Log compression storage method and electronic device

Abstract

The invention relates to a log compression storage method and an electronic device. The method comprises the following steps: step 1, pre-defining a log storage structure, wherein the defined log storage structure is divided into a parameter area, an index area and a log area, the index area comprises N indexes, n is an integer, and N is equal to the ratio of the total duration of one day to the defined minimum log cutting time unit; step 2, determining log compression custom parameters, and storing the parameters in a parameter area; and step 3, compressing the original log needing to be compressed according to the log compression self-defined parameters. According to the method, the logs are synchronously compressed, decompressed and displayed according to the time periods, the convenience degree of checking the logs by users is improved, and the problem positioning efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of data compression processing, in particular to a log compression storage method and an electronic device. Background technique [0002] Various websites and software will generate various logs. In order to facilitate finding and locating problems, it is often necessary to back up and archive these logs. When archiving, in order to reduce the storage space occupied by logs, historical logs are often compressed and stored. However, there are still contradictions between compressed storage and log query. If the log is not compressed, if the log volume is relatively large, more storage space will be wasted, which is not conducive to saving the cost of the enterprise; but after the log is compressed, it is very inconvenient to view the log. Generally, you need to decompress it before you can browse normally, which is time-consuming and inconvenient. [0003] 1. If the log content is large, it takes a long tim...

AI Technical Summary

Problems solved by technology

However, there are still contradictions between compressed storage and log query. If the log is not compressed, if the log volume is relatively large, more storage space will be wasted, which is not conducive to saving the cost of the enterprise; but after the log is compressed, it is very inconvenient to view the log. Generally, you need to decompress before you can browse normally, which is time-consuming and inconvenient

[0003] 1. If the log content is large, it takes a long time to compress and decompress; if there are too many decompressed files, it will also cause a serious waste of disk space; if the log queryer is not particularly sure about the date of the log, or needs to The logs may occur at the time of alternating dates, and it is often necessary to decompress multiple files to see the required logs, which has a large time cost for locating and analyzing problems

[0004] 2. Technologies such as elk have a certain learning cost; in addition, the elasticsearch used for storage does not solve the problem of occupying too much storage space; if self-built, there is a certain maintenance cost, and if cloud services are used, corresponding fees need to be paid storage cost

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