(dsv,jetstream) --> dataframe <--> bitemporal-tables
Project description
polars-hist-db
A Python library for building bitemporal data pipelines with Polars and MariaDB.
It ingests data from DSV files or NATS JetStream subjects, tracks history using MariaDB's system-versioned tables, and exposes everything as strongly-typed Polars DataFrames.
Features
- Typed uploads — push Polars DataFrames into MariaDB with automatic type mapping between Polars, SQL, and SQLAlchemy.
- Typed queries — read tables back into DataFrames with column types inferred from the database schema. Temporal query hints (
asof,span,all) let you slice history without writing SQL. - YAML-driven pipelines — define scrape specifications that handle column typing, enrichment via custom transform functions, normalization across tables, and foreign-key deduction.
- Deduplication — an audit log tracks what has already been ingested so re-runs skip previously processed files or messages.
- Transactional ingestion — each file or message is processed in its own transaction; failures roll back cleanly without affecting other items.
- Dual input sources — crawl directories for DSV files, or consume messages from NATS JetStream with configurable fetch/subscription strategies.
- Delta upserts — staging tables detect changed rows, handle duplicates (
take_first,take_last,error), and optionally mark disappeared rows as dropped.
Full documentation: jr200.github.io/polars-hist-db
Quick Start
uv sync
make test
Development
make check # ruff + mypy
make docs # render and preview quarto docs
make bump PART=patch
make release
License
This project is licensed under the MIT license.
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