10 SQL Server Partitioning Best Practices

SQL Server partitioning is a feature that lets you split a large table or index into smaller pieces, which can improve query performance and reduce storage costs. Partitioning can also make it easier to manage large data sets by allowing you to archive or delete data that is no longer needed.

When partitioning a SQL Server table or index, there are a few best practices to keep in mind in order to get the most benefit from the feature. In this article, we will discuss 10 of those best practices.

1. Partitioning is not a silver bullet

Partitioning can help improve query performance by allowing the database engine to more quickly narrow down the data it needs to search through, but it’s not a guarantee. In some cases, queries may actually run slower after partitioning is implemented.

It’s also important to remember that SQL Server partitioning comes with its own set of trade-offs. For example, partitioning can make some administrative tasks, like index maintenance, more complex.

Before implementing SQL Server partitioning, be sure to thoroughly test your workloads to ensure that performance improvements will outweigh any negative impacts.

2. Use partitioning to improve performance and manageability

Partitioning data can help improve query performance by allowing the database engine to access only the data that is needed for a particular query. For example, if you have a table that contains data for multiple years, you can partition the data by year, so that when a query only needs data for a specific year, the database engine can access only that data, rather than scanning the entire table.

Partitioning can also help improve manageability by making it easier to delete or archive old data. For example, if you have a table that contains data for multiple years, you can partition the data by year, and then delete or archive the partitions for the years that you no longer need. This can be much easier than deleting or archiving individual rows from the table.

3. Understand the data access patterns of your workloads

Partitioning can be an extremely effective way to improve performance and manageability of large data sets. However, it’s important to understand that partitioning comes with some overhead. In order to make sure that the benefits of partitioning outweigh the costs, you need to have a good understanding of how your data is accessed.

For example, if most of the queries against your data set only access a small subset of the data, then partitioning may not be worth the overhead. On the other hand, if most of the queries access a large portion of the data, then partitioning could be a very good idea.

Understanding the data access patterns of your workloads is therefore essential to making sure that SQL Server partitioning is used effectively.

4. Do not use partitioning for archiving purposes

Partitioning is designed to improve query performance by allowing the database engine to access only the data that is needed. However, when data is no longer needed, it should be removed from the database entirely to avoid performance issues.

If data is archived into a partitioned table, the database engine will still need to scan all of the partitions in order to find the data that is being requested, which negates the performance benefits of partitioning.

5. Consider using multiple filegroups instead of partitioning

Partitioning can be a great way to improve performance and manageability of very large tables. However, it can also add complexity to your database design and maintenance routines. In some cases, using multiple filegroups may be a better solution.

Multiple filegroups allow you to store different parts of your database in different physical files. This can be helpful if you have tables that are frequently accessed together, or if you want to put certain tables on faster storage devices.

Creating multiple filegroups is relatively easy to do and does not require any changes to your existing data. You can simply create a new filegroup and then move selected tables into it. This can be a good way to test the benefits of partitioning without making a commitment to a more complex design.

6. Be aware of the limitations of partitioned views

Partitioned views are a great way to improve query performance by allowing the optimizer to access only the data it needs. However, they have several limitations that can impact performance and stability:

-They can’t be used in distributed queries.
-They can’t be used in indexed views.
-They can’t be used in many types of DML operations, such as UPDATE, DELETE, or INSERT.
-They can’t be used in some system catalog views.

If you’re using partitioned views, be aware of these limitations and plan accordingly.

7. Avoid using non-aligned indexes on partitioned tables

When an index is not aligned with the partitioning of a table, SQL Server has to do extra work to maintain the index. This can lead to performance issues, as well as increased storage requirements.

To avoid these problems, make sure that all indexes on a partitioned table are aligned with the table’s partitioning scheme. You can do this by creating the index using the same partitioning column(s) as the table, or by using the ALIGN INDEX option when creating the index.

8. Take advantage of partition elimination

Partition elimination is the process of SQL Server ignoring partitions that are not relevant to the query. For example, if a query only references data in one partition, then SQL Server will only access that one partition and ignore the others.

This is important because it can dramatically improve query performance. If SQL Server only has to access one partition instead of all of them, then the query will run much faster.

To take advantage of partition elimination, you need to make sure that your queries only reference the partitions that they need to. This can be done by using table aliases or by qualifying column names with the partition name.

For example, let’s say you have a table named ‘Orders’ that is partitioned by ‘OrderDate’. If you want to query only orders from 2018, you would use the following query:

SELECT *
FROM Orders AS o
WHERE o.OrderDate >= ‘2018-01-01’ AND o.OrderDate < '2019-01-01'

By aliasing the ‘Orders’ table as ‘o’ and qualifying the ‘OrderDate’ column with the table alias, we are telling SQL Server that we only want to query the data in the ‘Orders’ table that is relevant to our query. In this case, that is only the data in the partition for 2018.

If we had not used table aliases or qualified column names, then SQL Server would have to check all partitions, even though only one is relevant to our query. This would waste time and resources, and could potentially cause performance problems.

Therefore, always take advantage of partition elimination when possible to improve query performance.

9. Use sliding windows when loading data into partitioned tables

Suppose you have a table that is partitioned by date, and today is March 1st. The current window would be from January 1st to March 1st. When loading data into the table, you would load data for the current window first, followed by the data for the previous window. In this case, you would load data for February 1st to March 1st first, followed by data for January 1st to February 1st.

This approach has several benefits. First, it allows you to keep your data organized in an efficient manner. Second, it makes it easier to query the data, since all of the data for a given time period is stored in a single partition. Finally, it makes it easier to manage the data, since you can simply drop partitions that are no longer needed.

10. Monitor query plans and statistics

Partitioning can have a profound impact on query performance. In some cases, it can make queries run faster. In other cases, it can make them run slower. And in still other cases, it can have no impact at all.

The only way to know for sure how partitioning will affect query performance is to monitor query plans and statistics before and after implementing partitioning. This will give you a baseline against which to measure the impact of partitioning.

If you see a significant improvement in query performance after implementing partitioning, then you know it was worth the effort. If you don’t see any improvement, or if query performance actually deteriorates, then you’ll know that partitioning wasn’t the right solution for your particular workload.