A search method, device, electronic device and storage medium

Abstract

The invention discloses a searching method. The method comprises the steps of: receiving a search request of a user; slicing the search request according to a preset slicing rule to obtain a pluralityof sliced query requests; judging whether a cache result corresponding to the sliced query requests exists in a cache, wherein the cache result in the cache is sliced in advance according to the preset slicing rule to obtain a plurality of sliced cache results; when determining that the cache result corresponding to the sliced query requests exists in the cache, obtaining the cache result corresponding to the sliced query requests from the cache and returning to the user; or when determining that the cache result corresponding to the sliced query requests does not exist in the cache, searching search results corresponding to the search request from a search server and returning to the user. The invention also provides a searching device, the server and a storage medium. By means of the method disclosed by the invention, the hit rate of the cache result can be increased and the search response time is reduced.

Description

technical field [0001] The present invention relates to the field of search technology, in particular to a search method, device, electronic equipment and storage medium. Background technique [0002] With the development of Internet technology, it is very common for users to search for information on the Internet. Usually, the website service system receives a search visit from a user, and returns corresponding search results to the user according to the search visit. Cache is an important component in a distributed system. It mainly solves the performance problems of hot data access in high concurrency and big data scenarios, and provides high-performance fast access to data. At present, the more common enterprise-level search application servers Solr, Elasticsearch, etc., all have various levels of caching, but the existing caching mechanism is that the query conditions must be exactly the same, that is, the same byte by byte can be considered the same , to get the cach...

AI Technical Summary

Problems solved by technology

[0003] In addition, in most enterprise-level application scenarios today, the query conditions are changeable. For example, only the data in the last 30 days and 31 days is searched. The common enterprise-level search application servers Solr and Elasticsearch caching mechanisms cannot hit the cache designed by themselves, that is to say It takes time and server resources to re-query the data within the search range if the time changes slightly during each retrieval. Therefore, the current search application server is not suitable for application scenarios with changing query conditions. The search time is too long and the user's search experience lower

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