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Message Queues (e.g., Kafka, RabbitMQ)Caching is a powerful strategy to speed up applications, reduce latency, and lessen the burden on backend systems like databases. Two of the most popular in-memory caching solutions used in system design are Redis and Memcached.
Caching stores frequently accessed data in memory (RAM) so that future requests for the same data can be served faster. Instead of going to a slower data store like a database, the application can quickly get the data from the cache.
Both Redis and Memcached offer lightning-fast data retrieval because they keep data in memory. This is crucial in high-scale systems like social media platforms, e-commerce sites, and search engines, where every millisecond matters.
Redis (REmote DIctionary Server) is an open-source, in-memory data structure store. It can be used as a cache, message broker, and even a lightweight database.
Imagine you're building a weather API service that returns the current temperature for a city. This data comes from a third-party service that charges money per call and is also relatively slow (around 2 seconds per request).
To reduce cost and speed up responses, you can use Redis:
// Pseudocode
if (redis.has("weather:delhi")) {
return redis.get("weather:delhi")
} else {
data = callWeatherAPI("delhi")
redis.set("weather:delhi", data, expiry=600) // Cache for 10 minutes
return data
}
Why is an expiry time set for the cached value?
Weather data changes frequently. Setting an expiry (TTL - Time to Live) ensures the cached value is not stale and is periodically refreshed from the source.
Memcached is another open-source, in-memory key-value store, optimized for simplicity and speed. It is widely used to cache small chunks of arbitrary data (like database query results, session tokens, etc.).
Suppose you're running an e-commerce site, and every time a user visits the product page, you query your database for product details. As traffic increases, the database load becomes a bottleneck.
With Memcached, you can cache query results:
// Pseudocode
if (memcached.has("product:123")) {
return memcached.get("product:123")
} else {
product = db.query("SELECT * FROM products WHERE id = 123")
memcached.set("product:123", product, expiry=300) // Cache for 5 minutes
return product
}
Why use Memcached instead of Redis in this case?
Memcached is lightweight and faster for simple key-value data. If you don’t need advanced data structures or persistence, Memcached is more memory-efficient and faster for this use case.
Feature | Redis | Memcached |
---|---|---|
Data Structures | Supports complex structures | Key-Value only |
Persistence | Yes | No |
Use Case | Advanced caching, message queues | Simple cache, session storage |
Memory Efficiency | Less efficient for large simple values | Highly efficient |
Multi-threading | Single-threaded | Multi-threaded |
Let's say you're storing user sessions (logged-in user data) temporarily in memory:
Can Redis and Memcached be used together?
Yes. Some systems use Memcached for fast, lightweight caching, and Redis for more persistent or complex needs like leaderboards or real-time chat systems.
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