Fragmentation in an SQL table with lots of inserts, updates and deletes
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I've met an interesting case today when we needed to manipulate data from tens of thousands of people daily. Assuming we would use table rows for the information, then we get a table in which rows are constantly added, updated and deleted. The issue is with the space allocated in table pages.
SQL works like this: If it needs space it allocates some as a "page" which can contain more records. When you delete records the space is not reclaimed, it remains as is (this is called ghosting). The exception is when all records in a page are deleted, in which case the page is reused as an empty page. When you update a record with more data then it held before (like when you have a variable length column), the page is split, with the rest of the records on the page moved to a new page.
In a heap table (no clustered index) the space inside pages is reused for new records or for updated records that don't fit in their allocated space, however if you use a clustered index, like a primary key, the space is not reused, since there needs to be a correlation between the value of the column and its position in the page. And here lies the problem. You may end up with a lot of pages with very few records in them. A typical page is 8 kilobytes, so a table with a few integers in a record can hold hundreds of records on a single page.
Fragmentation can be within a page, as described above, also called internal, but also external, between pages, when the recycled pages are used for data that is out of order. To get a large swathe of records the disk might be worked hard in order to jump from page to page to get what is logically a continuous blob of data. It is disk input/output that kills a database.
OK, back to our case. A possible solution was to store all the data for a user in a "blob", a VARBINARY column. For reads or changes only the disk space occupied by the blob would be changed, with C# code handling everything. It's what is called trading CPU for IO, which is generally good. However this NoSql-like idea itself smelled badly to me. We are supposed to trust our databases, not work against them. The solution I chose is monitoring index fragmentation and occasionally issuing clustered index rebuilding or reorganizing. I am willing to bet that reading/writing the data equivalent to several pages of table is going to be more expensive than selecting the changes I want to make. Also, rebuilding the index will end up storing all the data per user in the same space anyway.
However, this case made me think. Here is a situation in which the solution might have been (and it was in a similar case implemented by someone else) to micromanage the way the database works. It made me question using a clustered index/primary key on a table.
These articles helped me understand more:
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