Summary
FusionReactor OpsPilot AI assistant investigated application latency issues using Prometheus metrics and Tempo distributed tracing. OpsPilot identified flagd service EventStream operations timing out at exactly 600 seconds (10 minutes), causing cascading application latency across recommendation service, load-generator, checkout, and shipping services. Investigation used automated trace analysis to determine root cause was hanging EventStream connections requiring immediate timeout configuration review.
The Challenge: Application Latency Detection Without Alerts
User query: “Do we have any latency issues at the moment?”
This common question in DevOps teams highlights a critical challenge: application latency detection often occurs after user impact, not before. Traditional monitoring relies on alert thresholds, but latency can degrade performance without triggering alerts.
🔴 Critical: Investigate flagd EventStream configuration and connection handling. Resolve 25s+ response times
🔴 Critical: Check recommendation service's flagd integration and timeout settings. Resolve 11s+ response times
🟡 High: Review load-generator performance patterns. Improve overall system stability
🟡 Medium: Monitor checkout and shipping services for degradation. Prevent further performance issuesOpsPilot’s Automated Investigation Process
Step 1: Alert Status Check
- Action: Checked firing alerts across monitored infrastructure
- Result: Zero firing alerts across 50+ monitored services
- OpsPilot decision: Proceeded to metrics analysis despite no firing alerts, recognizing that application latency detection requires proactive investigation
Step 2: Prometheus Metrics Discovery for Application Latency Detection
OpsPilot discovered and queried metrics for comprehensive application latency detection:
- Response time metrics across all services
- Duration metrics for request-level performance
- Span metrics for detailed request-level performance data
This automated metrics discovery eliminates manual query construction, accelerating application latency detection.
Step 3: Service Performance Analysis
OpsPilot calculated average response times and identified slowest services for application latency detection:
- flagd service: 25+ second average response time
- recommendation service: 11+ second average response time
- load-generator service: 1+ second response time
OpsPilot queried 95th percentile response times for worst-case application latency analysis, providing deeper insight than average metrics alone.
Step 4: Tempo Distributed Trace Investigation
OpsPilot searched Tempo traces for flagd service slow traces to pinpoint application latency root cause.
Critical finding for application latency detection: EventStream operations duration exactly 600 seconds (10 minutes).
Trace data evidence:
- flagd EventStream operations: 599.999+ seconds duration
- recommendation EventStream operations: 600.007+ seconds duration
- Multiple error traces in the recommendation service related to EventStream calls
This trace-level application latency detection identified the exact operation causing performance degradation.
Step 5: Latency Trend Analysis
OpsPilot queried application latency trends over 6 hours:
- flagd service: 25.5s → 21.4s → 25.5s (consistently critical)
- recommendation service: 9.0s → 10.9s → 11.6s (worsening)
- load-generator service: 1.2s → 1.2s → 1.1s (stable but elevated)
Trend analysis confirmed this was ongoing application latency, not a temporary spike.
Root Cause Analysis: Application Latency Detection Success
Root cause: EventStream connection timeouts at exactly 600 seconds indicated hanging connections or misconfigured timeout settings.
Pattern: Consistent 10-minute timeout pattern across multiple traces indicated systemic application latency issue, not isolated incidents.
Cascading impact: flagd service application latency caused dependent service performance degradation across the system.
Root Cause Analysis: Application Latency Detection Success
Root cause: EventStream connection timeouts at exactly 600 seconds indicated hanging connections or misconfigured timeout settings.
Pattern: Consistent 10-minute timeout pattern across multiple traces indicated systemic application latency issue, not isolated incidents.
Cascading impact: flagd service application latency caused dependent service performance degradation across the system.
OpsPilot Recommendations for Application Latency Resolution
Critical priority actions:
- Investigate flagd EventStream configuration and connection handling to resolve application latency
- Check recommendation service flagd integration and timeout settings to prevent cascading application latency
High priority actions: 3. Review load-generator performance patterns to improve system stability
Medium priority actions: 4. Monitor checkout and shipping services for further application latency degradation
Immediate next steps for application latency detection and prevention:
- Check flagd service logs for connection errors or resource constraints
- Review EventStream timeout configurations in flagd and recommendation services
- Set up proactive alerts for response times exceeding 5 seconds for faster application latency detection
- Investigate feature flag evaluation performance and network connectivity issues
OpsPilot Application Latency Detection Capabilities
Multi-layer investigation approach:
- Alert monitoring: First line of application latency detection
- Metrics analysis: Prometheus-based application latency measurement across services
- Trace investigation: Deep dive into individual requests for application latency root cause identification
Automated Prometheus metrics discovery:
- Automatic identification of relevant performance metrics for application latency detection
- No manual query construction required
- Comprehensive service coverage
Tempo distributed trace analysis:
- Deep investigation into individual request traces for precise application latency detection
- Pattern recognition across multiple traces
- Identification of exact operation durations causing application latency
Pattern recognition for application latency detection:
- Identified exact 10-minute timeout pattern
- Recognized systemic issues versus isolated incidents
- Correlated application latency across dependent services
Cascading impact analysis:
- Understanding how one service’s application latency affects dependent services
- System-wide performance impact assessment
- Prioritization based on criticality
Actionable intelligence:
- Prioritized recommendations with expected impact
- Specific configuration areas to investigate
- Proactive monitoring suggestions for future application latency detection
Why Traditional Application Latency Detection Failed
This case demonstrates why traditional monitoring approaches struggle with application latency detection:
- No firing alerts: Despite severe application latency (25+ second response times), no alerts triggered
- Hidden timeout patterns: The exact 600-second timeout required trace-level investigation
- Cascading failures: Application latency in one service (flagd) impacted multiple dependent services
- Trend blindness: Without historical analysis, teams miss ongoing application latency patterns
OpsPilot’s AI-powered approach succeeded where traditional application latency detection failed by combining multiple data sources and automated investigation.
Key Metrics: Application Latency Detection Results
- Services monitored: 50+
- Firing alerts: 0 (highlighting need for proactive application latency detection)
- Critical application latency issues identified: 2 services (flagd, recommendation)
- EventStream timeout duration: 600 seconds (10 minutes)
- flagd average response time: 25+ seconds
- recommendation average response time: 11+ seconds
- Investigation timeframe: 6 hours trend analysis for application latency patterns
- Trace evidence: Multiple 599.999+ second duration traces confirming application latency
Technical Details: Application Latency Detection Tools
Observability tools integrated by OpsPilot:
- OpenTelemetry (OTEL): Native support for OpenTelemetry metrics, traces, and logs for comprehensive observability
- Prometheus: Metrics collection and querying for application latency measurement
- Tempo: Distributed trace storage and search for application latency root cause analysis
- Alert management system: Integration for comprehensive application latency detection
- Span metrics: Request-level performance data for detailed application latency analysis
Metrics queried for application latency detection:
- Average response time by service
- 95th percentile response times for worst-case application latency
- Error rates by service and status code
- Application latency trends over time
Trace analysis for application latency detection:
- Searched slow traces by service
- Identified exact operation durations causing application latency
- Correlated traces across dependent services for cascading application latency impact
Benefits of AI-Powered Application Latency Detection
Speed: Minutes instead of hours for application latency root cause identification
Comprehensiveness: Automated investigation across alerts, metrics, and traces for complete application latency detection
Accuracy: Trace-level precision identifies exact operations causing application latency
Proactive: Detects application latency before alerts fire or users report issues
Contextual: Understands cascading application latency impact across dependent services
Actionable: Provides prioritized recommendations for application latency resolution
Real-World Application Latency Detection Impact
For DevOps teams, this investigation demonstrates:
Reduced MTTR: Automated application latency detection and root cause analysis reduces mean time to repair
Proactive monitoring: Application latency detection before user impact or alert thresholds
Resource efficiency: AI-powered investigation replaces manual correlation across multiple monitoring tools
Knowledge retention: Systematic investigation approach captures troubleshooting methodology regardless of team member availability
Cost savings: Faster application latency detection and resolution minimizes business impact and user churn
Vendor-neutral observability: OpenTelemetry integration ensures compatibility with existing observability infrastructure
Get Started with OpsPilot Application Latency Detection
See how FusionReactor OpsPilot can transform your application latency detection and resolution:
Try OpsPilot: Experience AI-powered application latency detection in your environment.
Request a demo to see OpsPilot analyze your Prometheus metrics and Tempo traces.
Learn more about FusionReactor: Discover how FusionReactor’s APM platform provides comprehensive observability with integrated AI investigation.
Explore OpsPilot capabilities: Read our documentation on OpsPilot’s application latency detection features, OpenTelemetry integration, Prometheus metrics analysis, and Tempo trace investigation.
About FusionReactor OpsPilot
OpsPilot is your intelligent AI assistant for full-stack observability, designed to help every team member – from developers to SREs and engineering managers – understand, diagnose, and resolve issues faster than ever.
By combining FusionReactor’s powerful telemetry platform with advanced AI reasoning, OpsPilot transforms complex system data, code, and performance metrics into clear, actionable insights – all in natural, conversational language.
OpsPilot goes beyond traditional monitoring to analyze code, detect memory anomalies, interpret metrics, and even suggest the next steps to resolve problems. It’s observability, explanation, and action – all in one assistant.
OpsPilot integrates with industry-standard observability tools:
- OpenTelemetry (OTEL): Native support for OpenTelemetry metrics, traces, and logs
- Prometheus: Automated metrics discovery and querying
- Tempo: Distributed trace search and analysis
- Alert management systems: Comprehensive monitoring integration
OpsPilot application latency detection capabilities demonstrated in this case study:
- Automated Prometheus metrics discovery and querying
- Tempo distributed trace search and analysis for application latency root cause identification
- OpenTelemetry span metrics analysis for request-level performance data
- Alert status monitoring and correlation
- Multi-service application latency trend analysis
- Root cause identification with trace-level precision using OpenTelemetry distributed tracing
- Prioritized remediation recommendations for application latency resolution
- Cascading impact analysis across dependent services
- Natural language investigation accessible to all team members
FusionReactor APM Platform: FusionReactor provides comprehensive application performance monitoring for ColdFusion, Java, .NET, and other enterprise applications. With OpenTelemetry integration and AI-powered OpsPilot, FusionReactor delivers faster application latency detection, automated root cause analysis, and reduced mean time to repair for mission-critical applications.
Your Daily OpsPilot Routine
Essential Questions for OpenTelemetry Monitoring
Morning Health Check
8:00 AM - Start of DayMid-Morning Trace Analysis
10:00 AM - Active MonitoringPre-Lunch Resource Check
11:30 AM - Before Peak TrafficAfternoon Database Performance
2:00 PM - Performance Analysis| Table/Operation | Best | Worst | Current | Status |
|---|---|---|---|---|
| customers INSERT | 3ms | 529ms | 29ms | ✅ Good |
| customers SELECT | 0.2ms | 1,474ms | 1.8ms | ✅ Excellent |
| quotes SELECT | 0.4ms | 2,035ms | 201ms | ⚠️ Variable |
| services SELECT | 0.2ms | 786ms | 7ms | ✅ Excellent |
