Caching stores the results of expensive operations so repeated requests get instant responses instead of re-computing. It reduces API costs, database load, and response times by serving saved answers for identical queries. For businesses, this means faster systems and lower operational costs. Without it, you pay in time and money for every redundant request.
Every API call costs money. Every database query takes time.
Yet you run the same lookups hundreds of times a day.
The answer was the same an hour ago. It will be the same an hour from now.
Stop computing what you already know.
Part of the Orchestration Layer
Caching stores the results of expensive work so you do not repeat it.
Every time you look up a customer profile, query pricing data, or call an external API, you spend resources. Time, money, compute. For data that rarely changes, spending those resources repeatedly is pure waste.
Caching creates a fast-access copy of results. When the same request arrives, the system checks the cache first. Hit? Instant response. Miss? Do the work, then store the result for next time.
The trick is knowing what to cache, how long to keep it, and when to throw it away. Cache the wrong things and you serve stale data. Cache too little and you miss the performance gains. Cache just right and your systems feel instantaneous.
Caching is not about storing everything - it is about storing the right things for the right duration.
Do expensive work once, reuse the result many times.
When a request arrives, check if the answer already exists in fast storage. If yes, return it immediately. If no, compute the answer, store it for future requests, then return it.
Adjust the request volume and TTL duration. Watch how cache hit rate changes and costs spike or plummet.
Three approaches to caching, each with different trade-offs.
Cache expires after a fixed duration
Set a TTL when storing data. After time passes, the cache entry is considered stale. Simple to implement and understand. Works well when you can tolerate bounded staleness.
Cache clears when source data changes
Subscribe to change events. When source data updates, immediately invalidate affected cache entries. More complex but ensures freshness. Requires event infrastructure.
Cache updates happen alongside source updates
When data is written, update both the source and the cache in the same operation. Cache is always current. Adds write latency but eliminates stale reads entirely.
Answer a few questions to find your best caching strategy.
How critical is data freshness for your use case?
A manager opens the sales dashboard. The system queries five databases, calls two external APIs, and runs complex aggregations. Every. Single. Time. With caching, the first load does the work. The next 49 people today see instant results.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
Easy to add caching; hard to know when to clear it. Without a plan, caches grow stale and users see outdated data. The cache becomes a liability rather than an optimization.
Instead: Define invalidation rules before adding the cache. Every cache entry should have a clear expiration trigger: time-based, event-based, or both.
Cache key collision causes User A to see User B data. One of the most severe caching bugs, causing privacy violations and data leakage. Happens when user ID is forgotten in cache key.
Instead: Always include user identifier in cache keys for user-scoped data. Use structured key formats like "user:{id}:profile" that make scoping obvious.
Not all data benefits from caching. Highly personalized content, real-time data, and low-frequency queries may not justify cache complexity. Caching everything leads to cache bloat and management overhead.
Instead: Profile before caching. Identify high-frequency, expensive, stable queries. Cache those first. Leave dynamic or rare queries uncached.
When a popular cache entry expires, hundreds of requests simultaneously trigger the expensive operation. System overloads at exactly the worst moment. Happens with hot keys and synchronized TTLs.
Instead: Use jittered TTLs so expirations spread over time. Implement cache locking so only one request regenerates while others wait. Pre-warm critical entries before expiration.
Caching temporarily stores the results of expensive operations like database queries, API calls, or computations. When the same request comes again, the system returns the cached result instead of re-executing the operation. This dramatically reduces response times and resource consumption for frequently accessed data.
Use caching when the same data is requested repeatedly, when generating that data is expensive (slow API, complex query, heavy computation), and when data does not change frequently. Good candidates include user profiles, product catalogs, search results, and computed reports. Poor candidates include real-time data or highly personalized content.
Cache invalidation is the process of removing outdated cached data when the source changes. It is notoriously difficult because you must track what depends on what and update caches at the right time. Invalidate too early and you lose performance benefits. Invalidate too late and users see stale data. Most caching bugs are invalidation bugs.
In-memory caching stores data in a single server RAM, offering fastest access but limited by machine memory and lost on restart. Distributed caching like Redis spreads data across multiple machines, surviving restarts and scaling horizontally. Choose in-memory for single-instance apps, distributed for multi-server deployments or data that must persist.
Many APIs charge per request. If you call a pricing API 1000 times for the same product, you pay 1000 times. With caching, the first call is stored and subsequent identical requests serve from cache at zero API cost. For high-volume operations, caching can reduce API bills by 90% or more while improving response speed.
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Choose the path that matches your current situation
You have no caching in place.
You cache some things but not systematically.
You have solid caching but want more performance.
You now understand how caching speeds up repeated operations. Next, learn how to manage the broader state that caching is part of.