Trace Analysis

This section covers advanced trace analysis techniques and tools for understanding distributed system behavior through DeepTrace data.

Overview

Trace analysis helps you:

• Identify performance bottlenecks in distributed systems
• Understand service dependencies and communication patterns
• Debug complex issues across multiple services
• Monitor system health and reliability

Analysis Tools

Kibana Dashboard

Access the primary analysis interface through Kibana:

URL: http://YOUR_SERVER_IP:5601
Username: elastic
Password: YOUR_ELASTIC_PASSWORD

Key Analysis Features

Trace Data Structure

Span Information

Each span contains:

• Trace ID: Links spans belonging to the same request
• Span ID: Unique identifier for each span
• Parent ID: Creates the trace hierarchy
• Service Name: Identifies the source service
• Operation: Specific function or endpoint
• Duration: Time taken for the operation
• Tags: Additional metadata and labels

Correlation Data

DeepTrace provides correlation information:

• Network connections: TCP/UDP connection details
• Process information: PID, container ID, host details
• Timing data: Precise timestamps and latencies
• Protocol data: HTTP, database, and other protocol specifics

Analysis Techniques

Performance Analysis

Identify slow requests:

1. Sort traces by duration
2. Examine longest-running spans
3. Analyze service-to-service latencies
4. Look for patterns in slow operations

Example Kibana query:

duration:>1000 AND service.name:"product-page"

Error Analysis

Find failed requests:

1. Filter by error status codes
2. Examine error messages and stack traces
3. Correlate errors across services
4. Identify error propagation patterns

Example Kibana query:

tags.http.status_code:>=400 OR tags.error:true

Dependency Analysis

Understand service relationships:

1. Map service-to-service communications
2. Identify critical path dependencies
3. Analyze communication patterns
4. Detect circular dependencies

Traffic Pattern Analysis

Monitor system behavior:

1. Analyze request volume over time
2. Identify peak usage periods
3. Monitor service load distribution
4. Detect unusual traffic patterns

Common Analysis Scenarios

Debugging Slow Requests

1. Find the slow trace:

   • Sort by duration in Kibana
   • Identify traces exceeding SLA thresholds

2. Analyze the trace structure:

   • Examine span hierarchy
   • Identify the slowest spans
   • Check for blocking operations

3. Investigate root causes:

   • Database query performance
   • Network latency issues
   • Resource contention
   • External service delays

Service Health Monitoring

1. Error rate monitoring:

   • Track error percentages by service
   • Set up alerts for threshold breaches
   • Monitor error trends over time

2. Latency monitoring:

   • Track response time percentiles
   • Monitor SLA compliance
   • Identify performance degradation

3. Throughput analysis:

   • Monitor request volume
   • Analyze capacity utilization
   • Plan for scaling needs

Capacity Planning

1. Resource utilization:

   • Analyze service load patterns
   • Identify bottleneck services
   • Monitor growth trends

2. Scaling decisions:

   • Determine which services need scaling
   • Understand traffic distribution
   • Plan infrastructure changes

Best Practices

Effective Querying

• Use specific time ranges to improve query performance
• Combine multiple filters for precise results
• Save useful queries for repeated analysis
• Use wildcards carefully to avoid performance issues

Dashboard Creation

• Group related metrics on the same dashboard
• Use appropriate visualization types for different data
• Set up refresh intervals for real-time monitoring
• Share dashboards with team members

Alert Configuration

• Set meaningful thresholds based on SLA requirements
• Avoid alert fatigue with appropriate sensitivity
• Include context in alert messages
• Test alert conditions before deployment

Advanced Analysis

Custom Visualizations

Create specialized charts for:

• Service dependency graphs
• Request flow diagrams
• Performance heat maps
• Error correlation matrices

Data Export

Export trace data for:

• External analysis tools
• Long-term storage
• Compliance reporting
• Machine learning analysis

Integration with Other Tools

Connect DeepTrace data with:

• APM tools for enhanced monitoring
• Log aggregation systems
• Metrics collection platforms
• Incident management systems

Next Steps

• Web UI: Explore the web-based monitoring interface
• Database Setup: Advanced Elasticsearch configuration
• Basic Usage: Learn essential DeepTrace operations