Best practices for ABAP and SAP HANA Cloud Platform development
1. DEV207 – Customers present best practices for
ABAP and SAP HANA Cloud Platform
Public
Dr. Christopher Kaestner, Dinu Pavithran, Dr. Wolfgang Weiss
Product Management SAP HANA Platform, SAP SE
3. 3
What can you expect today?
• Build the bridge from today to tomorrow with custom & partner development
• Learn from the community: Insights and findings at successfully completed custom
developments on SAP HANA Platform
• SAP HANA and ABAP – combine the best of two worlds
• Explore – Evaluate – Develop: Quick start and high degree of flexibility in the Cloud
4. Build the bridge from today to
tomorrow
Business case for customer and partner developments at successful enterprises
6. Learn from the community:
Insights and findings at successfully
completed custom developments on
SAP HANA Platform
SAP HANA and ABAP – combine the best of two worlds
28. Comprehensive solutions in three key sectors
37
EWE brings together energy, telecommunications
and information technology, and
thereby possesses all the key expertise
for sustainable, intelligent energy supply systems
29. Our strengths are our excellent service and advice as well as
the proximity to our customers
38
2013
1.4 million electricity customers
1.6 million gas customers
680,000 telecommunications customers
30. HANA
39
Base Data
(Invoices,
Customizing)
Conversion
(12-24 h)
Derived Data
(statistics)
Reporting
(6 h)
The current reporting process of sales analysis
Optimize with
HANA
31. System Sketch of our prototype
40 40
easy+
AWS Cloud
One-time export of data HANA
Number of
Records:
Sales Analysis: > 170 Mio.
Sales Statistics: > 35 Mio.
32. What we did in the prototype
• Two colleagues with no to theoretical knowledge of HANA worked on this
• A whole year of derived data and surrounding customizing was dumped and transfered to a HANA
instance in the AWS cloud with an ABAP stack
- All data were previously anonymised
• The currently-in-use reporting programm was copied 1:1 to the ABAP system in the cloud
- Minor adjustments had to be done to the report, but the core business logic was not even touched
• HANA-Views were created to join the raw data with customizing
• Select statements were modified to use the built HANA views
41
33. Example of used SQL Statement
SELECT abrlfdat bukrs betrnr vbrart zulaufkz verdkz sv_data
SUM( kzanzvpart ) SUM( kzanzger )
SUM( abrmenge_oa ) AS abrmenge
SUM( apreis ) SUM( gpreis ) SUM( lpreis )
FROM ze_st_us_ohne_a
INTO CORRESPONDING FIELDS OF TABLE lt_us_einz_v
WHERE abrlfdat BETWEEN gv_datum_von AND gv_datum_bis
GROUP BY abrlfdat bukrs betrnr vbrart zulaufkz verdkz sv_data.
42
Intensive use of aggregate functions
34. Comparision showed a speedup of 1967 times,
calculating over 125 Mio. records
6000
5000
4000
3000
2000
1000
0
Runtime (s)
without HANA with HANA
Runtime (s) 5901 3
43
35. Comparing memory consumption showed a
compression ratio of 13
Table /EAS/ST_US_EINZ
• 676 Bytes per record
• Uncompressed 171.772.163 entries require 108 GB
• HANA compressed, this table needs 8,1 GB ratio 13
44
36. Using integrated XS engine and SAP UI5 to display dynamic
information on tablets
45
37. How time and effort was spent during the prototype
46
38. The planed next step to further simplify the process and gain
even more speedup
Conversion
(12-24 h)
HANA
Eliminate
Base Data
(Invoices,
Customizing)
Derived Data
(statistics)
Reporting
(6 h)
Eliminate
Reporting
(15 min?)
SAP Hana, Daniel Pawlowski / Hannes Schnieders, BTC AG 47
39. Based on initial experiences, an additional use case was
implemented
48
Real-time interactive reporting over 36,2 Millionen invoices
• All invoices grouped by booking date with aggregation (sum of amounts)
• Drill-Down with accounting area, contract type, due date,…
• Access with Integrated Data Access (ALV with IDA in NW7.4)
• Fuzzy search for booking date
40. Key take aways
49
• Fast ramp-up in project team despite
partial knowledge on SAP HANA at
project start
• New opportunities for interactive usage
of reports highly valuable for Line of
Business
• Prototypes from everyday business
accelerate innovation discovery
• Integration of sidecar database in
existing applications still not equivalent
to best practices
• Acceleration factor 2K and high
compression factor exceeded
expectations
41. Explore – Evaluate – Develop
Quick start and high degree of flexibility in the Cloud