SAN FRANCISCO — November 10, 2020 — InfluxData, creator of the time series database InfluxDB, today announced the general availability of the next-generation open source platform for time series data, InfluxDB 2.0. Developers can now ingest, query, store and visualize time series data in a single unified platform, leverage new tools and integrations, and use familiar skills — making it faster and easier than ever to develop and deploy modern time-based applications.

Today, we are proud to announce that InfluxDB Open Source 2.0 is now generally available for everyone. It’s been a long road, and we couldn’t have done it without the amazing support and contributions of our community. This marks a new era for the InfluxDB platform, but it truly is just the beginning. Before we talk about the future, let’s take a look at some of the amazing new capabilities our team has been working on.

On November 12, 2013, I gave the first public talk about InfluxDB titled: InfluxDB, an open source distributed time series database. In that talk I introduced InfluxDB and outlined what I meant when I talked about time series: specifically, it was any data that you might ask questions about over time.

This article was written by InfluxDB Community member and InfluxAce David McKay. Eighteen hours ago, I was meeting with some colleagues to discuss our Kubernetes initiatives and grand plan for improving the integrations and support for InfluxDB running on Kubernetes. During this meeting, I laid out what I felt was missing for InfluxDB to really shine on Kubernetes.

Downsampling is the process of aggregating high-resolution time series within windows of time and then storing the lower resolution aggregation to a new bucket. For example, imagine that you have an IoT application that monitors the temperature. Your temperature sensor might collect temperature data. This data is collected at a minute interval. It’s really only useful to you during the day.

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 API offers a unified approach to querying, writing data to, and assessing the health of your InfluxDB instances. In today’s Tech Tips post, we’re learning about how to create and list buckets. Buckets are named locations in InfluxDB where time series data is written to.

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 API, the read and write portions are available with InfluxDB v1.8+, offers a unified approach to querying, writing data to, and assessing the health of your InfluxDB instances. In today’s Tech Tips post, we learn how to create and list authentication tokens. Tokens provide secure data flow between an InfluxDB instance and its users.

At InfluxData, we’re obsessed with time to awesome — how quickly can you start working productively with time series data? What can we do to make things better? InfluxDB Templates are a great example of this mindset. Back in April, we announced Templates as a way to package up everything you need to monitor a particular technology — Telegraf configurations and InfluxDB Dashboards, Tasks, Alerts, and related artifacts — into a single configuration file.

In this post we translate subqueries, using InfluxQL in InfluxDB version 1.x, into Flux, a data scripting and functional query language in InfluxDB version 1.8 and greater in either OSS or Cloud. The subqueries translated here come from this blog. This blog assumes that you have a basic understanding of Flux. If you’re entirely unfamiliar with Flux, I recommend that you check out the following documentation and blogs.

This article describes an end-to-end solution built with open source components InfluxDB and Grafana and the ogamma Visual Logger for OPC, to collect industrial process control data, analyze it in streaming mode, and visualize it in a dashboard.