kimi-code/packages/minidb

minidb

A pure-Node.js (zero native addons) embedded key-value database that mixes ideas from Redis (in-memory KV speed, data structures, AOF-style rewrite) and SQLite (durable single-file persistence, WAL, indexed queries). Written in TypeScript, with strict types and zero runtime dependencies.

Built by studying the real source code of Redis, SQLite, NeDB, Bitcask, and a tiny SQLite clone — see DESIGN_NOTES.md for what each one taught us.

Features

• O(1) GET/SET from an in-memory index (Map), values as bytes.
• Optional larger-than-RAM values: valueMode: 'disk' keeps only value pointers in RAM and reads value bulk from the snapshot/WAL with synchronous positioned reads.
• Durable: append-only WAL with group commit + three fsync policies (always / everysec / no), exactly like Redis AOF.
• Crash-safe recovery: CRC-framed records; a torn tail from a crash is detected and truncated automatically.
• Snapshots + compaction (WAL rewrite) bound disk growth and speed up restart.
• TTL: lazy + active expiration (Redis-style, heap-backed).
• Secondary indexes: equality and range indexes over JSON documents, backed by a Redis-style skip list with O(log N) rank/range; unique and sparse supported; array fields indexed per element.
• RESP server (optional): talk to it with redis-cli / ioredis.
• ClusterDb (optional sharding layer): multi-process concurrent read/write over N sharded MiniDb directories, scaling write throughput with shard count.
• Value codecs: buffer, string, or json.
• Zero dependencies, pure node:* built-ins.

Install / layout

Written in TypeScript, zero runtime dependencies. Source is in src/; build output goes to dist/ (generated by npm run build).

src/
  index.ts          public API (MiniDb) + types
  codec.ts          binary frame encode/decode + CRC parser + resync
  crc32.ts          table-based CRC-32
  wal.ts            buffered append + group commit + fsync policies
  store.ts          in-memory index + TTL (lazy + active) + value refs
  value-reader.ts   synchronous positioned reads for disk-backed values
  snapshot.ts       chunked snapshot writer
  recovery.ts       load snapshot + replay WAL + resync/truncate
  compaction.ts     WAL rewrite / rotation
  skiplist.ts       comparator-driven skip list with span
  index-manager.ts  secondary indexes (equality + range)
  dt-index.ts       datetime-column indexes
  query.ts          jq-like value query engine
  text-index.ts     full-text index (in-RAM dictionary + on-disk postings)
  text-postings.ts  on-disk postings file for the full-text index
  lockfile.ts       exclusive file lock + read-only
  server.ts         optional RESP TCP server

npm run build      # compile src/ -> dist/ (JS + .d.ts)
npm run typecheck  # tsc --noEmit (strict)

Quick start (embedded)

import { MiniDb } from 'minidb';

const db = await MiniDb.open({
  dir: './data',
  valueCodec: 'string',     // 'buffer' | 'string' | 'json'
  fsyncPolicy: 'everysec',  // 'always' | 'everysec' | 'no'
});

await db.set('hello', 'world');
db.get('hello');            // 'world'

await db.set('temp', 'x', { ttl: 5000 }); // expires in 5s
db.ttl('temp');             // remaining ms (-1 none, -2 missing)

await db.del('hello');
await db.close();           // flush + fsync + close

Within this repo, import from ./src/index.ts (run with tsx). As an installed package, import from 'minidb' (the built dist/ output).

Re-open the same dir and your data is recovered from the snapshot + WAL.

Typed usage

MiniDb is generic over the value type:

interface User { name: string; age: number; city: string }
const db = await MiniDb.open<User>({ dir: './data', valueCodec: 'json' });
await db.set('u1', { name: 'Ann', age: 30, city: 'Paris' });
const u = db.get('u1'); // User | undefined

JSON + secondary indexes

const db = await MiniDb.open({ dir: './data', valueCodec: 'json' });

await db.createIndex('byCity', { field: 'city' });                 // equality
await db.createIndex('byAge',  { field: 'age',  type: 'range' });  // range (skip list)
await db.createIndex('byMail', { field: 'email', unique: true });  // unique

await db.set('u1', { name: 'Ann', city: 'Paris', age: 30, email: 'ann@example.com' });
await db.set('u2', { name: 'Bob', city: 'Paris', age: 41, email: 'bob@example.com' });

db.findEq('byCity', 'Paris');          // [{ key:'u1', value:{...} }, { key:'u2', ... }]
db.findRange('byAge', { min: 30, max: 40, count: 10 });

Index definitions are persisted and rebuilt from the store on startup.

Document model & querying (MongoDB-like subset)

A record is:

{ key: 'user:1234', value: { ...any JSON... }, dt1..dtN: <datetime columns> }

• key — string ≤ 128 chars, unique, ordered (range / prefix / ordered scan).
• value — any JSON object (jq-like filter + projection + full-text).
• dt1..dtN — any number of top-level datetime columns, each indexed for O(log N) range queries. Pass them as { dt: { created: Date.now() } } (ISO strings or epoch ms).

Key scans (ordered)

db.scan({ gte: 'user:1', lte: 'user:9', limit: 100 }); // range in key order
db.prefix('user:');                                    // prefix scan

Datetime column queries

await db.set('a', { n: 1 }, { dt: { created: '2024-01-01' } });
db.dtColumns();                              // ['created']
db.dtRange('created', { gte: t0, lte: t1 }); // O(log N), [{ key, value, dt, dtValue }]

Unified query

db.query(q) composes key range, dt range, full-text, and a value filter:

db.query({
  key:    { prefix: 'post:' },
  dt:     { created: { gte: t0, lte: t1 } },
  text:   { index: 'body', q: '北京', op: 'AND' },
  filter: { age: { $gte: 18 }, $or: [{ city: 'Paris' }, { city: 'London' }] },
  sort:   { age: -1 },
  project: ['name', 'age'],
  skip: 0, limit: 20,
});

Filter operators: $eq $ne $gt $gte $lt $lte $in $nin $regex $exists $contains $type, plus $and $or $nor $not. Paths use dot/bracket notation ("address.city", "tags[0]").

Indexed dimensions (key / dt / text / a createIndex'd field) are fast; db.query() automatically uses matching equality/range value indexes for simple top-level predicates (including $and), while a value filter with no matching index falls back to a full collection scan, exactly like MongoDB without an index.

Full-text search

await db.createTextIndex('body', { fields: ['bio'] }); // fields optional (default: all strings)
db.search('body', 'hello 世界', { op: 'AND' });         // [{ key, value, score }]

Latin words + CJK unigram/bigram tokenization (no dictionary, zero deps), with TF-IDF ranking.

API

Method	Description
MiniDb.open({ dir, valueCodec?, valueMode?, fsyncPolicy?, compactThresholdBytes?, autoCompact?, activeExpireIntervalMs?, recovery?, readOnly?, onLockFail?, maxMemoryBytes?, maxMemoryPolicy? })	Open / create a database
MiniDb.restore(srcDir, destDir, opts?)	Restore a db.backup() directory and open it
MiniDb.openOrRebuild(opts, { onRebuild? })	Open; on corruption, discard + reopen empty (cache use). Never deletes a live-locked db
get(key)	Decoded value or undefined
set(key, value, { ttl? })	Set; ttl in ms. Resolves per fsync policy
del(key)	Delete; returns true if it existed
has(key), size	Membership / live key count
mget([keys]), mset([[k,v],...])	Batch read / write
`batch([{op:'set'	'del', key, value?, ttl?, dt?}])`
expire(key, ttlMs), ttl(key)	Set / read TTL
createIndex(name, { field, type?, unique?, sparse? })	Secondary index (json codec)
dropIndex(name), listIndexes()	Manage indexes
findEq(name, value), findRange(name, opts)	Query value indexes
createCompoundIndex(name, { groupBy, orderBy, orderType? })	Compound index (group + order, e.g. workspace + updatedAt)
compoundRange(name, groupValue, opts)	Ordered range within a group, O(log N + limit), no full sort
dropCompoundIndex(name), listCompoundIndexes()	Manage compound indexes
scan({ gte, gt, lte, lt, limit, reverse })	Ordered key range scan
prefix(p, limit?)	Key prefix scan
dtColumns(), dtRange(col, opts)	Datetime column range query
query({ key, dt, text, filter, project, sort, skip, limit })	Unified Mongo-like query
createTextIndex(name, { fields? }), dropTextIndex(name)	Full-text index
search(name, q, { op?, limit? })	Full-text search
compact()	Force a snapshot + WAL rewrite now
backup(destDir, { compact? })	Write a consistent online backup directory
close()	Flush, fsync, close

Durability (fsyncPolicy)

Policy	Guarantees	Relative speed
always	fsync after every flush	slowest (safest)
everysec	fsync once per second (default)	fast, ≤1s loss window
no	OS decides when to flush	fastest, may lose seconds on power loss

Value storage mode (valueMode)

valueMode controls where value bulk lives:

const db = await MiniDb.open({
  dir: './data',
  valueCodec: 'json',
  valueMode: 'memory', // 'memory' | 'disk' | 'auto'
});

• memory (default): values are kept in RAM, Redis-style. Reads are memory-bound.
• disk: values stay inline in the durable snapshot/WAL, while RAM keeps only keys, metadata, indexes, and small { file, off, len } value pointers. Reads use synchronous positioned reads, so the public API stays synchronous. This allows value bulk to exceed RAM, at the cost of disk reads on cold get() paths.
• auto: at startup, compare the current db.snapshot + db.wal size with maxMemoryBytes. If the persisted files exceed the budget, open as disk; otherwise open as memory. If maxMemoryBytes is not set, auto falls back to memory.

Memory limits

With valueMode: 'memory' the budget bounds keys plus values. With valueMode: 'disk' it bounds the in-RAM key/metadata/index footprint, not the value bulk. You can bound writes with an approximate memory budget:

const db = await MiniDb.open({
  dir: './data',
  valueCodec: 'json',
  maxMemoryBytes: 512 * 1024 * 1024,
  maxMemoryPolicy: 'reject', // or 'evict-lru'
});

reject throws when a write would exceed the budget; evict-lru durably deletes least-recently-used keys first. In valueMode: 'disk', evict-lru trims keys to bound the metadata/index footprint; it does not delete value bytes from disk because values are already stored outside RAM. db.stats.evictions and db.stats.maxMemoryRejections track the behavior.

The budget is tracked in approximate logical bytes (key + value + dt metadata), not real JS heap usage. Actual per-key overhead is higher — on the order of several hundred bytes per key for the Map entry, the ordered index node, and bookkeeping — so a database of many small keys needs far more RAM than store.bytes suggests. (Measured: ~0.6 KB of heap per key for tiny records, so 1M small keys ≈ 600 MB of heap.) For millions of small keys, prefer valueMode: 'disk' and/or budget accordingly.

Online backup / restore

await db.backup('./backup');
const restored = await MiniDb.restore('./backup', './restored', { valueCodec: 'json' });

backup() flushes the WAL, optionally compacts, and copies the snapshot, WAL, index definitions, and text postings while writers are briefly parked, producing a consistent directory that restore() can reopen.

Write throughput & SSD endurance

MiniDb's write path is append-only and sequential, which is already SSD-friendly. A few choices have an outsized effect on write amplification and flash wear:

• Use batch writes. set() resolves after its own flush, so for (const x of xs) await db.set(...) issues one flush (and, under fsyncPolicy: 'always', one fsync) per key. Coalesce writes with db.batch([...]) / db.mset([[k, v], ...]) (single atomic WAL frame) or await Promise.all(xs.map(x => db.set(x))) (group commit collapses the tick into one writev + one fsync).
• Keep the default fsyncPolicy: 'everysec'. It bounds fsyncs to ~1/s. always is the most durable but the hardest on flash (one fsync per flush); reserve it for data that must survive any crash.
• Tune compactThresholdBytes for large datasets. Compaction rewrites all live data, so steady-state write amplification is roughly 1 + liveDataBytes / compactThresholdBytes. Raising the threshold (e.g. 256 MiB–1 GiB) trades a larger WAL / longer recovery for less rewrite.
• Observe it via db.stats. walBytesWritten, walFsyncs, snapshotBytesWritten, compactions, evictions, maxMemoryRejections, and queryIndexHits let you measure real write volume, fsync rate, memory pressure, and index usage under your workload.

RESP server (redis-cli compatible)

npm run server -- --dir ./data --port 6379
# or: node --import tsx src/server.ts --dir ./data --port 6379

Then in another shell:

redis-cli -p 6379 SET foo bar
redis-cli -p 6379 GET foo

Supported commands: PING ECHO GET SET DEL EXISTS MGET MSET TTL DBSIZE COMPACT INFO QUIT.

Benchmarks

Measured on Node v24, 100-byte values (npm run bench, N=100000):

Operation	Throughput
Raw Map set (baseline)	~8.6 M ops/s
Raw Map get (baseline)	~20 M ops/s
DB get (in-memory)	~8.0 M ops/s
DB set, fsync=everysec (concurrent, group commit)	~725 k ops/s
DB set, fsync=no (concurrent, group commit)	~396 k ops/s
DB set, fsync=always (sequential, 1 fsync/op)	~328 ops/s
Compact snapshot of 100k keys	~69 ms (12 MiB)

Reads are memory-bound and track the raw Map closely. Writes are disk-bound; group commit makes concurrent writes very fast, while synchronous (always) writes pay the fsync cost one would expect from any database. Numbers vary by machine/disk.

Query benchmarks (N=50k docs, 200 iters each, node bench/query.js):

Query	Throughput
dt range (small result)	~183 k ops/s
key prefix scan (~100 rows)	~23 k ops/s
full-text search (latin / CJK)	~350–490 ops/s
value filter, no index (full scan of 50k)	~23 ops/s

Indexed dimensions (key / dt / text) are fast; an unindexed value filter scans the whole collection — create a secondary index (createIndex) for hot value predicates.

For ordered pagination within a group (e.g. "sessions in a workspace ordered by updatedAt"), use a compound index instead of fetching the whole group and sorting in memory. On 10k sessions in one workspace, paginating with compoundRange is ~20–40× faster than "fetch-all + sort", and stays sub-millisecond at any offset.

Multi-process ClusterDb scaling across process counts × shard counts (the headline: shard-affinity writes scale ~linearly until the single-shard speed is reached; uniform cross-shard traffic pays lock handoffs):

pnpm bench:cluster   # bench/cluster.ts — spawns real writer/reader processes

Testing

Three layers of tests, run with the built-in node:test runner (no deps):

npm test            # unit tests (fast)
npm run test:e2e    # end-to-end stability suite
npm run test:all    # both

The unit tests (test/*.test.js) cover each module: frame codec/CRC, WAL group commit, store TTL, snapshot/compaction, recovery truncation, skip list, secondary/full-text indexes, and the RESP server.

The E2E stability suite (test/e2e/*.test.js) covers crash-safety and long-run behavior:

File	What it verifies
fuzz-model.test.js	thousands of random ops match a reference model (seeded, reproducible)
crash-recovery.test.js	kill -9 mid-write and mid-compaction → recovery is always consistent
index-consistency.test.js	key/dt/secondary/full-text indexes never drift from the store
compaction-race.test.js	heavy concurrent writes during compaction lose nothing
recovery-matrix.test.js	WAL corruption at head/mid/tail under resync vs strict
durability.test.js	always/everysec/no close-durability + many open/close cycles
boundary.test.js	key-length limits, large values, many keys, empty db
soak.test.js	sustained ops + heap stability (opt-in: SOAK=30 npm run test:e2e)

The cluster suite (test/cluster/*.test.ts) covers the ClusterDb sharding layer: topology/routing, merged scans, lock contention and lease renewal in-process, cross-shard indexes/compaction, true multi-process scenarios (concurrent writers on disjoint and shared shards, live cross-process read visibility, read/write storms) and crash takeovers (kill -9 → contiguous recovery + stale-lock handoff).

Design in one paragraph

Log-structured engine: all writes append to a CRC-framed WAL (group committed, configurable fsync); all reads hit an in-memory Map. When the WAL grows past a threshold, a snapshot of the live keys is written and the WAL is rotated — non-blocking: writers keep appending to the WAL (which doubles as a Redis-style rewrite buffer) while the snapshot is written, and pause only for a brief final rotation. Recovery loads the latest snapshot then replays the WAL, truncating any torn tail. Secondary indexes use a Redis-style skip list for range queries. See DESIGN_NOTES.md for the full rationale and the source-code study behind each choice.

Concurrency & multi-process

minidb is single-writer. Opening a directory for writing acquires an exclusive lock file (db.lock); a second writer is rejected with a LockError. A lock is taken over only when its owner PID is dead (stale-lock recovery), never merely because it is old.

// second process: throws LockError
await MiniDb.open({ dir: './data' });

// degrade to read-only instead of throwing
const ro = await MiniDb.open({ dir: './data', onLockFail: 'readonly' });
ro.get('k');        // ok (point-in-time view)
await ro.set('k');  // throws "read-only mode"

// open read-only alongside a writer
const r = await MiniDb.open({ dir: './data', readOnly: true });

For many clients, run the RESP server (single minidb process, many TCP clients) — that is the intended concurrent-access model, like Redis.

ClusterDb: multi-process sharding

When several processes must read and write the same logical database without a server, ClusterDb shards the key space over N ordinary minidb directories (each with its own WAL, snapshot, and db.lock):

import { ClusterDb } from '@moonshot-ai/minidb/cluster';

const db = await ClusterDb.open({ dir: './data', shardCount: 16, valueCodec: 'json' });
await db.set('user:1', { name: 'alice' });      // routed by hash to one shard
await db.mset([['a:1', v1], ['b:2', v2]]);      // grouped per shard, atomic per shard
const all = await db.scan({ prefix: 'user:' }); // merged over all shards
await db.close();

• Writes never meet a single global writer: each process acquires shard write locks on demand (with retry up to lockAcquireTimeoutMs), caches them briefly (lockPoolMaxShards, LRU), and yields a held shard after lockHoldMs (default 250ms) so other processes are never starved. Throughput scales with the number of distinct shards being written — up to the single-shard speed of MiniDb per shard.
• Reads never take locks: they use the cached writer when the local process holds the shard, else a read-only instance that is revalidated against the shard files on every use, so a read that starts after another process's commit always observes it.
• Consistency: single-key and same-shard batch ops are strongly consistent (single writer per shard, atomic WAL frames). Cross-shard mset/mdel/batch are best-effort (atomic per shard, not globally; crossShard: 'none' rejects them instead). Scans merge per-shard snapshots, so entries from different shards may reflect different points in time.
• Indexes (createIndex/createTextIndex) are recorded in a cluster-wide registry and applied by every shard writer on open; findEq/findRange/ search merge per-shard results (text scores are per-shard). Index management acquires every shard writer — run it from one process, off the hot path.
• Crash recovery is per shard: a lock left by a dead PID is taken over by the next opener, exactly like single MiniDb.

crossShard: '2pc' is reserved for a future two-phase commit and is rejected today. Performance numbers across process/shard counts: run pnpm bench:cluster (see bench/cluster.ts).

For a rebuildable cache, use openOrRebuild: a corrupt cache is discarded and reopened empty, while a live-locked db is never destroyed:

const db = await MiniDb.openOrRebuild(
  { dir: cacheDir, valueCodec: 'json' },
  { onRebuild: (err) => log.warn('cache rebuilt:', err.message) },
);

Caveats / roadmap

• In the default valueMode: 'memory', the dataset must fit in RAM Redis-style. Use valueMode: 'disk' for larger-than-RAM value bulk; cold reads then perform synchronous positioned reads against the snapshot/WAL.
• Full-text index postings are stored on disk (larger-than-RAM); only the term dictionary and per-doc metadata stay in memory. Postings are rebuilt from the store on open and on compaction. Search reads postings synchronously, so a cold, very large postings list can briefly block the event loop.
• Compaction is non-blocking for writes: the WAL itself acts as a BGREWRITEAOF-style rewrite buffer, so the (slow) snapshot is written while writers keep appending. Writes pause only for the final rotation (a flush, a tail copy, and two atomic renames). A pre-copy drains most of the tail beforehand when writes are slow enough; under sustained writes that outrun the pre-copy, the rotation absorbs a larger tail — the same bounded end-of-rewrite pause Redis accepts for its AOF diff flush — so compaction always terminates. Mid-compaction crashes leave db.*.tmp files behind, which the next writer open removes automatically.
• Snapshot encoding runs on the main thread (chunked + yielding); offloading to a worker_thread is a planned optimization.
• Single minidb directory = single process / single writer. For multi-process access use ClusterDb (above): sharding scales writes, but hash routing means whole-range scans fan out to all shards, and uniform cross-shard write patterns pay shard-lock handoff costs (lockHoldMs per yield) — workloads with per-process shard affinity scale best.

Credits

Design distilled from reading: Redis (references/redis), the SQLite WAL paper, NeDB (references/nedb), Bitcask (references/bitcask), and the cstack SQLite tutorial (references/db_tutorial).