At some point, every indexing system hits the same moment: something changes upstream—data format, schema, embedding model—and you realize your entire pipeline needs to adapt. But unlike analytics or training jobs, where a full re-run might be painful but straightforward, indexing systems don’t have that luxury.
Why? Because they’re long-lived, stateful, and often interdependent. You can’t just blow everything away and start fresh without breaking downstream assumptions—or burning through a ton of compute unnecessarily.
So, here’s how I think about handling updates in indexing pipelines. Not theoretically. Practically.
If this article is helpful, I would really appreciate a star ⭐ at my [open source project] (https://github.com/cocoindex-io/cocoindex) - a fresh index for AI.
1. Treat Indexing State as Durable
An indexing system isn’t just a transformation layer. It holds state—embeddings, relationships, metadata—all tied to source content. That state needs to persist across re-runs, updates, and crashes.
So, every time I change something—code, model, chunking logic—I ask:
- What already exists in the index?
- Can I reuse it?
- What must be invalidated?
This avoids brute-force reprocessing and sets up the foundation for safe, incremental updates.
2. Make Change Detection Explicit
You don’t want to guess what changed. You want to know.
I try to make change detection a first-class part of the pipeline:
- Hashes of source content
- Timestamps or version numbers
- Diffing at the chunk or field level
The goal is simple: figure out exactly what changed, and only reprocess that.
This might sound like overkill for small projects, but for large indexes or anything running continuously, it’s the only scalable way to stay fresh without breaking things.
3. Define What “Safe to Reprocess” Means
Not all data is equal. Some updates are cheap and stateless—like re-embedding a chunk. Others affect relationships, order, metadata, or user experience.
So, I bucket reprocessing into tiers:
- Safe & fast (e.g., rerun embedder)
- Requires coordinated update (e.g., chunking logic change)
- Requires index rebuild (e.g., schema overhaul)
This forces me to be honest about the real cost of updates—and helps teams plan them with eyes open.
4. Version Everything, Even Internals
If your embedding model changes, you version it.
But also version:
- Your chunking logic
- Your entity extraction rules
- Your join logic
Otherwise, you’ll end up with silently inconsistent data in the index. Two chunks might look the same, but were processed with totally different assumptions.
5. Don’t Overfit to a “Clean Slate” Mindset
It’s tempting to build indexing systems that assume clean runs. But in production, you’ll almost always be dealing with:
- Partial runs
- Interruptions
- Out-of-order updates
- Mixed-version data
So, I build systems expecting messiness from day one. That means:
- Checkpoints
- Retryable steps
- Idempotent operations
- Logs I can actually read and trust
If you can’t pause and resume the index safely, you don’t have a resilient system yet.
Indexing systems aren’t just “pipelines.” They’re living systems. And when you update them, you’re not just running code—you’re negotiating with history.
Every update is a chance to either lose consistency or reinforce it. So I treat them carefully, and design for traceability, flexibility, and forward motion—without starting from zero every time.
It’s not always clean, but it works. And it’s how I keep things stable even when everything around them is changing.