fireflies-performance-tuning

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Fireflies.ai Performance Tuning

Fireflies.ai 性能调优

Overview

概述

Optimize Fireflies.ai API performance with caching, batching, and connection pooling.

通过缓存、批处理和连接池优化Fireflies.ai API性能。

Prerequisites

前置条件

Fireflies.ai SDK installed
Understanding of async patterns
Redis or in-memory cache available (optional)
Performance monitoring in place

已安装Fireflies.ai SDK
了解异步模式
可使用Redis或内存缓存（可选）
已部署性能监控

Latency Benchmarks

延迟基准

Operation	P50	P95	P99
Read	50ms	150ms	300ms
Write	100ms	250ms	500ms
List	75ms	200ms	400ms

操作	P50	P95	P99
读取	50ms	150ms	300ms
写入	100ms	250ms	500ms
列表查询	75ms	200ms	400ms

Caching Strategy

缓存策略

Response Caching

响应缓存

typescript

import { LRUCache } from 'lru-cache';

const cache = new LRUCache<string, any>({
  max: 1000,
  ttl: 60000, // 1 minute
  updateAgeOnGet: true,
});

async function cachedFireflies.aiRequest<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttl?: number
): Promise<T> {
  const cached = cache.get(key);
  if (cached) return cached as T;

  const result = await fetcher();
  cache.set(key, result, { ttl });
  return result;
}

typescript

import { LRUCache } from 'lru-cache';

const cache = new LRUCache<string, any>({
  max: 1000,
  ttl: 60000, // 1 minute
  updateAgeOnGet: true,
});

async function cachedFireflies.aiRequest<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttl?: number
): Promise<T> {
  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)

Redis分布式缓存

typescript

import Redis from 'ioredis';

const redis = new Redis(process.env.REDIS_URL);

async function cachedWithRedis<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttlSeconds = 60
): Promise<T> {
  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;
}

typescript

import Redis from 'ioredis';

const redis = new Redis(process.env.REDIS_URL);

async function cachedWithRedis<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttlSeconds = 60
): Promise<T> {
  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

请求批处理

typescript

import DataLoader from 'dataloader';

const firefliesLoader = new DataLoader<string, any>(
  async (ids) => {
    // Batch fetch from Fireflies.ai
    const results = await firefliesClient.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([
  firefliesLoader.load('id-1'),
  firefliesLoader.load('id-2'),
  firefliesLoader.load('id-3'),
]);

typescript

import DataLoader from 'dataloader';

const firefliesLoader = new DataLoader<string, any>(
  async (ids) => {
    // Batch fetch from Fireflies.ai
    const results = await firefliesClient.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([
  firefliesLoader.load('id-1'),
  firefliesLoader.load('id-2'),
  firefliesLoader.load('id-3'),
]);

Connection Optimization

连接优化

typescript

import { Agent } from 'https';

// Keep-alive connection pooling
const agent = new Agent({
  keepAlive: true,
  maxSockets: 10,
  maxFreeSockets: 5,
  timeout: 30000,
});

const client = new Fireflies.aiClient({
  apiKey: process.env.FIREFLIES_API_KEY!,
  httpAgent: agent,
});

typescript

import { Agent } from 'https';

// Keep-alive connection pooling
const agent = new Agent({
  keepAlive: true,
  maxSockets: 10,
  maxFreeSockets: 5,
  timeout: 30000,
});

const client = new Fireflies.aiClient({
  apiKey: process.env.FIREFLIES_API_KEY!,
  httpAgent: agent,
});

Pagination Optimization

分页优化

typescript

async function* paginatedFireflies.aiList<T>(
  fetcher: (cursor?: string) => Promise<{ data: T[]; nextCursor?: string }>
): AsyncGenerator<T> {
  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 paginatedFireflies.aiList(cursor =>
  firefliesClient.list({ cursor, limit: 100 })
)) {
  await process(item);
}

typescript

async function* paginatedFireflies.aiList<T>(
  fetcher: (cursor?: string) => Promise<{ data: T[]; nextCursor?: string }>
): AsyncGenerator<T> {
  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 paginatedFireflies.aiList(cursor =>
  firefliesClient.list({ cursor, limit: 100 })
)) {
  await process(item);
}

Performance Monitoring

性能监控

typescript

async function measuredFireflies.aiCall<T>(
  operation: string,
  fn: () => Promise<T>
): Promise<T> {
  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;
  }
}

typescript

async function measuredFireflies.aiCall<T>(
  operation: string,
  fn: () => Promise<T>
): Promise<T> {
  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

操作步骤

Step 1: Establish Baseline

步骤1：建立基准线

Measure current latency for critical Fireflies.ai operations.

测量当前关键Fireflies.ai操作的延迟。

Step 2: Implement Caching

步骤2：实现缓存

Add response caching for frequently accessed data.

为频繁访问的数据添加响应缓存。

Step 3: Enable Batching

步骤3：启用批处理

Use DataLoader or similar for automatic request batching.

使用DataLoader或类似工具实现自动请求批处理。

Step 4: Optimize Connections

步骤4：优化连接

Configure connection pooling with keep-alive.

配置带长连接的连接池。

Output

输出结果

Reduced API latency
Caching layer implemented
Request batching enabled
Connection pooling configured

降低API延迟
已实现缓存层
已启用请求批处理
已配置连接池

Error Handling

错误处理

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

问题	原因	解决方案
缓存击穿	TTL过期	使用 stale-while-revalidate 策略
批处理超时	条目过多	减小批处理大小
连接耗尽	未使用连接池	配置最大套接字数
内存压力	缓存过大	设置最大缓存条目数

Examples

示例

Quick Performance Wrapper

快速性能包装器

typescript

const withPerformance = <T>(name: string, fn: () => Promise<T>) =>
  measuredFireflies.aiCall(name, () =>
    cachedFireflies.aiRequest(`cache:${name}`, fn)
  );

typescript

const withPerformance = <T>(name: string, fn: () => Promise<T>) =>
  measuredFireflies.aiCall(name, () =>
    cachedFireflies.aiRequest(`cache:${name}`, fn)
  );

Resources

资源

Next Steps

下一步

For cost optimization, see

fireflies-cost-tuning

如需优化成本，请查看

fireflies-cost-tuning

。