pipenv install still doing full lock resolution

[matteius]

In pipenv 2024.0.0 we made changes to install path, and my assumption was it would behave more like the upgrade path, but I saw first hand today its re-locking everything.

[matteius]

Analysis for Issue #6267:

Analysis of Pipenv Issue #6267

1. Problem Summary:

The issue describes a performance regression in Pipenv 2024.0.0, where the pipenv install command, even when installing a single package, triggers a full dependency resolution. This behavior was unexpected, as the user anticipated a more targeted resolution akin to the upgrade command.

2. Comment Discussion Analysis:

The comments reveal two key points:

• Confirmation of Issue: The maintainer acknowledges the problem, implying that the current behavior is unintentional.
• Initial Solution Flawed: The initial code suggestion by the analysis tool is deemed incorrect. While targeting the right area (do_install), it suggests creating a new constraint file, which is redundant given existing functionality in the upgrade path.

3. Proposed Resolution:

The goal is to leverage the existing upgrade logic for single-package installations to avoid unnecessary full resolution.

Code Changes:

• File: pipenv/routines/install.py
• Function: do_install

Current Code: (Simplified)

def do_install(
   project,
   packages=False,
   editable_packages=False,
   # ... other parameters
):
   # ... existing code ...
   if not packages and not editable_packages:
       # Perform full lock resolution
       do_init(project, ...)
   else:
       # Add packages to Pipfile
       # ... (Existing code) ...
       # Perform full lock resolution
       do_init(project, ...)
   # ... existing code ...

Proposed Code:

def do_install(
    project,
    # ... existing parameters
    packages=None,
    editable_packages=None,
    # ...
):
    # ... existing code ...
    if not packages and not editable_packages:
        # Perform full lock resolution
        do_init(project, ...)
    else:
        # Add packages to Pipfile
        # ... (Existing code) ...
        # Utilize the upgrade logic for targeted resolution
        from pipenv.routines.update import upgrade
        upgrade(project, packages=packages, editable_packages=editable_packages, ...)
    # ... existing code ...

4. Code Snippet:

The code snippet is included in the "Proposed Code" section.

5. Additional Steps/Investigations:

• Refactor do_install: The function should be refactored to clearly separate the logic for single-package installation and full installation, making the code more maintainable.
• Utilize Existing upgrade Function: Leverage the existing upgrade function for single-package installations instead of duplicating code. Ensure that upgrade handles single-package additions correctly and efficiently.
• Comprehensive Testing:
  • Develop test cases for single package installations, full installations, and edge cases (e.g., installing a package already present with a different version).
  • Test the modified code with various project structures and dependency complexities.
• Performance Benchmarking: Compare the performance of the modified install path to the existing full resolution approach, especially for projects with numerous dependencies.
• Documentation: Clearly document the updated behavior of pipenv install in the documentation, particularly when specific packages are provided as arguments.

Impact on Pipenv:

• Improved Efficiency: The proposed change will significantly improve performance when installing individual packages by avoiding unnecessary full resolution.
• Maintainability: Reusing the upgrade logic for single-package installations will reduce code duplication and improve maintainability.
• Clarity: Explicitly separating the logic for different installation scenarios will enhance code readability and understanding.

By implementing the suggested changes and conducting thorough testing, Pipenv can regain the efficiency it lost in version 2024.0.0 while maintaining accuracy and consistency in dependency management.

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[matteius]

fyi -- Testing out some Gen AI code analysis of recent issues