#Wechat windows architecture database cache keygen
Though Lambda functions are short-lived, the cached data can be used by subsequent instances of the same microservice to avoid backend calls. The compute layer is built using AWS Lambda. It requires data from multiple data sources deployed locally in the cloud or on premises. In both use cases, the microservices layer is created using Serverless on AWS offerings. In this blog post, we discuss a couple of these use cases that customers have built. Caching can be implemented in several ways. While working with our customers, we have observed use cases where data caching helps reduce latency in the microservices layer. When data is requested from a cache, it is delivered faster than if you accessed the data’s primary storage location. A cache is a high-speed data storage layer that stores a subset of data. In certain scenarios, it makes sense to maintain a cache close to the microservices layer to improve performance by reducing or eliminating the need for the real-time backend calls.Ĭaches reduce latency and service-to-service communication of microservice architectures.
The latency often ranges from milliseconds to a few seconds depending on size of the data, network bandwidth, and processing logic. These scenarios add latency to the microservice response time because multiple real-time calls are required to the backend systems. These can include data stores, legacy systems, or other shared services deployed on premises in data centers or in the cloud. However, a microservice might need to retrieve and process data from multiple disparate sources. This helps them gain agility and scalability and accelerate time-to-market for new features.Įach microservice performs a single function. Organizations are re-architecting their traditional monolithic applications to incorporate microservices.
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