distributed caching in microservices

When your microservices start to be "forgetful", what can distributed caching do?

Have you ever experienced this? The system ran very fast in the morning, but in the afternoon, users had to wait several seconds to click a button. The data is obviously there, but every call is like starting the query from scratch, services are waiting for each other, and the entire system is as slow as taking a walk. This is not because the server is tired, but because it lacks a mechanism to keep data "on call at any time" - this is the problem that distributed caching wants to solve.

Think about it, if every microservice has to repeatedly ask the database for the same data, the database will quickly become a bottleneck. It's like a busy coffee shop. Every time a customer orders, the barista has to re-grind the coffee beans and boil the water again, instead of preparing several cups of common styles in advance.分布式缓存，简单说，就是在你的服务网络中，设置一些共享的、高速的临时存储点，把常用数据放在离服务更近的地方，随用随取。

Caching is not only "fast", it is also stable

When many people hear cache, their first reaction is "speed up". Yes, accessing data in memory is much faster than querying the database, sometimes even hundreds of times faster. But that's only half the story.

More importantly, it helps you handle traffic peaks. When a sudden large number of requests strikes, the cache layer can absorb most of the pressure and the database behind it will not be overwhelmed. The resilience of the entire system has increased, and users experience a smooth and smooth experience instead of sudden lags. It's like adding a smart cushion to the system.

Moreover, it quietly reduces dependencies between services. Some basic data required by service A does not need to be called every time by service B. It can be obtained directly from the shared cache. Even if service B is temporarily in a minor condition, A can still work normally for a period of time, and the overall fault tolerance of the system will naturally be enhanced.

What should a good distributed cache look like?

When choosing, don’t just focus on performance numbers. You have to ask yourself a few questions: Is it really easy to fit into my existing microservices architecture? Is it easy or laborious to scale? When data needs to be updated, can it guarantee consistent results seen by multiple services?

A well-designed solution should seem tailor-made for a microservices environment. It doesn't require you to significantly refactor your code to use it, and access should be smooth. Horizontal expansion is also natural. As business grows, adding nodes will lead to greater capacity and higher throughput. It is best to automate this process without too much operation and maintenance burden.

Persistence and high availability cannot be ignored. If the cache server is restarted, will all the data be lost, or can it be partially restored? If one node in the cluster fails, can other nodes take over the work seamlessly? These characteristics determine how reliable it is in a production environment.

Some ideas to make caching really "work"

Introducing caching is not just about adding a tool and ending it. How to use it is more important.

What to cache? Not all data is worth caching. Data that changes extremely quickly and is different every time you query it will become cumbersome if left alone. The most suitable data is "hot" data that changes less frequently but is accessed more frequently, such as product catalogs, basic user information, country and region lists, etc.

Pay attention to the update strategy. Common "cache penetration" (querying for non-existent data, bypassing the cache and penetrating the database each time) and "cache avalanche" (a large number of caches failing at the same time, and all requests flowing to the database) need to be designed to avoid. For example, set a short-term null value mark for non-existent keys, or stagger the cache expiration time.

Another practice is to use multi-level caching. Set up an extremely fast cache (such as Ehcache) in the local memory of the service, coupled with a global distributed cache (such as Redis cluster). The local cache handles ultra-high-frequency requests, and the distributed cache ensures data sharing and consistency. The combination of the two often produces better results.

take a step forward

Technology is always evolving. Today, some advanced distributed caching solutions have begun to be deeply integrated with cloud-native environments, supporting containerized deployment and Kubernetes-based intelligent operation and maintenance. They are also exploring smarter data elimination and more fine-grained monitoring indicators, so that you can not only use them, but also see them clearly and manage them well.

After all, the introduction of distributed caching is not a purely technical decision. It's about how you deliver a stable and fast service experience to users, and how you keep your architecture calm when your business grows rapidly. When your microservices no longer "busy" for repeated queries, and when data can quietly and quickly appear where it should be when needed, the entire system will glow with a different kind of efficiency and vitality. This may be the rational and charming beauty in the architecture.

Established in 2005,kpowerhas been dedicated to a professional compact motion unit manufacturer, headquartered in Dongguan, Guangdong Province, China. Leveraging innovations in modular drive technology,kpowerintegrates high-performance motors, precision reducers, and multi-protocol control systems to provide efficient and customized smart drive system solutions.kpowerhas delivered professional drive system solutions to over 500 enterprise clients globally with products covering various fields such as Smart Home Systems, Automatic Electronics, Robotics, Precision Agriculture, Drones, and Industrial Automation.