Log report generation and monitoring method and device, equipment and storage medium

Abstract

The invention discloses a log report generation and monitoring method and device, equipment and a storage medium, and the method comprises the steps: setting a task monitoring time range, a monitoring latitude and an alarm threshold value of a basic alarm code table, and storing the time range, the monitoring latitude and the alarm threshold value into a database; starting a timed task according to the task monitoring time range set by the code table, and temporarily caching a result of the timed task in a system memory to obtain temporary cache data; performing secondary development on the temporary cache data and the basic information and the alarm threshold value in the code table, merging the associated data, generating a statistical report, and storing the statistical report in a database; starting a timing task which runs for a long time or runs according to a monitoring time range according to preset alarm information, reading latest data in a database statistical report, and pushing information for triggering an alarm condition. According to the method, the flattened data is efficiently read, the log statistical report is generated in real time, the readability is higher, persistent storage can be realized, and the reference value is improved.

Description

technical field [0001] The invention relates to the technical field of computer networks, in particular to a log statement generation and monitoring method, device, equipment and storage medium. Background technique [0002] With the increase of business volume, the amount of logs increases geometrically. It is very necessary to retrieve valid data from a large number of logs, count service capabilities, and monitor business status through logs. In the commonly used mass log retrieval framework, problems such as insufficient data retrieval granularity and low retrieval efficiency often occur. As a result, although massive logs are generated, they cannot be applied in actual business scenarios due to limited real-time performance and reliability. And caused a large backlog of expired logs. [0003] The existing technology for data monitoring and analysis is to retrieve logs through Elasticsearch and query business statistics reports in real time. [0004] The shortcoming of...

AI Technical Summary

Problems solved by technology

[0005] Using Elasticsearch to store logs is more suitable for data retrieval. Similar to most NoSql databases, it is flat and cannot realize the multi-table association scenario required by statistical reports, and cannot realize the persistence of statistical report logs. It is weak in log monitoring.

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