Optimizing Cache Strategies For Online Ticketing Systems By Analyzing User Query Behaviors

With the rapid development of Internet and mobile terminal technology,more and more passengers love inquiring and ordering tickets through the Internet.Great customer service demand spawned the third-party Internet ticket service platform.Therefore,airlines need to constantly improve and upgrade the user service platform to ensure the user experience,improve customer service quality,and enhance the core competitiveness of enterprises.The airline first needs to solve the user query traffic pressure growing with each passing day.In order to reduce the huge query pressure of background from the large number of users,flight query caching technology has come into being,and has gradually become the common measures.Flight query caching is a technology of caching ticket inventory information,which adds the cache system between the client and server that does not need to query the database when the results can be returned from the cache directly.On the one hand,the system can more quickly respond to the user's query,and enhance the user experience;on the other hand,it can significantly reduce the query flow of the background database and effectively solve the problem of excessive query flow.The key issue of cache technology is how to set the time-to-live(TTL)of the query keyword.Firstly,this paper designs an algorithm to extract the ticket inventory change interval based on the user's ticket query log.Secondly,it analysis the ticket inventory change rule based on the user query behaviors to extract the data features that influence the change of air ticket inventory.Thirdly,it designs a dynamic cache optimization strategy based on these features.Lastly,it can predict the change interval of the inventory by dynamic cache mechanism and dynamically set the TTL.In this paper,the real user query record of an online ticketing website is selected as the experimental data set.Meanwhile,the dynamic cache optimization algorithm is verified by experiments and is compared with traditional caching strategies.The experimental results show that the proposed dynamic cache algorithm can better adapt to the ticket inventory change rule,and the cache hit rate is greatly improved on the premise of ensuring high query accuracy.In addition,this paper designs and implements a cache module on the basis of the research of dynamic cache optimization algorithm.The cache module covers the function of data preprocessing,cache management,cache strategy learning and so on.And it has realized the logic process of request filtering,cache request processing,cache management,historical data management,configuration management,model learning and external error monitoring.The cache optimization strategy proposed in this paper can reduce the redundant query of the background service system,so as to reduce the pressure of the background database and constantly improve the quality of customer service,which will help to enhance the competitiveness of the airline market.