When it comes to indexing a table in SQL Server, there is often confusion and misinformation about whether to use a heap or a clustered index. In this article, we will dive into the concepts of heaps and clustered indexes, and discuss their impact on query performance.

What is a Heap?

A heap is a table that is stored without any underlying order. When rows are inserted into a heap, there is no guarantee of where the pages will be written or their order. The heap is comprised of an Index Allocation Map (IAM) that points to all pages within the heap. Each page can contain multiple rows of data. However, there is no linking or organization between the pages in a heap.

What is a Clustered Index?

A clustered index, on the other hand, provides an innate ordering for the table it is defined on. It follows the column order specified in the index definition. The data in a clustered index is stored as a binary tree structure, with leaf nodes containing the actual rows of data ordered by the clustered index columns. The index nodes organize the data based on the indexed column values.

Performance Comparison

Let’s compare the performance of tables with clustered indexes to heaps in terms of INSERT, UPDATE, DELETE, and SELECT operations.

INSERT Operations:

When inserting data into a heap and a table with a clustered index, the initial performance may be similar. However, as the number of rows increases, the heap requires significantly more reads compared to the clustered index table.

UPDATE Operations:

Updating rows in a heap can be more expensive than in a clustered index table, especially when searching via an indexed column. The heap may require additional key lookups, making it a less efficient option.

SELECT Operations:

While reading an entire table is not common, it is worth noting that heaps and clustered index tables have similar execution plans and reads for table scans. However, when seeking based on an indexed value, the heap may require more reads and additional operations compared to a clustered index.

DELETE Operations:

Deleting rows from a heap is generally more costly in terms of query cost and reads compared to a clustered index table. The cost of updating an index and deleting from a heap is higher, making the clustered index a better option for deletion operations.

Conclusion

In general, heaps tend to perform worse than tables with clustered indexes. While there may be some scenarios where a heap performs marginally better, it is important to thoroughly test and evaluate the specific use case before choosing a heap over a clustered index. Temporary objects, such as table variables and temporary tables, should also be given the same level of consideration and architectural planning as permanent objects.

It is crucial to avoid falling into the trap of treating exceptions as rules. Heaps should be used sparingly and only when there is a high level of certainty that they will not hinder performance. Thinking ahead and including a clustered index upfront can save future headaches when dealing with large amounts of data or complex applications.

Remember, the only way to truly determine performance is through rigorous testing with your own data and schema. Don’t rely on generalizations or disinformation found on the internet. Use heaps with caution and consider the long-term implications for your application.

For further reading, you can refer to Microsoft’s documentation on heaps and memory-optimized table variables.