Jun 15, 2021. By Aleena Mathew
In this highly competitive world, IT leadership is a clear differentiation. This calls for the need for developing high-end applications with the use of the latest technologies. IT organizations completely shifted from the use of the legacy system and monolithic architecture to the use of modern distributed microservice architecture. Therefore, most of the applications were developed in a distributed manner with microservices architecture. The major benefit the ITOps team gets here is that it is easier to build and maintain the applications. The productivity and speed of operations and delivery improved drastically. But will all of these benefits there was a downside for these applications. One of the challenges was in observing and monitoring these applications. Most of the custom applications wherein distributed microservice form made it difficult for IT operators to manage and monitor the application. These applications generated a huge set of metrics, logs, and traces. Identifying the right of metrics needed for monitoring became a challenge for the IT team. Moreover, most applications might not have the required or contain the right set of metrics needed for monitoring. This can create a big chaos in the system as the team is not aware of what metrics need to be collected here. Therefore, the right need here is to instrument the metrics that are right for the monitoring purpose. That's where OpenTelemenrty came in.
Instrumenting metrics with OpenTelemetry:
Today’s microservice-based architectures mean an application is an interconnected mesh of services, many of which are third-party and/or open-source, and understanding system performance from external outputs becomes far more challenging. The concept of instrumenting metrics came into the picture when it became difficult for IT operators to analyze and monitor the application metrics. Most of the applications do not generate meaningful metrics. Therefore, no proper inference was generated from them. That's where the concept of open telemetry came into the picture. Cloud-native telemetry has emerged as the observability solution of choice. OpenTelemetry is enabled in instrumenting custom metrics for custom applications that measure how well the internal state of a system can be inferred from knowledge of its external outputs. With instrumentation capability APIs or structured data, applications distributed trace data, metrics, logs, and traces are collectively collected by the telemetry platform. Once the instrumentation and collection of data are made possible with the telemetry platform, then the observable actions can be performed by backend systems such as Prometheus. This backend support enables in analyzing and providing insights into the custom applications.
Instrumenting OpenTelemerty for Custom Applications:
Open Telemetry opened an open-source gateway for creating/instrumenting custom metrics for custom applications. In this way, the ITOps team was able to observe the performance of the application with the right set of metrics. This enabled in providing intelligent alerts/events on the application which enabled the team to resolve them pro-actively. Let's see some of the benefits of instrumenting metrics. OpenTelemtry enables the creation of a set of unified libraries for cloud-native observability. Let's take a deeper look into how this instrumentation is made possible. Custom applications can be instrumented with custom metrics with OpenTelemetry trace APIs and metrics API. The metrics API sends metric data for storage as dimensional metric data in key:value pairs. Once these traces are made, they can be sent to the observability platform for an open observability from any source. This can be done by instrumenting everything and providing deeper analysis for eliminating blind-spots.
AI-based Observability for Custom Applications:
Observability is a major requirement in every IT organization. The need for monitoring every application is a must to understand the working of an IT organization both in business and IT perspective. Without proper observation or inference, it is hard to identify if there are any unknown issues in the applications. Manual operators can't sit 24/7 to continuously check if there are any issues in the application performance or availability at the end-user side. They might take days to understand the issues and by that time the problem might cause a bigger problem. Moreover, in the present microservice era, we need much more advanced observability, which is AI-based observability. AI-based observability enables in providing a single pane of glass view for observing every application present in the system. It enables in collecting every metrics even which are custom made. Based on the collected metrics, a continuous analysis will take place in such a way that any unknown problems or issues will be automatically be triggered. These events will be pro-actively alerted to the IT team and the IT team can take the necessary actions to resolve the issue. Moreover, with AI-based observability, we can perform anomaly detection mechanisms in which the AI-based models will automatically trigger an unknown/known anomaly and alert the IT team. In this way, custom applications are also intelligently monitored and issues are resolved proactively.
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