Node.js is a very popular JavaScript runtime for the backend. Its usage has grown steadily in the past years. Some notable users of Node.js include Netflix, PayPal, Uber, and eBay. In this post, you will learn how to add tracing to a Node.js application on AppSignal. You will use an existing Quotes app that talks to a PostgreSQL database to fetch the quotes. Let’s get going!
This post was written by Siddhant Varma. Scroll down for the author’s bio. Observability is an essential aspect of a healthy software architecture and a highly performant system. It enables developers and engineers to understand and dive deeper into how their application behaves. This in turn helps them monitor it effectively.
Message brokers like Kafka enable microservices to scale. But this same quality makes them hard to troubleshoot. How can developers avoid messages and errors getting stuck in oblivion? In this post we look at a few solutions: Kafka Owl, Redpanda, and Helios.
As the trend toward microservices-based architectures continues to gain momentum, it’s becoming increasingly clear that distributed tracing will be a crucial tool for monitoring and debugging these complex systems in the future. When designing a microservices-based architecture, breaking extensive services into smaller, more manageable components is standard practice. Communication between these components becomes crucial, but finding the root cause can be challenging when issues arise.
Honeycomb recently released our Query Assistant, which uses ChatGPT behind the scenes to build queries based on your natural language question. It's pretty cool. While developing this feature, our team (including Tanya Romankova and Craig Atkinson) built tracing in from the start, and used it to get the feature working smoothly. Here's an example. This trace shows a Query Assistant call that took 14 seconds. Is ChatGPT that slow? Our traces can tell us!
This post was written by Mercy Kibet, a full-stack developer with a knack for learning and writing about new and intriguing tech stacks. In today’s digital world, software applications are becoming increasingly complex and distributed, making it more challenging than ever to diagnose and troubleshoot issues when they arise.
The age-old dilemma of privacy and security vs. productivity pops up for developers every time they consider introducing a new technology to their stack. The dilemma is often viewed as a trade off: on one hand, privacy and security measures can slow down how quickly new features can be rolled out; on the other hand, prioritizing productivity and business enablement over privacy and security can increase the risk of breaches to an organization.
While more observability vendors are providing tracing ingestion and visualization as part of their core service, only Coralogix, the leading in-stream observability platform, supports a set of data optimization features that drive down cost, maximize insights and create a scalable tracing strategy unlike others.
How to filter metrics by labels using OpenTelemetry Collector.
As a big proponent of open source and all things open, I jumped at the opportunity to expand on Cribl Stream’s OpenTelemetry implementation. I’m happy to report that as of Cribl Stream 4.1, both our OpenTelemetry source and destination now support OTLP over HTTP!
It’s 5:00 pm on a Friday. You’re wrapping up work, ready to head into the weekend, when one of your high-value customers Slacks you that something’s not right. Requests to their service are randomly timing out and nobody can figure out what’s causing it, so they’re looking to your team for help. You sigh as you know it’s one of those all-hands-on-deck situations, so you dig out your phone and type the "going to miss dinner" text.
Organizations increasingly deploy and manage their applications using Kubernetes, which has emerged as the de facto standard for container orchestration. Monitoring these resources becomes critical for ensuring that the applications running on the platforms remain healthy.
This article was originally published in The New Stack and is reposted here with permission. They require different approaches for storage and querying, making it a challenge to use a single solution. But InfluxDB is working to consolidate them into one. Time series data has unique characteristics that distinguish it from other types of data. But even within the scope of time series data, there are different types of data that require different workloads.
Here at InfluxData, we recently announced InfluxDB 3.0, which expands the number of use cases that are feasible with InfluxDB. One of the primary benefits of the new storage engine that powers InfluxDB 3.0 is its ability to store traces, metrics, events, and logs in a single database. Each of these types of time series data has unique workloads, which leaves some unanswered questions. For example: Luckily this is where our work within OpenTelemetry comes into play.
To get visibility into highly distributed applications, organizations often use various tracing tools that are best suited to each individual service owner’s specifications. However, when a request travels between services that have been instrumented with different tools, the trace data may be formatted differently, resulting in broken traces.
Grafana Tempo 2.1 is out and comes with a host of TraceQL improvements. Tempo 2.1 comes with some nice incremental improvements to TraceQL and likely some breaking changes. There’s a section down below about those, too.
