Performance for query filtered by user preference

I have an app, that shows data based on user preference. I will simplify it in this theoretical example: **The app shows articles filtered based on user preference for their favorite authors or their favorite topics.** The data will be updated with high frequency since there will be new articles published any minute. The approach I am going for now: The app makes a request to an endpoint with user favorites in the url params like this: - https://example.com/api/articles/today?=fav_autohrs=1,5,3,7,10&topics=3,6,9 My query to db is something like this:

`
SELECT *
FROM articles_table
WHERE 
author_id IN (1,5,3,7,10) OR topic_id IN(3,6,9)
AND (published_datetime BETWEEN '$date_today 00:00:00' AND '$date_next 00:00:00')

` And I am creating an index for columns: published_datetime & author_id & topic_id. What I want to know if there is any caching mechanism or improvements I can do to this approach to achieve best performance and best utilization of db resources? My database server run on AWS RDS t3.medium that has 2 cpus and 4GB of ram. And I am expecting a high number of requests that reach thousands per minute, so I want to be sure I am solid about my approach and what I need to improve before I publish it to production. I am currently caching http requests for a short ttl, with varnish. But this won't help much in this case since most requests will be unique in their combination of preference. ***My setup:*** * WordPress - as CMS & REST API * Varnish - as caching layer * Mariadb - as database * InnoDB - as table engine **Whats the best approach for my case to allow users to query db based on preference with optimal caching?** **Edit:** I tested my query to see how fast they run and got: * 0.0117s on user first request with preference (unique). * 0.0040s when another request is made to a new date after the first request (preference unchanged, date changed). is this considered good?