polars-gdx

Lazy, prefiltered Polars access to GAMS GDX files — no GAMS installation required.

• scan_gdx(path, symbol=...) returns a pl.LazyFrame; nothing is read until collect(), and column projection is pushed down via Polars' IO-source interface. read_gdx(...) is the eager convenience wrapper (scan_gdx(...).collect(), same arguments).
• Predicates on key columns (key_filter={dim_index: allowed_labels}, filter(pl.col(key) == label) or filter(pl.col(key).is_in(labels))) are folded into a native, index-based prefilter inside the Rust read loop, so unwanted records are never materialized — unlike the official gamsapi reader, which must load everything into pandas first. The prefilter runs inside a bulk C callback (gdxDataReadRawFastEx): one FFI crossing for the whole symbol, with early termination for head(n).
• threads=n (on scan_gdx/read_gdx) parallelises the raw read across n independent file handles: each worker scans a contiguous range of the first key dimension, results are concatenated in range order so record order matches the serial read exactly, and a first-dimension filter lets whole non-matching ranges be skipped without touching the file. Parallel scanning requires rebuilding the vendored GDX C library with its internal mutexes enabled (the default in this repo).
• read_domains(path, symbol=...) lists the unique labels actually used per index dimension in a single bulk C scan (gdxGetDomainElements) — the cost of unique() over a key column without reading or materialising any records, valuable on very large symbols.
• Under the hood: hand-written FFI to the vendored MIT-licensed GDX C library, extracted from lolow/gdxcomp, building on GAMS-dev/gdx.

Usage

import polars as pl
from polars_gdx import scan_gdx, list_symbols

list_symbols("trnsport.gdx").filter(pl.col("type") == "Param")

# lazy: nothing read yet
x = scan_gdx("trnsport.gdx", symbol="x")

# read only the seattle rows, skipping all others natively
x.filter(pl.col("dim_0") == "seattle").collect()

# eager convenience: read_gdx = scan_gdx(...).collect()
from polars_gdx import read_gdx
df = read_gdx("trnsport.gdx", symbol="x", key_filter={0: ["seattle"]})

# unique labels per index dimension without reading the records:
from polars_gdx import read_domains
read_domains("trnsport.gdx", symbol="x")

Label matching

Key labels (key_filter and filter(pl.col(key) == label) predicates) are matched case-insensitively (ASCII-folded), the way GAMS does — "SEATTLE" matches a stored "seattle". The labels in the produced frame always keep the file's original casing. For predicate filters whose label only differs in case from the stored one, the plugin deliberately skips the native prefilter and re-applies a case-folded predicate on the full read, because Polars' == is case-sensitive and would otherwise silently drop the rows.

File handle lifetime

scan_gdx keeps the GDX file open until the returned LazyFrame is collected and released (Polars may read the source more than once). Use read_gdx, or an explicit Reader with its close() / context-manager support, to release the handle eagerly — relevant on Windows, where an open handle locks the file.

Benchmark

vs gamsapi 54.5.0 (pandas-backed), on a 2M-record parameter (80 MB GDX); best of 5 runs. See benchmarks/bench_vs_gamsapi.py (run as a CI job on every push).

¹ gamsapi has no lazy loading or pushdown: it must materialize the whole symbol, then filter. The advantage of polars-gdx grows with predicate selectivity; head(n)/n_rows terminates the native read after n records.

Build

maturin develop --release   # builds the vendored GDX C library + extension
pytest

Requires a C++17 compiler and CMake ≥ 3.5 at build time; runtime has no GAMS dependency.

Layout

crates/gdx-sys        # raw FFI; builds vendored GDX-dev/gdx via CMake
crates/gdx            # safe RAII wrapper (global lock; lazy UEL interning)
crates/polars-gdx     # PyO3 extension: symbol listing + Arrow streaming with prefiltering
python/polars_gdx     # Python layer: scan_gdx / list_symbols