For time series workloads the ability of serverless functions to scale up and down is a major advantage, especially for something like IoT devices that may have intermittent connectivity and might suddenly send data in bursts. In this type of situation, it doesn’t make sense to be paying for a server to be running 24/7 when you can use a serverless function and only pay for the compute you use.
Whether you’re using InfluxDB Cloud or InfluxDB OSS, the InfluxDB API provides a simple way to interact with your InfluxDB instance. The InfluxDB v2.0 API offers a unified approach to querying, writing data to, and assessing the health of your InfluxDB instances. Today we want to share a Postman project to help you use the API easily. Postman is “an API platform for building and using APIs”.
Monitoring is a critical aspect of cloud computing. At any time, you need to know what’s working, what isn’t, and have the ability to respond to changes occurring in a given environment. Effective monitoring begins with the ability to collect performance data from across an ecosystem and present it in a useful way. So the easier it is to manage monitoring data across an ecosystem, the more effective those monitoring solutions are and the more efficient that ecosystem is.
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.
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.
Wouldn’t it be nice to be able to perfectly predict the future? We are a long way from being able to do that, but that is basically the goal of anybody working in the data science field — take a bunch of historical data and then try to make future predictions based on that data.
Conventional databases such as PostgreSQL or MongoDB are great at safekeeping the state of your system in a tabular or document format, but what about time-dependent data: systems metrics, IoT device measurement or application state change? For those things, you need a more suitable type of database, one designed to manage better semi-structured data with a time characteristic.
It’s that season of sharing, and in the spirit of sharing, we have a new feature to share with you — notebook sharing. Now you can take your favorite InfluxDB notebooks and share them with whoever you would like. They don’t need to have an InfluxDB Cloud account. They just click on the link you share with them, and they can see the notebook that you shared, for the time range that you selected.
Do you have a bunch of smart home devices, such as IoT devices like smart switches, cameras, doorbells, alarm systems or appliances? Have you ever wanted to monitor and send events from those devices to InfluxDB? And wouldn’t it be amazing if you could do that with zero coding? With IFTTT Webhooks, you can! Let’s dive in.
