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This document discusses using the ETL tool Yertl and SAP HANA database for ad-hoc OLAP databases. Yertl can be used as a Perl-based ETL tool to extract, transform and load data from a source database into HANA for analytical queries. HANA is suitable for OLAP workloads as it uses a column-oriented architecture and in-memory storage to enable fast analytical queries. The document provides examples of using Yertl to extract data from a SQL source query, transform it using YAML, and load it into HANA using target SQL queries. It also discusses using HANA studio for bulk data imports and defining OLAP views in HANA for analysis.

Ad-Hoc OLAP databases with Yertl and HANA
Radek Kotowicz (radoslaw.kotowicz@sap.com)
http://blogs.perl.org/users/radek_kotowicz
http://www.ariba.com/about/sap-ariba

© 2013 Ariba - an SAP company. All rights reserved. 2Public
OLTP

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OLAP schema

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Difference
OLTP OLAP
Normalization De-normalization

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Consequences
OLTP OLAP
• Enforced integrity
• Easily extensible design
• Flexible analytics
• Slow for analytical queries
operating on large result
sets
Lack or loose integrity
Simple queries
Less flexible/extensible analytics
Performant analytics
Interface for operating on hyper-cubes

© 2013 Ariba - an SAP company. All rights reserved. 7Public
Goal
1. Get a DB where analytical queries can be executed without impacting
transaction database
2. No setup
3. Need an analytical tool for non-tech users
4. Report needs to be quick
5. Dataloading time not crucial

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RDBMS
JDBCJDBC
OLAP
cubes
JRuby
mondrian-olap
gem
MDX
A possible scenario

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RDBMS
JDBCXMLA/
HTTP
OLAP
cubes
Mondrian-XML-A-
Consumer
wxWidgets
More Perl-aware …

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RDBMS
JDBCXMLA/
HTTP
OLAP
cubes
Mondrian-XML-A-
Consumer
wxWidgets
Still not Perl-centric
Do I want to
build a pivot
table engine
from scratch?
SQL level filtering
too weak
No persistence

© 2013 Ariba - an SAP company. All rights reserved. 11Public
RDBMS
DBI/ODBCODBO
OLAP
cubesExcel
ETL::Yertl
Perl as an ETL tool + HANA DB + Excel for presentation

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The main concepts of HANA
On current CPUs, we can
expect to process 1 MB per ms and with parallel processing
on 16 cores more than 10MB per ms. To put this into con-
text, to look for a single dimension compressed in 4 bytes,
we can scan 2.5 million tuples for qualiﬁcation in 1 ms

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Compression
Column data is of uniform type; therefore, there are some opportunities for storage
size optimizations available in column-oriented data that are not available in row-
oriented data.

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Data Loading

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Yertl
• yfrom - Build YAML from another format (like JSON or CSV)
• ygrok - Build YAML by parsing lines of plain text
• ysql - Query SQL databases in a Yertl workflow
• ymask - Mask a data structure to display only the desired fields
• yq - Filter YAML through a command-line program
• yto - Change YAML to another format (like JSON)
EXTRACT
FILTER /
TRANSFORM
LOAD

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How it works?
YAML
YAML
[ DBI ]
[ DBI ]

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Get_recent_auctions.sql (source query file)

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insert_auctions.hsql (target query file)

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For complex filtering

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HCP architecture

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What about performance of such ETL process?
If you kick-off a load the data with a single ysql into a trial HANA instance you
probably won't get a speed above 20-60k rows per hour…

© 2013 Ariba - an SAP company. All rights reserved. 24Public
What about performance of such ETL process?
If you kick-off a load the data with a single ysql into a trial HANA instance you
probably won't get a speed above 20-60k rows per hour… but we need to bear
in mind that:
1. Yertl runs DMLs one by one
2. Auto-commits
3. You're loading within one ODBC connection that is routed through one
TLS tunnel
4. There are some constraints imposed on the connections in the trial
instance

© 2013 Ariba - an SAP company. All rights reserved. 25Public
If that’s still too slow
• Data Services
• HANA studio import

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HANA studio import

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HANA studio import

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Defining views
• Attribute views (dimensions) – typically modelling entities such as product, user,
commodity etc
• Analytical views – facts surrounded by dimensions with some defined aggregates
• Calculation views – extension of analytical views e.g. for multi-fact reporting

© 2013 Ariba - an SAP company. All rights reserved. 34Public
„Good” ETL tool

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References
• A Common Database Approach for OLTP and
OLAP Using an In-Memory Column Database,
Hasso Plattner Institute for IT Systems
Engineering University of Potsdam
• SAP HANA Essentials eBook, Jeffrey Word
http://saphanabook.com/
• CPAN: https://metacpan.org/release/ETL-Yertl
• Perl Blogs:
• http://blogs.perl.org/users/preaction/2015/01/man
aging-sql-data-with-yertl.html
• http://blogs.perl.org/users/radek_kotowicz/2015/0
8/moving-data-around-with-yertl-over-odbc-to-
hana.html

© 2013 Ariba - an SAP company. All rights reserved. 36Public
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1. Ad-Hoc OLAP databases with Yertl and HANA Radek Kotowicz (radoslaw.kotowicz@sap.com) http://blogs.perl.org/users/radek_kotowicz http://www.ariba.com/about/sap-ariba

5. © 2013 Ariba - an SAP company. All rights reserved. 5Public Consequences OLTP OLAP • Enforced integrity • Easily extensible design • Flexible analytics • Slow for analytical queries operating on large result sets Lack or loose integrity Simple queries Less flexible/extensible analytics Performant analytics Interface for operating on hyper-cubes

7. © 2013 Ariba - an SAP company. All rights reserved. 7Public Goal 1. Get a DB where analytical queries can be executed without impacting transaction database 2. No setup 3. Need an analytical tool for non-tech users 4. Report needs to be quick 5. Dataloading time not crucial

10. © 2013 Ariba - an SAP company. All rights reserved. 10Public RDBMS JDBCXMLA/ HTTP OLAP cubes Mondrian-XML-A- Consumer wxWidgets Still not Perl-centric Do I want to build a pivot table engine from scratch? SQL level filtering too weak No persistence

12. © 2013 Ariba - an SAP company. All rights reserved. 12Public The main concepts of HANA On current CPUs, we can expect to process 1 MB per ms and with parallel processing on 16 cores more than 10MB per ms. To put this into context, to look for a single dimension compressed in 4 bytes, we can scan 2.5 million tuples for qualiﬁcation in 1 ms

13. © 2013 Ariba - an SAP company. All rights reserved. 13Public Compression Column data is of uniform type; therefore, there are some opportunities for storage size optimizations available in column-oriented data that are not available in row- oriented data.

15. © 2013 Ariba - an SAP company. All rights reserved. 15Public Yertl • yfrom - Build YAML from another format (like JSON or CSV) • ygrok - Build YAML by parsing lines of plain text • ysql - Query SQL databases in a Yertl workflow • ymask - Mask a data structure to display only the desired fields • yq - Filter YAML through a command-line program • yto - Change YAML to another format (like JSON) EXTRACT FILTER / TRANSFORM LOAD

23. © 2013 Ariba - an SAP company. All rights reserved. 23Public What about performance of such ETL process? If you kick-off a load the data with a single ysql into a trial HANA instance you probably won't get a speed above 20-60k rows per hour…

24. © 2013 Ariba - an SAP company. All rights reserved. 24Public What about performance of such ETL process? If you kick-off a load the data with a single ysql into a trial HANA instance you probably won't get a speed above 20-60k rows per hour… but we need to bear in mind that: 1. Yertl runs DMLs one by one 2. Auto-commits 3. You're loading within one ODBC connection that is routed through one TLS tunnel 4. There are some constraints imposed on the connections in the trial instance

29. © 2013 Ariba - an SAP company. All rights reserved. 29Public Defining views • Attribute views (dimensions) – typically modelling entities such as product, user, commodity etc • Analytical views – facts surrounded by dimensions with some defined aggregates • Calculation views – extension of analytical views e.g. for multi-fact reporting

35. © 2013 Ariba - an SAP company. All rights reserved. 35Public References • A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database, Hasso Plattner Institute for IT Systems Engineering University of Potsdam • SAP HANA Essentials eBook, Jeffrey Word http://saphanabook.com/ • CPAN: https://metacpan.org/release/ETL-Yertl • Perl Blogs: • http://blogs.perl.org/users/preaction/2015/01/man aging-sql-data-with-yertl.html • http://blogs.perl.org/users/radek_kotowicz/2015/0 8/moving-data-around-with-yertl-over-odbc-to- hana.html

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