implementing-query-caching

name	implementing-query-caching
description	Implement query result caching with Redis and proper invalidation strategies for Prisma 6. Use when optimizing frequently accessed data, improving read-heavy application performance, or reducing database load through caching. Use when this capability is needed.
metadata	{"author":"djankies"}

Query Result Caching with Redis

Efficient query result caching for Prisma 6 applications using Redis: cache key generation, invalidation strategies, TTL management, and when caching provides value.

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Implement query result caching with Redis for Prisma 6, covering cache key generation, invalidation, TTL strategies, and identifying when caching delivers value. User mentions: caching, Redis, performance optimization, slow queries, read-heavy applications, frequently accessed data, reducing database load, improving response times, cache invalidation, cache warming, or optimizing Prisma queries. Query caching reduces database load and improves read response times, but adds complexity: cache invalidation, consistency challenges, infrastructure. Key capabilities: Redis-Prisma integration, consistent cache key patterns, mutation-triggered invalidation, TTL strategies (time/event-based), and identifying when caching provides value. **Phase 1: Identify Cache Candidates** Analyze query patterns for read-heavy operations; identify data with acceptable staleness; measure baseline query performance; estimate cache hit rate and improvement.

Phase 2: Implement Cache Layer Set up Redis with connection pooling; create cache wrapper around Prisma queries; implement consistent cache key generation; add cache read with database fallback.

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.

## When to Cache

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

## Basic Cache Implementation

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:  },
  });

   (user)  redis.(cacheKey, , .(user));
   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 =  redis.(cacheKey);
   (cached)  .(cached);

   posts =  prisma..({
    : filters,
    : { : , : , :  },
  });

   redis.(cacheKey, , .(posts));
   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;
}

**MUST:** * Use cache-aside pattern (not cache-through) * Consistent cache key generation (no random/timestamp components) * Invalidate cache on all mutations affecting cached data * Graceful Redis failure handling with database fallback * JSON serialization (consistent with Prisma types) * TTL on all cached values (never infinite) * Thorough cache invalidation testing

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

**Cache Hit Rate:** Monitor >60% for effective caching; <40% signals strategy reconsideration or TTL adjustment.

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.

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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

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