micsapp Customer.io Load & Scale
Overview
Load testing and scaling strategies for high-volume Customer.io integrations including k6 scripts, horizontal scaling, and message queue architectures.
Prerequisites
- Customer.io integration working
- Load testing tools (k6, Artillery)
- Staging environment with test workspace
Instructions
Step 1: Understand Rate Limits and Scaling Targets
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.
Step 2: Create Load Test Scripts
Build k6 load tests covering identify and track scenarios with ramping rates, error tracking, and latency thresholds.
Step 3: Configure Horizontal Scaling
Set up Kubernetes deployments with HPA autoscaling based on CPU utilization and queue depth metrics.
Step 4: Implement Message Queue Architecture
Use Kafka or similar message queue to buffer events between your application and Customer.io workers for reliable processing at scale.
Step 5: Add Rate Limiting
Use Bottleneck or similar library to stay within Customer.io's 100 req/sec limit with headroom for other services.
Step 6: Enable Batch Processing
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)
Output
- k6 load test scripts with identify/track scenarios
- Kubernetes deployment with HPA autoscaling
- Kafka-based message queue processor
- Rate limiter with Bottleneck
- Batch processing sender
- Load test execution scripts
Error Handling
| 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 |
Scaling Checklist
- Rate limits understood
- Load tests written and baselined
- Horizontal scaling configured
- Message queue buffering active
- Rate limiting implemented
- Batch processing enabled
- Monitoring during tests
Resources
Next Steps
After load testing, proceed to customerio-known-pitfalls for anti-patterns.
Examples
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.