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.
To paraquote H. L. Mencken: For every problem, there is a solution that’s cheap, fast, easy to implement – and wrong.
Silver bullets and magic wands don’t really exist, I’m afraid. There’s amble reasons for DBA’s being well-paid people.
There’s basically three options: Either increase the hardware capabilities to be able to handle the amount of data you want to deal with, decrease the amount of data so that the hardware you’ve got can handle it at the level of performance you want or… Live with the status quo.
If throwing more hardware at the issue was an option, I presume you would just have done so. As for how to viably decrease the amount of data in your active set, well, that’s hard to say without knowledge of the data and what you want to do with it. Is it a historical dataset or time series? If so, do you need to integrate the entire series back until the dawn of time, or can you narrow the focus to a recent time window and shunt old data off to cold storage? Is all the data per sample required at all times, or can details that are only seldom needed be split off into separate detail tables that can be stored on separate physical drives at least?