Difference between clustered and non-clustered indexes

Nonclustered indexes - data is physically arranged in random order, but logically ordered according to the index. This type of index is suitable for frequently changing datasets.

With cluster indexing, the data is physically ordered, which significantly increases the speed of data retrieval (but only in the case of sequential data access). Only one clustered index can be created per dataset.

Does it make sense to index data that has a small number of possible values?

Approximate rule that can be followed when creating an index is that if the amount of information (in bytes) that does NOT satisfy the sampling condition is less than the size of the index (in bytes) for the given sampling condition, then, in general, optimization will slow down the sampling.

When is full dataset scan superior to index access?

Full scan is performed by multi-block reading. Index scan - single block. Also, when accessing by index, the index itself is scanned first, and then blocks are read from the dataset. The number of blocks that must be read from the set depends on the clustering factor. If the total cost of all the required single-block reads is greater than the cost of full scan with multi-block read, then the full scan is more profitable and is chosen by the optimizer.

Thus, full scan is chosen when there is weak selectivity of overgrowth predicates and/or weak data clustering, or in the case of very small datasets.


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