hi my dears, I have an issue at work where we have to work with millions (150 mln~) of product data points. We are using SQL server because it was inhouse available for development. however using various tables growing beyond 10 mln the server becomes quite slow and waiting/buffer time becomes >7000ms/sec. which is tearing our complete setup of various microservices who read, write and delete from the tables continuously down. All the stackoverflow answers lead to - its complex. read a 2000 page book.
the thing is. my queries are not that complex. they simply go through the whole table to identify any duplicates which are not further processed then, because the processing takes time (which we thought would be the bottleneck). but the time savings to not process duplicates seems now probably less than that it takes to compare batches with the SQL table. the other culprit is that our server runs on a HDD which is with 150mb read and write per second probably on its edge.
the question is. is there a wizard move to bypass any of my restriction or is a change in the setup and algorithm inevitable?
edit: I know that my questions seems broad. but as I am new to database architecture I welcome any input and discussion since the topic itself is a lifetime know-how by itself. thanks for every feedbach.
Dunstabzugshaubitze@feddit.org8·3 days ago- spent time to generate/optomize your indexes.
- faster storage/cpu/ram for your rdbms
- get the data needed by specific services into the service, only get the data from a central place if you have to (spinning up a new instance, another service changes state of data you need, which is a warning sign in itself that your architecture is brittle…)
- faster storage/cpu/ram
- generate indexes
- 2nd level cache shared between services
- establish a faster datastore for often requested data thats used by multiple services (that might be something like redis, or another rdbms on beefier hardware)
- optimize queries
- generate indexes
- faster storage/cpu/ram