloads, and the group commit coalesces when appends from different collections overlap.

Why. One reader/writer lock covers the entire engine and is held across fsync, compaction and reply construction, so writes cannot use more than one core.

What. A catalog lock over the database and collection maps, plus a lock per collection. Ordering is catalog then collection, never the reverse, and never two collection locks at once — the only cross-collection operations are the TTL sweep and compaction, and both must take collections one at a time. Then cross-connection group commit: whichever writer finds no commit in flight becomes the leader, seals the buffer, compresses, writes and syncs once for everyone waiting.

Before touching anything, enumerate what the current code relies on. Entry.id aliases the docs map key. Engine.dup_index is engine-global state written by one call and read by the next. Log.scratch is a single shared buffer justified by appends being single-writer. Collections are stored by value in a hash map, so any insert can move them — that one needs fixing first regardless.

A durability guarantee changes here, and it should be a decision, not a surprise. Today the log always describes at least as much as memory — strictly stronger than MongoDB. Under leader/follower commit a reader can observe a write before its fsync completes, which is ordinary w:1, j:true semantics. Check what e2e2.js crash-b actually asserts before changing it.