The Day Our RPC Layer Became the Single Point of Failure


Everything worked perfectly in staging.

Transactions flowed, APIs responded, and latency stayed within limits. I assumed RPC was the least of our worries.

Production proved me wrong.

When the System Didn’t Break—But Users Did

There was no outage.
No node crash.
No dramatic alert.

Users simply experienced slow, inconsistent behavior. Requests timed out sporadically. Wallet actions felt unreliable.

Dashboards looked “mostly fine.”

The Mistake I Didn’t Know I Was Making

I had treated RPC as plumbing.
Something stable. Something external. Something “handled.”

In reality, our application load had turned RPC into a shared choke point—and we had no visibility into how bad it was getting.

Debugging Without a Clear Signal

We spent hours chasing symptoms:

  • Retrying requests

  • Scaling nodes

  • Adjusting timeouts

The real issue wasn’t failure—it was silent saturation.

That was the moment I realized RPC reliability isn’t about uptime. It’s about behavior under stress.

What I Changed After That Incident

After that day:

  • I treated RPC as a first-class system

  • I monitored queue depth and user-facing latency

  • I designed fallback behavior instead of assuming availability

It was a painful lesson—but one I’m glad happened early.

Why This Matters

Most teams won’t realize RPC is their bottleneck until users tell them.

If this post saves someone that moment, it’s worth writing.

— Peesh Chopra 


After stepping back from this incident, I wanted to understand why this failure pattern keeps repeating across blockchain systems. I later published a structured, industry-level breakdown explaining how RPC infrastructure becomes a hidden bottleneck in production.

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