InfluxData revamps its time-series database offerings with faster query performance and lower latency – SiliconANGLE

Time series database startup InfluxData Inc. is beefing up the capabilities of its flagship product InfluxDB with a major update rolled out today that’s designed to simplify the task of managing time series data at scale, while improving its overall performance.

In addition to InfluxDB 3.0, the startup announced InfluxDB Clustered, which is a self-managed version of its time-series database offering. It’s aimed at companies that want to deploy it on-premises or in private clouds.

The open-source InfluxDB platform is designed to handle information that’s processed chronologically, hence the “time-series” moniker. Time-series data is essential for many applications, as it provides critical context.

For instance, heat readings from industrial temperature sensors need to be recorded in the order they’re created, with precise timestamps, to allow companies to track how heat fluctuates over long periods of time. It can also be helpful for tasks such as application performance monitoring.

InfluxDB aims to make it simple to store and analyze time-series data, and has been optimized for high performance with its ability to process millions of data operations per second. It explained that the latest performance improvements are all about helping developers to analyze time-series data more effectively as their application and system data volumes grow. As workloads expand, there’s a need for more sophisticated database systems that can support real-time, high-resolution data retrieval and analysis.

The InfluxDB 3.0 release is supposed to solve this need, introducing support for unlimited cardinality, meaning it can process columnar data with an unlimited number of values. It also supports faster data ingestion and introduces enhanced data compression with native object storage, enabling it to power high-cardinality workloads such as real-time analytics, observability and internet of things applications, the startup said.

Other improvements pertain to query concurrency, scaling and lower latency to ensure InfluxDB can manage larger datasets without any performance degradation, so systems and applications will remain responsive even with high-cardinality data, it added.

In addition to the general improvements in InfluxDB 3.0., the company also detailed a number of improvements for its flagship products, including InfluxDB Cloud Dedicated, its fully managed database-as-a-service offering.

InfluxDB Cloud Dedicated gains a new dashboard that provides more comprehensive visual insights into the performance and health of dedicated clusters. With this, developers can better keep an eye on unintended workload changes and identify potential bottlenecks that might impact performance.

The offering also gains single-sign-on capabilities, making it easier for users to access individual clusters using their existing login credentials. In addition, there are new application programming interfaces for management and token management, which pave the way for administrative tasks such as user management to be automated.

As for InfluxDB Clustered, this finally hits general availability after a beta phase that lasted just under one year. It’s optimized for deployment on Kubernetes and features decoupled, independently scalable ingest and query capabilities to ensure high availability and scalability. The release gains all of the improvements delivered in the InfluxDB 3.0 update, as well as the option to use Helm Charts to simplify deployment.

InfluxData Chief Executive Evan Kaplan said today’s updates are critical in a world where intelligent, real-time systems are becoming increasingly powerful.

“[They] require an operational database capable of managing high-speed, high-resolution workloads,” he said. “InfluxDB 3.0 is engineered to meet this challenge head-on with industry-leading ingest performance, unlimited data cardinality, and exceptionally low latency querying, giving architects and developers tools to build real-time monitoring and control systems.”

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