Analyzing Response Times with New Relic

Analyzing Response Times with New Relic

1. Why Response Time Matters

• User Experience: Every 100 ms of additional latency can reduce user satisfaction by up to 16%.⁽¹⁾
• Business Metrics: Faster load times correlate with higher engagement, better conversion, and increased revenue.
• Operational Costs: Identifying slow endpoints early helps reduce infrastructure waste and improves resource utilization.

2. Key Metrics in New Relic

New Relic surfaces a range of metrics that illuminate response-time behavior:

Metric	Definition	Why It Matters
Response Time (Avg)	Average time to complete a request.	Baseline for normal behavior alerts on drift.
Apdex Score	Standardized satisfaction threshold (T).	Quickly assess user satisfaction. See Apdex on Wikipedia.
Percentiles (p50, p95, p99)	Distribution of response times.	Highlights outliers and tail latency issues.
Error Rate	Percentage of failed transactions.	Failures often correlate with slowdowns.

3. Instrumenting Your Application

1. Install the New Relic agent: Follow the official guide for your platform (New Relic APM Docs).
2. Configure thresholds: Set Apdex T values that reflect your SLA.
3. Enable Distributed Tracing: Correlate requests across microservices for end-to-end visibility.
4. Add Custom Instrumentation: Mark heavy SQL queries or external calls for deeper insight.

4. Building Dashboards

Dashboards bring metrics together in one place. A well-designed dashboard for response-time analysis typically includes:

• Trend lines for average and percentile response times.
• Apdex score over time with threshold overlays.
• Error rate and throughput side by side.
• Heatmaps or histograms to visualize distribution.

You can use New Relic One Query Language (NRQL) to craft custom charts:

SELECT percentile(duration, 50, 95, 99) 
FROM Transaction 
WHERE appName = YourApp
FACET name SINCE 1 hour ago
  

5. Drill-Down with Distributed Tracing

Response-time spikes often originate in downstream dependencies:

• Service-to-Service Calls: Identify which microservice or external API adds the most latency.
• Database Queries: Trace slow queries, examine query plans, and monitor connection pool usage.
• External Calls: Pinpoint third-party endpoints causing slowdowns.

Use the New Relic Distributed Tracing Docs to ensure spans are correctly registered.

6. Alerting on Response-Time Anomalies

Proactive monitoring relies on well-defined alert conditions:

Alert Type	Condition	Action
Threshold Breach	Avg. response time gt 500 ms for 5 min	Notify on-call team via Slack/Email.
Apdex Drop	Apdex lt 0.8 for 10 min	Create a Jira ticket automatically.
Anomaly Detection	Dynamic baselining detects unusual spikes	PagerDuty escalation.

7. Best Practices for Optimization

• Cache Strategically: Reduce repeated work with in-memory caches (Redis/Memcached).
• Optimize Database Access: Use indexing, pagination, and query optimization tools.
• Use CDNs: Offload static content delivery closer to users.
• Scale Horizontally: Add instances or containers to handle throughput spikes.
• Review Dependencies: Replace or optimize slow third-party services.

8. Case Study: E-Commerce Platform

An online retailer noticed a steady rise in p95 checkout time from 800 ms to 1.5 s during peak hours. Using New Relic:

1. Dashboard revealed that the PaymentService p99 spiked at 2.5 s.
2. Distributed Tracing broke down latency: 60% in external payment gateway, 30% in DB locks.
3. Short-term fix: increased thread pools and parallelized DB writes.
4. Long-term: moved payment gateway interactions to an asynchronous queue, reducing critical path latency by 70%.

Results:

• Checkout p95 average dropped from 1.5 s to 450 ms.
• Apdex score improved from 0.7 to 0.92.
• Error rate decreased by 40% under peak load.

9. Further Resources

• New Relic APM Documentation
• Distributed Tracing Guide
• Alerts and AI

10. Conclusion

Analyzing and optimizing response times is a continuous journey. With New Relic’s comprehensive tooling—real-time metrics, distributed tracing, dashboards, and alerts—you gain the visibility and context needed to diagnose performance issues swiftly. Implementing the practices detailed above will help you maintain a fast, reliable user experience and, ultimately, drive better business outcomes.

References:
(1) Google/SOASTA “Performance and User Experience”.