Time-Series Data Management Powered by Cloud-Native Architecture
Published on Aug 24, 2022
TDengine, the open-source platform for time-series data in IoT applications, announces the release of TDengine 3.0. The new version integrates caching, streaming processing, and data subscription into a cloud-native architecture optimized for Kubernetes deployments. The release addresses major pain points caused by large-scale IoT deployments, according to the company.
According to Jeff Tao, CEO and founder of TDengine, TDengine 3.0 has superior scalability and can handle billions of IoT sensors and data collection points. It is the first time-series database in the world that solves the high cardinality issue for time-series data processing and the only time-series database on the market to support big data components.”
Among the new features are Kubernetes and Serverless container support, which allows users to dynamically scale compute and storage resources.
A major feature of TDengine 3.0 is its scaling capabilities, which eliminates high-cardinality issues owing to TDengine’s clusters; a single cluster can host billions of time-series data points without sacrificing start-up performance. As a result of its high performance on time-series data, TDengine is considered an efficient platform, with 2-5x the speed of other time-series databases and 10x the read and write performance of general databases.
A cloud-native architecture was a highly requested feature for TDengine’s latest release; the addition enables deployment on public, private, and hybrid clouds.
The simplicity of both TDengine 3.0’s use and maintenance makes it ideal for enterprise users without the support of a big data team.
As TDengine 3.0 can handle large amounts of data, it is ideal for users with substantial amounts of devices or data collection points.
In addition to its nearly 140,000 instances in more than 50 countries worldwide, TDengine has over 370,000 lines of code and more than 230,000 lines of testing code.
A unique storage engine design makes it a good choice for processing time-series data, said Tao. Since it is a cloud-native database, both compute and storage resources can be dynamically changed based on the workload, as well as the ability to pay-as-you-go to reduce operational costs.
TDengine plans to increase support for third-party tools such as BI, visualization, and data collection agents, as well as offer abnormal detection and time-series forecasting in the future.
Linux cryptominers found in 241 npm and PyPI packages
The PyPI and npm open source registries have been infiltrated by more than 200 malicious packages this week. Each of these packages downloads a Bash script on Linux…
In November 2022, Spring Authorization Server 1.0 will be released
A GA release of Spring Authorisation Server 1.0 is planned for November 2022, just over two years after it was introduced to the Java community…