Backend Caching Cheat Sheet

Updated 2026-05-28

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Caching is a fundamental performance optimization technique in backend systems that stores frequently accessed data in temporary, fast-access storage layers closer to the application. By reducing redundant database queries and expensive computations, caching can cut response times from seconds to milliseconds and reduce database load by 90% or more. The critical challenge lies not just in storing data, but in choosing the right caching pattern, managing invalidation, and preventing common failure modes like cache stampedes and stale data — all while maintaining consistency across distributed systems.

What This Cheat Sheet Covers

This topic spans 13 focused tables and 89 indexed concepts, 89 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core Caching PatternsTable 2: Cache Invalidation StrategiesTable 3: Cache Eviction PoliciesTable 4: Distributed Caching ArchitectureTable 5: Multi-Tier CachingTable 6: Cache Warming StrategiesTable 7: Cache Problems and SolutionsTable 8: Advanced Caching TechniquesTable 9: Cache Key DesignTable 10: Caching for Specific Use CasesTable 11: Cache Monitoring and MetricsTable 12: Cache Configuration Best PracticesTable 13: Cache Technologies and Tools

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Table 1: Core Caching Patterns

The six foundational read/write patterns define how your application coordinates between the cache and the database; nearly every caching bug stems from choosing the wrong pattern for a given access profile.

Pattern	Example	Description
Cache-Aside (Lazy Loading)	`data = cache.get(key)` `if not data:` `data = db.query()` `cache.set(key, data)`	• Application checks cache first on read • on miss, loads from DB and populates cache. Most common pattern — gives full application control over caching logic.
Read-Through	`data = cache.get(key)`	• Cache itself handles DB fetch on miss • application treats cache as primary data source. Centralizes cache logic and simplifies application code.
Write-Through	`cache.set(key, data)` `db.write(data)`	• Every write goes to both cache and DB synchronously • ensures strong consistency but adds latency to writes. Best for read-heavy workloads needing fresh data.
Write-Behind (Write-Back)	`cache.set(key, data)` `queue_db_write(data)`	• Writes go to cache immediately; DB updated asynchronously in background • maximizes write performance but risks data loss if cache fails before flush.
Write-Around	`db.write(data)` `# cache not populated`	• Writes go directly to DB, bypassing the cache entirely • cache populated lazily on subsequent reads. Prevents cache pollution from infrequently-read write-heavy data.
Refresh-Ahead	`if access_count > threshold:` `async_refresh(key)`	• Proactively refreshes entries before expiration based on access patterns • eliminates cache-miss latency for frequently accessed data. Requires accurate prediction of hot keys.

Table 2: Cache Invalidation Strategies

Getting invalidation wrong is the most common source of correctness bugs in caching systems. The right strategy balances freshness, consistency guarantees, and operational complexity for your specific data volatility.

Strategy	Example	Description
TTL (Time-To-Live)	`cache.set(key, data, ttl=3600)`	• Entry expires after a fixed time period. Simplest approach — requires no coordination, but may serve stale data until expiry • Use jitter (`ttl + random(0,60)`) to distribute expirations

The rest of this section is part of the full cheat sheet

Table 3: Cache Eviction Policies

Eviction policy determines which entries are displaced when the cache fills up. The right choice depends heavily on your access pattern: temporal locality (recency) favors LRU, skewed popularity favors LFU, and modern web cache workloads (dominated by one-hit wonders) favor newer algorithms like SIEVE or S3-FIFO.

Policy	Example	Description
LRU (Least Recently Used)	`redis-cli CONFIG SET maxmemory-policy allkeys-lru`	• Evicts the least recently accessed items first. Best for temporal locality — workloads where recently accessed items are likely to be reused soon • Most widely deployed eviction policy

The rest of this section is part of the full cheat sheet

Table 4: Distributed Caching Architecture

Distributed caches introduce topology and consistency decisions that single-node caches avoid entirely. Understanding partitioning, replication, and coherence is essential before deploying a cache cluster at scale.

Concept	Example	Description
Distributed Cache Cluster	Redis Cluster, Memcached cluster	Cache data partitioned across multiple nodes. Provides horizontal scalability and higher aggregate throughput than any single-node cache.

The rest of this section is part of the full cheat sheet

Table 1: Core Caching Patterns

Pattern	Example	Description
Cache-Aside (Lazy Loading)	`data = cache.get(key)` `if not data:` `data = db.query()` `cache.set(key, data)`	• Application checks cache first on read • on miss, loads from DB and populates cache. Most common pattern — gives full application control over caching logic.
Read-Through	`data = cache.get(key)`	• Cache itself handles DB fetch on miss • application treats cache as primary data source. Centralizes cache logic and simplifies application code.
Write-Through	`cache.set(key, data)` `db.write(data)`	• Every write goes to both cache and DB synchronously • ensures strong consistency but adds latency to writes. Best for read-heavy workloads needing fresh data.
Write-Behind (Write-Back)	`cache.set(key, data)` `queue_db_write(data)`	• Writes go to cache immediately; DB updated asynchronously in background • maximizes write performance but risks data loss if cache fails before flush.
Write-Around	`db.write(data)` `# cache not populated`	• Writes go directly to DB, bypassing the cache entirely • cache populated lazily on subsequent reads. Prevents cache pollution from infrequently-read write-heavy data.
Refresh-Ahead	`if access_count > threshold:` `async_refresh(key)`	• Proactively refreshes entries before expiration based on access patterns • eliminates cache-miss latency for frequently accessed data. Requires accurate prediction of hot keys.

Table 2: Cache Invalidation Strategies

Strategy	Example	Description
TTL (Time-To-Live)	`cache.set(key, data, ttl=3600)`	• Entry expires after a fixed time period. Simplest approach — requires no coordination, but may serve stale data until expiry • Use jitter (`ttl + random(0,60)`) to distribute expirations

Table 3: Cache Eviction Policies

Policy	Example	Description
LRU (Least Recently Used)	`redis-cli CONFIG SET maxmemory-policy allkeys-lru`	• Evicts the least recently accessed items first. Best for temporal locality — workloads where recently accessed items are likely to be reused soon • Most widely deployed eviction policy

Table 4: Distributed Caching Architecture

Concept	Example	Description
Distributed Cache Cluster	Redis Cluster, Memcached cluster	Cache data partitioned across multiple nodes. Provides horizontal scalability and higher aggregate throughput than any single-node cache.