The global corporate landscape is on the brink of a complete premises lockdown in light of the COVID-19 crisis. Service disruption is inevitable, and enterprises’ business continuity plans are being put to the test. Despite this challenge, it’s heartening to see companies across nations take quick steps to ensure the health and safety of their employees during these trying times.
We’ve been working on something big. We’re building Request Metrics, a new service for web performance monitoring. TrackJS is a fantastic tool to understand web page errors, but what if your pages aren’t broken, just slow? What if the checkout page takes 10 seconds to load? What if that user API is slowing down from your recent database change? What pages have the worst user experience? Request Metrics will tell you that.
Monitoring has been around since the dawn of computing. Recently, however, there’s been a revolution in this field. Cloud native monitoring has introduced new challenges to an old task, rendering former solutions unsuitable for the job. When working with cloud native solutions such as Kubernetes, resources are volatile. Services come and go by design, and that’s fine—as long as the whole system operates in a regular way.
In today’s era of microservices, containers, and containerized applications, software architecture is more complex. Kubernetes is king in this environment, orchestrating an army of Docker containers in more distributed environments.
Are you building and deploying software manually and would like to change that? Are you interested in learning about building a Jenkins pipeline and better understand CI/CD and DevOps at the same time? In this first post, we will go over the fundamentals of how to design pipelines and how to implement them in Jenkins. Automation is the key to eliminating manual tasks and to reducing the number of errors while building, testing and deploying software.
In the context of logging, multiline logs happen when a single log is written as multiple lines in the log file. When logs are sent to 3rd party log monitoring platforms like Coralogix using standard shipping methods (e.g. Fluentd, Filebeat), which read log files line-by-line, every new line creates a new log entry, making these logs unreadable for the user.
Everyone knows it’s been a tough time for businesses. All flights, conferences and in-person meetings have been canceled. The novel coronavirus (COVID-19) has even made us all stand apart from each other and, if anything, bump elbows only. Times are tough. For those of you in the software business, you know you’ve got it easier than some industries. You CAN work from home. You CAN continue developing. And you should, too.
Social distancing measures, like remote working, school closures, and “shelter in place” have driven us onto the Internet more than ever before, creating unprecedented demand for a range of digital services from companies, many of whom weren’t set up for this type of pressure. As a digital operations company, we help teams ensure their websites and apps are running perfectly and partner with over 12,000 organizations around the world—from start-ups to 58 of the Fortune 100.
We’re really big fans of OpenTelemetry at Honeycomb. As we’ve blogged about before, OpenTelemetry is the next phase of the OpenTracing and OpenCensus projects. Instead of working on separate but similar efforts, those two projects have merged to create OpenTelemetry. This is wonderful for the larger community as it gives people a clear way to instrument their code for metrics and traces that isn’t specific to any tool or vendor. OpenTelemetry is a CNCF sandbox project.
BlackSky monitors the globe from space, the air, the ground, the internet, environmental sensors, asset tracking sensors, satellites in space, social media feeds, industrial IoT, and other sources too numerous to name. Once gathered in their Elastic-powered analytics engine, all of the data from these disparate sources is correlated, compared, and cleaned.
