The time-series model lets you store, query, and analyze time series data. It offers features such as data lifecycle, timeline indexing, hot and cold storage, and data compression for low-cost storage and high-performance querying and analysis of time series data. In connected vehicle scenarios, the time-series model is primarily for storing, querying, and analyzing vehicle trajectory data.
For more information about the time-series model, see Time-series model.
How it works
The Tablestore time-series model stores time series data in two-dimensional time-series tables. Each row represents the data for a timeline at a specific point in time. A row's primary key consists of the timeline identifier and the timestamp, and the data columns contain the data points for that timeline at that timestamp. A row can contain multiple data columns. You do not need to predefine the schema for the primary key or data columns; just specify the column names when you write data.
The TimeSeries model of Tablestore uses the multi-value model. You can specify multiple data values at one point in time. Each value corresponds to a column in the time series table, including the column name and column value. Column values support the following data types: Boolean, integer, floating-point, string, and binary.
A time series table can store time series data of different metric types. In the following figure, the time series table stores data of the following two metric types: temperature and humidity.
In the figure, time series identifiers consist of measurement, data source, and tags. You can call an API operation to update the properties in the metadata of a time series. The properties can be used to retrieve the time series.
After data is written to a time series table, the system automatically extracts the metadata of the time series and automatically creates a metadata index. You can retrieve a time series based on the combination of the metric name, data source, and tags.
Features
Designed for time series data, the Tablestore time-series model is ideal for scenarios such as monitoring IoT devices, collecting equipment data, and processing machine monitoring data.
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Provides a unified-common modeling method for time series data, which eliminates the need to predefine table schemas.
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Allows metadata indexes to be automatically created for time series and supports time series retrieval based on composite conditions.
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Supports queries and aggregation by using SQL.
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Supports automatic scale-out of service capabilities, high-concurrency writes and queries, and low-cost storage of petabytes of data.
Using the time-series model
You can use the time-series model with the console, CLI, or an SDK.