Throughout the third quarter of this year, Lightrun continued its efforts to develop a multitude of solutions and improvements focused on enhancing developer productivity. Their primary objectives were to improve troubleshooting for distributed workload applications, reduce mean time to resolution (MTTR) for complex issues, and optimize costs in the realm of cloud computing. Read more below the main new features as well as the key product enhancements that were released in Q3 of 2023!

Elastic® SQL inputs (metricbeat module and input package) allows the user to execute SQL queries against many supported databases in a flexible way and ingest the resulting metrics to Elasticsearch®. This blog dives into the functionality of generic SQL and provides various use cases for advanced users to ingest custom metrics to Elastic®, for database observability. The blog also introduces the fetch from all database new capability, released in 8.10.

Do you want to build software faster and release it more often without the risks of negatively impacting your user experience? Imagine a world where there is not only less fear around testing and releasing in production, but one where it becomes routine. That is the world of feature flags. A feature flag lets you deliver different functionality to different users without maintaining feature branches and running different binary artifacts.

Observability and security are converging, benefiting dev and security teams. Runtime observability is the missing component to this important endeavor, providing much-needed data and insights to DevSecOps and AppSec teams.

The shift from traditional monitoring to observability is widespread, and necessary. It's the way we make sense of increasingly complex and distributed systems. But when we capture all this data at scale... what do we do with it all? If this data itself had inherent value, we’d all be rich. But in the real world data does not provide us value until we can act on what it tells us.

Containers are an amazing technology. They provide huge benefits and create useful constraints for distributing software. Golang-based software doesn’t need a container in the same way Ruby or Python would bundle the runtime and dependencies. For a statically compiled Go application, the container doesn’t need much beyond the binary.

In order for fleet managers at Daimler Truck to manage the day-to-day operations of their vast connected vehicles service, they use tb.lx, a digital product studio that delivers near real-time data along with valuable insights for their networks of trucks and buses around the world. Each connected vehicle utilizes the cTP, an installed piece of technology that generates a small mountain of telemetry data, including speed, GPS position, acceleration values, braking force and more.

Organizations today must embrace a modern observability approach to develop user-centric and reliable software. This isn’t just about tools; it’s about processes, mentality, and having developers actively involved throughout the software development lifecycle up to production release. In recent years, the concept of observability has gained prominence in the world of software development and operations.

Sky-high observability costs or visibility gaps? This is the unfortunate trade-off many organizations have to make when it comes to determining how much telemetry data they should collect and send to their observability tools. Teams either collect more data than they need and pay the price, or they collect less and suffer visibility gaps. Today, this all changes.

Learn how full-stack observability can benefit your organization with real-time visibility into all layers of your IT infrastructure. With digital environments growing more complex, customer expectations are at an all-time high — and IT teams are being asked to manage more with fewer resources while also being “more strategic.” Impossible, right? Well, it can be without full-stack observability.

Our industry is in the early days of an explosion in software using LLMs, as well as (separately, but relatedly) a revolution in how engineers write and run code, thanks to generative AI. Many software engineers are encountering LLMs for the very first time, while many ML engineers are being exposed directly to production systems for the very first time.

Observability dashboards are powerful tools that enable teams to visualize and monitor the performance, health, and behavior of their applications and infrastructure. However, building observability dashboards is not a straightforward task, and many organizations make common mistakes hindering their ability to gain meaningful insights and respond to issues effectively.

OpenTelemetry is more than just becoming the open ingestion standard for observability. As one of the major Cloud Native Computing Foundation (CNCF) projects, with as many commits as Kubernetes, it is gaining support from major ISVs and cloud providers delivering support for the framework. Many global companies from finance, insurance, tech, and other industries are starting to standardize on OpenTelemetry.

TL;DR: Sometimes I get hung up in the scientific definition of "experiment." In daily work, take inspiration from it. Mostly, remember to look at the results.

Whether you’re a new Honeycomb user or a seasoned expert looking to uncover fresh insights, chances are you’ve sent tremendous amounts of data into Honeycomb already. The question is, now what? We have the answer: Board templates. Teams can now create Boards based on pre-built templates that generate visualizations with a single click.

In today's cloud-native landscapes, observability is more than a buzzword; it's a critical element for software development teams looking to master the complexities of modern environments like Kubernetes. There’s a multi-faceted nature to observability with all its various levels and dimensions — from basic metrics to comprehensive business insights. It’s complex and can continue indefinitely…if you let it.

This guest post is written by Ian Duncan, Staff Engineer - Stability Team at Mercury. To view the original post, go to Ian's website. At work, we use OpenTelemetry extensively to trace execution of our Haskell codebase. We struggled for several months with a mysterious tracing issue in our production environment wherein unrelated web requests were being linked together in the same trace, but we could never see the root trace span.

I’m launching a new Observability Series called the Observability Professor, and it is designed to cover some common topics and terms in a vendor agnostic way. That’s right, no marketing! So what’s special, what’s new, what’s it going to cover that everyone else in the industry missed? Background: There are endless amounts of blogs, papers, and books on Observability; what it is and what it offers.

In the dynamic world of IT, the way we monitor systems has seen a remarkable evolution. Gone are the days when monitoring was limited to basic server checks or infrastructure health. With the rise of cloud-native applications, serverless architectures, and container orchestration platforms like Kubernetes, the digital landscape has become a multi-dimensional maze.

The combination of Hewlett Packard Enterprise and OpsRamp is helping organizations to manage and transform their multi-vendor and multi-cloud IT estates with AI-driven operations, improving the performance and reliability of those environments while reducing complexity and technical debt.

Even as the global economy shows signs of a rebound, today’s observability customers are more focused than ever on driving utmost value from their investments. This isn’t simply because economics have forced organizations to closely review overhead and drive out unnecessary costs; the reality is that observability has become one of the leading budget items for every cloud software organization, full stop.

Frontend observability (or real user monitoring) is a critical, yet often overlooked, part of systems monitoring. Website and mobile app frontends are just as complex, if not more so, than the backend systems observability teams typically prioritize. They also represent the first interaction users have with our applications — so it’s important to have full visibility into that experience.

On the surface, business-critical IT infrastructure and cats may not seem like they have a lot in common. But they’re way more alike than you might think. Our feline friends contain multitudes, as any cat parent will tell you. They’re complex and can sometimes drive you up a wall. But once they warm up to you—and you warm up to them—the joys and benefits of having them in your life outweigh just about everything. Sounds a lot like technology, right?

To help simplify instrumenting Spring Boot applications with Grafana Cloud, we are excited to introduce the Grafana OpenTelemetry Starter, a project that connects the latest Micrometer enhancements from Spring Boot 3 with Grafana Cloud using OpenTelemetry. By using these tools, you will have logs, metrics, and traces in a single service — in the same easy way that you can use Prometheus with Spring Boot.

The OpenTelemetry Collector is a useful application to have in your stack. However, deploying it has always felt a little time consuming: working out how to host the config, building the deployments, etc. The good news is the OpenTelemetry team also produces Helm charts for the Collector, and I’ve started leveraging them. There are a few things to think about when using them though, so I thought I’d go through them here.

Observability and monitoring: These terms are often used interchangeably, but they represent different approaches to understanding and managing IT infrastructure. If you are new to these terms or are often confused between the two, this blog is for you! In this blog, we'll explore the key concepts of observability and monitoring, their evolution in IT operations, their differences and similarities, and their importance in modern infrastructure.

On July 25th, 2023, we experienced a total Honeycomb outage. It impacted all user-facing components from 1:40 p.m. UTC to 2:48 p.m. UTC, during which no data could be processed or accessed. This outage is the most severe we’ve had since we had paying customers. In this review, we will cover the incident itself, and then we’ll zoom back out for an analysis of multiple contributing elements, our response, and the aftermath.