Deep performance analysis using APM traces, metrics, and logs to identify bottlenecks and optimization opportunities. Correlates database queries, external API calls, and resource usage to pinpoint root causes of slow performance. Use when investigating latency issues, throughput problems, or resource optimization needs.
name performance-investigation argument-hint [service, endpoint, symptom] description Deep performance analysis using APM traces, metrics, and logs to identify bottlenecks and optimization opportunities. Correlates database queries, external API calls, and resource usage to pinpoint root causes of slow performance. Use when investigating latency issues, throughput problems, or resource optimization needs. compatibility {"tools":["datadog"],"dependencies":["search_datadog_spans","get_datadog_metric","analyze_datadog_logs","apm_search_spans"]} Performance Investigation Comprehensive performance analysis across the full application stack to identify and resolve bottlenecks. When invoked directly with /datadops:performance-investigation , use $ARGUMENTS as the performance context. If needed, ask for the service, endpoint, environment, and time window before starting the analysis. Capabilities 🔍 Multi-Layer Analysis Application Layer : Code-level bottlenecks, algorithm efficiency Database Layer : Query performance, connection pooling, indexing Infrastructure Layer : CPU, memory, I/O, network utilization External Dependencies : Third-party API performance, service meshes 📈 Performance Profiling Request flow visualization Latency breakdown by component Resource utilization correlation Throughput and concurrency analysis 🎯 Optimization Recommendations Specific performance improvements Infrastructure scaling suggestions Code optimization opportunities Caching strategy recommendations Investigation Methodology Phase 1: Performance Characterization Baseline Establishment Current performance percentiles (P50, P95, P99) Throughput and error rate baselines Resource utilization patterns Bottleneck Identification Slowest endpoints and operations Most resource-intensive requests Error-prone performance patterns Phase 2: Deep Trace Analysis Request Flow Mapping End-to-end request tracing Service dependency visualization Latency contribution breakdown Database Performance Query execution time analysis Connection pool utilization Index effectiveness assessment Phase 3: Resource Correlation Infrastructure Metrics CPU, memory, disk I/O patterns Network bandwidth and latency Container/host resource contention Scaling Patterns Performance under load Resource scaling effectiveness Bottleneck migration analysis Performance Patterns Database Bottlenecks Symptoms:
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Algorithm Efficiency
Concurrency Patterns
Resource Management
Scaling Strategies
Resource Allocation
Caching Layers