In database management systems, indexes play a crucial role in optimizing query performance. Among the various types of indexes, non-clustered indexes and filtered indexes are commonly used. While they both improve query performance, there are key differences between the two.
Non-Clustered Index
A non-clustered index is a data structure that sorts and stores a copy of the indexed columns separately from the actual data. This allows for quicker data retrieval and sorting operations. When a non-clustered index is created, a separate index tree is constructed, and the leaf nodes of this tree contain the index keys and pointers to the actual data rows.
Here are a few key points about non-clustered indexes:
- They can be created on one or more columns of a table.
- They do not define the physical order of the data in the table.
- They provide a faster way to retrieve data than scanning the entire table.
- They consume additional disk space.
Filtered Index
On the other hand, a filtered index is a special type of non-clustered index that includes only a subset of rows from a table. It is created with a filter predicate, allowing you to define specific conditions that the indexed rows must satisfy. A filtered index can be a useful solution when you frequently query a subset of data from a large table.
Consider the following characteristics of filtered indexes:
- They are defined with a filter predicate to specify the conditions for including or excluding rows.
- They are created on a subset of rows that match the filter criteria.
- They can improve query performance by narrowing down the search space.
- They reduce index maintenance overhead by excluding unnecessary data.
Conclusion
In summary, the main difference between a non-clustered index and a filtered index is that a non-clustered index is created on all rows of a table, while a filtered index is created on a subset of rows that satisfy a specified filter predicate. Understanding these differences can help you choose the appropriate index type based on your specific database optimization needs.
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