When Our Indexer Fell Behind and Nobody Noticed


Everything looked normal on the surface.

APIs responded. Dashboards were green. Queries returned results. No alerts fired.

But something felt off.

The Subtle Drift From Reality

Data started lagging—seconds at first, then minutes.

Users didn’t complain immediately. They simply trusted the system less. Numbers stopped lining up. Confidence eroded quietly.

That’s when I realized the worst failures aren’t outages, they’re misalignments.

The Mistake I Didn’t See Coming

I had optimized for query speed, not ingestion truth.

The indexer wasn’t broken. It was falling behind gracefully, and we treated that as success.

Reprocessing later revealed how far off we’d drifted.

Debugging After the Damage

By the time we investigated:

  • Backlogs were massive

  • State assumptions were invalid

  • Fixes required historical replay

The system had been lying politely for days.

What I Changed After That Experience

After this incident:

  • I tracked freshness, not just latency

  • I treated indexing delay as failure

  • I designed pipelines assuming reorgs and retries

It changed how I think about “data correctness” in production.

Why This Story Matters

Most teams won’t notice indexing failure until trust is already lost.

This experience forced me to design systems that fail loudly—before users do.

— Peesh Chopra


After stepping back from this experience, I wanted to understand why indexing failures like this aren’t isolated incidents. I later published a structured, industry-level breakdown explaining why blockchain indexing pipelines fail at scale.

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