When you build a full-stack application, one of the most important goals is to make it fast and responsive. Users don’t like waiting, and even a delay of a few seconds can make them leave your app. One way to make your application faster is by using distributed caching.
Distributed caches help reduce the time it takes to get data from your server or database. Instead of loading everything from the database every time, a cache stores frequently used data in memory. This makes your app much faster and more efficient.
In this blog, we will describe what distributed caching is, how it works, why it’s useful, and how to use it in your full-stack apps. If you’re learning through a full stack developer course in Hyderabad, this is an important topic to master for real-world development.
What Is a Distributed Cache?
A cache is a storage layer that saves data temporarily so it can be accessed. A distributed cache is a cache that is transmitted across multiple servers or machines.
This is different from a local cache, which is stored on just one server or device. Distributed caches are useful when your application runs on many servers and needs to share data between them.
Popular distributed caching systems include:
- Redis
- Memcached
- Hazelcast
- Apache Ignite
These tools store data in memory (RAM), which is much faster than reading from a hard disk or database.
Why Use Distributed Caching in Full-Stack Apps?
Full-stack apps often use a front-end framework like React or Angular and a back-end like Java, Node.js, or Python. The back-end talks to the database to get or save data. This process can be slow if done many times.
With caching, you reduce how often your back-end talks to the database. This speeds up your application.
Benefits of distributed caching:
- Faster data access
- Reduces load on the database
- Improves user experience
- Supports scalability for large apps
- Works well in cloud and multi-server environments
Many companies use Redis or Memcached in their systems. That’s why caching is often taught in advanced modules of a developer course.
How Does a Distributed Cache Work?
Let’s understand how it works with a simple example.
Imagine you have a web app that shows user profiles. Every time a user logs in, your server fetches their profile from the database. If thousands of users log in, your database can get overloaded.
With a cache:
- The first time a profile is requested, the app fetches it from the database and saves it in the cache.
- The next time, the app checks the cache first.
- If the profile is in the cache, it sends the data from the cache instead of going to the database.
- If not, it fetches from the database again and updates the cache.
This process is called cache lookup and cache update.
Types of Data You Can Cache
You can cache many types of data, such as:
- User sessions
- Product information
- Search results
- API responses
- Home page content
- Frequently accessed records
But be careful not to cache private or sensitive data unless it’s safe to do so.
Common Caching Strategies
To use caching properly, developers follow different strategies. Some of the most popular are:
1. Cache Aside (Lazy Loading)
In this strategy:
- The app checks the cache first.
- If data is not found, it loads from the database.
- Then it stores the data in the cache for next time.
This is the most common method and is easy to use in full-stack apps.
2. Write Through
In this method:
- Data is written to the cache and the database at the same time.
- This keeps both systems in sync.
This is useful when data changes often and needs to be available quickly.
3. Write Behind
Here:
- Data is written to the cache first.
- The cache then updates the database in the background.
This improves performance but is more complex to manage.
4. Time to Live (TTL)
All cache entries can have a TTL or expiration time. After that time, the data is removed from the cache. This keeps data fresh and avoids using old information.
Setting Up Redis Cache in a Java Full Stack App
If you’re using Java in your back-end, Redis is a great choice for caching.
Step 1: Install Redis
You can install Redis locally or use a cloud-based service like Redis Enterprise or AWS ElastiCache.
Step 2: Add Redis Dependency in Java Project
If you’re using Spring Boot, add the Redis starter to your pom.xml:
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-redis</artifactId>
</dependency>
Step 3: Configure Redis
In your application.properties:
spring.redis.host=localhost
spring.redis.port=6379
Step 4: Enable Caching
In your main application file:
@SpringBootApplication
@EnableCaching
public class MyApp {
public static void main(String[] args) {
SpringApplication.run(MyApp.class, args);
}
}
Step 5: Add Caching to a Method
Use the @Cacheable annotation:
@Cacheable(value = “users”, key = “#id”)
public User getUserById(String id) {
return userRepository.findById(id).orElse(null);
}
This saves the result in Redis the first time and fetches it from the cache after that.
Redis is a very useful tool and is commonly used in projects done during a developer course in Hyderabad.
Caching in Front-End Applications
Caching is not just for back-end. You can also cache data in the browser.
Common Front-End Caching Tools:
- Service Workers: Store content offline for Progressive Web Apps (PWAs)
- LocalStorage/SessionStorage: Save user settings or session data
- React Query / SWR: Cache API responses in React apps
Example using React Query:
const { data, isLoading } = useQuery(‘users’, fetchUsers);
This will automatically cache the response and reuse it the next time.
This improves speed, especially when users switch between pages.
Best Practices for Using Distributed Caches
- Always check if the data is already in the cache before hitting the database.
- Set proper TTL to keep data fresh.
- Avoid caching sensitive user data unless necessary.
- Monitor cache performance and hit ratio.
- Don’t overuse cache only use it for frequently accessed data.
- Handle cache misses and errors gracefully.
- Keep cache keys simple and organized.
When Not to Use Caching
Caching is not always the answer. Avoid using it when:
- Data changes very frequently
- You need real-time accuracy
- You are handling sensitive or secure data
- Your app is very small and doesn’t need optimization
Using caching at the wrong time can create bugs and confusion.
Final Thoughts
Distributed caching is a powerful way to speed up your full-stack applications. It helps ease the load on your database and gives users a faster experience.
You learned:
- What distributed caching is
- How Redis and other tools work
- Common caching strategies
- How to use Redis with Java
- Front-end caching options
- Best practices and tips
Whether you’re working on a big project or just starting, caching is an essential skill for modern developers. If you’re studying in a Java full stack developer course make sure to practice caching in your projects.
Start simple, learn step by step, and use caching to make your apps fast and efficient. It’s one of the easiest ways to improve performance and deliver a great user experience.
Contact Us:
Name: ExcelR – Full Stack Developer Course in Hyderabad
Address: Unispace Building, 4th-floor Plot No.47 48,49, 2, Street Number 1, Patrika Nagar, Madhapur, Hyderabad, Telangana 500081
Phone: 087924 83183
