I’m working with a big database from 2005. It has thousands of tables. Many of these tables grow by about 200,000 rows each year. I need to show stats based on when data was last accessed.
I want to group the data into time periods like 10 years, 5 years, 3 years, and so on, down to 2 weeks and the current date. I’m thinking of using partitions (p1 to p10) for this.
The tricky part is that these groups need to shift every week when I run my job. So p10 becomes p9 after a week, p9 becomes p8 after two weeks, and so on.
Here’s what I’m wondering:
Can I make this kind of moving partition scheme work?
Is it worth splitting up the tables like this instead of just querying the whole table each time? The database is about 31,556 MB in total.
I’m using SQL Server 2008 for this project. Any advice would be great!
I’ve dealt with similar time-based data analysis challenges before. While partitioning can be effective, it might introduce unnecessary complexity in your case. Consider using a date dimension table instead. This approach allows you to pre-calculate time periods and easily adjust them without altering your core data structure. You can join this dimension table to your fact tables, enabling efficient querying across various time ranges. This method is often more flexible and easier to maintain than a complex partitioning scheme, especially when dealing with shifting time periods. As for performance, ensure you have appropriate indexes on your date columns and test thoroughly before implementing any major changes.
hey man, partitioning sounds like a good idea but it might be overkill for ur situation. have u considered using indexed views instead? they can give u pre-calculated stats without the hassle of moving partitions. plus, with 31GB, its not THAT big. maybe try optimizing ur queries first? just my 2 cents
I’ve worked with large datasets like yours before, and I can share some insights. While partitioning sounds good on paper, it can be a nightmare to maintain, especially with shifting time periods. In my experience, a more effective approach is to create a separate summary table that stores aggregated data for different time ranges. You could set up a weekly job to update this summary table, which would contain pre-calculated stats for each of your time periods. This way, you’re not messing with the structure of your main tables, and queries against the summary table will be lightning fast. For the 31GB of data you’re dealing with, this approach should work well. It gives you the benefits of partitioning (faster queries for time-based analysis) without the headaches of managing a complex partition scheme. Plus, it’s much easier to adjust your time ranges if needed in the future. Just make sure to index the date columns in both your main tables and the summary table for optimal performance. And always test thoroughly before implementing any major changes to your production environment.