Transaction detail data comparison method and device

Abstract

The invention provides a transaction detail data comparison method and apparatus. The method comprises the steps of receiving a first identification field in transaction detail data sent by a servicesystem and a service element field corresponding to the first identification field; sorting the second identification fields and the first identification fields according to a preset sorting mode; comparing whether the field value of the sorted first identification field and the field value of the sorted second identification field are the same or not based on a double-cursor traversal mode; and determining that the field value of the first identification field is the same as the field value of the second identification field, and determining whether the service element field corresponding tothe first identification field is the same as the service element field corresponding to the second identification field. According to the method, the timeliness problem of big data checking between heterogeneous systems can be effectively solved, the service closed-loop period is shortened, the system performance and the service processing throughput are effectively improved, the system risk is reduced, and the usability is greatly improved.

Description

Technical field [0001] The invention relates to the field of big data processing, in particular to a method and device for comparing transaction detail data. Background technique [0002] With the gradual transformation of large commercial banks from the traditional centralized mainframe architecture to the distributed architecture of the development platform, the internal system of the bank has evolved into multiple independent development platform subsystems. A transaction involves multiple heterogeneous subsystems. In a distributed system, it is impossible to satisfy the "consistency", "availability" and "partition fault tolerance" of the "CAP Law" at the same time. [0003] In most scenarios under the distributed architecture, strong consistency needs to be sacrificed in exchange for the high availability and partition fault tolerance of the distributed system. Distributed systems often only need to ensure "eventual consistency". Due to the unreliability of remote service RPC ...

AI Technical Summary

Problems solved by technology

Banking business is characterized by high timeliness and large transaction volume. The daily business detailed logs of tens of millions or even hundreds of millions are checked one by one. The pros and cons of the data comparison algorithm may cause a catastrophic bottleneck in the system

[0004] However, with the growth of business volume, the existing data comparison method has low data processing efficiency, cannot meet the timeliness requirements of business, and has an impact on the performance of distributed systems

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