Clustering Factor and its Effects on Optimization in SQL

The clustering factor refers to a numerical value that denotes the extent of random data distribution in a table. To define it simply, it is the number of times a block changes when an index is in use for reading a table. It has a role of its own to play during optimization in SQL.

Another way to explain this term is to call it a measure of how sorted an index is as compared to the table it’s based on. Its main use is to find an estimate for a table lookup after index access. The cost of the operation is usually calculated by multiplying the selectivity of the index with the clustering factor.

 

A high clustering factor typically implies that the table doesn’t have its rows in a proper sequence. This will lead to excessive I/O consumption during extensive index range scans, hence requiring the user to performance tune SQL query. Calculating the clustering factor, at the time of index stats collection involves the following steps by Oracle:

 

●      Oracle draws comparisons between the row id block and that of the last index entry. It does this for each entry.

●      In case there is a difference when the comparison is made, the database increases the clustering factor by one.

 

The least possible value of the clustering factor is equivalent to the number of known blocks that were located from the index’s list. For example, the clustering factor for an index on one or more columns without nulls is normally the number of blocks that hold data. On the other hand, the greatest possible clustering factor would be the number of index entries.

 

Interpreting the clustering factor enables the DBA to estimate the number of table blocks involved in an index scan. This value also becomes useful later during optimization in SQL. If its value is somewhere near the index entry quantity, you can safely assume that a thousand-entry index scan might need to read around a thousand blocks from the table.

 

In another example, if the clustering factor is almost as high as the number of table blocks, the same magnitude of index scan could only require fifty blocks instead. You may compare it with the cost of one complete table-read to the high-water notch to find which type of scan is more efficient. You can use the multiblock input-output mechanism to find a winner between an index range and a full-table scan.

 

It is important to observe the number of large deletes made from the table. This is because such operations leave blocks empty of rows in many cases. The clustering factor takes these into account since these blocks are not about to show up in the index list. However, a complete table scan is still going to read every table block without considering if the block contains rows or not.

 

This is why, in extreme instances, the database might detect that reading a huge table with a million blocks entirely may only need it to read ten of those blocks. It can make an estimate of the number of blocks it has to read by going through the table and index statistics.

 

Given the presence of NOT NULL constraints informing the database about every table row within the index, an index scan will prove beneficial in terms of efficiency. A sample statement is a basic SELECT all query that would look something like this:

 

SELECT * FROM <tablename>;

 

But the execution will involve an index range scan rather than a complete table scan for better results. Otherwise, you may have to performance tune SQL query or queries. Another interesting thing to note is regarding the calculation of the clustering factor. It is done on the basis of the data present in the index, and the table is not used as a reference in any way.