brainlessdb 0.14.1

Typed collections backed by NATS JetStream KV

Brainless DB

Typed collections backed by NATS JetStream KV. In-memory with background sync.

Quick Start

from typing import Annotated
from msgspec import Meta
from brainlessdb import BrainlessDB, BrainlessDBFeat, BrainlessBucket

class UserV1(BrainlessBucket):
    id: Annotated[int, Meta(extra={"brainlessdb_flags": BrainlessDBFeat.INDEX})]
    name: Annotated[str, Meta()] = ""

db = BrainlessDB(nats, namespace="app")
users = db.collection(UserV1)  # sync - just registers
await db.start()  # loads all collections and starts watching

# Create
user = users.add(UserV1(id=1, name="Alice"))

# Update
user.name = "Bob"
user.save()  # marks dirty, schedules flush

# Query (sync - operates on in-memory data)
user = users.find(id=1)  # uses index
all_users = users.filter(lambda u: u.id > 0)

await db.stop()

When to Use What

Class	Use for	Has UUID	Stored in
BrainlessBucket	Main entities (User, Order, etc.)	Yes	Own bucket(s)
BrainlessStruct	Nested data, app-local data	No	Inside parent entity

from brainlessdb import BrainlessBucket, BrainlessStruct

# App-local data - BrainlessStruct (no UUID, not an entity)
class UcsLocal(BrainlessStruct):
    sid: int = 0
    pointer: int = 0

# Nested data - BrainlessStruct
class Address(BrainlessStruct):
    street: str = ""
    city: str = ""

# Main entity - BrainlessBucket (has UUID, stored in NATS)
class UserV1(BrainlessBucket):
    id: Annotated[int, Meta()]
    address: Optional[Address] = None  # nested struct
    _: Optional[UcsLocal] = None       # app-local struct

Global API

import brainlessdb

brainlessdb.setup(nats, namespace="app")  # sync
users = brainlessdb.collection(UserV1)  # sync
await brainlessdb.start()  # loads all registered collections
await brainlessdb.flush()  # manual flush
await brainlessdb.stop()

Field Types

Config Fields (default)

Persistent data synced across all instances:

class UserV1(BrainlessBucket):
    id: Annotated[int, Meta()]
    name: Annotated[str, Meta()] = ""

State Fields

Ephemeral data (separate bucket, faster sync):

class UserV1(BrainlessBucket):
    id: Annotated[int, Meta()]
    status: Annotated[int, Meta(extra={"brainlessdb_flags": BrainlessDBFeat.STATE})] = 0

Indexed Fields

Fast O(1) lookups:

class UserV1(BrainlessBucket):
    id: Annotated[int, Meta(extra={"brainlessdb_flags": BrainlessDBFeat.INDEX})]

Unique Fields

Enforces uniqueness constraint (also auto-indexed):

class UserV1(BrainlessBucket):
    email: Annotated[Optional[str], Meta(extra={"brainlessdb_flags": BrainlessDBFeat.UNIQUE})] = None

Combine flags with |:

counter: Annotated[int, Meta(extra={"brainlessdb_flags": BrainlessDBFeat.INDEX | BrainlessDBFeat.STATE})] = 0

App-Local Fields

Data private to each namespace:

from brainlessdb import BrainlessBucket, BrainlessStruct

class UcsLocal(BrainlessStruct):
    sid: int = 0

class AriLocal(BrainlessStruct):
    channel_id: str = ""

# Entity with app-local field
class UserV1(BrainlessBucket):
    id: Annotated[int, Meta()]
    _: Union[UcsLocal, AriLocal, None] = None

Each namespace only sees its own local data:

# In UCS app (namespace="ucs")
user = UserV1(id=1, _=UcsLocal(sid=123))
users.add(user)
user._.sid  # 123

# In ARI app (namespace="ari")  
user = users.find(id=1)
user._  # None - no ARI local data yet

CRUD Operations

# Add/update (validates types on add)
item = coll.add(MyStruct(...))

# Update via save()
item.field = value
item.save()

# Get by UUID
item = coll.get(uuid_str)

# Delete
coll.delete(item)
coll.delete(uuid_str)

# Clear all
coll.clear()

# Dict-style access
item = coll[uuid_str]
del coll[item]
len(coll)
for item in coll: ...
item in coll

Filtering

All filter/find methods are sync (operate on in-memory data):

# By predicate
items = coll.filter(lambda i: i.priority > 5)

# By field (uses index if available)
items = coll.filter(status=1)

# Nested fields
items = coll.filter(address__city="Prague")

# Combined
items = coll.filter(lambda i: i.active, status=1, limit=10)

# Find single
item = coll.find(id=123)

# Sort
items = coll.order_by("priority", reverse=True)

Events

Callbacks fire on remote changes by default. Set trigger_local=True to also fire on local changes.

# Any change
coll.on_change(lambda old, new: print(f"{old} -> {new}"))

# Deletion
coll.on_delete(lambda item: print(f"deleted: {item}"))

# Specific property
coll.on_property_change(
    status=lambda item, field, old, new: print(f"{field}: {old} -> {new}")
)

# Also trigger on local changes
coll.on_change(my_callback, trigger_local=True)

Watching

Watch starts automatically with start(). Manual control:

await db.unwatch()  # stop watching all
await db.watch()    # resume watching all

Flush Scheduling

• Changes schedule flush after flush_interval (default 100ms)
• Multiple changes batch into single flush
• flush_interval=0 flushes immediately
• await db.flush() forces immediate flush

Multi-Bucket Architecture

Each struct uses up to 3 NATS KV buckets:

• {StructName} - config fields (persistent)
• {StructName}-State - state fields (ephemeral)
• {StructName}-{LocalClass} - app-local fields (per namespace)

Example: UserV1 with UCS namespace creates:

• UserV1 (config)
• UserV1-State (if state fields exist)
• UserV1-UcsLocal (local data for UCS app)

Low-Level Bucket Access

Bucket exposes NATS KV metadata for consumers that need timestamps or revision info.

from brainlessdb.bucket import Bucket, WatchEntry

bucket = await Bucket.create(js, "UserV1")

# get_entry() — single key with metadata
entry = await bucket.get_entry("some-key")
entry.value       # bytes
entry.revision    # int
entry.created     # datetime (NATS server timestamp)

# all_entries() — all keys with metadata
entries = await bucket.all_entries()  # dict[str, WatchEntry]
for key, entry in entries.items():
    print(key, entry.created)

# watch() — created is included in every event
async for entry in bucket.watch():
    print(entry.key, entry.operation, entry.created)

# watch() with timeout — yields operation="TIMEOUT" on idle, watcher stays alive
async for entry in bucket.watch(timeout=3):
    if entry.operation == "TIMEOUT":
        continue
    ...

get() and all() remain unchanged (return raw bytes).

Dead Letter Queue

Entities that fail to deserialize (e.g. after a schema change) are automatically moved to the brainless_dlq NATS KV bucket and removed from the source bucket. This applies during both initial load and live watch updates.

DLQ key format: {SourceBucket}::{EntityUID}::{Timestamp}

Each entry contains source_bucket, key, error, timestamp, and the original value for recovery.