1. SQLCAT: Customer Experiences with Data Compression
Sanjay Mishra, Sunil Agarwal, Marcel van der Holst
Microsoft Corporation

2. SQL Server Customer Advisory Team (SQLCAT)
Works on the largest, most complex SQL Server projects worldwide
MySpace million concurrent users at peak time, 8 billion friend relationships, 34 billion s, 1 PetaByte store, scale-out using SSB and SOA
Bwin – Most popular European online gaming site – database transactions / second, motto: “Failure is not an option”; 100 TB total storage
Korea Telecom - Largest telco in Korea serves 26 million customers; 3 TB Data Warehouse
Drives product requirements back into SQL Server from our customers and ISVs
Shares deep technical content with SQL Server community
SQLCAT.com

3. SQL Server Design Win Program
Target the most challenging and innovative SQL Server applications
10+ TB DW, 3k/tran/s OLTP, Large 500GB+ Cubes, Competitive migrations, Complex deployments, Server Consolidation (1000+)
Invest in large scale, referenceable SQL Server projects across the world
Provide SQLCAT technical & project experience
Conduct architecture and design reviews covering performance, operation, scalability and availability
Offer use of HW lab in Redmond with direct access to SQL Server development team
Work with Marketing Team Developing PR

4. SQLCAT and SQL CSS Invite You…
To the SQL Server Clinic where the most experienced SQL Server experts in the world will be waiting to talk with you.
Bring your toughest Questions / Challenges to the experts who have seen it all
Architect and Design your future applications with experts who have done it before with some of the largest, most complex systems in the world
Or just stop in to say hello!
ROOM 611

5. Agenda Introduction What to Compress Planning Data Compression
Performance Impact of Data Compression
How does Data Compression work with other features
Unicode Compression in KJ

6. Data Compression in SQL Server 2008
New in SQL Server 2008 (Enterprise and Developer Edition only)
You CAN NOT restore / attach a database with compressed tables or indexes onto Standard Edition
Types of data compression
ROW Compression
Stores fixed length values as variable length
Row metadata optimized
PAGE Compression
Includes ROW Compression
Column Prefix and Dictionary
Data compressed on disk as well as in memory
Tables and indexes can be compressed independently
Individual partitions can be compressed independently

7. Deciding What to Compress
Estimate space savings
sp_estimate_data_compression_savings
Type of Data
IN_ROW_DATA => compressed
ROW_OVERFLOW_DATA ,LOB_DATA => not compressed
Application Workload
Use ROW Compression for all data if:
ROW compression results in space savings, and
10% increase in CPU usage can be accommodated,
Careful consideration for PAGE compression:
If no CPU headroom, don’t PAGE compress without thorough testing
For a DW applications, if sufficient CPU headroom available, PAGE compress all objects in the database.
For OLTP applications, detail analysis involves % of Update and % of Scan operations
Tables that contain the following patterns of data compress very well:
Columns with numeric or fixed-length character data types where most values don’t require all the allocated bytes: For example, integers where most values are less than 1000
Nullable columns where a significant number of the rows have a NULL value for the column
Significant amounts of repeating data values or repeating prefix values in the data
Some patterns of data that do not benefit much from compression are:
Columns with numeric or fixed-length character data types where most values require all the bytes allocated for the specific data type
Not much repeating data
Repeating data with non-repeating prefixes
Data stored out of the row
FILESTREAM data
Data in IN_ROW_DATA allocation unit is compressed
Data in ROW_OVERFLOW_DATA and LOB_DATA allocation units not compressed

8. Customer Example: An SAP Deployment
Inputs: sp_estimate_data_compression_savings, dm_db_physical_index_usage_stats, SAP knowledge Computed: S=% scans; U=% updates
Table
Size
(GB)
ROW
save %
PAGE
1-row
Read
>1-row
Update
Delete
Insert S U
Plan
Notes
COSP
398
80%
90%
2,797
58,735
886,187
15,747
584,017
3.80%
57.27%
Updates!
GLPCA
123
15%
89%
929,637
16,802
59,020
92.46%
0.0%
Scan mostly
COEP
185
30%
81%
19,036
2,927
48,182
27.14%
4.17%
Light use, but stay low risk
RESB
243
38%
83%
9,837
7,977,629
943,380
1,321
14,877
89.16%
10.54%
#updates
ACCTIT
210
21%
87%
54,580
0.00%
Append only
MSEG
183
28%
3,441,918
24,684,252 28
70,797
87.54%
FAGLFLEXA 98
29%
88%
298
58,882
0.50%
BSIS
148
9,069 67
5,773
64,366
11.44%
0.08%
Append mostly
COSB
150
84%
92% 88
100.00%
ROW ~=PAGE
GLFUNCA 40 6
read-mostly
ROW ~= PAGE => ROW
High Update, Low Scan => ROW
High Scan => PAGE
Append Only => PAGE
Read-only => PAGE
High non-append inserts, some update => ROW
High Update, High Scan => ROW

9. Customer Example: Gomez
Workload: OLTP (3000 inserts/sec) + Reporting
PAGE compressed the 5 largest tables
Some tables have 476 partitions
All partitions PAGE compressed
Size before compression: 7,437 GB
Size after compression: 4,540 GB (~40% savings)
Increase in CPU usage: 6%
No significant change in application performance

10. Planning Compression Estimating Workspace, CPU, I/O
Brand Transformation Presentation
Planning Compression Estimating Workspace, CPU, I/O
Compressing a table or index uses the same mechanism as rebuilding an index
Requires workspace, CPU, and I/O resources
The resource requirements depend upon:
Type of structure: heap, a clustered index, or a nonclustered index
Concurrency : ONLINE = ON or OFF
Recovery model: simple, bulk logged, or full recovery model
SORT_IN_TEMPDB option is ON (recommended) or OFF
When you compress a heap, the non-clustered indexes on the heap are rebuilt (behind the scene, after the compression is done)
One by one, if ONLINE = OFF
Simultaneously, if ONLINE = ON
Account for free space required for rebuilding the non-clustered indexes

11. WorkSpace Requirements General Considerations
Brand Transformation Presentation
WorkSpace Requirements General Considerations
Free workspace is required in:
User database
Transaction Log
TEMPDB
Side effect
When you compress a heap, the non-clustered indexes on the heap are rebuilt (behind the scene, after the heap is compressed)
One by one, if ONLINE = OFF
Simultaneously, if ONLINE = ON
Account for free space required for rebuilding the non-clustered indexes
When you compress a heap, the non-clustered indexes on the heap are rebuilt (behind the scene, after the compression is done)
One by one, if ONLINE = OFF
Simultaneously, if ONLINE = ON

12. Free Space Required For …
Brand Transformation Presentation
Free Space Required For …
The compressed table / index
In the user database
Estimate based on output of sp_estimate_data_compression_savings
Tip: If compressing many tables, compress in the order of smallest to largest
Reduce chances of the need to grow the data file(s)
Mapping Index
Internal structure to map old bookmarks to new bookmarks to support concurrent DML transactions
Only for ONLINE = ON
Only if you are compressing a Heap or a Clustered Index
In TEMPDB, if SORT_IN_TEMPDB = ON, else in the user database
Mapping Index: Typically 1.2 to 1.4 times the size of the compressed table / index
May be bigger if the clustered index is wider.

13. Free Space Required For …
Brand Transformation Presentation
Free Space Required For …
Transaction Log
Depends upon ONLINE = ON / OFF
Depends upon recovery model: FULL / SIMPLE
Version Store
Only if there are concurrent DML operations
Only for ONLINE = ON
In TEMPDB
Size of version store depends upon
Volume of ongoing data modifications
Duration of DML transactions
Transaction Log:
With ONLINE = ON and FULL recovery model, typically 1.1 to 1.4 times the size of the compressed table / index
May be bigger if the clustered index is wider.
Much less With ONLINE = OFF, or under SIMPLE recovery model.

14. Brand Transformation Presentation
CPU Requirements
On average:
ROW compression takes approx. 1.5 times the CPU time used for rebuilding an index
PAGE compression takes approx. 2 to 5 times the CPU time used for rebuilding an index
ONLINE operations require more CPU.
Can be parallelized. Caveats:
Few unique values for the leading column of the index may reduce parallelism
Compressing a heap with ONLINE = ON uses a single CPU for compression (or rebuild)
Transaction Log:
With ONLINE = ON and FULL recovery model, typically 1.1 to 1.4 times the size of the compressed table / index
May be bigger if the clustered index is wider.
Much less With ONLINE = OFF, or under SIMPLE recovery model.

15. Planning Compression How and When to Compress
Online vs. Offline:
Offline is faster and requires less resources, but reduces concurrency
Restrictions of online operations in SQL Server Books Online.
One table, index, or partition at a time vs. many concurrently:
One at a time is recommended in most cases
Multiple compressions at a time may be acceptable, if:
you have sufficient resources, and
You have efficient mechanism to reclaim the unused space after compression
Setting SORT_IN_TEMPDB to ON or OFF:
ON is recommended.
Uses TEMPDB space for the mapping index
Order of compressing the tables:
Matters if available space is at a premium
Start with the smallest in this list
Online vs. Offline: Whether to set the value of ONLINE to ON or OFF depends upon what else is running on the database at the same time. OFF is faster and requires less resources than ON, but the table is locked for the duration of the compression operation. Be aware of the restrictions of online operations as discussed in SQL Server Books Online.
One table, index, or partition at a time vs. many concurrently: Compressing one table, index, or partition at a time is recommended in most cases. When doing multiple simultaneous compressions, workspace, I/O, and CPU requirements outlined earlier must be available for all the compressions together. Be mindful of the risk of insufficient free space and the need to expand data files, as well as the likelihood of large amount of unused space in the data files at the end of compression. If you have sufficient resources (workspace, I/O, and CPU), and have an efficient mechanism to reclaim the unused space (refer to section 6 later in this white paper), executing multiple compressions simultaneously may be acceptable.
Order of compressing the tables: After you have decided on the list of tables and indexes to compress, compress the objects starting with the smallest in this list. Compressing smaller objects requires less workspace, and it releases space to the data files that can be used as workspace for compressing the larger objects subsequently. This approach minimizes the need for additional disk space during the compression process.
Setting SORT_IN_TEMPDB to ON or OFF: ON is recommended. This makes use of tempdb space for the mapping index, and therefore, it requires less workspace in the user database.

16. Reclaiming the Space Released by Data Compression
Brand Transformation Presentation
Reclaiming the Space Released by Data Compression
Option 1: May decide not to reclaim
Use released space for future data growth
Option 2: DBCC SHRINKFILE, but be aware:
Introduces index fragmentation
REORGANIZE (not REBUILD) after SHRINKFILE
ROW_OVERFLOW_DATA does move during CREATE CLUSTERED INDEX with DROP_EXISTING

17. Reclaiming the Space Released by Data Compression
Brand Transformation Presentation
Reclaiming the Space Released by Data Compression
Option 3: If you are compressing all the objects in a filegroup
Create a new filegroup
Move tables / indexes to the new filegroup while compressing
Need to create / recreate clustered index on the table if you want to move it during compression
Use CREATE CLUSTERED INDEX with DROP_EXISTING. Example:
CREATE UNIQUE CLUSTERED INDEX [PK_TRADE] ON [TRADE_BULK]
([T_ID] ASC) WITH (DATA_COMPRESSION=PAGE,
DROP_EXISTING=ON, SORT_IN_TEMPDB=ON, ONLINE=ON) ON [Data]
Caveat: LOB_DATA does NOT move during CREATE CLUSTERED INDEX with DROP_EXISTING.
Remove the old filegroup and its file(s) after it is empty
ROW_OVERFLOW_DATA does move during CREATE CLUSTERED INDEX with DROP_EXISTING

18. Reclaiming the Space Released by Data Compression
Brand Transformation Presentation
Reclaiming the Space Released by Data Compression
Option 4: Create an empty table in the new filegroup, compress it, and then copy the data over to the new table using INSERT … SELECT.
-- Create a new empty table in the new filegroup
ALTER DATABASE [TestDB] MODIFY FILEGROUP FG_COMP DEFAULT;
SELECT * INTO [Tab1] FROM [Tab] WHERE 1 = 2;
-- Compress the newly created empty table
ALTER TABLE [Tab1] REBUILD WITH (DATA_COMPRESSION = PAGE);
-- Create the appropriate indexes on the table
-- Copy the data over to the new table
INSERT INTO [Tab1] WITH (TABLOCK) SELECT * FROM [Tab]
-- The incoming data will be compressed as it gets inserted
-- TABLOCK is required if the target table is a heap
-- Trace flag 610 may help enable minimal logging
-- The LOB_DATA allocation unit will also be copied
-- Drop the old table and rename the new table as old table
-- After all the tables are copied like this, remove the old filegroup
If you have LOB data, you can’t move the LOB_DATA allocation unit while compressing the data.
Work around 1:
alter database [HELP] modify filegroup FG_COMP default
DROP TABLE [dbo].[test_help_2]
select * into [test_help_2] from [test_help] where 1 = 2
alter table [test_help_2] rebuild with (data_compression = page)
insert into [test_help_2] select * from [test_help]
Work around 2:
Use DBCC SHRINKFILE to shrink the file after moving the IN_ROW_DATA to new filegroup. Fragmentation OK with file containing only LOB_DATA.
ROW_OVERFLOW_DATA does move during CREATE CLUSTERED INDEX with DROP_EXISTING

19. Data Compression Performance and Resource Considerations
Compressed Data
More data in less pages
Less pages to read from disk
Less Logical I/Os, Less Physical I/Os
CPU savings from reduced Logical I/O
I/O-intensive workload will benefit performance gain
At the cost of extra CPU for compression / de-compression
Some workload may not gain performance
Doesn’t significantly reduce I/O for workloads dominated by singleton operations
Some workload performance presented here:
Bulk Load
Index Rebuild

20. BULK INSERT with Data Compression
To get PAGE compression while performing BULK INSERT on a heap, make sure to use TABLOCK
See next slide if you create a clustered index soon after the BULK INSERT

21. BULK INSERT and CREATE INDEX Choices
BULK INSERT on uncompressed heap, followed by CREATE CLUSTERED INDEX WITH (DATA_COMPRESSION = PAGE)
BULK INSERT on PAGE compressed heap, followed by CREATE CLUSTERED INDEX
PAGE compress CLUSTERED INDEX, followed by BULK INSERT

22. Index Rebuild with Data Compression
Rebuilding compressed indexes takes longer and consume more CPU, especially for PAGE compression.
SORT_IN_TEMPDB = ON, ONLINE = OFF

23. What Happens to New Rows
Table Organization
Table Compression Setting
ROW
PAGE
Heap
PAGE compressed:
If new row goes to an existing page with PAGE compression
If BULK INSERT with TABLOCK
If INSERT INTO ... (TABLOCK) SELECT ... FROM
Otherwise, the row is ROW compressed
Clustered Index
PAGE compressed, if new row goes to an existing page with PAGE compression
Otherwise, ROW compressed until page split. PAGE compression attempted before a page split
Two internal thresholds to be aware of:
A page is PAGE compressed only if the space savings exceed an internally defined threshold
When the number of DML (INSERT / UPDATE / DELETE) operations on a given page exceeds an internally defined threshold, the column prefix and page dictionary may get re-computed

24. Data Compression and Partition Manipulation
Brand Transformation Presentation
Data Compression and Partition Manipulation
Switch
CAN NOT switch if the source and target have different compression property
Split
New partition inherits the data compression property of the partition being split
Merge
Resultant partition inherits the data compression property of the destination partition
Whether the data in the resultant partition will be PAGE compressed or not, depends upon if you have a Heap or Clustered Index

25. Merging Partitions with Data Compression
Brand Transformation Presentation
Merging Partitions with Data Compression
Compressed
UnCompressed
UnCompressed
Compressed
CREATE PARTITION FUNCTION T_PF_LEFT(INT) AS RANGE LEFT FOR VALUES (1,2)
< = 1
> 1, < = 2
> 2
ALTER PARTITION FUNCTION T_PF_LEFT() MERGE RANGE (1) 2
< = 2
> 2
CREATE PARTITION FUNCTION T_PF_RIGHT(INT) AS RANGE RIGHT FOR VALUES(1,2)
Resultant partition inherits the data compression attribute of the destination partition
The colors can be switched and the above will hold true.
< 1
>= 1, < 2
> = 2
ALTER PARTITION FUNCTION T_PF_RIGHT() MERGE RANGE (1) 2
< 2
>= 2

26. Data Compression and TDE
TDE has negligible, if any, impact on data compression
TDE encrypts the pages when they are written to disk and decrypts them when they are read from disk into memory

27. Data Compression and Replication
Replication provides lots of flexibility
Replication can work between multiple SQL Server versions
Replication can work between Standard and Enterprise editions
Data compression respects replication’s flexibility
You can compress tables/indexes in the publisher and/or the subscriber as per your need
If the publisher is compressed, and you don’t want the subscriber to be compressed (or the subscriber doesn’t support compression),
When generating the initial schema through the snapshot agent, don’t enable 0x4 option
Don’t enable replication of compression in sp_addarticle
@schema_option parameter, 0x = replication of compression is enabled
If the publisher is NOT compressed, and you WANT the subscriber to be compressed,
Enable compression on the subscriber after establishing replication as per your convenience

28. Data Compression Enhancements in SQL Server 2008 R2

29. SQL Server 2008 R2 Unicode Storage Challenge
SQL Server uses UCS-2 encoding scheme for storing Unicode data
NCHAR and NVARCHAR data always takes 2 bytes of storage per character
Waste of 1 byte per character for commonly deployed locales (e.g. ASCII)

30. SQL Server 2008 R2 Unicode Solution
Use Standard Compression Scheme for Unicode (SCSU) technique
No application change needed
Compression Achieved
Locale
SCSU (SQL 2008 R2)
UTF-8
English
0.5
Japanese
.85
1.0
Korean
Turkish
.52
.53
German .5
Vietnamese
0.61
0.68
Hindi
SCSU => Standard Compression Scheme for Unicode

31. SCSU Compression vs UTF-8
SCSU compression not the same as UTF-8
Why not do UTF-8 encoding
Requires application change
UTF-8 optimized for ASCII. SCSU gives better compression for non-ASCII languages

32. Example: Unicode Compression
Create Table SQLCOMP (Name NVARCHAR(20))
HEADER
0x53514C “SQL”
Col-prefix
0x C “SQLSERVER”
0x53514C “SQLSERVER”
0x “?SERVER”
Talking Points
Show the row data before Unicode compression
Enable ROW compression and you get size reduction by 9 bytes (not 10). We need to make length odd to indicate it is Unicode compressed
Enable PAGE compression. Show the column prefix and also show that Unicode compression works with PAGE compression except that the relative savings (compared to PAGE compression in SQL2008RTM) will be less because we already got some savings with Unicode.
0x C E E0045 “SQLENGINE”
0x53514C454E47494E45 “SQLENGINE”
0x 03454E47494E45 “?ENGINE”
0x C004C004F “SQLLOADERS”
0x53514C4C4F “SQLLOADERS”
0x034C4F “?LOADERS”
PAGE COMPRESSION
ROW COMPRESSION

33. SQL Server 2008 R2: Enabling Unicode Compression
In SQL Server 2008 R2, Unicode compression is included in ROW compression
Supported types NVARCHAR and NCHAR
NVARCHAR(max) and NCHAR(max) not supported
NTEXT type not supported
Estimate space savings
sp_estimate_data_compression_savings
SQL2008R2: Can be used to estimate space savings due to data defragmentation.
Talk about changes in SQL2008R2
If you are already ROW compressed on SQL Server 2008, you can use this SP to estimate how much additional space you can save using Unicode compression in SQL Server 2008 R2.

34. SQL Server 2008R2: Unicode Compression Some Customer Results
Application
ROW Compression (2008)
ROW Compression (2008 R2)
SAP ERP Benchmark DB 9%
43%
Dynamics AX
30%
53.2%
Siebel
45%
64%
Customer
Projected Storage Cost Reduction
Microsoft ( MSIT/SAP)
$500 K
(1) ERP benchmark databases typically deliver lower compression than production because there are more fields that are empty.

35. Data Compression and Upgrade from SQL Server 2008 R2 to SQL Server 2008
Scenarios
ROW compression enabled in SQL Server 2008
No database changes when upgraded
Unicode value compressed only if it saves space. It happens when
An existing value is updated
A new row is inserted
Index is rebuilt with ROW or PAGE compression
PAGE compression enabled in SQL Server 2008
Same as with ROW compression
No changes needed to existing scripts and DDL

36. Key Takeaways
Space savings from data compression depends upon the data
Estimate space savings prior to compressing
Impact on performance and resource utilization depends upon the workload
Test with production-like workload to estimate impact on performance and resource utilization
General Guidelines:
Very minimal CPU increase with ROW compression (usually less than 5-10%)
If ROW compression results in space savings, and 10% increase in CPU usage can be accommodated, all data should use ROW compression
Careful consideration for PAGE compression:
If no CPU headroom, don’t PAGE compress without thorough testing
If CPU headroom available,
For Data Warehouse workload, PAGE compress all objects in the database
For OLTP workload, PAGE compress tables and indexes mostly involved in scan operations, and less involved in update operations
If using datatypes NCHAR and NVARCHAR, Unicode compression in SQL Server 2008 R2 can benefit significantly

37. Further Reading

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41. Appendix

42. Row Compression Variable Length Encoding (new row format)
Special Handling of NULL and 0s
4-byte integer
SQL Server 2005 1
4 bytes
SQL Server (Compression enabled) 1

43. Page Compression Column Prefix
9/18/2018
Page Compression Column Prefix
Factor out common prefix from the same column across all rows on the page
Store these ‘prefix’ values once and then reference them from respective columns
Example:
Student( Name VARCHAR (10),
ID INT,
CODE VARBINARY(3))
Emphasize
column prefix is not limited to character data. It could be any bytes. It is type agnostic.
the prefix does not need to appear in its entirety in every column
© 2008 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

44. Page Compression Column Prefix
TechReady7 Breakout Chalktalk Template
9/18/2018
Page Compression Column Prefix
Page Header
Lambert
NULL
Decimal
is HEX
0x4C4840
Lamb 4
0x5B8D80
Lambei 5i
20x41
0x41AABB
Lambert
20x42
0x5CAABB
Lee
20x43
0x9A4041
Lee
0x112233
© 2008 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

45. Page Compression Page Dictionary
Find repeating values across all columns on all the rows on the page, irrespective of data type
Store these values in a dictionary once, and reference them from respective columns

46. Page Compression Page Dictionary
TechReady7 Breakout Chalktalk Template
Page Compression Page Dictionary
9/18/2018
Page Header
Lambert
NULL
Column prefix
Lee
0x5B8D80
Page Dictionary 4 1
0x5B8D80
Decimal
is HEX
0x5B8D80 5i
20x41
0x41AABB
This is maintained per page
20x42
0x5CAABB
Lee
20x43
0x9A4041
Lee
600000 1
0x112233
© 2008 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

47. Free Space in Database Files
--Provided AS IS, without warranty of any kind SELECT a.file_id, LOGICAL_NAME = a.name, PHYSICAL_FILENAME = a.physical_name, FILEGROUP_NAME = b.name, FILE_SIZE_MB = CONVERT(DECIMAL(12,2),ROUND(a.size/ ,2)), SPACE_USED_MB = CONVERT(DECIMAL(12,2),ROUND(FILEPROPERTY(a.name,'SpaceUsed')/ ,2)), FREE_SPACE_MB = CONVERT(DECIMAL(12,2),ROUND((a.size-FILEPROPERTY(a.name,'SpaceUsed'))/ ,2)) FROM sys.database_files a LEFT OUTER JOIN sys.data_spaces b ON a.data_space_id = b.data_space_id

48. Data Volume in Each Allocation Unit Type
-- Provided AS IS, without warranty of any kind SELECT OBJECT_NAME(p.object_id) AS Object_Name , i.name AS Index_Name , ps.in_row_used_page_count AS IN_ROW_DATA , ps.row_overflow_used_page_count AS ROW_OVERFLOW_DATA , ps.lob_used_page_count AS LOB_DATA FROM sys.dm_db_partition_stats ps JOIN sys.partitions p ON ps.partition_id = p.partition_id JOIN sys.indexes i ON p.index_id = i.index_id AND p.object_id = i.object_id WHERE OBJECTPROPERTY (p.[object_id], 'IsUserTable') = 1

49. Percent of Update Operations on the Object
SELECT o.name AS [Table_Name], x.name AS [Index_Name], i.partition_number AS [Partition], i.index_id AS [Index_ID], x.type_desc AS [Index_Type], i.leaf_update_count * / (i.range_scan_count + i.leaf_insert_count + i.leaf_delete_count + i.leaf_update_count + i.leaf_page_merge_count + i.singleton_lookup_count ) AS [Percent_Update] FROM sys.dm_db_index_operational_stats (db_id(), NULL, NULL, NULL) i JOIN sys.objects o ON o.object_id = i.object_id JOIN sys.indexes x ON x.object_id = i.object_id AND x.index_id = i.index_id WHERE (i.range_scan_count + i.leaf_insert_count + i.leaf_delete_count + leaf_update_count + i.leaf_page_merge_count + i.singleton_lookup_count) != 0 AND objectproperty(i.object_id,'IsUserTable') = 1 ORDER BY [Percent_Update] ASC

50. Percent of Scan Operations on the Object
SELECT o.name AS [Table_Name], x.name AS [Index_Name], i.partition_number AS [Partition], i.index_id AS [Index_ID], x.type_desc AS [Index_Type], i.range_scan_count * / (i.range_scan_count + i.leaf_insert_count + i.leaf_delete_count + i.leaf_update_count + i.leaf_page_merge_count + i.singleton_lookup_count ) AS [Percent_Scan] FROM sys.dm_db_index_operational_stats (db_id(), NULL, NULL, NULL) i JOIN sys.objects o ON o.object_id = i.object_id JOIN sys.indexes x ON x.object_id = i.object_id AND x.index_id = i.index_id WHERE (i.range_scan_count + i.leaf_insert_count + i.leaf_delete_count + leaf_update_count + i.leaf_page_merge_count + i.singleton_lookup_count) != 0 AND objectproperty(i.object_id,'IsUserTable') = 1 ORDER BY [Percent_Scan] DESC