Lindorm time series engine is a high-performance, cost-effective, stable, and reliable online time series database engine service that provides capabilities such as efficient data reading and writing, storage based on a high compression ratio, time series data aggregation, and in-database machine learning.
Core capabilities
High performance: The time series engine supports high write throughput. Through the self-developed time series engine, it achieves high-concurrency time series data writing. In some scenarios, a single core can support a write throughput of ≥100,000 data points per second, a query throughput of ≥20 million queries per second, and more than 1,000 batch write and update operations per second.
Data compression: The time series engine has built-in data compression capabilities. Combined with self-developed time series compression algorithms and general block compression, the compression ratio can reach up to 10:1.
Time series indexing: The time series engine has built-in time series indexing capabilities, supporting high-performance multi-dimensional data queries for tens of billions of time series.
Time series computing: The time series engine provides rich time series aggregation computing capabilities. The aggregation engine supports more than 10 core aggregation operators, more than 20 filling policies, and more than 10 interpolation algorithms.
Auto Scaling: The time series engine uses a distributed architecture and supports online elastic scaling to adapt to data storage and processing requirements of any scale.
In-database machine learning: The time series engine has built-in in-database machine learning services, supporting mainstream time series forecasting and time series anomaly detection algorithms.
For more information about the features supported by Lindorm, see Features.
Scenarios
Lindorm time series engine is widely used in industry scenarios such as Internet of Things (IoT), Industrial Internet of Things (IIoT), and application performance monitoring (APM). It allows you to write tens of millions of time series data points in seconds and provides features such as high compression ratio with low-cost storage, pre-downsampling, interpolation, multi-dimensional aggregate computing, and visualized query results. This way, it can meet your requirements for storing and processing large amounts of time series data.
Methods of connecting to LindormTSDB
You can use SQL to connect to and access the Lindorm time series engine in the following three ways:
If you use Java to access the time series engine, see Use Lindorm JDBC Driver to connect to and use LindormTSDB.
If you use other programming languages to access the time series engine, see SQL API application guide.
If you are a database administrator or a data analyst, see Use Lindorm-cli to connect to and use LindormTSDB.