In basically every modern software organization, building software is not just a matter of writing code – it’s a matter of testing it to ensure it works properly, a matter of creating artifacts out of it that can be used by the end customers, and a matter of deploying them to a customer-accessible location for these customers to be able to actually use it.
Heaven knows we all could use some luck these days, and observability may be just the thing we need. But observability isn’t luck, and it isn’t really new either. A few people even know that observability is an aspect of control theory, which dates back to the 1800s! In this blog post, I’ll cover some of the history of observability vs.
The term ‘cloud native’ has become a much-used buzz phrase in the software industry over the last decade. But what does cloud-native mean? The Cloud Native Computing Foundation’s official definition is: From this definition, we can differentiate between cloud-native systems and monoliths which are a single service run on a continuously available server. Like Amazon’s AWS or Google Azure, large cloud providers can run serverless and cloud-native systems.
Juju re-imagines the world of operating software securely, reliably, and at scale. Juju realizes the promise of model-driven operations. Excellent observability is undeniably a key ingredient for operating software well, which is why the Charmed Operator ecosystem has long provided operators the ability to run a variety of open source monitoring software. We collectively refer to these operators as the Logs, Metrics, and Alerts (LMA) stack.
For you, one or more of these statements (and / or challenges) likely apply to you, and the organization for which you work. Which of these are you hearing or saying? Splunk can help you with these in many ways. Today, I am highlighting one way to address many of these statements, specifically with the Content Pack for Splunk Observability Cloud.
With the demand to meet service level agreements (SLAs), any on-call SRE can tell you that incidents always happen at the wrong time. Things break when you least expect them to (on a date, about to beat a new level in a video game, pizza delivery just arrived, asleep at 3am). During these inopportune moments, you want to make sure it's easy to get the data you need, no matter which device is nearby.
An application’s success depends on many factors. One of the most crucial is how it performs when actual users interact with it. The chances are that even if you properly prepare your entire software development strategy, problems will occur at some point or another.
Observability has gained a lot of momentum and is now rightly a central component of the microservices landscape: It’s an important part of the cloud native world where you may have many microservices deployed on a production Kubernetes cluster, and a need to monitor these microservices keeps rising. In production, quickly finding failures and fixing them is crucial. As the name suggests, observability plays an important role in this failure discovery.
Today, we announced that Honeycomb has raised $50M in Series C funding, in a round led by Insight Partners and joined by all existing investors from our Series B. We’re using this investment to support the growth of our customers and community, ensure the benefits of observability can be realized by all engineering teams, and expand the ways we can better serve you.
A digital evolution is taking place at Carhartt. Agent of Transformation Tim Masey shares in his own words how full-stack observability plays into it.
Log observability and monitoring are terms often used interchangeably, but really they describe two approaches to solving and understanding different things. Observability refers to the ability to understand the state of a complex system (or series of systems) without needing to make any changes or deploy new code.
You might already know that OpenTelemetry is the future of instrumentation. It’s an open-source and vendor-neutral instrumentation framework that frees you from the trap of using proprietary libraries simply to understand how your code is behaving. Best of all, you can instrument your applications just once and then take that instrumentation to any other backend system of your choice. This blog shows you exactly how to use OpenTelemetry to ✨break the vendor lock-in cycle.✨
Tucker Callaway is the CEO of LogDNA. He has more than 20 years of experience in enterprise software with an emphasis on developer and DevOps tools. Tucker drives innovation, experimentation, and a culture of collaboration at LogDNA, three ingredients that are essential for the type of growth that we've experienced over the last few years.
Which data sources do DevOps teams need in order to achieve observability? At a high level, that’s an easy question to answer. Concepts like the “three pillars of observability”—logs, metrics, and traces—may come to mind. Or, you may think in terms of techniques like the RED Method or Google’s Golden Signals, which are other popular frameworks for defining which types of data teams should collect for monitoring and observability purposes.
As product developers, our responsibility continues beyond shipping code. To keep our software running, we need to notice whether it’s working in production. To make our product smoother and more reliable, we need to understand how it’s working in production. We can do this by making the software tell us what we need to know. How can we notice when the software is running smoothly? Make it tell us!
In my previous post, we explored why Honeycomb is implemented as a distributed column store. Just as interesting to consider, though, is why Honeycomb is not implemented in other ways. So in this post, we’re going to dive into the topic of time series databases (TSDBs) and why Honeycomb couldn’t be limited to a TSDB implementation. If you’ve used a traditional metrics dashboard, you’ve used a time series database.
Observability and its implementation may look different to different people. But, underneath all the varying definitions is a single, clear concept: Most software that’s run today uses microservices or loosely coupled distributed architecture. While this design makes scaling and managing your system more straightforward, it can make troubleshooting issues more difficult. The three pillars of observability are different methods to track software systems, especially microservices.
Observability is a staple of high-performing software and DevOps teams. Research shows that a comprehensive observability solution, along with a number of other technical practices, positively contributes to continuous delivery and service uptime.
In the recent past, most engineering teams had a vague notion of what Service Level Agreements (SLAs) and Service Level Objectives (SLOs) were—mainly things that their more business-focused colleagues talked about at length during contract negotiations. The success or failure of SLAs were tallied via magic calculations (what is “available” anyway?!) at the end of the month or quarter, and adjustments were made in the form of credits or celebrations in the break room.
At ElasticON Global 2021, Tanya Bragin, VP Product, Observability, and the Elastic Observability team showed how ongoing innovations continue to deliver actionable insights and faster root cause detection, reducing mean time to resolution (MTTR). The adoption of cloud, microservices, and ephemeral infrastructure is driving increased complexity, requiring an observability solution to provide end-to-end visibility.
Some things just go better together. Like barbeque and blues, sunsets and beaches, cheese and fine wine — hey, even software and superheroes go better together! That’s why in this blog we are going to look at why IT Operations and Observability just go better together, through a superhero analogy. Enter the Dark Knight himself — Batman! He will represent observability. IT Operations will be represented by Lucius Fox.
Observability is a term that is becoming commonplace in both startups and enterprises. Log observability is different from monitoring, as it provides visualized metrics from a variety of different systems in a single pane of glass view. This is invaluable for organizations to understand the interdependencies and links between external events and internal performance.
The Query Data API is a Honeycomb Enterprise feature. With a Honeycomb Enterprise account, you can use this API today. Head over to our API docs to learn how to get access to your data. If you aren’t yet a Honeycomb Enterprise user, try it out by requesting an Enterprise Trial. Starting today, Honeycomb Enterprise customers can use the Honeycomb Query Data API to programmatically run queries and retrieve their results, and pull query results into any data visualization tool of their choice.
Ever since the very beginning, Honeycomb has poured a lot of heart and soul into our blog. We take pride in knowing it isn’t just your typical stream of feature updates and marketing promotions, but rather real, meaty pieces of technical depth, practical how-to guides, highly detailed retrospectives, and techno-philosophical pieces. One of my favorite things is when people who aren’t customers tell me how much they love our blog.
New Relic recently published a survey on the observability projections and trends for the year 2021. At observIQ, an emerging platform of choice in the observability space, we decided to give our users a rundown on what we agreed and disagreed within their survey results.
