datafusion-query-builder
Programmatic, injection-safe builder for DataFusion SQL.
Decision gist · record as of 2026-08-14
Yes, if you build DataFusion queries from user input or dynamic parameters. The injection-safe-by-construction design and readable SQL output make it worth the medium install friction. Alpha status and 26-day-old release suggest active development; verify dialect coverage for your specific query patterns before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; prebuilt wheels available for common platforms (macOS arm64/x86_64, Linux x86_64/aarch64).
- Medium install friction due to compiled Rust wheels; prebuilt abi3 wheels available for CPython 3.9+ on macOS (both architectures) and Linux (x86_64 and aarch64).
- Active maintenance with a release 26 days ago.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-07-19 (26 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 104,416 downloads/mo, #12,749 on PyPI
Alternatives
Verify before relying
from datafusion_query_builder import col, table, functions as f
q = table("records").filter(col("kind") == "span").select(
f.coalesce(col("service_name"), "(unknown)").alias("service")
).limit(200)
print(q.to_sql())- Whether the library's DataFusion SQL dialect coverage matches your specific query patterns.
- Performance characteristics when building very large or deeply nested queries.
- Stability guarantees given the Alpha development status.
What it is and what it does
datafusion-query-builder is a typed SQL query builder for DataFusion, written in Rust with a Python API exposed via PyO3. It replaces hand-rolled SQL strings with a composable, type-safe interface that automatically escapes values by construction—untrusted input like column values or user-supplied filters are safe to embed without manual quoting. The builder emits actual SQL text (not an AST or opaque object), so queries remain visible in logs, greppable, and cache-keyable.
The library provides a façade layer over sqlparser, handling expressions, filters, selections, grouping, and ordering through method chaining. Python scalars auto-promote to literals; specialized operators like JSONB key-exists checks (`has_key`, `has_any_key`, `has_all_keys`) are first-class. An explicit `raw()` escape hatch and `param()` placeholders allow stepping outside the standard grammar when needed. The crate is correctness-critical and tested against DataFusion itself as an oracle to catch silent encoding errors.
Use it for
- Build dynamic SQL filters from user input without manual escaping or string concatenation.
- Generate analytics queries programmatically while keeping SQL visible in application logs.
- Construct complex DataFusion queries with type safety and composable method chaining.
- Embed untrusted column names or filter values safely without SQL injection risk.
- Test query generation logic with deterministic, re-parseable SQL output.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you build DataFusion queries from user input or dynamic parameters.
The injection-safe-by-construction design and readable SQL output make it worth the medium install friction. Alpha status and 26-day-old release suggest active development; verify dialect coverage for your specific query patterns before production use.
Install
datafusion-query-builder on PyPI
Before you install
Medium install friction due to compiled Rust wheels; prebuilt abi3 wheels available for CPython 3.9+ on macOS (both architectures) and Linux (x86_64 and aarch64). Active maintenance with a release 26 days ago.
Requires Python 3.9 or later; prebuilt wheels available for common platforms (macOS arm64/x86_64, Linux x86_64/aarch64).
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
from datafusion_query_builder import col, table, functions as f
q = table("records").filter(col("kind") == "span").select(
f.coalesce(col("service_name"), "(unknown)").alias("service")
).limit(200)
print(q.to_sql())Verify before relying
- Whether the library's DataFusion SQL dialect coverage matches your specific query patterns.
- Performance characteristics when building very large or deeply nested queries.
- Stability guarantees given the Alpha development status.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 26 days since the last release |
| First released | |
| Downloads | 104,416 / month, #12,749 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3 :: OnlyProgramming Language :: RustTopic :: DatabaseTyping :: Typed |
Evidence: datafusion_query_builder-0.2.0-cp39-abi3-macosx_11_0_arm64.whl; datafusion_query_builder-0.2.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; datafusion_query_builder-0.2.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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