Heaps and clustered indexes are two different ways of storing data in SQL Server. Both have their advantages and disadvantages, and we will discuss them in this post.
A Bit about Heaps
Heaps are essentially piles of data that remain unsorted or unorganized, hence the name. Although you can find heaps on tables that don’t have clustered indexes, they may also be present with non-clustered indexes. Heaps provide the benefit of increased input speed which helps while adding data to a table. Data insertion is quicker because the process doesn’t require a logical order to do so.
A Bit about Clustered Indexes
A clustered index is a more organised way of data insertion. In fact, it is the go-to technique for logically sorting information in a table. A clustered index doesn’t need a primary key but you can create one on a predefined key-value. Most DBAs recommend creating them on the most-used columns that come under reference of highly frequent query executions. They also reduce the need for optimization since all the data gets sorted to fit them. The primary benefit of using a clustered index is that it speeds up data reads.
Knowing When to Use a Clustered Index
As noted above, using a clustered index leads to better read rates. Therefore, there are several instances where you may need to identify whether a clustered index will improve performance of SQL query rather than a heap.
To do this, you need to follow these steps:
- First, it is important to understand where there is a requirement for greater read speed.
- Check dynamic system views and look for large tables without a clustered index.
- Once you locate a few such tables, you can analyse the plans and stats of queries in the MSSQL system dynamic management views. Searching through the table name in the variable will show you the usage frequency of the plan. It will also show the text fetched and other necessary validation details that show whether a heap or non-clustered index is in use instead.
You will be able to view object names in the second result set in case the table in question is under use in SQL object. Once you have reviewed the query plans relevant to the use cases, you will have sufficient information to help you decide whether the table requires a clustered index or if a heap is more suitable for it. You will also have to choose all the columns that will have to be in the index in case of the former. Tables with several use cases that mostly share the same columns can provide result sets faster with a clustered index.
When Not to Use Clustered Indexes
This is just as important to know because believe it or not, there are instances where a clustered index can do more harm than good to oracle database performance.
A logging table is one such instance as it normally has far more insert operations than reads or updates. This is because their purpose is to log each occurrence but users may not refer to it as frequently. If you place an index on this kind of table, it can result in hot latches due to lagging data insertions for the last available page. Meanwhile, information keeps getting added onto the same page from other means. The one case where this issue doesn’t occur is when the index’s main column is a GUID, therefore, it isn’t sequential.
Using a clustered index in a table with an excessive number of columns isn’t the best idea, either. The reason behind this is simple: the index is supposed to define the default sort order. Too many columns mean repeated resorting with each new use case, slowing down the database. It will also result in an increase in the size of the non-clustered indexes present in the table.
Another situation where a clustered index can’t help is a column that isn’t usually static as they undergo frequent changes. Changing key values on an index have far greater chances of creating performance-related problems. This is because updating key values typically leads to page splits – these need maintenance, which takes resources and affects performance.