SQL Server Indexes

Article part of “The Better Developers” series

Indexes?

Indexes are a key factor for a database, relational or not. Without indexes the only option for a database would be to read all the data and discard what is not needed. Very inefficient.

If a query has a where or a join or a group by clause and you feel performances are not good, before trying to figure out how to cache results in order to improve overall application performances — which will surely help but it will put on you the burden to maintain the cache, making the solution more expensive and complex to develop, maintain and evolve — keep in mind that you will surely benefit from using an index. How much is hard to say, but I would expect performance improvements in the order of 10x to 1000x. Yes: index can make such difference.

More on caching and other stuff is discussed at the bottom of the article, in the “For those who want to know more” and “For those who know more” sections.

Notes

Wondering who I am and why I’m writing these posts? Read the introductory article here. Also be sure to read the notes about the “For the Better Developer” article series, so that you can know what to expect.

The indexes you have to know

There are two principal types of indexes in SQL Server: clustered and non-clustered. The first main difference between the two is that the clustered works directly on table data, while the non-clustered works on a copy of such data.

Both indexes can supports two different storage models: using a row-store or using a column-store.

There are therefore four possible combination that we have to choose from when we need to add an index to our tables.

Clustered Row-Store Indexes

An easy example to explain this index is the encyclopedia or the phone number directory. Data is stored accordingly the order specified by the index. More specifically, by the columns declared when the index is created.

create clustered index IXC on dbo.Users(Surname, Name)

In the example above, rows will be stored ordered by Surname and then Name. Just like the phone number directory, duplicates are allowed.

Each row will also be stored along with all the remaining columns values. If the table dbo.Users has the following definition:

create table dbo.Users
(
Id int not null,
Surname nvarchar(50) not null,
Name nvarchar(50) not null,
DateOfBirth date not null,
PhoneNumber varchar(50) not null,
Address nvarchar(100) not null,
City nvarchar(50) not null,
State nvarchar(50) null,
Country nvarchar(50) not null
)

the index row will contain not only the indexed columns but also the Id, DateOfBirth, PhoneNumber, Address, City, State and Country. This happens because, remember, a clustered index works directly on table data.

Of course, since a clustered row-store index works directly on table’s data, there can be only one clustered index per table.

When you should use this type of index? It is perfect if:

  • you are looking for zero, one or more rows in a table
  • you are looking for a range of rows: from “A” to “B” for example.
  • you need to have the rows returned in a specific order

As you can see it fits all usage scenarios and, in fact, the clustered row-store index is very flexible and can be useful in almost every scenario but unfortunately, as already said, only one can exist per table. This limitation is imposed by the fact that such index physically orders data in the table and, of course, you can order data only in one way at time.

If you need to order it in more than one way at the same time, you need to duplicate some or all data, and that’s why Non-Clustered Row-Store Indexes exists.

Non-Clustered Row-Store Index

A Non-Clustered Row-Store index is somehow like the index you can find at the beginning of a magazine, where the topics are listed along with a pointer to the page in which they can be found.

In the case of SQL Server, when you create a non-clustered row-store index on a column — and following the phone number directory example let’s say the PhoneNumber column — all values in that column will be indexed and each one will have a pointer that allows SQL Server to relate that value with the row in the table that contains it.

create nonclustered index IXNC on dbo.Users(PhoneNumber)

Again, please note that values in the chosen columns needs not to be unique.

The non-clustered index requires additional space to be stored, since it creates a copy of all the data in the columns you specified in the index definition, plus the space needed for the pointer.

When the index is used to locate some data, the result of that search is a pointer. This means that in order to get the real data a lookup operation needs to be done. For example, given a PhoneNumber you want to know who will be answering if you call it. SQL Server will used the index of the PhoneNumber and after having found the number you are looking for, will take the pointer and use it to find all the related values like Name, Surname, Address and so on. A lookup is nothing more that a jump to the location indicated by the pointer in order to access the real data.

Of course this additional operation has a little overhead. Overhead that is directly proportional to the number of rows for which the lookup is needed. Are you looking for a thousand rows? A thousand lookup needs to be done. The consequence of this behavior is that it doesn’t make sense to always use an index, even if it exists. If the effort to do the all the lookup (which can be an expensive operation) is too high, compared to reading the full table and just discarding the rows that doesn’t fall in the scope of the requested search, the index will not be used.

As a general rule, than, it can be said that this index is perfect when you are looking

  • for zero or one row in a table
  • for *very few* rows in a table

as a rule of thumb, “very few” mean 1% or less of the rows in your table.

Clustered Column-Store Index

This index effectively turns SQL Server into a columnar database. Really. Once created on a table, since it is a clustered index, it will change the way SQL Server stores that table: instead of saving data one row at time, data will be saved one column a time, following the principles of columnar databases. For that reason you don’t really need to specify any columns when creating such index, since it will be applied to the whole table and thus all columns will be indexed:

create clustered columnstore index IXCCS on dbo.TrainingSessions

Column-Store index are perfect for analytical queries. All queries in which data needs to be grouped, optionally filtered and aggregated fall into this scope. Performance can be easily increased by a factor of 10x and it’s quite common to see improvements of 30x and more. This is also a result of the fact that, to work properly, columnar solutions needs to compress data and SQL Server makes no exception: the clustered column-store index will compress data in your table using several techniques, and you may see you table space shrinking a lot, like up to 60%-70% or even more.

Column-Store index are especially good in read-intensive scenarios, while they do not excel in being written very often. There is an exception for bulk-load scenario. A bulk load is activated when when you use the T-SQL command BULK INSERT or load data into SQL Server using the SqlBulkCopy object or if you use any solution, like SQL Server Integration Services, that supports the Bulk Load option.

If you have a table in which the majority if the queries have this pattern:

SELECT 
<aggregation_function>(Column1),
<aggregation_function>(Column2),
...
FROM
<table>
WHERE
...
GROUP BY
Column3,
Column4,
...

and write operations, compared to read operations, are a small number, then give this index a try. You will probably be amazed by how quickly data can be aggregated and manipulated, even on huge tables. (Huge: hundreds millions rows or more)

The downside of this index is that while it is great for working with aggregations, is not that good retrieving one or few rows with all their columns. So you may want to mix the row-store index and the column-store index together, and that’s why the next, and last, index on the list may be very useful too.

Non-Clustered Column-Store Index

This index allows you to create a colum-store index only on a subset of columns of your table.

create nonclustered columnstore index IXNCCS on dbo.Users(City, State, Country)

This is helpful when you need to have your table both supporting a row-store index, that is quick for retrieving whole rows, but you also need to quickly aggregated on some columns of your data.

Using again our phone directory example, the above index will make any aggregation on City or State or Country or a combination of them, really really really fast.

Let’s make a more real-life scenario: if you have a database that stores training sessions, for example, and you what to be able to present to your user a nice dashboard with aggregated information of the last six month of the most useful indicators like calories burnt, number of training done and so on and you also need to be able to quickly retrieve a single or a bunch of rows to show all the data you have for a specific training session, the non-clustered column-store index, especially when used together with a clustered row-store index, is just perfect for this job.

Be Warned

Creating, altering or dropping and index may have huge impact on the server. The bigger the table the bigger the impact. Depending on the size of your data, such operation can take seconds or hours and in the meantime all users trying to use the database will be affected in a way or another. So before playing with indexes, test the script on a test server to evaluate the impact.

Conclusions

You now have the basic foundation to understand how to make application go faster. Much, much, much faster.

Take the sample done above, remove training and users and put your own objects like invoices, bills, orders, whatever you want, and you can easily understand how SQL Server can be perfect to manage a real-time analytic workload and at the same a time also a transactional workload, which is a quite typical requirement.

This is a really unique feature in the DBMS space today.

My opinion is that, if you have less than 1 TB of “hot” data (data updated quite frequently during that day) you don’t really need to look for Big Data solution since the overhead they will put on you and your team can be quite big that I can hardly justify for one 1 TB of data. If you have 1 PB or near, than, let’s talk.

External References

Indexes are really a huge topic, here we just scratched the surface and even just a bit. I’ll surely talk more about indexes in next posts, but if you don’t want to wait and you’re interested in the topic, which is my main objective with this post series, that you can go for these external references:

For those who want to know more

Caching

SQL Server has its own internal cache. Actually, it has several different caches. The most important is the buffer cache. Here is where data live. In an healthy situation 99% of your data comes from the cache. Yeah, right, 99%.

Row-Store Indexes

Row-Store indexes are implemented a B+Trees. The technical difference between clustered and non-clustered is that for a clustered index in the leaf pages you have the actual data, while in the non-clustered you have data related only to the columns used or included in the index, and the pointer.

Clustered Row-Store Index

SQL Server doesn’t really enforce the physical order of the data after the index has been created. Data is ordered following the index definition during the index creation phase, but after that, if there are operation that inserts, deletes or updated data, than the physical order is not preserved. What is preserved is the logical order of the data. This means that data will be read in the correct sequence which may be different than the sequence in which rows can be found on the disk. This logical/physical difference is the index fragmentation that is not necessarily a bad thing. It usually is a bad thing, but not in 100% of the cases. For example if you always access your data by one row at time, you care a little about fragmentation. Or if you want to insert data as fast as possible, you don’t really care about fragmentation; on the opposite, you may want to have fragmented data in order to reduce the chances of having hot spots where contention will happen and thus your performance will suffer. Anyway, as usual, edge cases like the two described must not be used as the general rule.

Non-Clustered Row-Store Index

You can have more than one non-clustered row-store index on a table. Of course you can create such index on more than one column. This enables some nice “tricks” like the covering index that I will describe in future articles.

Other Indexes

There are many more index type then the one I described. They are specific to a certain data type, like geospatial indexes or to a specific feature that I will discuss in another post, like the Hekaton, the In-Memory, Lock-Free, engine. The indexes I described in this articles are the most commonly used.

For those who know more

Caching

Caching is usually a nice thing to have and I also use caching solution quite a lot. I just don’t want to use it as a surrogate of performance I can have right from my database. Caching is perfect, in my opinion, in highly concurrent systems to reduce the stress on your database so that you can deliver the same user experience with just a less expensive solution (be a smaller hardware on-premises or a lower performance level in the cloud).

Keep also in mind that SQL Server, offers an extremely good option for creating customized caching solution using In-Memory Tables, as Bwin, the online betting giant, demonstrated. So if you already using a SQL Server version that supports this feature, it may be worth to give it a try. Otherwise Redis and the likes are just perfect.

The “User” table

Yes, the table used in the sample has several design problems (both logical and physical), for example:

  • there is a functional dependency between City, State and Country)
  • there is no Primary Key defined
  • data types are really not the best possible

but I just want to show something that is as close as reality as possible so it will be easier to relate the example to your own experience. In addition to that I’ll talk about Keys and Indexes in another dedicated articles, so here I went for something that just plain, simple and easy to understand.

You’re really come that far?

Well, thanks for reading. :)