Time series data storage method and device, computer equipment and storage medium

Abstract

The invention relates to a time series data storage method and device based on a big data processing technology, computer equipment and a storage medium. The method comprises the following steps: acquiring a current time series data point, wherein the time series data point comprises a timestamp field; determining a reference data point corresponding to the current time series data point accordingto the timestamp field; calculating a relative change value of the current time series data point and a reference data point; adding the relative change value to a compressed data point correspondingto the reference data point in a time sequence database according to a time sequence; and when a next time series data point is received, marking the next time series data point as a current time series data point, and returning to the step of determining the reference data point corresponding to the current time series data point. By adopting the method, the time series data storage cost can bereduced.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a time series data storage method, device, computer equipment and storage medium. Background technique [0002] In the operation and maintenance scenario, it is necessary to monitor the running status of business products at multiple time nodes, so more and more time series data are generated. Time series data is a series of data with a time stamp. For example, the attribute monitoring data of the unmanned vehicle at the first moment, such as coordinates, speed, direction, temperature, humidity, etc., is one piece of time-series data; the attribute monitoring data of the unmanned vehicle at the second moment is another piece of time-series data. At present, time series data is stored based on a dedicated time series database. Due to the continuous generation of time series data, the data volume of time series data is usually very large, and multiple time series data...

AI Technical Summary

Problems solved by technology

Due to the continuous generation of time series data, usually the data volume of time series data is very large, and multiple time series databases need to be deployed in a distributed manner to store massive time series data, which increases the storage cost of time series data

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