Sumit Bhatnagar, VP at JPMorgan Chase, has 2 decades of expertise in innovation, sustainability, and leadership excellence.
Caching is vital in enhancing microservices’ performance and firmness. It is a technique in which data often and recently used is stored in a separate storage location for quicker retrieval from the main memory, known as a cache. If caching is incorporated correctly into the system architecture, there is a marked improvement in the microservice’s performance and a lessened impact on the other systems.
Types Of Caches
1. In-Memory Caches: Data is always kept in memory (RAM) for easy access to enhance efficiency during usage. Some examples of KEY_VALUE stores are Redis and Memcached.
2. Distributed Caches: Data is not grouped in one server but spread across many nodes, which makes it highly available and scalable. Some examples are VMware Gemfire, Hazelcast and Apache Ignite.
3. Local Caches: These are cached locally on the same instance of the service and are good for small-scale applications with few nodes.
Each caching level serves different purposes and is suitable for various scenarios within a microservices architecture.
Caching In Microservices
Improved Performance: Popular data is stored nearer to the client or in memory, as caching minimizes the time spent obtaining it.
Reduced Latency: Caching can reduce the HTTP request-response time to get data from distant servers. Microservices regularly require information from other sources (data repositories, legacy systems, etc.). Real-time calls to these sources may involve latency. Caching helps minimize the number of backend calls made by your application.
Decreased Load On Databases: It also reduces the number of read operations on the database while making the capacity of the database more available for write operations and others.
Enhanced Scalability: Issues related to caching are vital for microservices to be able to serve more concurrent requests to the system, improving its scalability.
Improved Availability: Thus, microservices help overcome the problem of intensive backend consumption by serving the cached data for concurrent requests.
Caching Strategies
Read-Through Cache: This cache is placed in front of the database to act as the part of the application that communicates with the rest of the system. In the request handling aspect, when there is a request for any data, the first point of call is the cache. If the data is not found there, a new data call is made to the database, and this data is then stored in the cache, ready for the next request.
Write-Through Cache: This allows for the cache and the database to be written simultaneously so that they are in harmony.
Write-Behind (Write-Back) Cache: Data is saved initially in the cache, and as a background process, it is replicated to the database. This increases write performance but requires extra care to maintain strong consistency.
Cache-Aside (Lazy Loading): The application code maintains and delegates a cache mechanism. On the other hand, the cache only loads data when it is demanded; if the data is not in the cache, it searches for it in the database and loads it to the cache.
Cache Eviction Policies
Storing a large amount of cache data makes the data remain for a long time, making it stale. Cache replacement policies mean that stale content is flushed from the cache to always be fresh. Cache eviction policies are also part of the caching systems that help regulate the effectiveness of caching and the system’s efficiency. They decide when data should be erased from the cache to create space for storing new data. Several cache eviction policies need to be in place to ensure the fact of maintaining only the most frequently used data in the cache at any given instance for efficiency and to avoid wastage of resources. Common eviction policies include:
1. Time-To-Live (TTL): Caching of data is done for a specific time.
2. Least Recently Used (LRU): When the cache reaches its maximum size, the least used items will be promptly removed.
3. Least Frequently Used (LFU): The cache removes those elements that have not been used for the longest time, so such cache forms are frequently referred to as Least Recently Used.
Conclusion
Caching is quite a helpful technique that positively influences the service quality and the work of microservices. In more detail, these opportunities can enhance the productivity of the microservice-based system and increase its performance maintained by caching on various levels supplemented with the coarse-grained caching methods and the adaptive caching model, along with the adequate eviction policies to apply. As the microservices architectures grow more sophisticated, caching remains essential for maintaining the required level of performance and customer satisfaction.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
