brainlessdb 0.4.0

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, BrainlessStruct

class UserV1(BrainlessStruct):
    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 registered collections
await users.watch()  # start watching for remote changes

# 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
BrainlessStruct	Main entities (User, Order, etc.)	Yes	Own bucket(s)
Struct (msgspec)	Nested data, app-local data	No	Inside parent entity

from msgspec import Struct
from brainlessdb import BrainlessStruct

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

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

# Main entity - BrainlessStruct (has UUID, stored in NATS)
class UserV1(BrainlessStruct):
    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(BrainlessStruct):
    id: Annotated[int, Meta()]
    name: Annotated[str, Meta()] = ""

State Fields

Ephemeral data (separate bucket, faster sync):

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

Indexed Fields

Fast O(1) lookups:

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

Unique Fields

Enforces uniqueness constraint (also auto-indexed):

class UserV1(BrainlessStruct):
    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. Use plain Struct (not BrainlessStruct):

from msgspec import Struct

# Plain Struct - just data, no UUID
class UcsLocal(Struct):
    sid: int = 0

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

# Entity with app-local field
class UserV1(BrainlessStruct):
    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 single collection
await coll.watch()
await coll.unwatch()

# Watch all collections
await db.watch()
await db.unwatch()

Watch updates in-memory entities automatically when remote changes arrive.

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)