What do mall food courts and Honeycomb have in common? We both love sampling! Not only do we recommend it to many of our customers, we do it ourselves. But once Refinery (our tail-based sampling proxy) is set up, what comes next? Since sampling is inherently lossy, it’s good to be sure the organization’s most important measurements aren’t negatively affected.
For nearly a decade, Logz.io has offered a proven pathway for organizations using the world’s most popular open source tools to monitor and analyze their cloud systems—allowing them to enlist a far more efficient and cost-effective approach.
Observability architecture and design is becoming more important than ever among all types of IT teams. That’s because core elements in observability architecture are pivotal in ensuring complex software systems’ smooth functioning, reliability and resilience. And observability design can help you achieve operational excellence and deliver exceptional user experiences. In this article, we’ll delve into the vital role of observability design and architecture in IT environments.
When we launched Pyroscope in 2021, we had one clear goal: Give developers a powerful open source continuous profiling tool for collecting, storing, and analyzing profiling data. Grafana Labs had a similar goal when they released Grafana Phlare, a horizontally scalable, highly available open source profiling solution inspired by databases like Grafana Loki, Grafana Mimir, and Grafana Tempo.
Cloud deployments and containerization let you provision infrastructure as needed, meaning your applications can grow in scope and complexity. The results can be impressive, but the ability to expand quickly and easily makes it harder to keep track of your system as it develops. In this type of Kubernetes deployment, it’s essential to track your containers to understand what they’re doing.
Elastic Cloud Observability is the premiere tool to provide visibility into your running web apps. Google Cloud Run is the serverless platform of choice to run your web apps that need to scale up massively and scale down to zero. Elastic Observability combined with Google Cloud Run is the perfect solution for developers to deploy web apps that are auto-scaled with fully observable operations, in a way that’s straightforward to implement and manage.
When it comes to observability, your organization will have no shortage of options for tools and platforms. Between open source software and proprietary vendors, you should be able to find the right tools to fit your use case, budget and IT infrastructure. Observability should be cost-efficient, easy to implement and customers should be provided with the best support possible.
Our friends at Tracetest recently released an integration with Honeycomb that allows you to build end-to-end and integration tests, powered by your existing distributed traces. You only need to point Tracetest to your existing trace data source—in this case, Honeycomb. This guest post from Adnan Rahić walks you through how the integration works.
Observability solutions can easily and rapidly get complex — in terms of maintenance, time and budgetary constraints. But observability doesn’t have to be hard or expensive with the right solutions in place. The future of your observability can be a bright one.
The Accelerate State of Devops Report highlights four key metrics (known as the DORA metrics, for DevOps Research & Assessment) that distinguish high-performing software organizations: deployment frequency, lead time for changes, time-to-restore, and change fail rate. Observability can kickstart a virtuous cycle that improves all the DORA metrics.
Data Lakes can be difficult and costly to manage. They require skilled engineers to manage the infrastructure, keep data flowing, eliminate redundancy, and secure the data. We accept the difficulties because our data lakes house valuable information like logs, metrics, traces, etc. To add insult to injury, the data lake can be a black hole, where your data goes in but never comes out. If you are thinking there has to be a better way, we agree!
Understanding what's happening within your systems is a necessity. Have you ever wondered how experts keep an eye on systems to make sure everything's running smoothly? That's where observability tools come in! Observability tools are like helpers that give you a peek inside your tech. In this blog, we will talk about observability tools and how they can be used in different situations so it's easier for you to choose the right one for your organization.
#reliably #chaosengineering #honeycomb #slack #resilience
In this video, we will use a chaos engineering experiment, that we expect to fail, to verify our open tracing and communication platforms are correctly set up. Using the Honeycomb and Slack integrations provided by Reliably, we will send traces and messages and observe if they are triggered as expected.
The needs of observability workloads can sometimes be orthogonal to the needs of compliance workloads. Honeycomb is designed for software developers to quickly fix problems in production, where reducing 100% data completeness to 99.99% is acceptable to receive immediate answers. Compliance and audit workloads require 100% data completeness over much longer (or "infinite") time spans, and are content to give up query performance in return.
One of the hardest challenges in computer science is deciding what to name things. Adoption of consistent nomenclature is difficult because there is no one right answer. In fact, it’s not uncommon for different teams within organizations to choose different names for the same technologies. In the world of monitoring and observability, this can create quite a lot of confusion – not to mention wasted resources.
In the world of observability, having the right amount of data is key. For years Apica has led the way, utilizing synthetic monitoring to evaluate the performance of critical transactions and customer flows, ensuring businesses have important insight and lead time regarding potential issues.
Application performance monitoring (APM) is much more than capturing and tracking errors and stack traces. Today’s cloud-based businesses deploy applications across various regions and even cloud providers. So, harnessing the power of metadata provided by the Elastic APM agents becomes more critical. Leveraging the metadata, including crucial information like cloud region, provider, and machine type, allows us to track costs across the application stack.
Salary Finance is a UK-based financial well-being employee benefit program. Over the last seven years, the company grew from a startup to a scaleup, earning rave reviews along the way from its more than 4,000 customers. However, with fast growth also comes natural growing pains. As their customer base expanded, so did the number of incidents they experienced, which also became harder to diagnose due to lack of visibility into their increasingly complex environment.
In previous blogs, we explored how Elastic Observability can help you monitor various AWS services and analyze them effectively: One of the more heavily used AWS container services is Amazon ECS (Elastic Container Service). While there is a trend toward using Fargate to simplify the setup and management of ECS clusters, many users still prefer using Amazon ECS with EC2 instances.
This post will discuss remote debugging in VS Code and how to improve the remote debugging experience to maximize debugging productivity for developers. Visual Studio Code, or VS Code, is one of the most popular IDEs. Within ten years of its initial release, VS Code has garnered the top spot among popularity indices, and its community is growing steadily. Developers love VS Code not only for its simplicity but also due to its rich ecosystem of extensions, including the support for debugging.
The W3C trace context specification is an amazing standard and a massive leap in standardization of telemetry correlation in the current climate of microservices being the de facto for new systems (that’s a debate for another day).
Christophe is a self-taught engineer from France who specializes in site reliability engineering. He spends most of his time building systems with open-source technologies. In his free time, Christophe enjoys traveling and discovering new cultures, but he would also settle for a good book by the pool with a lemon sorbet.
In this age of complex software systems, code instrumentation patterns define specific approaches to debugging various anomalies in business logic. These approaches offer more options beyond the built-in debuggers to improve developer productivity, ultimately creating a positive impact on the software’s commercial performance. In this post, let’s examine the various code instrumentation patterns for Node.js.
We’re excited to announce that we just released the next-generation of our observability platform – the Circonus Telemetry Cloud™. Here’s a closer look at what it is and why we think it’s a standout in the monitoring and observability space.
Observability has traditionally been conceptualized in terms of three core facets: logs, metrics, and traces. For years, these elements have been seen as the “pillars” of observability, serving as the foundational components for system monitoring and delivering key insights to improve system performance. However, with the exponential growth in system complexity, a more comprehensive and unified perspective on observability has become necessary.
As we navigate our fast-paced digital era, organizations across various industries are in constant pursuit of strategies for efficient monitoring, performance tuning, and continuous improvement of their services. Elastic® and Kyndryl have come together to offer a solution for Mainframe Observability, engineered with an emphasis on organizations that are heavily reliant on mainframes, including the financial services industry (FSI), healthcare, retail, and manufacturing sectors.
Observability vs Telemetry vs Monitoring - What they are, differences and what lies in future.
A robust observability strategy forms the backbone of a successful cloud environment. By understanding cloud observability and its benefits, businesses gain the ability to closely monitor and comprehend the health and performance of various systems, applications, and services in use. This becomes particularly critical in the context of cloud computing. The resources and services are hosted in the cloud and accessed through different tools and interfaces.
As your infrastructure and applications scale, so does the volume of your observability data. Managing a growing suite of tooling while balancing the need to mitigate costs, avoid vendor lock-in, and maintain data quality across an organization is becoming increasingly complex. With a variety of installed agents, log forwarders, and storage tools, the mechanisms you use to collect, transform, and route data should be able to evolve and adjust to your growth and meet the unique needs of your team.
Tech debt is usually one of the most fraught topics on engineering teams. Engineers often feel they aren’t allowed enough time to address tech debt. Product partners wonder why engineers spend so much time working on it—or at least talking about it. “The business” always seems to insinuate that engineers should do less of it, instead focusing on shipping value to customers.
As today’s businesses increasingly rely on their digital services to drive revenue, the tolerance for software bugs, slow web experiences, crashed apps, and other digital service interruptions is next to zero. Developers and engineers bear the immense burden of quickly resolving production issues before they impact customer experience.
Most companies in today's business landscape that deal with large amounts of data want to integrate their applications so that they can pass data between them seamlessly and easily. Being able to ensure that you can see exactly what is happening at every stage of the process is key, and this is where approaching the process with observability in mind can make a real difference. Deciding at the outset that observability is something that you want to be baked into the process means that you can plan and execute with that in mind.
The OpenTelemetry Go project now supports automatic instrumentation via eBPF! This is a big milestone for the project and makes it significantly easier to generate data from your Go apps: The automatic instrumentation agent is still in s/alpha/beta today, but it’s ready for you to try on your applications!
Logz.io is excited to announce Easy Connect, which will enable our customers to go from zero to full observability in minutes. By automating service discovery and application instrumentation, Easy Connect provides nearly instant visibility into any component in your Kubernetes-based environment – from your infrastructure to your applications. Since applications have been monitored, collecting logs, metrics, and traces have often been siloed and complex.
