Which one is fast either Index or View both are used for optimization purpose both are implement on table's column so any one explain which one is more faster and what is difference between both of them and which scenario we use view and index.
An indexed view has a unique clustered index. The unique clustered index is stored in SQL Server and updated like any other clustered index. An indexed view is more significant compared to standard views that involve complex processing of large numbers of rows, such as aggregating lots of data, or joining many rows.
A SQL index is used to retrieve data from a database very fast. Indexing a table or view is, without a doubt, one of the best ways to improve the performance of queries and applications. A SQL index is a quick lookup table for finding records users need to search frequently.
It is not possible to create an index on a view. Indexes can be used for views processed using the merge algorithm. However, a view that is processed with the temptable algorithm is unable to take advantage of indexes on its underlying tables (although indexes can be used during generation of the temporary tables).
View and Table both are integral parts of a relational database, and both terms are used interchangeably. The view is a result of an SQL query and it is a virtual table, whereas a Table is formed up of rows and columns that store the information of any object and be used to retrieve that data whenever required.
VIEW
INDEX
ANALOGY:
Suppose in a shop, assume you have multiple racks. Categorizing each rack based on the items saved is like creating an index. So, you would know where exactly to look for to find a particular item. This is indexing.
In the same shop, you want to know multiple data, say, the Products, inventory, Sales data and stuff as a consolidated report, then it can be compared to a view.
Hope this analogy explains when you have to use a view and when you have to use an index!
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With