Disclaimer: This is work in progress intended to consolidate information
from various sources. It considers only on-premise SQL Server, for other
platforms please refer to the documentation.
Columnstore indexes will
be considered separately.
Last updated: 22-Jan-2024
Indexes
-
{def} a database object associated with a table or view that provides
efficient access path to data in the base object, based on one or more
columns
-
contains keys built from one or more columns stored physically as
B-trees
-
⇒ provides the path that is necessary to get to the data in the quickest
and most efficient manner possible [7]
-
⇒ can dramatically speed up the process of finding data as well as the
process of joining tables together [2]
-
on-disk structure
- index placement
- placed on either
- particular filegroup
- partitioned according to a predefined partition scheme
- {default} placed on the same filegroup as the base object [5]
-
must be maintained and updated as data are added or changed in the base
object
- each index brings with it a certain amount of overhead
- ⇐ the updates, inserts, and deletes are slower
-
{type}
clustered index
(CI)
- based on a clustering key
- tells SQL Server how to order the table's data pages [11]
- static columns (aka unchanging columns, non-volatile columns)
- the clustering key is never updated [11]
- nonstatic columns
-
when the clustering key value is updated, the data may need to be
moved elsewhere (aka record relocation) in the clustered index so that
the clustering order is maintained [11]
- triggers a nonclustered index update as well
-
so that the value of the clustering key is correct for the
relevant nonclustered index rows [5]
- wastes time and space
- causes page splits and fragmentation
-
adds unnecessary overhead to every modification of the column(s)
that make up the clustering key [5]
-
updating the clustering key is clearly more expensive than updating
a non-key column [11]
- can be either
- a single column key
- a composite key
- [SQL Server 2005] up to 16 columns
-
[SQL Server 2016] up to 32 columns can be combined into a single
composite index key
-
all the columns in a composite index key must be in the same table
or view [8]
- the most redundant data within the entire table
- {restriction}a table can have only one clustered index [5]
-
{restriction} maximum allowable size of the combined index values is 900
bytes
- {restriction} LOB data types can’t be specified as columns
- {subtype} unique clustered index (UCI)
- created with the UNIQUE keyword
- {subtype} non-unique clustered indexes
- created without the UNIQUE keyword
- SQL Server does not require a clustered index to be unique
- ⇒ must have some means of uniquely identifying every row
-
forces the index to be unique by appending a 4-byte value (aka
uniqueifier) to key values as necessary to differentiate identical key
values from one another [2]
- stored in every level of the B-tree [11]
- in both clustered and non-clustered indexes
- stored as a variable-length column [11]
-
if a table does not already contain any other variable-length
columns, each duplicate value is actually consuming 8-bytes of
overhead:
- 4 bytes for the uniqueifier value
- 4 bytes to manage variable-length columns on the row [11]
-
the overhead for rows with a NULL uniqueifier value is zero
bytes [11]
- added everywhere the clustering key is stored [11]
-
if there are many rows using the same clustering key value, the
operation can become quite expensive [11]
- is not readily visible and cannot be queried [11]
-
it is impossible to estimate how much additional storage overhead
will result from the addition of a uniqueifier, without first having
a thorough understanding of the data being stored [11]
- meaningless operation in the context of the data [11]
-
all clustered indexes must be unique so that nonclustered index
entries can point to exactly one specific row [5]
-
{type}
nonclustered index
(NCI)
-
the index is a completely separate structure from the data itself [5]
-
⇒ its presence or absence doesn’t affect how the data pages are
organized [5]
-
heap or a table with a clustered index affects the structure of
nonclustered indexes [5]
- best at singleton selects
- ⇐ queries that return a single row.
-
once the nonclustered B-tree is navigated, the actual data can be
accessed with just one page I/O, that is, the read of the page from
the underlying table
- each table can have up to 999 nonclustered indexes [8]
-
{subtype} nonclustered indexes with included columns
-
uses the INCLUDE keyword along with a list of one or more columns to
indicate the columns to be saved in the leaf level of an index [1]
-
the data for these columns are duplicated in index leaf-level pages
- ⇒ increases the disk space requirement [1]
- ⇒ fewer index rows can fit on index pages [1]
-
⇒ might increase the I/O and decrease the database cache
efficiency [1]
- ⇒ data is stored and updated twice [1]
-
⇒ index maintenance may increase in terms of the time that it
takes [1]
- can be specified up to 1,023 columns [1]
- the included columns are not part of the index key
- ⇒ keeps the key size small and efficient [1]
-
having the columns available at the leaf pages with the index means
avoid querying the base table if the included columns can satisfy the
query [1]
-
⇒ increases the chances that the query will find all it needs in the
index pages itself, without any further lookups [1]
-
provides the benefits of a covering index [1]
- results in smaller, efficient, and narrow keys [1]
- removes the 900 bytes/16 columns key size restriction [1]
-
{restriction} columns of data type text, ntext, and image are not
allowed as non-key included columns [1]
- {option} FILLFACTOR
-
its value determines the percentage of space on each leaf-level page to be
filled with data, reserving the remainder on each page as free space for
future growth [16]
-
it's applied when the index is first created but is not enforced afterward
- ⇒ isn't maintained over time
- affects only the leaf-level pages in the index
-
normally reserves enough empty space on intermediate index pages to
store at least one row of the index's maximum size [2]
-
the empty space is reserved between the index rows rather than at the end
of the index [16]
- its value is a percentage from 1 to 100
-
{default} 0 which means that the leaf-level pages are filled to capacity
[16]
- provided for fine-tuning index data storage and performance [16]
- affects performance
- creating an index with a relatively low fillfactor
- helps avoid page splits during inserts
-
with pages only partially full, the potential for needing to split
one of them in order to insert new rows is lower than it would be
with completely full pages
-
can be good for performance if the new data is evenly distributed
throughout the table [16]
-
if all the data is added to the end of the table, the empty space
in the index pages will not be filled [16]
-
the index will require more storage space and can decrease read
performance [16]
-
⇒ can decrease database read performance by an amount inversely
proportional to the fill-factor setting [16]
-
a high fillfactor can help compact pages so that less I/O is required to
service a query
- common technique with data warehouses
-
retrieving pages that are only partially full wastes I/O bandwidth
- {option} PAD_INDEX option
-
instructs SQL Server to apply the FILLFACTOR to the intermediate-level
pages of the index [2]
-
if FILLFACTOR setting is so high that there isn't room on the intermediate
pages for even a single row (e.g., a FILLFACTOR of 100%), the
percentage is overridden so that at least one row fits [2]
-
if FILLFACTOR setting is so low that the intermediate pages cannot store
at least two rows, SQL Server will override the fillfactor percentage on
the intermediate pages so that at least two rows fit on each page [2]
- {option} IGNORE_DUP_KEY
-
{enabled} when a UNIQUE index is created the option is used a duplicate
key error on a multiple-row INSERT won’t cause the entire statement to be
rolled back [5]
-
⇒ the nonunique row is discarded, and all other rows are inserted [5]
-
⇒ it makes a violation in a multiple-row data modification nonfatal to
all the nonviolating rows [5]
-
{disabled} during changes even if one row is found that would cause
duplicates of keys defined as unique, the entire statement is aborted and
no rows are affected [5]
- {option} STATISTICS_NORECOMPUTE
-
determines whether the statistics on the index should be updated
automatically [5]
- typically enabled
-
used to set a specific statistic or index to not update automatically [5]
-
overrides an ON value for the AUTO_UPDATE_STATISTICS database option [5]
-
if the database option is set to OFF, that behavior for a particular index
can’t be overridden
-
⇒ all statistics in the database must be updated manually
- via UPDATE STATISTICS or sp_updatestats [5]
- {option} MAXDOP
-
controls the maximum number of processors that can be used for index
creation [5]
-
can override the server configuration option [max degree of parallelism
for index building] [5]
-
allows multiple processors to be used for index creation can greatly
enhance the performance of index build operations [5]
-
each processor builds an equal-sized chunk of the index in parallel
- each parallel thread builds a separate tree
-
the math used to determine the theoretical minimum number of
required pages varies from the actual number [5]
- the tree might not be perfectly balanced
-
after each thread is completed, the trees are essentially
concatenated [5]
-
extra page space reserved during this parallel process can be used
for later modifications [5]
- {operation} index tuning
- iterative process
- {operation}index consolidation
- typically performed when indexes overlap
- includes duplicated indexes
- {caution} consider the various aspects when modifying indexes
- the number and design of indexes impact performance
-
⇐ the goal is to obtain the best performance with the smallest number
of indexes
- {operation} creation (aka index creation)
- [clustered index]
- the data becomes the leaf level of the clustered index [5]
-
data in the table is moved to new pages and ordered by the clustering
key
- maintained logically rather than physically
-
the order is maintained through a doubly linked list (aka page
chain)
- based on the definition of the clustered index
- same as the order of rows on the data pages
-
deciding on which column(s) to cluster is an important performance
consideration [5]
- can be ordered in only one way
-
although heap pages do have pointers to next and previous pages, the
ordering is arbitrary and doesn’t represent any ordering in the table
itself [5]
-
{operation} [SQL Server 2005] rebuilding (aka rebuilding indexes)
- {goal} generate new statistics [18]
-
{goal} change the number of pages used by the index (fill factor) [18]
-
{goal} reorganize the data on database pages in relative proximity to each
other [18]
-
introduced mainly for SQL Server's own internal purposes when applying
upgrades and service packs [6]
- drops and re-creates the index
- always rebuilds the entire index [3]
- regardless of the extent of fragmentation [3]
-
an index rebuild for a lightly fragmented index is really overkill [3]
-
there must be enough free space in the database to accommodate the new
index [3]
-
otherwise the database will grow to provide the required free space
[3]
- this can be problematic for large indexes [3]
-
reclaims disk space by compacting the pages based on the specified or
existing fill factor setting [15]
- reorders the index rows in contiguous pages [15]
- automatically rebuilds all index statistics [18]
- columns statistics aren’t touched
-
usually faster than reorganizing indexes if the logical fragmentation
is high [18]
-
when ALL is specified, all indexes on the table are dropped and rebuilt
in a single transaction
-
indexes with more than 128 extents are rebuilt in two separate phases
[15]
- logical phase
-
the existing allocation units used by the index are marked for
deallocation [15]
- the data rows are copied and sorted [15]
-
then moved to new allocation units created to store the rebuilt
index [15]
- physical phase
-
the allocation units previously marked for deallocation are
physically dropped in short transactions that happen in the
background, and do not require many locks [15]
- serial index rebuild
-
forces an allocation order scan, ignoring the key placement and
scanning the object from first IAM to last IAM order [13]
- via WITH(NOLOCK, INDEX=0)
- takes longer than a [parallel index rebuild]
- parallel index rebuild
-
during rebuild a portion of the data (range) is assigned to each of
the parallel workers [13]
-
for each parallel worker is assigned its own CBulkAllocator when
saving the final index pages [13]
-
a leap frog behavior of values, across extents occurs as the workers
copy the final keys into place [13]
-
each worker spreads 1/nth of the data across the entire file instead
of packing the key values together in a specific segment of the file
[13]
- can use minimal-logging to reduce transaction log growth [3]
- may require long-term locks on the table [3]
- ⇒ can limit concurrent operations [3]
- replaces
- DBCC DBREINDEX command
- DROP_EXISTING option of CREATE INDEX command
- can be executed
- online (aka online operation)
-
table and indexes are available while the operation is
performed
- offline (aka offline operation)
- permissions requirements
- ALTER permission on the table or view
- users must be members of either:
- sysadmin
- db_ddladmin
- db_owner
-
{operation} reorganize (aka reorganize indexes, index reorganization)
- doesn’t update statistics at all [3]
- always single-threaded [3]
- always fully logged [3]
-
⇐ doesn’t prevent transaction log clearing [3]
- doesn’t hold blocking locks
- always executed online
- won’t update statistics at all [3]
- uses minimal system resources
- requires 8KB of additional space in the database [3]
- doesn’t hold blocking locks [3]
- takes care of the existing fragmentation [3]
-
⇒ makes it a better choice for removing fragmentation from a
lightly fragmented index [3]
-
⇒ makes it a poor choice for removing fragmentation from a heavily
fragmented index [3]
- {limitation} can’t use index options
- replaces the DBCC INDEXDEFRAG command [6]
- removes some of the fragmentation from an index
- it’s not guaranteed to remove all the fragmentation
- just like DBCC INDEXDEFRAG
-
doesn’t have a corresponding option in the CREATE INDEX command [6]
-
because when creating an index there is nothing to reorganize [6]
-
it defragments the leaf level of clustered and nonclustered indexes on
tables and views by physically reordering the leaf-level pages to match
the logical, left to right, order of the leaf nodes [15]
- compacts the index pages
- based on the existing fill factor value
-
{operation} disabling (aka index disabling)
- makes an index completely unavailable
- ⇒ it can't be used for finding rows for any operations
-
⇒ the index won't be maintained as changes to the data are made
- disables one or ALL indexes with a single command
- indexes must be completely rebuilt to make them useful again
-
⇐ there is no ENABLE option
-
⇐ because no maintenance is done while an index is disabled
-
{warning} disabling a [clustered index] on a table, makes the
data unavailable [6]
-
⇐ because the data is stored in the clustered index leaf level [6]
- {operation} dropping (aka index dropping)
- {operation} changing index options
-
{warning} regular rebuilding of all the indexes in a database is a poor
way to manage indexes
-
leads to resources waste by rebuilding indexes that are not fragmented
-
it can affect the availability of a system when the indexes can’t be
rebuilt online
-
{concept}
index extent
- a group of eight index pages
-
{concept}
narrow index
- in terms of the number of bytes it stores [11]
- usually has few columns in the index key
-
can accommodate more rows into an 8KB index page than a wide index [1]
-
⇒ {benefit} reduces I/O
-
⇒ {benefit} reduces storage requirements
-
⇒ {benefit} improves database caching
- {benefit} reduces maintenance overhead
- small indexes might not have an intermediate level at all
-
the goal when defining indexes isn’t to have only very narrow indexes [5]
-
extremely narrow indexes usually have fewer uses than slightly wider
indexes [5]
-
{concept}
wide index
- typically an index with many columns in the index key
-
wider keys cause more I/O and permit fewer key rows to fit on each
B-tree page
-
indexes require a larger number of pages than it otherwise would and
causes it to take up more disk space a lot of space (and potentially
buffer pool memory) is wasted
- large indexes often have more than one intermediate level
- {benefit} covers more queries
- {downside} increases maintenance overhead
- {downside} can adversely affect disk space
-
{concept}
ever increasing index
-
e.g. a numeric value can be continuously incremented by the value defined
at creation
- {benefit} avoids fragmentation
-
results in sequential IO and maximizes the amount of data stored per
page
- the most efficient use of system resources
- {benefit} improve write performance
-
SQL Server can much more efficiently write data if it knows the row will
always be added to the most recently allocated, or last, page [11]
-
non-sequential key column can result in a much higher overhead during
insertion [11]
-
SQL Server has to find the correct page to write to and pull it into
memory
-
if the page is full, SQL Server will need to perform a page split to
create more space [11]
-
during a page split, a new page is allocated, and half the records are
moved from the old page to the newly-allocated page [11]
-
each page has a pointer to the previous and next page in the index, so
those pages will also need to be updated [11]
-
{concept}
fragmentation
- results from data modifications
-
can take the form of gaps in data pages, so wasting space, and a logical
ordering of the data that no longer matches the physical ordering [11]
-
{concept}
covering index
-
nonclustered index that contains all the columns requested by the query
without going to the base table
-
allows it to skip the bookmark lookup step and simply return the data
the query seeks from its own B-tree [2]
-
when a clustered index is present, a query can be covered using a
combination of nonclustered and clustered key columns since the
clustered key is the nonclustered index's bookmark [2]
-
the next best thing to having multiple clustered indexes on the same
table [2]
- included columns
- columns added in the INCLUDE clause of the CREATE INDEX command
-
allow exceeding the 900-byte or 16-key column limits in the leaf level
of a nonclustered index [5]
- appear only in the leaf level
- don’t affect the sort order of the index rows in any way [5]
- SQL Server can silently add an included column to indexes [5]
-
e.g. when an index is created on a partitioned table and no ON
filegroup or ON partition_scheme_name is specified [5]
-
when partitioning a non-unique, nonclustered index, the Database
Engine adds the partitioning column as a non-key (included) column
of the index, if it is not already specified [8]
- column names cannot be repeated in the list [8]
-
column namescannot be used simultaneously as both key and non-key
columns [8]
-
{restriction} all data types are allowed except text, ntext, and image
[8
-
{restriction} index must be created or rebuilt offline if any one of the
specified non-key columns are varchar(max), nvarchar(max), or
varbinary(max) data types [8]
-
[computed columns] that are deterministic and either precise or
imprecise can be included columns [8]
-
[computed columns] derived from image, ntext, text, varchar(max),
nvarchar(max), varbinary(max), and xml data types can be included in
non-key columns as long as the computed column data types is allowable
as an included column [8]
-
[SQL Server 2005] can be stored in the same filegroup as the underlying
table, or on a different filegroup [12]
-
{concept} [SQL Server 2008]
filtered indexes
-
indexes with simple predicates that restrict the set of rows included in
the index [4]
-
leaf level contains an index row only for keys that match the filter
definition [5]
- created using a new WHERE clause on a CREATE INDEX statement
- alternative to indexed views
- which are more expensive to use and maintain
- their matching capability is not supported in all editions
-
tend to be more useful for the more classical relational query
precomputation scenarios
- {benefit} have fewer rows and are also narrower than the base table
- require fewer pages as well
-
specific constraints that are used on queries with large tables where
space is an issue, this kind of index can be quite useful
- usage scenarios
- fields are used only occasionally
- resulting in many NULL entries for that column
-
a traditional index stores a lot of NULLs and wastes a lot of
storage space
-
updates to the table have to maintain this index for every row
-
querying a table with a small number of distinct values and are using
a multicolumn predicate where some of the elements are fixed
-
this might be useful for a regular report with special purpose
-
it speeds up a small set of queries while not slowing down updates
as much for everyone else
-
when there is a known query condition on an expensive query on a large
table
-
{concept}
indexed views
- similar to materialized views in Oracle
- without indexes, views are purely logical
- ⇒ the data involved has no physical storage [5]
- the first index built must be a clustered index
- data is stored in the leaf level of the index [5]
- further nonclustered indexes can be built
- {constraint} view’s columns must be deterministic
- {constraint} session-level restrictions
- options must be set to a specific value (see )
-
if these options aren’t set as specified, an error message is raised
when attempted to create an indexed view [5]
- {constraint} only deterministic functions are allowed
-
check via SELECT OBJECTPROPERTY (object_id('<function_name>'),
'IsDeterministic')
-
imprecise values (float or real values) can be used only if the computed
value is persisted [5]
-
{constraint} the definition of any underlying object’s schema can’t change
[5]
- must be enforced through WITH SCHEMABINDING option
- {constraint} if a view's definition contains GROUP BY
- the SELECT list must include the aggregate COUNT_BIG (*)
- COUNT_BIG returns a BIGINT, a 8-byte integer
- can’t contain HAVING, CUBE, ROLLUP, or GROUP BY ALL
- all GROUP BY columns must appear in the SELECT list
-
constraints apply only to view definition, not to the queries that might
use the indexed views [5]
- {benefit}materialize summary aggregates of large tables [5]
- eliminates the need of scanning the underlying, large tables [5]
- {activity} check whether a view is indexable
-
via SELECT OBJECTPROPERTY (OBJECT_ID ('<view_name>'),
'IsIndexable');
- {activity} check whether a view is indexed
-
via SELECT OBJECTPROPERTY (OBJECT_ID ('<view_name>'),
'IsIndexed');
- alternative solutions
- temporary tables
- persistent tables
-
[Query Optimizer] doesn’t always choses an indexed view for query’s
execution plan [5]
-
{restriction} considered only in [Enterprise], [Developer] and
[Evaluation] editions
- the base tables might be accessed directly [5]
-
use NOEXPAND hint in FROM clause to make sure that the indexed view
isn’t expanded into its underlying SELECT statement [5]
-
{recommendation} NOEXPAND hint should be used only when improves
query’s performance [5]
- indexes on computed columns
- without indexes, computed columns are purely logical
- the data involved has no physical storage [5]
- recomputed every time a row is accessed
- {exception}the computed column is marked as PERSISTED [5]
- {restriction} see the requirements for indexed views
- {exception} table-level restrictions don’t apply [5]
-
{restriction} allowed on deterministic, precise (and persisted
imprecise) computed columns where the resulting data type is otherwise
indexable [5]
- {activity} check whether a column is deterministic
-
via SELECT COLUMNPROPERTY (OBJECT_ID('<tabelle>'),
'<column>', 'IsDeterministic');
- {activity} check whether a column is precise
-
via SELECT COLUMNPROPERTY (OBJECT_ID('<tabelle>'),
'<column>', 'IsPrecise');
- {activity} check if a column is persisted
-
SELECT is_persisted
FROM sys.computed_columns
WHERE
object_id = object_Id('<tabelle>')
AND name =
'<column_name>'
-
{concept}
missing index
- index that would have lead to a optimal query plan
-
[query optimizer] when generating a query plan, it analyzes what are the
best indexes for a particular filter condition [17]
-
if the best indexes do not exist it generates a suboptimal query plan
[17]
- stores information about the missing indexes
- {activity} review missing indexes
- via sys.dm_db_missing_index_group_stats
-
when table metadata changes, all missing index information about the
table is deleted [17]
- e.g. column added/dropped, index adde
- via MissingIndexes element in XML Showplans
-
correlate indexes that the query optimizer considers missing with
the queries for which they are missing [17]
- turn on via SET STATISTICS XML ON option
- it’s not intended to fine tune an indexing configuration [17]
-
it cannot gather statistics for more than 500 missing index groups
[17]
-
after this threshold is reached, no more missing index group data is
gathered [17]
-
the threshold is not a tunable parameter and cannot be changed
[17]
-
it does not specify an order for columns to be used in an index [17]
-
for queries involving only inequality predicates, it returns less
accurate cost information [17]
- it reports only include columns for some queries
- index key columns must be manually selected [17]
-
it returns only raw information about columns on which indexes might
be missing [17]
-
information returned might require additional processing before
being used to create an index [17]
- it does not suggest [filtered indexes] [17]
-
it can return different costs for the same missing index group that
appears multiple times in XML Showplans [17]
-
it can occur when different parts of a single query benefit
differently from the same missing index group [17]
- it does not consider [trivial query plans] [17]
-
{concept}
fragmented index
-
index that is not in the same logic order as it is stored in memory or on
storage [19]
- caused by
-
modifications to data can cause the information in the index to become
scattered in the database (fragmented) [15]
- autoshrink option
-
heavily fragmented indexes can degrade query performance and cause your
application to respond slowly [15]
- index fragmentation
-
exists when indexes have pages in which the logical ordering, based on
the key value, does not match the physical ordering inside the data file
[15]
- {type} extent fragmentation (aka external index fragmentation)
-
the pages get out of physical order, as a result of data modifications
[11]
- can result in random I/O
- does not perform as well as sequential I/O [11]
-
is truly bad only when SQL Server is doing an ordered scan of all or
part of a table or an index [6]
-
only when the pages need to be fetched in logical order is needed to
follow the page chain.
-
if the pages are heavily fragmented, this operation is more
expensive than if there were no fragmentation [6]
-
if seeking individual rows through an index, it doesn't matter
where those rows are physically located [6]
- {type} page fragmentation (aka internal index fragmentation)
- occurs when there are gaps in the data pages
- reduces the amount of data that can be stored on each page
-
increases the overall amount of space needed to store the data
[11]
-
[scanning] the entire table or index involves more read operations
than if no free space were available on your pages [6]
-
some fragmentation is desirable in order to minimize the [page splits]
[6]
- fragmentation on small indexes is often not controllable
- the pages of small indexes are stored on mixed extents [15]
- mixed extents are shared by up to eight objects [15]
-
the fragmentation in a small index might not be reduced after
reorganizing or rebuilding the index [15]
- {affect} primarily impacts range-scan queries
- singleton queries would not notice much impact [11]
- can benefit from routine defragmentation efforts [11]
-
{affect} disk performance and the effectiveness of the SQL Server
read-ahead manager [10]
- disk latency
-
[small-scale environment] only the highest fragmentation levels had a
significant negative impact on disk latency [10]
-
[large-scale environment] significantly lower and never became an
issue
- due to increased I/O performance provided by the SAN [10]
- {action} detect fragmentation
- analyze the index to determine the degree of fragmentation
- via sys.dm_db_index_physical_stats
- controlled through
- fillfactor setting
- regular defrag operations
- {remedy} reorganize indexes
- {remedy} rebuild indexes
-
{concept}
page splits
-
when a new row is added to a full index page, half the rows are moved to a
new page to make room for the new row
-
the new page must be linked into the indexes page chain, and usually the
new page is not contiguous to the page being split [6]
- resource intensive operation
- occurs frequently
- can lead to external fragmentation [6]
-
{concept}
interleaving
-
occurs when index extents are not completely contiguous within the data
file, leaving extents from one or more indexes intermingled in the file
[18]
- can occur even when there is no logical fragmentation
-
because all index pages are not necessarily contiguous, even when
logical ordering matches physical ordering [18]
-
{concept}
leap frog behavior
- behavior occurs during [parallel index rebuild]
-
when the key range is leap frogged the fragmentation limits [SQL Server]’s
I/O size to 160K instead of 508K and drives the number of I/O requests
much higher [13]
-
{workaround} [partitioned table] partition the table on separate files
matching the DOP used to build the index
-
allows better alignment of parallel workers to specific partitions,
avoiding the [leap frog behavior]
-
{workaround} [non-partitioned table] aligning the number of files with the
DOP may be helpful [13]
-
with reasonably even distribution of free space in each file the
allocation behavior is such that alike keys will be placed near each
other [13]
-
{workaround} for single partition rebuild operations consider serial index
building behaviors to minimize fragmentation behaviors [13]
-
{concept}
defragmentation
- does not yield performance gains in every case [10]
- steps
- {activity} ensure there are no resource issues
- common resource issues are related to
- I/O subsystem performance
- memory usage
- CPU utilization
- physical disk fragmentation [10]
-
{recommendation} [small-scale environments] correct disk
fragmentation before running index defragmentation [10]
- not necessary on SAN environments [10]
- inappropriate schemas [10]
- out-of-date statistics
-
{recommendation} resolve any out-of-date statistics before
defragmenting [10]
- {activity} determine the amount of index fragmentation
- focus on the larger indexes
- their pages are less likely to be cached [10]
-
{best practice} monitor regularly the fragmentation levels on
indexes [10]
- {activity} determine workload type
- not all workload types benefit from defragmenting [10]
-
read-intensive workload types that do significant disk I/O benefit
the most [10]
-
DSS workload types benefit much more than OLTP workload types [10]
- {activity} determine queries that perform poorly [10]
- determine the amount of I/O performed by a query [10]
-
queries that scan large ranges of index pages are affected most by
fragmentation and gain the most from defragmenting [10]
-
{activity} understand the effect of fragmentation on disk throughput and
the read-ahead manager [10]
-
for queries that scan one or more indexes, the read-ahead manager is
responsible for scanning ahead through the index pages and bringing
additional data pages into the SQL Server data cache [10]
-
the read-ahead manager dynamically adjusts the size of reads it
performs based on the physical ordering of the underlying pages [10]
-
when there is low fragmentation, the read-ahead manager can read
larger blocks of data at a time, more efficiently using the I/O
subsystem [10]
-
as the data becomes fragmented, the read-ahead manager must read
smaller blocks of data [10]
-
the amount of read-ahead operations that can be issued is
independent of the physical ordering of the data; however, smaller
read requests take more CPU resources per block, resulting in less
overall disk throughput [10]
-
when fragmentation exists and the read-ahead manager is unable to
read the larger block sizes, it can lead to a decrease in overall
disk throughput [10]
- {constraint}UNIQUE
-
allows the columns that make up the constraint to allow NULLs, but it
doesn’t allow all key columns to be NULL for more than one row [5]
-
unique indexes created using the CREATE INDEX command are no different
from indexes created to support constraints [5]
-
[Query Optimizer] makes decisions based on the presence of the unique
index rather than on whether the column was declared as a constraint [5]
- ⇐ is irrelevant how the index was created [5]
- {constraint} PRIMARY KEY
- all the columns involved in the PRIMARY KEY don’t allow NULL values
-
if any of the columns allow NULL values, the PRIMARY KEY constraint
can’t be created [5]
- its value is unique within the table
- uniqueness defined via the UNIQUE constraint
-
⇒ a unique index is created on the columns that make up the constraint
[5]
-
when a PRIMARY KEY or UNIQUE constraint is created, the underlying index
structure that’s created is the same as though you had used the CREATE
INDEX command directly [5]
-
⇐ however usage and features have some differences
-
a constraint-based index can’t have other features added (e.g.
included columns, filters), while a UNIQUE index can have these
features while still enforcing uniqueness over the key definition of
the index [5]
-
when referencing a UNIQUE index - which doesn’t support a constraint -
through a FOREIGN KEY constraint, indexes can’t be referenced with
filters [5]
-
⇐ an index that doesn’t use filters or an index that uses included
columns can be referenced [5]
-
⇐ the names of the indexes built to support these constraints are the same
as the constraint names [5]
-
{concept}
index design
-
designing and using proper indexes is one of the keys to maximizing query
performance [1]
- over-indexing
- can be worse than under-indexing
- extreme case: index every column
- under-indexing
- extreme case: having no index
- very complex, time-consuming, and error-prone process
- even for moderately complex databases and workloads [12]
- requires knowing
- the data
- the workload
- how SQL Server works [5]
- index internals
- statistics
- query optimization
- maintenance
-
{activity} understand the characteristics of the database itself [12]
- e.g. characteristics of OLAP vs OLTP
-
{activity}understand the characteristics of the most frequently used
queries [12]
-
{activity} understand the characteristics of the columns used in the
queries [12]
- data type
- column uniqueness
-
unique indexes are more useful for the query optimizer than nonunique
indexes [12]
- data distribution in the column
- NOT NULL values percentage
- columns with categories of values
- columns with distinct ranges of values
- sparse columns
- computed columns
-
{activity} determine which index options might enhance performance when
the index is created or maintained [12]
-
e.g. creating a clustered index on an existing large table would benefit
from the ONLINE index option
-
{activity} determine the optimal storage location for the index [12]
-
storage location of indexes can improve query performance by increasing
disk I/O performance [12]
-
⇐ storing a nonclustered index on a filegroup that is on a different
disk than the table filegroup can improve performance because multiple
disks can be read at the same time [12]
-
[partitioning] clustered and nonclustered indexes can use a partition
scheme across multiple filegroups
-
⇐ [partitioning] makes large tables or indexes more manageable by letting
you access or manage subsets of data quickly and efficiently, while
maintaining the integrity of the overall collection
-
{default} indexes created on a partitioned table will also use the
same partitioning scheme and partitioning column [9]
-
⇐ the index is aligned with the table [9]
-
allows for easier management and administration, particularly with
the sliding-window scenario [9]
- {guideline} avoid over-indexing heavily updated tables
-
large numbers of indexes on a table affect the CRUD statements [12]
-
{guideline} use many indexes to improve query performance on tables with
low update requirements, but large volumes of data [12]
- {guideline} indexing small tables may not be optimal
-
because it can take the query optimizer longer to traverse the index
searching for data than to perform a simple table scan [12]
-
{guideline} indexes on views can provide significant performance gains
when the view contains
- aggregations
- table joins
- combination of aggregations and joins
-
views does not have to be explicitly referenced in the query for the
query optimizer to use it [12]
-
{guideline} keep your indexes as compact and narrow as possible while
still meeting the business needs your system was designed to address [2]
-
⇐ the index needs to be efficient
-
{guideline}use the Database Engine Tuning Advisor to analyze your database
and make index recommendations [12]
-
{guideline} create nonclustered indexes on the columns that are frequently
used in predicates and join conditions in queries [12]
-
{guideline} write queries that insert or modify as many rows as possible
in a single statement
- instead of using multiple queries to update the same rows
- exploits optimized index maintenance
-
{guideline} consider using filtered indexes on columns that have
well-defined subsets [12]
- e.g. sparse columns
- e.g. columns with mostly NULL values
- e.g. columns with categories of values
- e.g. columns with distinct ranges of values
- e.g. consider indexing computed columns
-
{recommendation} use a couple of relatively narrow columns that, together,
can form a unique key [11]
-
saves the cost of the uniqueifier in the data pages of the leaf level of
your clustered index [11]
-
still results in an increase increase of the row size for your
clustering key in the index pages of both your clustered and
nonclustered indexes
-
{recommendation} carefully design and determine when and what columns
should be included [1]
-
determine whether the gains in query performance outweigh the effect on
performance during data modification and the additional disk space
requirements [1]
- {recommendation} keep the index size small
- tends to be efficient [1]
-
features such as INCLUDE and filtered indexes can profoundly affect the
index in both size and usefulness [5]
-
{recommendation} have all the data required by the query obtained from the
covering index without touching the clustered index or table data
page
- results in lesser I/O and better query throughput
-
poorly designed indexes and a lack of indexes are primary sources of
database application bottlenecks [12]
-
the right index, created for the right query, can reduce query execution
time from hours down to seconds [5]
-
without a useful index the entire table is scanned to retrieve the
data
-
in joins some tables may be scanned multiple times to find all the
data needed to satisfy the query [2]
- {recommendation} create a clustered index on all user tables [14]
-
the performance benefits of having a clustered index on a table outweigh
the negatives ones [14]
-
tables with clustered indexes have a higher 'page splits/sec' values
[14]
-
heaps can have higher throughput (rows/sec) for individual or batch
CRUD operations [14]
-
concurrent insert operations performed are often susceptible to
encountering hot spots [14]
-
the contention at the particular insert location in the table
adversely affects performance [14]
- disk space required is smaller than for a heap [14]
-
deleting rows frees up more disk space when compared to a heap table
-
[clustered indexes] empty extents are automatically deallocated from
the object [14]
- [heaps] extents are not deallocated automatically [14]
- extents are held on for later reuse [14]
- results in table size bloating [14]
-
the residual disk space for heap tables could be reclaimed by using
maintenance tasks [14]
-
⇐ requires additional work and processing [14]
-
{recommendation} choose the optimum sort order by examining the individual
workload queries [17]
- {recommendation} limit changes to no more than 1-2 indexes
- metadata
-
⇐{recommendation}for the data not persisted make periodic copies to use
the data for further analysis
-
[SQL Server 2000] sys.sysindexes
- system table that stores system-level information about indexes
-
a pointer to each index's root node is stored in sysindexes' root column
- {limitation} doesn't support partition tables
- {warning} susceptible to be removed in further versions
- [SQL Server 2005] sys.indexes
-
contains a row per index or heap of a tabular object, such as a table,
view, or table-valued function
-
metadata's visibility is limited to the securables a user either
owns or to which has been granted permissions
-
[SQL Server 2005] sys.index_columns
-
contains one row per column that is part of an index or unordered table
(heap).
-
[SQL Server 2005]
sys.dm_db_index_physical_stats
-
returns the size and fragmentation information for the data and indexes
of the specified table or view
- useful for determining the size and health of indexes [5]
- replaces DBCC SHOWCONTIG
-
when the function is called, SQL Server traverses the page chains for
the allocated pages for the specified partitions of the table or index
[5]
- has similar parameters as sys.dm_db_index_physical_stats
-
[SQL Server 2008]
sys.dm_db_missing_index_group_stats
- returns summary information about missing index groups [17]
- updated by every query execution
-
⇒ not by every query compilation or recompilation
- not persisted
-
[SQL Server 2008] sys.dm_db_missing_index_groups
-
returns information about a specific group of missing indexes [17]
-
⇐ the group identifier and the identifiers of all missing indexes that
are contained in that group [17]
- not persisted
- {required permissions} VIEW SERVER STATE
- {required permissions}[SQL Server 2022] VIEW SERVER PERFORMANCE
-
[SQL Server 2008] sys.dm_db_missing_index_details
- DMV that returns detailed information about missing indexes [17]
- updated when a query is optimized by the query optimizer
- not persisted
- {limitation} the result set is limited to 600 rows
-
⇒ address the missing indexes issues to see further values
- {required permissions} VIEW SERVER STATE
-
{required permissions}[SQL Server 2022] VIEW SERVER PERFORMANCE STATE on
the server
-
[SQL Server 2008] sys.dm_db_missing_index_columns
-
returns information about the database table columns that are missing an
index [17]
- not persisted
- {required permissions} VIEW SERVER STATE
- {required permissions}[SQL Server 2022] VIEW SERVER PERFORMANCE
-
{undocumented} [SQL Server
2012] sys.dm_db_database_page_allocations
-
returns a row for every page used or allocated based on the given
parameters [5]
- replaces the DBCC IND undocumented command [5]
-
[SQL Server 2019] sys.dm_db_page_info
- function that returns information about a page in a database
-
returns one row that contains the header information from the page,
including the object_id, index_id, and partition_id
- replaces DBCC PAGE in most cases
-
{command} DBCC SHOWCONTIG
- determines how full the pages in a table and/or index really are
- shows three types of fragmentation:
- extent scan fragmentation
- logical scan fragmentation
- the percentage of out-of-order pages in an index [10]
- not relevant on heaps and text indexes [10]
-
high values for logical scan fragmentation can lead to degraded
performance of index scans [10]
- scan density
- not valid when indexes span multiple [10]
- {concept} average page density
- measure of fullness for leaf pages of an index [10]
-
low values for average page density can result in more pages that must
be read to satisfy a query [10]
-
reorganizing the pages so they have a higher page density can result
in less I/O to satisfy the same query [10]
-
generally, tables have a high page density after initial loading of
data [10]
-
page density may decrease over time as data is inserted, resulting
in splits of leaf pages [10]
-
value dependent on the fillfactor specified when the table was
created [10]
- key indicators
- Logical Scan Fragmentation
- Avg. Page Density
- {option} WITH FAST
-
allows DBCC SHOWCONTIG to avoid scanning the leaf pages of an index
[10]
-
⇒ it cannot report page density numbers [10]
- consider using it on a busy server [10]
-
{command} DBCC INDEXDEFRAG
- defragments index pages
- defragments only individual indexes [10]
-
⇒ cannot rebuild all indexes with a single statement [10]
- performs an in-place update of the pages to defragment them [20]
-
⇐ does not require locking the table for the entirety of the process,
[20]
-
⇒ allows users to access the table [20]
- only defragments the pages within the extents [20]
-
⇐ it does not defragment the extents [20]
-
⇐ doesn’t affect extent scan fragmentation
-
⇒ reduces logical scan fragmentation
- online operation
- skips locked pages as it encounters them
-
⇒ may not be able to completely eliminate fragmentation [18] [6]
- each unit of work performed occurs as a separate transaction [18]
-
⇒ can be stopped and restarted without losing any work [18]
-
{drawback} does not help to untangle indexes that have become interleaved
within a data file [10]
-
even if indexes can be rebuilt so that there is minimal interleaving,
this does not have a significant effect on performance [10]
- {drawback} does not correct extent fragmentation on indexes [10]
- {drawback} can produce large transaction log files [20]
-
{workaround} decrease the backup interval for transaction backups while
DBCC INDEXDEFRAG is running [20]
-
⇐ keeps the transaction log files from growing too large [20]
- {drawback} can make extent switching worse [20]
-
because a page order scan, which might be in order after DBCC
INDEXDEFRAG, might switch between more extents [20]
-
{phase: 1} compacts the pages and attempt to adjust the page density to
the fillfactor that was specified when the index was created
-
attempts to raise the page-density level of pages to the original
fillfactor [10]
-
{phase: 2} defragments the index by shuffling the pages so that the
physical ordering matches the logical ordering of the leaf nodes of the
index [10]
-
⇐ performed as a series of small discrete transactions
- has a small impact to overall system performance [10]
-
{command} DBCC DBREINDEX
- rebuilds an index or all indexes on a table
-
⇐ builds indexes the same way as CREATE INDEX [10]
-
restores the page density levels to the original fillfactor or to the
provided value [10]
- rebuilds statistics automatically during the rebuild
- have dramatic improvements on workload performance [10]
- can take advantage of multiple-processor computers [10]
-
can be significantly faster when rebuilding large or heavily
fragmented indexes [10]
-
rearranges the extents and the pages within a table so that they are in
index order [19]
-
⇐ pages scan faster when they have been written in heap memory in the
same order as their next page's pointers (index order) [19]
-
extents can be paged from disk faster during a scan operation when
they're written to storage in the same order as their next extent
pointers (index order) [19]
- offline operation
-
its effectiveness can be influenced by the available free space [10]
-
⇐ without large enough contiguous blocks of free space, DBREINDEX may be
forced to reuse other areas of free space within the data files,
resulting in indexes being rebuilt with a small amount of logical
fragmentation [10]
-
the amount of free space needed varies and is dependent on the number of
indexes being created in the transaction [10]
-
{guideline} [clustered indexes] required free space = 1.2 * (average
row size) * (number of rows) [10]
-
[nonunique clustered index] when rebuilt is also need free space for
both the clustered and any nonclustered indexes
-
implicitly rebuilt because SQL Server must generate new unique
identifiers for the rows [10]
-
{guideline} [nonclustered indexes] average row size *number of rows
-
average row size of each row in the nonclustered index considers
the length of the nonclustered key plus the length of clustering
key or row ID [10]
- runs as one atomic transaction [18]
- when stopped the changes are rolled back [18]
-
[clustered indexes][locking] puts an exclusive table lock on the table
[19]
- prevents any users from accessing the table [19]
- [nonclustered indexes][locking] puts a shared table lock
-
prevents all but SELECT operations from being performed on it [19]
- {good practice} specify individual indexes for defragmentation [10]
-
⇐ gives more control over the operations being performed [10]
-
⇐ can help to avoid unnecessary work [10]
-
{limitation} does not reduce page density levels on pages that currently
have a higher page density than the original fillfactor [10]
-
{advantage} as the fragmentation level and size of the indexes increase,
DBCC DBREINDEX can rebuild the indexes much faster than DBCC INDEXDEFRAG
[10]
- via DBCC DBREINDEX (‘<table_name>')
Acronyms:
CI - clustered index
CRUD - Create, Read, Update, Delete
DBCC -
Database Console Command
DMV - Dynamic Management View
IAM
- Index Allocation Map
NCI - nonclustered index
UCI - unique
clustered index
Resources:
[1] Scalability Experts (2005) "Microsoft® SQL Server 2005: Changing
the Paradigm",
[2] Ken Henderson (2003) "Guru's Guide to SQL Server
Architecture and Internals"
[3] Paul S Randal (2011) SQL Q&A: The Lore of Logs (link)
[4] Kalen Delaney et al (2009) "Microsoft® SQL Server® 2008
Internals"
[5] Kalen Delaney (2013) Microsoft SQL Server 2012
Internals
[6] Kalen Delaney (2006) "Inside Microsoft® SQL Server™ 2005:
The Storage Engine"
[7] Jason Strate & Grant Fritchey (2015)
"Expert Performance Indexing in SQL Server"
[8] Microsoft Learn (2023)
CREATE INDEX (link)
[9] Kimberly L Tripp (2005) Partitioned Tables and Indexes in SQL
Server 2005 (link)
[10] Mike Ruthruff (2009) Microsoft SQL Server 2000 Index
Defragmentation Best Practices [white paper]
[11] Michelle
Ufford (2011) Effective Clustered Indexes, (link)
[12] Technet (2016) SQL Server Index Design Guide (link)
[13] Bob Dorr (2015) How It Works: MAX DOP Level and Parallel Index Builds
(old link)
[14] Stuart Ozer
et al (2007) SQL Server Best Practices Article (link)
[15] Microsoft Learn (2023) Optimize index maintenance to improve query
performance and reduce resource consumption (link)
[16] MSDN (2015) Specify Fill Factor for an Index (link)
[17] Technet (2015) Finding Missing Indexes (link)
[18] MSDN Blogs (2010) Notes - SQL Server Index Fragmentation, Types
and Solutions, by Pankaj Mittal (old link)
[19] Wayne W Berry (2010) The DBCC DBREINDEX Command
[20] Wayne W Berry (2010) The DBCC INDEXDEFRAG Command
[21] Microsoft Learn
(2023) SQL Server and Azure SQL index architecture and design guide (link)