Hello, I'm Maneshwar. I'm working on git-lrc: a Git hook for Checking AI generated code.
AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.
Deleting a key k from a B+-tree begins the same way every structural change begins, with a search.
SQLite first performs a normal search to locate the leaf node where k would reside.
If the key is not found, the operation terminates immediately. There is nothing to fix. The tree remains unchanged.
If the key is found, we remove it from the leaf.
Internally, this means shifting all entries after that index one position left.
At this point, one of two things happens:
- If the node still satisfies the minimum occupancy requirement, the delete operation is complete.
- If the node falls below the lower occupancy bound, the tree must be rebalanced.
This lower bound is what keeps the tree shallow and efficient. If nodes are allowed to become too empty, the tree degenerates and performance suffers.
When a Leaf Becomes Underfull
Suppose after deletion, the leaf node contains fewer entries than allowed.
The tree must now redistribute or merge nodes to restore balance.
The restructuring process begins at that underfull node N and works its way upward if necessary.
The goal is simple:
Ensure that all nodes remain within the allowed occupancy bounds while keeping the tree height-balanced.
Redistribution: Borrowing from Siblings
The first strategy is redistribution.
Node N looks at its siblings usually the immediate left or right sibling.
If N has both, either may participate in balancing.
If it is the leftmost or rightmost child, then only one side is available.
If a sibling has extra entries (more than the minimum required), entries are redistributed between N and its sibling so that both nodes have roughly equal occupancy.
This avoids merging and keeps the number of nodes constant.
After redistribution, the parent’s separator keys must be updated to reflect the new boundary between children.
If redistribution succeeds, the delete operation ends here.
Merging: When Redistribution Is Not Enough
If the sibling does not have spare entries, meaning both nodes are at minimum occupancy, redistribution is impossible.
In that case, SQLite performs a merge.
Node N is merged with one of its siblings. Their entries are combined into a single node. The sibling node is freed.
This reduces the number of child pointers in the parent by one.
Naturally, the separator key in the parent corresponding to the removed node must also be removed.
At this point, the parent itself may become underfull.
Propagation Upward
If the parent node falls below its minimum occupancy after a merge, the restructuring process repeats, this time at the parent level.
The algorithm is recursive:
- Check occupancy
- Attempt redistribution
- Otherwise merge
- Update parent
- Repeat if necessary
This process continues upward until either a node remains valid after redistribution, or the root is reached
The Root Is Special
The root node has relaxed rules.
It is allowed to have fewer children than internal nodes lower in the tree.
In fact, if the root ends up with only one child after restructuring, the tree height can decrease by one.
In that case:
- The single child becomes the new root
- The tree becomes one level shorter
- Height balance is preserved
This is the only time the tree height decreases.
Just as insert may increase height when splitting the root, delete may decrease height when merging collapses the root.
Why Deletion Is Harder Than Insertion
Insertion is localized growth. It splits nodes only when necessary and propagates upward predictably.
Deletion is more delicate.
It must:
- Detect underflow
- Choose between redistribution and merge
- Adjust separator keys
- Possibly collapse levels
All while preserving sorted order, height balance, occupancy constraints
Yet despite this complexity, the cost remains logarithmic in the number of entries.
Connecting Back to SQLite
In SQLite’s implementation, these operations operate at the page level.
When nodes merge or redistribute:
- Page contents are rewritten
- Parent pages are updated
- The pager marks affected pages dirty
- Journaling ensures recoverability
The structural algorithm lives in the tree module, but its safety is guaranteed by the pager layer beneath it.
Delete is not just about shifting keys. It’s about carefully rewriting pages while preserving transactional atomicity.
The Full Picture
Now we’ve seen all four fundamental operations:
- Search → Navigate from root to leaf
- Search-next → Ordered traversal without leaf links
- Insert → Split upward to preserve balance
- Delete → Redistribute or merge downward to restore occupancy
Together, they define the dynamic life of a B+-tree.
The structure grows, shrinks, and reorganizes, but it never becomes unbalanced.
👉 Check out: git-lrc
Any feedback or contributors are welcome! It’s online, open-source, and ready for anyone to use.
⭐ Star it on GitHub:
HexmosTech
/
git-lrc
Check AI generated code with Git Hooks
git-lrc
Check AI-Generated Code With Git Hooks
AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.
See It In Action
A deliberately invalid Go line is staged for commit.
git lrccatches it and blocks the commit before it ever lands.
gitlrc_demo_fast.mp4
Why
- 🤖 AI agents silently break things. Code removed. Logic changed. Edge cases gone. You won't notice until production.
- 🔍 Catch it before it ships. AI-powered inline comments show you exactly what changed and what looks wrong.
- 🔁 Build a habit, ship better code. Regular review → fewer bugs → more robust code → better results in your team.
- 🔗 Why git? Git is universal. Every editor, every IDE, every AI toolkit…

Top comments (1)
"Interesting approach, Maneshwar! AI code can indeed introduce subtle bugs that are hard to trace back. Your git-lrc hook sounds like a vital tool for maintaining code integrity. Have you considered how it could handle complex diff scenarios, like those arising in large refactoring? 🤔"