SCA feature code updating method and device based on process splitting and storage medium

By splitting the process of SCA feature code analysis into multiple small processes, and using multiple devices for series processing, combined with the setting of state, the problem of inefficient resolution in the prior art is solved, and more efficient resource utilization and data analysis speed is achieved.

CN119690473AActive Publication Date: 2025-03-25SECZONE TECH CO LTD
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Patent Information

Application Number
CN202411537255.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-31
Publication Date
2025-03-25
Estimated Expiration
2044-10-31

AI Technical Summary

Technical Problem

The process line of the existing SCA source code feature analysis method is too long, resulting in the data stream processing time in memory and low parsing efficiency, which affects the efficiency of concurrent parsing of multiple components in the system.

Method used

By splitting the overall process of SCA feature code analysis into multiple small processes, and using multiple devices such as Java, Python, Flink and Doris for series processing, the process is connected in combination with state settings to reduce the dwell time of the data stream.

Benefits of technology

It effectively improves resource utilization efficiency and data parsing speed, reduces the processing time of data streams in memory, and improves the efficiency of system concurrent parsing multi-components.

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Abstract

The invention discloses an SCA feature code updating method and device based on process splitting and a storage medium, and the updating method comprises the steps: analyzing a component version file by Java, pushing the analyzed data to redis, and modifying a threshold exceeding state according to the use capacity of the redis; the Python modifies the migration state according to the over-threshold state or modifies the to-be-migrated state according to the use capacity of the redis; the Java completes analysis of the current component version file according to the to-be-migrated state and then modifies the migratable state; the Python modifies the migration state according to the migratable state; the Flink migrates data in the redis to the column to be merged according to the migration state and modifies the migration success state; and the Dores performs combination and deduplication on the to-be-combined column and the final result column according to the migration success state, and updates the to-be-combined column and the final result column to the final result column. According to the SCA feature code updating method based on process splitting, the whole process of SCA feature code analysis can be split and executed by combining a plurality of devices, the staying processing time of the data stream is shortened, and the data analysis speed is effectively improved.
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