SAP HANA Development Basics PDF

SAP HANA Development
Basics
December, 2017 Confidential
Agenda
• SAP HANA Architecture • SQLScript and Procedures
• SAP HANA Studio Overview • Export and Import
• Data Provisioning Overview and SAP • HANA Performance

Landscape Transformation (SLT)
• User Management & Security
• SAP HANA Overview
• A Data Consumption example using Microsoft
• SAP HANA Modeling: Working with Tables Excel
• SAP HANA Modeling: Calculation Views
• Variables and Input Parameters
© 2017 SAP SE or an SAP affiliate company. All rights reserved. Confidential 2

SAP HANA Architecture
Advances in Hardware made SAP HANA possible
Dramatically improved hardware economics made SAP HANA possible.

Understanding them is important to understand HANA’s performance characteristics.
Multi-core architecture
 8 CPUs x (8-16) cores per blade
 Parallel scaling with many blades
 64-bit address space – 4 TB in current server boards
 Dramatic decline in price/performance
https://global.sap.com/community/ebook/2014-09-02-hana-hardware/enEN/index.html

Overcoming old constraints with SAP HANA
Three-tier application architecture resulted

from several constraints:
 Data does not fit into server memory. Application Server
 Disk I/O is a performance bottleneck.
 The network is a performance bottleneck. CPU
 CPUs have one core, with no true parallelism. Core

…
CPU Cache
Some of these constraints have changed: Now
 Now data transfer between memory and CPU is Main Memory
the most critical performance bottleneck. Past
Disk

Advances in Software made SAP HANA possible
Row and Column Store
Compression
Partitioning
Avoiding Aggregate Tables
Insert Only on Delta +

+ ++

What about Data Modifications?
UPDATE ORDER
Technically, SAP HANA is an SET Sales = 1050
“insert-only” DBMS: WHERE ORDER = 457
 Speeds up data modifications Column Column Column
 Applies modifications to delta store first Order Product Sales
 Less optimized columnar store Value Value Value
Delta Store
1 456 1 corn 1 1000
2 457 2 wheat 2 1050
3 458 3 corn 3 600
4 459 4 rice 4 800
Applications can still insert,
update, and delete data.
Main Store
Value Value Value
… … … … … …
102 457 102 wheat 102 900
… … … … … …

Delta Store is merged with Main Store as required
Before delta merge operation: Write
 Delta store grows Main Delta

Store Store
During delta merge operation: Read

 Read/write access still works on old Merge Operation Write
stores and new delta store
Main New Delta New
Store Main Store Delta
After delta merge operation:
Read
 Data optimized for fast access again
Write
New New
Main Delta
Read

Expectations around SAP HANA
SAP HANA Architecture
3600x 460B 21%

Faster reporting Data records analyzed in Average increase
speed less than a second in revenue

Implications for developers
In-memory computing imperative:

 Avoid movement of large data volume.
 Calculate first, then move results.
Today:
Data-intense
Application Layer
computations in
application layer
SAP HANA:
Data Layer Delegate
data-intense
operations to
data layer

SAP HANA in the Cloud
• SAP HANA is also available in the Cloud

• You can use SAP HANA side-by-side as standalone appliance, or as SAP
Business Suite on SAP HANA
• SAP HANA One is available on subscription basis
• https://cloudplatform.sap.com/

Exercise: SAP HANA Architecture
There are no exercises for this Unit

SAP HANA Studio Overview
The SAP HANA Studio at a glance
• The SAP HANA Studio provides an environment for modeling, development,

administration, and data provisioning.
• The SAP HANA Studio is a Java based application that runs on the Eclipse Platform and
enables developers and modelers to create models and stored procedures, manipulate
tables and issue queries on top of SAP HANA.
• Modelers can create Information Models based on the raw tables.
• Developers can develop application logic on top of SAP HANA using SQLScript, a
collection of extensions to SQL that pushes down data intensive logic into the
database. They can also create UI5 JavaScript code to accompany server based
procedures.
• You can also monitor and configure some aspects of the data provisioning (replication,
extraction) and debug procedures, create and view traces, and monitor the system status.
• Available at: https://tools.hana.ondemand.com/#hanatools

Starting the SAP HANA Studio
When you start the SAP

HANA Studio you can
decide on several
perspectives, as
predefined Layouts for
different roles. Users can
choose their preferred
perspective according to
their role.

First steps with the SAP HANA Studio
• Developers will mostly work with the SAP HANA Development or Modeler perspective (you need
to switch to it manually the first time)
• The Administration console is useful to manipulate the SAP HANA data dictionary, manage
schemas, import/export data, perform interactive DB queries

Adding Systems (1)
How to add an SAP HANA System:
Right-click on the Systems panel:
Click on Add System
User names for each

SAP HANA system
1
SAP HANA
System names

Adding Systems (2)
• Enter Host Name and Instance Number and click on Next
• Enter User Name and Password and click on Finish

Adding Users: Changing Passwords
How do I change my password?

• Double-click on your User under Security  Users, enter new password and press Deploy (F8).
• Then don’t forget to change your password also in the Systems panel (right click on the system,
and then select Properties, and in the Database User Logon, change the previously saved
password)

Adding Users: Authorizations at a glance
Authorizations, Roles and Privileges:

• Using the Users Folder under Security, you can assign roles and different kinds of privileges to
users
• Double-click on your User under Security  Users
• Example: You can enable users to create schemas for their own SAP HANA projects by adding the
System Privilege “Create Schema”

Working with the Catalog
The Catalog in the SAP HANA Studio

• It contains the data Tables for your SAP HANA project
• Catalog data is organized in Schemas
• Data tables can be loaded from different sources, for
example, they can be replicated from SAP ERP, they can be
imported from files, etc.
• You can also create new tables with the SAP HANA Studio
• Under the Catalog you can also find Column Views (system
internal representations), Procedures and Views. However,
in most cases, Views and Procedures will be rather created
as Content (more details about the Content will be explained
in further lessons).

Working with Schemas
What are Schemas?

• They are separate name spaces for the different SAP HANA
catalog objects, for example, for replicated tables from SAP
ERP
• You can create different Schemas for your different purposes

A glimpse into the Development perspective
The SAP HANA Development Perspective:

• You can use the Development perspective to develop end-to-
end applications on top of SAP HANA
• You can create services, read/write procedures, Javascript
and UI5 objects, design time objects for tables, roles, etc.
• Debugging functionality is also available
• It contains a full development environment incl. SAP HANA
Repositories

Delivery Units to transport Content Packages
You can use the Quick launch to create Delivery Units:

• In this way you can transport your Content Packages (design time objects such as,
e.g. Column Views, Procedures, etc.) from one SAP HANA System to the other

Validation Rules

Active/Inactive Artifacts – Version History

Where used

Exercise: Get familiar with the SAP HANA Studio
The exercise for this Lesson is Exercise 1-1 in the “Student Exercise
Manual”.
You have 10 minutes for the Exercise

Data Provisioning Overview
and SAP Landscape
Transformation (SLT)
Different methods of data replication

ETL Based data provisioning: DataServices
BW
DSO
Openhub, RFC
Some Master
Data Tables ODBC HANA
DataServices
CRM
ABAP
Cluster tables Function calls
ODBC
Functions DS
Repo
DB2
Oracle (CVDS) ODBC
Tables, Views

Introducing the advantages of SAP LT Replication
▪ SAP LT uses trigger based approach. Trigger-based approach has no measureable performance
impact in source system
▪ It is based on the proven SLO technology (used for NearZeroDowntime, TDMS, etc.) used by
hundreds of customers with large DBs.
▪ It provides transformation and filtering capability

▪ It allows real-time (and scheduled) data replication, replicating only relevant data into HANA from
SAP and non-SAP source systems
▪ It is fully integrated with HANA modeler UI
▪ Replication from multiple source systems to one HANA system is allowed, also from one source
system to multiple HANA systems

SAP LT Key Building Blocks: SAP Source system
DB Trigger Write DB
Modules
Connection
Logging Read RFC Controler

Tables Modules Connection Modules
Application Tables
LT replication server does not have to be a separate

SAP system and can run on any SAP system with SAP
NetWeaver 7.02 ABAP stack (Kernel 7.20EXT)
Application Tables
Source system LT Replication Server SAP HANA Database
Efficient initialization of data Flexible and reliable replication Fast data replication via DB connect
replication based on DB trigger process, incl. data migration LT replication functionality is fully
and delta logging concept (as used for TDMS and SAP LT) integrated with SAP HANA Studio
(as with NearZero downtime approach)

COIL TDF Landscape Architecture
ECC + TDF S/4HANA + TDF
Terminal Server Terminal Server
ECC NW + TDF ECC NW + TDF
SLT HANA
HANA

Exercise: Data Provisioning overview and SAP LT
There are no exercises for this Unit
Demo
SAP HANA Overview
Content and Views in the SAP HANA Studio
• The SAP HANA Catalog contains run-time database

objects, such as tables, views, procedures, etc. and no
design-time objects
• Design-time objects and modeling tools are available in

the SAP HANA Content area in the Modeler perspective.
This allows modelers to design and model data Views out
of the “raw” tables.
• Using the SAP HANA Studio, users can define their

Views and start to model and “slice and dice” data.
Complex modeling may also take place somewhere else,
e.g. SAP BI or SAP BO.

Working with Content
Content in the SAP HANA Studio:

• It contains design-time objects, e.g. analytic models, for
database objects, that is, nested content Packages for Packages
• Attribute Views
• Analytic Views
• Calculation Views
• It also contains Analytic privileges representing row-level
authorization filters evaluated dynamically during query
execution
• It also contains Procedures, i.e. is used to store Design-Time
objects
• SQLScript or
• L procedures
to express complex calculation or processing logic

Creating Content Packages
Right-click on Content and click on New  Package
Type your Package Name and click on OK

The 3 SAP HANA Modeling Views in a Nutshell
• Attribute View: Gives context to, and selects subset of columns and rows from data tables. Used
for all type of joins. In most cases used to model master data like entities (like Product, Location,
Business Partner)
• Analytic View: Used for calculation and aggregation (star-schema-like). Adds transactional tables
and measures (key figures), calculates aggregates (e.g. No. of Products sold per year), joins
Attribute Views. It is defined at least on one fact table. In most cases used for exposing transactional
data by joining the fact table with Attribute Views.
• Calculation View: Performs Engines cooperate with each
complex calculations not other. The SQL Optimizer
possible with other views and decides the best way to call
uses SQLScript. It can be each engine depending on the
defined as graphical view models
or scripted view.

Attributes, Views, Facts, Cubes and Dimensions
• Attributes are the data • Fact Tables are measurements,

fields of one table row, e.g. metrics or facts of a business
the “Company Name”. process and are often located at the
Calculated Attributes center of a Star Schema (data
return simple calculations, cube) surrounded by its linked
Dimension Tables, typically master
e.g. table field text
data.
manipulations or measure Example: fact table can be the Sales
related operations. Order header table (you can also
use the sales order item table in the
• Views are combinations fact table if necessary).
and selections of data from
tables modeled to serve a
particular purpose.

Exercise: SAP HANA Engines
There are no exercises for this Lesson.

SAP HANA Modeling:
Working with Tables
Pros/Cons of Column store vs. Row store
When to use Row-based or Column-based Tables with SAP HANA?

• Column-based tables have advantages in the following cases:
• Calculations are performed only on few columns
• Table is searched based on values on few columns
• Table has large number of columns
• Table has large number of rows and columnar operations are required (aggregate, scan)
• High compression rates can be achieved because majority of columns contain only few distinct values
compared to number of rows
• Row-based tables have advantages in the following cases:
• The application needs to only process a single record at a time (many selects / updates of single records)
• The application typically needs to access a complete record or row
• Columns contain many distinct values so that the compression rate would be low
• Neither aggregation nor fast searching are required
• Table has a small number of rows

Altering Tables from/to Column store
Some Tips and Tricks regarding Row-based and Column-based Tables

• You can alter an existing table from columnar to row-based and vice versa
• SLT replicated tables are created as Column-based tables by default
• You can either alter the type of table manually for each table or write an SQL script to perform this
operation for a set of tables
In case you are wondering….

• In most cases, having the table as column table is the way to go. In many cases, the engines of
HANA can work more effectively with such tables.
• However there are certain use cases and valid scenarios for row tables:
• SAP BW on HANA uses row based tables for the so called Write Optimized DSOs, where the main
requirement is to have the best performance for uploading lot’s of records  So this row table (DSO) is an
interim staging area for data to be further processed (and transferred to column based tables)
• Generally, row based tables could be considered for storing configuration (which don’t appear in joins
with column tables) or staging tables for optimizing upload performance
Creating Tables Using SAP HANA Development
You can create Tables as design-time objects (.hdbtable):
The screen below shows you an example of the syntax used to create a table in a particular schema
using the SAP HANA Development perspective
Deprecated

Update
• CDS Artifacts are the recommended way of defining tables, views, entity
types, associations, as well as annotating data.
• It is the new and recommended way to define database artifacts.

Core Data Services (CDS)
• CDS (Code Data Services) is a data definition

language (DDL) artifact which provides the
design time definition of the runtime database
objects. It’s also a Query and Expression
Language.
With this artifact it is possible to define:
• Entities
• Views
• Associations
• Annotations
• Etc…
• HANA CDS vs ABAP CDS:
• Two implementations based on the same specification. ABAP CDS is open, while HANA CDS is HANA
specific. ABAP CDS is stored in the ABAP Data Dictionary and HANA CDS is stored in the HANA Repository
with the extension ‘.hdbdd’ or on newer systems with ‘.hdbcds’.
• More at: Education training HA450, Reference Guide (PDF)

CDS vs SQL
• CDS simplifies the creation and association of

Tables and Views.
• Upon activation, columns are created or updated.
• Simplifies the transport: just one activation instead of

a series of SQL Statements.
• No need to specify the join on querying associations

on views:

CDS – Reusable Types
• Create Reusable Types for common table structures.
• Example: easily add record tracking of user and timestamp in

a standardized way.

Creating Tables
• Standard SQL • SAP HANA • CDS: DDL File

Modeler Tools

Data Preview
• Easy way to Display Table/View Data.
• Select the table on the Catalog and right-click on it.
• Choose Open Data Preview
• You can see the Raw Data or perform some quick Analysis

Inserting Data
• Insert data to your table using standard SQL • Use the Import / Export tools in the SAP HANA
statements on a SQL Console Studio to import DDL and data from/to the SAP
HANA server or from/to your local client

Exercise: Creating tables
The exercises for this Lesson is Exercise 2-1 and 2-2 in the “Student
Exercise Manual”.
You have 10 minutes for this Exercise

SAP HANA Modeling:
Calculation Views
Calculation View: Concept
• A Calculation View is a column view of type calculation
• Calculation Views are used to address complex business scenarios. They are created using tables,
other calculation views or the former attribute and analytic views.
• Calculation Views can be created using a Graphical editor or an SQLScript editor.
Deprecated

Query on Calculation View
• It is a SQL View
• A Calculation View can be queried with a SQL statement just as a standard View
• The Calculation View is a Column View stored under the schema _SYS_BIC, as for Attribute Views,
Analytic Views and Procedures created using the Modeler Content tools in the SAP HANA Studio.
• The naming pattern is the following:
Syntax to select from Calculation View:

SELECT * FROM "_SYS_BIC".”<package name>/<calculation view name>”

Calculation over Attribute and Analytical Views
• Attribute and Analytical Views are deprecated. Currently you can create
a Calculation View and later on specify which type of Information View it
will be.
• Scripted Views are also deprecated. This feature has been replaced by
Table Functions.

Unsupported Features

Defining Dimension Calculation Views
• Dimension Views give context to Master Data tables.

• Example: A table with Product names and numeric Supplier IDs but
no Company Names for these Suppliers can be linked to another
table with more information about the Suppliers, including Company
Name. Company Names have given context to the first table in this
case.
• It is a select of a subset of columns and rows from a data table
• They define attributes, which are usually not measurable (hence
the former Attribute Views)
• Dimensions typically join master data tables to each other, e.g.
“Plant” to “Material” or “Business Partner” to “Product”
• They can also be used to join the text tables with the master data
tables

Creating Dimension Calculation Views: Choosing Name & Type
We aim to create a summary view of the Business

Partner data for our company ITelO, compared to the full
replicated EPM table SNWD_BPA. Right-click on
Calculation Views under your Content-Package and
click on New
Enter Name, select DIMENSION as Data Category and

click on Finish

Creating Dimension Calculation Views: Selecting Tables
Add tables to Data Foundation – via drag & drop from the Catalog, or press the Add Objects button
(with the “+” sign)
Enter the table name (beginning)

in the popup, select the right
tables and press OK. For our
example we need the EPM Business
Partner table SNWD_BPA.

Creating Dimension Calculation Views: Joining Tables
We create a Dimension Calculation View based on several joined tables, for example, to display more
contact details for our Business Partners:
• We join the SNWD_BPA and SNWD_BPA_CONTACT tables
• We may select the following attributes:

CLIENT,
NODE_KEY (Key Attribute),
EMAIL_ADDRESS,
COMPANY_NAME,
FIRST_NAME,
LAST_NAME

Business Partner contact details: The Joins
To join attributes, you may either right-click on the table and then click on Create Join or drag the join
arrow from one attribute on the first table to the attribute on the second table
Select the join type and cardinality
For our business scenario we will join the NODE_KEY on the left table with the PARENT_KEY on the
right table

Table Join Types and Unions
Returns rows when there is at least one match in both tables. Combines records
from the left and right table exactly when the specified criteria are met.
Inner Join If the criteria are not met, no record is created in the result set. Inner Join is used
even if not added (if there’s an implicit join defined using a SELECT from 2 tables).
Returns all rows from the left (right) table, even if there are no matches in the right
Left (Right) (left) table. If records match, they are combined, otherwise the columns are empty.
Outer Join It is the only “correct” type of join for Cube Calculation Views.
Returns all the rows from the left table, and all the rows from the right table.
If records fulfill the criteria, they are combined, otherwise columns will be empty.
Full Outer Join
Semantically an inner join that assumes that referential integrity is given, that is, the left table always
have an corresponding entry on the right table. Optimized or faster inner join where the right table is
Referential Join
not checked if no field from the right table is requested. Cannot be used if a filter is set on a field in
the right table.
Used to join a text table to a master data table (e.g. Material_ID  Description) as requirement for
description mapping. The text join always filters on one language, and thus one needs to specify a
Text Join language column in the text table. The language is filtered based on the view settings (per default
taken from the user profile)
Used to combine the result-set of two or more SELECT. The Union operation is popular for combining
plan and actual values like CO-PA. It is not supported in the former Attribute Views, Analytical Views.
Union
http://www.codinghorror.com/blog/2007/10/a-visual-explanation-of-sql-joins.html

Table Join Types: Text Joins
• Text Join are used to join a text table to

a master data table.
• Text Joins acts as a Left Outer join and

can be used with SAP tables where the
language column (SPRAS) is present.
• For each attribute it is possible to define

a description mapping that will be
specific to the end users language.

Creating Dimension Calculation Views: Choosing Attributes
Choose the attributes for the Dimension Calculation View.

Click on the circle or use context menu of the corresponding attribute to Add To Output as Attribute.
You can also use Apply Filter.
Use Column Properties to mark Key Attributes

Creating Dimension Calculation Views: Activating the View
Click on Save, to save the model in the repository
Right click on the Dimension Calculation View and click on Validate, to verify the syntax
Right click on the Dimension Calculation View and click on Activate or Redeploy. A runtime object
will be created in the Catalog Schema _SYS_BIC.
Save and Validate Save and Activate

Creating Dimension Calculation Views: Calculated Columns
Using a Calculated Column, we can clearly show

which Business Partners are Customers or Suppliers.
Right-click on Calculated Columns and click on New.
Enter Name and Expression as shown on the right

screen. Click on Validate.
Click on Add
Beware of the syntax, e.g. type of quotation marks for

”table_columns” vs. ‘values’. Functions differ from SQL!

Previewing Data from Dimension Calculation Views
Save, Validate and Activate as shown before
Right-click on your Attribute View and click on Data

Preview, or press the Data Preview button on the
Attribute View window

Displaying Charts for Dimension Calculation Views (1)
Switch from the Data Preview (Raw Data) View to Distinct Values and click on an Attribute to display
your first analytic graphs

Displaying Charts for Dimension Calculation Views (2)
Switch to the Analysis Tab to display more complex charts

Creating Dimension Calculation Views: Re-Opening the View
In your Content Package, open the Dimension Calculation View by double clicking on it.
Alternatively you can right-click on the Dimension Calculation View and click on Open.
Validate, Activate, Data Preview, etc.

Copying Dimension Calculation Views
To copy an Dimension Calculation View:

Right click on the Calculation View node and click New
Check the button Copy from
Browse in the Content Package repository, and select the
Attribute View you want to copy or derive from
Click on Finish

Exercise: Creating Dimension Calculation Views
The exercises for this Lesson are Exercises 2-3 and 2-4 in the
“Student Exercise Manual”.
You have 20 minutes for the Exercises

Defining Cube Calculation Views
• Cube Calculation Views are multidimensional reporting models (“cubes”) where

Fact Tables (Star Join) are joined against modeled dimensions
(Dimension Calculation Views)
• Cube Calculation Views do not store data: data is read from joined tables and Joins and calculated
measures are evaluated at run time
• Can be used for example to model transactional data (sales order header joined with material
master)
• A selection of Measures (Key Figures) is taken to build the Star Join based on transactional data
• Similar idea as the classic “star schema”

What is a Star Schema?
• Data Warehousing data model concept, similar to SAP BW Product Time

Infocube dimension dimension
• A single object (Fact Table with measures and “facts” which
change dynamically with time) sits in the middle and is Fact
radially connected to dimension lookup tables (relatively Table
static) like a star
• Primary keys in each dimension are related to foreign keys Partner Country
in fact table dimension dimension
• Cons: Fact tables are normalized (no redundancies) but dimension tables are de-normalized
(contain redundancies). No easy aggregate building. Performance issues with big dimension tables.
Changes in semantic data model mean complex table reorgs.
• Pros: Intuitive data model. Relatively few joins and tables. Easy to maintain.

Creating Cube Calculation Views: An EPM example
We create an Analytic View based on the EPM data model.

Our goal is to display aggregated Purchase Order amounts
classified by Business Partners and Products:
• We join the SNWD_PO (PO header) and SNWD_PO_I

(PO items) tables to build our Fact Table
• We use the following Dimension Calculation Views as

master data dimensions:
• Dimension Calculation View for Business Partners (based on
the joined tables SWND_BPA and SWND_BPA_CONTACT as
outlined on the last chapter)
• Dimension Calculation View for Products based on single
table SWND_PD

Creating Cube Calculation Views: Building Blocks
• Create a Cube Calculation View similarly as for Dimension

Calculation Views: Right-click on the Calculation Views node 
Click on New  fill the required fields (Name, Data Category:
CUBE, with Star Join)
• Add the required Dimension Calculation Views based on the
Business Partner and Product tables to the Star Join (you need to
previously create these Dimension Calculation Views as explained
before)
• Add the Purchase Order Header table (SNWD_PO) and Purchase
Order Items table (SNWD_PO_I) tables to the Join node.

Creating Cube Calculation Views: Attributes and Measures
• Click on Semantics and mark the desired columns as Attributes or Measures

• You can also hide some columns to hide them from the result view
Attribute
Measure

Creating Cube Calculation Views: Join data and Activate
• On the Star Join level, join the Dimension Calculation View with the Fact Table defined above
• Click on Validate and then on Activate
Best Practice: Use Left Outer Join, rather than

Inner Join, to join the Fact Table to the master
data dimensions (Dimension Calculation Views)

Creating Cube Calculation Views: Data Preview
• Check the Cube Calculation View data using Data Preview:

Check aggregates using the SQL Editor
• You can use the SQL Editor to check aggregates for the tables:
SELECT partner_guid, SUM(gross_amount)
FROM "<epm_schema>".snwd_po
GROUP by partner_guid
ORDER BY 2 DESC;
SELECT supplier_guid, SUM(gross_amount)
FROM "_SYS_BIC"."<package_name>/CV_PO"
GROUP BY supplier_guid
ORDER BY 2 DESC;

Exercise: Create Cube Calculation Views
The exercises for this Lesson are Exercises 2-5, 2-6 and 2-7 in the

Variables and Input Parameters
Concepts
In Analytic and Calculation views you can create two

types of objects to parameterize the result returned by the
view:
• Variables
These are bound to attributes and are used for filtering.
They can only contain the values available in the Attribute
they relate to.
• Input Parameters
Can contain any value the reporting user has to enter to
parameterize the result. Therefore, a data type must be
specified for each Input Parameter.
© 2017 SAP SE or an SAP affiliate company. All rights reserved. 92 Confidential 92

Variable types
The following types of Variables are supported:

Type Description
Single Value Use this to apply a filter to a Single Value
Interval Use this where you want the user to specify a set start and end to
a selected Interval.
Range Use this when you want the end user to be able to use operators
such as “Greater Than” or “Less Than”.
Range Variable Example:

Creating Variables
A variable restricts the results in the view for the

selected Attribute.
You select the Attribute in the view that you
want to filter on, and you also define the:
Selection Type: Whether selections should be
based on intervals, ranges or single values.
Multiple Entries: Whether multiple values of
the selection types should be allowed.
You can also define whether the Variable is
Mandatory or if it should have a Default Value.

Input parameter types
The following types of input parameters are supported:
Type Description
Currency Use this during currency conversion where the end user should
specify a source or target currency.
Date Use this to retrieve a date from the end user using a calendar
type input box.
Static List Use this when the end user should have a set list of values to
choose from.
Attribute Value When an Input Variable has this type, it serves the same
purpose as a normal Variable.
(none) If none of the above applies you do not have to specify an Input
Variable type. The Type can be left blank

Creating input parameters
If we want the end user to decide whether Gross or

Net amount should be shown in a View, what we
first need to do is to create an Input Parameter to
be used in a calculation.
The Input Parameter can be of any suitable type,
for example a StaticList type.
In this example to the left the user can choose
either ”Gross” or ”Net” as the selection.

Calling Input Parameters
We also need somewhere to call the Input

Parameter from.
In for example a Calculated Measure, we can
reference the result of the user selected Input
Parameter.
This is done by calling it within double dollar signs,
see example:
if('$$INP_GROSS_OR_NET$$'=
'Gross',"GROSS_AMOUNT","NET_AMOUNT")

Exercises: Variables and input parameters
There are no exercises for this Lesson

Using SQLScript and
Procedures
Concept
• SQLScript is a collection of extensions to Structured Query Language (SQL).
Data Type Extension Allows the definition of table types without corresponding tables
(CREATE TYPE)
Procedures To push data intensive logic into the database. Supported are
(CREATE PROCEDURE) declarative (side-effect free) and imperative procedures to
encapsulate complex queries and data flows
User Defined Functions Creates side-effect free Scalar or Table functions
(CREATE FUNCTION)
• SQLScript encourages to implement algorithms using a set-oriented paradigm instead of a

one tuple at a time paradigm.
• SQLScript allows using of imperative as well as declarative statements.
Data Type Extensions
Scalar Data Type :
The SQLScript type system is based on the SQL-92 type system and supports the following
primitive data types:
TINYINT, SMALLINT, INTEGER, BIGINT
DECIMAL(p, s), REAL, FLOAT, DOUBLE
VARCHAR, NVARCHAR, CLOB, NCLOB
VARBINARY, BLOB
DATE, TIME, TIMESTAMP
Data Type Extensions
• Table Type: SQLScript also allows the definition of table types.

• Table types are used to define parameters for a procedure that represents tabular results, for
example an output table displaying the results of a procedure used to encapsulate complex queries.
• A table type is created using statement CREATE TYPE and can be deleted using statement DROP
TYPE.
• Syntax:
• CREATE TYPE [schema.]name AS TABLE

(name1 type1 [, name2 type2,...])
• DROP TYPE [schema.]name [CASCADE]
SQLScript and Procedures
• Procedures are reusable processing blocks, and describe a sequence of data transformations.
• Procedures can have multiple input and output parameters (scalar or table types).
• DROP and CREATE statements are used to modify the definition of a procedure.
• A procedure can be created as read only (without side-effects) or read-write (with side-effects).
• Read-only procedures can only call other read-only procedures.
• Procedures can be created with different languages, but SAP HANA Studio supports only SQLScript.
Procedure
• The body of a procedure consists of a sequence of statements separated by semicolons.

• An intermediate variable, inside a procedure, is not required to be defined before it is bound by an
assignment.
• Cyclic dependencies that result from the intermediate result assignments or from calling other
functions are not allowed.
• A variable name is prefixed by “:” (colon) while used as input to another statement.
• A Procedure can be created using the SQL editor or using creation wizards available for the different
perspectives in the SAP HANA Studio (Modeler and Development perspectives).
Procedure creation using the SQL editor
• Syntax:
CREATE PROCEDURE {schema.}name
{({IN|OUT|INOUT} param_name data_type {,...})}
{LANGUAGE <LANG>} {SQL SECURITY <MODE>}
{READS SQL DATA {WITH RESULT VIEW <view_name>}} AS
BEGIN
...
END
▪ READS SQL DATA defines a procedure as read-only. Also optimizes Performance

▪ Implementation LANGUAGE can be specified . Default is SQLScript.
▪ WITH RESULT VIEW is used to create a column view for the output parameter of type table
▪ SQL SECURITY modes defines who can call the procedure:
- INVOKER: assigned privileges / - DEFINER: only the owner
Calling a Procedure
• CALL - Procedure Called From Client

• A procedure (or table function) can be called by a client on the outer-most level, using any of the
supported client interfaces.
Syntax
CALL [schema.]name (param1 [, ...])
• For table output parameters it is possible to either pass a (temporary) table name or to pass one of
? or NULL.
The option NULL will display the output directly on the client output screen.
Calling a Procedure – WITH OVERVIEW
• CALL...WITH OVERVIEW From Client

• This CALL statement returns one result set that holds the information of which table contains the
result of a particular table’s output variable.
• This is used to populate an existing table by passing it as parameter.
• When passing ? (or NULL) to the output parameters, temporary tables holding the result sets will be
generated.
• Help Documentation
Syntax
CALL [schema.]name (param1 [, ...]) WITH OVERVIEW
Calling a Procedure in Select Query
• When a procedure has been created with addition “WITH RESULT VIEW”, a column view is
generated. This column view can be used in an SQL SELECT statement.
• Column views also accept input parameters, which can be used in a query using the
‘PLACEHOLDER’ syntax as shown below.
Syntax
SELECT {[COL1, COL2,…] | * }

FROM <VIEW NAME>
WITH PARAMETERS (
‘PLACEHOLDER’ = (‘$$<attribute name>$$’,‘<value>’),
‘PLACEHOLDER’ = (‘$$<attribute name>$$’,‘<value>’),
…);
• Note: WITH RESULT VIEW is no longer supported on .hdbprocedure.

Use .hdbtablefunction if it is required.
Syntax Tips
• Single quote ‘ ’ is used for values and double quote “ ” for identifiers
• Identifier and character value are case sensitive
• Identifier names without double quote are converted into upper case. Double
quotes can be used to prevent this conversion.
Creation using the Modeler wizard
• Start Procedure creation wizard from context menu of package
Deprecated
• ! WITH RESULT VIEW not supported on .hdbprocedure. Use .hdbtablefunction instead.

Create Stored Procedures on Repositories
• Press Ctrl+N and search for ‘procedure’
• Choose a folder, name it and your

schema
SQLScript: Calculation Engine Plan Operators
• Calculation engine plan operators (CE_* operators) encapsulate data-

transformation functionality
• It is an alternative to using SQL statements as their logic is directly implemented in

Deprecated
the calculation engine, i.e. the execution environment of SQLScript.
• Using Calculation Engine operators may lead to higher performance in some

cases, and can help structure the code in a simpler way
• Calculation Engine (CE) Functions - Rest in Peace

https://scn.sap.com/community/hana-in-memory/blog/2014/12/11/calculation-
engine-ce-functions--rest-in-peace
• They are still supported but not recommended
Functions
• Syntax:
CREATE [OR REPLACE] FUNCTION <function_name> (<parameter_clause>)
RETURNS <return_type>
[LANGUAGE <lang>] [SQL SECURITY <mode>]
[DEFAULT SCHEMA <default_schema_name>] [DETERMINISTIC] [WITH ENCRYPTION]
AS {
BEGIN
<function_body>
END | HEADER ONLY
}
• DETERMINISTIC is used for scalar functions to cache results
• WITH ENCRYPTION encrypts the function definition permanently
• HEADER ONLY is used to declare the function signature without specifying the body
• Help documentation
Using a Function
• A function used inside a select statement to modify the result of a selected column.
Syntax
SELECT [schema.]function (param1 [, ...]) FROM …
Example
SELECT body_mass_index (height, weight) FROM People;
Result:
BODY_MASS_INDEX(1.70, 70)
1 24,22
• If the function returns a table type you can perform a select on the function.
Syntax
SELECT * FROM [schema.]function (param1 [, ...])
SQLScript: Control Statement CONDITIONAL
IF <bool-expr1>
Example:
THEN
SELECT count(*) INTO found
{then-stmts1} FROM books WHERE isbn = :v_isbn;
IF :found IS NULL THEN

{ELSEIF <bool-expr2>
CALL ins_msg_proc
THEN ('result of count(*) cannot be NULL');
{then-stmts2}} ELSE
{ELSE CALL ins_msg_proc

('result of count(*) not NULL - as expected');
{else-stmts3}}
END IF;
END IF
SQLScript: Control Statement LOOP
WHILE <bool-stmt> DO FOR <loop-var> IN {REVERSE} <start> .. <end>

DO
{stmts}
{stmts}
END WHILE
END FOR
Warning:
Control Statements negatively impact the planner parallelization.
Avoid them if possible. If not, push them to the end of the queries and views so that the planner can
execute as many as parallel tasks before waiting for conditions to be met.
SQLScript: Dynamic SQL
EXEC '<sql-statement>'
• Dynamic SQL statement is used to construct SQL statements at runtime in a procedure

• Dynamic SQL allows to use variables where they might not be supported in SQLScript. It
provides more flexibility in creating SQL statements.
• Optimization of dynamic SQL statement is limited
• It is not possible to bind result of a dynamic SQL statement to a SQLScript variable
• Dynamic SQL is prone to SQL injection
• EXEC '<sql-statement>‘ is used to construct dynamic SQL
• We recommend that you avoid dynamic SQL because it might have a negative impact
on security or performance
Exercise: Creating Procedures
The exercises for this Lesson are Exercises 3-1, 3-2 and 3-3 in the
Export and Import
Exporting and importing with SAP HANA
HANA supports export and import of Information Models, tables and more
Export and Import is handled via SAP HANA Studio
 Client-side export/import: to/from client PC running SAP HANA Studio
 Server-side export/import: to file system of SAP HANA database server
Information Models
DB Server
Tables
Client-Side Export/Import of Information Models
• Client side export allows to export models and tables (table import to and from client supported from
SP4) to client file system.
• It does not require access to file system of HANA server
• Exported objects (xml files) can be changed
• See Import / Export tools on the Quick Launch or clicking on the File Menu
Server side export and Import
• Server side export allows you to export information

models to the file system where the SAP HANA
server is installed
• Packages must be assigned to a Delivery Unit for

server side export
• You can also export Delivery units to your client, for

example for test transports of development content
between systems
Import with CDS
• CDS can also be used to import data from CSV files.
• Table Import configuration file:

import = [ {
table = "mycompany.tests.TiTest::inhabitants";
schema = "AMT";
file = "mycompany.tests.TiTest:inhabitants.csv";
header = false;
} ];
• Triggered upon file activation!
• More at: Import Data with CDS Table-Import
Exercise: Export and Import
“Student Exercise Manual”. Exercises 4-3 and 4-4 are optional.
SAP HANA Performance
Modeling
General Principles
▪Minimize the join operations ▪ Minimize data transfer between the

▪ Avoid long join chains engines
▪ Avoid joins between big tables ▪ Prefer execution of calculation in lower layers,
▪ Avoid joining tables with n:m cardinality instead of pushing data up and calculate there
▪Reduce data amount as early as possible

▪ Use data aggregation ▪ Avoid expensive calculation or data
▪ Use data filters where possible manipulation
▪ Row based data base expressions are expensive
▪Avoid huge query result sets ▪ Intermediate results needs to be written into
▪ Data transfer to the UI clients also requires time buffer and takes additional time
▪ Too many data can be anyway no analyzed properly ▪ Complex expressions, e.g. IF, CASE statements
Join operations
• Joins are well optimized in the join engine.

• Cascaded Joins are very expensive and not always lead to right results. Cascade carefully.
• Left outer join is normally faster than inner join. Left Join are almost equally fast as Referential
Join.
• Currently, one single join operation is executed by 1 core at maximum (Multiple Join are
parallelized across multiple cores).
• Avoid joins on calculated fields, e.g. concatenating two fields into one and execute a join on it.
Materialize the concatenated field (if not done by the engine).
• Avoid non-equijoin predicates on column table as column engine does not natively support join
predicates other than equality condition.
Filter Operations
CONSTRAINT
▪ applies on a table of a cube calculation view
▪ is forced with inner join, and optional with left / referential join
▪ is executed before join and aggregation
▪ reduces data amount very early and for that it is very fast (especially when constraint is applied on a dimension
table)
WHERE
▪ applies on a tuple and has different semantic than constraint
▪ is very fast on column tables
HAVING
▪ is used to reduce the final result set after aggregation. However it has no performance gain
e.g. HAVING salesAmount > 10000
Database Expressions
▪ Avoid database expressions which need to be executed before aggregation

▪ Performance is critical as calculation happens on every single record and needs to be written into
an intermediate runtime buffer, too
▪ In a Calculation View you can push the expressions to the top while doing the aggregations at the
bottom. Aggregate before and do database expressions later
▪ If not avoidable, check if persisting the calculated field in the table could be a valid option (see SQL
lesson)
SQLScript
• Query optimizer creates an execution plan that involves both storage engines even if the data reside
completely in the column store if feature used in query is not supported by column engine.
• Query should be formulated in such a way that minimizes data transfer between different engines.
• Avoid implicit type casting.
• Reduce complexity of SQL Statements. It helps in identifying common sub-expression.
• Reduce dependencies between different SQL statement and use declarative constructs to enable parallel
execution of SQL statements.
• Executing dynamic SQL is slow because compile time checks and query optimization must be done for
every invocation of the procedure.
• Avoid CURSOR (and other imperative constructs) as parallelization can’t be done.
• Consider the impact of expensive calculations (like unit and currency conversion).
SQLScript
The column engine can natively execute queries that are

logically equivalent to queries that can be expressed in
the following format.
SELECT <column engine expressions separated by comma>

FROM <column engine FROM specifications separated by comma>
[ WHERE <column engine filter condition> ]
[ GROUP BY <column engine expressions separated by comma> ]
[ HAVING <column engine filter condition> ]
[ ORDER BY <column engine expressions with optional
ASC | DESC separated by comma> ]
[ LIMIT <any expression> [OFFSET <any expression> ] ]
Partitioning
Typical use cases for partitioning
• Column table limitation of storing max. 2 billion records without partitioning
• Load balancing: individual partitions may be distributed over the landscape. Queries could be
processed on multiple servers.
• Parallelization: operations are parallelized by using several threads per table
• Partition Pruning: scans on relevant partitions only and reduces load. Precondition is, that the query
must match the given partition specification
Performance Analysis
Execution Time measurement
Query execution time can be measured directly by executing the

query in the SAP HANA Studio.
Measure Expensive SQL Statement
Configure the measurement of

expensive statements
 System context menu in the
Navigator  Administration
 Select Trace Configuration tab. Edit
config for the Expensive
Statements Trace.
Measure Expensive SQL Statements
Check the traced SQL statements:
Administrator view  Performance Tab  Expensive Statements Trace
Explain Plan
Explain Plan
Column name Description

OPERATOR_NAME Name of an operator.
OPERATOR_DETAILS Details of an operator. Predicates and expressions used by the operator are
shown here.
OBJECT_NAME Name of database tables and views accessed by an operator .
SUBTREE_COST Estimated cost of executing the subtree starting from an operator. The unit of
this value is time in seconds, but the absolute value would not match the actual
execution time. This value is only for relative comparison between different
subtrees.
INPUT_CARDINALITY Estimated input row count of an operator. This is available only for operators
accessing tables and views directly. For column store, estimation is done in row
granularity, but for row store, estimation is done in page granularity and there
can be large error for small tables.
OUTPUT_CARDINALITY Estimated output row count of an operator.
OPERATOR_ID Unique ID of an operator. IDs are integers starting from 1.
Visualize Plan
Right click on the SQL statement, and select Visualize Plan (from SP4), then
• Right click on the yellow box and select Execute
• Open the yellow box using the triangle to see the Column Plan
Configure Performance Trace
To trace all SQL statements, you can configure it. It can be restricted by user.
Execute Performance Trace
After Starting the trace, execute query. Stop tracing once execution is over.
Analyze Performance Trace
• Traced file is available under “Diagnosis Files”.

• In order to analyze trace file, access to the analyzer tool in HANA server is required.
• It is also helpful to download the traced file and attach it while contacting HANA team for
performance issue.
Analyze Performance Trace
To analyze trace file, application HDBAdmin.sh is used. It’s required to have HDB
administrator authorization to use it.
Debugging Tools
• It is also possible to debug your SQLScript code

• The Development Perspective in the SAP HANA Studio offers various tools to
debug your applications
Exercises: HANA Performance
User Management & Security
User Management and Security in SAP HANA
Create Users
 Assign Initial
Passwords
 Important User
Parameters
Manage Users
Assign Security  Lock Users
 Control Access to  Reset Passwords
Objects
 Check User
 Row-Level Security Privileges
 Restrict allowed actions  Integration with BI
Types of Privileges
When accessing the SAP HANA database using a client interface (such as ODBC, JDBC, MDX), any
access to data must be backed by corresponding privileges. Different schemes are implemented
Object Privilege System Privilege

Object SYSTEM
 Privilege SQL statement type  Used for administrative tasks.
(for example, SELECT, Privilege Privilege
 System Privileges are
UPDATE, and CALL…) assigned to users and roles.
Authorization
Concept
Package Privilege Analytic Privilege

Package Analytic
 Restrict the access to and  Analytic Privileges are used
Privilege Privilege
the use of packages in the to provide row-level
repository authorization Views.
Object Privilege
In the SAP HANA database, a number of privileges are available to control the authorization of
SQL commands. Following the principle of least privilege, users should only be given the
smallest set of privileges required for their role.
Two groups of SQL Privileges are available:
System Privileges
These are system-wide privileges that control some general system
activities mainly for administrative purposes, such as creating
schema, creating and changing users and roles.
Object Privileges
These privileges are bound to an object, for example, to a database
table, and enable object-specific control activities, such as SELECT,
UPDATE, or DELETE to be performed.
Analytic Privilege - The Concept
Analytic Privileges are used to control access Implement row-level security with Analytic
to SAP HANA data models Privileges
• Without Analytic Privilege, no data can be • Restrict access to a given data container to
retrieved from selected Attribute Values
▪ Attribute Views ▪ Field from Attribute View
▪ Analytic Views ▪ Field from Attribute View used in Analytic View
▪ Calculation Views ▪ Private Dimension of Analytic View
▪ Attribute field in Calculation View
▪ Combinations of the above
▪ Single value, range, IN-list
User Provisioning - User and Role Concept
You can use the Security to manage users, roles and assign privileges
Pre-delivered Roles
SAP HANA comes with several pre-defined / standard roles:

Roles that should (must) be used unchanged
 PUBLIC – minimal privileges for a user to work with the database at all
 Is implicitly granted whenever a user is created
Role Templates
 CONTENT_ADMIN – the only role in the system with vital privileges, e.g.:
▪ SQL Privileges on Schema _SYS_BIC – with GRANT OPTION
▪ SQL Privileges on Schema _SYS_BI – with GRANT OPTION
 MODELING – a very richly privileged role that enables
▪ Creation and activation of Information Models
▪ Creation and activation of Analytic Privileges
 MONITORING – role with full read-only access to all meta data, monitoring and statistics
Web-based Workbench Roles
• SAP HANA Web-based Development Workbench provides an easy access to HANA features.
• It has basically the same features as the SAP HANA Studio on Eclipse.
• To use them, you will need the role:

• sap.hana.xs.ide.roles::Developer
• And also one role for each feature:

• sap.hana.xs.ide.roles::CatalogDeveloper
• sap.hana.xs.ide.roles::EditorDeveloper
• sap.hana.xs.ide.roles::SecurityAdmin
• sap.hana.xs.ide.roles::TraceViewer
Exercise: User Management and Security
A Data Consumption example
using Microsoft Excel
Business Example
Microsoft Excel is still the primary tool of choice for

business users to analyze data
Using the PivotTable and PivotChart features,

multidimensional reporting can provide valuable insight
Using SAP HANA, business users can create reports in

MS Excel that give an overview on operational level
(OLTP) data in an aggregated (OLAP) way, thus providing
real-time insight into business processes
Microsoft Excel
Multidimensional reporting is
available via Excel Pivot Tables
“Quick and dirty” cross-tab style
reporting via Excel.
There are disadvantages as well:
 The report definition is only available locally
(workarounds exist)
 Subject to performance limitations of the
desktop machine where Excel runs
BusinessObjects Analysis, Edition for

Microsoft Office is also worth looking
into!
It’s the successor of BEx Analyzer for
Excel and offers rich functionality.
Procedure: Access HANA models from Excel I
1. From the Data tab on the Excel tabstrip, select

the “From Other Sources” button, and select the
“From Data Connection Wizard” option
2. Select the “Other/Advanced item” from the list

and click Next
3. Select the “SAP HANA MDX Provider” from the

list and click OK
4. Provide the login credentials to the SAP HANA

system
Procedure: Access HANA models from Excel II
5. Select one of the Views you have created earlier then

click Next
6. On the next window, click the Finish button
7. Select PivotChart and PivotTable report and click OK
8. You might need to provide your password to SAP HANA

again
9. Play around by selecting fields from the right hand side

list and create different charts
10. Think about possible business questions and answers

Demonstration: Access HANA models from Excel
Exercise: Access HANA models from Excel
(OPTIONAL)
Obrigado! Inovação + Parceiros + SAP
Ivan Mirisola Lucas Rigo

Partner Integration Expert Partner Integration Expert
SAP Co-Innovation Lab Brasil SAP Co-Innovation Lab Brasil
ivan.mirisola@sap.com lucas.rigo@sap.com
Learn More
• OpenSAP Courses: • Help Documentation:

• https://open.sap.com/courses/hana6
• SAP HANA Platform Portal
• https://open.sap.com/courses/hana4 • SAP HANA Master Guide
• https://open.sap.com/courses/hana2 • SAP HANA Administration Guide
• SAP HANA Modeling Guide
• SAP HANA Developer Guides:

• Quick Start
• For SAP HANA Studio
• For SAP HANA Web Workbench
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SAP HANA Development

• SAP HANA Architecture • SQLScript and Procedures

• SAP HANA Studio Overview • Export and Import

• Data Provisioning Overview and SAP • HANA Performance

• SAP HANA Modeling: Calculation Views

• Variables and Input Parameters

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Dramatically improved hardware economics made SAP HANA possible.

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Three-tier application architecture resulted

 CPUs have one core, with no true parallelism. Core

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Row and Column Store

Avoiding Aggregate Tables

Insert Only on Delta +

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Before delta merge operation: Write

 Delta store grows Main Delta

During delta merge operation: Read

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3600x 460B 21%

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In-memory computing imperative:

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• SAP HANA is also available in the Cloud

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There are no exercises for this Unit

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• The SAP HANA Studio provides an environment for modeling, development,

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When you start the SAP

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How to add an SAP HANA System:

Right-click on the Systems panel:

Click on Add System

User names for each

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• Enter Host Name and Instance Number and click on Next

• Enter User Name and Password and click on Finish

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How do I change my password?

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Authorizations, Roles and Privileges:

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The Catalog in the SAP HANA Studio

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What are Schemas?

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The SAP HANA Development Perspective:

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You can use the Quick launch to create Delivery Units:

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You have 10 minutes for the Exercise

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