5/30/2023 0 Comments Microsoft net monitor![]() ![]() Main Api enqueues enriched request to a RabbitMQ queue for asynchronous processing.Main Api enriches the request body with current day, obtained from Time Api.Main Api receives a request from a “source”.NET Core application we will be using the following asynchronous distributed transaction example: To illustrate how observability can be added to a. Other questions, such as custom KPIs or user behavior, require adding instrumentation to your code. Another example is through the usage of service meshes in Kubernetes ( Istio, Linkerd).īuilt-in and transparent tracing are typically covering basic scenarios and answering generic questions, such as observed request duration or CPU trends. Dapr for example, is a runtime to build distributed applications, transparently adding distribute tracing. There are many ways to add observability aspects to an application. Such as CPU usage being higher than before, payment error count spiking, and queued item count keeps growing.Īdding Observability to a. Application problems are often first detected through abnormal metric values. As opposed to logs and traces, the amount of data collected using metrics remains constant as the system load increases. It can be leveraged to build alerts, allowing proactive reactance to unexpected values. Metrics: provide a real-time indication of how the system is running. Like calls from service A to B are taking longer than normal, service payment calls are failing, etc. Once a problem has been recognized, traces are a good starting point in identifying the source in distributed operations. A trace is like a stack trace spanning multiple applications. Tracing: collects information to create an end-to-end view of how transactions are executed in a distributed system. ![]() As well as calls to external service with incorrect address, calls to external service returns with unexpected results, and incoming requests with unexpected input. Such as: service throwing out of memory exceptions and app configuration not reflecting expected values. Searching through the logs of suspect services can provide the necessary hint to identify the problem root cause. ![]() Helping the team analyze unexpected application behavior. Logging: collects information about events happening in the system. Such as canary, mirroring, rings, blue/green, etc. The impact of a bad system update can be minimized by combining the monitoring information with progressive deployment strategies. Has the CPU and/or Memory usage increased?.Has the throughput (req/sec) decreased?.Did the request duration unexpectedly increase compared to previous versions?.Are we observing more errors than before?.The collected data must provide the required information to analyze and identify a bad update. Identifying software error and business impact require a monitoring solution with the ability to observe and report how the system and users behave. We will be looking into OpenTelemetry and Application Insights SDKs to add observability to a sample distributed application. They have been collected based on interactions with customers using. This article explores options for adding observability to. These practices can only work when a monitoring solution is in place. As important as identifying bugs early, finding out if changes are improving business value are equally important. Modern software development practices value quick and continuous updates, following processes that minimize the impact of software failures. Thank you Sergey Kanzhelev for the support and review of this ASP.NET Core Apps Observability article. ![]()
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