Rerun parquet ingestion

ParquetReader is a pure reader: it maps a flat table onto the Rerun model by turning raw columns into grouped, time-indexed chunks of struct and scalar components. Column-name prefixes become entities, grouped columns become a single struct component, designated columns become timelines. The reader does not assemble archetypes anymore, mapping struct fields into typed Rerun components (Transform3D, Scalars, Points3D) is done with lenses on the reader's .stream(). The whole reader job is configuration; fill in the rerun-data-model mapping table first, then express it through stream(). Stream mechanics after .stream() are in rerun-chunk-processing.

The whole table is configuration, not code. If you find yourself building Chunk.from_columns from a parquet, or munging it in pandas first, stop, ParquetReader plus a lens almost certainly expresses it. Anything the reader cannot express (per-row entity routing, derived values, unit conversion) belongs in lenses downstream, not in pre-pandas munging; keep the pipeline columnar.

The API

from rerun.chunk import DeriveLens, IndexColumn
from rerun.experimental import ParquetReader

reader = ParquetReader(table_path)  # a lightweight handle over the file
stream = reader.stream(
    entity_path_prefix="/world",  # prepended to every entity path
    column_grouping="prefix",  # "prefix" | "individual" | "explicit_prefixes"
    delimiter="_",  # split for column_grouping="prefix"
    prefixes=None,  # required for "explicit_prefixes"
    use_structs=True,  # pack grouped columns into one struct component
    static_columns=["robot_type"],  # constant-per-file values, logged static
    index_columns=[IndexColumn.timestamp("timestamp", input_unit="us"), IndexColumn.sequence("frame_index")],
)