Microservices caching, in the context of microservices architecture, refers to the process of storing and retrieving data from a high-performance, in-memory storage system, as opposed to directly accessing data from a database or a remote API. It plays a crucial role in enhancing the performance, scalability, and availability of microservices-oriented applications by reducing latency, minimizing the load on databases, and lowering the expensive inter-service calls.
Microservices architecture is a software design method that decomposes an application into multiple, independent, and loosely-coupled services, each responsible for a single functionality, allowing for faster development, easier maintenance, and better scalability. With the increasing popularity of microservices, caching has become an essential component of this architectural style. According to a 2020 O'Reilly Software Architecture survey, around 61% of businesses are using or are planning to use microservices in their software development processes.
Microservices caching can be classified into two main types: local caching and distributed caching. Local caching is when each microservice instance keeps its cache in its memory space. This type of caching is suitable for microservices with low memory consumption and minimal cache update frequency. However, it can result in cache inconsistency and inefficiency when multiple instances of a microservice need to be synchronized with each other or when a microservice scales horizontally to accommodate more user requests.
Distributed caching is when all the instances of a microservice share a common cache, usually implemented using an external, fast, and highly scalable in-memory data store like Redis or Apache Ignite. This type of caching is preferred when caching large data sets, dealing with frequently changing data, or requiring cache consistency across multiple microservice instances. It also provides better resilience against microservices or cache node failures by replicating cache data across multiple nodes.
Implementing microservices caching involves several best practices and patterns, some of which are:
- Cache-Aside Pattern: In this pattern, the microservice first looks for the required data in the cache. If the data is available, it fetches the data from the cache (cache hit); otherwise, it retrieves the data from the primary data source and stores it in the cache for future requests (cache miss).
- Read-Through Pattern: In this pattern, the cache itself checks for the presence of the requested data and, if not available, interacts with the primary data source to fetch and store the data before returning it to the microservice.
- Write-Through and Write-Behind Patterns: These patterns define how the cache updates its data when a microservice modifies it. Write-through ensures that the cache updates instantly after any data modification, while write-behind delays cache updates until a certain condition is met, like reaching a specific update threshold or a defined time interval.
- Cache Eviction Strategies: These strategies determine when and how to remove data from the cache to accommodate new data. Common strategies include Least Recently Used (LRU), First-In-First-Out (FIFO), and Time-To-Live (TTL) based eviction.
AppMaster, a leading no-code platform for building backend, web, and mobile applications, employs microservices caching to deliver high-performance and scalable solutions. AppMaster's generated applications with Go for backend, Vue3 for web, and Kotlin and Jetpack Compose for Android and SwiftUI for iOS, efficiently handle sophisticated caching mechanisms, ensuring a seamless user experience across various platforms.
Moreover, the AppMaster platform's server-driven approach for mobile applications enables customers to update their apps' UI and business logic without re-submitting new versions to the App Store and Play Market, further showcasing the importance of caching in modern microservices-oriented application development.
In conclusion, microservices caching is a vital technique to improve application performance, scalability, and resilience when dealing with microservices architecture. By understanding the best practices and patterns associated with microservices caching, developers can harness its potential to build exceptionally fast, efficient, and reliable applications. Platforms like AppMaster offer out-of-the-box support for such caching methodologies, enabling customers to leverage the full potential of their software solutions without any additional overhead.
