Skip to main content

firebird-embedded

firebird-embedded packages prebuilt Firebird client and embedded-engine files as platform-specific Python wheels. It does not compile Firebird. The Python distribution is firebird-embedded; its import package is firebird.embedded within the implicit firebird namespace package.

The agent skill describes provider discovery and integration with firebird-driver for applications using the installed wheel.

Architecture

The small Python package exposes firebird.embedded.provider.FirebirdProvider through the firebird.runtime.providers entry-point group. Consumers discover the provider class, instantiate it, and obtain the installed runtime root, client-library and log paths, versions, and capabilities. The provider does not depend on firebird-driver or configure it.

Each wheel contains the Python package at firebird/embedded/ and one prepared native runtime at firebird/embedded/runtime/. hatch_build.py selects that runtime using FIREBIRD_EMBEDDED_TARGET and adds it to the wheel. The runtime includes an initially empty firebird.log at its root. The wheels have py3-none-<platform> tags because the native files do not depend on a particular Python ABI. A build host can assemble wheels for other platforms from their prebuilt Firebird archives.

Native runtime requirements

The Linux wheels use manylinux_2_17_i686 and manylinux_2_17_x86_64 tags so PyPI accepts them and installers select the matching architecture and glibc version. Firebird 4 also requires the target system to provide libtommath.so.0 and its dependencies. This library is not bundled in the wheels. Install the distribution package supplying it before using the embedded runtime; a matching manylinux tag alone does not establish that it is present.

The Windows wheels include msvcp140.dll and the required ICU DLLs. The supplied Firebird binaries also import VCRUNTIME140.dll and Universal CRT components, which must be available on the target system.

The package version has four components: <Firebird major>.<minor>.<revision>.<packaging revision>. The first three components must match the bundled Firebird release. The version is stored in src/firebird/embedded/__about__.py.

Repository layout

.
├── make_embedded.py              # prepare configured local runtimes
├── hatch_build.py                # select one runtime for a wheel
├── pyproject.toml                # metadata, targets, and Hatch scripts
├── src/firebird/embedded/        # provider and package metadata
└── firebird-binaries/
    ├── assets/
    │   ├── linux32/             # cached Firebird release archives
    │   ├── linux64/
    │   ├── linux32-arm/
    │   ├── linux64-arm/
    │   ├── win32/
    │   ├── win64/
    │   ├── manifest/             # this branch's <target>.txt mappings
    │   └── override/             # optional branch-local replacements
    └── <version>/<target>/       # prepared runtime directories

The cached archives and prepared runtimes are local build assets and are ignored by Git. Only this branch's flat manifests and overrides are tracked. Each manifest selects only the files needed for its embedded runtime, including files later replaced by an override. A preparation record at <version>/.prepared.json lets the wheel hook reject incomplete or mismatched runtimes.

The fb-3.0 and fb-4.0 branches build Linux x86/amd64 and Windows x86/x64 wheels. Their published ARM kits target Android, not Linux. fb-5.0 builds all six targets. main is a build-disabled scaffold for Firebird 6, with no release manifests or target settings.

Prepare local runtimes

Run these commands from the repository root. Use Python 3.11 or newer:

python3.11 make_embedded.py

The script reads firebird-version and target settings from pyproject.toml. An optional version argument must match that configuration. It checks firebird-binaries/assets/<target>/ for matching archives, downloads missing ones from the corresponding Firebird GitHub release, and prepares the versioned runtime trees from the branch-local manifests and optional overrides. It installs all configured targets together, with rollback on failure.

For a new Firebird release, update the first three components of src/firebird/embedded/__about__.py and the tool.firebird-embedded.firebird-version setting in pyproject.toml before building wheels. Update archive suffixes and branch-local manifests if an upstream kit changes. make_embedded.py does not edit package metadata.

Build wheels

From the repository root, run one Hatch script per target:

hatch run linux32
hatch run linux64
hatch run win32
hatch run win64

The scripts are defined in the default Hatch environment. Each runs a wheel build with its own FIREBIRD_EMBEDDED_TARGET; the wheels are written to dist/<Firebird-version>/. A target's versioned prepared directory and preparation record must exist before its wheel is built. Run only the scripts configured on the branch.

Metadata

Release files for firebird-embedded 4.0.7.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for firebird-embedded 4.0.7.3
File
firebird_embedded-4.0.7.3-py3-none-win_amd64.whl Python 3 none Windows x86-64 Details
firebird_embedded-4.0.7.3-py3-none-win32.whl Python 3 none Windows x86-32 Details
firebird_embedded-4.0.7.3-py3-none-manylinux_2_17_x86_64.whl Python 3 none Linux glibc 2.17+ x86-64 Details
firebird_embedded-4.0.7.3-py3-none-manylinux_2_17_i686.whl Python 3 none Linux glibc 2.17+ x86-32 Details

Total release size: 53.9 MB

Release files / firebird_embedded-4.0.7.3-py3-none-win_amd64.whl

Download URL firebird_embedded-4.0.7.3-py3-none-win_amd64.whl
Size 18.1 MB
Tags Python 3 Windows x86-64
SHA-256 checksum
How to use checksums
7db63dd00bc0ad33d4fa57939c7caad9ec3240c3ffa29c1f30a093ddc96a5a3f
BLAKE2b-256 checksum
How to use checksums
4e4475d5bcb4b5f790ff1b8cd78a5cf5183b3ee64083238783138cbf391d9c8d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.0

Release files / firebird_embedded-4.0.7.3-py3-none-win32.whl

Download URL firebird_embedded-4.0.7.3-py3-none-win32.whl
Size 17.2 MB
Tags Python 3 Windows x86-32
SHA-256 checksum
How to use checksums
6fbe9edb2eafd280274ad9c22428eacd5a98e3658656d63fde648e79da2e5403
BLAKE2b-256 checksum
How to use checksums
1b01b6c4781e442fe2ba6bde891df31559cd3dffd28a35f1ecd78958d50249b8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.0

Release files / firebird_embedded-4.0.7.3-py3-none-manylinux_2_17_x86_64.whl

Download URL firebird_embedded-4.0.7.3-py3-none-manylinux_2_17_x86_64.whl
Size 8.9 MB
Tags Linux glibc 2.17+ x86-64 Python 3
SHA-256 checksum
How to use checksums
fa9932286b6392d8ab6b068a37c5e05bab845b1c98165e0f6df986e55f3f9674
BLAKE2b-256 checksum
How to use checksums
73b9b20c287e2037a610f2acc851ebcb5a46c7818e9a41722b13488154b0faa2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.0

Release files / firebird_embedded-4.0.7.3-py3-none-manylinux_2_17_i686.whl

Download URL firebird_embedded-4.0.7.3-py3-none-manylinux_2_17_i686.whl
Size 9.8 MB
Tags Linux glibc 2.17+ x86-32 Python 3
SHA-256 checksum
How to use checksums
a2d36ea6b18080eed887441e507852ca0652ee5b2d2dfa3dfe70a7e227712695
BLAKE2b-256 checksum
How to use checksums
e6fc0c51e86cd787d6c34b5af8bf70a9e70e350c605510c8074678e0681deb0b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.0

Security for firebird-embedded 4.0.7.3

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page