Tips tricks to speed nw bi 2009

© 2009 Wellesley Information Services. All rights reserved.
20 technical tips and tricks
to speed SAP NetWeaver
Business Intelligence query,
report, and dashboard
performance
Dr. Bjarne Berg

2
What We’ll Cover …
• Introduction
• Performance Issues & Tips
 MultiProviders and Partitioning
 Aggregates
 Query Design & Caching
 Hardware & Servers
• Designing for Performance
 InfoCubes and DSOs
• BI- Accelerator
 Sizing and Implementation
 Management and Costs
• EarlyWatch Reports
• Wrap-up

3
 In this session we will cover the top 20 must-do technical
performance tricks to help you optimize SAP NetWeaver
BI reporting for your end users.
 We will look at performance modeling of InfoCubes, how
to improve memory utilization by caching and how to use
diagnostics to analyze performance issues.
 We will also explore best practices on how to develop and
manage aggregates and MultiProviders, and see what the
BI- Accelerator (BIA) can do for your organization.
 Finally, we will look at how to analyze EarlyWatch reports
from Solution Manager 4.0 so they become actionable.
In this session.

4
Capacity and Scalability Is the Top Concern for Your CxO
• Don’t under size your global BI system
 Spend adequate funding on hardware, memory, processing power and disk space
7.03
7.21
7.31
7.51
7.62
7.65
7.88
7.9
7 7.25 7.5 7.75 8
Difficult for end users to learn or use
Danger of distributing outdated data
Too many Business Intelligence tools in use
Data Integration
Security
Determining ROI
Server & Desktop processing capacity
Overall System Performance and capacity
Source: Intel, SAP & Business Week
"Seizing the BI Opportunity" 2006.
A survey of 353 top C-level officers in large companies, reported
that the top BI concern was the scalability of their solutions.
A survey of 353 top C-level officers in large companies, reported
that the top BI concern was the scalability of their solutions.

5
• Introduction
 Aggregates
BI- Accelerator
• Wrap-up

6
Problem: To reduce data volume in each InfoCube,
data is partitioned by Time period.
A query now have to search in all InfoProviders to find
the data (i.e. billing docs from 2007). This is very slow.
Solution: We can add “hints” to guide the query execution. In the
RRKMULTIPROVHINT table, you can specify one or several
characteristics for each MultiProvider which are then used to
partition the MultiProvider into BasicCubes.
If a query has restrictions on this characteristic, the OLAP processor is already
checked to see which part cubes can return data for the query. The
data manager can then completely ignore the remaining cubes.
An entry in RRKMULTIPROVHINT only makes sense if a few attributes of this
characteristic (that is, only a few data slices) are affected in the majority of, or
the most important, queries (SAP Notes: 911939. See also: 954889 and 1156681).
Tip 1: MultiProviders and Hints
2002 2003 2004 2005 2006 2007 2008

7
Tip 2: The Secret about MultiProviders & Parallel Processing
• To avoid an overflow of the memory, parallel processing is
cancelled as soon as the collected result contains 30,000 rows
or more and there is at least one incomplete sub process
 The MultiProvider query is then restarted automatically and processed
sequentially
 What appears to be parallel processing corresponds to sequential
processing plus the preceding phase of parallel processing up to the
termination
• Generally, it’s recommended that you keep the number of
InfoProviders of a MultiProvider to no more than 10. However,
even at 4-5 large InfoProviders you may experience performance
degradation.

8
MultiProviders and Parallel Processing (cont.)
• Consider deactivating parallel processing for those queries that
are MultiProvider queries and have large result sets (and ‘hints’
cannot be used).
 With SAP BW 3.0B SP14 (SAP BW 3.1 SP8 and later versions, you can change
the default value of 30,000 rows — refer to SAP Notes 629541, 622841, 607164,
and 630500.
• A larger number of base InfoProviders is likely to result in a
scenario where there are many more base InfoProviders than
available dialog processes, resulting in limited parallel processing
and many pipelined sub-queries
You can also change the number of dialogs (increase the use of parallel processing)
in RSADMIN by changing the settings for QUERY_MAX_WP_DIAG.

9
• Introduction
 Aggregates
• BI- Accelerator
• Wrap-up

10
Aggregates
• Aggregates are much less used by the SAP installation base than
training and common sense should dictate.
• The interface to build the summary tables (aggregates) are intuitive
and easy to master, but few are taking real advantage of them.
• Even among those that are using aggregates, many have poorly
defined solutions & seldom monitor the usage, thereby limiting the
benefits of this simple technology.
To avoid poor definition and usage, aggregates should
be developed after the system has been in production
for a while and real user statistics are captured.

11
Tip 3: Building aggregates is easy – Propose from statistics
• Select the run time of queries
to be analyzed (e.g., 20 sec)
• Select time period to
be analyzed
 Only those queries executed in
this time period will be reviewed
to create the proposal
This example shows how to
build aggregates by using
system statistics to generate
proposals
Note: To make this work, the BW
statistics must be captured.

12
Correct Aggregates Are Easy to Build – Propose from Query
We can also create proposals
from the Query user statistics.
To make this work, a
representative number of
queries must be executed to
gather the statistics to optimize
from.
We can also create
proposals for aggregates
based on individual queries
that are performing poorly.

13
Tip 4: Reduce the number of overlapping Proposals
High valuation and high usage is what we are looking for. This indicates high reduction
of records in aggregate and high benefits to users.
.
We reduce the overlapping proposals
by optimizing them.
This may reduce the proposals from 99
to less than a dozen
When using 3rd
party query tools and ODBC to query directly into the
DSO, you are bypassing the OLAP Processor. Therefore, you cannot
accurately performance tune the system using aggregates (statistics),
nor will the 3rd
party tool benefit from aggregates.

Activate the aggregate
The process of turning 'on' the
aggregates is simple

Fill aggregate with summary data

16
• Introduction
 Aggregates
BI- Accelerator
• Wrap-up

17
Tip 5: Use the Right Read Mode for Queries
Select the right read mode. Three query read modes in
BW determine the amount of data to be fetched from a
database:
1. Read all data (all data is read from a database and stored in user
memory space)
2. Read data during navigation (data is read from a database only
on demand during navigation)
3. Read data during navigation and when expanding the hierarchy
Reading data during navigation minimizes the impact on
the application server resources because only data that
the user requires will be retrieved.
Source: Catherine Roze,

18
Tip 6: Query read mode for large hierarchies
For queries involving large hierarchies with many nodes, it would
be wise to select Read data during navigation and when
expanding the hierarchy option to avoid reading data for the
hierarchy nodes that are not expanded.
Reserve the Read all data mode for special queries—for instance,
when a majority of the users need a given query to slice and
dice against all dimensions, or when the data is needed for data
mining. This mode places heavy demand on database and
memory resources and might impact other SAP BW processes
and tasks.
A query read mode can be defined either on an individual query
basis or as a default for new queries using the query monitor
(transaction RSRT).
Source: Catherine Roze

19
Tip 7: Condition & Exceptions
Minimize conditions-and-exceptions reporting. Conditions &
exceptions are usually processed by the SAP application
server. This generates additional data transfer between
database and application servers.
If conditions and exceptions have to be used, the amount of data to be
processed should be minimized with filters. When multiple drill-downs
are required, separate the drill-down steps by using free characteristics
rather than rows and columns.
This strategy results in a smaller initial result set, and therefore faster
query processing and data transport as compared to a query where all
characteristics are in rows.
This strategy does not reduce the query result set. It just
separates the drill-down steps. In addition to accelerating query
processing, it provides the user more manageable portions of data.
Source: Catherine Roze,

20
Some Performance settings for Query Execution
This decides how many records are read
during navigation.
Examine the
request status
when reading
the InfoProvider
New in 7.0 BI:
OLAP Engine can
read deltas into the
cache. Does not
invalidate existing
query cache.
Displays the level of
statistics collected.
Turn off/on parallel
processing
When will the
query program be
regenerated based
on database
statistics

21
Tip 8: Filters
Leverage filters as much as possible. Using filters contributes to
reducing the number of database reads and the size of the result set,
thereby significantly improving query runtimes.
Filters are especially valuable when associated with “big
dimensions” where there is a large number of characteristics such as
customers and document numbers.
If large reports have to be produced, leverage the BEx
Broadcaster to generate batch reports and pre-deliver
them each morning to their email, PDF or printer.
If large reports have to be produced, leverage the BEx
Broadcaster to generate batch reports and pre-deliver
them each morning to their email, PDF or printer.

22
Tip 8: Use RSRT Transaction to examine slow queries
P1 of 3

23
Look for patterns and see the performance details
P2 of 3
In this real case, aggregatesIn this real case, aggregates
was needed for those cubeswas needed for those cubes
flagged…flagged…

24
Real Example: This system has issues with the Oracle DB
P3 of 3
Work with the basis teamWork with the basis team
to research the settingsto research the settings
and the Oracle issues.and the Oracle issues.
Focus on SAP notes andFocus on SAP notes and
the index issue.the index issue.
The RSRT and RSRVThe RSRT and RSRV
codes are a gold mine forcodes are a gold mine for
debugging and analyzingdebugging and analyzing
slow queries.slow queries.

25
Look at the query details for each slow query
Notice the yellow flag for the 6 base
cubes in the MultiProvider and the
yellow flag for the 14 free chars.
(Note: no hints were used in this MultiProvider,
which led to very poor performance).
Notice the yellow flag for the 6 base
cubes in the MultiProvider and the
yellow flag for the 14 free chars.
(Note: no hints were used in this MultiProvider,
which led to very poor performance).
You can also trace the front-end data
transfers and OLAP performance by using
RSTT in SAP 7.0 BI (RSRTRACE in BW 3.5)

26
Tip 9: Use the BEx Broadcaster to Pre-Fill the Cache
Distribution Types
You can increase query speed by broadcasting
the query result of commonly used queries to
the cache.
Users do not need to execute the query from
the database. Instead the result is already in
the system memory (much faster).
You can increase query speed by broadcasting
the query result of commonly used queries to
the cache.
Users do not need to execute the query from
the database. Instead the result is already in
the system memory (much faster).

27
Tip 10: Debugging Queries - RSRT
Here you can execute the queryHere you can execute the query
and see each breakpoint, therebyand see each breakpoint, thereby
debugging the query and seedebugging the query and see
where the execution is slow.where the execution is slow.
Worth a try:Worth a try: Try running slowTry running slow
queries in debug mode withqueries in debug mode with
parallel processing deactivatedparallel processing deactivated
to see if they run faster..to see if they run faster..

28
Tip 11: Upgrade to AS-Java service pack 14 asap.
In service pack 14 we find several performance improvements
including:
- Better Java execution and performance
- Increased OLAP cache abilities (Enhanced Cluster table -BLOB)
In 7.0 BI at all service packs upto number 14, it is also impossible to
populate the OLAP cache by broadcasting query views. If you use
earlier service packs, you may be forced to create many different
queries to provide this performance.
The implementation of service pack 14 is highlyThe implementation of service pack 14 is highly
recommended by SAP for these performance reasons. Whenrecommended by SAP for these performance reasons. When
implemented the Java execution will also improve.implemented the Java execution will also improve.

29
A Real Example
This company saw aThis company saw a
39% decrease in Query39% decrease in Query
execution time afterexecution time after
implementing SP-14.implementing SP-14.
They had 38 cockpitsThey had 38 cockpits
and 82 queries thatand 82 queries that
improved substantiallyimproved substantially
without any furtherwithout any further
changes..changes..
Cockpit
#of
Queries
Baseline
4/18/08
SP 14
(4/21/08)
Improve
ment
Expense Query for Detailed program 1 145 9 94%
Financial dashboard expense (actual vs. target) 7 150 18 88%
Financial dashboard [expense] 2 70 12 83%
Financial dashboard [non-earnings] 2 42 13 69%
Expense Query for Detailed org objective 1 31 10 68%
Financial dashboard [workforce costs] 6 50 17 66%
Expense Query for Detailed 1 36 14 61%
Capital Query for Detailed work type 1 22 9 59%
Financial performance 9 43 21 51%
Workforce financials 9 29 16 45%
Expense query for detailed 1 16 9 44%
Balance Query for Detailed program 1 14 8 43%
Balance Query for Detailed cost element 1 14 8 43%
Balance Query for Detailed MWC 1 14 8 43%
Non-earnings Query for Detailed org objective 1 14 8 43%
Financial dashboard (other balance) 7 29 17 41%
Balance query for organization detailed 1 13 8 38%
Balance Query for Detailed work type 1 13 8 38%
Non-earnings Query for Detailed cost element 1 13 8 38%
Capital Query for Detailed cost element 1 14 9 36%
Labor query for detailed org 1 14 9 36%
Standard costs variance detailed report 1 20 13 35%
Financial dashboard (non-earnings) 7 30 20 33%
Non-earnings Query for Detailed program 1 12 8 33%
Balance Query for Detailed org objective 1 13 9 31%
Capital Query for Detailed MWC 1 14 10 29%
Non-earnings query for organization detailed 1 12 9 25%
Non-earnings Query for Detailed work type 1 12 9 25%
Financial dashboard [workforce cost] 2 23 18 22%
Capital Query for Detailed org objective 1 15 12 20%
Financial dashboard [other balance sheet] 2 21 17 19%
Labor query for detailed cost element 1 12 10 17%
Expense Query for Detailed cost element 1 13 11 15%
PCC Expense Query for detailed 1 14 12 14%
Headcount detail fin-DB 1 15 13 13%
Capital - Actual Vs. Target 7 24 22 8%
Financial dashboard capital trend 2 13 12 8%
Non-earnings Query for Detailed MWC 1 13 13 0%
Average 27.95 12.03 39%

30
1. When Restrictive Key Figures (RKF) are included in a query, conditioning is done for
each of them during query execution. This is very time consuming and a high number
of RKFs can seriously hurt query performance
Recommendation: Reduce RKFs in the query to as few as possible. Also, define
calculated & RKFs on the Infoprovider level instead of locally within the query. Why?:
Good: Formulas within an Infoprovider are returned at runtime and held in cache.
Bad: Local formulas and selections are calculated with each navigation step.
2. Line item dimensions are basically fields that are transaction oriented and therefore,
once flagged as a ‘line item dimension’, is actually stored in the fact table. This
results in faster query access (no table join).
Tip 12: Restrictive Key Figures & Line Item Dimensions
Explore the use line item dimensions for fieldsExplore the use line item dimensions for fields
that are frequently conditioned in queries.that are frequently conditioned in queries.

31
Problem: Calculated Key Figures (CKF) are computed
during run-time, and a many CKFs can slow down the
query performance.
Solution: Many of the CKF can be done during data loads & physically
stored in the InfoProvider. This reduces the number of computations and
the query can use simple table reads instead. Do not use total rows when
not required (this require additional processing on the OLAP side).
Problem: Sorting the data in reports with large result sets can be time
consuming.
Solution: Reducing the number of sorts in the default view can improve
the report execution & provide the users with data faster.
Tip 13: Reducing the Query processing time
PS! Reducing thePS! Reducing the texttext in query willin query will
also speed up the processing some.also speed up the processing some.

32
Web templates in SAP BI can become
really large. Since they contain both
scripts and Cascading Stylesheets
(CSS), the code can become really
comprehensive.
To reduce the CSS, you can try several
compression tools that may help you
limit the overall size of your web
templates.
There are no lack of free tools available,
and the quality varies. Therefore you
must remember to test, test and test….
(but the benefits can also be great).
Compression tools for CSS and Java scripts can reduce the overall web
template size. If you have thousands of users, this can be a ‘life saver’’
Tip 14: Make your web templates Smaller
CSSTidy

33
• Introduction
 Aggregates
BI- Accelerator
 Management and Costs EarlyWatch Reports
Wrap-up

34
Tip 15: Is the Memory Cache Is Set Too Low?
Cache has a system default of 100 MB for local and 200 MB for global
cache. This may be too low for a system that can be optimized via
broadcaster.
The Cache is not used when a query contains a virtual key
figure or virtual characteristics, or when the query is
accessing a transactional DSO, or a virtual InfoProvider
Review the settings with the
Basis team and look at the
available hardware.
Use the transaction code
RSCUSTV14 in SAP NetWeaver
BI to increase the cache. Focus
particularly on the global cache.

35
Tip 15: Monitor and adjust Cache Size
To monitor the usage of the cache, use transaction code RSRCACHE
and also periodically review the analysis of load distribution using
ST03N – Expert Mode
The size of OLAP Cache is physically limited by the amount
of memory set in system parameter rsdb/esm/buffersize_kb.
The settings are available in RSPFPAR and RZ11.
Source: V. Rudnytskiy, 2008

36
Tip 16: The Right OLAP Cache Persistence Settings
CACHE OLAP Persistence settings
Note When What t-code
Default Flatfile
Change the logical file
BW_OLAP_CACHE when
installing the system (not
valid name) FILE
Optional Cluster table Medium and small result sets
RSR_CACHE_DBS_IX
RSR_CACHE_DB_IX
Optional
Binary Large Objects
(blob) Best for large result sets
RSR_CACHE_DBS_BL
RSR_CACHE_DB_BL
SP 14
Blob/Cluster
Enhanced (new in
SAP 7.0 BI)
No central cache directory or
lock concept (enqueue). The
mode is not available by
default.
Set
RSR_CACHE_ACTIVATE_NE
W RSADMIN VALUE=x
Source: SAP AG 2008.

37
Monitor Memory Usage – Do you need more?
Roll memory was never maxed out in
the period 12/23/07 through 1/27/08
Paging memory was never maxed out
in the period 12/23/07 through 1/27/08
Extended memory was never maxed out in
the period 12/23/07 through 1/27/08
Only 3GB of 9 GB of Heap memory was ever
used in the period 12/23/07 through 1/27/08

38
• Introduction
 Aggregates
BI- Accelerator
• Wrap-up

Tip 17: Avoid Outdated Indexes and Database statistics
Database statistics are used by the optimizer to route queries. Outdated
statistics leads to performance degradation. Outdated indexes can lead
to very poor search performance in all queries where conditioning is
used (i.e. mandatory prompts).
For high volume Infocubes, or cubes that have a high number of users, the
percentage used to build the DB stats can be increased from the default 10%
to 20%. This may yield more accurate query routing and better query
performance (consider this especially for cubes with ‘old data’ partitioned)
Real example

Tip 18: Avoid replicating the transaction system in SAP BI
It is tempting to load cross-reference tables and do lookups inside SAP BI instead of
extending extractors. This creates DSOs that cannot be queried efficiently without
many table joins. In this example, ¼ of all DSOs contains less than 9 fields, & six
have less than 4.
Programs that can help you monitor
the system design:
1.SAP_ANALYZE_ALL_INFOCUBES
2.ANALYZE_RSZ_TABLES
3.SAP_INFOCUBE_DESIGNS
As much logic as possible should be moved to the extraction,
and needed data fields should be denormalized and stored in
logically organized ODSs and Infocubes.
Real example

41
InfoCube Design & Indexes
When you flag a dimension as “high cardinality” SAP BI
will use a b-tree index instead of a bit-map index.
This can be substantially slower if the high cardinality
does not exist in the data in general (star-joins cannot be
used with b-trees).
Info Cube Line Item
dims
DIM 1 DIM 3 DIM 6 DIM 8
CBBL_CB02 0 H
CBPD_CB06 0 H
CBPR_CB11 0 H
CBPR_CB18 0 H
CBSV_CB01 0 H
CBSV_CB02 0 H
Validate the high-cardinality of the data and reset the flag if
needed – this will give a better index type and performance
Real example

42
• Introduction
 Aggregates
BI- Accelerator
• Wrap-up

43
TIP 19: Use BI Accelerator ASAP
The SAP BI
Accelerator makes
query response time
50-10,000 faster.
You use process
chains to maintain
the HPA engine after
each data load
HP, Sun and IBM have standard solutions ranging
from $32K to $250K+ that can be installed and
tested in as little as 2-4 weeks (+ SAP license fees)
SAP
BW
Any
tool
32 Gb Blades are now
certified by SAP (July 2008)

44
Currently, the BIA performs aggregation and data selection
for the query, all other processing is done by the OLAP
analytical engine. (this means that 99% of the previous
recommendations in this session still holds true)…
SAP BIA is not used when the result set exceeds 3 million records
(max. default). When the result set is less, the data is sent
as one large data package to the application server (need
fast network).
In the next SAP NetWeaver release the BIA will handle more of the
analytics processing such as “top-5 products sales” which
is currently done in the OLAP analytical engine.
How does SAP BIA Work?
You get BIA sizing estimates by running the
SAP program available in SAP Note: 917803

45
BIA Currently reads data from
InfoCubes. DSOs & InfoObjects
are still read from
base/physical tables (even
when the InfoObject is indexed
as part of master data).
Performance Benchmarks for BIA
BIA’s strength resides in
its near-linear scalability.
Performance is measured
in terms of:
1.BIA index creation time
2.Multi-user throughput per hr.
3.Average report response time
4.Average number of records
touched by each report.

46
The BIA should be sized for critical applications. Most companies use
BIA only for Production, while others have a complete landscape
Hardware Example
Environment Area Recommended size IBM example*
Production Blade servers 14 Blades BladeCenter HS21 -8853G6U
Production Memory 2x8 GB (2x4) DDR2 total 16 GB 39M5797
Production Processors 2 x Quad Core Intel Xeon Processor 2 x Quad Core Intel Xeon Processor
Production Processor speed 3.00 GHz+ 3.00 GHz
Production Network cards 2 x Gigabit Cisco cards 32R1760
Production External storage Dedicated disks (500 GB+) DS-4800
Production File system General Parallel file system (GPFS) GPFS
Production Chassis 14 blades capacity H-series (rack-mount/9U) 88524XU
QA Blade servers 14 Blades BladeCenter HS21 -8853G6U
QA Memory 2x8 GB (2x4) DDR2 total 16 GB 39M5797
QA Processors 2 x Quad Core Intel Xeon Processor 2 x Quad Core Intel Xeon Processor
QA Processor speed 3.00 GHz+ 3.00 GHz
QA Network cards 2 x Gigabit Cisco cards 32R1760
QA External storage Dedicated disks (500 GB+) DS-4800
QA File system General Parallel file system (GPFS) GPFS
QA Chassis 14 blades capacity H-series (rack-mount/9U) 88524XU
Development Blade servers 4 Blades BladeCenter HS21 -8853G6U
Development Memory 2x8 GB (2x4) DDR2 total 16 GB 39M5797
Development Processors 2 x Quad Core Intel Xeon Processor 2 x Quad Core Intel Xeon Processor
Development Processor speed 3.00 GHz+ 3.00 GHz
Development Network cards 2 x Gigabit Cisco cards 32R1760
Development External storage Dedicated disks (300 GB+) DS-4800
Development File system General Parallel file system (GPFS) GPFS
Development Chassis 14 blades capacity H-series (rack-mount/9U) 88524XU

47
Once you exceed a few hundred critical users and/or 3-4 Tb of
data you should seriously consider SAP BIA
BIA is becoming mainstream
BIA is no longer
something exotic.
Many of the large BI
systems have already
implemented BIA and
many more projects are
under way in Europe
and in the Americas.
Some of SAP reference clients

48
• Introduction
 Aggregates
BI- Accelerator
EarlyWatch Reports
Wrap-up

49
Tip 20: SAP Solutions Manager - EarlyWatch Reports Are Great!
•EarlyWatch reports provide a
simple way to confirm how your
system is running and to catch
problems
 A “goldmine” for system
recommendations
•Run them periodically & read the
details
•This is a real EarlyWatch report
from a mid-sized company that
has been running SAP BW for the
last four years
On a large global project, system issues
can be hard to pin-down without access
to EarlyWatch reports. The monitoring
reports allows you to tune the system
before the user community gets access
and complaints arise.
On a large global project, system issues
can be hard to pin-down without access
to EarlyWatch reports. The monitoring
reports allows you to tune the system
before the user community gets access
and complaints arise.

50
EarlyWatch Performance Info
1 Performance Overview
The performance of your system was analyzed with respect to the average response times and total
workload. We did not detect any major problems that could affect the performance of your system.
The following table shows the average response times for various task types:
Task type Dialog
Steps
Avg. Resp.
Time in ms
Avg. CPU
Time in ms
Avg. Wait
Time in ms
Avg. Load
Time in ms
Avg. DB
Time in ms
Avg. GUI
Time in ms
DIALOG +
RFC
195240 3253.3 728.7 1.8 2.5 1110.9 6.3
UPDATE 5 984.2 28.2 26.0 15.2 585.4
UPDATE2 48 133.2 17.1 0.7 3.3 80.8
BATCH 59288 11599.3 2091.2 0.6 8.5 5772.6
HTTP 257762 693.5 183.7 4.4 2.2 405.0
1.1 Current Workload
The following table lists the number of current users (measured from our workload analysis) in your system.
Users Low Activity Medium Activity High Activity Total Users
Measured in System 98 11 7 116
In a 24-hour operational systems
due to time-zones, you will have
less time to react and fix issues.
Therefore, early detection of
system issues are critical to the
success of a global project.
In a 24-hour operational systems
due to time-zones, you will have
less time to react and fix issues.
Therefore, early detection of
system issues are critical to the
success of a global project.

51
EarlyWatch Reports – Finds Oracle fixes
In this real example, we can the EarlyWatch report identified that the
system was several Oracle notes are behind that needed to be applied
to optimize DB performance.
Before this was done, this system took 24 to 26 minutes to execute
some queries.
SAP Note
number
Description
841728 Oracle 10.2.0: Composite note for problems and workarounds
871096 Oracle Database 10g: Patch sets/Patches for 10.2.0
871735 Current Patchset for Oracle 10.2.0
850306 Oracle Critical Patch Update Program
1021454 Oracle Segment Shrinking may cause LOB corruption.
952388 Kernel <= 6.40:UNIX error due to 9i Client software
Real example

52
EarlyWatch Reports – Finds Backup Problems
In this real example,
the EarlyWatch
report identified that
there were no valid
backups for almost
one month.
0.1 Backup Frequency
When we checked the backup log files, we detected that your backup strategy does not follow the SAP backup
recommendations.
In the time period from 09.01.2008 to 05.02.2008 , we noticed the following problems:
- There was no successful backup on Friday 01.02.2008
- There was no successful backup on Thursday 31.01.2008
- There was no successful backup on Wednesday 30.01.2008
- There was no successful backup on Tuesday 29.01.2008
- There was no successful backup on Monday 28.01.2008
There are 5 working days without successful backup this week.
There is no successful backup at all for this period. Real example

53
• Introduction
 Aggregates
BI- Accelerator
EarlyWatch Reports
Wrap-up

54
7 Key Points to Take Home
• Use best practices for query design before you start massive hardware
performance tuning efforts.
• Plan for growth – what is the plan when you have 200,500, 1000+ users?
• Start with aggregates (poor man’s BIA), thereafter go with caching.
• Monitor the system usage- do you need more app servers, memory, HW?
• Check database statistics and indexes and keep them up to date.
• If you are building an Enterprise Data Warehouse, plan and budget for a
BIA installation.
• EarlyWatch reports are a tool to live (and ‘die’) by. Use the report before
you have performance issues.

55
Presentations, tutorials & articles
www.Comerit.net
SAP SDN Community web page for Business Intelligence Performance Tuning
https://www.sdn.sap.com/irj/sdn/bi-performance-tuning
ASUG407 - SAP BW Query Performance Tuning with Aggregates by Ron Silberstein
(requires SDN or Marketplace log-on). 54 min movie.
https://www.sdn.sap.com/irj/sdn/go/portal/prtroot/docs/media/uuid/d9fd84ad-0701-
0010-d9a5-ba726caa585d
Large scale testing of SAP BI Accelerator on a NetWeaver Platform
https://www.sdn.sap.com/irj/sdn/go/portal/prtroot/docs/library/uuid/b00e7bb5-3add-
2a10-3890-e8582df5c70f
Resources

56
Your Turn!
How to contact me:
Dr. Bjarne Berg
bberg@comerit.net

Tips tricks to speed nw bi 2009

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