For high throughput systems that focus on gathering continuous data or have a heavy read-only traffic, NoSQL databases came as a blessing. NoSQL databases, due to their unstructured nature of data, allow relatively faster inserts as well as reads compared to relational databases. One such database that’s quite popular today is MongoDB. In this article, our focus would be to understand how to extract metrics out of MongoDB and ship them to Signoz using the Open Telemetry collector.

Let's dive deep into the realm of Memcached, where we'll uncover the power of monitoring with OpenTelemetry and SigNoz. This isn't just about caching data; it's about watching over Memcached like a vigilant guardian, ensuring it performs at its best, and optimizing your application's speed. In this tutorial, you will install OpenTelemetry Collector to collect Memcached metrics that should be monitored for performance and then send the collected data to SigNoz for visualization and alerts.

Application security testing tools are designed to ensure that applications are put through rigorous security assessments to identify security flaws within the application and its code. Even though applications are tested thoroughly (in static and dynamic ways), attackers always seem to find new ways of compromising them.

Enabling auto-instrumentation for your Lambda functions provides detailed insights into the performance and security of your serverless applications. Developers often also use custom instrumentation to fine-tune visibility and further tailor telemetry to their business needs. However, different teams within your organization might use a variety of instrumentation libraries, and achieving more granular visibility can come at the expense of data portability and interoperability.

To monitor and troubleshoot the performance of microservice-based applications, Jaeger and Zipkin are examples of the most commonly used open-source distributed tracing systems. They both supply users with insight into the flow of requests through various components of a system, which can be utilized to find latency bottlenecks, errors, and performance problems in the system.

Database monitoring is an important aspect to look at for a high-volume or high-traffic system. The database performance drastically impacts the response times for the application. In this tutorial, you will install OpenTelemetry Collector to collect MySQL metrics and then send the collected data to SigNoz for monitoring and visualization. In this tutorial, we cover: If you want to jump straight into implementation, start with this pre-requisites section.

In the realm of modern software development, achieving true observability is paramount for understanding application behavior and performance. This demonstration focuses on a.NET application that harnesses the capabilities of OpenTelemetry to seamlessly integrate logging and tracing functionalities. OpenTelemetry, a key player in the Cloud Native Computing Foundation, provides a unified framework for comprehensive observability.

OpenTelemetry provides a component called OpenTelemetry Collector, which can be used to collect data from multiple sources. Prometheus is a popular metrics monitoring tool that has a wide adoption. If you’re using Prometheus SDKs to generate metrics, you can collect them via OpenTelemetry collector and send them to a backend of your choice.

Monitoring Apache web server metrics ensures your web server performs efficiently, securely, and reliably. In this tutorial, you will configure OpenTelemetry Collector to collect Apache metrics and send them to SigNoz for monitoring and visualization. We cover: If you want to jump straight into implementation, start with this prerequisites section.

When you use OpenTelemetry SDKs to collect logs, metrics, and traces from infrastructure or an application, you’ll find many references to people using Grafana Agent and OpenTelemetry Collector. They start with an application or infrastructure that sends telemetry, and that data is sent to a collector, which then sends it to a backend like Grafana that may perform many functions, including visualization.

Unlocking the full potential of observability and tracing in modern software ecosystems has become imperative for businesses striving to deliver improved reliability and user experience. In this comprehensive roundup, we will dive into the world of Jaeger-incorporated observability and tracing dashboards, offering a curated selection of the best use cases that empower DevOps teams, engineers, and developers to gain unparalleled insights into the inner workings of their applications.

OpenTelemetry stands at the forefront of modern observability practices, revolutionizing how developers gain insights into their applications' performance and behavior. As a powerful distributed tracing framework, it empowers engineers to effortlessly instrument their applications, providing comprehensive visibility into the intricacies of microservices architectures. This tutorial discusses how OpenTelemetry can be used to get insights from a Java application.

Manually deploying and managing OpenTelemetry components in a Kubernetes environment can be a complex and time-consuming task. It involves creating various Kubernetes resources, setting up configurations, and ensuring the components are properly integrated with the applications.

The Splunk Add-on for OpenTelemetry Collector is a variation of the Splunk Distribution of the OpenTelemetry Collector that simplifies metrics and traces data collection, configuration and management. Since it is an add-on, users can deploy it alongside Universal Forwarders using tools like Deployment Server to start collecting high-fidelity metrics and traces from 1000s of their hosts easily. We’re happy to announce that the Add-On is now generally available in Splunkbase.

The OpenTelemetry project provides many different components and instrumentations that support different languages and telemetry signals. However, new users often find it hard to pick the right ones and configure them properly for their specific use cases. For this reason, OpenTelemetry defines the concept of a distribution, which is a tailored and customized version of OpenTelemetry components. Here at Grafana Labs, we are all-in on OpenTelemetry.

Collecting data in real-time plays a crucial role in securing, monitoring, and troubleshooting applications. This real-time data, often referred to as ‘runtime data,’ provides unique insights into the application’s behavior, which aren’t available through other collection techniques. But the tricky part is that collecting runtime data comes with challenges.

Open Telemetry (OTel) is an open source observability framework that has garnered significant attention for its powerful capabilities in monitoring metrics, logs and traces.. It is second only to Kubernetes in the CNCF velocity chart with contributions being made from major players in the cloud industry, and has a growing community helping build out a thriving ecosystem.

The Grafana LGTM Stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics) offers the freedom and flexibility for monitoring application performance. But we’ve also heard from many of our users and customers that you need a solution that makes it easier and faster to get started with application monitoring.

When all your IT systems, your apps and software, and your people are spread out, you need a way to see what’s happening in all these minute and separate interactions. That’s exactly what distributed tracing does. Distributed tracing is a way to tracking requests in applications and how those requests move from users and frontend devices through to backend services and databases.

An Essential Guide to OpenTelemetry In today’s expeditious, highly distributed software landscape, achieving true observability is no simple task. As you strive to understand how your applications and services perform and behave, you face multiple challenges. Moreover, you need to instrument applications and services that generate data effectively, have a reliable means to transmit it, and, most importantly, find a way to visualize and derive insights from it.

What are OpenTelemetry, and OpenCensus and how to migrate from OpenCensus to OpenTelemetry.

In telemetry jargon, a pipeline is a directed acyclic graph (DAG) of nodes that carry emitted signals from an application to a backend. In an OpenTelemetry Collector, a pipeline is a set of receivers that collect signals, runs them through processors, and then emits them through configured exporters. This blog post hopes to simplify both types of pipelines by using an OpenTelemetry extension called the Headers Setter.

Modern development teams increasingly rely on open-source packages to rapidly build and deploy applications. In fact, most, if not all applications consist of far more open-source and 3rd-party code than the code that’s‌ written by their dev teams.

What is OpenTelemetry? Simply put, OpenTelemetry is an open source observability framework. It offers vendor-agnostic or vendor-neutral APIs, software development kits (SDKs) and other tools for collecting telemetry data from cloud-native applications and their supporting infrastructure to understand their performance and health. Managing performance in today’s complex, distributed environment is extremely difficult.

Application Performance Monitoring (APM), tracing, and observability are fundamental software development and system management approaches. Each of these three concepts uniquely ensures that your applications operate, efficiently, smoothly, and reliably. Your organisation will more than likely already adopt one of these approaches, or even two, potentially all three.