Welcome to the first SigNal of 2024! It is a year that we’re looking forward to accomplishing great things. We recently crossed 16,000+ GitHub stars as we continue to be amazed by the support of the developer community in our mission of open-source observability. Let’s see what humans of SigNoz were up to in January 2024.

When you have questions about your software, telemetry data is there for you. Over time, you make friends with your data, learning what queries take you right to the error you want to see, and what graphs reassure you that your software is serving users well. You build up alerts based on those errors. You set business goals as SLOs around those graphs.

A new year is a natural time to reflect on past achievements — and consider future aspirations. When I think about the observability space, specifically, in 2023, OpenTelemetry felt omnipresent. It was a hot topic at every industry event, with at least one dedicated talk at ObservabilityCON, Monitorama, PromCon, and KubeCon + CloudNativeCon, both NA and EU. A notable highlight from KubeCon was OpenTelemetry going GA, marking a significant milestone in the project’s development.

OpenTelemetry is a Cloud Native Computing Foundation(CNCF) project aimed at standardizing the way we instrument applications for generating telemetry data(logs, metrics, and traces). However, OpenTelemetry does not provide storage and visualization for the collected telemetry data. For OpenTelemetry visualization, you need to use a backend that can ingest the collected data and provide a web UI to visualize it.

Both OpenTelemetry and Prometheus are open-source projects under the Cloud Native Computing Foundation. OpenTelemetry is a more comprehensive observability framework with support for metrics, traces, and logs. In contrast, Prometheus is focused specifically on time-series metrics. OpenTelemetry is more versatile, and if you’re confused between choosing between the two, go for OpenTelemetry. We will delve deeper into the reason for choosing OpenTelemetry over Prometheus in this article.

Observability isn't just a buzzword; it's a vital compass guiding us through the maze of system health and performance. As we’ve adopted microservice architectures, the ability to know ‘what is currently happening in our system’ has diminished as our operational resilience has increased. We find services scattered among a maze of interconnections and interdependencies. And even the logs that used to guide are now scattered throughout this maze.

OpenTelemetry can auto-instrument your Java Spring Boot application to capture telemetry data from a number of popular libraries and frameworks that your application might be using. It can be used to collect logs, metrics, and traces from your Spring Boot application. In this tutorial, we will integrate OpenTelemetry with a Spring Boot application for traces and logs. OpenTelemetry is a vendor-agnostic instrumentation library that is used to generate telemetry data like logs, metrics, and traces.

In the ever-evolving world of software development, one constant remains - the pursuit of better performance. As applications grow in complexity and demand, the need for tools to uncover performance bottlenecks becomes paramount. Flamegraphs, a brainchild of Brendan Gregg, has emerged as an important visualization of insight, showing those dark corners of your codebase that need optimization.

OpenTelemetry Collector is a stand-alone service provided by OpenTelemetry. It can be used as a telemetry-processing system with a lot of flexible configurations to collect and manage telemetry data. Let's do a deep dive on OpenTelemetry Collectors to understand how it works. The first step in setting up observability with OpenTelemetry is instrumentation. The application code is instrumented with OpenTelemetry client libraries that help generate telemetry data like logs, metrics, and traces.

With Grafana Tempo 2.3, we introduced a new storage format (vParquet3), which enabled an exciting new feature (dedicated attribute columns) that focused on the read path. Dedicated attribute columns offer a wide range of benefits primarily centered around query performance and memory usage. These columns can improve read speed across most queries, and they can have a major impact on resource utilization.