Even with less than 30 QPS and normal DB CPU, a connection pool can run out in two hours because connections are held too long—often due to leaks, long‑running transactions, or async misuse—so the root cause must be traced back to the application code rather than just slow SQL.
This article walks readers through each section of the top command output—system overview, process list, CPU, memory, and I/O metrics—explaining what the values mean, how to interpret warnings, and which interactive shortcuts and command-line options help quickly pinpoint performance bottlenecks.
When a core order‑query suddenly slowed from milliseconds to seconds, the team discovered that using PostgreSQL’s EXPLAIN ANALYZE revealed hidden plan regressions, outdated statistics, and index misuse, leading to a systematic, production‑grade methodology for diagnosing and fixing query performance issues.
This guide walks you through every line of the Linux top command output, explaining system summaries, CPU and memory metrics, process details, and advanced shortcuts, so you can quickly diagnose performance bottlenecks and become proficient at real‑time system troubleshooting.
This article explains Bilibili's large‑scale big‑data platform architecture, the huge volume of offline and real‑time tasks it handles, common failure and slowdown causes, and why the company is exploring AI‑driven assistants to help engineers troubleshoot these issues efficiently.
This article details a step‑by‑step analysis of a server experiencing sustained high load, uncovering a stuck NFS mount and an overactive Zabbix monitoring job, and explains how targeted process termination and monitoring adjustments reduced the load from 85 to normal levels.
The article details how a Go service’s default GOGC setting caused overly frequent garbage‑collection pauses that spiked request timeouts, and how adjusting GOGC dynamically with debug.SetGCPercent and setting memory limits reduced GC CPU usage, extended pause intervals, and eliminated timeout spikes.
The article details how a midnight interface timeout in Vivo’s e‑commerce activity system was traced to a logging bottleneck: a synchronous Log4j call blocked all threads while a cron‑driven log‑rotation script copied a 2.6 GB file, and the issue was resolved by switching to asynchronous logging with a non‑blocking appender.
This article walks through a systematic analysis of MySQL slow‑log entries, correlating them with error‑log flush events, disk‑I/O monitoring, and semi‑synchronous replication traces to pinpoint performance bottlenecks and provide practical troubleshooting steps.
The article describes how a MongoDB test environment on a single VM experienced persistent high CPU load despite low visible QPS, how the root cause was traced to thousands of short‑lived processes spawned by Zabbix monitoring, and how execsnoop was used to identify and eliminate the offending processes.
This article explains what Linux load average measures, how to view the 1‑, 5‑, and 15‑minute values, interprets the numbers using traffic analogies, presents stress‑test scenarios across different CPU cores, and shows how load average guides effective troubleshooting of CPU and I/O bottlenecks.
