10 SQL Server Index Best Practices

SQL Server indexes are a vital part of performance tuning. But with so many best practices, it can be hard to know where to start. This article covers the 10 most important SQL Server index best practices.

Indexes are a vital part of SQL Server performance tuning. They are used to speed up the retrieval of data from the database. However, indexes can also have a negative impact on performance if they are not used correctly.

In this article, we will discuss 10 SQL Server index best practices that you should follow to ensure optimal performance of your database.

1. Use the correct index type

The main index types are clustered and nonclustered. A clustered index is used to store the data rows in the table in order, based on the key columns of the index. A nonclustered index stores a copy of the key columns of the index in order, but the data rows are stored separately from the index pages.

There are also special index types, such as filtered indexes and columnstore indexes. Filtered indexes can be used to index only a subset of the data in a table, which can be useful for performance tuning. Columnstore indexes are used to store data in a columnar format, which can provide significant performance gains for data warehousing workloads.

The correct index type to use depends on the workload and the data in the table. For example, if the table will be mostly read-only and the data is not highly compressible, then a columnstore index might be the best choice. If the table will be updated frequently and the data is highly compressible, then a clustered index might be the best choice.

It’s important to choose the right index type because using the wrong index type can negatively impact performance. For example, using a clustered index on a table with highly compressible data can cause the index pages to become fragmented, which can lead to performance issues.

2. Index columns with high cardinality

Cardinality refers to the number of unique values in a column. A column with high cardinality has many unique values, while a column with low cardinality has few unique values. For example, a column containing gender would have low cardinality because there are only two possible values: male and female. A column containing email addresses would have high cardinality because there are potentially millions of different values.

Columns with high cardinality are good candidates for indexing because they can be used to quickly narrow down the data that needs to be searched. For example, if you want to find all the rows containing a particular email address, an index on the email address column can be used to quickly locate those rows without having to search the entire table.

Indexing columns with high cardinality is especially important for columns that are frequently used in WHERE clauses, ORDER BY clauses, and GROUP BY clauses.

3. Avoid using indexes on computed columns

When a computed column is added to a table, the values in that column are not actually stored in the database. Instead, they are calculated when needed based on the expression used to create the column. This means that if an index is created on a computed column, the database will need to recalculate the values every time the index is used, which can be very costly in terms of performance.

It’s much better to store the values in a separate column and create an index on that column. That way, the values will only need to be calculated once and they can be quickly retrieved from the index when needed.

4. Don’t create an index for every query

If you have too many indexes, your inserts, updates, and deletes will take longer because those operations will need to update all of the indexes. In addition, more indexes means that SQL Server will need to use more storage.

It’s important to strike a balance between having too many and too few indexes. You can use tools like SQL Server Management Studio to help you identify which queries could benefit from an index, and then test different indexes to see which ones provide the best performance improvement.

5. Keep your indexes small and narrow

Small indexes fit entirely in memory, which means they can be scanned more quickly. Narrow indexes have fewer columns, which means each index row is smaller and requires less IO to read.

Of course, there’s a trade-off here. Smaller indexes mean more indexes, which can slow down inserts, updates, and deletes. And narrower indexes can make it harder to find the right index for a given query. But on balance, the benefits of small, narrow indexes outweigh the drawbacks.

6. Know when to use a clustered index

A clustered index is a type of index that reorders the way records in the table are physically stored. This means that a clustered index can have a major impact on query performance, since it can drastically reduce the amount of disk I/O needed to retrieve data.

However, a clustered index can also have a negative impact on query performance if it’s not used correctly. For example, if a table is frequently updated, and the clustered index is based on a column that is often updated, then the index will need to be constantly rebuilt, which can cause performance problems.

Therefore, it’s important to carefully consider whether or not a clustered index is appropriate for a given table, and to choose the right columns on which to base the index.

7. Understand how SQL Server uses statistics

Statistics are used by the query optimizer to help it choose the best execution plan for a query. The query optimizer uses statistics to estimate how many rows will be returned by each operator in the execution plan, and then uses that information to calculate the overall cost of the plan.

If the statistics are inaccurate, the query optimizer may choose a sub-optimal execution plan, which can lead to poor query performance. Therefore, it’s important to make sure that the statistics are accurate, and that they’re updated regularly.

There are two ways to update statistics: manually, using the UPDATE STATISTICS command, or automatically, using the AUTO_UPDATE_STATISTICS option. It’s generally recommended to use the latter, as it’s less likely to cause performance problems.

8. Rebuild or reorganize fragmented indexes

When data is inserted, updated, or deleted in a SQL Server table, the indexes on that table can become fragmented. Fragmented indexes can cause performance issues because they require more disk I/O to read. They can also cause SQL Server to use more memory.

To avoid these performance issues, you should regularly rebuild or reorganize your indexes. How often you need to do this depends on your workload, but it’s generally recommended to do it monthly or weekly.

You can use the sys.dm_db_index_physical_stats dynamic management view to check the fragmentation level of your indexes. If the fragmentation level is above 30%, you should consider rebuilding or reorganizing the index.

9. Monitor your indexes

If you’re not monitoring your indexes, you won’t know when they become fragmented and need to be rebuilt. Additionally, if you’re not monitoring your indexes, you won’t know if they’re being used or not.

You can use the built-in SQL Server tools to monitor your indexes, or you can use a third-party tool. Whichever method you choose, make sure you’re monitoring your indexes on a regular basis.

10. Test before implementing

Suppose you’re tasked with improving the performance of a query that’s currently taking 30 seconds to run. After doing some analysis, you come up with an indexing strategy that you think will help. You implement your indexes and re-run the query. It now takes 20 seconds to run.

You’ve just saved 10 seconds, but is that the best you can do? What if there was another indexing strategy that would have saved 15 seconds? The only way to know for sure is to test different strategies and compare the results.

Testing is especially important when you’re working with complex queries. A small change in one part of the query can have a big impact on performance, so it’s important to test each change before implementing it.


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