What is a Trace?
What is a Trace? A trace refers to the end-to-end monitoring and tracking of a user request as it flows through multiple microservices. It is part of distributed tracing, a technique used to understand the flow of requests and responses across various microservices that collaborate to provide a complete service. Tracing is one of the […]
What is Application Performance Monitoring (APM)?
Application Performance Monitoring (APM) incorporates telemetry data and monitoring tools to track and manage the performance of business-critical applications. It is widely used in applications to help monitor system availability, reliability, service performance optimization, and response times. It is crucial for optimizing application performance and presenting clients with an exceptional user experience. Features of Application Performance Monitoring […]
How Novacy Shortened Troubleshooting Time by 90% with Helios
When I first met Uria Franko, the CTO of Novacy, I immediately knew we’d hit it off. He was looking for an observability solution for his team with a specific need around Celery, after they had been using logs but found they lacked the depth and granularity they needed. Luckily, our mission at Helios is […]
How we slashed detection and resolution time in half (Salt Security)
Salt Security had deployed OpenTelemetry but found it insufficient. So the company engineers evaluated Helios, which visualizes distributed tracing for fast troubleshooting. My role as the Director of Platform Engineering at Salt Security lets me pursue my passion for cloud-native tech and for solving difficult system-design challenges. One of the recent challenges we solved had to […]
Debugging and troubleshooting microservices in production—All you need to know
What do you do when things break in production? Debugging microservices isn’t a walk in the park. Microservices are designed to be loosely coupled, which makes them more scalable and resilient, but also more difficult to debug. When a problem occurs in a microservices app, it can be difficult to track down the source of […]
API latency in microservices – Trace based troubleshooting
In microservices architectures, apps are broken down into small, independent services that communicate with each other using APIs in a synchronous or asynchronous way. Microservices carry many advantages, such as Increased flexibility and scalability (microservices can be scaled independently of each other, and APIs help to scale microservices by adding or removing instances of the […]
OpenTelemetry .NET Distributed Tracing – A Developer’s Guide
Modern applications are becoming increasingly distributed due to a wide range of benefits including enhanced scalability, high availability, fault tolerance, and better geographical distribution. But it also makes the overall system complex making it challenging to understand how they function internally. Distributed tracing helps to address it by tracking how requests flow through various system […]
Serverless observability, monitoring, and debugging – Overview and best practices
Serverless, as you may already know, is a cloud computing model where the cloud provider dynamically manages and allocates resources to execute code without the need for server provisioning or infrastructure management on the developer. This article overviews serverless observability, monitoring, and debugging, based on distributed tracing and OpenTelemetry (OTel). It is gaining […]
API monitoring vs. observability in microservices- Troubleshooting guide
Monitoring APIs through enhanced observability has gained traction with the popularity of microservices. Since microservice applications are built as independent and scalable modules, the number of microservices can grow dramatically as the application grows, increasing the complexity drastically. Since APIs work as the connective tissue between microservices, the number of APIs also grows in parallel. […]
Kafka monitoring: Message brokers observability and troubleshooting
Message brokers like Kafka enable microservices scalability. But monitoring them lacks context. Here are 3 solutions: Redpanda, Kafka Owl, and trace-based Helios
