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SYSTEM DESIGN NEWSLETTER Subscribe Sign in Share this post TOP 5 CACHING PATTERNS newsletter.systemdesign.one Copy link Facebook Email Note Other DISCOVER MORE FROM SYSTEM DESIGN NEWSLETTER A weekly newsletter to learn system design. Over 29,000 subscribers Subscribe Continue reading Sign in TOP 5 CACHING PATTERNS #11: READ NOW - HOW TO UPDATE THE CACHE (3 MINUTES) NK Oct 5, 2023 43 Share this post TOP 5 CACHING PATTERNS newsletter.systemdesign.one Copy link Facebook Email Note Other 5 Share Get the powerful template to approach system design for FREE on newsletter sign-up: Subscribe -------------------------------------------------------------------------------- A cache is a temporary storage that provides quick access to frequently used data. And it’s usually stored in memory for low latency. But available memory is limited. So it’s important to update the cache the right way. I will teach you the popular caching patterns in this post. A cache hit occurs when queried data is available in the cache. And a cache miss occurs when queried data isn’t available in the cache. CACHING PATTERNS 5 popular caching patterns are: 1. CACHE ASIDE Cache Aside Pattern Here is the workflow: 1. Read data from the cache 2. Read data from the database on a cache miss 3. Write data to the cache 4. And return data It’s also called lazy loading. Because only the queried data gets cached. Also the cache doesn't interact with the database. But the app does. ADVANTAGES * Easy implementation * Finer control over cache population. Because only frequently accessed data is cached DISADVANTAGES * Degraded latency. Because there are many trips * Potential data inconsistency USE CASES * General purpose caching * Read-heavy workloads 2. WRITE THROUGH Write Through Pattern Here is the workflow: 1. Write data to the cache 2. And the cache writes data synchronously to the database The app doesn't interact with the database. But the cache does. ADVANTAGES * Data consistency * Low read latency. Because the cache contains fresh data DISADVANTAGES * High write latency. Because both cache and database need to be updated * Most data in the cache is never read USE CASES * A low number of writes expected * Data freshness is important 3. READ THROUGH Read Through Pattern Here is the workflow: 1. Read data from the cache 2. Read data from the database on a cache miss 3. Write data to the cache 4. And return data The app doesn't interact with the database. But the cache does. And this is what makes it different from the cache aside pattern. ADVANTAGES * Low read latency for frequently accessed data * Improved read scalability DISADVANTAGES * Potential data inconsistency. Because the cache might contain stale data * High latency on a cache miss USE CASES * Read-heavy workloads * A high cache miss rate is acceptable 4. WRITE BACK Write Back Pattern Here is the workflow: 1. Write data to the cache 2. And the cache writes data asynchronously to the database It’s also called the write-behind pattern. ADVANTAGES * Low write latency * Improved performance. Because writes to the database get batched DISADVANTAGES * Increased risk of data loss. Because the cache might fail before writing to the database * Complex implementation USE CASES * Write-heavy workloads * Data durability is not important 5. WRITE AROUND Write Around Pattern Here is the workflow: 1. Write data to the database 2. Read data from the cache 3. Read data from the database on a cache miss 4. And write data to the cache ADVANTAGES * Reduced risk of data loss * Reduced cache pollution. Because cache stores only frequently accessed data DISADVANTAGES * High read latency * High cache miss rate USE CASES * No data update expected * A low number of reads expected Redis and Memcached are popular cache implementations. And a combination of caching patterns is used in real-world systems. -------------------------------------------------------------------------------- Consider subscribing to get simplified case studies delivered straight to your inbox: Subscribe -------------------------------------------------------------------------------- Follow me on LinkedIn and Twitter -------------------------------------------------------------------------------- Thank you for supporting this newsletter. Consider sharing this post with your friends and get rewards. Y’all are the best. Share -------------------------------------------------------------------------------- 6 PROVEN GUIDELINES ON OPEN SOURCING FROM TUMBLR NK · October 1, 2023 Read full story THIS IS HOW STRIPE DOES RATE LIMITING TO BUILD SCALABLE APIS NK · September 28, 2023 Read full story And don’t forget to hit the Like button if you enjoyed this post ❤️ -------------------------------------------------------------------------------- * https://www.prisma.io/dataguide/managing-databases/introduction-database-caching * https://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast * https://www.alachisoft.com/resources/articles/readthru-writethru-writebehind.html 43 Likes · 3 Restacks 43 Share this post TOP 5 CACHING PATTERNS newsletter.systemdesign.one Copy link Facebook Email Note Other 5 Share Previous Next 5 Comments Share this discussion TOP 5 CACHING PATTERNS newsletter.systemdesign.one Copy link Facebook Email Note Other Duck Dickson Oct 9, 2023·edited Oct 9, 2023Liked by NK This is a great idea for an article but unfortunately the descriptions are not easy to read. For example, the workflow for Read-through is WORD FOR WORD identical to Cache Aside. The words are insufficient to explain what's going on, so is the diagram. Readers have to read both the words and the diagram to stitch together hat's going on. I would encourage rewriting these to explain in more detail. Expand full comment Like (2) Reply Share 2 replies by NK and others Anton Zaides Writes Leading Developers Oct 5, 2023Liked by NK Interesting! I've worked only with the Cache aside implementation, as it's the easier to maintain. Expand full comment Like (1) Reply Share 3 more comments... 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