InfluxData Innovates New Time Series Engine for InfluxDB, Catering Towards Scale and Performance

The innovator behind InfluxDB, InfluxData, is deploying its next-generation time series engine designed to transform InfluxDB to a columnar, real-time data platform optimized for the full range of time series data and able to ingest data at high volumes with unbound cardinality. The announcement also brings SQL language support for queries, merging the interests of InfluxDB with that of users employing the popular data programming language. 

“InfluxData’s new storage engine is a significant advancement in how our customers work with time series data, transforming InfluxDB into a real-time analytics platform,” said Paul Dix, founder and CTO at InfluxData. “Limited scale is a thing of the past; now developers can run unlimited time series workloads in InfluxDB and contextualize data by any dimension and without restrictions, improving performance for the largest applications in IoT, cloud observability, and other resource-intensive analytics applications.”

Since its beginnings as the open source project InfluxDB IOx in 2020, the latest iteration of the storage engine accelerates the collections, storage, and orchestrations of massive time series data workloads. Employing Rust, a modern programming language, the new storage engine is fundamentally built to accommodate for speed, efficiency, reliability, scale, and performance, greatly optimizing real-time delivery of data in InfluxDB, according to the vendor. The addition of SQL query support boosts accessibility for developers, allowing them to use the languages they know, love, and trust.

"As organizations increasingly ramp up their usage of real-time data analytics, the challenge of real-time data collection, storage, and extraction grows along with that demand," said Stephen O'Grady, principal analyst with RedMonk. "Historically, databases have attacked the problem of new and emerging data workloads via the introduction of purpose-built storage engines, and that's exactly what InfluxDB has done with the introduction of a new columnar engine designed to remove limits on cardinality for very large scale, time series data."

InfluxDB’s new storage engine comes with a variety of benefits that expands time series data value for advanced analytics use cases. With real-time query speed, users can query data across any series within milliseconds; queries are 100x faster against high-cardinality data, according to the vendor. Limits and restrictions on the number of time series are now a thing of the past, enabling InfluxDB users to execute various actions with accompanying metadata. SQL support enables developers to query through the InfluxDB API, Flux, and InfluxQL. The new engine also offers full support of observability use cases, supporting data such as metrics, logs, and traces, from a single platform.

For more information regarding the new InfluxDB storage engine, please visit