Git UNDO : How to Rewrite Git History with Confidence

· Source: Towards Data Science · Field: Technology & Digital — Software Development & Engineering · Depth: Intermediate, extended

Summary

This article details how to confidently rewrite Git history by understanding its internal mechanisms and utilizing key commands. It begins by explaining Git's three states: the working directory, the index (staging area), and the repository, illustrating how changes are recorded through `git add` and `git commit`. The core of the article focuses on `git reset` with its `--soft`, `--mixed`, and `--hard` options, demonstrating how each option progressively undoes commits, staging, and working directory changes. It also introduces `git cherry-pick` for applying specific commit changes to another branch and `git revert` for safely undoing pushed commits by creating a new "undo" commit. Finally, the `git reflog` command is presented as a crucial tool for recovering lost commits, even after a `git reset --hard`, by tracking HEAD's past positions.

Key takeaway

For Data Scientists and Software Engineers managing Git repositories, understanding the nuanced effects of `git reset`'s `--soft`, `--mixed`, and `--hard` options is crucial for precise history manipulation. Before executing any complex undo operation, visualize the current and desired repository states. This practice, combined with knowing when to use `git revert` for public history and `git reflog` for recovery, will prevent data loss and streamline collaborative development workflows.

Key insights

Mastering Git's internal states and undo commands enables confident history rewriting and recovery.

Principles

Method

To undo Git changes, use `git reset --soft` to undo commits, `--mixed` to also undo staging, and `--hard` to additionally undo working directory changes. For pushed commits, `git revert` creates a new undo commit. Recover lost commits via `git reflog`.

In practice

Topics

Best for: Data Scientist, Software Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.