centaurodb 0.9.0

Pydantic-native storage for time-series and evolving data models. SQLite + PostgreSQL, schema evolution without migrations, built-in Polars.

CentauroDB

Docs: centaurodb.dev

Pydantic-native storage for time-series and evolving data models. SQLite + PostgreSQL backends, schema evolution without migrations, built-in Polars DataFrames.

pip install centaurodb

Optional extras:

pip install "centaurodb[polars]"     # for .df / sql_select() → DataFrame
pip install "centaurodb[postgres]"   # for PostgreSQL backend

---

Why CentauroDB?

CentauroDB is not another ORM. It targets a different problem:

• You define data with Pydantic models, not table schemas.
• Models are stored as JSON blobs, so adding a field never requires a migration — just give it a default value.
• Time-series data is a first-class citizen via TimeSeriesCollection, with one-line conversion to a Polars DataFrame.
• Same API on SQLite (zero-config, embedded) and PostgreSQL (production) — pick the URL, the dialect adapts.

Who is this for?

Built for data and analytics pipelines, IoT/sensor ingestion, quant backtesting, and analytics-shaped backends — workloads where schemas keep evolving, where the read path ends in a DataFrame, and where the write side is a process or a small fleet of workers, not a thousand concurrent web requests.

You want	Use
Web app with relational schema, FK constraints, complex joins	SQLAlchemy / SQLModel
High-concurrency web backend with auth / RLS / connection pooling	Supabase + SQLAlchemy
Document store with full-text search and replication	MongoDB
Pydantic models in, structured storage out, DataFrames back	CentauroDB
Time-series + metadata with evolving schemas	CentauroDB

---

Quickstart

from centaurodb import Engine, Collection, CentauroModel

class Book(CentauroModel):
    __centauro_name__ = "book"
    title: str = ""
    author: str = ""
    rating: float = 0.0

engine = Engine("library.db")          # or "sqlite://" for in-memory
                                       # or "postgresql://user:pw@host/db"
books = Collection(engine, "library")

# Write
dune = Book(title="Dune", author="Herbert", rating=4.8)
books.write_object(dune)

# Update
dune.rating = 5.0
books.update_object(dune)

# Query (JSON fields, AND/OR conditions)
top = books.read_objects(Book.fields.rating > 4.5)
for b in top:
    print(b.title, b.rating)

# Paginate large result sets
page = books.read_objects(Book.fields.rating > 4.5, limit=20, offset=40)
total = books.count_objects(Book.fields.rating > 4.5)

# Delete
books.delete_object(dune)

---

Time-series with Polars

from datetime import datetime
import polars as pl
from centaurodb import Engine, TimeSeriesCollection, CentauroModelSeries

class StockPrice(CentauroModelSeries):
    __centauro_name__ = "stock"
    ticker: str = ""
    exchange: str = "NYSE"

engine = Engine("stocks.db")
prices = TimeSeriesCollection(engine, "portfolio")

df = pl.DataFrame({
    "time":  [datetime(2026, 1, 1), datetime(2026, 1, 2)],
    "value": [185.20, 187.55],
})

apple = StockPrice(ticker="AAPL", values=df)
prices.write_object(apple)

# Read back, filtered by JSON metadata
[apple] = prices.read_objects(StockPrice.fields.ticker == "AAPL")

# .df is a Polars DataFrame with (time, value)
print(apple.df)

If your tool today is pandas.read_csv plus a folder of parquet files, this is the next step from that.

---

Schema evolution without migrations

Add a field — give it a default. Old rows still load fine:

class Book(CentauroModel):
    __centauro_name__ = "book"
    title: str = ""
    author: str = ""
    rating: float = 0.0
    pages: int | None = None      # NEW — old rows just see None

Rename a field — declare the old name as an alias:

from centaurodb import renamed_from

class Book(CentauroModel):
    __centauro_name__ = "book"
    page_count: int = renamed_from("pages", default=0)

Three guardrails enforced at class-definition time keep stored data readable across versions:

1. All fields must have a default value.
2. extra='forbid' is rejected (old keys must be silently dropped).
3. __centauro_name__ is mandatory — it's the stable storage identifier.

---

Async

AsyncCollection and AsyncTimeSeriesCollection mirror the sync API and run under asyncio:

from centaurodb import Engine, AsyncCollection

async def main():
    engine = Engine("postgresql://localhost/mydb")
    books = AsyncCollection(engine, "library")
    results = await books.read_objects(Book.fields.rating > 4.5)

---

Status

• 591 tests passing (610 collected; the 14 PostgreSQL integration tests skip without a CENTAURODB_PG_URL env var and run in CI against a real postgres:16 service container), ~4,000 LOC, fully type-hinted (PEP 561 py.typed).
• v0.9.0 — beta. Stable storage format (five canonical columns — id, name, write_time, edit_time, meta — committed per ADR-0002, storage-format guarantees; future system features land in meta._sys.* or sibling tables, never as new columns) and stable public API on the surfaces marked stable.
• See CHANGELOG.md for release history.

Production status & known limits

Honest about what's not in the box yet — so you can decide whether the gaps matter for your workload:

• No connection pooling. A PostgresEngine holds a single connection. Fine for batch jobs, ETL, ingestion workers, and pipelines. Not yet sized for high-concurrency web backends.
• AsyncCollection runs sync psycopg inside asyncio.to_thread. It works under FastAPI, but it does not unlock native-async concurrency. Treat it as ergonomic compatibility, not a perf win. A native psycopg.AsyncConnectionPool path is on the roadmap.
• No automatic pagination. read_objects accepts limit / offset (and count_objects for totals); use them on any list endpoint that could grow unbounded.
• No row-level security / built-in auth. This is a storage library, not a backend. Enforce auth at the application boundary.

If your use case is data pipelines, analytics, time-series, IoT, or quant research, none of these are blockers. If it's a high-concurrency multi-tenant web app, reach for SQLAlchemy + Supabase instead.

License

MIT — see LICENSE.