𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐢𝐬 𝐮𝐩, 𝐛𝐮𝐭 𝐧𝐨𝐭𝐡𝐢𝐧𝐠 𝐢𝐬 𝐟𝐚𝐢𝐥𝐢𝐧𝐠. 𝐖𝐡𝐞𝐫𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐥𝐨𝐨𝐤 𝐟𝐢𝐫𝐬𝐭? 🔍 One of the worst production situations: Latency is growing 📈 Users feel it 😐 Logs are clean 🧼 Nothing is obviously broken ❌ Most teams waste time here. They search for errors 🔎 Restart pods 🔄 Jump between dashboards 📊 But when nothing is failing, the problem is rarely an exception. It is usually one of these: 1. 𝗦𝗰𝗼𝗽𝗲 𝗳𝗶𝗿𝘀𝘁 🎯 One endpoint or all? One instance or all? Reads, writes, or async? If you skip this, you debug the whole system instead of a slice 2. 𝗧𝗵𝗿𝗲𝗮𝗱 𝗽𝗼𝗼𝗹𝘀 🧵 Active threads, queue size, blocked threads. If all workers are busy, requests are not failing - they are waiting to run. 3. 𝗧𝗵𝗿𝗲𝗮𝗱 𝗱𝘂𝗺𝗽 📸 Look for: * repeated stack traces * WAITING / BLOCKED threads * DB connection waits * socket reads * lock contention This shows where execution is actually stuck. 4. 𝗚𝗖 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 ♻️ Pause time, frequency, heap pressure. If latency spikes in waves, GC is often involved. 5. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗽𝗼𝗼𝗹𝘀 🧩 DB, HTTP clients, Redis, broker. Exhausted pool = requests wait instead of fail. Classic “slow but no errors”. 6. 𝗤𝘂𝗲𝘂𝗲𝘀 & 𝗹𝗮𝗴 📊 Queue depth, consumer lag, retries. The system may look fine while work silently accumulates. 7. 𝗗𝗼𝘄𝗻𝘀𝘁𝗿𝗲𝗮𝗺𝘀 🌐 DB, internal services, external APIs. Your service might be slow because it is efficiently waiting on something else. The key shift: No errors does not mean no problem. ❗ It usually means the bottleneck is in waiting, saturation, contention, or backlog. Stop hunting for exceptions first. Start finding where time is spent. How do you usually localize the bottleneck first in this situation? 🤔 #backend #java #springboot #observability #performance #distributedsystems #productionengineering
This is spot on — “no errors” scenarios are usually the hardest to debug. One pattern I’ve seen repeatedly in production: 👉 It’s often downstream slowness disguised as application latency We had a case where: - APIs were slow - CPU looked fine - No exceptions Turned out: ➡️ DB connection pool exhaustion + slow queries ➡️ Threads waiting, not failing A couple of things that helped us: - End-to-end tracing (to see where time is actually spent) - Thread dump + pool metrics correlation - Looking at saturation signals (queue depth, connection usage) instead of errors +1 on “stop hunting exceptions first” — that mindset shift is huge. Curious — do you rely more on tracing (Jaeger/Zipkin) or metrics-first when debugging these?
Really resonates — I’ve seen similar cases where everything looked healthy, but latency kept increasing. In one case, it was connection pool exhaustion + a slow downstream call — nothing failed, just more waiting. Breaking down request time helped us spot saturation quickly instead of chasing logs. Agree — these are usually waiting problems, not failures.
This LinkedIn post is a classic case of "The Junior's Guide to Senior Debugging." It sounds smart on paper, but following it in a real production outage is a one-way ticket to a 4-hour downtime and an angry CTO. Writing this because you mentioned "teams" and then completely ignore them -) Step 0 is missing: Communication. If "users feel it," you must sync with Support and SREs first. Never troubleshoot in a silo while the ship sinks. The Change Rule: 80% of latency comes from recent changes. Check your Deployment logs and Feature Flags before touching a single Thread Dump. Inverted Priorities: Checking Downstreams (Step 7) should be Step 1. In modern systems, the bottleneck is usually the DB or an external API, not your GC behavior. A better flow for Production: Sync & Declare: Ensure stakeholders are aware. Correlate with Changes: If it matches a deploy → Rollback first, debug later. Check the "Waterfall": Use APM/Tracing to see where time is actually spent (DB, Network, or IO). App Internals: Only dive into Thread Pools and GC once Infra and Downstreams are cleared. The Golden Rule: "No errors" means your timeouts are likely too high. Don't look for what's broken; look for what's waiting. #SRE #Engineering #DevOps #Observability