I have been super fortunate to work with incredibly innovative, talented teams that create powerful technology to help manage the world's data. When I met with Thomas Hazel, CHAOSSEARCH founder and CTO, and Les Yetton, CHAOSSEARCH co-founder and CEO to talk about CHAOSSEARCH — scalable, performant text search to your object storage without having to move any data — I knew it was special and was eager to join.

The next major version of the LogDNA agent is right around the corner, and we’re introducing some significant improvements. Most notably, we’ve completely rewritten the agent using Rust instead of Node.js. As we gear up for the release, we wanted to explain why we chose Rust for the LogDNA agent, the benefits that it offers over other languages, and how it can help you log faster. Rust is a systems programming language that aims to offer both performance and safety.

One of the benefits of using Logstash in your data pipelines is the ability to transform the data into the desired format according to the needs of your system and organization. There are many ways of transforming data in Logstash, one of them is using the mutate filter plugin. This Logstash filter plugin allows you to force fields into specific data types and add, copy, and update specific fields to make them compatible across the environment.

In the previous post, we discussed the various environments that Log360 helps you audit and secure. Having established the ease of Log360’s use and the breadth of its auditing scope, now we’ll examine some of the critical areas it can help you monitor. With over 1,000 predefined reports and alerts for several crucial types of network activity, Log360 provides comprehensive network auditing.

The use cases and requirements of a logging platform in an organization varies between teams and job functions. The problem isn’t in collecting log data (we are a logging company after all), but in deciding how to manage these logs for each team. For example, our backend developers need detailed, short-lived logs in order to build and test new features; while our infrastructure team needs lengthy retention periods for auditing and compliance.

Looking to learn about Logstash as quickly as possible? This Logstash Tutorial is for you: we’ll install Logstash and push some Apache logs to Elasticsearch in less than 5 minutes. Logstash is a good (if not the) swiss-army knife for logs. It works by reading data from many sources, processing it in various ways, then sending it to one or more destinations, the most popular one being Elasticsearch.

Over the past few years, and coupled with the growing adoption of microservices, distributed tracing has emerged as one of the most commonly used monitoring and troubleshooting methodologies. New tracing tools and frameworks are increasingly being introduced, driving adoption even further. One of these tools is Jaeger, a popular open source tracing tool. This article explores the integration of Jaeger with the ELK Stack for analysis and visualization of traces.

Apache Tomcat is the most popular application server for serving Java applications. Widely-used, mature and well documented, Tomcat can probably be defined as the de-facto industry standard. Some sources put Tomcat’s market share at over 60%! Tomcat is particularly popular for serving smaller applications since it doesn’t require the full Java EE platform. It consumes a relatively small amount of resources and provides users with simpler admin features.