instantly-performance-tuning
Optimize Instantly API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Instantly integrations. Trigger with phrases like "instantly performance", "optimize instantly", "instantly latency", "instantly caching", "instantly slow", "instantly batch". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore <jeremy@intentsolutions.io>
Allowed Tools
No tools specified
Provided by Plugin
instantly-pack
Claude Code skill pack for Instantly (24 skills)
Installation
This skill is included in the instantly-pack plugin:
/plugin install instantly-pack@claude-code-plugins-plus
Click to copy
Instructions
# Instantly Performance Tuning
## Overview
Optimize Instantly API performance with caching, batching, and connection pooling.
## Prerequisites
- Instantly SDK installed
- Understanding of async patterns
- Redis or in-memory cache available (optional)
- Performance monitoring in place
## Latency Benchmarks
| Operation | P50 | P95 | P99 |
|-----------|-----|-----|-----|
| Read | 50ms | 150ms | 300ms |
| Write | 100ms | 250ms | 500ms |
| List | 75ms | 200ms | 400ms |
## Caching Strategy
### Response Caching
```typescript
import { LRUCache } from 'lru-cache';
const cache = new LRUCache({
max: 1000,
ttl: 60000, // 1 minute
updateAgeOnGet: true,
});
async function cachedInstantlyRequest(
key: string,
fetcher: () => Promise,
ttl?: number
): Promise {
const cached = cache.get(key);
if (cached) return cached as T;
const result = await fetcher();
cache.set(key, result, { ttl });
return result;
}
```
### Redis Caching (Distributed)
```typescript
import Redis from 'ioredis';
const redis = new Redis(process.env.REDIS_URL);
async function cachedWithRedis(
key: string,
fetcher: () => Promise,
ttlSeconds = 60
): Promise {
const cached = await redis.get(key);
if (cached) return JSON.parse(cached);
const result = await fetcher();
await redis.setex(key, ttlSeconds, JSON.stringify(result));
return result;
}
```
## Request Batching
```typescript
import DataLoader from 'dataloader';
const instantlyLoader = new DataLoader(
async (ids) => {
// Batch fetch from Instantly
const results = await instantlyClient.batchGet(ids);
return ids.map(id => results.find(r => r.id === id) || null);
},
{
maxBatchSize: 100,
batchScheduleFn: callback => setTimeout(callback, 10),
}
);
// Usage - automatically batched
const [item1, item2, item3] = await Promise.all([
instantlyLoader.load('id-1'),
instantlyLoader.load('id-2'),
instantlyLoader.load('id-3'),
]);
```
## Connection Optimization
```typescript
import { Agent } from 'https';
// Keep-alive connection pooling
const agent = new Agent({
keepAlive: true,
maxSockets: 10,
maxFreeSockets: 5,
timeout: 30000,
});
const client = new InstantlyClient({
apiKey: process.env.INSTANTLY_API_KEY!,
httpAgent: agent,
});
```
## Pagination Optimization
```typescript
async function* paginatedInstantlyList(
fetcher: (cursor?: string) => Promise<{ data: T[]; nextCursor?: string }>
): AsyncGenerator {
let cursor: string | undefined;
do {
const { data, nextCursor } = await fetcher(cursor);
for (const item of data) {
yield item;
}
cursor = nextCursor;
} while (cursor);
}
// Usage
for await (const item of paginatedInstantlyList(cursor =>
instantlyClient.list({ cursor, limit: 100 })
)) {
await process(item);
}
```
## Performance Monitoring
```typescript
async function measuredInstantlyCall(
operation: string,
fn: () => Promise
): Promise {
const start = performance.now();
try {
const result = await fn();
const duration = performance.now() - start;
console.log({ operation, duration, status: 'success' });
return result;
} catch (error) {
const duration = performance.now() - start;
console.error({ operation, duration, status: 'error', error });
throw error;
}
}
```
## Instructions
### Step 1: Establish Baseline
Measure current latency for critical Instantly operations.
### Step 2: Implement Caching
Add response caching for frequently accessed data.
### Step 3: Enable Batching
Use DataLoader or similar for automatic request batching.
### Step 4: Optimize Connections
Configure connection pooling with keep-alive.
## Output
- Reduced API latency
- Caching layer implemented
- Request batching enabled
- Connection pooling configured
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| Cache miss storm | TTL expired | Use stale-while-revalidate |
| Batch timeout | Too many items | Reduce batch size |
| Connection exhausted | No pooling | Configure max sockets |
| Memory pressure | Cache too large | Set max cache entries |
## Examples
### Quick Performance Wrapper
```typescript
const withPerformance = (name: string, fn: () => Promise) =>
measuredInstantlyCall(name, () =>
cachedInstantlyRequest(`cache:${name}`, fn)
);
```
## Resources
- [Instantly Performance Guide](https://docs.instantly.com/performance)
- [DataLoader Documentation](https://github.com/graphql/dataloader)
- [LRU Cache Documentation](https://github.com/isaacs/node-lru-cache)
## Next Steps
For cost optimization, see `instantly-cost-tuning`.