When developers think of log files and log analysis, their minds typically transports into the world of contributing factors and incident remediation. However, analyzing log events doesn’t always need to be about a specific bug and its corresponding resolution. In fact, log analysis can be a very useful resource for organizations looking to develop a more high-level and large-scale plan for their application moving forward.
Logging is probably not the first item to come to mind when most of us think about DevSecOps, a term that refers to the integration of security into DevOps processes, but it should be. Logging and log management play a critical role in helping to put DevSecOps principles into practice by ensuring that developers, IT operations staff, and security teams have the visibility and communication pipelines they need to prioritize security at all stages of the DevOps delivery cycle.
Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.
Today is an exciting day for LogDNA! I have two wonderful announcements to make. First, we’ve officially announced that LogDNA has closed a $25 million series C round led by Emergence Capital. Second, and most importantly, I’m thrilled to share that Tucker Callaway, LogDNA’s current President and Chief Revenue Officer, is transitioning into a new role as the company’s Chief Executive Officer (CEO).
This was originally posted on The New Stack. Once upon a time, log management was relatively straightforward. The volume, types, and structures of logs were simple and manageable. However, over the past few years, all of this simplicity has gone out the window. Thanks to the shift toward cloud native technologies—such as loosely coupled services, microservices architectures, and technologies like containers and Kubernetes—the log management strategies of the past no longer suffice.
Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.
When thinking about serverless applications, one thing that comes to mind immediately is efficiency. Running code that gets the job done as swiftly and efficiently as possible means you spend less money, which means good coding practices suddenly directly impact your bottom line. How does logging play into this, though? Every logging action your application takes is within the scope of that same performance evaluation.
When thinking about serverless applications, one thing that comes to mind immediately is efficiency. Running code that gets the job done as swiftly and efficiently as possible means you spend less money, which means good coding practices suddenly directly impact your bottom line. How does logging play into this, though? Every logging action your application takes is within the scope of that same performance evaluation.
At first glance, microservices logging may seem simple. You just take the same principles you’ve always followed for monoliths and apply them to each microservice in your application, right? Well, no. The differences between microservices and monolithic architecture amount to much more than a difference in the number of services involved.
Managing stateful applications has been challenging for engineering and operations teams long before the debut of Kubernetes. In this post, we’ll explore all aspects of your deployments of stateful applications on Kubernetes, from the underlying hardware to Pod update strategies, and provide insights into how LogDNA uses stateful Kubernetes to build one of the world’s fastest log management platforms.
