rypipe documentation¶
rypipe is a format-agnostic columnar engine that turns byte streams into
Apache Arrow record batches. It separates format-specific parsing from
format-agnostic execution, so the same engine can parse XML, JSON, CSV,
HTML, or any other row-oriented format once you provide a small adapter.
What rypipe is¶
- A Rust workspace with three crates:
rypipe-core: the generic engine.rypipe-xml: Crystal Reports XML adapter.rypipe-python: PyO3 bindings.- Zero-copy friendly: decoders emit borrowed strings; the engine copies only when necessary.
- GIL-free parsing: all heavy work runs outside Python's GIL.
- Memory-bounded and parallel by design.
What rypipe is not¶
- Not a full query engine. It handles projection, renaming, dropping, casting, filtering, and dictionary encoding, not joins, aggregations, or SQL.
- Not a one-size-fits-all parser. Each format needs a
RecordParser+Splitteradapter.
Quick start¶
From Python¶
import rypipe
table = rypipe.read(
"data.xml",
row_tag="Row",
fields={"amount": "float64"},
filter={"field": "status", "op": "==", "value": "active"},
)
print(table.num_rows, table.num_columns)
From Rust¶
use rypipe_core::{ExecutionPlan, FieldType, Pipeline};
use rypipe_xml::xml_pipeline;
let batch = xml_pipeline("Row")
.with_plan(
ExecutionPlan::new()
.type_as("amount", FieldType::Float64)
.filter_eq("status", "active"),
)
.read_path("data.xml", false, false)?;
Guides¶
- Architecture: how the pieces fit together.
- Python API:
_rypipefunctions and options. - Rust API: using
rypipe-coreand writing custom adapters. - Writing a format adapter: adding CSV, JSON, etc.
- Performance: benchmarks and tuning knobs.