Crypto Dev Peesh Chopra

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 ...

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 C...

  For a long time, I trusted our dashboards. They were clean. Metrics looked healthy. Alerts were configured. On paper, everything was “production-ready.” Then came the incident. When Users Felt Pain Before Metrics Did Users started reporting delayed confirmations. Support tickets arrived before any alert fired. At first, I assumed this was edge-case noise. The dashboards showed normal throughput and acceptable latency. Nothing looked broken. That assumption cost us time. The False Comfort of Green Metrics What I didn’t realize then was simple: our monitoring reflected infrastructure health, not user reality . Blocks were still being produced. Nodes were still online. But the system was no longer behaving the way users expected. By the time alerts triggered, we were already in damage control. The Moment My Mental Model Broke Sitting there during the incident, I stopped trusting the graphs. We were debugging from logs, tracing requests manually, trying to understand where ...