Volvo CQRS framework
Cloud-agnostic CQRS/Event Sourcing framework with optimized write and read paths for high-throughput scenarios.
Raymond Clanan
Performance Optimization
Deep expertise in systems performance, scalability, and optimization across backend services, databases, and distributed systems
Performance isn't just about making things fast — it's about making the right trade-offs between latency, throughput, cost, and developer experience at every layer of the stack.
My expertise
I've spent years optimizing systems at scale, from database query patterns to distributed system architectures. My approach combines deep technical knowledge with pragmatic business impact.
What I optimize
Backend services
API latency reduction, async processing, connection pooling, request batching, and efficient serialization.
Database performance
Query optimization, indexing strategies, connection management, read replicas, caching layers, and partitioning.
Distributed systems
Message queue optimization, event-driven architectures, load balancing, circuit breakers, and retry strategies.
Caching strategies
Multi-layer caching, cache invalidation patterns, CDN optimization, and in-memory data structures.
Concurrency
Thread pool tuning, async I/O, parallel processing, lock contention reduction, and non-blocking patterns.
Profiling and analysis
CPU profiling, memory analysis, latency percentiles (p50/p95/p99), flamegraphs, and bottleneck identification.
Case studies
Real-world performance improvements I've delivered:
LLM routing optimization
Routing, caching and observability tradeoffs across multiple LLM providers; production benchmark figures are not documented.
Performance philosophy
My approach to performance optimization:
1. Measure First
"You can't improve what you don't measure."
Before any optimization, I establish baseline metrics: latency percentiles (p50, p95, p99), throughput, error rates, and resource utilization. This ensures we're solving real problems, not imaginary ones.
2. Focus on Business Impact
Not all performance improvements matter equally. I prioritize optimizations that:
- Directly improve user experience (e.g., reducing checkout latency)
- Reduce infrastructure costs significantly
- Unblock scaling to the next order of magnitude
3. Systematic Profiling
I use profiling tools to identify actual bottlenecks:
- CPU: Flamegraphs, CPU profilers
- Memory: Heap analysis, allocation tracking
- I/O: Database query logs, network tracing
- Concurrency: Thread dumps, lock contention analysis
4. Iterative Improvement
Performance work is iterative:
- Measure baseline
- Identify bottleneck
- Apply targeted fix
- Measure impact
- Repeat
Each iteration validates that we're moving the needle on what matters.
Common performance patterns
Database Optimization
- N+1 Query Elimination: Use eager loading, batch queries, or DataLoader patterns
- Indexing: Composite indexes for multi-column filters, covering indexes for read-heavy queries
- Query Rewriting: Replace subqueries with joins, use CTEs for readability
- Connection Pooling: Tune pool size based on workload and database capacity
Caching
- Cache Keys: Design predictable, invalidatable cache keys
- TTL Strategy: Balance freshness with hit rate
- Prewarming: Load critical data at startup
- Cache Aside vs. Write Through: Choose based on read/write ratio
Async Processing
- Queue Workers: Offload heavy work from request path
- Batching: Aggregate operations to reduce overhead
- Backpressure: Prevent overwhelming downstream systems
- Dead Letter Queues: Handle failures gracefully
Related writing
- Benchmarks that matter: latency, throughput, dollars — which benchmarks matter when user experience, throughput, and cost are on the line.
- Rust LLM streaming bridge — a practical way to cut perceived latency in LLM-powered applications.
- ONNX Runtime patterns: perf without pain — the patterns that matter most for startup cost, memory behavior, and inference speed.
Common questions
How do you approach performance optimization projects?
I start with profiling and measurement to identify actual bottlenecks (not assumptions). Then I prioritize fixes by business impact and implementation cost. Each change is validated with metrics before and after.
Typical engagement: 2-4 weeks for performance audit + quick wins, or 2-3 months for comprehensive optimization across the stack.
What tools do you use for performance analysis?
My toolkit includes: Application profilers (async-profiler, py-spy), APM tools (DataDog, New Relic), database query analyzers (EXPLAIN, pg_stat_statements), distributed tracing (OpenTelemetry, Jaeger), load testing (k6, Locust), and custom profiling scripts.
Can you help with cloud cost optimization?
Yes. Performance and cost are often linked. By optimizing resource utilization, right-sizing instances, implementing better caching, and improving database efficiency, I typically reduce cloud costs by 20-40% while improving performance.
Do you work with specific languages or frameworks?
I'm language-agnostic but have deep experience with Python, Rust, Go, Node.js, and Java/JVM. Performance principles apply across languages — the specifics of profiling and optimization techniques vary, but the methodology remains consistent.
Related topics: Production AI Systems · Observability · Case studies