Getty Images
InfluxData targets performance, adds self-managed version
The time series database specialist's update addresses performance to better handle complex real-time workloads and includes a new option for on-premises and private cloud users.
InfluxData on Wednesday unveiled new features for its InfluxDB 3.0 product suite aimed at speeding and simplifying time series data management at scale, including performance improvements and a new operational dashboard.
In addition, the vendor made generally available InfluxDB Clustered, a self-managed version of its database for on-premises and private cloud deployments first unveiled in September 2023.
Based in San Francisco, InfluxData is a time series database specialist and the creator and lead sponsor of InfluxDB, an open source database designed specifically to manage the data that enables time series analysis.
The vendor raised $81 million in financing in February 2023 to bring its total funding to more than $200 million. Two months later, InfluxData unveiled InfluxDB 3.0. The product suite includes InfluxDB Cloud Serverless and InfluxDB Cloud Dedicated, both of which are managed by InfluxData, and now InfluxDB Clustered as well for self-managed users.
One of the key upgrades in InfluxDB 3.0 was enabling unlimited cardinality, which refers to the uniqueness of the values in a database column -- a high level of distinctness means the column has high cardinality.
Other key upgrades included high throughput to enable users to ingest, transform and analyze hundreds of millions of time series data points per second, significantly faster real-time query response times, increased data compression to reduce storage costs and support for SQL to simplify analysis.
The new features add to those that initially comprised InfluxDB 3.0 and are aimed at helping InfluxData stand out in a competitive market, according to IDC analyst Carl Olofson. Other time series database specialists include Grafana and Prometheus while tech giants AWS, Google, IBM and Microsoft are among others offering time series databases.
"The [keys] are size and speed," Olofson said. "The time series field has, in recent years, become very competitive. InfluxData is clearly looking to stand out, realizing that as users develop more complex networks of data sources -- including edge devices -- the challenge of applying a single analysis against all that data is becoming overwhelming."
New capabilities
Time series data is data that is time stamped so that an enterprise's changes can be observed over time.
Meanwhile, just as more data sources are resulting in an increase in the overall volume of data enterprises now collect, the number of sources and resulting data volume that enable changes to be tracked over time are also rising.
In response, InfluxData and its peers have developed databases that specialize in managing time series data. Common characteristics of such databases include optimization for large-scale workloads, high-performance reading and writing capabilities to enable real-time analysis, processes for managing data lifecycles so that older data can be retained and found, and filters specific to time-based queries.
InfluxDB 3.0's initial launch represented a complete overhaul of the database's underlying engine. Along with the new underlying engine, the release addressed and added some of those common characteristics such as high performance and capabilities to enable real-time analysis.
Now, the latest release of InfluxDB 3.0 is aimed at increasing the database engine's performance as well as simplifying its use.
The update includes improved query concurrency and scaling to better handle high-cardinality data. In addition, InfluxDB 3.0 now has a new operational dashboard that provides visual insights into the performance and health of data clusters so that developers can address unintended workload changes, identify bottlenecks and optimize performance. A new single sign-on streamlines the log-in process. And new APIs have been added that let users automate certain repetitive tasks.
"High cardinality is the key here," Olofson said. "You can do time series queries and analysis on much larger data sets with high performance than was possible before."
Rachel Stephens, an analyst at RedMonk, similarly said that continuing to address cardinality is key for InfluxData.
She noted that time series databases have historically struggled with high cardinality use cases. InfluxDB 3.0's initial release improved InfluxData's handling of high-cardinality workloads with the new release adding further performance.
"InfluxDB 3.0 potentially opens up new space in the market for the database to be a performant option in [high cardinality] situations," Stephens said.
While the InfluxDB 3.0 update addresses performance, the launch of InfluxDB Clustered extends the database engine's capabilities to more of the vendor's users.
When InfluxDB 3.0 was first released, it was only available to users of InfluxDB Cloud Serverless and InfluxDB Cloud Dedicated, which are both fully managed database services. On-premises and private cloud users had only InfluxDB Enterprise -- which was not built with InfluxDB 3.0's engine -- as an option.
InfluxDB Clustered essentially replaces InfluxDB Enterprise. Its significance, therefore, is that it provides on-premises and private cloud customers with the same capabilities as users of InfluxData's fully managed databases, according to Stephens.
"InfluxDB Clustered is the successor product to InfluxDB Enterprise," she said. "InfluxDB Clustered brings the columnar database engine to customers' self-managed environments."
The impetus for the InfluxDB 3.0 improvements and launch of InfluxDB Clustered came from InfluxData's goal of providing developers tools that allow them to efficiently manage time series workloads at scale, according to Gary Fowler, the vendor's vice president of products.
In particular, enabling developers to process large datasets in real time is essential given the increasing demand for real-time decision-making.
"As workloads continue to expand, developers need sophisticated systems that can handle large datasets without compromising performance," he said. "InfluxDB 3.0 is engineered to meet these challenges head-on, offering the tools necessary to manage time series data at scale."
In the future
With the full suite of InfluxDB 3.0 products now generally available, InfluxData's roadmap is focused on continuing to add new features and functionality, according to Fowler.
In addition, Fowler said the vendor is planning to improve the performance of Amazon Timestream for InfluxDB, a managed offering resulting from InfluxData's partnership with AWS.
Currently, Amazon Timestream for InfluxDB is based on a pre-InfluxDB 3.0 engine, which makes it an option for open source users with small, low cardinality workloads. Now, InfluxData is working to bring InfluxDB 3.0 to Amazon Timestream for InfluxDB along with other features not yet available to open source users.
"These enhancements will provide greater flexibility, performance and security for our users as they manage their time series data in the cloud," Fowler said.
Olofson, meanwhile, suggested that InfluxData would be wise to develop a fully cloud-based version of its managed databases to provide customers with more choice. However, doing so is not as simple as building a new version of its database, he noted.
"For efficient execution, this may require special agreements with the public cloud platform providers," Olofson said.
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
