Microservices are hot!

Microservice architecture is all the rage, but it’s certainly no fad: Kubernetes (K8S) has become a dev-household name. Building large applications out of small modules increases flexibility; enables scalability where it’s needed (without requiring it where it’s not); makes individual services easier to replace or upgrade, and minimizes unanticipated consequences of changes.

Generally, each microservice represents a single business capability. Microservice architecture works best with small, agile cross-functional team structures that strengthen identification with business goals and customer-orientation.

The Microservices Debugging Challenge

Microservice architectures require precise choreography and event collaboration. When it works, it’s a well-oiled machine. But a small bug in one service can lead far-flung services to fail or fall out of sync. Tracing individual users or requests back to the cause, through multiple processes and boundaries, can be frustrating, time-consuming and daunting.

Microservices often leverage Kubernetes or Mesos and other encapsulated DevOps services. Handing off control to these layers means sacrificing observability as well.

‘Breaking into’ microservice-based applications in production to add code for gathering data is time-consuming, resource-intensive and risky, even with full visibility. When visibility is limited, even small changes can knock microservices out of sync — or entirely out of operation.

Rookout for Microservices

Rookout closes the gap between modern DevOps architecture and ‘old-school’ debugging capabilities by enabling developers to collect data on demand and pipeline it wherever they need it, without prior planning. The ability to dip in as needed, instead of trying to anticipate predictable cases and create “periscopes” for observing them , aligns perfectly with the dynamic nature of microservice principles and architectures.
By decoupling data collection from the CI/CD pipeline, Rookout eliminates the friction and bottlenecks that slow iterations for gathering data. It adds observability that is essential for debugging microservices that leverage Kubernetes, Mesos and other widely-used devOp services.

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