this post was submitted on 02 May 2025
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Programming

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database greenhorn (discuss.tchncs.de)
submitted 5 days ago* (last edited 5 days ago) by [email protected] to c/programming
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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.

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[–] [email protected] 1 points 1 day ago* (last edited 1 day ago)

"They simply go through the whole table".. that's the problem. A full table scan should be avoided at all costs.

Learn: how to run and read an explain plan, indexes, keys, constraints, and query optimization (broadly you want to locate individual records as quickly as possible by using the most selective criteria).

You also need to learn basic schema design and to familiarize yourself with normalization.

Avoid processing huge result sets in your application. The database is good at answering questions about data it contains. It isn't just a big bucket to throw data into to retrieve later.