“If you can’t measure something to get results, you can’t possibly get better at it. Worse yet, you won’t know what you should be focusing on,” says Dennis Brazil, Sr. Engineering Manager, SRE Monitoring at PayPal. Brazil and his team needed a scalable end-to-end host monitoring solution to keep pace with the company’s infrastructure modernization to a container-based architecture.

2019 was a great year for InfluxData. But before I get into the details, I need to emphasize that none of this was possible without the incredible open source community around InfluxDB, the customers that rely on our products, and the investors that believe in our team.

Redis is an extremely fast NoSQL data store. While it is used mainly as a cache, it can be applied to uses as diverse as graph representation and search. Client libraries are available in all of the major programming languages, and it is provided as a managed service by all of the top cloud service providers. For the past three years, Redis has been named the most loved database by the Stack Overflow Developer Survey.

As part of Elasticsearch 7.5.0, we introduced a couple of ways to control the index age math that’s used by index lifecycle management (ILM) for phase timings calculations using the origination_date index lifecycle settings. This means you can now tell Elasticsearch how old your data is, which is pretty handy if you’re indexing data that’s older than today-days-old.

A few blogs posts ago I wrote about new BI for digital companies and in that blog I alluded that quite a bit of that BI is based on log data. I wanted to follow up on the topic of logs, why they exist and why they contain so much data that is relevant to BI. As I said in that post, logs are an artifact of software development and they are not premeditated, they are generated by developers almost exclusively for the purpose of debugging pre-production code. So how is it that logs are so valuable for BI?

My holiday challenge to explain serverless InfluxDB to my family produced a useful Flux script anyone can put to work today. Before we dive into the code, let me outline the high-level approach to gathering and visualizing how long it takes a website to respond to an HTTP request.

Today we announce InfluxDB 2.0 Open Source’s official move to Beta. This represents a huge step forward from where we started out nearly a year ago and is one step closer to general availability. You can download the latest version on our downloads page. Since we announced the first Alpha for InfluxDB 2.0 back in January ‘19, we have been working hard to build out and harden InfluxDB 2.0’s capabilities.

Curious about how to write more idiomatic concurrent code in Go? It’s not always easy or intuitive, even if you’ve done lots of concurrent programming in other languages. I’ve been lucky to have worked in a well-written code base, and had the expert advice of Beats core area lead Steffen Siering along the way. In this post I’ll walk you through how we implemented a new scheduler for Heartbeat that is part of our upcoming 7.6.0 release.

In this three-part series, we will take a hard look at Amazon’s Elastic Kubernetes Service: how it helps organizations run Kubernetes on AWS, what insights can be gained in EKS and how it’s monitored, and finally, how organizations can get the most out of EKS with the help of Sumo Logic. For the first installment, we’ll learn how it works and how organizations can get started with Amazon’s Elastic Kubernetes Service.

Gathering logs that contain IP addresses are quite common across your infrastructure. Your firewalls, web servers, wireless infrastructure and endpoints can contain IP addresses outside your organization. Having additional data on those logs that gives you the Geolocation of the IP address helps in your investigations and understanding of your traffic patterns. For Example, if you can see logs on a World Map, you know if you are communicating to a country you don’t normally talk to.