Reducing latency in query-based search engines

A system comprising:

• at least one processor;
• and
• memory storing instructions executable by the at least one processor to perform operations comprising:
• receiving, from a client device, a query specifying one or more criteria for a travel reservation;
• transmitting (i) a first request for first live travel data associated with a first time, (ii) a second request for second live travel data, and (iii) a third request for cached travel data satisfying the one or more criteria, wherein the first request, second request, and third request are transmitted in parallel;
• receiving the cached travel data satisfying the one or more criteria;
• generating, based at least in part on the cached travel data, a list of query results, the list of query results associated with a data record format configured to be input into a machine learning model;
• receiving the first live travel data from a travel provider system, the first live travel data satisfying the one or more criteria;
• prior to receiving the second live travel data, generating live travel data predictions based at least in part on the cached travel data and the first live travel data utilizing the machine learning model;
• storing the first live travel data and the live travel data predictions in association with the query as new cached travel data, wherein each cache result of the new cached travel data is associated with a unique key configured to facilitate lookup and retrieval of the new cached travel data;
• transmitting the live travel data predictions to the client device for presentation to a user;
• and
• updating the live travel data predictions based at least in part on receiving the second live travel data.

A non-transitory computer-readable storage medium storing instructions executable by at least one processor to perform operations comprising:

• receiving, from a client device, a query specifying one or more criteria for a travel reservation;
• transmitting (i) a first request for first live travel data associated with a first time, (ii) a second request for second live travel data, and (iii) a third request for cached travel data satisfying the one or more criteria, wherein the first request, second request, and third request are transmitted in parallel;
• receiving the cached travel data satisfying the one or more criteria;
• generating, based at least in part on the cached travel data, a list of query results, the list of query results associated with a data record format configured to be input into a machine learning model;
• receiving the first live travel data from a travel provider system, the first live travel data satisfying the one or more criteria;
• prior to receiving the second live travel data, generating live travel data predictions based at least in part on the cached travel data and the first live travel data utilizing the machine learning model;
• storing the first live travel data and the live travel data predictions in association with the query as new cached travel data, wherein each cache result of the new cached travel data is associated with a unique key configured to facilitate lookup and retrieval of the new cached travel data;
• transmitting the live travel data predictions to the client device for presentation to a user;
• and
• updating the live travel data predictions based at least in part on receiving the second live travel data.