Rethinking the 5 Non-Clustered Indexes Rule for Wide Data Warehouse Tables

Data Warehouse

Introduction

Hey there, SQL Server enthusiasts! You’ve probably heard the age-old advice that having 5 non-clustered indexes per table is a good rule of thumb. But what happens when you’re dealing with those extra-wide data warehouse tables that have more than five columns that users frequently search on? Is this rule still applicable, or should we rethink our approach? In this article, we’ll dive deep into this topic, sharing personal insights and examples to help you make informed decisions when designing indexes for your wide tables.

The 5 Non-Clustered Indexes Rule

Before we get into the nitty-gritty, let’s quickly recap the reasoning behind the 5 non-clustered indexes rule:

1. Each additional index increases the storage overhead and maintenance costs.
2. Too many indexes can negatively impact the performance of write operations.
3. Having a limited number of indexes encourages careful consideration of which columns to index.

While these points are valid, the rule might not always be the best fit for wide data warehouse tables. Let’s explore why.

Wide Data Warehouse Tables: A Different Beast

Data warehouse tables are often designed to store large amounts of historical data, with many columns to support various analytical queries. These tables can have dozens or even hundreds of columns, making them significantly wider than typical OLTP tables.

In such scenarios, users may frequently search on more than five columns, making it tempting to create additional non-clustered indexes. However, before you go index-crazy, consider the following:

1. Storage Overhead: Each non-clustered index requires additional storage space. With wide tables, the storage overhead can quickly add up, especially if you have multiple indexes on large varchar or nvarchar columns.
2. Maintenance Costs: As data is inserted, updated, or deleted, indexes need to be maintained. The more indexes you have, the higher the maintenance costs. This can lead to slower write operations and increased fragmentation.
3. Query Performance: While indexes can improve query performance, having too many indexes can sometimes result in the optimizer choosing a suboptimal execution plan. It’s crucial to strike a balance and only create indexes that provide significant benefits.

Finding the Sweet Spot

So, how do you determine the right number of non-clustered indexes for your wide data warehouse tables? Here are a few strategies:

1. Analyze Query Patterns: Examine the most common and critical queries run against the table. Identify the columns frequently used in WHERE, JOIN, and ORDER BY clauses. These columns are prime candidates for indexing.
2. Consider Columnstore Indexes: If you’re using SQL Server 2012 or later, columnstore indexes can be a game-changer for data warehouse workloads. They provide high data compression and excellent query performance for analytical queries. In many cases, a single columnstore index can replace multiple non-clustered indexes.
3. Partition the Table: If your table is large and has a natural partitioning key (e.g., date), consider partitioning the table. This allows you to create targeted indexes on specific partitions, reducing the overall index maintenance costs.
4. Monitor and Adjust: Continuously monitor your query performance and index usage. If you notice that certain indexes are rarely used or causing performance issues, don’t hesitate to drop or modify them.

Conclusion

In summary, while the 5 non-clustered indexes rule is a good starting point, it’s not a one-size-fits-all solution. When dealing with wide data warehouse tables, it’s essential to analyze your specific requirements and find the right balance between query performance and index maintenance costs.

Remember, indexes are powerful tools, but they’re not magical solutions. By carefully considering your workload, leveraging columnstore indexes when appropriate, and monitoring your indexes’ effectiveness, you can optimize your wide data warehouse tables for peak performance.

So go forth, experiment, and find the indexing strategy that works best for your unique scenario. Happy indexing, friends!