Implementing Query Caching

Phase 3: Implement Invalidation Identify mutations affecting cached data; add invalidation to update/delete operations; handle bulk operations and cascading invalidation; test across scenarios.

Phase 4: Configure TTL Determine appropriate TTL per data type; implement time-based expiration; add event-based invalidation for critical data; monitor hit rates and adjust. </workflow>

Strong Candidates:

• Read-heavy data (>10:1 ratio): user profiles, product catalogs, configuration, content lists
• Expensive queries: large aggregations, multi-join, complex filtering, computed values
• High-frequency access

: homepage data, navigation, popular results, trending content

Weak Candidates:

• Write-heavy data (<3:1 ratio): analytics, activity logs, messages, live updates
• Frequently changing: stock prices, inventory, bids, live scores
• User-specific: shopping carts, drafts, recommendations, sessions
• Fast simple queries: primary key lookups, indexed queries, already in DB cache

Decision Tree:

Read/write ratio > 10:1?
├─ Yes: Strong candidate
│  └─ Data stale 1+ minutes acceptable?
│     ├─ Yes: Long TTL (5-60min) + event invalidation
│     └─ No: Short TTL (10-60sec) + aggressive invalidation
└─ No: Ratio > 3:1?
   ├─ Yes: Moderate candidate, if query > 100ms → short TTL (30-120sec)
   └─ No: Skip; optimize query/indexes/pooling instead

Example 1: Cache-Aside Pattern

import { PrismaClient } from '@prisma/client';
import { Redis } from 'ioredis';

const prisma = new PrismaClient();
const redis = new Redis({
  host: process.env.REDIS_HOST,
  port: parseInt(process.env.REDIS_PORT || '6379'),
  maxRetriesPerRequest: 3,
});

async function getCachedUser(userId: string) {
  const cacheKey = `user:${userId}`;
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached);

  const user = await prisma.user.findUnique({
    where: { id: userId },
    select: { id: true, email: true, name: true, role: true },
  });

  if (user) await redis.setex(cacheKey, 300, JSON.stringify(user));
  return user;
}

Example 2: Consistent Key Generation

import crypto from 'crypto';

function generateCacheKey(entity: string, query: Record<string, unknown>): string {
  const sortedQuery = Object.keys(query)
    .sort()
    .reduce((acc, key) => {
      acc[key] = query[key];
      return acc;
    }, {} as Record<string, unknown>);

  const queryHash = crypto
    .createHash('sha256')
    .update(JSON.stringify(sortedQuery))
    .digest('hex')
    .slice(0, 16);
  return `${entity}:${queryHash}`;
}

async function getCachedPosts(filters: {
  authorId?: string;
  published?: boolean;
  tags?: string[];
}) {
  const cacheKey = generateCacheKey('posts', filters);
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached);

  const posts = await prisma.post.findMany({
    where: filters,
    select: { id: true, title: true, createdAt: true },
  });

  await redis.setex(cacheKey, 120, JSON.stringify(posts));
  return posts;
}

Example 3: Cache Invalidation on Mutation

async function updatePost(postId: string, data: { title?: string; content?: string }) {
  const post = await prisma.post.update({ where: { id: postId }, data });

  await Promise.all([
    redis.del(`post:${postId}`),
    redis.del(`posts:author:${post.authorId}`),
    redis.keys('posts:*').then((keys) => keys.length > 0 && redis.del(...keys)),
  ]);
  return post;
}

Note: redis.keys() with patterns is slow on large keysets; use SCAN or maintain key sets.

Example 4: TTL Strategy

const TTL = {
  user_profile: 600,
  user_settings: 300,
  posts_list: 120,
  post_detail: 180,
  popular_posts: 60,
  real_time_stats: 10,
};

async function cacheWithTTL<T>(
  key: string,
  ttlType: keyof typeof TTL,
  fetchFn: () => Promise<T>
): Promise<T> {
  const cached = await redis.get(key);
  if (cached) return JSON.parse(cached);

  const data = await fetchFn();
  await redis.setex(key, TTL[ttlType], JSON.stringify(data));
  return data;
}

SHOULD:

• Redis connection pooling (ioredis)
• Separate cache logic from business logic
• Monitor cache hit rates; adjust TTL accordingly
• Shorter TTL for frequently changing data
• Cache warming for predictably popular data
• Document cache key patterns and invalidation rules
• Use

Redis SCAN vs KEYS for pattern matching

NEVER:

• Cache authentication tokens or sensitive credentials
• Use infinite TTL
• Pattern-match invalidation in hot paths
• Cache Prisma queries with skip/take without pagination in key
• Assume cache always available
• Store Prisma instances directly (serialize first)
• Cache write-heavy data </constraints>

Invalidation Testing: Verify all mutations invalidate correct keys; test cascading invalidation for related entities; confirm bulk operations invalidate list caches; ensure no stale data post-mutation.

Performance: Measure query latency with/without cache; target >50% latency reduction; monitor P95/P99 improvements; verify caching doesn't increase memory pressure.

Redis Health: Monitor connection pool utilization, memory usage (set maxmemory-policy), connection failures; test application behavior when Redis is unavailable. </validation>

References

• Redis Configuration — Connection setup, serverless
• Invalidation Patterns — Event-based, time-based, hybrid
• Advanced Examples — Bulk invalidation, cache warming
• Common Pitfalls — Infinite TTL, key inconsistency, missing invalidation