Monitoring and observability have increased with software applications moving from monolithic to distributed microservice architectures. While observability and application monitoring share similar definitions, they also have some differences. The purpose of both monitoring and observability is to find issues in an application. However, monitoring aims to capture already known issues and display them on a dashboard to understand their root cause and the time they occurred.

Some popular monitoring tools in the market can complicate and create blind spots in your Elasticsearch monitoring. That’s why we made monitoring Elasticsearch simple, straightforward and actionable. Read along as we dive into the steps to monitor Elasticsearch using observIQ’s distribution of the OpenTelemetry collector. To monitor Elasticsearch we will configure two OpenTelemetry receivers, the elasticsearch receiver and the JVM receiver.

The scope of the OpenTelemetry project encompasses how telemetry data is collected, processed, and transmitted. The OpenTelemetry project is not involved with how the data is stored, displayed, or used beyond the collection and transmission phases. The OpenTelemetry Collector is an application written in Go.

PHP is a widely popular server-side language and enjoys the top spot in terms of market share. Many world-famous organizations like Facebook have their applications written in PHP. WordPress, which powers 43% of all websites, is also built on PHP. In this tutorial, we will use OpenTelemetry to instrument a PHP application for telemetry data. It’s essential to monitor your PHP application for performance issues and bugs.

For this post, we’ll use a small Flask app that allows users to input a city name and they receive the current weather information for that city. We’ll make an API call to openweathermap.org to get the weather information for the city.

Snapshot of the content that AppDynamics’ OpenTelemetry experts, Erwan Paccard and Severin Neumann find valuable.

At Mezmo, we see a massive opportunity to reduce Mean Time to Detection (MTTD) and Mean Time to Resolution (MTTR) by making log data more valuable and actionable. Today, we’re thrilled to announce the release of the Mezmo Exporter for OpenTelemetry- the first step in our continued work with the project to further simplify the ingestion of log data and make that data more actionable with enrichment of key OpenTelemetry attributes.

Frontend technologies typically talk to several services in your backend, and those services talk to other services. At the root of every issue is a single event that causes a domino effect. A domino effect that impacts every operation from the first experience on the frontend to the backend API call. Sentry can show you how these exceptions and latency issues impact every one of your services. For example, take the ever common and seemingly simple to resolve 500 - Internal Server Error.

C# (pronounced C-Sharp) is a simple, modern, object-oriented, and type-safe programming language. ASP.NET is one of the top frameworks for building modern applications using C#, F#, or Visual Basic. OpenTelemetry is one of the popular CNCF projects. Some other notable projects under CNCF include Kubernetes, Helm, and Fluentd. The OpenTelemetry project aims to create an open source web standard for instrumenting cloud-native applications.

Are you thinking of using Jaeger as a distributed tracing tool? What if there is a better alternative that does both distributed traces and metrics monitoring so that your engineering team does not have to use multiple tools.