Load testing and scaling strategies for high-volume Customer.io integrations including k6 scripts, horizontal scaling, and message queue architectures.
Review Customer.io rate limits (100 req/sec per workspace for Track and App APIs) and choose architecture based on volume: direct API for < 1M events/day, queue-based for 1-10M, distributed for > 10M.
Build k6 load tests covering identify and track scenarios with ramping rates, error tracking, and latency thresholds.
Set up Kubernetes deployments with HPA autoscaling based on CPU utilization and queue depth metrics.
Use Kafka or similar message queue to buffer events between your application and Customer.io workers for reliable processing at scale.
Use Bottleneck or similar library to stay within Customer.io's 100 req/sec limit with headroom for other services.
Implement a batch sender that groups operations and processes them with controlled concurrency.
For detailed implementation code and configurations, load the reference guide:
Read(${CLAUDE_SKILL_DIR}/references/implementation-guide.md)
| Issue | Solution |
|---|---|
| Rate limited (429) | Reduce concurrency, check limiter config |
| Timeout errors | Increase timeout, check network |
| Queue backlog | Scale workers, increase concurrency |
| Memory pressure | Limit batch and queue sizes |
After load testing, proceed to customerio-known-pitfalls for anti-patterns.
Basic usage: Apply customerio load scale to a standard project setup with default configuration options.
Advanced scenario: Customize customerio load scale for production environments with multiple constraints and team-specific requirements.
