Forum Discussion
What are the best practices for managing and optimizing a SQL table that contains 10 million records
I have a table that currently holds 10 million records, and it continues to grow daily. As the dataset expands, I'm encountering significant challenges in retrieving data efficiently and generating reports. The increasing volume of data is causing performance bottlenecks, leading to slow query execution times and delays in report generation. To address these issues, I need to implement strategies for optimizing data retrieval and managing the growing dataset effectively and to ensure that the system remains responsive and capable of handling the increasing data load.
You need to locate the real bottleneck first. You'd better maintain a test enviroment, in which you can make tune and record performent metrics to compare with baseline, it will help you to make choice.
You can use Monitor Resource Usage (Performance Monitor) - SQL Server | Microsoft Learn to make a basic judgment on whether the bottleneck is in CPU or IO or memory. After this, make adjustments, check the performence if it goes better.
CPU overload, more CPU cores is a easy solution, but optimize indexes and query might be cheaper and get huge gain sometimes. This requires analyzing the query plan to find the direction of optimization.
IO overload can be resolved by using SSD, more RAID disks, better storage infrastructure.
The most easy way to resolve memory not enought is more money -- adding memory.
Optimizing queries and indexes may help reduce the pressure on IO and memory, but it usually need more skill.
I'm not sure if this is the best practice, but I'm sharing what I do.
My SQL data table contains around 100 million rows. I analyze the key column used for filtering data (Mostly date in year/month/day) in reports and split the data across different physical disks (SAN) for storage. This approach makes it easier for me to detach data partition when I want to archive it.
Indexes and statistics are updated weekly.
