In-Memory features is the most perspective trend in the area of high performance. Columnstore Indexes is one of such features, and even with their restrictions, they can accelerate your queries at times! How to get more from this feature? In which situations should we use them? Which internal mechanisms help to achive that? You can get answers on these questions on this session.

1. Columnstore Indexes
Deep introduction into columnar storage and
indexes in SQL Server 2012
Denis Reznik

2. Sponsors

3. About me
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Denis Reznik
Kiev, Ukraine
Database Architect at The Frayman Group
Microsoft MVP
Community enthusiast

4. Agenda
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Columnar storage
Creation of Columnstore index
Usage scenarios and limitations
Performance accelerators
 Columnstore Storage internals
 Columnstore Execution mode internals
 Columnstore index maintenance
 Columnstore Future (actually Present :)
4 |

5. Row Store and Column Store
 In row store, data is stored tuple by tuple.
 In column store, data is stored column by
column

6. Row Store and Column Store

Most of the queries does not process all the attributes of a particular
relation.
nam address
e
id
SELECT c.Name, c.Address
FROM Customers c
WHERE c.City = 'Sofia'
city
state
age

7. Creating a columnstore index
T-SQL
SSMS

8. Usage scenarios and limitations
 Primary focus of Columnstore Indexes is DW
databases
 In SQL Server 2012 Columnstore Indexes
are read-only
 Supported operators and data types are
limited

9. DEMO
 Incredible Performance of Columnstore
Indexes

10. How Are These Performance Gains
Achieved?
 Two complimentary technologies:
 Storage
 Data is stored in a compressed columnar data format (stored
by column) instead of row store format (stored by row).
 New “batch mode” execution
 Vector-based query execution capability
 Data can then be processed in batches versus row-by-row
 Depending on filtering and other factors, a query may also
benefit by “segment elimination” - bypassing million row
chunks (segments) of data, further reducing I/O

11. Compression
 Patented VERTIPAQ algorithms
 So, there is no public information about how the
data actually compressed
 But some info we have 
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Dictionary encoding
Run Length encoding
Bit-Vector encoding
…

12. DEMO
 Columnstore Indexes Internals

13. Columnar storage structure
Row store:
…
C1
Column store:
C2
C3
C4
C5
C6
Pages

14. Column Segments and Dictionaries
segment 1
C1
C2
C3
C4
C5
C6
Set of
about 1M
rows
…
segment N
Column
Segment
dictionaries

15. DEMO
 Columnstore Indexes – Segments and
Dictionaries

16. Memory management
• Memory management is automatic
• Columnstore is persisted on disk
• Needed columns fetched into memory
• Columnstore segments is a unit of data between disk and memory
T.C
T.C
T.C T.C
4
1
3
T.C 2
T.C
1
T.C
T.C
T.C T.C 3
4
1
3
T.C 2
T.C
1
T.C
T.C 3
T.C
T.C 3
1
4
2
SELECT C2, SUM(C4)
FROM T
GROUP BY C2;
T.C
2
T.C
2
T.C
4
T.C
4

17. Batch mode processing
Batch object
bitmap of qualifying rows
Column vectors
 Process ~1000 rows at a
time
 Vector operators
implemented
 Greatly reduced CPU time
(7 to 40X)

18. Segment Elimination
• Segment (rowgroup) = 1 million row chunk
• Min, Max kept for each column in a segment
• Scans can skip segments based on this info
column_i
d
segment_i
d
min_data_i
d
max_data_id
1
1
20120101
20120131
1
2
20120115
20120215
1
3
20120201
20120228
skipped
select Date, count(*)
from dbo.Purchase
where Date >= '20120201'
group by Date

19. DEMO
 Segment Elimination

20. Maintaining Data in a Columnstore Index
 Once built, the table becomes “read-only”
and INSERT/UPDATE/DELETE/MERGE is
no longer allowed
 ALTER INDEX REBUILD / REORGANIZE not
allowed
 How can I modify index data?
 Drop columnstore index / make modifications /
add columnstore index
 UNION ALL (but be sure to validate performance)
 Partition switches (IN and OUT)

21. Columnstore Index Future
 Actually it is already become 
 Columnstore indexes can be clustered (in
SQL server 2014)
 Clustered Columnstore indexes can be
updatable (in SQL Server 2014)
 Update data (deltas) store in rowstore until
segment can be created

22. Summary
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Columnar storage
Columnstore Performance Demo
Creation of Columnstore index
Usage scenarios and limitations
Performance accelerators
 Columnstore Storage internals
 Columnstore Execution mode internals
 Columnstore index maintanance
 Columnstore Future (actually Present :)
22 |

23. Sponsors

24. Thank you!
 Denis Reznik
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Twitter: @denisreznik
Email: denisreznik@live.ru
Blog (in russian): http://reznik.uneta.com.ua
Facebook: https://www.facebook.com/denis.reznik.5
LinkedIn: http://ua.linkedin.com/pub/denis-reznik/3/502/234