Users with real-time and other analytic workloads want or need to keep large volumes of historical data to aid in important activities, such as ad hoc historical trend analysis and training AI models. However, storing this much data in a way that also makes it easily queryable becomes prohibitively expensive. As a result, users must balance data availability and usability with sacrificing data fidelity and storage costs. That is until now.

If we turn the clocks back to September 2013, we released InfluxQL alongside InfluxDB. InfluxQL is a SQL-like query language, specifically designed to query time series data. For many of our users, InfluxQL still remains the primary way they interact with InfluxDB. Based on this feedback, InfluxQL has been reborn in InfluxDB 3.0 alongside native support for the SQL query language. So what do I mean by reborn?

We recently introduced a new version of InfluxDB, rewritten from the ground up to improve performance across the board. As with any undertaking of this nature, developers will need to make some adjustments to their applications in order to incorporate the new database. We even faced this challenge internally. We had many Telegraf instances sending data to legacy versions of InfluxDB.

This post was written by Pete Osah, a software developer who is familiar with web technologies, passionate about new software technologies, and keen on developing ways to pass knowledge to others in a simple manner. Thanks to new technologies, developers can release software and features to production at a faster pace and with greater efficiency. But maintaining software dependability and integrity requires having the necessary tools in place.

InfluxDB 3.0 (previously known as InfluxDB IOx) is a (cloud) scalable database that offers high performance for both data loading and querying, and focuses on time series use cases. This article describes the system architecture of the database. Figure 1 shows the architecture of InfluxDB 3.0 that includes four major components and two main storages.

InfluxDB Cloud 3.0 is a versatile time series database built on top of the Apache ecosystem. You can query InfluxDB Cloud with the Apache Arrow Flight SQL interface, which provides SQL support for working with time series data. In this tutorial, we will walk through the process of querying InfluxDB Cloud with Flight SQL, using C++. The C++ Flight SQL Client is part of Apache Arrow Flight, a framework for building high-performance data services.

InfluxDB 3.0 now offers support for connecting Tableau to InfluxDB 3.0 to query data for visualization using the Apache Arrow Flight SQL JDBC driver (Flight SQL driver). In this blog post, we will explore the capabilities and benefits of this integration and provide some instructions on how to connect them.

Telegraf is an open-source plugin-driven agent for collecting, processing, aggregating, and writing time series data. When collecting metrics it is common to filter out or pass through metrics with specific names, tags, fields, or timestamp values. The Common Expression Language (CEL) is an open-source language that provides a set of semantics for expression evaluation.

Open source Zipkin offers a robust set of features that make it easier for developers to understand and optimize complex distributed systems. Distributed tracing is a technique you can use to trace and monitor requests propagating through a distributed system. It can work in environments where multiple services process a request, making it an essential tool for modern microservices architectures. Zipkin is an open source distributed tracing system for monitoring and troubleshooting complex systems.