Caching Design Patterns
Let’s illustrate cache design patterns using problem statement, their definitions, and how each pattern can be applied to solve these problems:
Problem Statement
Imagine an e-commerce platform that frequently displays product information, including images and prices, to users. Retrieving this information from the database each time a user visits a product page is slow and resource-intensive, leading to poor user experience.
1. Cache-Aside (Lazy Loading) Pattern
Definition: Cache-Aside is a pattern where the application first looks or data in the cache. It the data isn’t found, it fetches it from the main source, stores it in the cache, and returns it to the user.
Application: In the e-commerce scenario, the product details, images and prices can be stored in the cache. When a user requests this information, the application checks the cache. If the information is present, it’s served from the cache directly. If not, it’s fetched from the database, stored in the cache, and served to the user. Subsequent requests for the same product fetch the data from the cache, improving response time.
2. Write-Through Pattern
Definition: Write-Through pattern ensure that whenever new data is added or updated, it’s written to both the main storage and the cache, keeping them synchronized.
Application: When a new product is added or the like price changes, the system updates the database. The same change is also written to the cache immediately. This way, when users ask for the updated product detail, they get the latest information directly from the cache without needing to access the database, enhancing speed and consistency.
3. Write-Behind (Write-Back) Pattern
Definition: Write-Behind involves first writing to new or updated data to the cache, and then updating the main storage at a later, more convenient time.
Application: In the e-commerce situation, if there’s a sudden surge in product views and updates, the system writes these changes into the cache immediately. Then, instead of slowing down the user experience by instantly updating the database, it batches these changes and updates the main storage at intervals. This pattern improves speed by allowing the system to manage heavy traffic without immediately updating the main source.
4. Cache Invalidation Pattern
Definition: Cache Invalidation ensures that when data in the main source changes, related information in the cache is removed to present the use of outdated or incorrect data.
Application: In the e-commerce platform, if a product’s price or availability changes, the system triggers a cache invalidation process for that specific product. This ensures that the the outdated product information is removed from the cache, prompting the system to fetch the updated details from the database and refresh the cache with current information. Users always receive the most recent product data.
5. Refresh-Ahead Pattern
Definition: Refresh-Ahead Pattern involves proactively updating data in the cache before it becomes stale or outdated.
Application: In the e-commerce context, the system might predict high traffic for certain products during a sale event. Using the Refresh-Ahead pattern, the platform preloads and updates the cache with information about these products in advance, ensuring that product details are readily available. This avoids potential delays caused by a sudden influx of users and keeps the cache up-to-date before it’s actually needed.
6. Read-Through Pattern
Definition: Read-Through Pattern retrieves data from the cache. If the data isn’t present, it automatically fetches it from the main storage and adds it to the cache.
Application: In the e-commerce system, when a user requests product information, the Read-Through Pattern checks the cache. If the data is there, it’s retrieved directly. If not, it automatically pulls the information from the database, adds it to the cache, and then serves it to the user. This ensures that subsequent requests for the same product data are served directly from the cache, improving overall response time.
Caching Design Pattern
In today’s digital world, speed and efficiency matter a lot. When we use apps and websites, we want things to happen quickly. But making applications run fast is a bit tricky. That’s where the caching design pattern comes in.
Imagine caching as a fast storage trick. It stores important data from apps in a special place so that the app doesn’t need to do the hard work repeatedly. Caching design patterns are clever ways to use this storage trick. They make apps quicker in various ways.
Important Topics for the Caching Design Pattern
- Understanding Caching
- Caching Design Pattern
- Caching Design Patterns
- Advantages of the Caching Design Pattern
- Disadvantages of the Caching Design Pattern
- Use Cases of Caching
- Caching Design Pattern example
- Conclusion