MultiforaDB/README.md

# mongo-lite

A lightweight, embedded MongoDB-compatible document database written in
Zig 0.16. Like SQLite, it stores everything in a single file; unlike SQLite,
it speaks the MongoDB wire protocol, so real clients — `mongosh`, the Node.js
driver, PyMongo — connect over TCP and just work.

## Quick start

```sh
zig build                # build the server
zig build test           # run the unit test suite

zig-out/bin/mongo-lite --port 27017 --db data.log --compact-threshold 256m

# in another terminal:
mongosh --port 27017
> db.users.insertOne({name: "alice", age: 30})
> db.users.find({age: {$gt: 25}}).toArray()
> db.users.updateOne({name: "alice"}, {$set: {vip: true}})
> db.users.deleteOne({name: "bob"})
> db.sessions.createIndex({expireAt: 1}, {expireAfterSeconds: 3600})
```

## Features

- **Wire protocol**: OP_MSG (2013) plus legacy OP_QUERY/OP_REPLY (2004/2001)
  for the driver handshake; hello/isMaster with `maxWireVersion: 8`, so
  modern drivers (Node, Python, mongosh) connect without workarounds.
- **BSON**: full parse/serialize round-trip for all common types
  (including binary, regex, timestamps, ObjectId), canonical MongoDB
  comparison order for sorting and range queries.
- **CRUD**: `insert`, `find` (filter, sort, skip/limit, projection),
  `update` (multi/upsert), `delete`, `findAndModify`, `count`,
  `aggregate` (`$match`, `$sort`, `$skip`, `$limit`, `$project`, `$count`,
  `$group` with `$sum`), plus `create`/`drop`/`listCollections`/
  `listDatabases`/`dropDatabase`.
- **Query operators**: `$eq` `$ne` `$gt` `$gte` `$lt` `$lte` `$in` `$nin`
  `$exists` `$regex` (hand-rolled engine: anchors, `.`, `* + ?`, character
  classes, groups, alternation, `i`/`s` options) `$not` `$and` `$or` `$nor`
  `$size` `$all` `$elemMatch`, with dot paths and array multikey semantics.
- **Secondary indexes**: `createIndex`/`listIndexes`/`dropIndex` via the
  three driver commands, single-field and compound, with `unique`,
  `sparse` and `expireAfterSeconds` (TTL) options, persisted in the log
  and rebuilt on open (compaction re-emits them). A background sweeper
  expires TTL-indexed documents through the ordinary logged write path.
  The query planner turns equality / `$in` / range
  predicates into index lookups across `find`, `count`, `update`,
  `delete`, `findAndModify`, and a leading `$match` in `aggregate`; every
  candidate is re-checked against the full filter, so an index that
  over-approximates is merely slow, never wrong.
- **Update operators**: `$set` `$unset` `$inc` `$push` (`$each`) `$pull`
  `$rename`, with dot-path creation (including array indices).
- **Storage**: append-only record log, LZ4-compressed in 256 KiB blocks
  (XxHash3-checked, `fsync` per write, torn-tail tolerant: a crash
  mid-append truncates cleanly, interior corruption is rejected) with
  in-memory indexes rebuilt on open and automatic compaction (rewrite +
  atomic rename when the log grows past `--compact-threshold`, default
  16 MB). Killed mid-write (`kill -9`), the database recovers all
  committed writes; the log and compaction both work with relative or
  absolute `--db` paths. Records up to the announced 16 MB
  `maxBsonObjectSize` replay correctly.
- **Concurrency**: a writer-preferring read/write lock splits command
  execution — reads (`find`, `count`, `aggregate`, `list*`) run concurrently
  across connections, writes (CRUD, DDL) are exclusive and totally ordered,
  and handshake/no-op commands run lock-free. The log append + `fsync` still
  happen under the write lock, so the crash guarantees are unchanged. Fine
  for light workloads.

## Layout

```
src/
  bson.zig     BSON parse/serialize, ObjectId, canonical comparison order
  wire.zig     OP_MSG/OP_QUERY framing, message + reply builders
  commands.zig command dispatch (hello, CRUD, aggregate, admin, indexes)
  server.zig   TCP accept loop, per-connection handlers, TTL sweep monitor
  db.zig       in-memory engine: db → collection → _id → document maps
  storage.zig  append-only log: records, replay, CRC validation
  query.zig    filter matcher, regex engine, sort, projection
  index.zig    secondary indexes: entries, search, query planner
  update.zig   update operators with dot-path navigation
  main.zig     CLI: --port, --bind, --db, --ttl-sweep-secs, --compact-threshold
```

## Indexes

`collection.createIndex({field: 1})` works against every driver; the index
is persisted in the log, survives restarts and compaction, and is used by
the query planner to narrow scans.

- **Key patterns**: single-field and compound (up to 32 fields), each key
  `1` or `-1`. Descending order is metadata (entries are always stored
  value-ascending); the default index name is MongoDB's `a_1_b_-1`.
  `createIndex({_id: 1})` is an idempotent no-op — the docs map is the
  `_id_` index — and `dropIndex("_id_")` errors.
- **Options**: `unique` (a conflicting write fails with E11000 naming the
  index; per-document entries are deduped first, so `{a: [1,1]}` is legal)
  and `sparse` (documents missing an indexed field are skipped).
- **TTL**: `createIndex({expireAt: 1}, {expireAfterSeconds: 60})` deletes a
  document once its indexed date is that many seconds old. A background
  sweeper runs every `--ttl-sweep-secs` seconds (default 60, `0` disables
  it) and deletes through the ordinary write path, so each expiry is logged
  and fsynced and holds across a restart. As in MongoDB the option is
  single-field only (a compound key is `CannotCreateIndex`, code 67),
  `expireAfterSeconds` must be a whole number in `[0, 2147483647]` (`0`
  means "expire at the stored instant"), a non-date value at the path never
  expires, an array of dates expires on its earliest member, and expiry is
  coarse: a document stays visible until the next sweep. Re-creating an
  index with a different expiry is `IndexOptionsConflict` (85) and an
  expiry on `{_id: 1}` is `InvalidIndexSpecificationOption` (197), both as
  MongoDB has them.
- **Multikey**: an array at an indexed path is indexed as a whole *and*
  element-wise, mirroring the query matcher exactly, so both
  `{tags: "a"}` and `{tags: ["a","b"]}` hit the index. A compound index
  over two array paths rejects the document with MongoDB's "cannot index
  parallel arrays".
- **Planner**: picks the index covering the longest leading run of
  equality/`$in` predicates (cartesian product capped at 100 lookups),
  optionally with a range on the next key. Ranges with both bounds fall
  back to a scan on multikey indexes (a doc with `{a: [1,2]}` can satisfy
  `{a: {$gt: 5, $lt: 25}}` across two entries), and sparse indexes are
  never used for `null`-valued predicates. The `_id_` fast path resolves
  `{_id: ...}` through the docs map unless the value's compare class is
  serialization-ambiguous (int32 1, int64 1, double 1.0 compare equal but
  hash differently — those fall back to a scan, as do string/symbol/code).

v1 limits: no hashed/text/geo/partial indexes, and entry *insert* is O(n)
(a sorted array memmoves the tail) — fine for a light database, with a
B-tree as the follow-up. Removal is no longer a scan: entry generation is
a pure function of the document, so the entries to drop are regenerated
and found by binary search. A TTL sweep
walks every entry of every TTL index and holds the write lock for the
whole pass, so the interval is the tuning knob: expiry is never more
precise than `--ttl-sweep-secs`, and a very large TTL index wants a
longer one.

## Not (yet) implemented

- Authentication (SCRAM) — run without credentials
- Real cursors (all results are returned in one batch, cursor id 0)
- Transactions, change streams, replicasets
- Compression (OP_COMPRESSED)
- `collMod`, so an index's `expireAfterSeconds` cannot be changed in
  place — drop the index and re-create it with the new expiry
- `dropCollection`/`dropDatabase` write no log record, so a dropped
  collection (and its index definitions) resurrect on restart

## Working with large collections

Everything lives in RAM (db → collection → _id → document maps) and every
write command is logged with `fsync` before it is acknowledged (one sync per
command via group commit — a 500-doc `insertMany` syncs once, not 500
times), so multi-GB collections work, with cost/behavior notes measured by
the `tests/e2e/big.js` harness (12-core/32 GB Mac):

- **Build in ReleaseFast** — `zig build` defaults to it. A Debug server is
  10-200x slower on every path (the matcher alone was 70 µs/doc in Debug
  vs 0.4 µs in ReleaseFast), which dwarfed every other difference in the
  MongoDB comparison below.
- **Compaction no longer needs tuning for bulk loads.** It triggers on the
  share of the log that is garbage rather than on bytes appended, so a pure
  insert workload — which has no garbage — is never rewritten, and a
  rewrite-heavy one is reclaimed once about a fifth of the log is dead,
  keeping the file near 1.25x the live data. `--compact-threshold` is now
  only a floor below which small logs are left alone. (It used to fire
  every 16 MB regardless, rewriting the whole log each time: quadratic
  total traffic, and the reason bulk loads needed a raised threshold.)
- **`findOne({_id})` is O(1) only for ObjectId ids.** Integer, int64 and
  double ids compare equal but hash differently, so the docs-map fast path
  is skipped and every `_id` lookup becomes a full scan. Use the driver's
  default ObjectIds (or a secondary index) on big collections. The
  order-preserving key encoding already removes the ambiguity that forces
  this; lifting the restriction waits on an ordered `_id` index.
- **Secondary-index entry insert is O(n)** (sorted array — see v1 limits
  above), so inserting into a collection that already has an index is
  quadratic. Building an index over existing data is not: entries are
  appended unsorted and ordered once. Still cheapest to create indexes
  after the load.

## Performance vs MongoDB

`tests/e2e/compare-run.sh` runs the same driver workload (1 GB, 65,536 ×
16 KB docs, every write durable — mongo-lite fsyncs per command, mongod
runs with `j: true`) against each server and prints a side-by-side table.
With the ReleaseFast default build (MongoDB 8.3.7 on the same Mac):

| benchmark | mongo-lite | mongodb | winner |
|---|---|---|---|
| insertOne (sequential) | 0.17 ms | 4.2 ms | **mongo-lite ×25** |
| bulk insert (insertMany) | 722 MB/s | 824 MB/s | mongodb ×1.1 |
| createIndex({k: 1}) | 54 ms | 85 ms | **mongo-lite** |
| countDocuments({}) | 1.5 ms | 11.5 ms | **mongo-lite ×7** |
| findOne({_id}) | 0.57 ms | 0.50 ms | mongodb |
| findOne indexed | 0.77 ms | 0.86 ms | mongo-lite |
| range-scan count | 22.5 ms | 14.5 ms | mongodb ×1.6 |
| sort + limit(20), on `_id` | 2.5 ms | 2.3 ms | mongodb ×1.1 |
| sort + limit(20), indexed field | 1.0 ms | — | — |
| aggregate $group | 10.2 ms | 15.4 ms | **mongo-lite** |
| updateOne({_id}) | 0.13 ms | 0.22 ms | **mongo-lite** |
| updateMany (65 docs) | 2.4 ms | 5.8 ms | **mongo-lite ×2.4** |
| deleteOne + insert | 0.64 ms | 3.9 ms | **mongo-lite ×6** |
| server RSS | 2.0 GB | 1.6 GB | mongodb (×0.8) |
| kill -9 → reopen | 0.8 s | 1.3 s | **mongo-lite** |
| db on disk | 97 MB | 104 MB | **mongo-lite** |

The log is now LZ4-compressed in 256 KiB blocks, so the on-disk size is
on par with MongoDB's compressed files. The remaining losses are
structural rather than incidental. RSS trails because every document
carries its own arena and a second full copy as a `Pair` tree; 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. And bulk
insert is compress-bound (the LZ4 codec runs at ~1.7 GB/s; deflate would
cap writes below the insert rate, which is why the roadmap chose LZ4).

Reproduce the table with `bash tests/e2e/compare-run.sh 1g 16k`; the
pre-tree baseline is recorded in `tests/e2e/results/phase1.txt`, and the
runs with the B+tree, ordered `_id` index and compressed log (roadmap
items 1–3) in `tests/e2e/results/phase2.txt`, `phase3.txt` and
`phase4.txt`.

### What is left (highest impact first)

Each is written up with its design decisions, ordering constraints and
traps in [ROADMAP.md](ROADMAP.md).

1. **Stop giving every document its own arena** — the source of both the
   RSS gap and the range-scan gap. Storing canonical BSON bytes in a
   per-collection slab and matching against them (parsing only the fields a
   filter names) makes scans contiguous instead of a pointer chase.
2. **Decompose the global lock** — one reader/writer lock covers the whole
   engine and is held across fsync, compaction and reply construction.
   Per-collection locks plus cross-connection group commit are the path to
   using more than one core on writes.

Done so far, with the measurement that drove each:

- **Record integrity hash CRC32 → XxHash3.** `std.hash.Crc32` is
  table-driven and byte-at-a-time: 408 MB/s against XxHash3's 31 GB/s, or
  38 µs versus 0.5 µs on a 16 KB document — about two thirds of the entire
  bulk-insert cost. Insert 260 → 700 MB/s.
- **Compaction triggers on garbage, not on bytes written**, and syncs once
  per rewrite instead of once per document. Bulk load at the default
  threshold 41.6 → 703 MB/s.
- **Index builds append then sort once** instead of inserting into a sorted
  array. `createIndex` over 65,536 documents 649 → 44 ms.
- **Index entries hold encoded byte keys**, so comparing them is a memcmp
  rather than a walk over values in unrelated arenas.
- **A B+tree over the encoded keys** (roadmap item 1): fixed 4 KiB slotted
  pages in a flat u32-addressed node array, an overflow slab for long
  records, no rebalancing on delete, and bulk bottom-up packing. Entry
  insertion and removal are a descent plus a leaf-local edit instead of a
  tail memmove, so writes into an already-built index stopped being
  quadratic. `updateMany` 17.3 → 1.6 ms (2.8x slower than MongoDB → 4x
  faster); `createIndex` 62 → 51 ms.
- **An ordered `_id` index** (roadmap item 2): every collection carries an
  implicit `_id_` index (kept out of the secondary list, so the listing,
  drop and log-format surfaces are unchanged; rebuilt after replay like
  the secondaries). Its encoded keys are canonical, so the old
  serialization-guarded docs-map fast path is gone and integer/string
  `_id` point lookups, `$in` and ranges hit the tree instead of a full
  scan. `sort({_id: ...})` is now an index-ordered scan with an early stop:
  `sort+limit(20)` 6.2 → 2.4 ms (parity with MongoDB).
- **A block-framed, LZ4-compressed log** (roadmap item 3): a file header
  plus ~256 KiB blocks, each holding the existing record framing with the
  integrity hash covering the stored bytes (so the decompressor only ever
  sees input already proven intact). Records never straddle blocks; a
  short read, impossible length or hash mismatch in the final block is a
  torn tail (truncate cleanly), anywhere else is corruption. The hand-rolled
  LZ4 codec runs at ~1.7 GB/s and falls back to raw per block when
  compression does not help. `db on disk` 1025 → 97 MB — now smaller than
  MongoDB's own compressed files.
- **Entry removal is a binary search**, not a scan of the whole index.
  `updateMany` 15.4 → 5.5 ms.
- **Top-k sort selection** and an allocation-free decorate pass, plus
  **index-supplied ordering** when an index already holds candidates in the
  requested order. `sort+limit(20)` 40 → 4.3 ms, or 1.0 ms on an indexed
  field.
- **`limit` reaches the scan**, which used to materialize the whole
  collection before slicing, and `countDocuments` is answered by counting
  rather than by materializing and discarding every match.
- **Matching collects candidates on the stack**, resolves operators to an
  enum once per filter field rather than by string per document, and reuses
  one reply arena per connection.

Several real bugs surfaced while benchmarking:

- `plan_id` returned a pointer to a stack temporary (`&.{e}`) that dangled
  after the frame returned — Debug tolerated it, ReleaseFast read garbage,
  silently breaking every `findOne({_id: <ObjectId>})`. It now heap-copies
  the lookup value and frees it.
- Multi-doc writes fsynced once per document; they now group-commit (one
  fsync per command, same crash guarantees — verified by the kill -9
  crash suites).
- Compaction fsynced once per live document, because the log it wrote into
  never had deferred syncing enabled — 65,536 fsyncs to rewrite a 1 GB
  collection.
- `remove` never checked the compaction threshold, so a delete-heavy
  workload grew the log without bound.


## Code style

Zig 0.16 idioms (`std.Io` threaded through everything, unmanaged
containers); user-declared functions use `snake_case` per this repo's house
style.