Health Management and Diagnostics in Microservices

Handling unexpected failures is one of the hardest problems, especially in distributed systems. Most developer code deals with exception handling, which consumes the most testing effort, and detecting a failing microservice and restarting it is a complex challenge.

Microservices must be resilient to failures and be able to restart on another machine to maintain availability. Resilience includes both compute resilience (processes can be restarted at any time) and state or data resilience (no data loss and consistency is preserved).

When failures occur during application upgrades, resilience becomes even more complex. A microservice deployed with a deployment system must decide whether to move forward to a newer version or roll back to a previous version to keep a consistent state, requiring health information so that the application and orchestrator can make these decisions.

Cloud‑based systems must accept failure and automatically recover. For partial failures such as network or container issues, client applications need retry policies (e.g., exponential back‑off) or circuit‑breaker patterns. Libraries like Polly illustrate these techniques for .NET Core.

Health Management and Diagnostics in Microservices

Microservices must report their health and diagnostics; otherwise, operators have little insight. Correlating diagnostic events across independent services and handling clock skew to understand event order is challenging. A standardized protocol and log format are needed so that different teams can agree on a single logging schema for querying and viewing diagnostic events.

Health Checks

Health differs from diagnostics. Health is the microservice reporting its current status so that appropriate actions can be taken, such as maintaining availability during upgrades. Even if a process crashes or a machine restarts, the service may still be considered running. Health events enable informed decisions and help create self‑healing services.

The ASP.NET Core health‑check library can be used to expose health information to monitoring services.

An open‑source library, Beat Pulse, provides two kinds of checks: liveness (whether the service can accept requests) and readiness (whether its dependencies like databases or queues are ready).

Using Diagnostic and Logging Event Streams

Logs convey how an application or service runs, including exceptions, warnings, and informational messages. Typically each log entry is a single line, but stack traces may span multiple lines.

In monolithic applications, logs can be written to disk files and analyzed with any tool. In distributed applications, multiple services run on many nodes, making correlation of events a challenge.

Microservice‑based applications should write their event streams to standard output, which the execution environment collects. Examples of event‑stream routers include Microsoft.Diagnostic.EventFlow, Azure Monitor, Azure Diagnostics, and third‑party platforms such as Splunk.

Orchestrator Management of Health and Diagnostic Information

Building microservice‑based applications introduces complexity: high availability, addressability, recoverability, health, and diagnostics must be handled for dozens or hundreds of services and thousands of instances.

Orchestrators or microservice clusters aim to solve infrastructure complexity, allowing development teams to focus on business logic rather than low‑level operational concerns.

Different orchestrators provide varying levels of diagnostic and health‑check capabilities, often depending on the underlying operating system platform.

Subsequent sections will detail platforms based on Spring Cloud and Kubernetes.