open-brainy/docs/guides/optimistic-concurrency.md

---
title: Optimistic concurrency with _rev
slug: guides/optimistic-concurrency
public: true
category: guides
template: guide
order: 8
description: Use the per-entity `_rev` counter and `update({ ifRev })` to coordinate concurrent writes safely. Covers the lock pattern, idempotent inserts with `ifAbsent`, and recovery on conflict.
next:
  - concepts/consistency-model
  - guides/find-limits
---

# Optimistic concurrency with `_rev`

Brainy 7.31.0 adds a per-entity revision counter so multiple writers can coordinate without a global lock or external coordinator. The pattern is the same one CouchDB, PouchDB, and ETag-based HTTP caches use: read the current revision, do your work, write back with `ifRev: <whatRevYouSaw>`. If the revision moved, your write is rejected and you retry against the latest state.

## What gets added

| Surface | Behavior |
|---|---|
| `entity._rev: number` | Returned on every `get()`, `find()`, `search()`. Initialized to `1` on `add()`. Bumped by `1` on every successful `update()`. Pre-7.31.0 entities without `_rev` are surfaced as `1`. |
| `update({ id, ..., ifRev: number })` | If the persisted `_rev` does not equal `ifRev`, throws `RevisionConflictError`. Omitting `ifRev` keeps the prior (unconditional) update behavior. |
| `RevisionConflictError` | Carries `{ id, expected, actual }` for principled recovery. |
| `add({ id, ifAbsent: true })` | By-ID idempotent insert. Returns the existing `id` if one is already present; no throw, no overwrite. |
| `addMany({ items, ifAbsent: true })` | Applies `ifAbsent` to every item. Per-item `ifAbsent` overrides the batch flag. |

`_rev` is the **per-entity** counter. Its store-wide counterpart is the generation counter behind the [Db API](../concepts/consistency-model.md): `brain.transact(ops, { ifAtGeneration })` is CAS over the whole store, `update({ ifRev })` (and `ifRev` on `transact()` update operations) is CAS over one entity.

## The lock pattern

Every distributed-job scheduler eventually wants this exact loop:

```ts
import { Brainy, RevisionConflictError } from '@soulcraftlabs/brainy'

const LOCK_ID = '...uuid for this job slot...'

// Bootstrap the lock document once (idempotent).
await brain.add({
  id: LOCK_ID,
  type: NounType.Document,
  data: { owner: null, expiresAt: 0 },
  ifAbsent: true
})

async function tryAcquireLock(workerId: string, ttlMs: number) {
  const lock = await brain.get(LOCK_ID)
  if (!lock) throw new Error('lock document missing')

  const state = lock.data as { owner: string | null; expiresAt: number }
  const now = Date.now()

  // Only take the lock if it's free or expired.
  if (state.owner && state.expiresAt > now) return false

  try {
    await brain.update({
      id: LOCK_ID,
      data: { owner: workerId, expiresAt: now + ttlMs },
      ifRev: lock._rev
    })
    return true
  } catch (err) {
    if (err instanceof RevisionConflictError) {
      // Another worker grabbed it between our read and write.
      return false
    }
    throw err
  }
}
```

No external lock service, no Redis SETNX, no Cloud Tasks. The CAS check is the lock.

## Read-modify-write with retry

The other common shape is "update a counter / config object" with bounded retries on conflict:

```ts
async function incrementCounter(id: string, by: number) {
  for (let attempt = 0; attempt < 5; attempt++) {
    const entity = await brain.get(id)
    if (!entity) throw new Error('counter does not exist')
    const current = (entity.data as { value: number }).value
    try {
      await brain.update({
        id,
        data: { value: current + by },
        ifRev: entity._rev
      })
      return
    } catch (err) {
      if (err instanceof RevisionConflictError) continue   // refetch + retry
      throw err
    }
  }
  throw new Error('counter update conflict after 5 attempts')
}
```

The retry bound matters — without one, two unlucky writers can ping-pong forever.

## Idempotent bootstrap with `ifAbsent`

For singletons (config rows, well-known seed entities, job-state documents) where the natural ID is deterministic:

```ts
await brain.add({
  id: 'config:singleton',
  type: NounType.Document,
  data: { tenantQuota: 1000 },
  ifAbsent: true
})
```

- First caller writes; gets back `'config:singleton'`.
- Every subsequent caller short-circuits at the pre-read; gets back the same `'config:singleton'` without touching the existing entity.
- `_rev` is **not** bumped on the no-op path (no write happened).

`ifAbsent` is only meaningful when you supply an `id`. With no `id`, Brainy generates a fresh UUID that can never collide, so the flag is silently ignored.

`addMany({ items, ifAbsent: true })` applies the flag to every item. Mixing per-item overrides with the batch flag works as you'd expect: per-item `ifAbsent: false` opts an individual row out, per-item `ifAbsent: true` opts it in.

### Why no `addIfMissing({ match, add })` for attribute-based dedup?

You may want "create if no entity with this email exists" (lookup by attribute, not by ID). That's a different operation:

```ts
// What we DID NOT ship in 7.31.0 — the attribute-based variant.
await brain.addIfMissing({                       // ← not a real API
  match: { type: 'Person', where: { email: 'x@y.com' } },
  add:   { data: '...', metadata: { email: 'x@y.com' } }
})
```

It's race-prone as a plain read-then-write: two concurrent imports both see "not found," both insert, you get duplicates. Without a unique-index primitive (which Brainy doesn't have today), close the race with whole-store CAS — read at a pinned generation, then commit only if nothing moved:

```ts
import { GenerationConflictError } from '@soulcraftlabs/brainy'

async function addIfMissingByEmail(email: string, data: string) {
  for (let attempt = 0; attempt < 5; attempt++) {
    const db = brain.now()
    try {
      const existing = await db.find({
        type: NounType.Person,
        where: { email },
        limit: 1
      })
      if (existing.length > 0) return existing[0].id

      const committed = await brain.transact(
        [{ op: 'add', type: NounType.Person, subtype: 'customer', data, metadata: { email } }],
        { ifAtGeneration: db.generation }   // rejects if ANYTHING committed since the read
      )
      return committed.receipt!.ids[0]
    } catch (err) {
      if (err instanceof GenerationConflictError) continue   // world moved — re-read + retry
      throw err
    } finally {
      await db.release()
    }
  }
  throw new Error('addIfMissingByEmail conflict after 5 attempts')
}
```

`ifAtGeneration` is deliberately coarse — *any* committed write invalidates it — so keep the retry bound. When you control the ID, `ifAbsent` stays the cheaper tool.

## How `_rev` relates to generations

Brainy 8.0 has exactly two write-coordination counters, at two granularities:

| Counter | Scope | What it tracks | CAS surface | Conflict error |
|---|---|---|---|---|
| **`_rev`** | One entity | Per-entity write count, bumped on every successful update | `update({ ifRev })`, `{ op: 'update', ifRev }` in `transact()` | `RevisionConflictError` |
| **Generation** | Whole store | One tick per committed `transact()` batch or single-operation write | `transact(ops, { ifAtGeneration })` | `GenerationConflictError` |

They compose: a `transact()` batch can carry per-entity `ifRev` checks *and* a whole-store `ifAtGeneration`; any failed check rejects the entire batch before anything is staged. Generations also power snapshots and time travel (`brain.now()`, `brain.asOf()`, `db.persist()`) — see the [consistency model](../concepts/consistency-model.md) and [Snapshots & Time Travel](./snapshots-and-time-travel.md).

A snapshot or historical view captures each entity *including* its `_rev` at that moment, so reading the past and writing back with `ifRev` against the live state works exactly as you'd hope: the write fails if the entity moved since the state you copied from.

## The transact envelope: batch size, budget, and bulk imports

`transact()` applies its batch atomically under one commit — which means the whole batch
shares one **apply budget**. Since 8.7.0 the budget scales with the batch:
`max(30 s, opCount × 2 s)`, or exactly what you pass as `timeoutMs`. A tripped budget rolls
the entire batch back (nothing partial survives) and throws a retryable
`TransactionTimeoutError` that names the operation it stopped at, the batch size, and the
elapsed vs budgeted time — a diagnosis, not just a failure:

```
Transaction timed out at operation 41/120 ('add') — 246012ms elapsed, budget 240000ms.
The batch rolled back atomically; retry with a higher timeoutMs or a smaller batch.
```

Practical envelope guidance for bulk work:

1. **Precompute embeddings outside the commit path.** Embedding inside `transact()` spends
   the budget on model inference. Use `brain.embedBatch(texts)` and pass each vector via
   the op's `vector` field — the commit then pays only storage costs, and a retried batch
   never re-pays inference. (The win is *where* the inference happens, not raw embedding
   throughput: on the default WASM engine, batch and sequential embedding measure
   comparably, ~160 ms/text; native embedding providers may batch faster.)
2. **Chunk very large imports** into batches of a few hundred ops with one `transact()`
   each. You lose whole-import atomicity but keep per-chunk atomicity, bounded memory, and
   resumability — pair with `ifAbsent` upserts so a retried chunk is idempotent.
3. **Slow disks change the math, not the contract.** On network-attached storage a single
   op can cost ~2 s (canonical write + fsync + index maintenance). The scaled default
   absorbs that; pass an explicit `timeoutMs` only when you know better than the scale.
4. **`addMany`/`relateMany` are the convenience tier** — they chunk and batch-embed for
   you, with per-item error reporting instead of batch atomicity. Choose by what you need:
   atomic-all-or-nothing → `transact()`; resilient bulk load → `addMany`.