# Welcome to oups!
## What is oups?
*oups* stands for Ordered Unified Processing Stack — out-of-core processing for ordered data (batch + live).
*oups* is a Python toolkit for building end-to-end pipelines over ordered data with the same code in offline training and live streaming/batch contexts.
It centers on ``StatefulLoop`` (``loop.bind_function_state``, ``loop.iterate``, ``loop.buffer``), which binds and persists function/object state, orchestrates chunked iteration, and buffers DataFrames under a memory cap with flush-on-limit or last-iteration semantics.
Complementing the loop, ``stateful_ops`` provides vectorized, chunk-friendly primitives like ``AsofMerger`` for multi-DataFrame as-of joins (with optional windows of previous values) and ``SegmentedAggregator`` (planned) for streamed segmentation and aggregation.
The ``store`` package manages ordered Parquet datasets via schema-driven keys (``@toplevel``), supports incremental updates (``store[key].write(...)``) and duplicate handling, and offers synchronized iteration across datasets via ``store.iter_intersections(...)`` with optional warm-up (``n_prev``).
Together these pieces enable out-of-core processing with resumability, and deterministic buffering. The design favors explicit, minimal APIs and reproducible results, aligning offline feature generation with online serving.
## Links
- 📖 **[Documentation](https://pierrot.codeberg.page/oups/)** - Guides and API reference
- 📋 **[Changelog](CHANGELOG.md)** - Release notes and version history
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"description": "# Welcome to oups!\n\n## What is oups?\n*oups* stands for Ordered Unified Processing Stack \u2014 out-of-core processing for ordered data (batch + live).\n\n*oups* is a Python toolkit for building end-to-end pipelines over ordered data with the same code in offline training and live streaming/batch contexts.\n\nIt centers on ``StatefulLoop`` (``loop.bind_function_state``, ``loop.iterate``, ``loop.buffer``), which binds and persists function/object state, orchestrates chunked iteration, and buffers DataFrames under a memory cap with flush-on-limit or last-iteration semantics.\nComplementing the loop, ``stateful_ops`` provides vectorized, chunk-friendly primitives like ``AsofMerger`` for multi-DataFrame as-of joins (with optional windows of previous values) and ``SegmentedAggregator`` (planned) for streamed segmentation and aggregation.\nThe ``store`` package manages ordered Parquet datasets via schema-driven keys (``@toplevel``), supports incremental updates (``store[key].write(...)``) and duplicate handling, and offers synchronized iteration across datasets via ``store.iter_intersections(...)`` with optional warm-up (``n_prev``).\n\nTogether these pieces enable out-of-core processing with resumability, and deterministic buffering. The design favors explicit, minimal APIs and reproducible results, aligning offline feature generation with online serving.\n\n## Links\n\n- \ud83d\udcd6 **[Documentation](https://pierrot.codeberg.page/oups/)** - Guides and API reference\n- \ud83d\udccb **[Changelog](CHANGELOG.md)** - Release notes and version history\n\n",
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