I saw a question asked recently on the #sqlhelp hash tag:
Might SQL Server retrieve (out-of-row) LOB data from a table, even if the column isn’t referenced in the query?
Leaving aside trivial cases like selecting a computed column that does reference the LOB data, one might be tempted to say that no, SQL Server does not read data you haven’t asked for.
In general, that is correct; however, there are cases where SQL Server might sneakily read a LOB column.
Brad Schulz recently wrote about optimizing a query run against tables with no indexes at all. The problem was, predictably, that performance was not very good. The catch was that we are not allowed to create any indexes (or even new statistics) as part of our optimization efforts.
In this post, I’m going to look at the problem from a different angle, and present an alternative solution to the one Brad found.
Many people believe that whenever SQL Server creates an execution plan that uses parallelism, an alternative serial plan is also cached.
The idea seems to be that the execution engine then decides between the parallel and serial alternatives at runtime. I’ve seen this on forums, in blogs, and even in books.
In fairness, a lot of the official documentation is not as clear as it might be on the subject. In this post I will show that only a single (parallel) plan is cached. I will also show that SQL Server can execute a parallel plan on a single thread.
This post covers a little-known locking optimization that provides a surprising answer to the question:
If I hold an exclusive lock on a row, can another transaction running at the default read committed isolation level read it?
Most people would answer ‘no’, on the basis that the read would block when it tried to acquire a shared lock. Others might respond that it depends on whether the READ_COMMITTED_SNAPSHOT database option was in effect, but let’s assume that is not the case, and we are dealing simply with the default (locking) read committed isolation level.
It is frequently useful to generate sequences of values within SQL Server, perhaps for use as surrogate keys. Using the IDENTITY property on a column is the easiest way to automatically generate such sequences:
CREATE TABLE dbo.SomeTable
(
row_id integer IDENTITY PRIMARY KEY,
[data] sql_variant NOT NULL,
);
Sometimes though, the database designer needs a more flexible scheme than is provided by the IDENTITY property. One alternative is to use a Sequence Table.
If you look up Table Hints in the official documentation, you’ll find the following statements:
If a clustered index exists, INDEX(0) forces a clustered index scan and INDEX(1) forces a clustered index scan or seek.
If no clustered index exists, INDEX(0) forces a table scan and INDEX(1) is interpreted as an error.
The interesting thing there is that both hints can result in a scan. If that is the case, you might wonder if there is any effective difference between the two.
This blog entry explores that question, and highlights an optimizer quirk that can result in a much less efficient query plan when using INDEX(0). I’ll also cover some stuff about ordering guarantees.
A detailed look at costing, and more undocumented optimizer fun.
The SQL Server query optimizer generates a number of physical plan alternatives from a logical requirement expressed in T-SQL. If full cost-based optimization is required, a cost is assigned to each iterator in each alternative plan, and the plan with the lowest overall cost is ultimately selected for execution.
When you write a query to return the first few rows from a potential result set, you’ll often use the TOP clause.
To give a precise meaning to the TOP operation, it will normally be accompanied by an ORDER BY clause. Together, the TOP…ORDER BY construction can be used to precisely identify which top ‘n’ rows should be returned.
You might recall from Inside the Optimizer: Row Goals In Depth that query plans containing a row goal tend to favour nested loops or sort-free merge join over hashing.
This is because a hash join has to fully process its build input (to populate its hash table) before it can start probing for matches on its other input. Hash join therefore has a high start-up cost, balanced by a lower per-row cost once probing begins.
In this post, we will take a look at how row goals affect grouping operations.
One of the core assumptions made by the SQL Server query optimizer cost model is that clients will eventually consume all the rows produced by a query.
This results in plans that favour the overall execution cost, though it may take longer to begin producing rows.