The latest News and Information on Log Management, Log Analytics and related technologies.
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.
