You’re likely familiar with local debugging—the ability to go through your code line by line to find and eliminate bugs. However, with the ever-increasing complexity of development environments, working efficiently with remote systems is becoming more necessary. In this case, “remote” can mean any machine you don’t have native OS-level access to, such as Virtual Machines, Docker containers, and entirely separate devices accessed over the network.
“Observability,” is the process in DataOps of recording data generated by digital systems as they go about their processes. There are some great companies in the observability space, generating a whopping $17 billion annually, and contributing a significant portion to the modest 2.5 quintillion bytes of data created every year.
Keeping the experience of your end user in mind is important when developing applications. Observability tools help your team measure important performance indicators that are important to your users, like uptime. It’s generally a good practice to measure your service internally via metrics and logs which can give you indications of uptime, but an external signal is very useful as well, wherever feasible.
Honeycomb is known for its incredibly fast performance: you can sift through billions of rows, comparing high-cardinality data across thousands of fields, and get fast answers to your queries. And none of that is possible without our purpose-built distributed column store. This post is an introduction to what a distributed column store is, how it functions, and why a distributed column store is a fundamental requirement for achieving observability.
In modern software systems, it is common for several developers to work on the same project simultaneously. Siloed working with infrequent merging of code in a shared repository often leads to bugs and conflicts that are difficult and time-consuming to resolve. To solve this problem, we can adopt continuous integration.
Every now and then, issues occur that disrupt the very fabric of global software engineering. Chief amongst them is the recent mass outage of Github. Github is a fundamental building block in software productivity, hosting over 190 million code repositories. Github hosts our code and libraries, runs build pipelines, and much more. It is a central hub of activity and it is consumed by tens of thousands of organizations.
The content delivery network (CDN) has been an integral part of application infrastructure for more than two decades. A CDN is critical to the end-user experience, but it is no longer considered to be just a caching server. It has evolved to provide security from cyber threats, including DDOS attacks along with front end optimization. Although CDN services are now an indispensable part of any application infrastructure, visibility into CDN performance remains limited.
A selection of questions and answers from our recent webinar on leveraging AIOps to run sustainable, blameless retrospectives.
Is GitOps changing observability as we know it? GitOps has been the buzz word in the DevOps space for several years. GitOps, to those that are not familiar, is an operational methodology for DevOps that leverages a continuous deployment approach with Git as the single source of ‘truth’ for declarative control over both infrastructure and applications.
After building Lightrun for the JVM – an easier way to get a better grasp on production applications written in Java, Scala, and Kotlin – we’re pleased to announce the release of Lightrun’s developer-native observability platform for Python!
