Redis patterns including caching strategies, pub/sub, streams for event processing, Lua scripts, and data structures

Redis Patterns

Caching Strategies

async function getUser(userId: string): Promise<User> {
  const cacheKey = `user:${userId}`;
  const cached = await redis.get(cacheKey);

  if (cached) {
    return JSON.parse(cached);
  }

  const user = await db.user.findUnique({ where: { id: userId } });
  if (user) {
    await redis.set(cacheKey, JSON.stringify(user), "EX", 3600);
  }

  return user;
}

async function invalidateUser(userId: string): Promise<void> {
  await redis.del(`user:${userId}`);
  await redis.del(`user:${userId}:orders`);
}

async function cacheAside<T>(
  key: string,
  ttlSeconds: number,
  fetcher: () => Promise<T>
): Promise<T> {
  const cached = await redis.get(key);
  if (cached) return JSON.parse(cached);

  const value = await fetcher();
  await redis.set(key, JSON.stringify(value), "EX", ttlSeconds);
  return value;
}

Rate Limiting with Sliding Window

async function isRateLimited(
  clientId: string,
  limit: number,
  windowSeconds: number
): Promise<boolean> {
  const key = `ratelimit:${clientId}`;
  const now = Date.now();
  const windowStart = now - windowSeconds * 1000;

  const pipe = redis.multi();
  pipe.zremrangebyscore(key, 0, windowStart);
  pipe.zadd(key, now, `${now}:${crypto.randomUUID()}`);
  pipe.zcard(key);
  pipe.expire(key, windowSeconds);

  const results = await pipe.exec();
  const count = results[2][1] as number;
  return count > limit;
}

Pub/Sub

const subscriber = redis.duplicate();
await subscriber.subscribe("notifications", "orders");

subscriber.on("message", (channel, message) => {
  const event = JSON.parse(message);
  switch (channel) {
    case "notifications":
      handleNotification(event);
      break;
    case "orders":
      handleOrderEvent(event);
      break;
  }
});

async function publishEvent(channel: string, event: object): Promise<void> {
  await redis.publish(channel, JSON.stringify(event));
}

Streams for Event Processing

async function produceEvent(stream: string, event: Record<string, string>) {
  await redis.xadd(stream, "*", ...Object.entries(event).flat());
}

async function consumeEvents(
  stream: string,
  group: string,
  consumer: string
) {
  try {
    await redis.xgroup("CREATE", stream, group, "0", "MKSTREAM");
  } catch {
    // group already exists
  }

  while (true) {
    const results = await redis.xreadgroup(
      "GROUP", group, consumer,
      "COUNT", 10,
      "BLOCK", 5000,
      "STREAMS", stream, ">"
    );

    if (!results) continue;

    for (const [, messages] of results) {
      for (const [id, fields] of messages) {
        await processMessage(fields);
        await redis.xack(stream, group, id);
      }
    }
  }
}

Streams provide durable, consumer-group-based event processing with acknowledgment and replay.

Lua Script for Atomic Operations

const acquireLock = `
  local key = KEYS[1]
  local token = ARGV[1]
  local ttl = ARGV[2]
  if redis.call("SET", key, token, "NX", "EX", ttl) then
    return 1
  end
  return 0
`;

const releaseLock = `
  local key = KEYS[1]
  local token = ARGV[1]
  if redis.call("GET", key) == token then
    return redis.call("DEL", key)
  end
  return 0
`;

async function withLock<T>(
  resource: string,
  ttl: number,
  fn: () => Promise<T>
): Promise<T> {
  const token = crypto.randomUUID();
  const acquired = await redis.eval(acquireLock, 1, `lock:${resource}`, token, ttl);
  if (!acquired) throw new Error("Failed to acquire lock");
  try {
    return await fn();
  } finally {
    await redis.eval(releaseLock, 1, `lock:${resource}`, token);
  }
}

Anti-Patterns

Storing large objects (>100KB) in Redis without compression
Using KEYS * in production (blocks the server; use SCAN instead)
Not setting TTL on cache entries (memory grows unbounded)
Using pub/sub for durable messaging (messages are lost if no subscriber is connected)
Relying on Redis as the sole data store without persistence strategy
Not using pipelines for multiple sequential commands

Checklist

Cache keys follow a consistent naming convention (entity:id:field)
All cache entries have a TTL to prevent memory leaks
SCAN used instead of KEYS for pattern matching in production
Lua scripts used for operations requiring atomicity
Streams used instead of pub/sub when durability is needed
Connection pooling configured for high-throughput applications
Rate limiting uses sliding window with sorted sets
Distributed locks include fencing tokens and TTL

Redis Patterns (skill)

Redis Patterns

Caching Strategies

Rate Limiting with Sliding Window

Pub/Sub

Streams for Event Processing

Lua Script for Atomic Operations

Anti-Patterns

Checklist