1. Database Caching And Its Key Concepts

Types Of Data Caching

In-Memory Caching: In-memory caching involves storing data in the server's RAM. It is speedy because accessing RAM is much quicker than disk storage or eternal storage fetches. Database caching techniques like Redis and Memcached are popular choices for in-memory cache solutions.

Query Caching: Query caching stores the results set in the query; when an identical query is received, the cache can serve the results without re-executing the query against the database. However, this strategy is very effective when the data changes frequently, and the cache must be invalidated and often prefetched.

Distributed Caching: A distributed cache spreads the cache across multiple machines or nodes to allow data for grander scale and resilience. This type is mainly used for traffic applications that require a large amount of storage to maintain cache availability, even if one or two nodes fail.

Performance Improvement: The main advantage is the reduction in response time for data retrieval, where data can be accessed easily in the database without any delay.

Scalability: Caching helps manage the increased load without proportionally increasing the database load.

Cost Efficiency: Reduce the need for additional database resources and infrastructures.

Cache Aside: In this technique, the application is responsible for the cache. Whenever the data is requested, the application first checks the cache; if the data is available, then it simply returns it; otherwise, data is retrieved from the database and stored in the cache for future usage.

Read Through: In the read-through approach, the cache is positioned between the application and the database. If the data request comes, the application first goes to the cache. If data is found, it returns so it can be fetched from the database. In this technique, the cache is responsible for fetching data from the database.

Write Through: In this technique, the application writes the data to the cache and database simultaneously. If any data is added or updated, it will be written in the cache and database at the same time, which reduces read delays and slows down the write operations because of double-time work. Read and write caching is mainly used to improve application performance.

Write Back: In the write-back technique, the application directly writes the data to the cache first, and then, after some delay, the data is written in the database. This process ensures the data is present in the cache so that it is easily accessible. This technique is suitable for applications with heavy written loads. It reduces data loss when it fails before writing in the database.

Write Around: This method combines both the read-through method and the cache aside method. The application always writes the data into the database and reads the data from the cache. This read-and-write caching method ensures data fetching details and retrieving details in the database. This strategy works best when the data is rarely updated and read in very often.

Time Base Eviction: Time-based eviction is straightforward, removing data from the cache after the specified duration. This approach is simple to implement but might not always reflect the current state of the database if data changes before the expiration time.

Event-Driven Invalidation: Event-based invalidation removes or updates the cache data when a specific event occurs, such as updates or deletions in the database. This strategy aims to keep the cache as fresh as possible.

Read Heavy Applications: Applications like news websites where the content changes infrequently, but the read frequently benefits immediately from the caching.

Session Storage: Storing session information in a cache can significantly reduce the databases laid for websites with many concurrent users.

E-Commerce Platforms: Caching product information, prices, and availability can improve the responsiveness of online shopping experiences.

Cache invalidation: Determining when and how to invalidate the fresh data is complex but crucial for maintaining data consistency.

Memory management: Especially managing the cache memory to avoid running out of space while maximising the cache hits is a delicate balance.

Complexity: Implementing and maintaining the caching logic adds complexity to the application architecture.

Identification Of Cacheable Data: The first step in implementing the effective caching strategy is identifying which data benefits most. In the fetching process, not all the data is suitable for caching; data that doesn't change frequently but is often an ideal position in a database. When identifying the cacheable data, consider the read-and-write caching ratio. The high read-write ratio indicates that it is accessed frequently in the database and that it is prime usage for fetching.

Measurement And Monitoring Cache Performance: To ensure your caching strategy delivers the intended benefits, it's crucial to measure and monitor its performance to implement the metrics of data. If it occurs, data is available in case fetches, and if a failure occurs, data must be fetched from the primary database; it indicates the best optimization cache performance in the application.

Tips for students to achieve speed and scalability in applications

Students should choose the right caching strategy.

Use cache invalidation effectively.

Choose the right cache tool for better optimisation of cache performance.

Redis: This tool is a source memory storage system that supports an advanced data structure in software applications, and it helps fetch data for future uses.

Mem Cached: This tool is a lightweight storage access tool, most used in performance applications to boost the data efficiency used to achieve the data needed in applications

CDN For Static: This tool is used in networks to support static data, such as image scripts, and offer access. Primarily, this data is used to help improve the cache optimisation performance in many caching systems.

Wrapping Up!