Implementing the Sidecar Pattern in Microservices-based ASP.NET Core Applications

"Today's applications require monitoring, logging, configuration, etc. Each of these concerns can be implemented as a component or a service. These cross-cutting concerns can be tightly integrated into the application. While this tight coupling ensures effective use of shared resources, an outage in any of these components can take your application down."

"Enter the sidecar design pattern. The sidecar design pattern helps keep dynamic services (i.e., microservices) fueled with the resources and data they need while keeping them lightweight and free from the burden of carrying large amounts of internal logic."

"Sidecars can be built alongside your microservices, but they can be built using a different technology than the one used to build your microservices. Sidecars can be reused across multiple services to provide out-of-the-box support for configuration, logging, tracing, and publish-subscribe messaging."

"While sidecars are well-suited for providing out-of-the-box support for implementing cross-cutting capabilities, you may often prefer not to use them for ultra-latency sensitive workloads to avoid additional network hops and resource overhead."

Applications need monitoring, logging, configuration, and similar cross-cutting capabilities. These concerns can be implemented as separate components or services, but tight integration can create failure coupling where an outage can take down the whole application. The sidecar design pattern keeps microservices lightweight by providing required resources and data through an adjacent sidecar process. Sidecars decouple cross-cutting logic from business logic, reducing complexity and improving maintainability. Sidecars can be built alongside microservices and even using different technologies. They can be reused across multiple services to provide out-of-the-box support for configuration, logging, tracing, and publish-subscribe messaging. Sidecars may be avoided for ultra-latency sensitive workloads due to extra network hops and resource overhead.

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