“Pro SQL Server Internals” eBook is on sale until July 22nd

I just want to share that APress is running Independence Day eBook sale now. You can buy the copy of “Pro SQL Server Internals” eBook for $17.76 from their web site. The sale ends on July 22nd, 2015.

You can read the reviews at Amazon.

I would also appreciate if you post a short review at Amazon. It would help me to convince APress to publish the 2nd edition with SQL Server 2016 content.

PASS Summit 2015: I am Speaking!

SQL Server professionals are very strange. They live in their own world. They even have a calendar, which does not make any sense to the regular people.

They call Spring as Abstract Time and spend days in the dark rooms writing abstracts for upcoming PASS Summit. Summer becomes either Time of Work and Happiness or Time of Sadness depending on the Call for Speakers results. Autumn is, obviously, Gathering Time – time when they meet their friends and #sqlfamily in Seattle. And, finally, Winter is Time of Memories and Stories –  the season of neverending tales from the Summit – the season when they are boring other, non-SQL Server folks, to death.

This year is Time of Work and Happiness for me. I just found that two of my abstracts have been selected, including the pre-con session. It is hard to express how proud and honored I feel and I want to thank Program Committee, PASS and all of you for your trust and support. Thank you, it means a lot!

I am delivering the well known pre-con: SQL Server Internals: The Practical Angle. This pre-con explains how SQL Server components work under the hood and how their behavior affect your systems. It is based on my book Pro SQL Server Internals or, to be exact, that pre-con inspired me to write that book in the first place.

The second session, Thinking Outside of the In-Memory Box, talks about In-Memory OLTP and focusing how to address the surface area limitations of the technology in SQL Server 2014 and how to benefit from it even when migrations of existing systems are cost ineffective. There is also the book behind it.

I really hope, I will see some of you in Seattle. I urge you to attend the Summit – especially, if you have never been there. Trust me, it is life changing experience – the one, which is impossible to forget. You will see our community in action, you will make new friends and, of course, you will learn a lot.

Looking forward to see you there!

Thinking Outside of In-Memory Box: Supporting Uniqueness and Referential Integrity in In-Memory OLTP

As with any new technology, adoption of In-Memory OLTP comes at a cost. You need to acquire and/or upgrade to SQL Server 2014, spend time learning the technology and, if you are migrating an existing system, refactor code and test the changes.

Unfortunately, system refactoring can be complex and time consuming. SQL Server 2014 In-Memory OLTP has several important limitations, which can dramatically increase the cost of migration. To name just  few – it does not support off-row storage limiting you to 8,060-byte rows nor support CHECK, UNIQUE, FOREIGN KEY constraints and triggers. All those limitations can be addressed by schema and code refactoring; however, in some cases, that refactoring can require significant amount of efforts.

Today, I would like to start the series of the blog posts discussing how we can address specific limitations that exist in the first release of In-Memory OLTP. I will start with supporting uniqueness and referential integrity in the system. Unfortunately, In-Memory OLTP does not allow you to define foreign keys nor unique indexes and constraints besides a primary key. Such limitation rarely prevents us from adoption of the new technology. Nevertheless, those constraints help to keep the data clean and allow to detect data quality issues and bugs in the code at early stages of development.

To make matter worse, lock-free nature of In-Memory OLTP makes the code approach tricky. In-Memory OLTP transactions do not see any uncommitted changes done by the other transactions. For example, if you ran the code from Figure 1 in default SNAPSHOT isolation level, both transactions would successfully commit without seeing each other changes violating ProductName uniqueness.

Figure 1. Uniqueness Violation in SNAPSHOT Isolation Level

Fortunately, that situation can be addressed by using SERIALIZABLE transaction isolation level. As you remember, In-Memory OLTP validates serializable consistency rules by maintaining transaction scan set. As part of serializable rules validation, In-Memory OLTP checks for the phantom rows making sure that other sessions did not insert any rows that were previously invisible for the active transactions.

Code below creates memory-optimized table and natively compiled stored procedure that inserts data there running in SERIALIZABLE isolation level. Any inserts done through this stored procedure guarantee uniqueness of the ProductName even in multi-user concurrent environment.

The SELECT query builds transaction scan set, which will be used for serializable rule validation. That validation would fail if any other sessions inserted a row with the same ProductName while transaction is still active. Unfortunately, the first release of In-Memory OLTP does not support subqueries and it is impossible to write the code using IF EXISTS construct.

create table dbo.ProductsInMem
(
    ProductId int not null identity(1,1)
        constraint PK_ProductsInMem
        primary key nonclustered hash
        with (bucket_count = 65536),
    ProductName nvarchar(64) 
        collate Latin1_General_100_BIN2 not null,
    ShortDescription nvarchar(256) not null,

    index IDX_ProductsInMem_ProductName nonclustered(ProductName)
)
with (memory_optimized = on, durability = schema_and_data);
create procedure dbo.InsertProduct
(
    @ProductName nvarchar(64) not null
    ,@ShortDescription nvarchar(256) not null
    ,@ProductId int output
)
with native_compilation, schemabinding, execute as owner
as
begin atomic with
(
    transaction isolation level = serializable
    ,language = N'English'
)
    declare
        @Exists bit = 0

    -- Building scan set and checking existense of the product
    select @Exists = 1
    from dbo.ProductsInMem
    where ProductName = @ProductName

    if @Exists = 1
    begin
	;throw 50000, 'Product Already Exists', 1;
	return
    end

    insert into dbo.ProductsInMem(ProductName, ShortDescription)
    values(@ProductName, @ShortDescription);

    select @ProductID = scope_identity()
end

You can validate behavior of the stored procedure by running it in two parallel sessions as shown in Figure 2 below. Session 2 successfully inserts a row and commits the transaction. Session 1, on the other hand, would fail on commit stage with Error 41325.

Figure 2. dbo.InsertProduct Call from Two Parallel Sessions

Obviously, this approach would work and enforce the uniqueness only when you have full control over data access tier and have all INSERT and UPDATE operations performed through the specific set of stored procedures and/or code. INSERT and UPDATE statements executed directly against a table could easily violate uniqueness rules. However, you can reduce the risk by revoking INSERT and UPDATE permissions from the users giving them the EXECUTE permission on the stored procedures instead.

You can use the same technique to enforce referential integrity rules. Code below creates Orders and OrderLineItems tables and two stored procedures InsertOrderLineItems and DeleteOrders enforcing referential integrity between those tables. I am omitting OrderId update scenario, which is very uncommon in the real life.

create table dbo.Orders
(
    OrderId int not null identity(1,1)
        constraint PK_Orders
        primary key nonclustered hash 
        with (bucket_count=1049008),
    OrderNum varchar(32) 
        collate Latin1_General_100_BIN2 not null,
    OrderDate datetime2(0) not null
        constraint DEF_Orders_OrderDate
        default GetUtcDate(),
    /* Other Columns */
    index IDX_Orders_OrderNum
    nonclustered(OrderNum)
)
with (memory_optimized = on, durability = schema_and_data);

create table dbo.OrderLineItems
(
    OrderId int not null,
    OrderLineItemId int not null identity(1,1)
        constraint PK_OrderLineItems
        primary key nonclustered hash 
        with (bucket_count=4196032),
    ArticleId int not null,
    Quantity decimal(8,2) not null,
    Price money not null,
    /* Other Columns */

    index IDX_OrderLineItems_OrderId
    nonclustered hash(OrderId)
    with (bucket_count=1049008)
)
with (memory_optimized = on, durability = schema_and_data);
go

create type dbo.tvpOrderLineItems as table
(
    ArticleId int not null
        primary key nonclustered hash
        with (bucket_count = 1024),
    Quantity decimal(8,2) not null,
    Price money not null
    /* Other Columns */
)
with (memory_optimized = on);
go

create proc dbo.DeleteOrder
(
    @OrderId int not null
)
with native_compilation, schemabinding, execute as owner
as
begin atomic
with 
(
    transaction isolation level = serializable
    ,language=N'English'
)
    -- This stored procedure emulates ON DELETE NO ACTION 
    -- foreign key constraint behavior
    declare
        @Exists bit = 0

    select @Exists = 1
    from dbo.OrderLineItems
    where OrderId = @OrderId

    if @Exists = 1
    begin
        ;throw 60000, 'Referential Integrity Violation', 1;
        return
    end
    
    delete from dbo.Orders where OrderId = @OrderId
end
go

create proc dbo.InsertOrderLineItems
(
    @OrderId int not null
    ,@OrderLineItems dbo.tvpOrderLineItems readonly 
)
with native_compilation, schemabinding, execute as owner
as
begin atomic
with 
(
    transaction isolation level = repeatable read
    ,language=N'English'
)
    declare
        @Exists bit = 0

    select @Exists = 1
    from dbo.Orders
    where OrderId = @OrderId

    if @Exists = 0
    begin
        ;throw 60001, 'Referential Integrity Violation', 1;
        return
    end
    
    insert into dbo.OrderLineItems(OrderId, ArticleId, Quantity, Price)
        select @OrderId, ArticleId, Quantity, Price
        from @OrderLineItems
end

It is worth noting that InsertOrderLineItems procedure is using REPEATABLE READ isolation level. In this scenario, we need to make sure that referenced Order row has not been deleted during the execution and REPEATABLE READ enforces that introducing less overhead than SERIALIZABLE isolation level.

That technique comes with another small benefit. It can demonstrate advantage of having dedicated data access tier to application developers helping to convince them to build one. All of us, database professionals, would like it, don’t we? :)

Source code is available for download.

Compressing LOB (XML) Data in the Database

Some time ago we have discussed the several techniques that can help reducing the database size. I have received quite a few responses on that post and several people asked if I could provide more details and examples. Today I will try to follow up on one of the methods, such as reducing the size of LOB data (in particular XML) in the database.

As you know, SQL Server stores the data in regular B-Tree indexes in three different sets of the data pages called allocation units. The main data row structure and fixed-length data are stored in IN-ROW data pages. Variable-length data greater than 8,000 bytes in size is stored in LOB (large object) pages. Such data includes (max) columns, XML, CLR UDT and a few other data types. Finally, variable-length data, which does not exceed 8,000 bytes, is stored either in IN-ROW data pages when it fits into the page, or in ROW-OVERFLOW data pages. You can read more about it in the previous post and here.

Enterprise Edition of SQL Server allows you to reduce the size of the data by implementing data compression. However, data compression is applied to IN-ROW data only and it does not compress ROW-OVERFLOW and LOB data. Any large objects that do not fit into IN-ROW data pages remain uncompressed.

Unfortunately, there is very little we can do to reduce the size of the LOB data. SQL Server does not provide any build-in functional to address it. The only remaining option is compressing it manually – either on the client or with CLR routines. Obviously, compression and decompression adds overhead and, from SQL Server load standpoint, it is preferable to do it on the client side. However, in the large number of cases, you will need to access compressed data from T-SQL, and CLR integration is the only choice. Ideal implementation in that case would combine compression and decompression code in both tiers and use CLR only when it is necessary.

Fortunately, .Net implementation of the compression code is very simple and can be done with DeflateStream or GZipStream classes. Below you can see the code of CLR functions that perform compression and decompression. You can also download entire project with the link at the end of the post.

/// <summary>
/// Compressing the data
/// </summary>
[Microsoft.SqlServer.Server.SqlFunction(IsDeterministic = true, IsPrecise = true,
        DataAccess = DataAccessKind.None)]
public static SqlBytes BinaryCompress(SqlBytes input)
{
    if (input.IsNull)
        return SqlBytes.Null;

    using (MemoryStream result = new MemoryStream())
    {
        using (DeflateStream deflateStream = 
            new DeflateStream(result, CompressionMode.Compress, true))
        {
            deflateStream.Write(input.Buffer, 0, input.Buffer.Length);
            deflateStream.Flush();
            deflateStream.Close();
        }
        return new SqlBytes(result.ToArray());
    } 
}

/// <summary>
/// Decompressing the data
/// </summary>
[Microsoft.SqlServer.Server.SqlFunction(IsDeterministic = true, IsPrecise = true,
        DataAccess = DataAccessKind.None)]
public static SqlBytes BinaryDecompress(SqlBytes input)
{
    if (input.IsNull)
        return SqlBytes.Null;
    
    int batchSize = 32768;
    byte[] buf = new byte[batchSize];

    using (MemoryStream result = new MemoryStream())
    {
        using (DeflateStream deflateStream = 
            new DeflateStream(input.Stream, CompressionMode.Decompress, true))
        {
            int bytesRead;
            while ((bytesRead = deflateStream.Read(buf, 0, batchSize)) > 0)
                result.Write(buf, 0, bytesRead);
        }
        return new SqlBytes(result.ToArray());
    } 
}

You can define the functions in the database with the following code (you either need to get byte sequence of the compiled assembly from the demo script or compile CLR project).

create assembly LOBCompress
authorization dbo
from /*..*/
go

create function dbo.BinaryCompress(@input varbinary (max))
returns varbinary (max)
as external name [LOBCompress].[Compress].[BinaryCompress];
go

create function dbo.BinaryDecompress(@input varbinary (max))
returns varbinary (max)
as external name [LOBCompress].[Compress].[BinaryDecompress];
go

Now let’s see the process in action, create the test table and populate it with some data.

create table dbo.DataWithXML
(
    ID int not null,
    Data xml not null,

    constraint PK_DataWithXML
    primary key clustered(ID)
)
go

declare
    	@X xml

select @X = 
    (
        select *
        from master.sys.objects
        for xml raw, root('Data')
    )

;with n1(c) as (select 0 union all select 0) -- 2 rows
,n2(c) as (select 0 from n1 as t1 cross join n1 as t2) -- 4 rows
,n3(c) as (select 0 from n2 as t1 cross join n2 as t2) -- 16 rows
,n4(c) as (select 0 from n3 as t1 cross join n3 as t2) -- 256 rows
,n5(c) as (select 0 from n4 as t1 cross join n3 as t2) -- 4,096 rows
,ids(id) as (select row_number() over (order by (select null)) from n5)
insert into dbo.DataWithXML(ID,Data)
    select id, @X
    from Ids;

update dbo.DataWithXML
set Data.modify('replace value of (/Data/row/@object_id)[1]
with sql:column("ID")');

Let’s examine the size of the table and average size of XML there using the following queries:

select 
    index_id, partition_number, alloc_unit_type_desc
    ,index_level
    ,page_count
    ,page_count * 8 / 1024 as [Size MB]
from 
    sys.dm_db_index_physical_stats
    (
        db_id() /*Database */
        ,object_id(N'dbo.DataWithXML') /* Table (Object_ID) */
        ,1 /* Index ID */
        ,null /* Partition ID – NULL – all partitions */
        ,'detailed' /* Mode */
    )
go

select avg(datalength(Data)) as [Avg XML Size]
from dbo.DataWithXML;

As you can see in Figure 1, LOB allocation units are using 96MB of space to store the data. The average size of the XML data is 20,897 bytes per row. It is also worth mentioning that actual storage size for XML is 3 data pages per row, which is 24,576 bytes.

1. Uncompressed data: Storage space and XML Size

Let’s see what we can achieve by using compression. The code below creates another table and copies the data there, compressing it on the fly. As you see, the data is stored in varbinary(max) column.

create table dbo.DataWithCompressedXML
(
    ID int not null,
    Data varbinary(max) not null,

    constraint PK_DataWithCompressedXML
    primary key clustered(ID)
)
go

insert into dbo.DataWithCompressedXML(ID,Data)
    select ID, dbo.BinaryCompress(convert(varbinary(max),Data))
    from dbo.DataWithXML

Let’s check the size of the data in compressed table:

select avg(datalength(Data)) as [Uncompressed] 
from dbo.DataWithXML;

select avg(datalength(Data)) as [Compressed] 
from dbo.DataWithCompressedXML
go

select 
    index_id, partition_number, alloc_unit_type_desc
    ,index_level
	,page_count
	,page_count * 8 / 1024 as [Size MB]
from 
    sys.dm_db_index_physical_stats
    (
        db_id() /*Database */
        ,object_id(N'dbo.DataWithCompressedXML') /* Table (Object_ID) */
        ,1 /* Index ID */
        ,null /* Partition ID – NULL – all partitions */
        ,'detailed' /* Mode */
    )

As you can see in Figure 2, we were able to reduce the size of the table from 96 to 10MB decreasing the size of the XML in every row from 20,897 to 2,674 bytes.

2. Compressed data: Storage size and compressed XML size

It is also worth noting that in our case, the size of compressed data is less than 8,000 bytes and SQL Server was able to accommodate all the data using IN-ROW allocation units. Even though the new table is almost 10 times smaller than the old one, it has significantly more IN-ROW data pages in the index. This could introduce some performance side effects in some cases. For example, when system has poorly optimized queries that perform clustered index scans. Again, it could become the issue only if compressed data is less than 8,000 bytes. Otherwise, SQL Server will still store it using the LOB data pages.

Obviously, this implementation requires schema and code changes. We can mitigate it a little bit by abstracting it with the views as it is shown below.

create view dbo.vDataWithXML(ID, Data)
as
    select ID, convert(xml,dbo.BinaryDecompress(Data))
    from dbo.DataWithCompressedXML

We can even create INSTEAD OF trigger (yuck!) on the view to minimize the changes if absolutely needed.

Speaking of the overhead, compressing and decompressing are CPU intensive and there is the performance penalty of calling CLR functions. It is not that noticeable when you need to decompress the single or very few rows; however, it could be very significant on the large data sets. For example, when you need to decompress and shred XML and use some of its elements in a where clause of the query. The code below shows such an example.

set statistics time on
select count(*) 
from dbo.DataWithXML 
where Data.value('(/Data/row/@object_id)[1]','int') = 3;

select count(*) 
from dbo.vDataWithXML 
where Data.value('(/Data/row/@object_id)[1]','int') = 3;
set statistics time off

The second SQL that accesses the compressed table has to decompress XML for every row in the table. The execution times of the statements on my laptop are 116ms and 6,899ms respectively. As you see, the compression definitely comes at cost.

One of the ways to reduce such an overhead, is storing XML elements that are used in the queries in the separate table columns. Unfortunately, by-the-book approach with persisted calculated columns would not always works. It is possible to create and persist such a column using user-defined functions; however, SQL Server would ignore it in some cases. The code below shows the example that creates calculated column that contains the attribute we are using in our queries.

create function dbo.fnGetCompressedObjectId(@Compressed varbinary(max))
returns int
with schemabinding
as
begin
    return (convert(xml,dbo.BinaryDecompress(@Compressed))
                .value('(/Data/row/@object_id)[1]','int'))
end
go	 

alter table dbo.DataWithCompressedXML
add
    ObjId as dbo.fnGetCompressedObjectId(Data)
        persisted
go

-- It is a good practice to rebuild index after alteration
alter index PK_DataWithCompressedXML 
on dbo.DataWithCompressedXML rebuild
go

alter view dbo.vDataWithXML(ID, Data, ObjId)
as
    select ID, convert(xml,dbo.BinaryDecompress(Data)), ObjId
    from dbo.DataWithCompressedXML
go

Unfortunately, if you ran the following query: select count(*) from dbo.vDataWithXML where ObjId = 3, you’d notice that SQL Server recalculates the value of the calculated column even though it is persisted and functions are defined as deterministic and precise. This is just the limitation of the Query Optimizer. Figure 3 shows that ObjId is recalculated.

3. Execution plan of the query

There is still the possibility of using persisted calculated columns. For example, you can define CLR function, which decompress and parse XML and return ObjID as the integer. Something like that:

.Net CLR:
[Microsoft.SqlServer.Server.SqlFunction(IsDeterministic = true, IsPrecise = true,
            DataAccess = DataAccessKind.None)]
public static SqlInt32 GetObjId(SqlBytes input)
{
    if (input.IsNull)
        return SqlInt32.Null;
    /* Parsing XML with XmlReader and return ObjId attribute */
}

SQL: 
create function dbo.GetObjId(@input varbinary (max))
returns int
as external name [LOBCompress].[Compress].[GetObjId]
go

alter table dbo.DataWithCompressedXML drop column ObjId
go

alter table dbo.DataWithCompressedXML
add
    ObjId as dbo.GetObjId(Data)
        persisted
go

alter index PK_DataWithCompressedXML 
on dbo.DataWithCompressedXML rebuild
go

Now, if you run the previous query: select count(*) from dbo.vDataWithXML where ObjId = 3, it would work just fine and does not recalculate the column value. Figure 4 shows the execution plan in this case.

4. Execution plan that utilizes calculated column

It is worth mentioning that execution time of this query on my laptop is just 3 milliseconds comparing to 116 milliseconds of the query against dbo.DataWithXML table. The query is significantly faster because it does not need to shred XML to obtain ObjID value.

Obviously, creating separate CLR methods for each calculated column can lead to some coding overhead. You can consider using regular columns instead and populate them in the code or even in the triggers. Each approach has the own set of benefits and downsides based on the use-cases implemented in the system.

Compressing LOB data in the database could help you to significantly reduce the database size in the large number of cases. However, it adds an overhead of compressing and decompressing data. In some cases, such overhead would be easily offset by the smaller data size, less I/O and buffer pool usage but in any case, you should be careful and take all other factors into the consideration.

Source code is available for download.

Optimizing Substring Search Performance in SQL Server

The requirement of searching data by part of the value is very common in business applications. All of us are familiar with it – users want to be able to search by entering just a few letters from the client or article name; locate postal address by typing just a part of the street; or do something similar in dozens of the other cases.

Obviously, there are many ways to skin the cat and implement such a search. In some complex and performance-critical cases we can use external to SQL Server solutions, for example Apache Lucene. In others, we can use Full-Text Search or even do the brute force approach with LIKE operator. Today, I would like to talk about the latter one. After all, even though LIKE is not necessarily the fastest solution, its performance could often be acceptable especially with relatively small tables. Last but not least, it comes with very little implementation cost.

Unfortunately, LIKE operator cannot use Index Seek unless you are performing the prefix search. In that case, when you are searching by the beginning of the string – for example, LastName LIKE ‘Smit%’ condition – SQL Server is able to locate subset of the data where predicate needs to be evaluated. In our example, the predicate, in the nutshell, is the following condition: LastName >= ‘Smit’ and LastName < ‘Smiu’, which is perfectly SARGable and suitable for the fast Index Seek.

This is not the case, however, when LIKE expression requires SQL Server to find patterns in the middle of the string – for example, in LastName LIKE ‘%Smit%’ situation. The only option for SQL Server is evaluating expression against every row from the index, which leads to the Index Scan.

As strange as it sounds, you can often improve search performance by challenging business requirements. Even though customers want to be able to search by substring, in the very large number of cases prefix search would do. For example, when you are calling customer service somewhere and asking them to look up your account, you’d usually provide them the first few letters of your name rather than some letters from the middle of it.

Unfortunately, changing the business requirements is not always possible. In some cases, we do not have any choices but implementing substring search. In those occasions, there are two ways to improve the performance – reduce the number of rows where LIKE predicate must be evaluated and reduce predicate evaluation time.

Reducing the number of rows for predicate evaluation greatly depends on the indexes. While you cannot do much when LIKE is the only predicate in the query, such condition is usually an exception rather than the rule. In case when query has multiple predicates, the right composite indexes would help. The key here is adding evaluation column as the key or included column of the index and avoiding post-Key Lookup predicate evaluation.

For example, consider multi-tenant shopping cart system and the query that need to return the list of the articles that belong to particular tenant. The query could be implemented as follows:

select ..
from dbo.Articles
where TenantID = @TenantID and Name LIKE '%' + @paramName + '%'
order by Name

Such query would benefit from the following index, which will limit LIKE predicate evaluation to the single tenant scope. As the side note, adding Name as the key rather than included column would help to avoid SORT operator in the execution plan – data in the index would be sorted according to order by clause of the query.

create index IDX_Articles_TenantID_Name
on dbo.Articles(TenantID,Name)

Reducing the predicate evaluation time is the trickier subject. Fortunately, you can often achieve significant performance increase by utilizing binary collations during such an evaluation.

Let’s take a look at the example. As the first step, we will create a table and populate it with some random data. Col1, Col2 and Col3 columns are populated with randomly generated GUIDs and VarCol and NVarCol store concatenated values from them using SQL_Latin1_General_CP1_CI_AS collations. Finally, I am creating nonclustered indexes on VarCol and NVarCol columns to minimize amount of data pages SQL Server need to read during our tests and make those tests consistent.

create table dbo.Data
(
    ID int not null,
    Col1 uniqueidentifier not null
        default NEWID(),
    Col2 uniqueidentifier not null
        default NEWID(),
    Col3 uniqueidentifier not null
        default NEWID(),
    VarCol varchar(108) null,
    NVarCol nvarchar(108) null,
    
    constraint PK_Data
    primary key clustered(ID)
)
go

;with N1(C) as (select 0 union all select 0) -- 2 rows
,N2(C) as (select 0 from N1 as T1 cross join N1 as T2) -- 4 rows
,N3(C) as (select 0 from N2 as T1 cross join N2 as T2) -- 16 rows
,N4(C) as (select 0 from N3 as T1 cross join N3 as T2) -- 256 rows
,N5(C) as (select 0 from N4 as T1 cross join N4 as T2) -- 65,536 rows
,IDs(ID) as (select row_number() over (order by (select NULL)) from N5)
insert into dbo.Data(ID)
    select ID from IDs;

update dbo.Data
set
    VarCol =
        convert(varchar(36),Col1) +
        convert(varchar(36),Col2) +
        convert(varchar(36),Col3)
    ,NVarCol =
        convert(nvarchar(36),Col1) +
        convert(nvarchar(36),Col2) +
        convert(nvarchar(36),Col3)
go

create nonclustered index IDX_Data_VarCol
on dbo.Data(VarCol);

create nonclustered index IDX_Data_NVarCol
on dbo.Data(NVarCol);

Next, let’s randomly choose substring to search using one of the rows from the table. You would obviously have different data in your case/

select * from dbo.Data where ID = 10000

01. Choosing test substring for the search.

Now, let’s run SELECT statements that perform substring search against both columns and measure the execution time of the selects. I am disabling parallelism with MAXDOP 1 hint to avoid any parallelism overhead during queries execution.

declare
    @V varchar(32) = '9D81AB12'
    ,@NV nvarchar(32) = N'9D81AB12'

set statistics time on

select count(*)
from dbo.Data
where VarCol like '%' + @V + '%'
option (maxdop 1);

select count(*)
from dbo.Data
where NVarCol like '%' + @NV + '%'
option (maxdop 1);

set statistics time off

In my environment, CPU times of the first and second statements are 203 and 844 milliseconds respectively. Obviously, you would get the different results in your system and performance would greatly depend on the data.

It is also worth mentioning, that index on NVarCol is about two times larger than index on VarCol column due to the fact, that unicode data uses 2-bytes per character to store the data as the opposite to 1-byte per character with non-unicode varchars. However, the overhead of the extra logical reads is minimal.

Now let’s measure execution time using binary collations. First, we will alter the table adding two calculated columns that represent our strings in binary collation and creating nonclustered indexes afterwards.

alter table dbo.Data
add VarColBin as upper(VarCol) collate Latin1_General_100_Bin2
persisted;

alter table dbo.Data
add NVarColBin as upper(NVarCol) collate Latin1_General_100_Bin2
persisted;

create nonclustered index IDX_Data_VarColBin
on dbo.Data(VarColBin);

create nonclustered index IDX_Data_NVarColBin
on dbo.Data(NVarColBin);

PERSISTED keyword tells SQL Server to materialize calculated columns and store them in the data row. Technically speaking, you do not need to persist calculated columns in our case – you can index them even when they are not persisted, which helps to avoid clustered index row size increase. However, you need to be careful making sure that SQL Server always uses nonclustered index for the search. Otherwise, search performance could be even slower than with nonbinary collation – SQL Server will need to calculate column values on the fly before evaluating LIKE predicate.

Another very important factor to remember is case sensitivity of the binary collation. You need to convert your data to upper or lower case if your system uses case-insensitive collation. Obviously, you need to use the same conversion rule for the search predicate.

declare
    @V varchar(32) = '9D81AB12'
    ,@NV nvarchar(32) = N'9D81AB12'

set statistics time on

select count(*)
from dbo.Data
where VarColBin like '%' + Upper(@V) + '%' collate Latin1_General_100_Bin2
option (maxdop 1);

select count(*)
from dbo.Data
where NVarColBin like '%' + Upper(@NV) + '%' collate Latin1_General_100_Bin2
option (maxdop 1);

set statistics time off

The execution times in my environment are 125 and 62 milliseconds respectively. You can see all the results in Figure 2 below.

02. Test results

As you can see, we got more than 13 times performance improvement in case of the unicode data. Performance improvement with non-unicode strings are less drastic; however, it still ran about 40% faster than before. It is also worth mentioning that with the binary collation, predicate evaluation against unicode data is faster than with varchar data. At least with my test data.

Lastly, the word of caution. While that technique can be help to improve performance of substring search and reduce CPU load in the system, you should not treat it as the replacement of the proper indexing. After all, you can get much better ROI by investing your time into query optimization. However, it is the great technique to use in conjunction with query optimization and index tuning when you need to get the most from your queries.

Off-Topic: The Author’s Dilemma

The journey of a thousand miles begins with a single step. The journey of writing, on the other hand, begins with the planning. There are hundreds of the ways how to structure the information in the book. And, unfortunately, the author’s choice is not always the best one – it is easy to forget that readers can have different preferences.

Today I need your feedback. I would like to ask all of you – how you, as the readers, prefer books or large articles to be structured. Let I elaborate a little bit.

Usually, every large article or book consists of several interrelated topics. Moreover, each topic can be split to basic (introductory) and advanced content. Let’s think about Clustered Columnstore Indexes as the example. If I ever wrote a book on them, I would have to cover their internal structure, explain how they are handling data modifications, how to design ETL processes in the most efficient way and, perhaps, explain what is the batch-mode execution and how to utilize it. Please, do not consider it to be the full coverage on the topic – this is just an example.

The conventional approach for such book would introduce Table of Content similar to the one below. Please, do not judge its quality – it is something I just invented on the fly to illustrate the point.

  • Overview and Introduction
  • Internal Structure
    • Row Groups, Delta Store, Delete Bitmaps
    • Internals of data modifications
  • ETL and Design Considerations
    • Best practices for ETL processes
    • CCI and Data Partitioning
    • ETL and Index Maintenance
  • Batch-Mode Execution
    • Batch-Mode Execution – overview and implementation
    • Query patterns that lead to Batch-Mode Execution

There is another approach, however. We can split the article or book based on the level of content, such as basic and advanced. With such an approach, you would be able to read half of the book, get some ideas on the topic and start using it. Meanwhile, you can master your skills with advanced content.

Obviously, there are some downsides. First, basic content would have plenty of references to advanced chapters. For example, it can say “using batches of 1M rows for import would provide the best results. We will cover why it happened in Chapter X“. Second, the same topics will be covered in two different chapters, granted on the different levels. And it will look poorly structured, especially if you are already familiar with the topic.

The table of content for such an approach could look like that:

  • CCI: Basics Concepts
    • Overview and Introduction
    • CCI components overview
    • Best practices during data modification and ETL (*mainly tips)
    • Batch-Mode execution overview and tips and tricks how to utilize it
  • CCI: Advanced Concepts
    • Internal Structure of CCI components
    • Internals of Data Modifications
    • ETL processes – design and performance considerations
    • CCI maintenance
    • Deep dive into Batch-Mode execution.

So, what would be your choice? What would you prefer to read?

Can you please answer one question survey or leave the reply to the post?

I really appreciate all your help! Trust me, it matters!

Size does matter: 10 ways to reduce the database size and improve performance in SQL Server

As the database professional, I like multi-terabyte databases. They are fun to deal with; they give you priceless experience and look cool in your resume. Last but not least, customers with multi-terabyte databases do not have problems with multi-kilodollar invoices. Customers usually understand amount of work involved in such projects.

The problem, however, is that the large databases are not good for the customers. Those databases are more expensive to support and maintain. You need to have highly skilled professionals in the team. Professionals, who can design nontrivial solutions in all areas – architecture, availability, maintenance, performance tuning, to name just a few.

Large databases usually require powerful hardware to run. There is, of course, very subtle difference between the size of the database and size of the active (hot) data. It is entirely possible that applications deal only with the fraction of the data stored in the database and, therefore, even mediocre server can handle the load. However, on the bare minimum, there is always the storage cost.

The projects when you have to reduce the size of the databases are very common. Even though size reduction is rarely the primary objectives of such projects, reducing the size often helps to achieve other goals. Think about designing Disaster Recovery (DR) strategy. Plenty of things that can help to meet strict RTO requirements and smaller database size definitely helps.

Today, I am going to discuss several methods that can help in reducing database size. Some of them are fully transparent to the client applications; others require regression testing and/or code refactoring. I would also focus on the data files only – troubleshooting and reducing transaction log size is the different topic.

0. Find the worst offenders

In the nutshell, the database files are just the containers for the data. Some space in the data files is allocated and used by the database objects; however, there is usually some unallocated space. For example, if you created the database with one 1GB data file, you would have 1 GB file on disk. However, immediately after creation, it would have just a handful of the data pages allocated inside the file and most part of the file would be free (unallocated).

It is completely normal to have free space in the data files, especially if amount of data is constantly growing. However, excessive amount of the free space could consume unnecessary space on the disks. The script below helps you to analyze amount of allocated and unallocated space on per-database file basis.

select 
    f.type_desc as [Type]
    ,f.name as [FileName]
    ,fg.name as [FileGroup]
    ,f.physical_name as [Path]
    ,f.size / 128.0 as [CurrentSizeMB]
    ,f.size / 128.0 - convert(int,fileproperty(f.name,'SpaceUsed')) / 
        128.0 as [FreeSpaceMb]
from 
    sys.database_files f with (nolock) left outer join 
        sys.filegroups fg with (nolock) on
            f.data_space_id = fg.data_space_id
option (recompile)

Figure 1 illustrates the output of the script from one of the production servers. Even though the output shows significant amount of free space in the data files, it may or may not be a problem. The decision if that space needs to be deallocated depends on the other factors, such as data growth expectations, disk system configuration and a few others.

01. Allocated and Free space in the database files

We will talk how to handle the situations when free space needs to be deallocated later, for now, let’s focus on the data, and discuss what we can do to reduce its size.

All of us are familiar with Pareto principle, which is also known as 80/20 rule. To simplify, in most projects, you can achieve 80% of improvements by spending 20% of time or resources. That ratio is even more severe when we search for the most space consuming objects in the database. Even with very large databases, usually the most part of the space is consumed by just a handful of tables. Obviously, we would like to focus on them – at least at the beginning of the process.

Let’s do one step backwards, however, and remember how SQL Server stores the data. Every on-disk table has the main copy of the data, which stored either in the clustered index or in heap. In addition, every table can have the set of nonclustered indexes that store the copy of the data for some of columns and reference the main copy of the rows (in the clustered index or heap). For the purpose of this discussion, let’s talk about generic indexes without any further differentiation between their types.

Every index consists of the data pages that can store the data that belong to the different allocation units. IN_ROW allocation unit contains main data rows, which includes internal row structures, fixed-length column data and variable-length column data that fits in IN_ROW data page. LOB allocation unit contains data for variable-length column data (including data types such as XML, CLR UDT, etc)that is greater than 8,000 bytes in size. Finally, ROW_OVERFLOW allocation unit contains data for variable-length column data that does not exceed 8,000 bytes but does not fit IN_ROW.

For example, if you created the table of the following structure and inserted one row there as it is shown below, you would have data for column C5 stored in LOB allocation units, data for one of either C3 or C4 columns stored in ROW_OVERFLOW allocation unit and data for all other columns stored in IN_ROW allocation unit. It is also worth mentioning that main row data in IN_ROW data would have the pointers to the data stored in the other allocation unit.

create table dbo.T1
(
    C1 int,
    C2 datetime,
    C3 varchar(5000),
    C4 varchar(5000),
    C5 varchar(max)
);

insert into dbo.T1(C1, C2, C3, C4, C5)
values
(
    1 /* C1 */
    ,GetUtcDate() /* C2 */
    ,Replicate('A',5000) /* C3 */
    ,Replicate('B',5000) /* C4 */
    ,Replicate(convert(varchar(max),'A'),10000) /* C5 */
)

You can read more about row structure and allocation units here: http://aboutsqlserver.com/storage-engine/

The script below provides you the information about space allocation on per-index basis in the database. You can easily modify it to aggregate on per-table basis or drill-down on per-allocation unit basis; however, at that stage I usually prefer to work on the index level.

;with SpaceInfo(ObjectId, IndexId, TableName, IndexName
    ,Rows, TotalSpaceMB, UsedSpaceMB)
as
( 
    select  
        t.object_id as [ObjectId]
        ,i.index_id as [IndexId]
        ,s.name + '.' + t.Name as [TableName]
        ,i.name as [Index Name]
        ,sum(p.[Rows]) as [Rows]
        ,sum(au.total_pages) * 8 / 1024 as [Total Space MB]
        ,sum(au.used_pages) * 8 / 1024 as [Used Space MB]
    from    
        sys.tables t with (nolock) join 
            sys.schemas s with (nolock) on 
                s.schema_id = t.schema_id
            join sys.indexes i with (nolock) on 
                t.object_id = i.object_id
            join sys.partitions p with (nolock) on 
                i.object_id = p.object_id and 
                i.index_id = p.index_id
            cross apply
            (
                select 
                    sum(a.total_pages) as total_pages
                    ,sum(a.used_pages) as used_pages
                from sys.allocation_units a with (nolock)
                where p.partition_id = a.container_id 
            ) au
    where   
        i.object_id > 255
    group by
        t.object_id, i.index_id, s.name, t.name, i.name
)
select 
    ObjectId, IndexId, TableName, IndexName
    ,Rows, TotalSpaceMB, UsedSpaceMB
    ,TotalSpaceMB - UsedSpaceMB as [ReservedSpaceMB]
from 
    SpaceInfo		
order by
    TotalSpaceMB desc
option (recompile)

Figure 2 shows the output of this script (table and index names are obscured). As you can see, this could quickly pinpoint the indexes that consume most part of the space in the database.

02. Indexes that consume the most space in the database

Now, let’s see what we can do to reduce their size.

1. Reducing Index Fragmentation

As you know, SQL Server stores on-disk table data on the 8KB data pages. Each data page contains data for one or multiple rows. With the exception of index creation or rebuild, SQL Server tries to populate pages in full during normal data modification operations. When data does not fit, for example, when the data pages does not have enough space to accommodate the new row, SQL Server performs the page split operation. In the nutshell, SQL Server allocates another data page and moves about half of the data from original to the new page, which frees up some space to accommodate the new row on the original data page.

Page split operations lead to the index fragmentation, which exists in two kinds. External fragmentation means that the logical order of the pages does not match their physical order, and/or logically subsequent pages are not located in the same or adjacent extents (extent is the group of 8 pages). Such fragmentation forces SQL Server to jump around reading the data from the disk, which makes read-ahead less efficient and increases the number of physical reads required. Moreover, it increases random disk I/O, which is far less efficient when compared to sequential I/O in the case of magnetic hard drives.

Internal fragmentation, on the other hand, means that data pages in the index have free space. As a result, the index uses more data pages to store data. It also increases the number of reads during query execution and amount of memory in buffer pool to cache index pages.

A small degree of internal fragmentation is not necessarily bad. It reduces page splits during insert and update operations when data is inserted into or updated from different parts of the index. Nonetheless, a large degree of internal fragmentation wastes index space and reduces the performance of the system. Moreover, for indexes with ever-increasing keys, for example on identity columns, internal fragmentation is not desirable because the data is always inserted at the end of the index.

You can monitor both, internal and external fragmentation with sys.dm_db_index_physical_stats DMV. Internal fragmentation can be monitored with avg_page_space_used_in_percent column. Lower value in the column indicates higher degree of internal fragmentation.

Let’s take a look at the example and analyze internal fragmentation of one of the indexes with the script below. For simplicity sake, I am using relatively small table; however, you would obviously like to focus on the largest indexes during the tuning process.

select 
    index_id, partition_number, alloc_unit_type_desc
    ,index_level, page_count, avg_page_space_used_in_percent
from 
    sys.dm_db_index_physical_stats
    (
        db_id() /*Database */
        ,object_id(N'dbo.MyTable) /* Table (Object_ID) */
        ,1 /* Index ID */
        ,null /* Partition ID – NULL – all partitions */
        ,'detailed' /* Mode */
    )

Figure 3 illustrates partial output of the script. The table is partitioned and, as result, you will see separate rows in the result – one per partition per allocation unit.

03. Internal Fragmentation in the Index

You can remove internal fragmentation by rebuilding the index. Figure 4 illustrates the output of sys.dm_db_index_physical_stats after the index rebuild with FILLFACTOR=100 (more on it later)

04. Internal Fragmentation after Index Rebuild

Figure 5 illustrates amount of space index used before (on the left side) and after (on the right side) rebuild. As you can see, index rebuild was able to free up more than 40% of the space index consumed before the rebuild.

05. Index Size before and after rebuild

FILLFACTOR parameter controls amount of free space SQL Server reserves on the pages during index creation and rebuild operation. For example, FILLFACTOR=80 forces SQL Server to make data pages about 80% full keeping 20% of the page space reserved. This could reduce the number of page splits and internal fragmentation when new rows are inserted to the middle of the index or updated in the way that increase their size. It is very important to remember that FILLFACTOR is applied only during index creation or rebuild stages. After index is created, SQL Server continue populates pages in full up to 100% performing page splits as needed.

As you can guess, the optimal solution would require fine-tuning FILLFACTOR and designing index maintenance strategy in the way that keeps internal fragmentation at minimum most part of the time. Unfortunately, there is no “one size fits all” advice in terms of FILLFACTOR. You should try to figure out the most optimal value by using different FILLFACTOR values and monitor how it would affect your fragmentation. You can start with FILLFACTOR value close to 100 and gradually decrease it by 5 until you find the sweet spot. Alternatively, you can monitor page splits in real time using transaction_log extended event tracking LOP_DELETE_SPLIT operation changing value based on amount of splits (you can see more on it at Jonathan Kehayias’ blog),

Lastly, you should remember than index rebuild creates another copy of the index during the process. In fact, it could increase the size of the data file on disk during the operation. Moreover, it generates large amount of transaction log records that could also affect transaction log size, network load and size of send and redo queues if database mirroring or AlwaysOn AG are in use.

2. Implementing Data Compression

If you are the lucky enough to have Enterprise Edition of SQL Server, you can reduce the size of the data by implementing Data Compression. There are two types of Data Compression supported in SQL Server – Row and Page.

Row compression addresses the storage inefficiency introduced by fixed-length data types. By default, in non-compressed row, size of the fixed-length data is based on the data type size. For example, INT column would always use 4 bytes, regardless of the value – even when it is NULL. Row compression addresses that and removes such an overhead. For example, INT value of 255 would use just 1 byte rather than 4 bytes.

Page compression goes one-step further and implements dictionary-based compression removing repetitive sequences of bytes on the page. I am not going to dive deep in the storage format of the compressed rows here and will follow up with additional blog post at some point. Alternatively, you can read about it in my book.

The actual results would greatly depend on the data and the schema. As you can guess, row compression would be beneficial when table has fixed-length data columns. More fixed-length columns you have, better the space savings are. Results of the page compression, on the other hand, depend on how repetitive is the data on the page. You can use sp_estimate_data_compression_savings system stored procedure to estimate compression results for your data. That procedure works by copying and compressing the sample of your data in tempdb measuring compression results.

It is also very important to remember that data compression works with IN_ROW allocation units only. It does not compress LOB nor ROW_OVERFLOW data.

Obviously, there is an overhead. Compression and decompression adds additional CPU load to the system. That overhead is relatively small for the ROW compression, especially when you read the data; however, for PAGE compression that overhead is more significant. There is the catch, though. While compression adds the load to CPU, it reduces I/O load in the system – SQL Server needs to issue less I/O operations due to the smaller data size. In the end, the queries could execute even faster especially on the systems that are not heavily CPU bound.

There is also the overhead during the batch operations. Batch inserts and updates could take more time when data is compressed. The same applies to the index maintenance. Just to give you some numbers, I ran a few tests at time when I worked on the book. I was using the data from one of the production tables with a decent number of fixed- and variable-length columns. Obviously, different table schema and data distribution will lead to slightly different results. However, in most cases, you would see similar patterns.

At the beginning of the tests, I have created three different heap tables and inserted one million rows into each of them. After that, I created clustered indexes with different compression settings and FILLFACTOR=100. This workflow led to zero index fragmentation and fully populated data pages.

During the first test, I ran SELECT statements to scan all of the clustered indexes accessing some row data. The second test updated every row in the tables, changing the value of the fixed-length column in a way that did not increase the row size. The third test inserted another batch of a million rows in the tables. Finally, I rebuilt all of the clustered indexes. You can see the execution statistics in Figure 5 below. All tests ran with warm cache with the data pages cached in the buffer pool. Cold cache could reduce the difference in execution times for the queries against compressed and non-compressed data, because queries against compressed data perform less physical I/O.

All statements were forced to run on a single CPU by using a MAXDOP 1 query hint. Using parallelism would decrease the query execution times, however it would add the overhead of parallelism management during query execution, which I wanted to avoid during the tests.

Figure 6 demonstrates the results of the tests.

6. Data Compression – Storage Size and Performance

Obviously, it is impossible to provide generic advice how and when to compress the data. However, in case, if system is not heavily CPU bound, I would usually suggest implementing ROW compression on the indexes with volatile data. PAGE compression, on the other hand, could be the good choice for the old static data, especially when that data accessed infrequently.

It is also impossible to avoid mentioning data partitioning when we are discussing compression. It is very common to have the situation when just small subset of the data in the table is volatile. Unfortunately, you cannot apply different compression methods to hot and cold data unless the data is partitioned (either with table partitioning or with multiple tables utilizing partitioned views). Such partitioning helps you to implement different compression schemas to different table partitions (or tables) and will allow you to reduce index maintenance overhead by rebuilding the index on partition scope.

A word of caution, however. Partitioning is the great tool that can help you to address multiple challenges especially in database administration area. Even though, table partitioning can be implemented transparently to the client applications, it could introduce plan regressions. One of such examples is described here: http://aboutsqlserver.com/2012/07/10/cautionary-tale-about-triggers-version-store-and-fragmentation/. Make sure to carefully test your application if you decided to implement table partitioning.

Finally, if you are interested in data partitioning, I would like to reference my book again. The chapter on data partitioning is the largest one there and it discusses various examples and approaches of partitioning in various editions of SQL Server.

3. Removing unused indexes

It is often possible to reduce the size of the data during the index tuning process. The main goal of the index tuning is creating the right set of indexes, which also requires you to drop existing unused and redundant indexes. Keep in mind that you always need to carefully test your system when you change the indexes making sure that there is no plan regressions after the tuning.

There are two data management views that can help you to detect non-efficient indexes. The first one, sys.dm_db_index_usage_stats, shows you the statistics on the various index operations, such as index seek and scan, index updates and a few others, along with the time of the last operation. The second DMV, sys.dm_db_index_operational_stats, dives deeper and provides an information on I/O, access methods and locking statistics on the index.

The key difference between two DMOs is how they collect data. Sys.dm_db_index_usage_stats tracks how many times an operation appeared in the execution plan. Alternatively, sys.dm_db_index_operation_stats tracks operations at the row level. For example, if query execution plan includes Key Lookup operation and SQL Server ran it twice during query execution, sys.dm_db_index_usage_stats would track the single lookup operation, while sys.dm_index_operation_stats would track two of them.

You can obtain the information about index usage with sys.dm_db_index_usage_stats by running the statement below. You can use similar approach with sys.dm_db_index_operation_stats if you need more detailed analysis.

select 
    s.Name + N'.' + t.name as [Table]
    ,i.name as [Index] 
    ,i.is_unique as [IsUnique]
    ,ius.user_seeks as [Seeks], ius.user_scans as [Scans]
    ,ius.user_lookups as [Lookups]
    ,ius.user_seeks + ius.user_scans + ius.user_lookups as [Reads]
    ,ius.user_updates as [Updates], ius.last_user_seek as [Last Seek]
    ,ius.last_user_scan as [Last Scan], ius.last_user_lookup as [Last Lookup]
    ,ius.last_user_update as [Last Update]
from 
    sys.tables t with (nolock) join sys.indexes i with (nolock) on
        t.object_id = i.object_id
    join sys.schemas s with (nolock) on 
        t.schema_id = s.schema_id
    left outer join sys.dm_db_index_usage_stats ius on
        ius.database_id = db_id() and
        ius.object_id = i.object_id and 
        ius.index_id = i.index_id
order by
    s.name, t.name, i.index_id
option (recompile)

As you can see in the output in Figure 7, you can easily pinpoint the problematic indexes. Indexes in red ovals were not used in any queries for the duration of statistics collection. Those indexes consume space in the database. Moreover, they introduce update overhead in the system. As the side note, it is also beneficial to look at the indexes where update overhead exceeds their usefulness – for example, index in green oval is used only for scans even though it is constantly updated.

07. Index Usage Statistics

Obviously, you need to be careful making sure that you captured valid usage statistics. SQL Server does not persist statistics and it resets on restarts. Statistics is also cleared whenever the database is detached or shut down when the AUTO_CLOSE database property is on. Moreover, in SQL Server 2012 and 2014, statistics resets when the index is rebuilt.

You must keep this behavior in mind during index analysis. It is not uncommon to have indexes to support queries that execute on a given schedule. As an example, you can think about an index that supports a payroll process running on a bi-weekly or monthly basis. Index statistics information could indicate that the index has not been used for reads if SQL Server was recently restarted or, in the case of SQL Server 2012 and 2014, if index was recently rebuilt.

One of the ways to address statistics reset is collecting usage statistics based on some schedule and persists results in one of the tables in the database. This could help to catch the situations when index is required to support some of the rarely executed processes. As the side note, you can consider to recreate such indexes as part of the process dropping them when processes are completed.

You should also be careful with unique indexes. It is entirely possible that such indexes are created to support uniqueness constraints and removal of such indexes would violate business requirements for the system.

4. Removing Redundant Indexes

As you know, SQL Server can use composite index for an Index Seek operation as long as a query has a SARGable predicate on the leftmost query column. Ok, I know, it is confusing so let’s look at the example and create a table with clustered and two nonclustered indexes.

create table dbo.Employee
(
    EmployeeId int not null,
    LastName nvarchar(64) not null,
    FirstName nvarchar(64) not null,
    DateOfBirth date not null,
    Phone varchar(20) null,
    Picture varbinary(max) null
);
 
create unique clustered index IDX_Employee_EmployeeId 
on dbo.Employee(EmployeeId);
 
create nonclustered index IDX_Employee_LastName_FirstName
on dbo.Employee(LastName, FirstName);
 
create nonclustered index IDX_Employee_LastName
on dbo.Employee(LastName);

SQL Server can utilize IDX_Employe_LastName_FirstName index if query has the predicate on the LastName regardless of existence of the predicate on the FirstName. For example, both of the queries below would be able to use that index:

select EmployeeId, LastName, FirstName, DateOfBirth
from dbo.Employee
where LastName = @LastName and FirstName = @FirstName

select EmployeeId, LastName, FirstName, DateOfBirth
from dbo.Employee
where LastName = @LastName

Thus, the index IDX_Employee_LastName is redundant and can be dropped. There are, of course, always exceptions from the rule. IDX_Employee_LastName index stores less data and, therefore, it is more compact. If you have a process that constantly scan the index, smaller index size could be beneficial. However, those situations are very rare and usually update overhead of the extra index is not worth such small performance improvement on SELECT queries.

The script below shows you potentially redundant indexes by checking the indexes that have the same leftmost columns.

select
    s.Name + N'.' + t.name as [Table]
    ,i1.index_id as [Index1 ID], i1.name as [Index1 Name]
    ,dupIdx.index_id as [Index2 ID], dupIdx.name as [Index2 Name] 
    ,c.name as [Column]
from 
    sys.tables t join sys.indexes i1 on
        t.object_id = i1.object_id
    join sys.index_columns ic1 on
        ic1.object_id = i1.object_id and
        ic1.index_id = i1.index_id and 
        ic1.index_column_id = 1  
    join sys.columns c on
        c.object_id = ic1.object_id and
        c.column_id = ic1.column_id      
    join sys.schemas s on 
        t.schema_id = s.schema_id
    cross apply
    (
        select i2.index_id, i2.name
        from
            sys.indexes i2 join sys.index_columns ic2 on       
                ic2.object_id = i2.object_id and
                ic2.index_id = i2.index_id and 
                ic2.index_column_id = 1  
        where	
            i2.object_id = i1.object_id and 
            i2.index_id > i1.index_id and 
            ic2.column_id = ic1.column_id
    ) dupIdx     
order by
    s.name, t.name, i1.index_id

For example, for dbo.Employee table, script would provide the output shown in Figure 8.

08. Potentially Redundant Indexes

You can use such information for further analysis performing further index consolidation. In some cases, consolidation is trivial. For example, if a system has two indexes: IDX1(LastName, FirstName) include (Phone) and IDX2(LastName) include(DateOfBirth), you can consolidate them as IDX3(LastName, FirstName) include(DateOfBirth, Phone).

In the other cases, consolidation requires further analysis. For example, if a system has two indexes: IDX1(OrderDate, WarehouseId) and IDX2(OrderDate, OrderStatus), you have three options. You can consolidate it as IDX3(OrderDate, WarehouseId) include(OrderStatus) or as IDX4(OrderDate, OrderStatus) include(WarehouseId). Finally, you can leave both indexes in place. The decision primarily depends on the selectivity of the leftmost column and index usage statistics.

5. Implementing Filtered Indexes

Filtered indexes, introduced in SQL Server 2008, allowed you to index only a subset of the data and, therefore, reduce the index size. Consider a table with some data that needs to be processed as an example. This table can have a Processed bit column, which indicates the processing status as it is shown below.

create table dbo.Data
(
    RecId int not null,
    Processed bit not null,
    /* Other Columns */
)

Let’s assume that you have a backend process that loads unprocessed data based on the following query.

select top 1000 RecId, /* Other Columns */
from dbo.Data
where Processed = 0
order by RecId;

This query can benefit from the following index: CREATE NONCLUSTERED INDEX IDX_Data_Processed_RecId ON dbo.Data(Processed, RecId). Even though SQL Server rarely uses indexes on bit columns due to their low selectivity, such a scenario might be an exception if there are just a handful of unprocessed rows. SQL Server can use that index to select them; however, the index will never be used for selection of processed rows if a Key Lookup is required.

As a result, all index rows with a key value of Processed=1 would be useless. They will increase the index’s size, waste storage space, and introduce additional overhead during index maintenance.

Filtered indexes solve that problem by allowing you to index just unprocessed rows, making the index small and efficient as it is shown below.

create nonclustered index IDX_Data_Unprocessed_Filtered
on dbo.Data(RecId)
include(Processed)
where Processed = 0;

Obviously, there are several catches to remember. First, Query Optimizer has a design limitation, which can lead to suboptimal execution plans when columns from the filter are not present in leaf-level index rows. Always add all columns from the filter to the index, either as key or included columns. In our example, Processed column is added as an included column.

Second problem is filtered index statistics. SQL Server does not count the changes in the filtered columns towards statistics update threshold, which can lead to very inaccurate statistics. You should factor this behavior to statistics maintenance and, perhaps, update statistics manually on the regular basis.

Finally, SQL Server is very conservative when to use filtered indexes in case of plan caching. SQL Server would not generate and cache the plan with filtered index if there is the possibility that this plan would be invalid for some parameter values. For example, our filtered index would not be used for the case below:

select top 1000 RecId, /* Other Columns */
from dbo.Data
where Processed = @Processed
order by RecId;

As you can guess, auto-parameterization would make the matter worse. The bottom line, you should carefully test your system after implementing filtered indexes making sure that there is no plan regressions.

Finally, if you are using XML indexes in SQL Server 2012 and above, you can reduce their size (which, by the way, could be gigantic) by implementing Selective XML Indexes , which index just subset of the data. Pretty much the same approach with as filtered indexes.

6. Using Appropriate Data Types

All approaches we have already discussed could be implemented transparently to the client applications. Obviously, transparency here is very misleading – index tuning and partitioning require careful regression testing to be performed. However, all those changes are located in the database tier and you do not need to change any application code.

Now, it is the time to talk about several approaches that require such code changes. I would like to start with the general design principle that is often ignored during database design stage. The principle is simple – you should choose appropriate data types for the job.

Let’s consider the system that collects GPS location information from the multiple devices as the example. The main transaction entity in such system is Positions. One of the approaches to define such a table is the following (just a few columns from there):

create table dbo.Positions
(
    ATime datetime not null, -- 8 bytes 
    Latitude float not null, -- 8 bytes 
    Longitude float not null, -- 8 bytes 
    IsPrecise int not null,	-- 4 bytes
    IsAssistanceUsed int not null, -- 4 bytes
    -- Total: 32 bytes
    ...
)

Alternatively, you can define the same table a little bit differently:

(
    ATime datetime2(0) not null, -- 6 bytes 
    Latitude decimal(9,6) not null, -- 5 bytes 
    Longitude decimal(9,6) not null, -- 5 bytes 
    IsPrecise bit not null,	-- 1 bytes
    IsAssistanceUsed bit not null, -- 0 bytes
    -- Total: 17 bytes
    ...
)

As you see, even in the scope of those 6 columns you can save 15 bytes per row. It does not sound as significant saving; however, it quickly adds up as amount of data growth. For example, if such system collects 1M rows per day and stores it for a year, 15 bytes per row would become ~5.4GB of data on the leaf level of the index without counting any fragmentation overhead. And trust me, 1M positions per day is very small number for such systems.

While row compression can help to address some overhead, it would not help much when data types store excessive information. For example, row compression would cut an extra space from the boolean data stored in int columns; however, it would not help much with datetime in case if it has more precision that needed. Moreover, compression is the Enterprise Edition feature, which would not help you with the other editions.

One of the questions you should answer during database design stage is how precise the information should be. This could help you to choose correct data type for the column. As the example, consider the OrderDate column in Order Entry/Shopping cart system. Do you really need to store the time when order was placed with up to 3-millisecond precision provided by datetime column (8 bytes)? If this was not the case, you could use 1-second precision of datetime2(0) type (6 bytes). Or, for 1 minute precision, you can end up with 4-byte smalldatetime data.

You should also remember that smaller data rows help with the performance during the scans. The table with smaller rows would have more rows stored on the data page and, therefore, would have less data pages stored in the index. Queries that need to perform scans (including range scans) will be faster due to the less I/O operations involved. Last but not least, such indexes will use less memory in the buffer pool, which allows to cache more data and reduces the number of physical I/O in the system.

Finally, you should remember that table alteration never ever decreases size of the data row. You should rebuild the indexes that reference altered columns in order to see the space saving.

7. Storing LOB data outside of the database

So far, we have discussed how to reduce the size of IN_ROW data. Let’s talk a bit about LOB data. First, let’s discuss the situation with the binary data, which does not require any in-database processing. For example, images, binary documents and other similar entities. With such entities, you always have the question of how to store them. Either within the database or externally, keeping just a reference (perhaps file name) in the database.

There was the rule of thumb introduced by Microsoft at time of FILESTREAM release. The binary data greater than 1MB would benefit from external storage. The data smaller than 200KB should live within the database. Well, everything in between is in the grey area. While I do not want to charge the numbers, there are usually more factors involved that just a size. Obviously, I am not talking about huge binary objects when the choice is obvious, but in general, you should make the decision on case-by-case basis.

In-database storage of binary data is usually the simplest solution to implement. The obvious downside of this approach is the increase of the database size. However, you can mitigate it up to degree with proper architecture. For example, you can put binary data to the separate filegroup(s) that reside on the slower disk arrays. You can also implement partial backup and exclude static binary data from the dayly backup files. In Enterprise Edition of SQL Server, you can utilize piecemeal restore and achieve strict RTO requirements even with the binary data in the database; however, in non-Enterprise Edition, RTO requirements could become the deal breaker. Binary data could significantly slow down restore time (due to the database and backup size), which can prevent you from meeting RTO requirements.

In case, if you decided to store binary data outside of the database, there are several questions to answer. The first, and perhaps the most important one, is how to handle redundancy and high availability of external data. For example, if you decided to store binary data as the files and reference them in the database, you need to make sure that such schema is compatible with SQL Server High Availability solution and file storage itself is redundant. Redundancy question mainly relies on storage administrators; however, High Availability aspect could be tricky in this scenario. Especially, if you have geo-redundancy and/or hybrid solutions in place. Obviously, you can implement something based on SAN replication moving files across multiple data centers; however, it requires significant investments into the hardware and software as well as incur the implementation cost.

Consistency of the data is another important question. If binary data needs to be transactionally consistent, you have a little choice but using FILESTREAM. While it is technically possible to implement the consistency in the code without FILESTREAM – for example, if transaction modifies the data, application generates another file and replace the reference to this file in the database; it would be extremely hard to support disaster recovery in this scenario. As you can guess, you can easily have “out of sync” situation when restoring data from the backup.

FILESTREAM could help you here; however, it has a few caveats. It is incompatible with some of SQL Server features, for example with database mirroring and, in some cases, it is complicated to implement. Performance-wise, you should use Streaming API on the client side to get the most from it.

As I already said, there is no right or wrong solutions. You should consider pros and cons of all approaches and consider other requirements in the system. With Enterprise Edition, I personally prefer to store relatively small (up to a few MB) data in the database carefully architecting filegroup layout and backup/restore strategy in case if I am using Enterprise Edition of SQL Server. With Standard Edition, the choices are much more limited.

8. Compressing LOB data in the database

As you already know, data compression is Enterprise Edition feature that compresses IN_ROW data only. However, it is entirely possible, that large amount of space in the database is consumed by LOB data. Do not forget, that there are plenty of data types that are, in the nutshell, LOBs. Think about XML as the example – it is not uncommon to see that XML-centric systems with XML data that consume large amount of space in the system.

One of the approaches to address such an overhead is manually compress LOB data in the code. It is very easy to create the methods to compress and decompress data utilizing one of the classes from System.IO.Compression namespace, for example using GZipStream class. Moreover, that method could be implemented in CLR stored procedures and used directly in T-SQL code.

I am not going to provide the examples of how to code that; you should be able to find quite a few of them searching in Internet. I would like, however, to discuss a couple implementation-related questions.

First, compression is CPU intensive. It is better to run such code on the client whenever it is possible. I would still, however, suggest implementing CLR routines in the database and have them available to T-SQL code. This could help to address some of the use-cases, when client needs to work with uncompressed data. Consider, for example, some external analytical or reporting tools that query the database directly. You can create the view that call CLR function and decompress the data on the fly providing it to the clients.

You should be careful with the version management in such scenario making sure that the code is the same on both, client side and in the database and that algorithms remain the same and data can be decompressed on either side.

The second important consideration is performance. Obviously, decompression adds an overhead, which you would like to avoid on the large scope. For example, it is the bad idea to have a query that scans large amount of data and performs decompression on every row to evaluate the predicate against one of compressed attributes. For example, query shown below would be highly inefficient.

select 
from T
where convert(xml,dbo.DecompressData(CompressedXML)).value('..') = 1

One of the ways to address such an issue is creating persisted calculated columns for the attributes that are used in where clauses of the queries. The downside of this approach is that SQL Server would not be able to use parallel execution plans in such queries – this is one of the limitations of Query Optimizer when you are using columns calculated with scalar UDFs. However, it is often the small price to pay comparing to constant decompression overhead.

With all being said, compressing LOB data manually is definitely the option, which is worth considering. You can use sys.dm_index_physical_stats view to evaluate amount of such data on per-index basis. Obviously, Pareto 80/20 principle still applies – do not add extra complexity if benefits are not worth it.

UPDATE (2-15-04-07): More details about this method are here.

9. Storing Data in Clustered Columnstore Indexes

In case of Enterprise Edition of SQL Server 2014, you have another option to consider. You can store some of the data in columnstore format utilizing Clustered Columnstore Indexes. This format can provide significant space saving comparing to the regular B-Tree row-based storage. Moreover, you can also utilize Archival Columnstore Compression that applies gzip-like compression on columnstore data and reduces the size even further at cost of extra CPU load.

Just to give you an example, Figure 9 below shows you the difference in the storage space between row-based and column-based storage. The table in this example was generated based on FactSalesBig table from AdventureWorks2012DW database. Obviously, different data leads to the different results; however, in the most cases, clustered columnstore indexes would give you order of magnitude decrease on the storage space. It is also worth mentioning that nonclustered indexes on B-Tree tables would contribute to additional storage space, which is not the case with clustered columnstore indexes that are the single copy of the data in the table.

09. Storage Size based on different compression methods and storage formats

Obviously, clustered columnstore indexes are not for everyone. They are very beneficial for Data Warehouse workload that requires to scan and aggregate large amount of data. The same time they are the very bad choice for OLTP workload – they do not support point-lookups nor small range scans.

Same time, it is not uncommon to have different use-cases for the old and new data in OLTP systems. Customers can generate OLTP workload to support day-to-day operations with the new data; however, the old data can be used for analysis and reporting, which is mainly Data Warehouse workload. In such scenario, you can consider to partition your data into the multiple tables using columnstore format for the tables with the old data. You can abstract all those changes via partitioned views making the differences in the schema and storage format transparent to the clients.

This is by no means not the simplest thing to implement. However, such design could lead to significant performance improvements and storage space saving for the certain kind of workloads.

10. Reducing amount of free space in the database

Finally, let’s discuss what we can do when data files have large amount of free space.

As strange as it sounds, one of the best possible options in that case is leaving everything as is. Consider the situation when system implements sliding-window pattern keeping 1-year worth of data in the system. Typically, such systems purge the data based on some schedule. For example, it is possible that every 1st day of the month system purges the 13th month of data – the one with the data older than 1 year.

Let’s assume that system collects 500GB of data per month. In this scenario, if you measured amount of free space in the data files right after the purge, you would notice that files have more than 500GB of free space available. Obviously, you can shrink the files and release such space to OS; however, the database would reclaim it as data growth.

However, for the purpose of this discussion, let’s assume that we have legitimate case to decrease the size of the files. Unfortunately, it is not very easy to do. DBCC SHRINKFILE command is the terrible way to reduce the size of the database. That command works in the very simple matter – it starts to move the allocated extents from the end of the file to unallocated space in the beginning of the file. As you can guess, this leads to the terrible index fragmentation. Moreover, it generates excessive amount of log records, which can affect the system in the multiple ways.

Obviously, you can perform index maintenance after you are done with the shrink. However, there is the catch – index rebuild will grow the file again (it needs space to accommodate the new version of the index). Index reorg could be the better choice in this scenario even though it does not provide results in par with the index rebuild. In the end, everything depends on amount of the space you are clearing and the size of your data. For example, if you had terabytes of free space and your biggest index is just a couple hundred gigabytes, you could consider to shrink and run index rebuild afterwards. Files would grow; however, such growth is much smaller comparing to space saving after the shrink.

Another, and often the better way to accomplish the task is moving all the data to another filegroup dropping original filegroup afterwards. The actual implementation would vary based on the version and edition of SQL Server. In Enterprise Edition, you can perform online index rebuild to the different filegroup, which will keep system available during the process. In Standard Edition, you can rebuild indexes only offline.

There is another catch though – index rebuild does not move LOB data between filegroups by default. The only way to workaround it is by rebuilding index to the new partition schema instead of the filegroup. However, it requires Enterprise Edition, which supports partitioning. Unfortunately, in Standard Edition you are out of luck.

I am not providing the examples here; however, I would like to reference my book again where I have discussed it in details. Alternatively, you can download book demo scripts and see how data movement works in action.

Lastly, there is always the option of creating another table on another filegroup, copying data there and dropping original table and renaming the new table afterwards. This approach would work in either edition; however, in the most part of the cases it needs be done offline. Online implementation is, of course, possible but it is usually complicated if table has volatile data.

Wrapping Up

This blog post ended up being much bigger than I expected. Unfortunately, even with such size it was impossible to cover all the details for some of the methods. Anyway, I hope you found this information useful, at least as the high-level overview.

Please, do not take the order in which I outlined approaches as the guideline. Every system is unique and you need to design the solution targeted to particular system taking hardware, software and business requirements into consideration.

Reducing Offline Index Rebuild and Table Locking Time in SQL Server

Enterprise Edition of SQL Server is all about availability features. It allows you to build robust High Availability and Database Maintenance strategies, which are the must for the mission critical systems. Unfortunately, all those benefits come at cost. At very high cost. Enterprise Edition of SQL Server is anything but cheap. Enterprise Edition licenses usually several times more expensive comparing to the Standard Edition licenses. It applies to both, on-premises and Cloud installations. For example, in Microsoft Azure, VMs with Enterprise Edition are about 4 times more expensive comparing to Standard Edition VMs.

Not every business can afford Enterprise Edition. And, to be honest, not every business even need Enterprise edition. It is entirely possible to build relatively big and complex solutions with Standard Edition of SQL Server especially with SQL Server 2014, which can utilize 128GB of RAM. I have personally worked with several multi-TB databases using Standard Edition of SQL Server.

One of the common challenges with non-Enterprise Editions, however, is Index Maintenance. Enterprise Edition allows you to rebuild indexes keeping table online. It uses row-versioning similar to optimistic transaction levels under the hood. With exception of short-time shared (S) at the beginning and schema-modification (Sch-M) locks at the end of the execution, there are no full table locks held. Other users can access the table, and read, and modify data from there.

Unfortunately, this is not the case with the Standard Edition where only offline index rebuild is supported. Offline index rebuild acquires schema modification (Sch-M) lock for the duration of the execution, which blocks entire access to the table even in read uncommitted transaction isolation level. Based on the index size, rebuild can be very time consuming operation. Obviously, you can use index reorganize instead of rebuild., which is online operation. However, it does not work as good as index rebuild on heavily fragmented tables.

Today, I am going to show you very simple technique that can help to decrease offline index rebuild time, and, therefore, time when table is inaccessible for the other sessions. It is not the magic silver bullet that helps everywhere. In fact, in some of the cases, it could even hurt rather than help. We will discuss that later in the post.

I am going to use a table from one of my production systems. That table has a couple dozen columns including one nvarchar(max) column, which is hosting some LOB data. I have rebuilt the index before the tests making sure that initial fragmentation does not affect execution time on the first test run.

As you see in Figure 1 below, leaf level of the clustered index for IN-ROW data consist of 20,374 pages, which is about 159MB of data. LOB data consists of 76,708 pages, which is about 600MB of data.

01. Index Physical Stats

As the first step, let’s run ALTER INDEX REBUILD statement and measure execution time for the operation. I am clearing buffer pool emulating a cold cache before the test (DO NOT RUN IT IN PRODUCTION!). This is, obviously, the worst case scenario – in the real life, at least some of the data pages would be cached at time of the index rebuild operation.

I am also running index rebuild without parallelism involved just to make sure that results are consistent and simplify the analysis. As the side note, my test server has pretty slow I/O subsystem, and execution time without MAXDOP=1 hint is just about the same as with the hint.

-- Clearing buffer pool. DO NOT RUN IN PRODUCTION
checkpoint
go

dbcc dropcleanbuffers
go

-- Rebuilding Index
alter index IDX_CI on dbo.MyTable rebuild with (maxdop = 1)

As you can see in Figure 2, index rebuild time in my system is 1 minute and 24 seconds. As you know, other sessions would be unable to access the table during this time due to schema modification (Sch-M) lock held on the table.

02. Execution Time of Index Rebuild with the Cold Cache

Let’s repeat our test (again, with cold cache) and analyze session waits during the index rebuild. We can do it by creating Extended Event session with the code shown below. This code will work in SQL Server 2012 and 2014 and will require slight modifications in SQL Server 2008/2008 R2.

create event session [Waits] on server
add event sqlos.wait_info
(
	action (sqlserver.session_id)
	where
	(
		sqlserver.session_id = 141 -- @@SPID of the session with index rebuild)
		and opcode = 1 -- END of wait
	)
)
add target package0.event_file(SET filename=N'C:\ExtEvents\Waits.xel',max_rollover_files=(0))
with
(
	max_memory=4096 KB
	,event_retention_mode=allow_single_event_loss
	,max_dispatch_latency=10 SECONDS
)
go

alter event session [Waits] on server state = start

After index rebuild command, let’s stop the session and look at the waits that occur during index rebuild.

alter event session [Waits] on server
state = stop
go

;with EventData(event_data)
as
(
	select cast(event_data AS XML) AS event_data
	from sys.fn_xe_file_target_read_file
		('C:\ExtEvents\Waits*.xel',null,null,null)
)
,Waits
as
(
	select
        event_data.value ('(/event/@timestamp)[1]'
			, 'datetime') as [Time],
        event_data.value ('(/event/data[@name=''wait_type'']/text)[1]'
			,'varchar(255)') AS [Wait],
        event_data.value ('(/event/data[@name=''duration'']/value)[1]'
			,'bigint') AS [Duration],
        event_data.value ('(/event/data[@name=''signal_duration'']/value)[1]'
			,'bigint') AS [Signal]
	from EventData
)
select 
	w.[Wait]
	,count(*) as [Count]
	,sum(w.Duration) as [Total Duration (ms)]
	,sum(w.Duration) - sum(w.Signal) as [Resource Waits (ms)]
	,sum(w.Signal) as [Signal Waits (ms)]
from Waits w
group by w.[Wait]

As you can see in the Figure 3 below, the vast majority of the waits are PAGELATCHIO*, which occur when SQL Server is waiting for the data page to be read from the disk.

03. Waits during Index Rebuild with the Cold Cache

That discovery can point us to the right direction. If large portion of index rebuild was waiting for the data to be read from disk, we could speed up the process by pre-fetching and caching data in the memory. SQL Server would not need to perform physical read operations, which should reduce rebuild time.

Let’s try that and run the following select below (again, making sure that we are using cold cache for test sake):

checkpoint
go

dbcc dropcleanbuffers
go


declare
	@T1 bigint, @T2 bigint

select 
	@T1 = avg(RecId) -- Making sure that IN_ROW pages are read
	,@T2 = avg(len(LOB_Column)) -- Making sure that LOB pages are read
from dbo.MyTable with (nolock, index=IDX_CI) 
option (maxdop 1)

I am forcing the scan of the clustered index – the one, which we are going to rebuild. I am using read uncommitted transaction isolation level with NOLOCK hint. As you can guess, in this situation I do not worry about any data consistency issues that can arise.

However, there is very important part – we need to make sure that SQL Server reads LOB data pages in addition to IN_ROW data pages. We can force it by accessing our LOB column in the select list. You can use the same technique dealing with ROW_OVERFLOW pages in case, if index has such allocation units.

Finally, I am also referencing one of columns from IN_ROW data to make sure that those pages are also read into the memory. In some cases, with the large tables and read uncommitted transaction isolation level, SQL Server can choose to use allocation unit scans instead of index scan. I seriously doubt, that SQL Server can perform such operation and scan only LOB data pages in our select, especially with the index hint in use, but it is better to be safe than sorry.

The execution time of the select in my system is 1 minute and 3 seconds as it is shown in Figure 4 below.

04. Execution Time of Select Statement

Now, let’s run index rebuild again. As you see, with index pages cached in memory, index rebuild time is just 10 seconds as it is shown in Figure 5. Even though, total time of both operations (select and index rebuild) are comparable with original index rebuild over cold cache, the time when table was inaccessible due to schema modification (Sch-M) lock is about 8 times less than before.

05. Execution Time of Index Rebuild when Data is Cached

If you looked at the waits during the last index rebuild, you would see that PAGELATCHIO* waits practically disappeared as it is shown in Figure 6.

06. Waits During Index Rebuild with the Warm Cache

While this technique can significantly reduce blocking during offline index rebuild, it is not for everyone. First, and foremost, you should be careful in case of the large indexes especially when system has limited amount of memory and/or low page life expectancy. Caching large indexes will require significant amount of memory and, therefore, it could lead to the situations when you flush the buffer pool possibly flushing the index pages you just read. This is especially important if you expect data growth in the future. Small indexes today can become the large ones tomorrow.

Second, it is beneficial to analyze how many pages in the index are typically cached in memory. Obviously, if most part of the pages have been already cached, you would not get much gain from running the select and pre-fetching remaining pages to the memory. On the other hand, logical reads are fast anyway so select over warm cache would not take much time.

You can use the script below to analyze number of pages from the index that have been cached. You can compare those numbers with results of sys.dm_db_index_physical_stats to estimate the number of cached pages per allocation unit from the index.

select i.name, au.type_desc, count(*) as [Cached Pages]
from 
	sys.dm_os_buffer_descriptors bd with (nolock) 
        join sys.allocation_units au with (nolock) on 
		    bd.allocation_unit_id = au.allocation_unit_id
	join sys.partitions p with (nolock) on
		(au.type in (1,3) and au.container_id = p.hobt_id) or
		(au.type = 2 and au.container_id = p.partition_id)
	join sys.indexes i with (nolock) on
		p.object_id = i.object_id and 
		p.index_id = i.index_id
where
	bd.database_id = db_id() and
	p.object_id = object_id (N'dbo.MyTable') and 
	p.index_id = 1 -- ID of the index
group by
	i.name, au.type_desc

Finally, that technique is useful when the biggest bottleneck in the system is I/O performance as it happened in my case. This often happens in the Cloud-based systems (SQL Server in VMs) where I/O latency is usually high and Standard Edition of SQL Server is commonly used due to its cost. In that scenario, pre-fetching data to the cache could significantly reduce the locking time for offline index rebuild when you have enough memory for the index data.

Plan Cache: Plan Reuse

Last time we have discussed how parameter sniffing can affect the quality of generated execution plans. Today, I would like to talk about another aspect of plan caching, which is plan reuse. Plans, cached by SQL Server, must be valid for any combination of parameters during future calls that reuse the plan. In some cases, this can lead to situations where a cached plan is suboptimal for a specific set of parameter values.

One of the common code patterns that leads to such situations is the implementation of stored procedure that search for the data based on a set of the optional parameters. Let’s look at the typical implementation of such stored procedure as shown in the code below. That code uses dbo.Employees table from the “parameter sniffing” post and it also creates two nonclustered indexes on that table.

create proc dbo.SearchEmployee
(
	@Number varchar(32) = null
	,@Name varchar(100) = null
)
as
begin
	select Id, Number, Name, Salary, Country
	from dbo.Employees
	where 
		((@Number is null) or (Number=@Number)) and
		((@Name is null) or (Name=@Name))
end
go

create unique nonclustered index IDX_Employees_Number
on dbo.Employees(Number);

create nonclustered index IDX_Employees_Name
on dbo.Employees(Name);

There are several different approaches how SQL Server can execute the query from the stored procedure based on what parameters were provided. In the large number of cases, the most efficient approach would be using Nonclustered Index Seek and Key Lookup operators. Let’s run the stored procedure several times with different parameter combinations and check the execution plans:

exec dbo.SearchEmployee @Number = '10000';
exec dbo.SearchEmployee @Name = 'Canada Employee: 1';
exec dbo.SearchEmployee @Number = '10000', @Name = 'Canada Employee: 1';
exec dbo.SearchEmployee @Number = NULL, @Name = NULL;

01.Execution Plans When Plans Are Cached

SQL Server compiles stored procedure at time of the first call when only @Number parameter was provided. Even though, the most efficient execution plan for this case is IDX_Employees_Number Index Seek operation, SQL Server cannot cache such execution plan because it would not be valid for the case, when @Number parameter is NULL. Therefore, SQL Server generated and cached execution plan that utilizes Index Scan operation, which is highly inefficient in case, when @Number parameter is not provided. In that case, SQL Server performs Key Lookup operation for every row in the nonclustered index optionally evaluating predicate on @Name parameter afterwards.

Similar to parameter sniffing issues, you can address this problem with statement-level recompilation as it is shown below. SQL Server recompiles the query on every call, and therefore it can choose the most beneficial execution plan for every parameter set.

It is also worth mentioning that the plans are not cached in cases where statement-level recompile is used.

alter proc dbo.SearchEmployee
(
	@Number varchar(32) = null
	,@Name varchar(100) = null
)
as
begin
	select Id, Number, Name, Salary, Country
	from dbo.Employees
	where 
		((@Number is null) or (Number=@Number)) and
		((@Name is null) or (Name=@Name))
	option (recompile)
end

02.Execution Plans With Statement-Level Recompile

As we already discussed, query recompilation adds CPU overhead. That overhead can be acceptable in case of large and complex queries when compilation time is just the fraction of the query execution time and system is not CPU-bound. In that case, recompilation can even help producing the better execution plans, which would be generated based on the current parameter values, especially if table variables are involved. However, this recompilation overhead is usually not acceptable in case of OLTP queries that are called very often.

One of the options you can use to address the issue is writing multiple queries using IF statements covering all possible combinations of parameters. SQL Server would cache the plan for each statement in the procedure. Listing below shows such an approach, however it quickly becomes unmanageable with a large number of parameters. The number of combinations to cover is equal to the number of parameters squared.

alter proc dbo.SearchEmployee
(
	@Number varchar(32) = null
	,@Name varchar(100) = null
)
as
begin
	if @Number is null and @Name is null
		select Id, Number, Name, Salary, Country
		from dbo.Employees
	else if @Number is not null and @Name is null
		select Id, Number, Name, Salary, Country
		from dbo.Employees
		where Number=@Number
	else if @Number is null and @Name is not null
		select Id, Number, Name, Salary, Country
		from dbo.Employees
		where Name=@Name
	else 
		select Id, Number, Name, Salary, Country
		from dbo.Employees
		where Number=@Number and Name=@Name
end

In the case of a large number of parameters, dynamic SQL becomes the only option. SQL Server will cache the execution plans for each dynamically generated SQL statement. Remember that using dynamic SQL breaks ownership chaining, and it always executes in the security context of CALLER. You should also always use parameters with dynamic SQL to avoid SQL Injection.

alter proc dbo.SearchEmployee
(
	@Number varchar(32) = null
	,@Name varchar(100) = null
)
as
begin
	declare
		@SQL nvarchar(max) = N'	
select Id, Number, Name, Salary, Country
from dbo.Employees
where 1=1'
	
	if @Number is not null
		select @Sql = @SQL + N' and Number=@Number'
	if @Name is not null
		select @Sql = @SQL + N' and Name=@Name'
	exec sp_executesql @Sql, N'@Number varchar(32), @Name varchar(100)'
		,@Number=@Number, @Name=@Name
end

While most of us are aware about danger of using optional parameters and OR predicates in the queries, there is another less known issue associated with filtered indexes. SQL Server will not generate and cache a plan that uses a filtered index, in cases when that index cannot be used with some combination of parameter values.

Listing below shows an example. SQL Server will not generate the plan, which is using the IDX_RawData_UnprocessedData index, even when the @Processed parameter is set to zero because this plan would not be valid for a non-zero @Processed parameter value.

create unique nonclustered index IDX_RawData_UnprocessedData
on dbo.RawData(RecID)
include(Processed)
where Processed = 0;

-- Compiled Plan for the query would not use filtered index
select top 100 RecId, /* Other columns */
from dbo.RawData
where RecID > @RecID and Processed = @Processed
order by RecID;

In that particular case, rewriting the query using IF statement would be, perhaps, the best option.

if @Processed = 0
	select top 1000 RecId, /* Other Columns */
	from dbo.RawData
	where RecId > @RecId and Processed = 0
	order by RecId;
else 
	select top 1000 RecId, /* Other Columns */
	from dbo.Data
	where RecId > @RecId and Processed = 1
	order by RecId;

Unfortunately, IF statement does not always help. In some cases, SQL Server can auto-parametrize the queries and replace some of the constants with auto-generated parameters. That behavior allows SQL Server to reduce the size of the plan cache; however, it could lead to all plan reuse issues we have already discussed.

By default, SQL Server uses SIMPLE parametrization and parametrize only the simple queries. However, if database or query are using FORCED parametrization, that behavior can become the issue. Let’s look at a particular example and create a database with a table with a filtered index and populate it with some data, as shown below.

use master
go

create database ParameterizationTest
go

use ParameterizationTest
go

create table dbo.RawData
(
	RecId int not null identity(1,1), 
	Processed bit not null, 
	Placeholder char(100),
	constraint PK_RawData
	primary key clustered(RecId)
);

/* Inserting:
	Processed = 1: 65,536 rows
	Processed = 0: 16 rows */
;WITH N1(C) AS (SELECT 0 UNION ALL SELECT 0) -- 2 rows
,N2(C) AS (SELECT 0 FROM N1 AS T1 CROSS JOIN N1 AS T2) -- 4 rows
,N3(C) AS (SELECT 0 FROM N2 AS T1 CROSS JOIN N2 AS T2) -- 16 rows
,N4(C) AS (SELECT 0 FROM N3 AS T1 CROSS JOIN N3 AS T2) -- 256 rows
,N5(C) AS (SELECT 0 FROM N4 AS T1 CROSS JOIN N4 AS T2 ) -- 65,536 rows
,IDs(ID) AS (SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) FROM N5)
insert into dbo.RawData(Processed)
	select 1
	from Ids;

insert into dbo.RawData(Processed)
	select 0
	from dbo.RawData
	where RecId <= 16;

create unique nonclustered index IDX_RawData_Processed_Filtered 
on dbo.RawData(RecId)
include(Processed)
where Processed = 0;

For the next step, let’s run the queries that count a number of unprocessed rows in both SIMPLE and FORCED parametrization modes.

select count(*)
from dbo.RawData
where Processed = 0			
go

alter database ParameterizationTest set parameterization forced
go

select count(*)
from dbo.RawData
where Processed = 0

If you examine the execution plans shown in Figure 3, you will notice that SQL Server utilized a filtered index in the case of a SIMPLE parametrization. SQL Server can cache this plan because of the constant in the Processed=0 predicate. Alternatively, with FORCED parametrization, SQL Server parametrizes the query using the parameter in the Processed=@0 predicate. Therefore, it cannot cache the plan with the filtered index because it would not be valid for the case when a query selects processed (Processed=1) rows. SQL Server generated the execution plan with a Clustered Index Scan, which is far less efficient in this case.

03.Execution Plans and Parametrization

The workaround in this case is using SIMPLE parametrization for the database, or forcing it on query level with plan guide (more on it later). In some cases, you would also need to rewrite the query and use one of the constructs that prevent parametrization in SIMPLE parametrization mode, such as IN, TOP, DISTINCT, JOIN, UNION, subqueries and quite a few others.

Plan caching and plan reuse are the great features that help to reduce CPU load on the server. However, they introduce several side effects you need to be aware of and keep them in mind when you write queries and stored procedures.

Source code is available for download

Plan Cache: Parameter Sniffing

There are several reasons why it is beneficial to use parameters in the queries. It mitigates security risks by protecting from SQL Injection. Moreover, it helps with the performance. Query optimization is expensive and resource-intensive process. SQL Server reduces that cost by keeping execution plans in the special part of the memory called Plan Cache and reusing them on subsequent calls.

Even though, this approach benefits the system by reducing the number of query compilations, it could also introduce some issues. Cached plans can become inefficient due to the data distribution and/or parameter values change. SQL Server tracks changes in the data by counting number of changes of the index columns outdating statistics and recompiling queries when number of changes reach about 20% of number of rows in the table (trace flag 2371 can reduce that threshold). However, tracking parameter values change is more complicated.

When SQL Server compiles parametrized query or stored procedure, it analyzes parameters and generates execution plan, which is optimal for those specific values. This process is called Parameter Sniffing. Furthermore, generated plans are cached and reused regardless of parameter values at time of reuse. That behavior can lead to the situations when query is compiled and plan is generated based on atypical parameter set. Cached plan, in this case, would be suboptimal for the calls that reuse that plan.

All of us experienced the situations when query or stored procedure suddenly became very slow even when they ran fine before. Parameter sniffing could be the reason of why it happened. SQL Server decided to recompile the query due to statistics update and the query that triggered recompilation had atypical parameter values. Even though, generated execution plan was efficient for parameters at time of compilation; that cached plan is not necessarily efficient for the other queries that reuses it.

Historically, parameter sniffing has the negative connotation in SQL Server community. It is used to explain why queries have suboptimal execution plans, like I just did in the previous paragraph. This is vastly wrong, however. Parameter sniffing is just the name for SQL Server behavior of analyzing parameters during query compilation.

Let’s look at a few examples of parameter sniffing and discuss how to address the issues it can introduce. In this post, I am using the stored procedure; however, everything would work the same in case of parametrized queries.

As the first step, let’s create a table dbo.Employees. We will populate it distributing data in the way that most rows have Country column value of USA with a few employees with Country value of Canada. Listing below shows the code.

create table dbo.Employees
(
	ID int not null,
	Number varchar(32) not null,
	Name varchar(100) not null,
	Salary money not null,
	Country varchar(64) not null,

	constraint PK_Employees
	primary key clustered(ID)
);

;with N1(C) as (select 0 union all select 0) -- 2 rows
,N2(C) as (select 0 from N1 as T1 cross join N1 as T2) -- 4 rows
,N3(C) as (select 0 from N2 as T1 cross join N2 as T2) -- 16 rows
,N4(C) as (select 0 from N3 as T1 cross join N3 as T2) -- 256 rows
,N5(C) as (select 0 from N4 as T1 cross join N4 as T2 ) -- 65,536 rows
,Nums(Num) as (select row_number() over (order by (select null)) from N5)
insert into dbo.Employees(ID, Number, Name, Salary, Country)
	select 
		Num, 
		convert(varchar(5),Num), 
		'USA Employee: ' + convert(varchar(5),Num), 
		40000,
		'USA'
	from Nums;

;with N1(C) as (select 0 union all select 0) -- 2 rows
,N2(C) as (select 0 from N1 as T1 cross join N1 as T2) -- 4 rows
,N3(C) as (select 0 from N2 as T1 cross join N2 as T2) -- 16 rows
,Nums(Num) as (select row_number() over (order by (select null)) from N3)
insert into dbo.Employees(ID, Number, Name, Salary, Country)
	select 
		65536 + Num, 
		convert(varchar(5),65536 + Num), 
		'Canada Employee: ' + convert(varchar(5),Num), 
		40000,
		'Canada'
	from Nums;

create nonclustered index IDX_Employees_Country
on dbo.Employees(Country);

As a next step, let’s create a stored procedure that calculates the average salary for employees in a specific country.

create proc dbo.GetAverageSalary @Country varchar(64)
as
begin
	select Avg(Salary) as [Avg Salary]
	from dbo.Employees
	where Country = @Country
end

As you can guess, when we call this stored procedure with parameter @Country=’USA’, stored procedure needs to touch almost every row in the table and the most optimal execution plan is Clustered Index Scan. However, when we call the stored procedure with parameter @Country=’Canada’, the most efficient execution plan is Nonclustered Index Seek with Key Lookup operators.

Let’s call the stored procedure twice: the first time with @Country=’USA’ and the second time with @Country=’Canada’, as shown below.

exec dbo.GetAverageSalary @Country='USA';
exec dbo.GetAverageSalary @Country='Canada';

As you can see in Figure 1, SQL Server compiles the stored procedure and caches the plan with the first call and reuses it later. Even though such a plan is less efficient with the @Country=’Canada’ parameter value, it may be acceptable when those calls are rare, which is expected with such a data distribution.

01. Execution plans when the stored procedure is compiled with @Country=’USA’

Now let’s take a look what happens if we swap those calls when the plan is not cached. We will use the DBCC FREEPROCCACHE command, which clears plan cache (DO NOT RUN IT ON PRODUCTION SERVER). As I already mentioned, another instance when this might happen is with a statistics update that forces query to recompile.

dbcc freeproccache
go

exec dbo.GetAverageSalary @Country='Canada';
exec dbo.GetAverageSalary @Country='USA';

As you can see in Figure 2, SQL Server now caches the plan based on the @Country=’Canada’ parameter value. Even though this plan is more efficient when the stored procedure is called with @Country=’Canada’, it is highly inefficient for @Country=’USA’ calls.

02. Execution plans when the stored procedure is compiled with @Country=’Canada’

There are a few ways to address the issue. You can force the recompilation of either stored procedure using EXECUTE WITH RECOMPILE or a statement-level recompile with OPTION (RECOMPILE) clauses. Obviously, a statement-level recompile is better because it performs the recompilation on a smaller scope. SQL Server sniffs the parameter values at the time of the recompilation, generating the optimal execution plan for each parameter value. It is also worth mentioning, that statement-level recompile allows SQL Server to obtain number of rows stored in table variables, which can help to generate more efficient execution plans in some cases.

alter proc dbo.GetAverageSalary @Country varchar(64)
as
begin
	select Avg(Salary) as [Avg Salary]
	from dbo.Employees
	where Country = @Country
	option (recompile)
end
go

exec dbo.GetAverageSalary @Country='Canada';
exec dbo.GetAverageSalary @Country='USA';

As you can see in Figure 3, SQL Server recompiles the statement on every call, and it generates the most efficient execution plan for every parameter value. It is also worth noting that SQL Server does not cache plans for the statements when statement-level recompile is used.

03. Execution plans with OPTION (RECOMPILE) hint

The statement-level recompile may be a good option when the queries do not execute very often or in the case of complex queries, when the compilation time is just a fraction of the total execution time. However, it is hardly the best approach for OLTP queries, which are constantly running in the system due to the extra CPU load that recompilation introduces.

Another option is using an OPTIMIZE FOR hint, which forces SQL Server to optimize a query for the specific parameter values provided in the hint. Listing below illustrates such an approach.

alter proc dbo.GetAverageSalary @Country varchar(64)
as
begin
	select Avg(Salary) as [Avg Salary]
	from dbo.Employees
	where Country = @Country
	option (optimize for(@Country='USA'))
end
go

exec dbo.GetAverageSalary @Country='Canada';
exec dbo.GetAverageSalary @Country='USA';

As you can see in Figure 4, SQL Server ignores the parameter value during the compilation and optimizes the query for the @Country=’USA’ value.

04. Execution plans with OPTIMIZE FOR hint

Unfortunately, the OPTIMIZE FOR hint introduces supportability issues, and it can lead to suboptimal execution plans in cases where the data distribution has changed. Let’s consider the situation, albeit unrealistic, when a company and all of its employees moved from the United States to Germany.

update dbo.Employees set Country='Germany' where Country='USA';

exec dbo.GetAverageSalary @Country='Germany';

Statistics are outdated at the time of the update, which forces SQL Server to recompile the statement in the stored procedure. At this point, there are no rows in the table with Country=’USA’, and the recompilation produces a suboptimal execution plan, as shown in Figure 5. As a side note, the query uses more reads than before due to the index fragmentation introduced by the update.

05. Inefficient execution plan after data distribution change

SQL Server 2008 introduced another optimization hint, OPTIMIZE FOR UNKNOWN, which helps to address such situations. With this hint, SQL Server performs an optimization based on the most statistically common value in the table. Listing below shows the code involved in doing this.

alter proc dbo.GetAverageSalary @Country varchar(64)
as
begin
	select Avg(Salary) as [Avg Salary]
	from dbo.Employees
	where Country = @Country
	option (optimize for(@Country UNKNOWN))
end
go

exec dbo.GetAverageSalary @Country='Canada';

Figure 6 illustrates the execution plan. Country=’Germany’ is the most statistically common value in the table, and therefore SQL Server generates an execution plan that is optimal for such a parameter value.

06. Execution plan with OPTIMIZE FOR UNKNOWN hint

You can achieve the same results with an OPTIMIZE FOR UNKNOWN hint by using local variables instead of parameters. This method also works with SQL Server 2005, where the OPTIMIZE FOR UNKNOWN hint is not supported. Listing below illustrates that approach. It introduces the same execution plan with a Clustered Index Scan, as shown in Figure 6 above.

alter proc dbo.GetAverageSalary @Country varchar(64)
as
begin
	declare
		@CountryTmp varchar(64)
	set @CountryTmp = @Country

	select Avg(Salary) as [Avg Salary]
	from dbo.Employees
	where Country = @CountryTmp
end

Finally, in case when you are dealing with 3rd party systems and cannot add optimization hints to the code, you could use plan guides to achieve the same goal. We will discuss plan guides in one of the future blog posts.

Source code is available for download.