In this post, we will describe a simple way to share data from multiple InfluxDB 2.0 OSS instances with a central cloud account. This is something that community members have asked for when they have OSS running at different locations, but then they want to be able to visualize some of the data or even alert on the data in a central place. Please note that while the method presented here is simple and fast to set up, it has many limitations which may make it inappropriate for your product use case.

One of the things we needed to either adopt or build for InfluxDB IOx is a database catalog. If you haven’t heard us talk about it yet, InfluxDB IOx (pronounced eye-ox) is the new in-memory columnar database that uses object storage for persistence. We’re building it as the future core of InfluxDB. A database catalog usually contains the definitions of a database’s structure like schema and indexes.

Since the initial release of InfluxDB OSS 2.0 in November 2020, more than 10% of the community has successfully upgraded, and the pace of the upgrades continues at a steady rate. We have released a number of maintenance releases to address defects, expand platform coverage, and enhance the update experience based on feedback.

This article will show how we kept cardinality under control with a few tweaks in the Telegraf configuration. If you’re not yet familiar with it, Telegraf is the native and open-source plugin-driver metrics collection agent of InfluxDB. As you may know, cardinality is the combination of measurements, tags, sets, fields, and values in a time-series database, and having high cardinality can be a challenge.

One of the great things about SaaS applications is that users in the platform automatically have access to any available software updates. Yet, having a beta program requires a separate environment, creating a potential challenge for users and development teams. In this context, having a tool where you can control features and flag certain users is important because sometimes features are too early or not relevant for all users.

One of the best things about working at InfluxData is getting to know the worldwide InfluxDB community. It’s always fun getting to meet new users through our Community Slack, social media, team members and virtual/in-person events. I recently met David Ko, a DevOps engineer at Index Exchange. Index Exchange is a global marketplace for digital media advertising; I recently chatted with David over Zoom to discuss how they use InfluxDB at Index Exchange.

Docker is a power tool for deploying applications or services, and there are numerous Docker orchestration tools available that can help to simplify the management of the deployed containers. But what if you are wanting to deploy a small number of services and not wanting to undertake setting up and managing another application stack just to run a handful of containers. I will cover how I deployed a handful of services on a single Docker host.

InfluxDB 2.0’s Checks and Notifications system is likely the most powerful and flexible system available for creating alerts based on time series data. To get the most out of the system, it is helpful to understand the different pieces and how they fit together. After reading this article, you should be able to create precise alerting using the InfluxDB 2.0 User Interface (UI), as well as be able to extend and customize the system to suit your specific needs.

In March, we released Telegraf 1.18, which included a wide range of new input and output plugins. One exciting new addition was an XML Parser Plugin that added support for another input data format to parse into InfluxDB metrics.

SAN FRANCISCO — April 14, 2021 — InfluxData, creator of the leading time series database InfluxDB, today announced the general availability of InfluxDB Notebooks, a new capability that improves communication for software development teams, ultimately enhancing productivity within InfluxDB Cloud. InfluxDB Notebooks is the first of the company’s new capabilities designed to make it easier for developers to collaborate around time series data within the platform.