Clay Load & Scale

Overview

Load testing, scaling strategies, and capacity planning for Clay integrations.

Prerequisites

k6 load testing tool installed
Kubernetes cluster with HPA configured
Prometheus for metrics collection
Test environment API keys

Capacity Metrics

Metric	Warning	Critical
CPU Utilization	> 70%	> 85%
Memory Usage	> 75%	> 90%
Request Queue Depth	> 100	> 500
Error Rate	> 1%	> 5%
P95 Latency	> 1000ms	> 3000ms

Instructions

Step 1: Create Load Test Script

Write a k6 test with ramp-up (2m to 10 VUs), steady state (5m), stress (2m to 50 VUs), and ramp-down stages. Set thresholds: P95 < 500ms, error rate < 1%.

Step 2: Configure Auto-Scaling

Set up Kubernetes HPA targeting 70% CPU utilization, 2-20 replicas, with custom metric on queue depth.

Step 3: Implement Connection Pooling

Use generic-pool with 5-20 connections and 30s idle timeout to reuse Clay client connections.

Step 4: Run Load Test and Analyze

k6 run --env CLAY_API_KEY=${CLAY_API_KEY} clay-load-test.js

Record results in the benchmark template: total requests, success rate, P50/P95/P99 latency, max RPS.

For complete k6 scripts, HPA YAML, connection pool code, and capacity estimator, load the reference guide: Read(${CLAUDE_SKILL_DIR}/references/implementation-guide.md)

Output

Load test script created
HPA configured
Benchmark results documented
Capacity recommendations defined

Error Handling

Issue	Cause	Solution
k6 timeout	Rate limited	Reduce RPS
HPA not scaling	Wrong metrics	Verify metric name
Connection refused	Pool exhausted	Increase pool size
Inconsistent results	Warm-up needed	Add ramp-up phase

Resources

Next Steps

For reliability patterns, see clay-reliability-patterns.

Examples

Basic usage: Apply clay load scale to a standard project setup with default configuration options.

Advanced scenario: Customize clay load scale for production environments with multiple constraints and team-specific requirements.

clay-load-scale