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APM

The latest News and Information on Application Performance Monitoring and related technologies.

Latest Top 11 Log Monitoring Tools [Includes Open-Source]

For any software company, a log monitoring tool is a must for collecting, storing, and providing a centralized view of all logs from different applications and hosts for faster anomaly detection, incident resolution, and troubleshooting. They can also help detect security threats and provide audit trails. They are effective in capacity planning, decision-making, and ensuring optimized performance.

OpenTelemetry Flask Instrumentation Complete Tutorial

In this article, we will use OpenTelemetry to instrument a sample Flask app for traces. Flask is one of the most popular web application frameworks of Python. It consists of Werkzeug WSGI toolkit and Jinja2 template engine. Instrumentation is the biggest challenge engineering teams face when starting out with monitoring their application performance. OpenTelemetry is the leading open-source standard that is solving the problem of instrumentation.

Monitoring apps based on Falcon Web Framework with OpenTelemetry

Falcon is a minimalist Python web API framework for building robust applications and microservices. It also compliments many other Python frameworks by providing extra reliability, flexibility, and performance. Using OpenTelemetry, you can monitor your Falcon applications for performance by collecting telemetry signals like traces. Instrumentation is the biggest challenge engineering teams face when starting out with monitoring their application performance.

Elastic APM for iOS and Android Native apps

Elastic APM for native apps provides auto-instrumentation of outgoing HTTP requests and view-loads, captures custom events, errors, and crashes, and includes pre-built dashboards for data analysis and troubleshooting purposes Elastic® APM for iOS and Android native apps is generally available in the stack release v8.12. The Elastic iOS and Android APM agents are open-source and have been developed on-top, i.e., as a distribution of the OpenTelemetry Swift and Android SDK/API, respectively.

Datadog on Kubernetes Autoscaling

Datadog, the observability platform used by thousands of companies, runs on dozens of self-managed Kubernetes clusters in a multi-cloud environment, adding up to tens of thousands of nodes, or hundreds of thousands of pods. Also, this infrastructure is used by a wide variety of engineering teams at Datadog, with different features and capacity needs that may also change overtime.

This Month in Datadog: Dynamic Instrumentation, Log Pipeline Scanner, Network Device map, and more

Datadog is constantly elevating the approach to cloud monitoring and security. This Month in Datadog updates you on our newest product features, announcements, resources, and events. This month, we put the Spotlight on Dynamic Instrumentation..

Livestream: Client side monitoring & metrics for Kafka using OpenTelemetry & SigNoz

In this livestream, we will walk through a demo of how to get client side insights from Kafka using distributed tracing. We will take a NodeJS producer and consumer setup communicating via Kafka to show how one can instrument this with OpenTelemetry, and get metrics from a client perspective. We will also touch on getting Kafka metrics using OpenTelemetry receivers.

Monitoring Django application performance with OpenTelemetry

Django is a popular open-source python web framework that enables rapid development while taking out much of the hassle from routine web development. It also helps developers to avoid common security mistakes. As such, many applications are built with Django. Django is very popular among web developers and has a huge community behind it. It gives web developers ready-to-use components for common things that you will need to accomplish for a web application.

Beyond deployment: The ongoing challenges in application performance monitoring implementation

In the age of digital acceleration, application performance monitoring (APM) acts as a sentinel, empowering organizations to maintain, analyze, and optimize the health of their digital ecosystems. However, as organizations navigate the intricacies of distributed architectures, hybrid cloud deployments, and dynamic workloads, they confront a complex terrain marked by data proliferation, siloed environments, and a scarcity of skilled personnel.