BI2013_Berg_Amsterdam_BW_EDW_v6

Report
Best practices to ensure efficient data
models, fast data activation, and
performance of your SAP NetWeaver
BW 7.3 data warehouse
Dr. Bjarne Berg
COMERIT
© Copyright 2013
Wellesley Information Services, Inc.
All rights reserved.
What We’ll Cover …
•
•
•
•
•
•
•
•
Introductions
EDW Data Design and Data Modeling
Data Loading and Fast Activations
Tips and Tricks for Faster Query Times
Real-Time and Near-Time reporting for the EDW
EDW Reduction and Cleanup
In Memory Options with HANA
Wrap-Up
1
In This Session …
•
This is a advanced technical presentation intended for
developers with significant experience with SAP BW in a
hands-on role
•
We will look at many EDW technical design options and the
pros and cons of some of the new design features in HANA
and BW 7.3
•
During the presentation we will look at many real examples
from 5 real implementations and explore what can be
learned from these
2
What We’ll Cover …
•
•
•
•
•
•
•
•
Introductions
EDW Data Design and Data Modeling
Data Loading and Fast Activations
Tips and Tricks for Faster Query Times
Real-Time and Near-Time reporting for the EDW
EDW Reduction and Cleanup
In Memory Options with HANA
Wrap-Up
3
Data Design The Use of Layered Scalable Architecture (LSA)
SAP BW 7.3 SP-3 has a set of 10
templates to help build a layered
data architecture for large-scale
data warehousing
The LSA consists logically of:






Acquisition layer
Harmonization/quality layer
Propagation layer
Business transformation layer
Reporting layer
Virtualization layer
4
EDW Design Vs. Evolution
An organization has two fundamental choices:
1.
Build a new well architected EDW
2.
Evolve the old EDW or reporting system
Both solutions are feasible, but organizations
that selects an evolutionary approach should
be self-aware and monitor undesirable add-ons
and ‘workarounds”.
Failure to break with the past can be
detrimental to an EDW’s long-term success…
5
Data Design - Real Example of LSA Implementation
This company
implemented a full LSA
Architecture and also
partitioned the
Infoproviders for faster
data loads and faster
query performance.
While this architecture
has benefits, there are
significant issues around
data volumes and the
Total cost of Ownership
when changes are made
to the data model(s)
6
Data Design Example of LSA Simplification In HANA
Since many of the benefits
sought by the LSA
architecture are inherent in
HANA, significant
simplifications can be
made to the data design
and data flows
This design has a dramatically
smaller cost of ownership
(fewer objects to maintain,
design and change) than the
traditional LSAs
7
EDW - Complex Layered Architectures
•
•
This BPC on BW system was experiencing
substantial load performance issues
Some of this was due to underlying SAP BW
configuration, while some was due to the
technical configuration of the data store
architecture and data flow inside SAP BW
Consolidation
Cube
(OC_CON)
Consolidation Processes:
1) Clearing
2) Load
3) Foreign Exchange
4) Eliminations
5) Optimizations
BPC Staging
Cube
(BPC_C01)
GL Summary
Cube
(FIGL_C03)
Production Issues included:
1) Dependent jobs not running
sequentially, i.e., load from
Summary cube to Staging cube is
sometimes executed before the
summary cube data is loaded and
activated, resulting in zero
records in the staging cube.
2) Long latency with 6 layers of
PSA, DSOs, and InfoCubes
before consolidation processes
can be executed.
Conformed
Reportable
DSO
Write
Optimized
DSO
FIGL_D21
FIGL_D20
FIGL_D17
FIGL_D14
FIGL_D18
FIGL_D15S
FIGL_D13S
FIGL_D10S
FIGL_D08
FIGL_D11S
R/3 3.1i
EU
ECC 4.7
ASIA
Persistent Staging Area (PSA)
ECC 6.0
AsiaPacific
ECC 6.0
NorthAmerica
ECC 4.7
LatinAmerica
8
Fixes to Complex EDW Architecture
•
•
The fix to this system included removing the conformed DSO
layer, with BEx flags for data stores that are never reported on.
Also, the BPC staging cube served
little practical purpose since the data is
Consolidation Processes:
1) Clearing
2) Load
already staged in the GL Summary cube
3) Foreign Exchange
4) Eliminations
and the logic can be maintained in the
5) Optimizations
load from this cube directly to the
consolidation cube.
Consolidation
Cube
(OC_CON)
GL Summary
Cube
(FIGL_C03)
Long-term benefits included
reduced data latency, faster
data activation, less data
replication, smaller system
backups as well as simplified
system maintenance.
Write
Optimized
DSO
FIGL_D15S
FIGL_D13S
FIGL_D10S
FIGL_D08
FIGL_D11S
R/3 3.1i
EU
ECC 4.7
ASIA
Persistent Staging Area (PSA)
ECC 6.0
AsiaPacific
ECC 6.0
NorthAmerica
ECC 4.7
LatinAmerica
9
EDW Data Design - Use of MultiProvider Hints in BW
Problem: To reduce data volume in each InfoCube,
data is partitioned by Time period.
2002
2003
2004
2005 2006
2007
2008
A query must now search in all InfoProviders to find
the data. 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 of the 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).
10
EDW Data Design - Semantic Partitioned Objects (SPO)
•
•
•
When data stores and InfoCubes are allowed to grow over time, the data load
and query performance suffers
Normally objects should be physically partitioned when the numbers of records
exceed 100 – 200 million
 However, this may be different depending on the size of your hardware and
the type of database you use
In SAP NetWeaver BW 7.3 we get an option to create a Semantic Partitioned
Object (SPO) through wizards
 You can partition based on fields such as calendar year, region, country, etc.
11
Data Design - Semantic Partitioned Objects (cont.)
•
When an SPO is created, a reference structure keeps track of the
partitions. The structure is placed in the MultiProvider for querying.
SPO Wizards create all Data Transfer Processes (DTP),
transformations, filters for each data store, and a process chain
12
•
•
•
In this example, many of the
InfoCubes are very large and not
partitioned
Several have over 100 million
records and one is approaching
500 million
In this system SPOs in BW 7.3 can
be very helpful. For BW 7.0 many
of these cubes can be physically
partitioned with hints on the
MultiProviders
Millions
InfoCube Design - Size
425
400
375
350
325
300
275
250
225
200
175
150
125
100
75
50
25
-
InfoCubes Number of Records
InfoCubes should be performance tuned if the number of records exceeds 100
million and partitioned before they are approaching 200+ million records. This
creates faster loads, better query performance, and easier management.
13
InfoCube Design - Use of Line Item Dimensions
•
•

Line item dimensions are basically
fields that are transaction oriented
Once flagged as a line item dimension,
the field is actually stored in the fact
table and has no table joins
This may result in improvements to query speeds for cubes not
in BWA or HANA
Explore the use of line item dimensions for fields that
are frequently conditioned in queries. This model
change can yield faster queries.
14
InfoCube Design — High Cardinality Flags
•
High-Cardinality flag for large InfoCubes with more than 10 million rows
InfoCube
FIUC_C03
ZGAT_C01
FIIF_C02
FIGL_C01
•
•
•
Number of rows
12,859,780
20,793,573
68,090,967
156,738,973
Entries in dimension
compared for F table
37%
46%
102%
88%
There are currently 11 InfoCubes with a ratio of more than 30% of the records in
the dimensions vs. fact table
SAP recommends for Indexing and performance reasons to flag these as “highcardinality” dimensions. However, it has minor impact to smaller cubes.
In this example, there were four medium and large InfoCubes that are not
following the basic design guidelines, and subsequently had slow performance
Many companies should redesign large InfoCubes with high-cardinality to
take advantage of the standard performance enhancements available.
15
DSO Design and Locks on Large Oracle Tables
Additionally, 101 DSO objects
were flagged as being reportable.
This resulted in System IDs (SIDs)
being created during activation.
Combined, these resulted in
frequent locks on the Oracle
database and failed parallel
activation jobs
Millions
In this example, many of the very
large DSOs are not partitioned,
and several objects have over 250
million records
425
400
375
350
325
300
275
250
225
200
175
150
125
100
75
50
25
-
DSO Number of Records
Partition DSOs. The lock on very large DSOs during parallel loads are well
known and SAP has issued several notes on the topic: 634458 'ODS object:
Activation fails - DEADLOCK' and 84348 'Oracle deadlocks, ORA-00060.'
16
What We’ll Cover …
•
•
•
•
•
•
•
•
Introductions
EDW Data Design and Data Modeling
Data Loading and Fast Activations
Tips and Tricks for Faster Query Times
Real-Time and Near-Time reporting for the EDW
EDW Reduction and Cleanup
In Memory Options with HANA
Wrap-Up
17
An Example of a - BW System Review
Database:
Oracle version 11.2g
BW system:
BW version 7.3
Operation systems: HP-UNIX; Linux for BWA; AIX 6.4 for three app servers
Area
Value
Trend
Active Users
106
steady
Avg. Availability per Week
100 %
up
DB Size
Last Month DB Growth
3514.24 GB steady
131.88 GB up
In this section, we take a look at a real example of a BW Implementation
and explore what we can learn from it.
18
System Background
•
•
There were over 600
InfoProviders in the system
The system had been in
production for 6 years and
has was upgraded in 2012
InfoProviders
400
353
350
300
250
200
150
113
101
95
100
•
Most InfoCubes followed
standard development
guidelines, but some had
abnormalities such as
InfoCubes feeding DSOs.
50
39
11
0
9
10
0
Structured design review sessions should be undertaken as part of
every project to assure that this design did not continue.
19
Faster Data Load and Design Options — Activation
•
During activation, SAP NetWeaver BW 7.0 has to lookup in the
NIRV table to see if the object already exists
 This can be a slow process
•
In SAP NetWeaver BW 7.0 we may buffer the number ranges to
compare the data load with records in-memory
 This speeds up data activation
•
However, in SAP NetWeaver BW 7.3, the data activation is
changed from single lookups to package fetch of the active table,
resulting in faster activation and less locks on the lookup tables
 This activation method results in 15-30% faster data activation
20
Example: Data Activation and Options
• During
activation, there was limited use
of buffering of number ranges for
dimensions and InfoObjects, even
when the number of entries were large.
Table
/BIC
/BI0
InfoObject
SGOBJKEY
SCO_ITEM_NO
SDOC_NUMBER
SAC_DOC_NO
SREF_DOC_NO
SBILL_NUM
SBA_BELNR
SCO_DOC_NO
SMAT_DOC
STCTSTEPUID
Buffer range
(thousands) level (thousands)
12,792
18,699
87,423
93,099
65,184
71,713
16,814
19,693
15,321
18,663
10,389
14,704
9,433
13,421
11,468
12,282
10,427
12,040
9,951
11,793
Rows
Buffer range
(thousands) level (thousands)
FIGL_C012 138,286
163,271
FIGL_C01
FIGL_C016
47,005
53,133
FICP_C053
10,172
29,655
FICP_C05
FICP_C059
6,268
18,808
3,281
15,740
FICP_C02 FICP_C021
FICP_C013
9,149
46,308
3,804
64,388
FICP_C01 FIAR_C014
FIAP_C017
19,139
25,844
69,264
79,758
FIIF_C02 FIIF_C022
InfoCube Dimension
Rows
This resulted in many reads on the
NRIV table that slowed down data
activation and process chains
(see SAP notes: 857998, 141497, 179224 and 504875)
Start buffering of number ranges of dimensions and
InfoObjects or use 7.3 data activation instead (‘packet fetch’)
21
More Data Load Ideas
•
In BW 7.3 for data transformations, the option “Read
from DataStore” for a faster data lookup is also available
•
Additionally, the use of navigational attributes as
sources in Masterdata transformations reduce overhead
for lookups
 Combined, this may lead to an additional 10-20%
improvement
The 7.3 initial load runtime option “Insert only” and the “Unique
data records only” prevents all lookups during activation and
can dramatically improve data loads when used correctly
22
More 7.3 Performance and Cockpit Capabilities
•
BW 7.3 monitors and cockpit capabilities also include:






Monitor of database usage and object sizes (i.e., InfoCubes, DSOs)
Query usage statistics are more visible (similar to RSRT, RSRV, RSTT)
We can see more of the use of SAP NetWeaver BW Accelerator and sizes
Monitor for the actual use of OLAP/MDX Cache and hit ratios
You can now selectively delete internal statistics in RSDDSTATWHM by
date through the updated RSDDSTAT_DATA_DELETE ABAP program
There is also a MDX Editor for coding and syntax assistance
Solution Manager has been
updated to take advantage of
these new monitors.
23
BW 7.3 Performance and Monitoring
•
Additionally BW 7.3 monitors include:

DEAMON update information (i.e., RDA capacity status, usage)

A performance monitoring workbench for performance trends

Process chain monitoring (transaction: RSPCM) with error and
active chain monitoring, user specific displays, and
performance threshold monitoring (i.e., for SLAs)
In SAP NetWeaver BW 7.3, the Near Line Storage (NLS) has been
enhanced to include archiving, support for write optimized DSOs
24
ETL Options for EDWs
In SAP NetWeaver BW 7.3 you can
create generic delta extraction for
the Universal Data (UD) and
Database Connect (DB) options,
as well as for flat files
• Additionally, you can use the
new DataSource adapter “Web
Service Pull” to load data from
external Web services
 You can even create generic Web services delta loads and load the
new data straight into the staging area of SAP NetWeaver BW 7.3
• While Web services does not fully support hierarchies yet, there is
integration of hierarchies into the standard process flow such as
transformation and DTPs, as well as being able to load hierarchies from
flat files using a new DataSource
•
25
The 7.3 DataFlow Generation Wizard
• SAP
NetWeaver BW 7.3 has a new, step-bystep wizard that allows you to generate data
flows from flat files or existing data sources
•A
great benefit is that the
wizards work against any
InfoProvider; i.e., you can use
the wizards to create loads from
DSOs to DSOs or InfoCubes
This wizard reduces the number or manual steps needed to load data. It also
simplifies the development process and makes ETL work much easier.
26
Example of Poor EDW Data Load Design
•
•
On an average daily data
extraction, transformation,
and load process takes
44.8 hours if run sequentially
A substantial amount of the
time is spent on data
transformation (51%) and
lookups are often done on
large DSOs without secondary
indexes.
ETL Statistics (average per day)
44.8
Total Load time (sequential) - hrs
11.1
- Time spent on source extraction - hrs
0.5
- Time spent on error filtering - hrs
22.9
- Time spent on transformations - hrs
10.4
- Time spent on target - hrs
371,407,881
Number of records extracted from source
125,102,791
Number of records written to target
100%
24.7%
1.1%
51.1%
23.1%
33.7%
•
Of the 371 million records extracted from
the source, only 33.7% are written to disk
•
This is due to lack of ability to do delta
processing for some files and also a
substantial amount of transform and
lookup logic in some of the ABAP rules
For this System, Developers should revisit extractor design
for lookups on source system instead of inside BW
27
What We’ll Cover …
•
•
•
•
•
•
•
•
Introductions
EDW Data Design and Data Modeling
Data Loading and Fast Activations
Tips and Tricks for Faster Query Times
Real-Time and Near-Time reporting for the EDW
EDW Reduction and Cleanup
In Memory Options with HANA
Wrap-Up
28
Database Performance (non-HANA systems)
•
Database statistics are used by the
database 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)
The current sampling rates for this example were too low, and
statistics should only be run after major data loads, and could be
scheduled weekly
•
For many systems, database statistics are outdated and may cause database
performance to perform significantly poorer than otherwise would be the case.
Sampling should often be changed and process chains may be re-scheduled.
29
Database Design for EDW: B-Tree Indexes on Large Objects
•
InfoCubes that are not flagged as high-cardinality use a Bitmap
index instead of a classical b-tree index for the joins.
•
This type of index does not get “unbalanced” since it uses
pointers instead of “buckets”
•
When updating these large InfoCubes, dropping and recreating
Indexes in the process chain can be very time consuming and
actually take longer than the inserts
•
It can also result in locks when the objects are very large (100
million+ records) and when attempting to do this in parallel (see
ORA-0060)
Rebuilding bitmap indexes in load processing for large objects
should not be a default answer for all designs. Any process
chains that do that, may need to be revisited.
30
Aggregates Are Not Needed with BW and HANA (or BWA)
•
•
•
•
At this company, there are 11 aggregates
in the system
Four are related to the cube ZIR_C01
and seldom used and (two has never
been used by any query)
The 7 other aggregates are used only
by the statistical cubes.
Every day, 1.9 million records are
inserted into the aggregates and take
35.6 minutes of processing time
Infocube
0TCT_C01
0TCT_C21
0TCT_C01
0TCT_C01
0TCT_C21
0TCT_C22
0TCT_C23
Number of records in
Aggregate
895,070
420,314
313,540
188,875
87,771
82,051
69,725
Delete unused aggregates. By reducing the data volume in the
underlying statistical cubes (cleanup), the remaining aggregates
will reduce in size and processing time.
31
The OLAP Memory Cache Size Utilization
•
The OLAP Cache is by default 100 MB for local and 200 MB
for global use
•
The system at this company was consuming no more than
80MB on average
This means that most queries were re-executing the same
data (good hit ratio of over 90%)
•
32
OLAP Cache — Turned Off
•
At one client, the OLAP cache was turned off for 131 out of
690 queries (excluding 4 planning queries in BW-IP)
•
The cache was also turned off for 24 out of 256 InfoCubes
•
The OLAP Cache mode for many of their queries could also
have been stored as “Binary Large Objects (BLOB),” that
could speed up caching and very large reads
For most companies queries are using CKF and
sums and sorts extensively, the cache read mode
for most queries should be turned on
33
Broadcast to Pre-Fill the Cache
•
•
This company’s Java Stack did not
communicate properly with SAP NetWeaver
BW, and multiple logons were required
As a result, broadcasting could not be used
until the connectivity was set up correctly
Set up Java connectivity ASAP and use the Broadcasting feature to prefill
the MDX cache (OLAP Universes) for BI analytical processing intensive
functions such as CKF, Sorts, Exceptions, Conditions
34
What We’ll Cover …
•
•
•
•
•
•
•
•
Introductions
EDW Data Design and Data Modeling
Data Loading and Fast Activations
Tips and Tricks for Faster Query Times
Real-Time and Near-Time reporting for the EDW
EDW Reduction and Cleanup
In Memory Options with HANA
Wrap-Up
35
HybridProvider and Real-Time Data
•
•
The “HybridProvider” (HP) is new in SAP NetWeaver BW 7.3. The core idea is to
link the historical data inside BW with real-time data.
There are two ways of implementing an HP:
 HP based on a DSO
 HP based on a Virtual InfoCube
Real-Time Data
Acquisition
HybridProvider
Transaction
Data
Indexing
ECC
and
External
Systems
BWA based
InfoCube
Direct
reads
Virtual
InfoCube
RDA
Real-Time
Data
Acquisition
Query
(real time)
Direct
Access
36
Option 1: The DSO-Based HybridProvider for EDWs




Real-time data is in the DSO and historical data in the SAP NetWeaver BW
Accelerator-based InfoCube
The DSO use real-time data acquisition (RDA) to load data
SAP NetWeaver BW automatically creates a process chain for the
HybridProvder’s data flow
The process chain is executed for every closed request
Real-Time Data
Acquisition
HybridProvider
Transaction
Data
Indexing
ECC
and
External
Systems
BWA based
InfoCube
Direct
reads
RDA
Real-Time
Data
Acquisition
Query
(real time)
Direct
Access
Virtual
InfoCube
This solution provides for really fast queries, but delta
logic has to be custom designed
37
The DSO-Based HybridProvider for EDW (cont.)
•
This solution provides for really fast queries, but delta logic has to
be custom designed and may be complex. However, the solution
allows for high-frequency updates and very rapid query response.
This is a good option if you
have a low volume of new
records and a high number
of queries or operational
dashboards
38
Option 2: The Virtual Cube-Based HybridProvider for EDW




Data is read in real-time from SAP ECC, while historical data is read from SAP
NetWeaver BW Accelerator
The difference depends on how often SAP NetWeaver BW Accelerator is loaded
Non-complex data logic can be applied
DTP is permitted if you do not filter the data set
Real-Time Data
Acquisition
HybridProvider
Transaction
Data
Indexing
ECC
and
External
Systems
BWA based
InfoCube
Direct
reads
RDA
Real-Time
Data
Acquisition
Query
(real time)
Direct
Access
Virtual
InfoCube
Warning: Virtual cubes with many users may place high-stress on the ERP system
39
What We’ll Cover …
•
•
•
•
•
•
•
•
Introductions
EDW Data Design and Data Modeling
Data Loading and Fast Activations
Tips and Tricks for Faster Query Times
Real-Time and Near-Time reporting for the EDW
EDW Reduction and Cleanup
In Memory Options with HANA
Wrap-Up
40
Database Growth and Cleanup - Example
The database has grown between
12 and 344 GB each month for
the last year
Three months of the year saw
data, logs, and PSA cleaned. Data
volume declined between 63 and
275 Gb those months
•
•
Oracle DB (Mb)
3600
3500
3400
3300
3200
3100
3000
2900
2800
2700
2600
2500
Oracle DB (Mb) Growth
The database has grown by
732Gb (26%) in the last year,
and the growth is uneven.
400
300
200
100
0
Feb-2010 Mar-2010
-100
-200
-300
Apr-2010 May-2010 Jun-2010
Jul-2010
Aug-2010 Sep-2010
Oct-2010
Nov-2010 Dec-2010
Jan-2011
Feb-2011
Schedule “housekeeping”
jobs. Better management of
cleanup would result in more
predictable patterns.
(i.e. we found PSA data that had 10
months of load history).
41
BW Data Reduction – Statistical Cubes
•
In our example, there were many statistical cubes with significant volume and
no real benefits. During the BW upgrade, most of these were not cleared and
are now creating poor system performance. For example:





0TCT_C02 has 408 million rows; others also had millions of rows
Stats are collected for over 1,900 objects, queries, InfoProviders,
templates, and workbooks (could be reduced significantly)
There are 8 aggregates with over 12 million rows on the stats cubes
Creating aggregates on stats cubes inserts 1.9 million rows and took 35.6
minutes for refresh each night
High-cardinality flags are set for small cube with only one million rows
(0TCT_C21)
Use RSDDSTAT and select “Delete Data” for old stats and also
schedule periodic jobs using standard process chains.
42
EDW on SAP HANA: 12 Cleaning Pre-Steps
1. Clean the Persistent Staging Area (PSA) for data already loaded to DSOs
2. Delete the Aggregates (summary tables). They will not be needed again.
3. Compress the E and F tables in all InfoCubes. This will make InfoCubes
much smaller.
4. Remove data from the statistical cubes (they starts with the technical
name of 0CTC_xxx). These contain performance information for the BW
system running on the relational database. You can do this using the
transaction RSDDSTAT or the program RSDDSTAT_DATA_DELETE to
help you.
5. Look at log files, bookmarks and unused BEx queries and templates
(transaction RSZDELETE).
6. Remove as much as possible of the DTP temporary storage, DTP error
logs, and temporary database objects. Help and programs to do this
is found in SAP Notes 1139396 and 1106393.
43
EDW on SAP HANA: 12 Cleaning Pre-Steps
7) For write-optimized DSOs that push data to reportable
DSOs (LSA approach), remove data in the writeoptimized DSOs. It is already available in higher level
objects.
8) Migrate old data to Near-Line Storage (NLS) on a small server.
This will still provide access to the data for the few users who
infrequently need to see this old data. You will also be able to query
it when BW is on HANA, but it does not need to be in-memory.
7) Remove data in unused DSOs, InfoCubes and files used for staging
in the BW system. This include possible reorganization of
masterdata text and attributes using process type in RSPC
44
EDW on SAP HANA: 12 Cleaning Pre-Steps
10) You may also want to clean up background information stored in the table
RSBATCHDATA. This table can get very big if not managed. You should
also consider archiving any IDocs and clean the tRFC queues. All of this
will reduce size of the HANA system and help you fit the system tables on
the master node.
11) In SAP Note 706478, SAP provides some ideas on how to keep the basis
tables from growing too fast too fast in the future, and if you are on Service
Pack 23 on BW 7.0, or higher, you can also delete unwanted masterdata
directly (see SAP Note: 1370848).
12) Finally, you can use the program RSDDCVER_DIM_UNUSED to delete any
unused dimension entries in your InfoCubes to reduce the overall
system size.
45
PSA Cleanup
•
For this company there were over 1.6 billion rows in the PSA
AGE of PSA
<1 MONTH
1-3 MONTHS
3-6 MONTHS
6-12 MONTH
1-2 YEARS
Total
•
•
Number of Records
591,774,063
738,204,015
144,476,834
173,651,469
35,421,883
1,683,528,264
Percent
35%
44%
9%
10%
2%
100%
Estimated
Gb
14.9
18.6
3.6
4.4
0.9
42.3
An estimated 29Gb could be freed up if data older than one month is
removed
A formal retention policy that is communicated and enforced should be
implemented
Create standard practices for PSA cleanup and
schedule regular jobs that take care of this in the future
46
What We’ll Cover …
•
•
•
•
•
•
•
•
Introductions
EDW Data Design and Data Modeling
Data Loading and Fast Activations
Tips and Tricks for Faster Query Times
Real-Time and Near-Time reporting for the EDW
EDW Reduction and Cleanup
In Memory Options with HANA
Wrap-Up
47
Looking Inside SAP HANA — In-Memory Computing Engine
Metadata
Authorization
Transaction
Manager
Manager
Manager
Relational
Engine
SQL Script
SQL Parser
-Row Store
-Column Store
Calculation
Disk Storage
Data
Volumes
Log
Session
Manager
MDX
Engine
Volumes
Load
Controller
Replication Server
Inside the Computing Engine of SAP HANA, we have many different components
that manage the access and storage of the data. This includes MDX and SQL
access, as well as Load Controller (LC) and the Replication Server.
BusinessObjects Data Services
48
Moving EDW to SAP HANA – Automated tool 1
SAP has a checklist tool for
SAP NetWeaver BW powered by
HANA (thanks Marc Bernard).
In this tool, SAP provided
automatic check programs for
both the 3.5 version and the 7.x
version of BW. These are found
in SAP Note: 1729988.
In version 2.x of this tool,
hundreds of checks are done
automatically in the BW system.
This includes platform checks
on database and application
There are even basis checks for support packs, ABAP/JAVA
and system information.
stacks, Unicode, BW releases, and add-ons to your system.
49
Moving EDW to SAP HANA – Automated tool 1
The idea of the checklist tool
from SAP is that you run it
several times throughout the
project.
Once before you start, then
periodically as you resolve
issues and upgrade
requirements, and then finally
when the system has been
migrated to HANA.
The checklist tool also has specific checks for the HANA system that can help
you identify any issues before turning over the system to end users..
50
Moving EDW to SAP HANA – Automated tool 2
SAP has released a new ABAP based tool that generates a report significantly
better sizing fro SAP BW than using just the QuickSizer above. This program
takes into consideration existing database compression, different table types
and also include the effects of non-active data on the HANA system.
The higher precision
you run the estimate at
the longer the program
is going to run.
With 14 parallel
processors and 8Tb
data warehouse, it is
not unusual to see 4575 minutes run time.
To increase speed,
you can also
suppress analysis
tables with less
than 1 MB size.
51
Moving EDW to SAP HANA – Automated tool 2
Since timeouts are common when
running the sizing program, you
can temporarily change the
parameter in rdisp/max_wprun_time
to 0 in BW transaction RZ11.
Finally, you estimate the growth for
the system as a percentage, or as
absolute growth.
After you have downloaded and installed the program, and-selected the parameters
above, you can go to SE38 and run SDF/HANA_BW_SIZING as a background job.
The output is stored in the file you specified and the file can now be email emailed
to hardware vendors for sizing input and hardware selection.
This program is attached to SAP Note: 1736976 on SAP Marketplace
52
Moving EDW to SAP HANA – Automated tool 3
Many experienced developers are
not aware that moving BW to
HANA can in some cases result in
slower transformations during
data loads.
a. Select for all entries (SFAE)
statements without HANA DB hints
--> add hints
b. Select * --> specify fields to select
c. Database access in the field routines
--> move to start routine
d. Loops which do not assign field
symbols --> use field symbols
e. Selects from master data tables -->
Use the read master data rule
f. Selects from DSOs --> Use the read
DSO rule
g. Direct updates to BW object tables -> Do not update tables directly
53
Hardware Options for EDW on SAP HANA
There are currently 7 different
certified HANA hardware vendors
with 13 different products.
Memory
Hardware
128GB
Cisco C260
X
Cisco C460
256GB
X
X
X
X+
X
Dell R910
X
X
X
Hitachi CB 2000
X
X
X
X
X
X
X
NEC Express 5800
Fujitsu RX 600 S5
X
Fujitsu RX 900 S2
HP DL 580 G7
X
X
X
HP DL 980 G7
X+
X+
X
X
X
X+
HP BL 680
X
X
X
IBM x3690 X5
X
X
X
X
X
IBM x3950 X5
1024GB
X
Cisco B440
Some boxes can be used as single nodes
with others are intended for scale-out
solutions for large multi-node systems
512GB
X+
54
A HANA Hardware Example
In this box, we are
see the inside of an
IBM x3950 HANA
system.
The system
basically consists
of memory, disk,
processors and
network cards
The hardware vendor will install, connect and to a health check on your system
before handing it over to you. A 3-year service plan is also normally required.
55
What We’ll Cover …
•
•
•
•
•
•
•
•
Introductions
EDW Data Design and Data Modeling
Data Loading and Fast Activations
Tips and Tricks for Faster Query Times
Real-Time and Near-Time reporting for the EDW
EDW Reduction and Cleanup
In Memory Options with HANA
Wrap-Up
56
SAP BW Roadmap
Source: SAP May 2013,
https://scn.sap.com/docs/DOC-36273
BW 7.4 SP05 Should be released this fall. BW 7.3 SP09 is already available
57
Additional Resources
•
An Introduction to SAP HANA

•
SAP NetWeaver 7.4 on SAP Community Network

•
http://scn.sap.com/docs/DOC-35096
LSA Templates and Architecture in SAP NetWeaver BW 7.3

•
Dr. Berg and Penny Silvia, SAP Press 2013, 2nd Edition
www.sdn.sap.com/irj/scn/weblogs?blog=/pub/wlg/24800
Roadmap – SAP NetWeaver BW 7.3

www.sdn.sap.com/irj/sdn/bw-73?rid=/library/uuid/300347b59bcf-2d10-efa9-8cc8d89ee72c
58
7 Key Points to Take Home
•
SAP BW has come a long way since version 7.0 and there are
significant new capabilities for EDW in later releases
•
Do not rely on old ‘knowledge’ – Big Data is changing everything!
•
Near-time and Real-time EDWs are gradually becoming the norm
•
Start a long-term plan for moving to HANA (you will do it eventually)
•
Costs of EDWs are too high due to complex data logic, design and
end of useful life of relational databases for high data volumes
•
All queries in your future EDW should run under 20 seconds, and
50% of these should be under 10 seconds.
•
Design is not evolutionary, it is deliberately created.
59
Your Turn!
How to contact me:
Dr. Bjarne Berg
[email protected]
Please remember to complete your session evaluation
60
Disclaimer
SAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet®, PartnerEdge, and other SAP products and services mentioned herein as well as their
respective logos are trademarks or registered trademarks of SAP AG in Germany and in several other countries all over the world. All other product and
service names mentioned are the trademarks of their respective companies. Wellesley Information Services is neither owned nor controlled by SAP.
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