Traffic Monitoring Best Practice

Follow these best practices guidelines for monitoring traffic to diagnose and prevent issues.

Why you should monitor Traffic

It's always best practice to have logging and monitoring on your systems, and this extends to the traffic between your site and Fredhopper.

This gives you an understanding of your traffic, the ability to detect and handle anomalies, and if there is an issue, data to diagnose the problem.

The following is some best practice of what to monitor, and what to look for.

What to Monitor

Metric / Data Point	Why it Matters
Query Volume (per endpoint)	Helps track overall usage and detect surges
Round-Trip Time (RTT)	Identifies latency issues or back-end slowness
Status Codes (2xx/4xx/5xx)	Reveals malformed queries, outages, or timeouts
Unique IP/User-Agent count	Identifies spikes in new or bot-like clients
Query Parameters (e.g. fh_location)	Detects abusive patterns (e.g., brute-force faceting)
Rate per user/session/token	Helps enforce throttling and usage fairness
Cache hit/miss ratio	Indicates inefficiencies in caching strategy

How to Implement It

Structured Logging

In your middleware or proxy service:

• Log all FHR-bound requests with:

  • Timestamp

  • User/session ID (if available)

  • Client IP and User-Agent

  • FHR endpoint and query parameters

  • FHR response status and time

• Prefer JSON-formatted logs for easy parsing in log aggregators

Example:

{ "timestamp": "2025-07-22T13:00:12Z", "userId": "user-123", "ip": "192.0.2.45", "userAgent": "Mozilla/5.0", "fh_endpoint": "/fredhopper/query", "fh_location": "//catalog01/en_GB/brand=adidas", "status": 200,"rtt_ms": 128 }

Performance Metrics

Use an APM tool (e.g. Grafana) to track:

• Average/median/max round-trip times

• Request rate per minute/hour

• Cache efficiency

• Error rate

Set alerts for:

• RTT > 500ms (indicates FHR or network issues)

• 4xx/5xx status rate spikes

• Unusual request rates from specific IPs or tokens

Correlation by Page Type

Monitor FHR usage broken down by page context:

• Category (PLP)

• Product detail (PDP)

• Search results

• Suggest/autocomplete

Look for out-of-pattern traffic, such as:

• Excessive hits to Suggest API

• PDPs requesting unusual or irrelevant fh_location paths

• Non-search pages generating fh_search queries

Irregular Traffic Patterns

Watch out for the following irregular traffic patterns:

Pattern	Possible Cause
Sudden spike in RTT	Back-end issues, query complexity, or bot load
High 400s/500s rate	Malformed queries, missing fallback logic
Repeated suggest queries with random strings	Automated bots brute-forcing autocomplete
Uncached repeat queries with minor parameter changes	Bot or misconfigured client with no caching
High volume from single IP or unknown UA	Malicious bot or unmonitored integration