When you send telemetry into Honeycomb, our infrastructure needs to buffer your data before processing it in our “retriever” columnar storage database. For the entirety of Honeycomb’s existence, we have used Apache Kafka to perform this buffering function in our observability pipeline.

SaaS is exploding and so it should; it takes commoditized work and infrastructure away from tech teams so that they can focus on differentiating features. But what happens when it goes wrong? How do SaaS platforms make sure they aren't letting their customers down and in turn, letting their customers down? Observability, bolstered with AI gives all the partners the best chance to optimize availability and customer experience. Here's how.

Achieving full, 360-degree observability across your entire IT ecosystem and application components can be thwarted by the disconnect between technical monitoring and business outcomes - running the risk of catastrophic service failures.

One of the most common questions we get at Honeycomb is “What insights can you get in the browser?” Browser-based code has become orders of magnitude more complex than it used to be. There are many different patterns, and, with the rise of Single Page App frameworks, a lot of the code that is traditionally done in a backend or middle layer is now being pushed up to the browser. Instead, the questions should be: What insights do frontend engineers want?

In the past few years, the word “observability” has steadily gained traction in the discussions around monitoring, DevOps, and, especially, cloud-native computing. However, there is significant confusion about the overlap or difference between observability and monitoring.

It’s one thing to set up an observability strategy. But what’s it like to introduce and scale observability effectively across an organization? In a wide-ranging conversation at ObservabilityCON 2021, three technical pros from Snyk, TripAdvisor, and Citibank joined Grafana Labs VP Global Solutions Engineering Steve Mayzak and — with more than 75 years experience between them — they shared the triumphs and turbulence in their respective observability journeys.

Dear Trapped, Thanks for asking the question! Approaching observability as an all-or-nothing problem often leads to the project feeling daunting. But that’s not specific to observability—any project can be overwhelming if you think it needs to be done all at once, perfectly. Such as, erm, writing an entire book on observability! *looks around worriedly*

Moving beyond traditional monitoring to embrace full stack observability offers a seemingly endless range of benefits. Beyond unifying logs, metrics, and traces in a single platform, the opportunity to enlist advanced analytics and engage a more predictive approach represents another huge step forward.

A developers perspective is different. While managing various sectors in a software, sometimes it would be difficult to monitor the activities and identify the bug that is disrupting the functions. What if you can spot the error beforehand, and resolve it at the earliest? The strategies that we focus on, and implement are the ones that help us effectively manage our tasks. That is possible by knowing about Observability. Let's learn in detail about it through this blog. TABLE OF CONTENTS.

Observability is a measure of how well the internal state of a system can be inferred from its external outputs. It helps us understand what is happening in our application and troubleshoot problems when they arise. It’s an essential part of running production workloads and providing a reliable service that attracts and retains satisfied customers.

Distributed tracing has been growing in popularity as a primary tool for investigating performance issues in microservices systems. Our recent DevOps Pulse survey shows a 38% increase year-over-year in organizations’ tracing use. Furthermore, 64% of those respondents who are not yet using tracing indicated plans to adopt it in the next two years. However, many organizations have yet to realize just how much potential distributed tracing holds.

From September to early October, Honeycomb declared five public incidents. Internally, the whole month was part of a broader operational burden, where over 20 different issues interrupted normal work. A fraction of them had noticeable public impact, but most of the operational work was invisible. Because we’re all about helping everyone learn from our experiences, we decided to share the behind-the-scenes look of what happened.

Today, we are launching a new Grafana Labs product, Grafana Enterprise Traces. Powered by Grafana Tempo, our open source distributed tracing backend,.and built by the maintainers of the project, this offering is an exciting addition to our growing self-managed observability stack tailored for enterprises.

There’s something wrong with the pricing of observability services. Not just because it costs a lot – it certainly does – but also because it’s almost impossible to discern, in many cases, exactly how the costs are calculated. The service itself, the number of users, the number of sources, the analytics, the retention period, and extended data retention, and the engineers on staff who maintain the whole system are all relevant factors that feed into the final expense.

The moment of launching something new at a game studio (titles, experiences, features, subscriptions) is a blockbuster moment that hangs in the balance. The architecture—distributed and complex, designed by a multitude of teams, to be played across a variety of devices in every corner of the world—is about to meet a frenzy of audience anticipation, along with the sky-high expectations of players, executives, and investors.

After years of helping developers monitor and debug their production systems, we couldn’t help but notice a pattern across many of them: they roughly know that metrics and traces should help them get the answers they need, but they are unfamiliar with how metrics and traces work, and how they fit into the bigger observability world. This post is an introduction to how we see observability in practice, and a loose roadmap for exploring observability concepts in the posts to come.

Credit: Unsplash What is monitoring? What is observability? Monitoring shows you how a Kubernetes environment and all of its layers are operating. Observability, on the other hand, is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.

Observability vs monitoring, what is the difference? Monitoring is the what to observability’s why. Here we dig into the differences.

Monitoring cloud-native systems is hard. You’ve got highly distributed apps spanning tens and hundreds of nodes, services and instances. You’ve got additional layers and dimensions—not just bare metal and OS, but also node, pod, namespace, deployment version, Kubernetes’ control plane and more. To make things more interesting, any typical system these days uses many third-party frameworks, whether open source or cloud services.

Software development firms need to develop and deploy software solutions and changes quickly, safely, and as demanded by the client. DevOps can help! There’s a major subset of DevOps you can’t overlook: observability.

The best way to be sure that you keep a secret is not to know it in the first place. Managing secrets is a notoriously difficult engineering problem. Across our industry, secrets are stored in a bewildering variety of secure (and sometimes notoriously insecure) systems of varying complexity. Engineers are often trying to balance the least worst set of tradeoffs. At Honeycomb, we asked: What if we didn’t need to know your secrets to begin with?

Databases have always been the backbone of applications – both web and enterprise. Now, more than ever before, you need to know not just overall statistics about your database, but you must identify how database performance interacts with the network, operating system, servers, configuration, and even third party dependencies.

Two popular deployment architectures exist in software: the out-of-favor monolithic architecture and the newly popular microservices architecture. Monolithic architectures were quite popular in the past, with almost all companies adopting them. As time went on, the drawbacks of these systems drove companies to rework entire systems to use microservices instead.

Anodot recently took part in the 2021 Data Agility Day, an event dedicated to examining how organizations are extracting value from data. CEO and Co-Founder David Drai was joined by David Ashirov, VP of Data at Freshly, where he has worked to build a data stack that departments across the company could leverage to drive business. Ashirov is a senior executive with two decades of experience in data engineering, business intelligence, and marketing.