Caching in Backend Systems — A Practical Guide for Developers
Modern backend systems are built around performance and scalability.
As applications grow, two problems quickly appear:
repeated heavy computations
repeated retrieval of the same data
If every request recomputes everything or fetches data from slow storage, systems quickly become slow and expensive to run.
This is where caching becomes one of the most important techniques in backend engineering.
In this article we will explore:
what caching actually means
where caching is used in real systems
network-level caching (CDN, DNS)
hardware-level caching
software-level caching (Redis, Memcached)
caching strategies
cache eviction policies
real backend use cases
The goal is to understand caching as a backend engineer, not as a theoretical concept.
What Is Caching?
In the simplest terms:
Caching is storing frequently used data in a faster location so it can be retrieved quickly instead of recomputing or fetching it again.
A slightly more technical way to think about it:
You have primary data storage (database, disk, external API).
You keep a subset of that data somewhere faster to access.
When a request comes, you check the fast storage first.
If the data exists there, you return it instantly.
This reduces:
computation time
database load
response latency
Caching is one of the main reasons why large-scale applications remain fast even under millions of requests.
Why Caching Matters in High-Performance Systems
In high-performance applications, engineers often measure latency in:
microseconds
milliseconds
Even saving 10–20 milliseconds per request can dramatically improve system performance.
Caching helps achieve this by avoiding:
heavy computations
large data transfers
repeated database queries
Many of the systems you use every day rely heavily on caching.
Let’s look at a few real-world examples.
Example 1 — Google Search
When you search something like:
weather today
Google must:
crawl billions of web pages
analyze indexes
rank results
This computation is extremely expensive.
If Google recomputed this for every request, servers would be overwhelmed.
Instead, Google caches results of frequently searched queries.
Workflow:
User searches a query
System checks cache
If result exists → return instantly (cache hit)
If not → compute results and store them (cache miss)
This dramatically reduces:
computation
server load
latency
Example 2 — Netflix Streaming
Netflix streams huge amounts of data globally.
Movies are stored in multiple resolutions:
1080p
720p
480p
Instead of sending all video traffic from a single central server, Netflix uses Content Delivery Networks (CDNs).
A CDN stores copies of content in servers located around the world.
When a user requests a video:
Request goes to the nearest server
That server checks if the content is cached
If cached → serve immediately
If not → fetch from origin server and cache it
This dramatically reduces:
buffering
network latency
server load
Example 3 — Trending Topics on Social Media
Platforms like X (Twitter) analyze millions of tweets to detect trending topics.
This computation is extremely expensive.
If it ran every time a user opened the trending page, the system would collapse.
Instead:
Twitter periodically computes trending topics.
The results are cached.
Users receive cached results instantly.
Since trends don’t change every second, caching works perfectly.
A Pattern Behind Caching
Across these examples, caching appears when:
Heavy Computation Exists
Examples:
ranking algorithms
machine learning predictions
analytics queries
Large Data Must Be Delivered Frequently
Examples:
videos
images
web assets
Whenever we want to avoid repeating expensive work, caching becomes the solution.
Levels of Caching
In backend systems, caching generally appears at three levels:
Network-level caching
Hardware-level caching
Software-level caching
Network-Level Caching
Two major examples:
CDN caching
DNS caching
CDN (Content Delivery Network)
CDNs cache content in servers located around the world.
These servers are called edge servers.
Instead of every request going to the origin server, requests are served from the nearest edge location.
Typical flow:
User requests a resource
DNS resolves the domain
Request is routed to the nearest CDN server
Server checks cache
Two possibilities:
Cache Hit
Resource exists in cache → returned instantly.
Cache Miss
Edge server fetches resource from origin server and caches it.
CDNs also use TTL (Time To Live).
TTL defines how long cached data remains valid.
Example:
Cache image for 6 hours
After expiration, new requests fetch fresh data.
DNS Caching
DNS converts domain names into IP addresses.
Example:
example.com → 93.184.216.34
Without caching, each lookup would require contacting multiple DNS servers.
Instead, DNS caching exists at multiple layers:
Operating System cache
Browser cache
ISP resolver cache
Authoritative server cache
Because of caching, DNS lookups are usually extremely fast.
Hardware-Level Caching
Caching also exists inside the computer hardware itself.
Memory hierarchy:
CPU registers
L1 cache
L2 cache
L3 cache
RAM
Disk storage
The closer the memory is to the CPU, the faster it is but the smaller it becomes.
For example:
| Memory Type | Speed | Size |
|---|---|---|
| CPU cache | Extremely fast | Very small |
| RAM | Fast | Moderate |
| Disk | Slow | Very large |
Processors automatically cache frequently accessed data to reduce computation time.
Why RAM Is Used for Software Caches
Technologies like Redis store data in RAM.
RAM is faster than disk because:
disk access involves mechanical operations (especially HDDs)
RAM uses electrical signals
However RAM has two limitations:
limited capacity
volatile (data disappears when power is off)
Because of this, RAM cannot replace disk storage.
Instead, RAM is used for temporary fast-access storage — caching.
Software-Level Caching
In backend development, caching is typically implemented using in-memory databases.
Popular technologies include:
Redis
Memcached
AWS ElastiCache
These are often called:
In-memory key-value databases
Why They Are Called Key-Value Stores
Unlike relational databases with tables and schemas, these systems store data as:
key → value
Example:
user:123 → {name:"Alice", age:24}
Values can be:
strings
JSON
lists
sets
counters
This simplicity makes them extremely fast.
Caching Strategies
Two common caching strategies exist.
1. Lazy Caching (Cache Aside)
In this strategy, data is cached only when requested.
Flow:
Client requests data
Check cache
If present → return
If absent → fetch from database
Store in cache
Return result
This is the most common caching strategy.
2. Write-Through Caching
In this strategy, whenever data changes:
Database is updated
Cache is updated immediately
This ensures cache is always fresh.
Tradeoff:
writes become slightly slower
reads remain extremely fast
Cache Eviction Policies
Cache memory is limited.
Eventually, the cache becomes full.
When this happens, the system must decide which data to remove.
This decision process is called the eviction policy.
LRU (Least Recently Used)
Remove the item that hasn't been accessed recently.
Example:
Cache: A B C D
New item: E
If A was accessed longest ago, it is removed.
LFU (Least Frequently Used)
Remove the item accessed the least number of times.
Example:
A → accessed 10 times
B → accessed 2 times
C → accessed 5 times
B gets removed.
TTL (Time To Live)
Each cached item has an expiration time.
Example:
cache weather data for 1 hour
After 1 hour, the entry is automatically removed.
Database Query Caching
One of the most common backend use cases.
Imagine a complex SQL query:
SELECT u.name, SUM(o.amount)
FROM users u
JOIN orders o ON u.id = o.user_id
GROUP BY u.name;
If this query runs frequently, it may:
consume CPU
slow down the database
Solution:
Run query once
Store result in Redis
Serve future requests from cache
Product Page Caching (E-Commerce)
Platforms like Amazon cache product information.
Example data:
product title
images
price
description
These rarely change.
Without caching:
millions of users hitting the same product page
millions of database queries
With caching:
one database query
millions of cache reads
Huge performance improvement.
User Profile Caching
Social media platforms cache user profiles.
Example:
username
profile photo
bio
These values rarely change but are read frequently.
Caching prevents constant database access.
Session Storage
When users log in, applications generate session tokens.
These sessions must be validated on every request.
Instead of querying the database every time, session tokens are stored in Redis.
Benefits:
faster authentication
reduced database load
API Response Caching
Sometimes your backend depends on external APIs.
Example:
Weather API
Calling the API repeatedly can cause:
high latency
rate limit errors
higher cost
Solution:
Cache the API response.
Example:
cache weather data for 1 hour
Future requests use cached data.
Rate Limiting Using Redis
Rate limiting protects APIs from abuse.
Example rule:
max 50 requests per minute per IP
Implementation:
Extract IP from request
Store counter in Redis
Example key:
ip:192.168.1.1 → 27 requests
Each request increments the counter.
If it exceeds the limit:
HTTP 429
Too Many Requests
Redis is ideal here because:
extremely fast
minimal latency
avoids database overload
This is part of the series Backend First Principles.Next: Background Jobs in Backend Systems.