If you think log files are only necessary for satisfying audit and compliance requirements, or to help software engineers debug issues during development, you’re certainly not alone. Although log files may not sound like the most engaging or valuable assets, for many organizations, they are an untapped reservoir of insights that can offer significant benefits to your business.
Telegraf comes included with over 200+ input plugins that collect metrics and events from a comprehensive list of sources. While these plugins cover a large number of use cases, Telegraf provides another mechanism to give users the power to meet nearly any use case: the Exec and Execd input plugins. These plugins allow users to collect metrics and events from custom commands and sources determined by the user.
A common DevOps use case involves alerting when hosts stop reporting metrics, aka a deadman alert. This can be done using the monitor.deadman() Flux function. One can easily create a deadman (or threshold) check in the InfluxDB UI Alerts section or craft a custom task to alert as well. Check out InfluxDB’s Checks and Notifications system post for more details. It’s also possible to use the monitor.deadman() function directly in a dashboard cell.
Organizations using Amazon Web Services (AWS) cloud traditionally leveraged Reserved Instances (RI) to realize cost savings by committing to the use of a specific instance type and operating system within the AWS region. Nearly 2 years ago, AWS rolled out a new program called Savings Plans, which give companies a new way to reduce costs by making an advanced commitment of a one-year or three-year fixed term.
If a picture is worth a thousand words, then a well-done data visualization is worth a million. The quality of a dashboard can make or break an application. In this tutorial, you will learn how to make high-quality data visualizations easily by using the Nivo charting library with ReactJS. You will also learn how to query data stored in InfluxDB to make your charts dynamic and versatile.
This is a short blog post about a pattern that we’ve observed more frequently among some of the large enterprises: the use of AWS S3 as both an observability lake and a data bus. AWS S3’s simple API, ubiquitous language support, unmatched reliability and durability, retention options, and numerous pricing plans have made it the de facto standard for storing massive amounts of data.
