openyxdb 1.3.0

Read and write Alteryx YXDB files

OpenYXDB

Read and write Alteryx YXDB files from Python and C++.

Built on Alteryx's official open-source YXDB implementation, released under GPLv3. This fork modernizes the build system, fixes bugs, adds cross-platform support, and wraps the C++ core in a Python package with first-class PyArrow, Pandas, and Polars integration.

What changed from the Alteryx original

• Cross-platform support -- builds and runs on Linux, macOS, and Windows (the original targeted Windows only)
• Python bindings via nanobind, published to PyPI as openyxdb
• PyArrow, Pandas, Polars, and DuckDB read/write integration with automatic type mapping
• Bug fixes -- block index was never written to disk (broke files with more than 65,536 records), reference-counting crash on GCC
• Modern CMake (3.20+, target-based), pixi for dependency management, Catch2 test suite, GitHub Actions CI

What is YXDB?

YXDB is the native binary format used by Alteryx Designer. It is row-oriented: each file contains UTF-16 XML metadata describing the schema, followed by LZF-compressed blocks of records, and a block index at the end for random access.

This library supports E1 (non-AMP) YXDB files only.

Install

pip install openyxdb

Usage

import openyxdb

# Read to PyArrow, Pandas, or Polars
table = openyxdb.to_pyarrow("data.yxdb")
df = openyxdb.to_pandas("data.yxdb")
df = openyxdb.to_polars("data.yxdb")

# Write from any of them
openyxdb.from_polars(df, "output.yxdb")
openyxdb.from_pandas(df, "output.yxdb")
openyxdb.from_pyarrow(table, "output.yxdb")

All 17 YXDB field types are supported: Bool, Byte, Int16, Int32, Int64, FixedDecimal, Float, Double, String, WString, V_String, V_WString, Date, Time, DateTime, Blob, and SpatialObj.

Polars integration

Importing openyxdb automatically monkey-patches polars with YXDB support (no-op if polars is not installed). No extra setup is required.

Top-level aliases

import polars as pl
import openyxdb  # registers everything on import

# Eager read — returns a DataFrame
df = pl.read_yxdb("data.yxdb")

# Lazy scan — returns a LazyFrame with projection & predicate pushdown
lf = pl.scan_yxdb("data.yxdb")
df = lf.select("col_a", "col_b").filter(pl.col("col_a") > 10).collect()

Namespace plugins

# Write a DataFrame directly
df.yxdb.write("output.yxdb")

# Stream a LazyFrame through the streaming engine and write in chunks
lf.yxdb.sink("output.yxdb", chunk_size=65_536)

lf.yxdb.sink() (and the top-level openyxdb.sink_yxdb(lf, path)) execute the lazy plan on Polars' streaming engine and write the result to the YXDB file in chunks, so the writer never buffers more than chunk_size rows in Python at a time. Polars 1.x does not expose a public plugin API for custom sink_* formats, so the query result still passes through a single collect() call before being chunk-written; true per-batch push sinks require upstream Polars support.

Pushdown support

scan_yxdb implements pushdown against the underlying reader:

Optimization	Supported	Notes
Projection pushdown	Yes	Only requested columns are decoded from disk.
Row-limit pushdown	Yes	head(n) / fetch(n) stops decoding once n rows produced.
Batched streaming	Yes	Default 65 536 rows; honours Polars' batch_size hint.
Predicate pushdown	Partial	Predicates evaluate per batch after decode. YXDB has no per-block statistics, so genuine file-level skipping isn't possible; combined with head predicates still short-circuit once enough rows are collected.

Manual registration

If you import openyxdb after polars is already loaded, everything is registered automatically. To re-register or verify:

openyxdb.register_polars()

DuckDB integration

import duckdb, openyxdb

con = duckdb.connect()

# Register a YXDB file as a SQL view
openyxdb.register_duckdb(con, "yx", "data.yxdb")
con.execute("SELECT COUNT(*) FROM yx WHERE score > 90").fetchone()

# Or get a relation directly
rel = openyxdb.to_duckdb("data.yxdb", con)

# Write the result of a DuckDB query to YXDB
openyxdb.from_duckdb(
    "SELECT id, score FROM yx WHERE score > 90",
    "top_scores.yxdb",
    con=con,
)

DuckDB support is provided through Arrow interop: YXDB files are loaded into an Arrow table and registered on the connection. A native DuckDB table function (SELECT * FROM read_yxdb('file.yxdb')) would require a C++ extension, which is out of scope for this package.

Building from source

pixi install
pixi run build
pixi run test

Limitations

• Spatial indexes are skipped on read and not created on write.
• Spatial objects are accessible only as raw blobs (SHP-encoded binary).
• Little-endian architectures only (x86, ARM).

Testing

The reader has been validated against 1,012 real-world E1 YXDB files sourced from the community corpus at Sigilweaver/YXDB-Sources, covering a wide range of field types, encodings, record counts (0 to 200k+), and filenames including non-ASCII characters — 100% pass rate.

The 1.3.0 streaming sink (sink_yxdb) and DuckDB round-trip paths are exercised against a sampled 25-file subset of the corpus in addition to the per-feature unit tests. Full Python suite: 1,040 passed.

License

GPLv3 -- see LICENSE.