A method and system for quickly establishing an index based on a local cache

Abstract

A system for rapidly creating index based on a local cache comprises a basic information center, a scheduling center, a resource operation reader, a local cache builder, a data buffering queue and an index data submitter, wherein the scheduling center initiates a task according to task time stored in the basic information center; the resource operation reader reads data to the local cache builder from a database according to a data source; the local cache builder completes caching of data in local; the basic information center is used for storing cache building and basic information of an updated task; the scheduling center executes the task regularly in dependent of task time configured by the basic information center, and acquires the basic information dependent on task run from the basic information center; the local cache builder is used for managing the local cache; and the data buffering queue is used for storing the data read from the local cache.

Description

technical field [0001] The invention belongs to the technical field of e-commerce search, and in particular relates to a method and system for improving search index update through local cache optimization. Background technique [0002] With the popularity of e-commerce platforms at present, merchants hope to release more and more products to e-commerce platforms, and consumers also hope to quickly and accurately find the products they need on e-commerce platforms; while e-commerce platforms Generally speaking, in order to meet the needs of merchants and consumers, a mechanism for quickly indexing products is usually established at the technical level. Traditionally, relying entirely on data to update product index data has been difficult to meet the fast and efficient indexing tasks of e-commerce search platforms. [0003] As people have higher and higher requirements for the timeliness of products on e-commerce search platforms, the current search systems on the market usu...

AI Technical Summary

Problems solved by technology

[0009] All of the above factors may cause a full update of the index data. Just imagine, if there are 20 million data in our database, and this database is also used by the product list page of the e-commerce platform, and it takes 10 hours to rebuild once, then During these 10 hours, first of all, there was a certain pressure on the data, and then indirectly affected the product list page of the e-commerce platform

[0010] For such a problem, the existing solutions usually choose a trough of visits, that is, when the foreground traffic is not large and the database pressure is low to rebuild the index, so many urgent needs to rebuild the index of the search platform cannot be obtained. Satisfy

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