In platform engineering, configuring the right alerts in Grafana is essential for monitoring system performance and ensuring high availability. This guide describes key alerts you should consider implementing.
Alerts, Platform Engineering, Grafana, Monitoring, System Performance, High Availability
// Example Alert Configuration
{
"alert": "High CPU Usage",
"expr": "avg(rate(node_cpu_seconds_total{mode=\"system\"}[5m])) * 100 > 80",
"for": "5m",
"labels": {
"severity": "critical"
},
"annotations": {
"summary": "High CPU usage detected",
"description": "CPU usage has exceeded 80% for more than 5 minutes."
}
}
{
"alert": "Memory Usage",
"expr": "node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes < 0.1",
"for": "10m",
"labels": {
"severity": "warning"
},
"annotations": {
"summary": "Low memory available",
"description": "Available memory is less than 10% of total memory."
}
}
{
"alert": "Disk Space Usage",
"expr": "node_filesystem_avail_bytes / node_filesystem_size_bytes < 0.1",
"for": "10m",
"labels": {
"severity": "critical"
},
"annotations": {
"summary": "Insufficient disk space",
"description": "Disk space is below 10% capacity."
}
}
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?