Indexing for Performance for SQL Server 2005

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Indexing for Performance for SQL Server 2005

Single - Table OptimizationChapter Four

Jeff Garbus – [email protected] Cannizzo – [email protected]

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Acknowledgements

Microsoft SQL Server and Microsoft SQL Server Management Studio are trademarks of Microsoft Inc.

This presentation is copyrighted. This presentation is not for re-sale This presentation shall not be used

or modified without express written consent of Soaring Eagle Consulting, Inc.


Topics

Examine detailed topics in query optimization Indexes with SARGs Improvised SARGs Clustered vs. nonclustered indexes Queries with OR Index covering Forcing index selection


SQL Server 2005 Search Techniques SQL Server 2005 uses three basic search techniques for

query resolution Table Scans

Index Searches

Covered Index Searches


Table Scans

If SQL Server 2005 can’t resolve a query any other way, it does a table scan Scans are expensive

Table scans may be the best way to resolve a query

If there is a clustered index on the table, SQL Server will try and use it instead of performing a table scan

Table Scan Search

select * from pt_tx where id = 1


Table Scans (Cont’d)

Query Plan

Verify table scans with: set statistics io on

Table 'pt_tx'. Scan count 1, logical reads 38, physical reads 0, read-ahead reads 0


Table Scan Output: Update

showplan

update pt_tx set id = id + 1


Index Selection

Topics Optimizer selection criteria

When indexes slow access

When indexes cause deadlocks

Index statistics and usage


Optimizer Selection Criteria

During the index selection phase of optimization the optimizer decides which (if any) indexes best resolve the query

Identify which indexes match the where and join clauses Estimate rows to be returned Estimate page reads


SARG Matching

Indexes usually correspond with SARGs

Useful indexes will specify a row or rows or set bounds for the result set

An index may be used if any column of the index matches the SARG

where dob between '3/3/1941' and '4/4/65'

create unique index nci on authors

(au_lname, au_fname)


SARG Matching (Cont’d)

Which of the following queries (if any) could be helped by the index?

If there are not enough rows in the table, indexes that look useful may never be used

select * from authorswhere au_lname = 'Smith' or au_fname = 'Jim'

select * from authors where au_fname = 'Jim'

select * from authors where au_fname = 'Jim' and au_lname = 'Smith'

create unique index nci on authors

(au_lname, au_fname)


Index Selection

Review of index types

Optimizer selection criteria

When indexes slow access

When indexes cause deadlocks

Index statistics and usage

Topics


Index Types

SQL Server provides three types of indexes

Clustered Nonclustered Full text

One clustered index per table

Data is maintained in clustered index order

248 nonclustered indexes per table

Nonclustered indexes maintain pointers to rows Full text is beyond scope


Clustered Index Mechanism

With a clustered index, there will be one entry on the last intermediate index level page for each data page

The data page is the leaf or bottom level of the index

(Assume a clustered index on last name)

Houston

Exeter

Brown

Albert

Quincy

Mason

Jones

Albert

Loon

Klein

Jude

Jones

Paul

Parker

Neenan

Mason

Alexis, Amy, ...

Root Page

Intermediate PageData Page

Amundsen, Fred, ...

Baker, Joe, ...

Best, Elizabeth, ...

Albert, John, ...

Masonelli, Irving, ...

Narin, Mabelle, ...

Naselle, Juan, ...

Neat, Juanita

Mason, Emma, ...

...

...

...


Nonclustered Index Mechanism

The nonclustered index has an extra, leaf level for page / row pointers

Data placement is not affected by non-clustered indexes

(Assume an NCI on first name)

Dave

Bob

Amy

Zelda

Elizabeth

Elizabeth

GeorgeGeorge

Amy

...

...

...

...

...

...

Sam

Sam

Alexis, Amy, ...

Root Page

Intermediate PageData Page

Amundsen, Fred, ...

Baker, Joe, ...

Best, Elizabeth, ...

Albert, John, ...

Masonelli, Irving, ...

Narin, Anabelle, ...

Naselle, Amy, ...

Neat, Juanita

Mason, Emma, ...

Zelda

...

...

...

Amy

Amy

...

...

Emma

...

Leaf Page

Anabelle

...


Clustered vs. Nonclustered A clustered index tends to be 1 I/O faster than a nonclustered

index for a single-row lookup Clustered indexes are excellent for retrieving ranges

of data Clustered indexes are excellent for queries with

order by Nonclustered indexes are a bit slower, take up much more disk

space, but are the next best alternative to a table scan Nonclustered indexes may cover the query for maximal

retrieval speed For some queries; covered queries, nonclustered indexes can

be faster When creating a clustered index, you need free space in your

database approximately equal to 120% of the total table size


Using Indexes

Clustered Index Indications Columns searched by range of values Columns by which the data is frequently sorted

(order by or group by) Sequentially accessed columns Static columns Join columns (if other than the primary key)

Nonclustered Index Indications NCI selection tends to be much more effective if less than about

20% of the data is to be accessed NCIs help sorts, joins, group by clauses, etc., if other column(s)

must be used for the CI Index covering


Other Index Limitations

Maximum 16 columns Maximum 900 bytes column width


Primary Key vs. Clusteringvs. Nonclustering A primary key is a logical concept, not a physical concept Indexes are physical concepts, not logical concepts There is a strong correlation between the logical concept of

a key and the physical concept of an index By default, when you define relationships as part of table

design, you will build indexes to support the joins / lookups By default, when you define a primary key, you will create a

unique clustered index on the table Unique is good, clustered isn’t always good

When you define a clustered index, the server automatically appends the key column(s) (plus a unique identifier, if necessary) to the nonclustered indexes


Key / index features(New in SQL Server 2005) Columns that are not part of the index key can be included in

nonclustered indexes. Including the nonkey columns in the index can speed queries (Index covering) and can exceed the current index size limitations of a maximum of 16 key columns and a maximum index key size of 900 bytes

The new ALLOW_ROW_LOCKS and ALLOW_PAGE_LOCKS options in CREATE INDEX and ALTER INDEX can be used to control the level at which locking occurs for the index

The query optimizer can match more queries to indexed views than in previous versions, including queries that contain scalar expressions, scalar aggregate and user-defined functions, interval expressions, and equivalency conditions

Indexed view definitions can also now contain scalar aggregate and user-defined functions with certain restrictions. (More in “Views”)


Optimizer Selection Criteria During the index selection phase of optimization the

optimizer decides which (if any) indexes best resolve the query

Identify which indexes match the clauses Estimate rows to be returned Estimate page reads


Index Selection Examples

1. What index will optimize this query?

2. What indexes optimize these queries?

3. In the second query, what would the net effect be of changing the range to this?

select title from titles where title = ‘Alleviating VDT Eye Strain’

select title from titles where price between $5. and $10.

between $500 and $600


CI vs. NCI

Table facts:2,000,000 titles (= 14492 pages)138 rows / page1 million rows in the range

Index used Page readsClustered index 7,247 + index levelsNon-clustered index (worst case) 1,000,000 + index

pagesNo index (table scan) 14492

select title from titles where price between $5. and $10.


CI vs. NCI

It is feasible, occasionally likely, that a table scan is faster than using a nonclustered index for specific queries

The server evaluates all options at optimization time and selects the least expensive query


Or Indexing

Questions What indexes should (could) be used? Will a compound index help? Which column(s) should be indexed?

select title from titles where price between $5. and $10. or type = 'computing'


Or Indexing (Cont’d)

How is the following query different(from a processing standpoint)?

What is a useful index for?

select title from titles where price between $5. and $10. and type = 'computing'

select * from authors where au_fname in ('Fred', 'Sally')


Or Clauses

Format

select * from authors where au_lname = 'Smith' or au_fname = 'Fred'

select * from authors where au_lname in ('Smith', 'Jones', 'N/A')

(How many indexes may be useful?)

SARG or SARG


Or Strategy

An or clause may be resolved via a table scan, a multiple match index or using or strategy

Table Scan

Each row is read, and criteria applied Matching rows are returned in the result set The cost of all the index accesses is greater than the cost

of a table scan At least one of the clauses names a column that is not

indexed, so the only way to resolve the clause is to perform a table scan


Or Strategy (Cont’d)

Multiple match index

Using each part of the or clause, select an index and retrieve the row

Only used if the results sets can not return duplicate rows Rows are returned to the user as they are processed


Or: Query Plan

select company, street2 from pt_sample

where id = 2017 or id = 2163

Query Execution Plan


Index Selection and the Select List

Questions What is the best index? Do the columns being selected have a bearing on the

index?

select * from publishers where pub_id = 'BB1111'


Index Selection and the Select List

Question Should there be a difference between the utilization of

the following two indexes?

select royalty from titles where price between $10 and $20

create index idx1 on titles (price)/* or */create index idx2 on titles (price, royalty)


Index Covering

The server can use the leaf level of a nonclustered index the way it usually reads the data pages of a table: this is index covering

The server can skip reading data pages

The server can walk leaf page pointers

A nonclustered index will be faster than a clustered index if the index covers the query for a range of data (why?)

Adding columns to nonclustered indexes is a common method of reducing query time

This has particular benefits with aggregates


Index Covering (Cont’d)

Beware making the index too wide; As index width approaches row width, the benefit of covering is reduced

# of levels in the index increases Index scan time approaches table scan time

Remember that changes to data will cascade into indexes


Composite Indexes

Composite (compound) indexes may be selected by the server if the first column of the index is specified in a where clause, or if it is a clustered index

create index idx1 on employee (minit, job_id , job_lvl)


Composite Indexes (Cont’d)

Which queries may use the index?

select * from employee where minit = 'A' and job_id != 4 and job_lvl = 135

select * from employee where job_id != 4 and job_lvl = 135

select * from employee where minit = 'A' and job_lvl = 135

create index idx1 on employee (minit, job_id , job_lvl)


Composite vs. Many Indexes

Each additional index impacts update performance

In order to select appropriate indexes, we need to know how many indexes the optimizer will use, and how many rows are represented by the where clause

select pub_id, title, notes from titles where type = 'Computer' and price > $15.


Options

CI or NCI on type

CI or NCI on price

One index on each of type & price

Composite on type, price

Composite on price, type

CI or NCI on type, price, pub_id, title, notes

Which are the best options in which circumstances?

select pub_id, title, notes from titles where type = 'Computer' and price > $15.


Index Usefulness It is imperative to be able to estimate rows returned for an

index. Therefore, the server will estimate rows returned before index assignation

If statistics are available (When would they not be?) the server estimates number of rows using distribution steps or index density

SQL Server 2005 automatically generates statistics about index key distributions using efficient sampling algorithms

If you have an equality join on a unique index, the server knows only one row will match and doesn't need to use statistics

The query analyzer index analyzer can analyze a query and recommend indexes

The more selective an index is, the more useful the index


Data Distribution

You have a 1,000,000 row table. The unique key has a range (and random distribution) of 0 to 10,000,000

Question How many rows will be returned by the following query? How does the optimizer know whether to use an index or

table scan?

select * from table where key between 1000000 and 2000000


Index Statistics

SQL Server keeps distribution information about indexes in a “statblob” column in the sysindexes table

There is distribution for every index

The optimizer uses this information to estimate the number of rows returned for a query

The distribution information is built at index creation time and maintained by the server if set to automatically do so


Distribution Steps

The server creates the statistics by walking the index, and storing appropriate key values at each step increment

10,000,000 rows have an integer key.1 page has (2005 bytes / 4 bytes + 2 between) =~ 500 steps10,000,000 rows / 500 steps = 20,000 rows / step


Distribution Steps

The optimizer will walk the index, storing the key value every 20,000 rows

When a query is executed

The number of keys in the range * 20,000 rows / key is the approximate number of rows affected

select * from table where key between 1000000 and 2005000


Viewing Index Statistics

Viewed with the dbcc show_statistics

Continued next page

dbcc show_statistics (table_name,index_name)


Viewing Index Statistics (Cont’d)

Continued next page


Explaining DBCC Show Statistics Updated date and time: When the statistics were last updated Rows: Number of rows in the table Rows Sampled: Number of rows sampled for statistics

information Density: Selectivity of the index Average key length: Average length of an index row All density: Selectivity of the specified column prefix in the

index Columns: Name of the index column prefix for which the all

density is displayed Steps: Number of histogram values in the current distribution

statistics for the specified target on the specified table


Estimating Logical Page I/O

If there is no index, there will be a table scan, and the estimate will be the number of pages in the table

If there is a clustered index, estimate will be the number of index levels plus the number of pages to scan

For a nonclustered index, estimate will be index levels + number of leaf pages + number of qualifying rows (which will correspond to the number of physical pages to read)

For a unique index and an equality join, the estimate will be 1 plus the number of index levels


When to Force Index Selection Don't Do it

With every release of the server, the optimizer gets better at selecting optimal query paths

Forcing the optimizer to behave in a specific manner does not allow it the freedom to change selection as data skews

It also does not permit the optimizer to take advantage of new strategies as advances are made in the server software


When to Force Index Selection (Cont’d)

Exceptions When you (the developer) have information about a

table that SQL Server 2005 will not have at the time the query is processed (i.e., using a temp table in a nested stored procedure)

Occasions when you've proven the optimizer wrong


How to Force Index Selection

How to Force Index Selection

To force the server to use a specific index for a specific table, you must first know the index id of the index you want to use

In this example, the titles index with the id of 2 will be used for the titles table, and the publishers index with an id of 1 will be used for publishers

select * from titles (2), publishers (1) where titles.pub_id = publishers.pub_id


When to Force Index Selection (Cont’d)

The following SQL will list all table names and their corresponding index ids

Allowing you to use the following syntax to force indexes

Instead, identify why the optimizer picked incorrectly

select * from titles (index(titleind)), publishers (index( UPKCL_pubind) )where titles.pub_id = publishers.pub_id

select 'table'=o.name, 'index'=i.name, indid from sysindexes i, sysobjects owhere i.id = o.id


Summary

The optimizer uses indexes to improve query performance when possible

Watch out for improvised SARGs

Queries with OR may require a table scan

Try to take advantage of covered queries

Be careful when forcing an index

GOT BLAME?

Database

Network Application

The Blame Game: “My piece is fine..”

Storage

Insight: Drill into the Problem Tier

Case Study #1 – Problem in the Database

Pilot of a newly developed internet pharmacy application SQL Server running on a 2-CPU system currently supporting 120 users

System crashing frequently CPU (per MS Perf Mon) was pegging the box at 100%

Solution after 6 weeks? add 2 more CPUs to double server capacity performance was “acceptable” but CPU was still at 95%

Panic had set in going to 800+ users on initial production roll out how much more hardware would they need? so far, this increased their software license costs by $70K

Here is what we found Monday at 10AM

Time to Value – 1 Day

Monday95% of 4 CPU’s

Tuesday7% of 2 CPU’s

Drill Down: Instance> Database> Statement

Drilling down into root causes

Further drill down

Click Here

Statement-level information

Significant io wait

i3 traps, tracks, and stores all query plans…

… And will tell you when & why the plans change

Drilling Down into Stored Procedures

REPLACE WITH MORE RECENT PICTURE

Who is Doing the Work?

What tables are being accessed the most? Drill down and identify the queries hitting them.

You’ve found the high-activity table; how is it being accessed?

Ever wonder which indexes may be safely removed?

New Wait States

Procedure Cache

Actual Plan

Case Study #2: Not in the Database 4-processor SQL Server in an Application Service Provider (ASP)

environment Key queries (stored procedures) were rewritten to run in under a

half second, from 5-20 seconds. Further follow-up found that application response time was still

in the 5-second + category We knew the issue was not in the database; but where was it?

How to demonstrate that it’s not the database

Where is the application spending its time?

When the vast majority of elapsed time is not in the database, you have evidence that it’s not your fault.

In this case, I had been brought in to tune the database, knocked all the queries down from 5-10 second range to significantly subsecond. Performance was still in the 3-6 second range for much of the application. I knew it was not a database issue, but what was it?

In this case, it was the result of significant requests to an SSL layer; it turned out that some of the screens were taking 5-6 seconds to encrypt due to the quantity of dynamic data displayed.

Why APM is Important 70% - studies have shown that 70%+ of all application performance issues are directly

attributable to the source code, NOT INFRASTRUCTURE It’s critical to monitor and track performance of the application code components

25% - studies have shown that only 25% of performance issues identified in production could have been anticipated and resolved in test/dev/QA Monitoring production performance is critical to application availability and

performance 60% of i3’s customer’s cancel or defer hardware upgrade purchases within the first year

of ownership Multi-tier web-based applications are extremely complex with great interdependency

among hardware/software/application components. This makes it virtually impossible to determine root cause in a timely fashion – a typical response to the frustration is to upgrade expensive hardware. I3 streamlines your application components.

100% - i3’s customers have found that their staff can double their IT responsibility without additional workload

3 – i3 has an average payback of 3 months, 1.8 months at one large Federal agency 1-3% - i3 has an average overhead of 1-3% in production environments 24% of IT staff time is devoted to addressing application performance delays

Documents

Indexing for Performance for SQL Server 2005