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Understanding DataHub CLI Dev Builds: Installation, Identification, and Usage

Avery
Updated

Based on the research provided, here is a comprehensive guide formatted as a Help Center article.

When working with DataHub Support to resolve specific bugs or test new features, you may be asked to install a Dev Build (Development Build) of the DataHub CLI. This guide explains what dev builds are, how to identify them, and how to manage them safely.

What is a "Dev Build"?

An Official Release of the DataHub CLI is a versioned package (e.g., v0.15.0) published to PyPI and installed via standard pip commands.

A Dev Build is an unreleased version of the CLI installed directly from a specific branch in the DataHub source code repository. DataHub Support may provide a dev build when:

  • A critical bug fix is available but has not yet been included in an official release.
  • You are testing a patch for a customer-specific issue.
  • You need early access to a feature currently in development.

Unlike official releases, dev builds usually display their version as unavailable or 0.0.0.dev0 because they are not yet tagged for release.

How to Identify if You Are Using a Dev Build

There are three ways to determine if your current environment is running a Dev Build or an Official Release.

Method 1: Check the CLI Version Command

Run the following command in your terminal:

datahub version

Official Release Output:
You will see a standard version number.

DataHub CLI version: 0.15.0
Models: ...

Dev Build Output:
You will see a warning that the version is unavailable.

DataHub CLI version: unavailable (installed in develop mode)
Models: ...

Method 2: Check via PIP

Run the following command to see package details:

pip show acryl-datahub

Official Release Output:

Name: acryl-datahub
Version: 0.15.0
Location: /usr/local/lib/python3.9/site-packages

Dev Build Output:
Look for "Editable project location" or a version ending in .dev0.

Name: acryl-datahub
Version: 1!0.0.0.dev0
Location: /path/to/editable/install
Editable project location: /path/to/git/repo

Method 3: Python Check

If you are unsure, you can run this Python one-liner to check the internal version flags:

python -c "import datahub._version as v; print(f'Version: {v._version_}'); print(f'Is Dev: {v.is_dev_mode()}')"
  • Official: Returns Is Dev: False
  • Dev Build: Returns Is Dev: True

How to Install a Dev Build

If DataHub Support provides you with a Git branch URL (e.g., git+https://github.com/acryldata/datahub-fork.git@fix/issue-123), follow these steps:

1. Uninstall the existing CLI
To avoid conflicts, remove the official version first.

pip uninstall acryl-datahub

2. Install from the Git URL
Run the install command using the URL provided.
Note: You MUST include #subdirectory=metadata-ingestion at the end of the URL, as the CLI code resides in a subdirectory of the repo.

# Syntax
pip install "git+https://github.com/acryldata/datahub-fork.git@{BRANCH_NAME}#subdirectory=metadata-ingestion"

# Example
pip install "git+https://github.com/acryldata/datahub-fork.git@fix/customer-issue-123#subdirectory=metadata-ingestion"

3. Verify Installation
Run datahub version. It should now return unavailable (installed in develop mode), confirming you are on the custom branch.

How to Switch Back to an Official Release

Dev builds are temporary solutions. Once the fix has been merged and released, or if you are done testing, you should revert to the official version.

  1. Uninstall the Dev Build:

    pip uninstall acryl-datahub

  2. Install the Latest Official Release:

    pip install acryl-datahub --upgrade

    Or install a specific stable version:

    pip install acryl-datahub==0.15.0

Risks & Best Practices

When using Dev Builds, keep the following in mind:

  • No Automatic Updates: Dev builds are locked to the specific commit you installed. You will not receive security patches or bug fixes automatically.
  • Not Production-Tested: These builds may contain incomplete features or bugs. Only use them for the specific purpose discussed with Support.
  • Documentation: Keep a record of which branch you installed and when.
  • Isolation: We highly recommend using Python Virtual Environments to test dev builds without affecting your main system or other projects.

Example: Using a Virtual Environment for Testing

# Create a fresh environment for the dev build
python -m venv ~/venv-datahub-dev

# Activate the environment
source ~/venv-datahub-dev/bin/activate

# Install the dev build here
pip install "git+https://..."

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