home / skills / jeremylongshore / claude-code-plugins-plus-skills / perplexity-load-scale

This skill helps you perform perplexity load testing, auto-scaling, and capacity planning to optimize integrations and ensure reliable performance.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill perplexity-load-scale

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---
name: perplexity-load-scale
description: |
  Implement Perplexity load testing, auto-scaling, and capacity planning strategies.
  Use when running performance tests, configuring horizontal scaling,
  or planning capacity for Perplexity integrations.
  Trigger with phrases like "perplexity load test", "perplexity scale",
  "perplexity performance test", "perplexity capacity", "perplexity k6", "perplexity benchmark".
allowed-tools: Read, Write, Edit, Bash(k6:*), Bash(kubectl:*)
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---

# Perplexity Load & Scale

## Overview
Load testing, scaling strategies, and capacity planning for Perplexity integrations.

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

## Load Testing with k6

### Basic Load Test
```javascript
// perplexity-load-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';

export const options = {
  stages: [
    { duration: '2m', target: 10 },   // Ramp up
    { duration: '5m', target: 10 },   // Steady state
    { duration: '2m', target: 50 },   // Ramp to peak
    { duration: '5m', target: 50 },   // Stress test
    { duration: '2m', target: 0 },    // Ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<500'],
    http_req_failed: ['rate<0.01'],
  },
};

export default function () {
  const response = http.post(
    'https://api.perplexity.com/v1/resource',
    JSON.stringify({ test: true }),
    {
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${__ENV.PERPLEXITY_API_KEY}`,
      },
    }
  );

  check(response, {
    'status is 200': (r) => r.status === 200,
    'latency < 500ms': (r) => r.timings.duration < 500,
  });

  sleep(1);
}
```

### Run Load Test
```bash
# Install k6
brew install k6  # macOS
# or: sudo apt install k6  # Linux

# Run test
k6 run --env PERPLEXITY_API_KEY=${PERPLEXITY_API_KEY} perplexity-load-test.js

# Run with output to InfluxDB
k6 run --out influxdb=http://localhost:8086/k6 perplexity-load-test.js
```

## Scaling Patterns

### Horizontal Scaling
```yaml
# kubernetes HPA
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: perplexity-integration-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: perplexity-integration
  minReplicas: 2
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
    - type: Pods
      pods:
        metric:
          name: perplexity_queue_depth
        target:
          type: AverageValue
          averageValue: 100
```

### Connection Pooling
```typescript
import { Pool } from 'generic-pool';

const perplexityPool = Pool.create({
  create: async () => {
    return new PerplexityClient({
      apiKey: process.env.PERPLEXITY_API_KEY!,
    });
  },
  destroy: async (client) => {
    await client.close();
  },
  max: 20,
  min: 5,
  idleTimeoutMillis: 30000,
});

async function withPerplexityClient<T>(
  fn: (client: PerplexityClient) => Promise<T>
): Promise<T> {
  const client = await perplexityPool.acquire();
  try {
    return await fn(client);
  } finally {
    perplexityPool.release(client);
  }
}
```

## Capacity Planning

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

### Capacity Calculation
```typescript
interface CapacityEstimate {
  currentRPS: number;
  maxRPS: number;
  headroom: number;
  scaleRecommendation: string;
}

function estimatePerplexityCapacity(
  metrics: SystemMetrics
): CapacityEstimate {
  const currentRPS = metrics.requestsPerSecond;
  const avgLatency = metrics.p50Latency;
  const cpuUtilization = metrics.cpuPercent;

  // Estimate max RPS based on current performance
  const maxRPS = currentRPS / (cpuUtilization / 100) * 0.7; // 70% target
  const headroom = ((maxRPS - currentRPS) / currentRPS) * 100;

  return {
    currentRPS,
    maxRPS: Math.floor(maxRPS),
    headroom: Math.round(headroom),
    scaleRecommendation: headroom < 30
      ? 'Scale up soon'
      : headroom < 50
      ? 'Monitor closely'
      : 'Adequate capacity',
  };
}
```

## Benchmark Results Template

```markdown
## Perplexity Performance Benchmark
**Date:** YYYY-MM-DD
**Environment:** [staging/production]
**SDK Version:** X.Y.Z

### Test Configuration
- Duration: 10 minutes
- Ramp: 10 → 100 → 10 VUs
- Target endpoint: /v1/resource

### Results
| Metric | Value |
|--------|-------|
| Total Requests | 50,000 |
| Success Rate | 99.9% |
| P50 Latency | 120ms |
| P95 Latency | 350ms |
| P99 Latency | 800ms |
| Max RPS Achieved | 150 |

### Observations
- [Key finding 1]
- [Key finding 2]

### Recommendations
- [Scaling recommendation]
```

## Instructions

### Step 1: Create Load Test Script
Write k6 test script with appropriate thresholds.

### Step 2: Configure Auto-Scaling
Set up HPA with CPU and custom metrics.

### Step 3: Run Load Test
Execute test and collect metrics.

### Step 4: Analyze and Document
Record results in benchmark template.

## 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 |

## Examples

### Quick k6 Test
```bash
k6 run --vus 10 --duration 30s perplexity-load-test.js
```

### Check Current Capacity
```typescript
const metrics = await getSystemMetrics();
const capacity = estimatePerplexityCapacity(metrics);
console.log('Headroom:', capacity.headroom + '%');
console.log('Recommendation:', capacity.scaleRecommendation);
```

### Scale HPA Manually
```bash
kubectl scale deployment perplexity-integration --replicas=5
kubectl get hpa perplexity-integration-hpa
```

## Resources
- [k6 Documentation](https://k6.io/docs/)
- [Kubernetes HPA](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/)
- [Perplexity Rate Limits](https://docs.perplexity.com/rate-limits)

## Next Steps
For reliability patterns, see `perplexity-reliability-patterns`.

Overview

This skill implements Perplexity load testing, auto-scaling, and capacity planning strategies for production and staging integrations. It provides a reusable k6 test script, Kubernetes HPA patterns, connection-pooling guidance, and capacity estimation logic. Use it to validate performance, exercise autoscaling, and produce benchmark reports for decision-making.

How this skill works

The skill generates and runs k6 load tests against Perplexity endpoints, capturing latency, error rate, and throughput. It maps observed metrics to scaling policies by recommending HPA settings (CPU and custom queue-depth metrics) and connection pool sizes. It also computes capacity estimates and headroom to inform scale-up/scale-down decisions and produces a benchmark template for documentation.

When to use it

  • Before releasing a Perplexity integration to production
  • When validating autoscaling behavior in Kubernetes (HPA)
  • During capacity planning or cost optimization cycles
  • To reproduce performance regressions with k6 benchmarks
  • When tuning connection pools or API concurrency limits

Best practices

  • Include a ramp-up phase to warm caches and avoid false negatives
  • Set SLO-aligned thresholds (e.g., p95 < 500ms) in k6 to catch regressions early
  • Expose custom queue-depth metrics to Prometheus for HPA scaling
  • Use a pool for Perplexity clients to limit concurrent connections and control resource use
  • Run tests from an isolated environment with realistic API keys and network conditions

Example use cases

  • Run a 10–20 minute k6 scenario to validate P95 and error-rate before cutover
  • Configure an HPA using CPU and perplexity_queue_depth to scale a deployment from 2 to 20 pods
  • Estimate current vs. max RPS and calculate headroom to decide whether to provision more replicas
  • Benchmark SDK versions and record results in a standardized markdown template
  • Adjust connection pool max/min and re-run load tests to find optimal concurrency

FAQ

What prerequisites are required?

Install k6, have a Kubernetes cluster with HPA and Prometheus, and use test environment API keys.

How do I avoid k6 rate limits from Perplexity?

Start with conservative RPS, add a ramp-up, and monitor API rate-limit responses; reduce RPS or increase backoff if you hit limits.