Lecture 9

Ahsan AbdullahAhsan Abdullah
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Data WarehousingData Warehousing
Lecture-9Lecture-9
Issues of De-normalizationIssues of De-normalization
Virtual University of PakistanVirtual University of Pakistan
Ahsan Abdullah
Assoc. Prof. & Head
Center for Agro-Informatics Research
www.nu.edu.pk/cairindex.asp
National University of Computers & Emerging Sciences, Islamabad
Email: ahsan@cluxing.com

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Issues of De-normalizationIssues of De-normalization

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Why Issues?Why Issues?

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Issues of DenormalizationIssues of Denormalization
 StorageStorage
 PerformancePerformance
 Ease-of-useEase-of-use
 MaintenanceMaintenance

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Industry CharacteristicsIndustry Characteristics
Master:Detail RatiosMaster:Detail Ratios
 Health care 1:2 ratioHealth care 1:2 ratio
 Video Rental 1:3 ratioVideo Rental 1:3 ratio
 Retail 1:30 ratioRetail 1:30 ratio

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Storage Issues: Pre-joining FactsStorage Issues: Pre-joining Facts
 Assume 1:2 record count ratio between claimAssume 1:2 record count ratio between claim
master and detail for health-care application.master and detail for health-care application.
 Assume 10 million members (20 millionAssume 10 million members (20 million
records in claim detail).records in claim detail).
 Assume 10 byte member_ID.Assume 10 byte member_ID.
 Assume 40 byte header for master and 60Assume 40 byte header for master and 60
byte header for detail tables.byte header for detail tables.

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With normalization:With normalization:
Total space used = 10 x 40 + 20 x 60 = 1.6 GBTotal space used = 10 x 40 + 20 x 60 = 1.6 GB
After denormalization:After denormalization:
Total space used = (60 + 40 – 10) x 20 = 1.8 GBTotal space used = (60 + 40 – 10) x 20 = 1.8 GB
Net result is 12.5% additional space requiredNet result is 12.5% additional space required
in raw data table size for the database.in raw data table size for the database.
Storage Issues: Pre-joining (Calculations)Storage Issues: Pre-joining (Calculations)

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Consider the queryConsider the query “How many members“How many members
were paid claims during last year?”were paid claims during last year?”
With normalization:With normalization:
Simply count the number of records in the masterSimply count the number of records in the master
table.table.
After denormalization:After denormalization:
The member_ID would be repeated, hence need aThe member_ID would be repeated, hence need a
count distinct. This will cause sorting on a largercount distinct. This will cause sorting on a larger
table and degraded performance.table and degraded performance.
Performance Issues: Pre-joiningPerformance Issues: Pre-joining

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Depending on the query, the performanceDepending on the query, the performance
actually deteriorates with denormalization!actually deteriorates with denormalization!
This is due to the following three reasons:This is due to the following three reasons:
 Forcing a sort due to count distinct.Forcing a sort due to count distinct.
 Using a table with 1.5 times header size.Using a table with 1.5 times header size.
 Using a table which is 2 times larger.Using a table which is 2 times larger.
 Resulting in 3 times degradation inResulting in 3 times degradation in
performance.performance.
Bottom Line: Other than 0.2 GB additionalBottom Line: Other than 0.2 GB additional
space, also keep the 0.4 GB master table.space, also keep the 0.4 GB master table.
Why Performance Issues: Pre-joiningWhy Performance Issues: Pre-joining

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Continuing with the previous Health-CareContinuing with the previous Health-Care
example, assuming a 60 byte detail table andexample, assuming a 60 byte detail table and
10 byte Sale_Person.10 byte Sale_Person.
 Copying the Sale_Person to the detail tableCopying the Sale_Person to the detail table
results in all scans taking 16% longer thanresults in all scans taking 16% longer than
previously.previously.
 Justifiable only if significant portion of queries getJustifiable only if significant portion of queries get
benefit by accessing the denormalized detailbenefit by accessing the denormalized detail
table.table.
 Need to look at the cost-benefit trade-off for eachNeed to look at the cost-benefit trade-off for each
denormalization decision.denormalization decision.
Performance Issues: Adding redundant columnsPerformance Issues: Adding redundant columns

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Other issues include, increase in table size,
maintenance and loss of information:
 The size of the (largest table i.e.) transaction table
increases by the size of the Sale_Person key.
 For the example being considered, the detail table size
increases from 1.2 GB to 1.32 GB.
 If the Sale_Person key changes (e.g. new 12 digit
NID), then updates to be reflected all the way to
transaction table.
 In the absence of 1:M relationship, column movement
will actually result in loss of data.
Other Issues: Adding redundant columnsOther Issues: Adding redundant columns

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Horizontal splitting is a Divide&Conquer techniqueHorizontal splitting is a Divide&Conquer technique
that exploits parallelism. The conquer part of thethat exploits parallelism. The conquer part of the
technique is about combining the results.technique is about combining the results.
Lets see how it works forLets see how it works for hash basedhash based
splitting/partitioning.splitting/partitioning.
 Assuming uniform hashing, hash splitting supportsAssuming uniform hashing, hash splitting supports
even data distribution across all partitions in a pre-even data distribution across all partitions in a pre-
defined manner.defined manner.
 However, hash based splitting is not easilyHowever, hash based splitting is not easily
reversible to eliminate the split.reversible to eliminate the split.
Ease of use Issues: Horizontal SplittingEase of use Issues: Horizontal Splitting

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?

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Round robin and random splitting:Round robin and random splitting:
Guarantee good data distribution.Guarantee good data distribution.
Almost impossible to reverse (or undo).Almost impossible to reverse (or undo).
Not pre-defined.Not pre-defined.

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Range and expression splitting:Range and expression splitting:
Can facilitate partition elimination with aCan facilitate partition elimination with a
smart optimizer.smart optimizer.
Generally lead to "hot spots” (unevenGenerally lead to "hot spots” (uneven
distribution of data).distribution of data).

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Performance Issues: Horizontal SplittingPerformance Issues: Horizontal Splitting
P1 P2 P3 P4
1998 1999 2000 2001
Dramatic cancellation
of airline reservations
after 9/11, resulting in
“hot spot”
Splitting based on year
Processors

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Performance issues: Vertical Splitting FactsPerformance issues: Vertical Splitting Facts
Example:Example: Consider a 100 byte header for theConsider a 100 byte header for the
member table such that 20 bytes providemember table such that 20 bytes provide
complete coverage for 90% of the queries.complete coverage for 90% of the queries.
Split the member table into two parts as follows:Split the member table into two parts as follows:
1.1. Frequently accessed portion of table (20 bytes),Frequently accessed portion of table (20 bytes),
andand
2. Infrequently accessed portion of table (80+2. Infrequently accessed portion of table (80+
bytes).bytes). Why 80+?Why 80+?
Note that primary key (member_id) must beNote that primary key (member_id) must be
present in both tables for eliminating the split.present in both tables for eliminating the split.

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Scanning the claim table for mostScanning the claim table for most
frequently used queries will be 500% fasterfrequently used queries will be 500% faster
with vertical splittingwith vertical splitting
IronicallyIronically, for the “infrequently” accessed, for the “infrequently” accessed
queries the performance will be inferior asqueries the performance will be inferior as
compared to the un-split table because ofcompared to the un-split table because of
the join overhead.the join overhead.
Performance issues: Vertical Splitting Good vs. BadPerformance issues: Vertical Splitting Good vs. Bad

Lecture 9

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