Performance Tuning Oracle's BI Applications

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Jeff McQuigg | Senior BI Architect
April 24, 2013
Performance
Tuning Oracle’s
BI Applications

①Introducing The Performance Layer
②Building The Performance Layer
③Mapping Into Oracle BI
④Implementation Considerations
⑤Q&A
Agenda
Performance Tuning Oracle’s BI Applications 3

Introducing The
Performance Layer
4

Targeted At Organizations
Who Have:
Large Data Volumes
Custom Tables & Data Sets
Aggressive Performance Targets
Questionable Design Extensions
Slow Hardware
5
Introducing The Performance Layer
Performance Tuning Oracle’s BI Applications

These performance concepts
are applicable to any BI system
Custom Built Systems
Custom Stars in OBIA
SQL Server
6

Wider tables slow you down
Dashboards only need a few tables
Smaller is faster
7

Eliminate conflicting priorities
Singular focus on performance
Peak performance starts with design
8

Great performance requires perfect design for how it is used
Mandate a top-down approach to tuning your BI application
Specialized design for specialized usage
9

Pre-built logic
Clean star models
Reduced data weight
Tables which match usage by Oracle BI
10
Top-Down design yields…

11Performance Tuning Oracle’s BI Applications
Introducing The Performance Layer >> Oracle’s View On Data Warehouse Architecture

Keep the BI Apps model
mostly as-is & add a
performance layer…
① Source data from BI Apps tables
② Bring only what you need
③ Denormalizations & pre-calculations
④ Use all database performance tools
*Optimize To Usage*
12
Performance
Mini-Fridge
BIAppsDataFridge
ETL

① The Performance Layer is industry
standard architecture
② Design is driven only by report
performance improvement
③ Travel light
④ No need to alter BI Apps or DW
Introducing The Performance Layer - Takeaways

Building The
Performance Layer
14

① Start with priority areas (select a Fact table)
② Identify the use cases (reports w/ prompts & data security)
③ Analyze resulting physical SQL
④ Try to tune the BI Apps model first!
15
Building The Performance Layer

⑤ Prototype a new data model to match those
needs
⑥ Adjust SQL & benchmark (SQL handcrafting needed)
⑦ Map into Oracle BI & test (Unit & Regression)
⑧ Benchmark the Oracle BI report using
prototyped tables
16

9. Build the tables using INFA & DAC - Complete
Oracle BI RPD mapping
10. Formal Regression Test
11. Deploy
12. Enjoy praise from users
17

Reduce I/O with extreme prejudice
• Tune the BI Apps model first! It may work for you with low effort
• Employ techniques to eliminate I/O wherever possible
• Partition Elimination, Compression, Indexes, Aggregates, Star Transformations
• Let the Performance Layer do the work, not the report query
• Follow the KISS principle: Use a simple and clean Star. No
Snowflakes!
• Ensure OBI is mapped properly and uses correct tables with perfect
SQL
• Favor a general approach as opposed to a case-by-case approach
• A rising tide lifts all boats
18

 There are 4 kinds of tables in the Performance Layer:
1. Skinny Dimension and Fact tables
2. New Dimension tables
3. Mini-Dimension tables
4. Fact Aggregate tables
 Built directly from the base BI Apps or DW tables
 Goal: use these tables in as many reports as possible (80/20 rule)
 Guiding principles and performance influences:
1. Application use cases drive the layer’s design
2. Use minimal data for the job at hand
3. Aggregate Fact data when needed
4. Denormalize dimensions to eliminate extra joins
5. Pre-Build calculations to eliminate extra joins
6. Pre-Split data sets based on logical usage
19
BI Apps or
DW Perf. Layer

20
① Single column, local bitmap
indexes on all Fact table FKs
(_WIDs) and filter fields
(DELETE_FLG)
② Single column bitmap indexes
on all dimensional fields used in
any sort of prompt or report filter
③ Special composite B-Tree
indexes to assist Snowflaked
areas
④ Composite B-Tree indexes on
large dimensions for join backs
(for list reports)
Query Indexing (4 Types)
 For all Fact tables of a reasonable
size (e.g., > 5M rows)
 Usually partition on Month (Range
or Interval)
 The Database can easily
eliminate the majority of the table
 Allows for smaller, local indexes
Table Partitioning
Before Beginning:
Tune the OOTB Model

 Skinny Tables are highly selective versions of the BI Apps or DW
tables
• “Horizontal Aggregation” – use only 10 columns vs. 100 from the base table
 Both Dimensions and Facts
 Very easy to build and use
 Goal: Reduce Avg. Row Length to 1/5th - 1/20th original size
 Include only the columns you will need for top-down reporting
analysis
• If you don’t need Customer Address, don’t include it
• Ignore Meta Data columns (e.g., INTEGRATION_ID, etc.)
 Row sets are identical (1:1) with the base tables
• For Dimensions use the same ROW_WIDs - can be used with existing fact tables easily
21

 Build using:
1. Create Table as Select (CTAS)
2. Insert /*+ APPEND */
3. Materialized Views
 Compress the table
 Use Parallel hints & options
 Don’t forget partitions
 Enhance the tables with
calculation logic
 Database is very fast at these
operations – expect only a few
minutes for 100M rows
22
CREATE TABLE WC_ACCT_BUDGET_SF
COMPRESS NOLOGGING PARALLEL (DEGREE 8)
PARTITION BY RANGE(PERIOD_END_DT_WID)
INTERVAL(NUMTOYMINTERVAL(1, 'MONTH'))
(PARTITION Part_01 VALUES LESS THAN
(20100101))
AS SELECT /*+ PARALLEL(F,8) */
F.PERIOD_END_DT_WID,
F.X_PERIOD_END_DT_WID,
F.COMPANY_ORG_WID,
F.GL_ACCOUNT_WID,
F.X_POSTED_TOTAL_AMT,
case when GL_D."GL_ACCOUNT_NUM" =
'S250' then F."X_POSTED_TOTAL_AMT" end
as PLAN_CASES,
FROM W_ACCT_BUDGET_F F,
W_GL_ACCOUNT_D GL_D
WHERE F.GL_ACCOUNT_WID = GL_D.ROW_WID;

 Enhance _SF tables with logic
 Identify CASE WHEN statements
which require other dimensions
• Potential great benefit if the join can be
eliminated
• Don’t over do it – table will get less skinny
with each column
 Identify any data set splitting from
the RPD
• HR Workforce Events table has both
Events and Snapshots records but they
are always used separately in the RPD
• Usage drives design: Split them out!
• Huge benefit for Event counting metrics
(~10% of table)
23
case when
GL_D."GL_ACCOUNT_NUM" = 'S250'
then F."X_POSTED_TOTAL_AMT" end
as PLAN_CASES,
FROM W_ACCT_BUDGET_F F,
W_GL_ACCOUNT_D GL_D
WHERE F.GL_ACCOUNT_WID =
GL_D.ROW_WID;
Create table
WC_WRKFC_EVT_EVENTS_SF …
… WHERE SNAPSHOT_IND = 0
Create table
WC_WRKFC_EVT_MONTH_SNP_SF …
… WHERE SNAPSHOT_IND = 1

24
Real Examples I/O Benefit
Sub Ledger
(custom)
24X
Workforce Snap 11X
Workforce Events 56X
GL Balance 21X
Acct Budget 32X
Typical to get a 10X to 20X and even
50X I/O benefit in the _SF vs. the base
_F in size
 Reduced AVG_ROW_LEN
 COMPRESSION
 Record Set Splitting
 All without any aggregation
Skinny Dimensions also have benefits:
1. All I/O is a killer and slows down the entire system
2. De-normalize into a Star (eliminate snowflakes & outer joins)
3. Real World Ex, #1: Swap out 2 wide dims for 2 skinny dims improved query
time by 6X
4. Real World Ex. #2: One query going from 11s to 4s with one skinny dim
5. Real World Ex. #3: GL Account Dimension: 37X I/O benefit

 Pre-build major pieces of commonly used but complex logic into the
Data Model
• Over-relying on the RPD or Reports for logic can harm performance
• Let the ETL for the Performance Layer do the work not the query
 Example #1: Large binning and bucketing
CASE WHEN FACT.ORDER_AMT BETWEEN 0 and 100 THEN ‘0-100’ ELSE CASE WHEN FACT.ORDER_AMT
BETWEEN 101 and 200 THEN ‘101-200’ … END
• Build a new dimension table to hold these values –
WC_CUST_ORDER_QTY_BAND_D
 Example #2: Date format conversions – dynamically building a new
column with a string concatenation statement:
substring(T66755."PER_NAME_MONTH" , 1, 4) , '') + '-' +
isnull(right(T66755."PER_NAME_MONTH" , 2) , '')
• Build a new column in the W_DAY_D table & index it
25

 Simply a higher level or levels of
a larger dimension
• A combination of several Kimball
concepts
• Granularities will be mixed
 Make a new table from the
large, base dimension
• Contains distinct combinations
• Use only commonly used fields
• Get cues from dashboard
prompts, column selectors, report
filters
 Create a new ROW_WID
 Compress and index as normal,
Parallel if needed for creation
 Easy to build and map
26
Create table WC_EMPLOYEE_MD
COMPRESS as
select
ROWNUM AS ROW_WID,
W_ETHNIC_GRP_DESC,
WC_RACE_ETHNIC_DIVRSE_GRP_DESC,
W_SEX_MF_CODE, W_SEX_MF_DESC,
WC_NON_EMPLOYEE_VENDOR_NAME
from (
select distinct
W_ETHNIC_GRP_DESC,
WC_RACE_ETHNIC_DIVRSE_GRP_DESC,
W_SEX_MF_CODE,
W_SEX_MF_DESC,
WC_NON_EMPLOYEE_VENDOR_NAME
from W_EMPLOYEE_D);
This real world example created 5,400
records from a W_EMPLOYEE_D of 9+
Million rows.

_MD tables are used in the Performance Layer in two places:
1. Link into Skinny Facts
• Use a separate FK in addition to the base _WID
• Fact table has both EMPLOYEE_WID and EMPLOYEE_MD_WID
 Thus the _SF can join to all of the following:
• New Mini Dimension (_MD) (~1% rows, some columns)
• New Skinny Dimension (_SD) (100% rows, some columns)
• Base BI Apps/DW Dimension (_D) (100% rows, 100% columns)
 The OBI RPD can select which one is best for each query
 Benefits of linking into the _SF
1. The same set of fact rows are selected – no benefit
2. Reduced dimension I/O, CPU and buffer space
3. Faster join-back on list reports
4. Very fast prompts, especially when constrained
27
_D
_SD
_MD
Conceptual
Size
Difference
s

2. Use them for very high level Fact Aggregates
• Build a Fact aggregate at the Mini-Dimension level
 Allows greater field inclusion at no expense to
aggregation compression ratios
• Multiple fields are available - not just one
 A good Mini Dimension and Skinny Fact/Aggregate
can serve a large % of dashboard queries
 MD’s & Fact Aggregates offer extreme performance:
1. Real World Ex #1: From time-out after 10 minutes to 4 seconds
2. Real World Ex #2: GL Account MD: 131X I/O benefit
28

 Aggregates are used when summary reports exist
 Pre-aggregate the dataset to make it smaller & faster
 Sometimes they are the only solution
 Typically a minimum of a 10:1 ratio is used
 Use all database tools as with any fact table
• Partitioning, Indexing, Star Transformations, Compression
 For extreme needs, consider merging facts together
• Ex: Monthly Actuals and Budgets
• Be mindful of gaps in datasets and non-conformed dimensions
 Advanced implementations use partition management to
build only changed data for faster load times
29

① Building the underlying tables is relatively simple
② Take cues from dashboard, report & RPD configuration
③ Any savings in I/O helps the overall system
④ Use all of the available database performance tools
⑤ Tremendous benefits are possible with a few tables
Building The Performance Layer- Takeaways

Link as much as possible to allow for the best performance across all
scenarios
32
BI Apps
Performance
Layer
Best
Better
Base
Mapping Into Oracle BI

 The 3 Fact tables are mapped
like any aggregate
 The Skinny Fact (_SF) will
have fewer dimensions and
fewer metrics mapped to it
• Along the Employee dimension however it
is the same as the base _F
 OBI will prefer to use the _SF
over the _F
• Uses regular aggregate navigation
concepts
• Uses the _F when needed as a “backup
plan”
33
_A
_SF
_F

 Raise the priority group on the base _D to have
OBI prefer the _SD
As both LTS grains are identical, OBI needs more info to make a
choice
34
_D _SD

Create a dummy hierarchy level and map the LTSs for the Mini
Dimension and Fact Aggregate to it
 The grain of the Mini Dimension is arbitrary
 As long as OBI knows it is higher than the other LTSs it will
be preferred (Priority groups not needed)
35
_MD
_A

Table Mapping
The mapping of tables is straightforward
Link Tables As Much As Possible
Let Oracle BI make the best choice
Mapping Into Oracle BI - Takeaways

Implementation
Considerations
37

The whole prototyping process can be done on a
simple star in roughly two weeks
Allow for more time if you have:
• Large data volumes
• Difficult performance targets
• More complex models and logic
• Many disparate report patterns or lots of reports to consider
• More stars are needed (e.g., Actuals and Budgets together)
Development effort depends on # new objects
• Typically only another two weeks needed (ETL & OBI RPD)
• A few more for regression test and deployment
Implementation Considerations

 Use Production data volumes for
accurate analysis
 Use Production DDL, ETL code,
OBI RPD and OBI Webcat
 Quiet, unused machine for
accurate benchmarking
 Use hardware that is as similar to
Prod as possible
• KPI uses a database benchmarking tool to
compare environments

 Additional ETL & RPD Development
• Use SQL scripts instead of Informatica mappings (less effort, faster
execution)
 Additional Testing – Regression test is easy
 Additional ETL Run Time – may be critical
 Additional Database size - minor
 Customization Propagation / Impact Analysis
• True of any aggregate
 Complex logic will be more difficult
• Financial Analytics - snowflake with multiple segment hierarchies
• HR Workforce Event & Snapshot logic uses effective dates for future
dated events

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