In Part 1 of this series, we talked about the origins of observability and why you need it. In this blog (Part 2), we will cover exactly what observability is, what it isn’t, and how to get started. Before we can dive into how to approach observability, let’s get one thing clear: You can’t buy a one-size-fits-all observability solution.
We’ve got a lot of OpenTelemetry-flavored honey to send your way, ranging from OpenTelemetry SDK distribution updates to protocol support. We now support OpenTelemetry logs, released a new SDK distribution for OpenTelemetry Go, and have some updates around OpenTelemetry + Honeycomb to share. Let’s see what all the buzz is about this time! 🐝🐝
At VMware, we are on a mission to build a comprehensive, extensible, and intelligent monitoring and observability platform to help businesses run seamlessly. Over the past few years, we have evolved our platform to deliver invaluable end-to-end observability across applications and infrastructure.
With its market size reaching more than $2 billion in 2020, you’d think that a universal definition of the term observability would have emerged by now. But it turns out that a clear definition of a term or industry isn’t necessarily a prerequisite for the rapid growth of its market size — just ask everyone at your next dinner party to define blockchain for you and see how many different answers you get!
Someone once described dashboards to me as “expensive TV for software engineers.” At first, I stood there quietly shocked—dashboards had informed many root cause analyses (RCAs) in my life as a developer.
Logs are key to monitoring the performance of your applications. Kubernetes offers a command line tool for interacting with the control plane of a Kubernetes cluster called Kubectl. This tool allows debugging, monitoring, and, most importantly, logging capabilities. There are many great tools for SREs. However, Kubernetes supports Site Reliability Engineering principles through its capacity to standardize the definition, architecture, and orchestration of containerized applications.
SLOs—or Service Level Objectives—can be pretty powerful. They provide a safety net that helps teams identify and fix issues before they reach unacceptable levels and degrade the user experience. But SLOs can also be intimidating. Here’s how a lot of teams feel about them: We know we want SLOs, we’re not sure how to really use them, and we don’t know how to debug SLO-based alerts. Don’t worry, we’ve got your answer—observability!
Kubernetes is the most popular container orchestration tool for cloud-based web development. According to Statista, more than 50% of organizations used Kubernetes in 2021. This may not surprise you, as the orchestration tool provides some fantastic features to attract developers. DaemonSet is one of the highlighted features of Kubernetes, and it helps developers to improve cluster performance and reliability.
Roaming in the world of cloud technology not only helps you take a glance at the realm of cutting-edge technology but also helps you get familiar with concepts such as monitoring and observability. This article will cover an introduction to monitoring and the need for monitoring applications. From here, we will look at how you can utilize the data received when monitoring an application. This will allow us to understand how the concept of observability fits in with monitoring.
In this post, we’ll look at how you can use OpenTelemetry to monitor your unit tests and send that data to Honeycomb to visualize. It’s important to note that you don’t need to adopt Honeycomb, or even OpenTelemetry, in your production application to get the benefit of tracing. This example uses OpenTelemetry purely in the test project and provides great insights into our customer’s code. We’re going to use xUnit as the runner and framework for our tests.
There have been amazing articles on the subjects of migrating from a monolith to a microservice architecture e.g. this is probably one of the better examples. The benefits and drawbacks of the architectures should be pretty clear. I want to talk about something else though: the strategy. We build monoliths since they are easier to get started with. Microservices usually rise out of necessity when our system is already in production.
For most businesses, effective digital transformation is a key strategic objective, and as computing infrastructure grows in complexity, end-to-end observability has never been more important to this cause. However, the amount of data and dynamic technologies required to keep up with demand only continues to increase, and current tools are not equipped to handle it- with any discrepancies resulting in rising costs and reduced competitiveness.
The scenario: you want to see distributed traces, maybe for your web app. You’ve set up an OpenTelemetry collector to receive OTLP traces in JSON over HTTP, and send those to Honeycomb (how to do that is another post, and we’ll link it here when it’s up).
Honeycomb Play is an interactive sandbox that lets users explore Honeycomb’s data-enriched UI through a guided scenario. The hands-on experience takes a deep dive into how Honeycomb enables you to identify issues, assess their impact, and diagnose their causes for remediation. There is no requirement to sign up—simply dive in and get started right away!
In the race to create more sophisticated apps, microservices and scalable cloud environments, monitoring is not enough. As environments gain complexity, an observability mindset is paramount in transforming data into mission-critical insights.
This is how the developer story used to go: You do your coding work once, then you ship it to production—only to find out the code (or its dependencies) has security or other vulnerabilities. So, you go back and repeat your work to fix all those issues. But what if that all changed? What if observability were applied before everything was on fire? After all, observability is about understanding systems, which means more than just production.
Power has a way of flowing towards people managers over time, no matter how many times you repeat “management is not a promotion, it’s a career change.” It’s natural, like water flowing downhill. Managers are privy to performance reviews and other personal information that they need to do their jobs, and they tend to be more practiced communicators.
Businesses today need to react instantly to changes or alerts that impact the digital experience. Full-stack visibility can help.
Cloud native and microservice architectures bring many advantages in terms of performance, scalability, and reliability, but one thing they can also bring is complexity. Having requests move between services can make debugging much more challenging and many of the past rules for monitoring applications don’t work well. This is made even more difficult by the fact that cloud services are inherently ephemeral, with containers constantly being spun up and spun down.
How do you pass context from events that concern Security teams to Development teams who can make changes and address those events? Often this involves a series of meetings and discussion that can take days or weeks to filter down from security event to developer awareness. Compounding the problem, developers generally do not have access to Splunk Core, Cloud or Enterprise indexes used by security teams, and indeed, may use only Splunk Observability for their metrics, traces and even logs.
If you were pulled into a meeting right now and asked to give your thoughts on how to achieve better outcomes with monitoring and observability, what would you recommend? Would you default to suggesting that your team improve Mean Time To Detect (MTTD)? Sure, you might make some improvements in that area, but it turns out that most of the opportunities lie in what comes after your system detects an issue. Let’s examine how to measure improvements in monitoring and observability.
I care a lot about instrumentation and telemetry and OpenTelemetry, so I was thinking of joining the observability engineering team at my company… but it seems like they spend all their time managing Prometheus and Grafana. I guess I was expecting something very different?
Organizations in every industry are becoming increasingly dependent upon data to drive more efficient business processes and a better user experience. As the data collection and preparation processes that support these initiatives grow more complex, the likelihood of failures, performance bottlenecks, and quality issues within data workflows also increases.
Google’s Core Web Vitals (CWVs) are used to rank the performance of mobile sites or pages. It’s easy to see when your CWV scores are low, but it’s not always clear exactly why that’s happening. In Honeycomb’s new guide, Tracking Core Web Vitals with Honeycomb and Vercel, you can learn how to capture, analyze, and debug your real-world CWV performance using a free Honeycomb account.
How much does monitoring and observability actually cost us? We all collect logs, metrics, traces, and possibly other telemetry data. Yet, this can get expensive pretty quickly, especially in today’s microservices-based systems.
