Best Practices for Optimal Data Models in SAP HANA and Analytics in SAP BusinessObjects 4.0 - Webinar Presentation

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SAP HANA 1.0

Best Practices for optimal data

models in SAP HANA and analytics in

SAP BusinessObjects 4.0

Patrice Le Bihan

Werner Steyn

Customer Solution Adoption (CSA)

January 2012

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Disclaimer

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Agenda

 General Modeling Principles

 Restricted Measures vs Logical Partitioning

 Calculated Attributes - Row vs Sets

 Calculation Before Aggregation

 Calculation Views - SQL vs Calculation Engine Functions

 Join Types and Referential Integrity

 Do’s and Don’ts with SBO Explorer

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General Modeling Principles

Column Store Analytical Views Attribute Views Calculation Views Client / Application A B C D A B C D G Y A G Y A G Y Z

Filter data amount as early as possible in the lower layers (CONSTRAINTS, WHERE Clause, Aggregate data records (e.g using GROUP BY, reducing Coulmns)

Avoid transfer data of large resultsets between the HANA DB and client application

- Do calculation after aggregation.

- Avoid Complex expressions (IF, CASE, ... )

Join on Key Columns or Indexed Columns

Avoid calculations before aggregation on line item level Reduce data transfer between views

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Agenda

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Modeling Example 1

Restricted Measures vs Logical Partitioning

Analytical View

• Define 3 Restricted Measures - once for each PLANT (A,B & C) within a single Analytical View

Multiple Analytical Views + 1 Calculation View • Create 3 Analytical views – one for each PLANT

• Define a design time filter (PLANT) on each Analytical View • Create a Calculation view that reads each Analytical view • Combine the results using Union Constants

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Agenda

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Modeling Example 2

Calculated Attributes - Row vs Sets

Calculation View

• Re-use the same Analytical View 3x

• Define a Current Year Key filter in each Projection Node • Combine results using Union Constants

Analytical View

• Define 4 Calculated Attributes

Filter [3] Filter [2]

Analytical View + 1 Calculation View

• Create a Analytical View without any Calculated Attributes, instead define Calculated Columns in a Calculation View • Create a Calc. View reading from a single Analytical View • Add one Projection Node above the Analytical View & add

Calculated Columns

• Stack 3 Projection nodes and Filter by Current Year Key

• Combine results using Union Constants

Filter [1]

Filter [1] Filter [3]

Filter [2]

Current Year Filter

Current Year [midstr(now(),1,4)]

Previous Year [ (“current_year")-1] Earliest Year [(“previous_year")-1]

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Agenda

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Modeling Example 3

Calculate Before Aggregation

Analytical View

• Define Calculated Measure threshold

(i.e. NETWR > 99) using the Expression Editor • Check Calculate Before Aggregation

• The result will either be 1 or 0 per row

• Use regular SUM aggregation to calculate count based on threshold calculation

Use with caution, row based processing significantly slower than set based processing

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Agenda

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Modeling Example 4

Calculation View – SQL vs CE Functions

Calculation Engine (CE) Functions

• Preferred over SQL

• Improved performance, can be optimized by the engine (i.e. field pruning & parallelized)

SQL

CE Functions

Even though only MATNR & NETWR are requested by the front end tool all fields will be

retrieved from the table including all calculations will be done irrespective.

Only MATNR & NETWR will be retrieved from the database. Only NETWR will be calculated. Columns requested by the front end tool can be

parallelized

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Agenda

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Join Types – Definitions and Referential Integrity

Join Type Use when you need to report on…

Be aware that…

INNER facts with matching dimensions only

facts without any dimension will be excluded dimensions without any fact will be excluded JOIN is always performed

LEFT OUTER all posted facts whether there is a matching dimension or not

dimensions without any fact will be excluded best for performance because JOIN is omissible

RIGHT OUTER all dimensions whether there are matching facts or not

facts without any dimension will be excluded JOIN is always performed

REFERENTIAL facts for the requested dimensions

AND referential integrity is ensured

• it is the default join type

• acts as an INNER for Attributes Views

• join on attribute views is “optional”, which facts are returned will depend on which attributes are

queried for

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Join Types - Example

Customer

( … Right table / Header / Dimension for sales) ( … Central / Left / Many table for state

Sales

( … Fact table / Central / Left / Items / Many)

C_ID ORDER_ID AMOUNT

1 1 100

1 2 100

2 3 100

4 4 100

77 8 100

C_ID CNAME STATE AGE

1 WERNER MI 10 2 MARK MI 11 3 TOM TX 12 4 BOB TX 13 STATE SNAME MI MICHIGAN AL ALABAMA

State

( … Right table / Header / Dimension for customer)

Notes:

 Order 8 does not have a customer master record  Customer TOM does not have any orders

 State TX does not have a description  No customers reside in Alabama

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Join Types – Referential Join in Attribute Views

C_ID CNAME STATE AGE SNAME

1 WERNER MI 10 MICHIGAN

2 MARK MI 11 MICHIGAN

SELECT

C_ID, CNAME, STATE, AGE, SNAME

C_ID CNAME AGE

1 WERNER 10

2 MARK 11

SELECT

C_ID, CNAME, AGE

*** Referential join is a feature available only in OLAP engine, when testing Attribute views outside the context of a Analytical view then the Join Engine will perform a Inner Join. As a result TOM and BOM will not be returned.

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Customer

Sales

2 table referential join in Data Foundation. No

dimensions.

*** Customer table join will be omitted since only fields are selected from the left table

*** Since the field AGE is selected from the right table then a inner join is preformed resulting in order 8 not being returned.

*** Since there are no dimensions joined to C_ID all the facts will be returned.

C_ID AMOUNT

1 200 2 100 4 100 77 100

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Join Types – Text Join for multilingual reporting



Text Join is used when

translation for a dimension is available



Designed for ERP table (and typically SPRAS field)



Act as an INNER join



User language is used as a filter at runtime to find the right translation for that attribute

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Agenda

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Do’s and Don’ts with SBO Explorer

Recommendation Description

1 Only include dimensions (facets) the end user absolutely needs, 6-10

facets seem to work for most cases

2

If the end user needs more facets but response time is the priority, split the Information Space in two spaces.

Restrict each Information Space to one or two hierarchies (navigation paths)

3 The first measure on the list should be one that leads to the easiest /

lightest calculation (among all available measures)

4a Use the "Show facets in the defined order" option when the facet order _{matters to end users and select a “fast” dimension first} 4b Use the "Order facets by filtering capability“ option if the facet order is not

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SBO Explorer – Recommendation #1

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SBO Explorer – Recommendation #1

Only include dimensions (facets) the end user absolutely needs 6-10 facets seem to work for most cases

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SBO Explorer – Recommendation #2

If the end user needs more facets but response time is the priority, split the

Information Space in two spaces. Restrict each Information Space to one or two hierarchies (navigation / drill paths).

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SBO Explorer – Recommendation #3

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SBO Explorer – Recommendation #4a

Use the "Show facets in the defined order" option when the facet order matters to end users and select a “fast” dimension first

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SBO Explorer – Recommendation #4b

use the "Order facets by filtering capability“ option if the facet order is not so important, Explorer will determine the optimal order

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Agenda

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Drill functionality in SBO WebIntelligence



Navigate along a hierarchy defined as a navigation path in a universe



Only fetch data you need as you select a member and drill one level down



Query data on demand as opposed to everything upfront



Leads to (much) lighter reports, faster response times and better user experience

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Drill functionality in SBO WebIntelligence

Navigate along a hierarchy defined as a navigation path in a universe

Only fetch data you need as you select a member and drill one level down

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Drill functionality in SBO WebIntelligence

WebIntelligence

Query only the top level of the hierarchy

As opposed to all levels upfront

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Drill functionality in SBO WebIntelligence

Start Drilling and navigate along hierarchies…

Down Up

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More Info

 http://help.sap.com/hana

 Previous Know How Webinars on SAP HANA :

 http://www.sdn.sap.com/irj/scn/khnc

 Building Advanced Data Models with SAP HANA:

http://www.sdn.sap.com/irj/scn/events?rid=/library/uuid/208df7db-74bd-2e10-adac-b47ea848e5af

 How to best leverage SAP BusinessObjects BI 4.0 on SAP HANA 1.0:

http://www.sdn.sap.com/irj/scn/events?rid=/library/uuid/201d220d-11cc-2e10-39bf-e6cf6d42438b

 Lessons Learned from the Provimi HANA Project:

http://www.sdn.sap.com/irj/scn/events?rid=/library/uuid/30f565bb-2703-2f10-bab9-845b7a71da88

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Thank You!

Patrice Le Bihan – [email protected] Werner Steyn – [email protected]

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Best Practices for Optimal Data Models in SAP HANA and Analytics in SAP BusinessObjects 4.0 - Webinar Presentation

SAP HANA 1.0