Introduction to Language Runtime Observability.

Nov 16, 2021. By Anil Abraham Kuriakose

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Introduction to Language Runtime Observability

Language runtime observability refers to the metrics, time taken, and resources utilized by the machine to execute a piece of code in different programming languages. The language runtime observability is measured using time complexity and space complexity. Here the term system can be a platform, runtimes, and applications.

What is the Mechanism by Which Observability Operates? Observability platforms continuously discover and collect performance telemetry through integration with existing instrumentation built into application and infrastructure components and providing tools for instrumenting these components. Observability is organized around four distinct types of Telemetry: Logs: Logs are detailed, timestamped, complete, and immutable records of application events that happened at a particular time. Metrics: Metrics (occasionally referred to as time series metrics) are fundamental indicators of application and system health over a specified period, such as the amount of memory or CPU capacity consumed by an application over five minutes or the amount of latency experienced by an application during a spike in usage. Traces: Traces document each user request's end-to-end 'journey' from the user interface or mobile application through the distributed architecture and back to the user. Dependencies: Dependencies show how each application component is dependent on another application, features, and IT resources. OpenTelemetry shows excellent promise and already enjoys widespread support from many vendors and customers, which is quite impressive for a project in Beta at the time of this writing. If the current momentum continues, OpenTelemetry will truly simplify observability for everyone.

A Quick View of Observability Anatomy Mainly three layers in the observability stack. These layers are the collection layer, storage layer, and analytics/Monitoring layer. 1. Collection:- Choose a portable and safer way of gathering data ( OpenTelemetry can be the safest option) 2. Storage:- Store the Telemetry with the utmost care in a multi-modal ops datastore. Any solution stores only partial data is not helpful here. 3. Analytics and Monitoring:- Observability also requires monitoring and anomaly detection algorithms to detect system performance issues and unknown issues from the telemetry data. Telemetry has no intrinsic value. Only the workflows and applications that are built on top can be considered valuable. Just because observability is essential, it doesn't mean that it needs to be complex and confusing.

What is OpenTelemetry? OpenTelemetry is a collection of APIs, SDKs, tooling, and integrations that enable the collection and management of telemetry data from your services, resulting in increased observability. In addition, open Telemetry enables DevOps and IT groups to better understand system behavior and performance by standardizing instrumentation across all services. You have probably heard of OpenTelemetry by now; it is an open-source observability framework created by merging OpenTracing and OpenCensus. So let's move on with our discussion on how to improve observability with OTel.

Why is Language Runtime Observability Important in Programming? Language runtime Observability helps developers learn the multi-layered architectures and know what's slow and can improve performance or speed. Observability in programming provides the real-time pulse of a system. In simple words, you can say that lack of observability is similar to driving a car without a fuel indicator. In this blog, we will discuss the language runtime observability with Open Telemetry( OTel). Telemetry data is used to fix problems and create a better experience for customers.

Software Observability: Observability in software is a term that refers to the ability to deduce an application's performance from its output data or Telemetry. This Telemetry can be classified into three broad categories in distributed systems: Observability does not end with telemetry data collection; the most critical aspect of the practice is what happens to the data after collection. You can use tools that provide an unparalleled analysis of your telemetry data, including correlation detection, historical context, and automatic point-in-time snapshots. Broadly we can classify language run time features into two categories. The first one is the fundamental features. These features have a broader impact on the application execution—the features include garbage collection, memory safety, type safety, and high-level programming language metrics. The second one is the secondary features, including application domain program isolation and program security. Modern languages, including statically typed languages like golang, needs several observability requirements. The main requirements are fully automated distributed tracing across HTTP and gRPC requests, getting code-level insights, code-level CPU profiling, and the ability to import all metrics.

Undoubtedly, better observability of language runtime is very important because all application and business observability starts from the language runtime observability. To learn more about Algomox Observability solutions, please visit our obsevability page

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