Ever since BW on HANA had been introduced, I read many articles discussing the improvement that it can deliver. These articles provided these conceptual experiences that resulted in specific benefits, features and overall improvements. I recently had the experience to execute a true POC that compared a BW on Oracle system with a BW on HANA environment. This was the first time I was able to turn the conceptual into practical and I wanted to share these results with you. Though the system that I was working with was not terabytes, I think I was able to clearly quantify the HANA benefit. Let me know what you think and I hope that you enjoy.

Key Benefits

Company A is currently reviewing the capabilities to implement BW on HANA.  The main reasons to implement this tool are the following:

• Improve end user data accessibility – A few teams are limited with the data sets that they are trying to execute.  The current data model has been effectively implemented but one of the main reasons for the deficiencies is the way the tool is currently used.  End Users are executing queries that are returning over a million plus rows and have complex restricted and calculated key figures.
• Reduce development life cycles – Due to consistent changes as the system matures, we need to be able to reduce the development life cycles
• Reduce data model – The Company A data model has become very complex over the last few months and it continues grow in complexity.
• Future development – As the data model continues to grow, the volume will also continue to expand. Leveraging BW on HANA will allow the database to be scalable moving forward.  Not only does it provide the in-memory capabilities but it will also provide the following:
  • Scalability – As Company A introduces additional functional areas, we will not have an immediate concern for performance implications.
  • Predictive Analytics – In the future, Company A may implement both a fully functional planning solution and predictive capabilities.  BW on HANA provides the extended and improved capabilities with the integration with the Predictive Analytical Library
  • Real Time Operational Reporting –– With the enterprise solution, HANA Live can be introduced to support real time operational reporting
  • BW real time extraction – With BW 7.4, SLT will support real time (trigger based) data extraction.  Please note, not all DataSources provide these capabilities but this will continue to grow until the main capabilities are supported.

The following methods were measured to determine the immediate improvements between BW on an Oracle Database and BW on a HANA Database:

• DSO Activation times
• InfoCube Load Times
• InfoCube Size
• Query run times

In order to successfully achieve the above measurements, the following systems, transactions and tools were used:

• Data Model Benchmarks
  • BWP vs. BWX
    • Please note that these systems were a snapshot at a specific time so the data sets are not the exactly the same
  • Transaction RSA1 – This transaction is used for all data modeling executions.  These benchmarks use SAP Best Practices in order to create a Pulse Check to compare the systems
• Query Runtime
  • Business Explorer Analyzer (BEx) – This is the tool that leverages Microsoft Excel and is embedded
  • RSRT – This is the embedded BW tool that leverages the application server and mimics the execution within the Web

Data Model Benchmarks

The following prerequisites were executed prior to all benchmarks:

• Optimize all BW on HANA InfoCubes
• Optimize all BW on HANA DSO’s (new DSOs that were copied from original source DSO)
• Creation of improved Data Model

BW on Oracle (BWP)

The following measurements are 2 of the larger DataStore Objects within BWP.  We are documenting how long it takes to activate a DSO.

DSO	Activation Time (seconds)	Activation Time (minutes)	Activation Time (hours)
DSO1	1232	20.53333333	0.342222222
DSO2	8206	136.7666667	2.279444444

The following measurements are the 4 main InfoCubes that are leveraged within the Sales and Distribution functional area.  These are the main sources for reporting for both the Finance and Demand Planning teams. None of these InfoCubes have aggregates but 2 of them are compressed within a 7 day time period (every 7 days).

InfoCube	Load Duration (minutes)	Load Duration (hours)	# of Records
InfoCube1	22	0.366666667	8,456,590
InfoCube2	18	0.3	3,195,159
InfoCube2	97	1.616666667	3,592,243
InfoCube2	75	1.25	3,745,513

BW on HANA (BWX)

Since the BW on HANA System is not connected to the PRD system, we are unable to e51xtract from this system directly into BWX.  In addition, we delete the Persistent Staging area on a weekly basis.  In order to compare the above data loads, we have replicated the DSOs and InfoCubes, which will be sourced from the above Data Targets. This will not provide an exact comparison but similar since the structures are the same.  As stated below, the number of records will be less in our BW on HANA System.

BWX DSO’s

DSO	Activation Time (seconds)	Activation Time (minutes)	Activation Time (hours)	# of Records
DSO1	51	.85	0	8,458,642
DSO2	38	.63	0	3,218,010

BWX InfoCubes

InfoCube	Source Target	Load Duration (minutes)	Load Duration (hours)	# of Records
ZHANA01	DSO1	4	.06	8,458,642
ZHANA02	DSO2	11	.18	3,723,589
ZHANA02	DSO3	9	.15	3,791,004
ZHANA02	DSO4	9	.15	3,176,558

Comparisons

In the below DSO Activation comparisons, you can see they are all greater than or equal to 95%.

DSO	Activation Time (seconds) – BWP	Activation Time (seconds) – BWX	Variance	% improvement
DSO1 vs. DSO3	1232	51	-1181	95%
DSO 2 vs. DSO4	8206	38	-8168	99.5%

Please note***we are loading DSO1 into DSO2, while DSO1 is loaded from 2 different sources so there is more records that need to be aggregated together.

In the below InfoCube comparisons, you can see how changes to the data model will be reduced with maintenance time with BW on HANA.  The load times have decreased by >50% in all cases and in some instances at least 90% better

InfoCube	Load Duration (minutes) – BWP	Load Duration (minutes) – BWX	Load Duration Variance	% improvement
InfoCube1 vs. ZHANA01	22	4	-18	81%
InfoCube2 vs. ZHANA02	18	11	-7	38%
InfoCube2 vs. ZHANA02	97	9	-88	90%
InfoCube2 vs. ZHANA02	75	9	-66	88%

Query Runtime

There were 2 approaches taken in order to execute queries:

• Executing the reports against the current architecture
• Creating a more efficient architecture and recreating a similar query to see performance improvements

With BW on HANA, there are areas that improvement will be realized but other areas that it can’t be realized. All improvements will be recognized within the database.  After the data is moved from the backend database to the frontend database, the

Within the query runtime, we want to measure some of the following metrics:

OLAP Related events

Event	Description
OLAP: Query Generation	This event measures the time that is needed to check the query definition and, if necessary, to generate the query
Data Manager	Structure-/format-converting for frontend presentation. It can be optimized with help of compression & aggregates.  If the duration time of all events 9000 (Data Manager) is higher than all other times then BW on HANA will provide a significant improvement
Total DBTRANS	Total Number of Transported Records
Total DBSEL	Total Number of Read Records
OLAP: Data Selection	This event measures e.g. the time taken to sort the read data (from DM) according to structure element selections or restricted key figures. The calculation of virtual key figures is done here as well.
OLAP: Datatransfer	In this part of the coding many OLAP features are processed, e.g. exception aggregations are carried out, formulas are calculated, and the correct number of decimal places for the data cells is determined. The time can be optimized by using cache. The number indicates the number of cells.
OLAP: Read Texts	Texts are read to sort the data. This event measures the time taken to call the text read class

Front-End related events in detail

Event	Description
Bytes Transferred	The number indicates the transfered bytes for one step.
Load Workbook	This is the time for transfering the workbook from backend to temp. folder of client.
Generate Workbook	This time is needed for generation of the workbook.
Not Assigned	This is a collective event for all parts that are not explicitly assigned to an event. The parts that are not assigned to an event are not belonging to the backend or frontend. This time is spend somewhere in between, durring the frontend backend communication. In most of the cases this is the network load (WAN). Especially if your Citrix Servers are located far from the application servers and the network load is high. This is the time which belongs to data transfer in the landscape after leaving the BW server and before reaching the frontend tool, it includes complete time for network-traffic and occurs for every data transfer between BW Server and frontend. To improve the performance in the frontend/backend communication you should apply following notes: 1352375 – Performance improvements in a WAN (roundtrip reductions) > ANA_USE_SIDGRIDWBUF/ANA_USE_SIDGRIDMASS 1179647 – Performance: Network load in BEx Analyzer > ANA_USE_TABLE
Total DBSEL	How many cells reads the database
Number of Excel Cell	This is the amount of data which is sent across the network.
Render Item	This is the Excel formatting time for rendering the BEx items. In column Statistic Object Name you can identify which BEx Item consumes the time. You can reduce it by using the following analysis grid properties: – Apply Formatting: deactivate – Display Hierarchy Icons: “display +/-” See Online Help: Analysis Grid
Wait Time, User	It is the time when the application was waiting for user action.

The first query that was executed was Query XXXX, which is a report which supports the Team X. 

The following were used:

• Transaction RSRT
• HTML
• No Cache
• Track Statistics

The navigation that occurred within this report is the following:

• Drill Across on Calendar Year/Month
• Drill Down on Material

Event (BWX)	Duration  (BWX)	Event (BWP)	Duration (BWP)	# of records	% Improvement
Data Manager	1.993154	Data Manager	34.062514	0	94.1%
Total DBTRANS	0.000000	Total DBTRANS	0.000000	114,476
Total DBSEL	0.000000	Total DBSEL	0.000000	5,008,276
OLAP: Data Selection	3.944361	OLAP: Data Selection	21.840990	0	81.9%
Cache Commit	0.000444	Cache Commit	0.000548	0
OLAP: Read Data	0.003076	OLAP: Read Data	0.004482	23,263
OLAP: Read Texts	0.153941	OLAP: Read Texts	0.436043	0
OLAP: Data Transfer	18.624689	OLAP: Data Transfer	39.310077	570,102	53.8%

Query XXXXX

The following were used:

• Transaction RSRT
• HTML
• No Cache
• Track Statistics
• Selection Criteria – 0CALMONTH Range – 01/2014 – 12/2014
• Created a Y query in both systems using the original Query XXXXX as the baseline
  • Characteristics included in the initial layout are the following:
    • Sold-To – Sales District
    • Sold-To – Customer Group
    • Sold-To – Industry
    • Sold-To Party
    • Sold-To – Broker
    • Material – Brand
    • Material
    • Calendar Month (Columns)
    • Key Figures Included are the following:
      • Restricted Key Figure 1
      • Restricted Key Figure 2
      • Restricted Key Figure 3
      • Calculated Key Figure 1
      • Restricted Key Figure 4
      • Restricted Key Figure 5
      • Restricted Key Figure 6
      • Restricted Key Figure 7
      • Calculated Key Figure 2
      • Calculated Key Figure 3

System	Transaction	Report (Technical Name)	Executed?	Time	# of Rows	Comments
BWP	RSRT	Query XXXXX – Non HANA	Yes	11 minutes	1,409,796
BWX	RSRT	Query XXXXX – HANA based	Yes	4 minutes	1,383,192

     

Event	BWP Duration	# of rows	BWX Duration	# of rows	Comments	%
Data Manager	79.439548	0	11.515888	0		85%
Quantity Conversion	7.105138	0	3.508917	0
Total DBTRANS	0.000000	1,186,632	0.000000	972,297
Total DBSEL	0.000000	2,435,735	0.000000	2,142,308
OLAP: Data Selection	82.334437	0	23.708521	0		71%
Cache Commit	0.000854	0	0.000578	0
OLAP: Read Data	20.268199	1,376,757	2.968213	903,120
OLAP: Read Texts	0.487236	0	0.280800	0
OLAP: Data Transfer	425.246616	9,688,374	193.350133	4,253,660	Restricted Key Figures	54%

In the below 2 tables, you can see the aggregated access to the main tables in BWP and BWX.  Within BWP, you will see both F and E Tables since there are compressed and uncompressed tables that need to be read. Within BWX, since there are no dimension or fact tables, this is skipped and the read time is drastically lower. This provides a huge improvement within the database access and the amount of time that it takes to start rendering the data.  This provides further explanation on why it took 11 minutes in BWP vs. 4 minutes in BWX.

Basic InfoProvider – BWX	Aggregate	Table Type	Read Time
InfoCube1	InfoCube1$X		0.045058
InfoCube2	InfoCube2$X		0.242016
InfoCube3	InfoCube3$X		0.074789
InfoCube4	InfoCube4$X		4.390364
InfoCube5	InfoCube5$X		7.740808 0.000000

Scenario 1:

Query: XXXXXX

Variable Values: 01/2014 – 03/2014

Key Figures to be included are the following:

• Restricted Key Figure 1
• Restricted Key Figure 2
• Restricted Key Figure 3
• Restricted Key Figure 4
• Calculated Key Figure 1

Execution Steps:

• Execute Report in RSRT
• Drill Down on Free Characteristic 1
• Drill Across with Key Figures
• Filter Key Figures not required
• Sort Restricted Key Figure 4
• Filter Hierarchy on NODEX
• Drill down on Item
• Swap Item with Characteristic 2
• Delete Filter on NODEX and switch to NODEY
• Start back with NODEX and Free Characteristic 1
• Select Free Characteristic 2
• Filter down by Free Characteristic 3 on Free Characteristic 2
• Complete

BWP – 6:14 minutes (368 seconds)

BWX – 2:40 minutes (144 seconds)

1. 60.8% improvement

Basic InfoProvider – BWP	Aggregate	Table Type	Read Time
InfoCube6	InfoCube6	F	0.922992
InfoCube5	InfoCube5	F	4.884891
InfoCube3	InfoCube3	F	4.968414
InfoCube4	InfoCube4	F	8.535001
InfoCube7	InfoCube7	F	11.981518
InfoCube1	InfoCube1	F	40.520607
InfoCube4	InfoCube4	E	74.700701 0.000000

Event	Duration BWX	Duration BWP
Data Manager	1.175035 50.377441
Quantity Conversion	0.240998 2.132771
Total DBTRANS	0.000000 0.000000
Total DBSEL	0.000000 0.000000
OLAP: Data Selection	0.181115 19.870039
Cache Commit	0.000088 0.000111
OLAP: Read Data	0.006416 0.015813
OLAP: Data Transfer	0.002840 0.003708

In the above screen shot, you can see the substantial improvement within the Data Manager.  As SAP has stated that if the Data Manager time duration is longer than all of the other events, SAP HANA can greatly improve the overall performance

Execution Steps:

The time that is listed is the overall duration that it took to get to that step.  It does not mean the amount of time it took that step to execute.  The final execution is the overall duration for the end to end processing.

Step	BWP	BWX
Execute Report in RRMX	1:39 minutes	:17 seconds
Drill Down on Free Characteristic 1	2:56 minutes	:42 seconds
Drill Across with Key Figures	3:26 minutes	:54 seconds
Filter Key Figures not required	3:48 minutes	1:16 minutes
Sort Key Figure 1	3:58 minutes (1733 records)	1:26 minutes (1414 records)
Filter Hierarchy on NODEX	4:44 minutes	1:42 minutes
Filter on Top 10 Characteristic 2	4:49 minutes	1:48 minutes
Drill down on Free Characteristic 3	5:03 minutes	1:56 minutes
Swap Characteristic 3 with Free Characteristic 4	6:01 minutes (361 seconds)	2:56 minutes (176 seconds)
Delete filter on NODEX
Switch to NODEY
Return to Free Characteristic 1/Free Characteristic 2
Select Free Characteristic 2 (Top 10)
Filter down by Free Characteristic 5 and 6

Improved Architecture:

One of the benefits of BW on HANA is to reduce the overall data model, remove duplication and simplify the loading process.  In order to do this moving forward, Company A will be required to review the overall data model that has been currently created for the implemented modules.  In order to determine if there is a significant performance improvement, we created a quick remodel of a few of the objects (testing purposes only and will not include the complete data set).

Our main objective is to remove the InfoCube layer and leverage the DSO layer for all reporting. A MultiProvider will still be the reporting layer in order to create the necessary unions.

Prior Architecture object type	Prior Technical name	New Object Type	New Technical Name
InfoCube	InfoCube4	DSO	DSOHANA – HANA POC
InfoCube	InfoCube2	DSO	DSOHANA2
MultiProvider	MultiProvider1	MultiProvider	ZHANAM – HANA MultiProvider

A few notes on the below stats:

• The number of objects in the MultiProvider were less
• Only the data that is used in the report were included in the data targets
• SPO was used against the Forecasting data by version which may not be feasible within production.  SPO capabilities may still be used in production but by year

Step	BWX (new architecture	BWX	% Improvement
Execute Report in RRMX	:04 seconds	:17 seconds	76%
Drill Down on Free Characteristic 1	:07 seconds	:42 seconds	83%
Drill Across with Key Figures	:12 seconds	:54 seconds	77%
Filter Key Figures not required	:21 seconds	1:16 minutes	72%
Sort Key Figure 1	35 seconds (1317 records)	1:26 minutes (1414 records)	59%
Filter Hierarchy on NODEX	48 seconds	1:42 minutes	53%
Filter on Top 10 Characteristic 2	51 seconds	1:48 minutes	53%
Drill down on Free Characteristic 3	54 seconds	1:56 minutes	54%
Swap Characteristic 3 with Free Characteristic 4	1:35 minutes (95 seconds)	2:56 minutes (176 seconds)	47%
Delete filter on NODEX
Switch to NODEY
Return to Free Characteristic 1/Free Characteristic 2
Select Free Characteristic 2(Top 10)
Filter down by Free Characteristic 5 and 6

As you can see the largest improvements occur in the beginning when the database is accessed. After this, the improvements start to level off because the application server is working through the most of the tasks while the database is accessed less.