High-concurrency scene processing method and device based on Redis and Kafka online snap-up system

Abstract

The invention provides a high-concurrency scene processing method and device based on a Redis and Kafka online snap-up system; the method comprises the following steps: generating a login token basedon a user login request, writing the login token into caches of front-end Cookie and rear-end Redis, checking whether the front-end login token is matched with the rear-end login token; if not, returning an operation failure result; if yes, judging whether the user login token is valid; if not, returning an operation failure result; if yes, judging whether a commodity lock of a corresponding commodity is obtained or not; if not, returning an operation failure result; if yes, pushing request data of snap-up success to a Kafka information queue. According to the method and the device in the invention, loading pressure of a server and a database is effectively reduced through current-limiting peak-clipping processing; a large number of concurrent requests can be borne in short time; the throughput capacity of the cluster is greatly increased; through front-rear end check on the number of user access times, a cheating user can be quickly found out, so that the event is guaranteed to be carried out stably and fairly.

Description

technical field [0001] The invention belongs to the technical field of high-concurrency scene processing, and in particular relates to a high-concurrency scene processing method and device for an online snap-up system based on Redis and Kafka. Background technique [0002] With the popularity of the Internet, more and more users choose to use the Internet to purchase goods, participate in house purchases and lottery. In order to attract users, merchants often hold promotional activities such as flash sales and limited-time panic buying, that is, they provide a limited number of discounted products within a limited time. Due to the low price, they are often sold out in a second. Due to the situation of too many monks and too few porridges, a large number of users frequently access the server before and after the event, and the traffic will surge several times or even dozens of times than usual, which will bring huge pressure to the server. After a certain pressure capacity is...

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