Corréler les métriques pour trouver les causes profondes

A spike in response time is a symptom. Performance analysis is about finding the cause. Doing that means correlating what the load tool saw with what the server experienced at the same moment.

Every load test has two data sources:

• Client-side metrics from JMeter or k6 (latency, throughput, errors).
• Server-side metrics (CPU, memory, GC, DB queries).

Correlation overlays both on a shared timeline.

To correlate, all metrics must use the same clock. Synchronize systems with NTP and align charts on identical time ranges so a latency spike lines up exactly with the server event that caused it.

If response time climbs while CPU pegs at 100%, the bottleneck is compute. Throughput plateaus no matter how many virtual users you add. This is one of the most common correlations.

Sawtooth response-time spikes that align with garbage-collection pauses point to memory pressure. Overlay GC pause logs with latency to confirm.

When CPU is low but latency is high, the app is often waiting on the database. Correlate slow query logs and connection-pool saturation with the slow requests.

Tools like Grafana let you put client metrics (from InfluxDB) and server metrics (from Prometheus) on one dashboard. Stack the panels so trends are visually aligned.

Plot throughput against the number of virtual users. The point where throughput flattens while users keep rising marks the system's saturation point, a key correlation target.

Watch how p99 separates from p50 as load grows. A widening gap signals queuing or contention long before average latency looks alarming.

A good correlation finding states: the symptom, the correlated server metric, the time window, and the hypothesized cause. This turns raw charts into actionable engineering tickets.

Two metrics moving together do not prove causation. Confirm a correlation with a controlled change: fix the suspected cause and verify the symptom disappears before declaring root cause.

Interpret a correlation pattern.

You learned to correlate metrics for root-cause analysis.

• Overlay client and server metrics on a synchronized timeline.
• Recognize CPU, GC, and database wait patterns.
• Use the throughput-vs-users curve to find saturation.