Time series data management method, device and equipment and readable storage medium

Abstract

The invention discloses a time sequence data management method, which can ensure the automatic recovery writing of each piece of write-in failure data, automatically store the write-in failure data in a log file of a time sequence database when finding that the write-in failure exists in the time sequence database, and then automatically read the data in the log file of each database for the recovery writing of the data. Manual intervention is not needed in the whole process from failure monitoring to writing recovery, the situation of data loss caused by manual misoperation is avoided, and automation and stability of time sequence data management are guaranteed; and meanwhile, the reliability of the data recovery writing process is high, even if the system power-off process is lost, the data in the log file stored in the disk cannot be lost, the continuation of the data recovery process after power-on can be guaranteed, and thus the consistency of data writing is guaranteed. The invention further discloses a time series data management device and equipment and a readable storage medium, which have corresponding technical effects.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a time series data management method, device, equipment and readable storage medium. Background technique [0002] Time series data refers to time series (TimeSeries) data, which is a data column recorded in chronological order by the same indicator. Time-series data is almost everywhere. For example, the number of calls to a system interface per second in the past hour can form a set of time-series data. Each data point in the data contains the specific value of the time point and the number of calls; Similarly, the daily sales volume in the past month can also form a set of time series data, and each data point is the month and sales volume value. Through the analysis of time series, it is possible to find out the statistical characteristics and development regularity of the time series in the sample, construct a time series model, and perform out-of-sample for...

AI Technical Summary

Problems solved by technology

However, the data recovery process in this solution is complex, requires manual intervention, and the steps are cumbersome. In the process of processing, it is easy to cause data loss due to human error, which affects the normal use of users; in addition, this method does not solve the problem of data consistency. , it is easy to lose data. When using the buffer to access data, if the service fails and the process disappears, the data in the buffer will be lost, which will lead to data consistency problems

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