Commodity panic buying request distribution method

Abstract

The invention discloses a commodity panic buying request distribution method, and relates to the technical field of electronic commerce, wherein the method comprises the steps: dividing a commodity inventory into a plurality of commodity single examples according to the SKU of a commodity, and storing the plurality of commodity single examples in a key-value form; obtaining the number of servers and the inventory number of each commodity case from a redis database; calculating the number of the commodity panic buying requests processed by each server, and reallocating the servers to the commodity panic buying requests according to the number of the commodity panic buying requests and the weights of the server response threads, so that the situations of overselling and wrong selling are avoided, the fairness of commodity panic buying is ensured, and the user experience is improved.

Description

technical field [0001] The invention belongs to the technical field of electronic commerce, and in particular relates to a method for allocating commodity snap-up requests. Background technique [0002] At present, various companies issue coupons to customers online to achieve important business functions such as city promotion, channel rewards, new activities, and settlement compensation. As a type of commodity, coupons are an important acquisition channel. How to efficiently and accurately allow users to obtain coupons has become an important problem to be solved in program design. [0003] At present, the solution to the high-concurrency rush-buying scenario is as follows: [0004] Inventory oversold is the primary problem to be solved in high-concurrency panic-buying scenarios. It can be solved based on pessimistic locks and queues, but it has a great impact on relative performance and concurrency; redis-based operations frequently read and write inventory values ​​unde...

AI Technical Summary

Problems solved by technology

[0004] Inventory oversold is the primary problem to be solved in high-concurrency panic-buying scenarios. It can be solved based on pessimistic locks and queues, but it has a great impact on relative performance and concurrency; redis-based operations frequently read and write inventory values ​​under high concurrency will easily lead to server performance degradation. , reducing the concurrent processing capability of the system; regular queues can only fix the number of queues to be processed, and cannot be configured for business scenarios, which increases the failure scenarios and reduces the usability and user experience; the rush-purchase processing logic based on multi-load servers is applied to different hosts, causing users There is a problem of inconsistent numerical order in the request, which violates the principle of first-in first-out, making the rush purchase lack of fairness

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