SQL Server Performance | DBA Diaries

The following tips do not promise that your database server will magically become blisteringly fast and go like a Ferrari but they are important things to bear in mind and relatively simple to implement either when building your database or if you have an existing performance problem and are looking for ways in which to make your database run faster.

**1/ SELECT * FROM**

The rest of my points are in no particular order but I must place this one at the top of the list as it really annoys me. I’ve seen this many times where developers take the lazy approach to writing their queries by including all columns for the table, or in the case of there being joins involved, then this means every column for every table involved in the query. It is far better to limit the query to only bring back the columns required reducing I/O, memory pressure and on bandwidth between the database and the application layers.

2/ Incorrect sizing of data types – less is more

Database design is sometimes a neglected aspect of building an application. The need to rapidly build a database to store the data and get coding results in tables containing data types which are incorrectly sized.

So lets look at a simple example of what I mean using MS SQL Server.

The system we are designing for allows advertisements to be placed in one county in England per advert. Whilst this does not sound like a terribly flexible system, it serves the purpose in trying to demonstrate the issue with badly sized data types 🙂

So here is the create table script. (I have omitted the Customer create table script which would theoretically exist here to ensure that the relationship on Advert.CustomerID could be created).

CREATE TABLE County
(
CountyID INT PRIMARY KEY IDENTITY(1,1),
County VARCHAR(20)
);

CREATE TABLE Advert
(
AdvertID INT PRIMARY KEY IDENTITY(1,1),
CustomerID INT NOT NULL REFERENCES Customer(CustomerID),
DatePlaced DATETIME DEFAULT GETDATE() NOT NULL,
CountyID INT NOT NULL REFERENCES County(CountyID),
AdvertText VARCHAR(MAX) NOT NULL,
);

So, two tables and where in this design are there issues?

How many counties are there in the England? Well certainly not this many: 2,147,483,647 which is the limit of the data type above 0. Yes you can store negatives also up to -2,147,483,648.

So what, they’re just numbers right? What’s the problem? The problem is the storage needed. The INT data type will require 4bytes of storage. The number of English counties is less than 255. Is it ever going to get larger than 255? I can’t see that happening any time soon so better to design the County table to be CountyID TINYINT and the related field in Advert to also be a TINYINT. This data type uses 1byte of storage. That’s a 75{3a76cbff1b5ce5ace3bd34bbdd68f71285d81fb9af60114c7a6db80d3a8688de} reduction and if you had an extremely large Advert table it could all start to add up.

The other field in question is the DatePlaced. Always question whether you need the increased size of the types you are setting. In my example, I don’t think I am too fussy about the extra precision which the DATETIME data type gives me, so I should have designed that as a SMALLDATETIME.

But storage is cheap right? Yes but this is not the point. It’s all about I/O and memory pressure. The smaller the data types are, the less pages have to be read from disk into the memory buffers. If the memory buffers are not constantly being emptied to make way for other data, then your database server and application will be a lot happier as there will be less read requests to disk.

3/ Adding and Removing Indexes

Identify queries which are performing badly. Can you add indexes which would make them faster? SQL Server 2008 Management Studio will even recommend which indexes to add when you view the execution plan. Identify where you need to add clustered indexes and put them in.

Identify unused indexes and remove them. For each record inserted to a table, every index is updated. If you do not need an index, get rid of it. It will help speed up inserts.

4/ Use stored procedures instead of in-line SQL

There are many advantages to using stored procedures. From a performance perspective, having them in place means that each time the procedure is executed, the execution plan is pulled out of cache. As the server does not need to work out the execution plan, the process from calling the procedure to it completing its execution is typically faster.

5/ Set up maintenance plans to rebuild indexes and statistics

All indexes will suffer from fragmentation and statistics will get out of date. Set up maintenance plans in SQL Server or batch tasks in MySQL which will periodically rebuild statistics and indexes to help the optimizer choose the best execution plan.

6/ Replace use of functions in WHERE clauses with SARG’s

It’s all about the search arguments (SARG’s)

Here’s an example. Two queries, the first uses a LIKE, the second uses the SUBSTRING function. There is an index on the LastName column in my table and the first query reports using an index seek which should normally be faster compared with the second which reports using a clustered index scan.

SELECT FirstName, LastName
FROM Employee
WHERE LastName LIKE 'Hay{3a76cbff1b5ce5ace3bd34bbdd68f71285d81fb9af60114c7a6db80d3a8688de}';

SELECT FirstName, LastName
FROM Employee
WHERE LastName SUBSTRING(LastName,1,3) = 'Hay';

Note that use of LIKE will not always reference an index for example this query would not use an index

WHERE LastName LIKE '{3a76cbff1b5ce5ace3bd34bbdd68f71285d81fb9af60114c7a6db80d3a8688de}es'

Other examples of SARG’s:

LastName = 'Hayes'
Amount > 0
Amount BETWEEN 10 AND 20

I’ve also seen UDF’s in place in WHERE clauses and unsuprisingly, performance is poor.

Get the query using a SARG and there is a higher chance that the optimizer will use an index.

7/ Ensure that foreign keys in are in place

Please refer to my post on the importance of foreign keys and how they can help improve query performance.

8/ Increasing chances of using the query cache (MySQL)

Minor changes to queries to make them deterministic can increase query cache hit rate. For example if a query uses CURDATE(), then the results will not be cached.

9/ Deal in sets not cursors

Try and build SQL queries which deal with sets of data. Leave any row by row processing to the application layer.

10/ Allocate more memory to buffers

You need a solution quickly and you have identified your server is under memory pressure. You have spare RAM available and so it is an easy task to allocate more memory to MS SQL or MySQL database memory buffers. Personally, I will always try and carry out a more exhaustive analysis of the server but if time does not permit that then allocate the memory to improve performance and analyse a typical workload on a staging server to determine where other performance improvements can be made.

It is important to ensure that these issues are not brushed under the carpet after increasing hardware resource. Think about it for a moment. Your application has been slowing down and user complaints have increased. What’s an obvious cause of that? – Growth.

So in order to effectively deal with growth, from a DBA perspective, the database server needs to be monitored for performance problems with issues reported to the relevant technical staff at the earliest opportunity. Adding hardware will always help improve performance but the application will never scale very well unless the issues “underneath the bonnet” in the database layer are effectively dealt with.

StaffID FirstName LastName Department Region Salary ----------- ---------- ---------- ---------- ---------- ----------- 1 John Smith Sales South East 45000 2 Raymond Jones Finance South East 40000 3 Tracey Carter Technology South East 42000 4 Andy Milward Sales South West 41000 5 Joanne Baldock Finance South West 37000 6 Simon Jackson Technology South West 40000 7 Greg Benson Sales Midlands 47500 8 Jack Andrews Finance Midlands 39000 9 James Michaels Technology Midlands 38000 10 Mark Taylor Sales North West 39000 11 Frances Keagan Finance North West 42000 12 Jane Coleson Technology North West 35000 13 Jennifer Seymour Sales North East 35500 14 Rebecca Morgan Finance North East 38000 15 Alex Parker Technology North East 38500 (15 row(s) affected)

Department Region AverageSalary --------------- --------------- ----------- Finance Midlands 39000 Sales Midlands 47500 Technology Midlands 38000 NULL Midlands 41500 Finance North East 38000 Sales North East 35500 Technology North East 38500 NULL North East 37333 Finance North West 42000 Sales North West 39000 Technology North West 35000 NULL North West 38666 Finance South East 40000 Sales South East 45000 Technology South East 42000 NULL South East 42333 Finance South West 37000 Sales South West 41000 Technology South West 40000 NULL South West 39333 NULL NULL 39833 Finance NULL 39200 Sales NULL 41600 Technology NULL 38700 (24 row(s) affected)

SELECT Department , Region , AVG(Salary) AS AverageSalary FROM vwStaff GROUP BY Department , Region UNION SELECT Department , NULL AS Region , AVG(Salary) AS AverageSalary FROM vwStaff GROUP BY Department UNION SELECT NULL AS Department , Region , AVG(Salary) AS AverageSalary FROM vwStaff GROUP BY Region UNION SELECT NULL AS Department , NULL AS Region , AVG(Salary) AS AverageSalary FROM vwStaff

---GROUPING SETS Table 'Department'. Scan count 0, logical reads 60, physical reads 1, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Region'. Scan count 0, logical reads 60, physical reads 1, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Staff'. Scan count 2, logical reads 4, physical reads 1, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. ---UNION Table 'Department'. Scan count 0, logical reads 60, physical reads 1, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0 Table 'Region'. Scan count 0, logical reads 120, physical reads 1, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Staff'. Scan count 4, logical reads 8, physical reads 1, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

10 simple tips on improving query and server performance