Business/Customer Intelligence & Data Science

InfluxData deplos next-generation time series engine

Rebuilt and reimagined storage engine built on open source project InfluxDB IOx delivers faster queries, unlimited time series, and introduces SQL for writing queries and BI tool support

InfluxData, creator of the leading time series platform InfluxDB, today announced the deployment of its next-generation time series engine. The new engine reimagines InfluxDB as a columnar real-time data platform, delivering high-volume data ingestion with unbounded cardinality, optimized for the full range of time series data. InfluxData also adds SQL language support for queries, bringing the hugely popular data programming language to InfluxDB for the first time. With the introduction of SQL, InfluxDB now enables broad analytics use cases through business intelligence and machine learning tools.

First introduced in 2020 as the open source project InfluxDB IOx, the new storage engine is the product of sustained development by InfluxData and considerable contribution from the InfluxDB open source developer community. Today, the new engine based on IOx arrives first in InfluxData’s multi-tenant InfluxDB Cloud service, available to developers worldwide.

Developed with Rust, a modern programming language designed for speed, efficiency, and reliability, InfluxDB’s new storage engine doubles down on scale and performance, enabling its largest customers to collect, store, and intelligently orchestrate massive time series data workloads. InfluxDB delivers true real-time – developers can query data immediately after it is written, often measured in milliseconds, as opposed to many other data warehouses that rely on batch ingestion and delayed processing. With new SQL query support, InfluxDB meets developers where they are, allowing them to work in yet another language they love. Together, these enhancements raise the bar for time series analytics, adding even more real-time use cases that rely on metrics, events, traces, and other high-cardinality data.

“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, 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.”

“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.”

Build and Scale Time Series Applications Without Limitations or Caps

InfluxDB’s new storage engine improves system performance, scalability, and resilience with new capabilities that expand the utility of time series data to new and advanced analytics use cases. InfluxDB developers will make use of the following new capabilities:

  • Real-time query speed. Query data across any series within milliseconds. 100X faster queries against high-cardinality data enable new use cases that were previously impossible.
  • Unlimited time series volume. Eliminate limits and restrictions on the number of time series, opening InfluxDB to even more use cases with massive metadata and other data dimensionality.
  • SQL language support. With SQL, InfluxData adds the world’s most popular data programming language to its platform, enabling developers to query via the InfluxDB API, Flux, and InfluxQL.
  • Full support of observability use cases. Support for the golden triangle of observability data – metrics, logs, and traces – all in a single database.

“Edge Delta’s platform relies on InfluxDB to help deliver the next generation of observability – an effort that relies on the highest cardinality data streams,” said Ozan Unlu, CEO and Founder, Edge Delta. “Unbounded cardinality in the InfluxDB platform will allow our team to analyze log data in any dimension to detect anomalies faster and deliver real-time insights to our most demanding customers.”

“One goal at Cleverse is to provide our portfolio of web3-focused startups the tools and platforms that won’t stand in the way of their goal to create impact through rapid innovation,” said Panjamapong Sermsawatsri, CTO, Cleverse. “One of our products requires storing very high cardinality time series data. With support for workloads with unbounded cardinality, InfluxData’s new storage engine will widen the possibilities of our product expansion to best serve our customer needs.”

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