There’s a lot of confusion surrounding the differences between structured and unstructured data. To better understand why, let’s review which data formats the industry currently is using, and some of the challenges they pose. Simply put, structured data typically refers to highly organized, stored information that is efficiently and easily searchable. Unstructured data is not.
As applications are getting more complex, it’s becoming harder to deliver high-quality applications. Tools likeJavaScript has come a long way in recent years. Browsers are becoming more robust and machines are growing more powerful. Pair this with the recent development of Node.js for execution of JavaScript on servers, and you can understand why JavaScript has exploded in popularity.
The days when you could simply SSH into a server and perform a fancy grep are long gone. If you’re reading this article, chances are either you are looking to move from that obsolete approach to a centralized logging approach with a log management tool, or you are looking for an alternative log management tool to replace your existing solution. Problem is, there are so many different tools out there, making a choice can be overwhelming. So how do you pick the right solution?
When I first joined this startup that makes an observability platform, I was a front-end Javascript developer who had never ssh’ed into production–I didn’t even know what tracing or monitoring or metrics were, let alone what it meant for logs to be structured or how they could be useful to me. But within a couple months I joined the on-call rotation, and now share responsibility in our services along with the rest of my team.
Views may seem straightforward at first, but they hide a lot of power. On a very basic level, a view is a shortcut to a specific search query or filter. You can use views to display only a subset of logs, create alerts and graphs, export specific events, and even embed your log event feed on another website. In this post, we’ll present several tips and tricks for making the most out of views.
In a previous post, we introduced a new integration with Microsoft Azure that makes it easy to ship Azure logs and metrics into Logz.io using a ready-made deployment template. Once in Logz.io, this data can be analyzed using the advanced analytics tools Logz.io has to offer — you can query the data, create visualizations and dashboards, and create alerts to get notified when something out of the ordinary occurs.
In order to analyze logs efficiently, they must be structured effectively. Often, logs from different sources label data fields differently and/or provide data that’s completely unstructured. The problem is that both types of data need to be structured appropriately in order to key in on particular elements within the log data, such as: Monitoring on source address, Applying rules associated with user names, and Creating alerts for destination addresses.
When logging applications to a centralized location like LogDNA, developers have two options: using a logging agent or using a logging library. Both approaches will get your logs to their destination, but choosing one over the other can have a significant impact on the design of your applications and infrastructure. In this article, we’ll explain the difference between logging via agents and logging via libraries, and which approach works best in modern architectures.
