How does Caching on the Backend work? (System Design Fundamentals)

Summary

This video dives into the complexities of backend caching, explaining its importance for performance optimization in systems with millions of users. It covers key topics like caching strategies, invalidation, eviction policies, and when caching may not be necessary. The speaker demonstrates how caching with Redis can reduce latency and database load while enhancing user experience. It also highlights common pitfalls like stale data and provides strategies for managing cache consistency. The tutorial offers practical insights for developers to effectively implement caching in complex architectures using tools like Node.js, Redis, and Memcached.

Takeaways

• ⚡ Caching is essential in large-scale systems to reduce latency, reduce database load, and improve user experience.
• 🧠 Cache invalidation is one of the hardest problems in computer science because data can become stale if not managed properly.
• 🏗️ There are multiple cache layers such as client-side, CDN, web server, database, and application-level caching—with application caching being the most impactful for developers.
• 🌍 Caches can be global (shared across servers) or distributed (multiple cache nodes with replication), improving performance but adding complexity in synchronization.
• 🔄 Common caching strategies include Cache Aside (lazy loading), Write-Through (proactive), and Write-Back (asynchronous), each with trade-offs between performance and consistency.
• 📉 Eviction policies such as LRU (Least Recently Used), LFU (Least Frequently Used), FIFO (First In First Out), and Random Replacement help manage limited memory and remove stale or infrequently accessed data.
• ⏳ Time-to-Live (TTL) and background cache repair strategies ensure that cached data does not become outdated over time.
• 🛠️ Redis is highly recommended for backend caching due to its speed, robustness, and built-in features like TTL and distributed cache support.
• 🚫 Caching should not be used when data is highly random, frequently updated, or when caching provides no measurable performance benefit.
• 📊 Effective caching can reduce infrastructure costs by lowering database queries and network bandwidth usage, especially at scale.
• 🧪 The script demonstrates a practical implementation using Node.js, Redis, and API requests, showing how cache hits significantly improve responsiveness.

Q & A

What is the main challenge discussed in the video related to caching?

The main challenge is cache invalidation, which is one of the most difficult aspects of caching in complex systems. Ensuring that the cache remains up-to-date without serving stale data is crucial.

How does caching help improve system performance?

Caching improves system performance by reducing latency, decreasing database load, and saving network costs. Cached data can be retrieved faster than making a database request, thus optimizing the user experience and reducing operational costs.

What are some common types of caches mentioned in the video?

The video mentions several types of caches: client-side caching (e.g., in mobile apps or web browsers), CDN caching (for static assets), web server caching (e.g., Varnish), and database caching (e.g., built-in caches in MySQL). The focus, however, is on application-level caching using tools like Redis.

What is the difference between a global cache and a distributed cache?

A global cache is a shared cache that is accessed by multiple servers or nodes in a system, whereas a distributed cache has multiple cache nodes, each communicating with specific servers or microservices, optimizing the architecture by distributing the cache load.

What are the pros and cons of the 'cache-aside' caching strategy?

The 'cache-aside' strategy (also called lazy loading) has the advantage of keeping the cache slim, storing only necessary data. However, it may result in an initial cache miss, causing extra trips to the database and slower responses during the first request.

What is 'write-through caching' and what are its benefits and drawbacks?

'Write-through caching' updates the cache automatically every time data is written to the database. It ensures that the cache is always in sync with the database, reducing database reads. However, it can result in a larger cache with less frequently accessed data stored, which is inefficient for certain systems.

How does 'write-back caching' work, and what are the challenges associated with it?

'Write-back caching' performs writes to the database asynchronously, allowing for some delay. This improves performance by reducing the number of writes to the database but can lead to synchronization issues if the cache and database become out of sync, especially in a multi-node system.

What are some cache eviction policies discussed in the video?

The video mentions several cache eviction policies, including Least Recently Used (LRU), Least Frequently Used (LFU), and First In, First Out (FIFO). These policies help decide which data to remove from the cache when it reaches its limit.

What is the 'LRU' eviction policy and how does it work?

The Least Recently Used (LRU) eviction policy removes the least recently accessed data from the cache when space is needed for new data. It prioritizes keeping data that is frequently accessed, making it a common choice for mobile and web applications.

When should you avoid using caching in a system?

Caching should be avoided when the data is highly random or frequently updated, as it would result in unnecessary complexity and overhead. Additionally, if adding a cache doesn't result in noticeable performance improvements, it’s best to omit it. Use cases like live streams with random comments are also not suitable for caching.