Claude-code-plugins-plus appfolio-performance-tuning
install
source · Clone the upstream repo
git clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/jeremylongshore/claude-code-plugins-plus-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/saas-packs/appfolio-pack/skills/appfolio-performance-tuning" ~/.claude/skills/jeremylongshore-claude-code-plugins-plus-appfolio-performance-tuning && rm -rf "$T"
manifest:
plugins/saas-packs/appfolio-pack/skills/appfolio-performance-tuning/SKILL.mdsource content
AppFolio Performance Tuning
Overview
AppFolio's property management API handles bulk tenant queries, property portfolio pagination, and work order batch processing. Large portfolios with thousands of units generate heavy read traffic on listing endpoints. Optimizing cache lifetimes for slow-changing property data, batching work order updates, and pooling HTTP connections reduces API call volume by 60-80% and cuts dashboard load times from seconds to sub-second.
Caching Strategy
const cache = new Map<string, { data: any; expiry: number }>(); const TTL = { properties: 300_000, tenants: 120_000, units: 300_000, workOrders: 60_000 }; async function cached(key: string, ttlKey: keyof typeof TTL, fn: () => Promise<any>) { const entry = cache.get(key); if (entry && entry.expiry > Date.now()) return entry.data; const data = await fn(); cache.set(key, { data, expiry: Date.now() + TTL[ttlKey] }); return data; }
Batch Operations
async function batchWorkOrders(client: any, ids: string[], batchSize = 25) { const results = []; for (let i = 0; i < ids.length; i += batchSize) { const batch = ids.slice(i, i + batchSize); const res = await Promise.all(batch.map(id => client.http.get(`/work_orders/${id}`))); results.push(...res.map(r => r.data)); if (i + batchSize < ids.length) await new Promise(r => setTimeout(r, 200)); } return results; }
Connection Pooling
import { Agent } from 'https'; const agent = new Agent({ keepAlive: true, maxSockets: 10, maxFreeSockets: 5, timeout: 30_000 }); // Pass to axios/fetch: { httpsAgent: agent }
Rate Limit Management
async function withRateLimit(fn: () => Promise<any>): Promise<any> { const res = await fn(); const remaining = parseInt(res.headers['x-ratelimit-remaining'] || '100'); if (remaining < 5) { const retryAfter = parseInt(res.headers['retry-after'] || '2') * 1000; await new Promise(r => setTimeout(r, retryAfter)); } return res; }
Monitoring
const metrics = { apiCalls: 0, cacheHits: 0, errors: 0, totalLatency: 0 }; function track(startMs: number, hit: boolean, error?: boolean) { metrics.apiCalls++; metrics.totalLatency += Date.now() - startMs; if (hit) metrics.cacheHits++; if (error) metrics.errors++; } // Log: avg latency, cache hit rate, error rate per minute
Performance Checklist
- Cache property and unit listings with 5-min TTL
- Use incremental sync via last_modified timestamps
- Batch work order updates in groups of 25
- Enable HTTP keep-alive with connection pooling
- Parse rate limit headers and back off proactively
- Parallelize independent dashboard queries with Promise.all
- Monitor cache hit ratio (target > 70%)
- Set request timeouts to 30s to avoid hung connections
Error Handling
| Issue | Cause | Fix |
|---|---|---|
| 429 Too Many Requests | Exceeded API rate limit | Parse Retry-After header, exponential backoff |
| Stale tenant data | Cache TTL too long | Reduce tenant cache to 2 min, add cache-bust on writes |
| Timeout on portfolio list | Large dataset with no pagination | Add page_size=100 and cursor-based iteration |
| Connection reset | Socket exhaustion | Enable keep-alive agent with maxSockets cap |
Resources
Next Steps
See
appfolio-reference-architecture.