Meshping

Meshping is a lightweight network monitoring service that concurrently pings multiple targets and records detailed latency histograms. It runs traceroutes, performs Path MTU discovery for each hop, and renders visual maps and heatmaps to help locate weak links and routing issues.

Key Features

• Concurrent ICMP probing of many targets with per-target latency histograms (not simple averages)
• Traceroute integration with hop-by-hop Path MTU discovery and AS information
• SVG network topology maps that highlight outage locations and routing loops
• Heatmaps and histogram visualizations for spotting multimodal latency distributions
• Prometheus-compatible /metrics endpoint for scraping and integration with existing monitoring stacks
• Dynamic target management (add/remove targets at runtime) and optional peering between Meshping instances
• Docker images and a simple web UI including a mobile-friendly layout

Use Cases

• Troubleshooting WAN and datacenter connectivity by pinpointing where latency or packet loss occurs
• Comparing latency across multiple endpoints or monitoring locations for capacity planning and performance trends
• Distributed measurements via peered Meshping instances to observe end-to-end behavior from multiple vantage points

Limitations and Considerations

• ICMP probing and traceroute functionality require privileges or container capabilities (e.g., CAP_NET_RAW) to send raw packets
• Default local storage uses SQLite, which may not scale for very large deployments or extremely high sampling rates
• Meshping focuses on measurement and visualization; it has no built-in advanced alerting engine and relies on Prometheus or external tools for alerting
• When deployed behind some proxies, specific proxy settings may be required for good UI responsiveness

Meshping is suited for network operators and engineers who need detailed, visual latency and path visibility without heavy infrastructure. It complements metric and alerting stacks by providing raw-response histograms, traceroutes, and topology visualizations for root-cause analysis.