An in-memory table is a table in some platforms that’s stored entirely in memory. These don’t really exist in MS SQL, although you could say that it is a table that’s been entirely cached, and so doesn’t result in any physical (hard disk) reads when queried. In earlier versions, the DBCC PINTABLE command allowed the “pinning” of tables in memory, but this was deprecated in SQL Server 2005.

Often, a table-valued variable, @tablename, might be stored in memory (although this is not guaranteed), and declared in a batch or function, with no persistence.

A temporary table is a table that will be automatically dropped when it’s no longer needed, usually when the creating session is terminated. In MS SQL, they begin with a # (or two hashes if they’re global temporary tables, shared between multiple sessions), and are often created with a SELECT INTO #TEMPTABLE … style query.

A pivot table is a special form of query where the values in several rows are summarised, “pivoted” on an axis, and become columns, where the summary data then becomes the rows. Frequently this happens where you’ve rows sorted on dates; these may then be “pivoted” so you end up with a column for January, one for February, one for March, etc.

The main issue with null values and performance is to do with forward lookups.

If you insert a row into a table, with null values, it’s placed in the natural page that it belongs to. Any query looking for that record will find it in the appropriate place. Easy so far….

…but let’s say the page fills up, and now that row is cuddled in amongst the other rows. Still going well…

…until the row is updated, and the null value now contains something. The row’s size has increased beyond the space available to it, so the DB engine has to do something about it.

The fastest thing for the server to do is to move the row off that page into another, and to replace the row’s entry with a forward pointer. Unfortunately, this requires an extra lookup when a query is performed: one to find the natural location of the row, and one to find its current location.

So, the short answer to the question of performance is yes, making those fields non-nullable will help search performance. This is especially true if it often happens that the null fields in records you search on are updated to non-null.

Of course, there are other penalties (notably I/O, although to a tiny extent index depth) associated with larger datasets, and then you have application issues with disallowing nulls in fields that conceptually require them, but hey, that’s another problem!