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Copyright © 2015 IBM Corporation
All rights reserved
IBM DB2 Analytics Accelerator
Hands-On Experiences
Netezza In-Database Analytics Functions and
Accelerator Only Table (AoT) Support for QMF 11.2
May 10, 2016
IBM New York City, NY
Dave Trotter
Analytics Technical Sales
North America – Midwest

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System z Business Analytics Performance Integration
Please Note:
IBM’s statements regarding its plans, directions, and intent are subject to change or
withdrawal without notice at IBM’s sole discretion.
Information regarding potential future products is intended to outline our general product
direction and it should not be relied on in making a purchasing decision.
The information mentioned regarding potential future products is not a commitment, promise,
or legal obligation to deliver any material, code or functionality. Information about potential
future products may not be incorporated into any contract. The development, release, and
timing of any future features or functionality described for our products remains at our sole
discretion.
Performance is based on measurements and projections using standard IBM
benchmarks in a controlled environment. The actual throughput or
performance that any user will experience will vary depending upon many
factors, including considerations such as the amount of multiprogramming in
the user’s job stream, the I/O configuration, the storage configuration, and the
workload processed. Therefore, no assurance can be given that an individual
user will achieve results similar to those stated here.

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Acknowledgements and Disclaimers
© Copyright IBM Corporation 2014. All rights reserved.
– U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract
with IBM Corp.
IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International Business Machines Corporation in the United
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effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms
and conditions of the applicable license agreement governing the use of IBM software.
All customer examples described are presented as illustrations of how those customers have used IBM products
and the results they may have achieved. Actual environmental costs and performance characteristics may vary
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that any activities undertaken by you will result in any specific sales, revenue growth or other results.

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IDAA – INZA Functions and AoTs using QMF 11.2
DB2 Analytics Accelerator
Users’ Group

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Agenda
• Brief Overview of IDAA Interfaces
• Netezza In-database Analytics / INZA functions
• SPSS Modeler/Data Studio/Stored Procedure Examples
• Installation Steps and Documentation for INZA Support
• Accelerator Only Tables / QMF 11.2 Support
• Summary / Questions

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IBM DB2 Analytics Accelerator
Product components
CLIENT
Data Studio with
Studio
Plug-in
z System
DB2 for z/OS enabled for IBM
IBM DB2
Analytics
Accelerator
v5.1
Dedicated highly available
network connection
PureData System
for Analytics
(Netezza Technology)
SPSS Modeler 17
or
SPSS Modeler 18
(GA March 2016)

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Accelerator-only table type in DB2 for z/OS
Creation (DDL) and access through DB2 for z/OS in all cases
Non-accelerator DB2 table
• Data in DB2 only
Accelerator-shadow table
• Data in DB2 and the Accelerator
Accelerator-archived table / partition
• Empty read-only partition in DB2
• Partition data is in Accelerator only
Accelerator-only table (AOT)
• “Proxy table” in DB2
• Data is in Accelerator only
Table 1
Table 4
Table 3
Table 2
Table 2
Table 4
Table 3

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Netezza In-database Analytics (INZA) functions
Enable acceleration of predictive analytics applications
In-database analytics enables SPSS/Netezza
Analytics (INZA) data mining and in-database
modeling to be processed within IDAA.
 Accelerate SPSS/Netezza Analytics data mining and in-database modeling through
SQL Stored Procedure calls.
 Allow frequent model refreshes to enable adequate scoring
 Reduce need of data movement processes (ETL) to other platforms for predictive
analytics purposes
 Support the full lifecycle of a real-time analytics solution on a single, integrated
system, combining transactional data, historical data, and predictive analytics

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In-database analytics – Technical basics
Support for INZA function calls on IDAA
Set of stored procedures contained in the IBM Netezza In-Database Analytics
Package (INZA) available to be installed on the Accelerator.
Currently there are 19 functions, that support:
 Decision Tree
 Regression Tree
 Naive Bayes
 K-means Clustering
 TwoStep Clustering
Stored procedures use accelerator-shadow tables or accelerator-only tables as
input and create accelerator-only tables and data models as output
DB2 for z stored procedures invoke the INZA function code on the Accelerator
itself. These procedures can be called from DB2 for z/OS client applications or
from SPSS Modeler 17 and beyond.

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In-database Analytics – Technical basics
 Only SPSS Modeler 17 and newer are supported, which take advantage
of the following 19 INZA stored procedures
 dectree - Builds a Decision Tree model by growing and pruning a tree
 grow_dectree - Builds a Decision Tree model
 predict_dectree - Applies a Decision Tree model to generate classification predictions
 prune_dectree - Prunes a previously built Decision Tree model
 regtree - Builds a Regression Tree model by growing and pruning a tree
 grow_regtree - Builds a Regression Tree model
 prune_regtree - Prunes a previously built Regression Tree model
 predict_regtree - Applies a Regression Tree model to generate regression predictions for
a dataset
 naivebayes - Builds a Naive Bayes model
 predict_naivebayes - Applies a Naive Bayes model to generate classification predictions
for a dataset
 kmeans - Builds a Clustering model that clusters the input data into k centers. The centers
are calculated as the mean value of the nearest input data records
 predict_kmeans - Applies a K-means Clustering model to cluster records of a dataset

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In-database Analytics – Technical basics
 two_step - Builds a TwoStep Clustering model that first distributes the input data into a
hierarchical tree structure according to the distance between the data records, then
reduces the tree into k clusters. A second pass over the data associates the input data
records to the next cluster
 predict_twostep - Applies a TwoStep Clustering model to score records of a dataset
 split_data - Randomly splits the input data into two separated subsets
 pmml_model - Stores the given analytics model as PMML document to a table
 export_pmml - Exports the given analytics model as PMML document to a file, or it
exports a model from a PMML table to a file. If no PMML table exists containing the
PMML document for this model, one can be created automatically when requested.
Optionally, instead of writing to a file, the result can be returned by the procedure.
 model_exists - Checks if the given model exists. The model can be searched in the current
or in another given database.
 drop_model - Drops the given model. All managed tables of this model are also dropped
 Many other functions exist in the INZA Analytics Package, but support for
these extended functions in IDAA is still in development.

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Developer’s Guide: https://ibm.biz/Bd4bRE
Reference Guide: https://ibm.biz/Bd4zdA
These guides are hosted out on IBM’s DeveloperWorks Community Pages as PDF
downloads. They are also available for download from the IBM FixCentral site, along
with the install packages.
Netezza Analytics Reference Information

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In-Database Analytics
Data Preparation and SPSS modeling in the Accelerator
Transaction Processing
Systems (OLTP)
With embedded scoring
Advantages:
• Allows fast model refreshes
• Better performance and
reduced latency
• Ensures adequate scoring
• Scoring outside accelerator
with SPSS Modeler Server
Scoring Adapter for
DB2 for z/OS
Data for transactional and analytical processing
Customer
Transactions
Customer
Data
Customer Txn
Data Prep AOTs
Customer
Transactions
Customer
Data
Modeling
Model
Model

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IBM SPSS Modeler 17 - Stream Processing
• Simple stream to select some rows from a table
and display the result:
• Enable Stream Properties in "File"->"Stream
Properties"->"Optimization"
• Executed statement on the Accelerator STRIPER:
• SELECT T0."R_REGIONKEY" AS
"R_REGIONKEY",T0."R_NAME" AS
"R_NAME",T0."R_COMMENT" AS
"R_COMMENT"
FROM (SELECT * FROM STKNOL.REGION)
T0
WHERE NOT((LOCATE('A', T0."R_NAME",
CODEUNITS32) = 1))

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IBM SPSS Modeler 17
Stream Processing with caching enabled
• With caching enabled on the SELECT node of the stream, SPSS creates
an accelerator-only table on the Accelerator
• Executed statements on the Accelerator:
• CREATE TABLE "SESSION".CLEMTMP79C49C041 ( "R_REGIONKEY" INTEGER,"R_NAME"
VARCHAR(25),"R_COMMENT" VARCHAR(152) ) IN ACCELERATOR "STRIPER" CCSID UNICODE
INSERT INTO "SESSION".CLEMTMP79C49C041 ("R_REGIONKEY","R_NAME","R_COMMENT")
SELECT T0."R_REGIONKEY" AS "R_REGIONKEY",T0."R_NAME" AS "R_NAME",T0."R_COMMENT" AS
"R_COMMENT"
FROM (SELECT * FROM STKNOL.REGION) T0 WHERE NOT((LOCATE('A', T0."R_NAME", CODEUNITS32) = 1))
SELECT T0."R_REGIONKEY" AS "R_REGIONKEY",T0."R_NAME" AS "R_NAME",T0."R_COMMENT" AS
"R_COMMENT" FROM "SESSION".CLEMTMP79C49C041 T0
Find more details here:
https://www.ibm.com/developerworks/community/wikis/home
?lang=en#!/wiki/W494c1ca765dc_4cbe_a8cb_dc15fd30847c/p
age/Use%20of%20IBM%20DB2%20Analytics%20Accelerator
%20in%20SPSS%20Modeler
Will be enhanced with Modeling examples with DB2 Analytics
Accelerator V5.1

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A large petroleum and energy products supplier did a pilot project with
SPSS Modeler.
Working together with IBM, they deployed IBM SPSS Modeler software
with IBM DB2 Analytics Accelerator for z/OS.
Using accelerated DB2 queries, the company now harnesses SPSS
predictive analytics insights to generate personalized sales suggestions
based on customers’ purchasing histories.
Pilot Project with SPSS Modeler

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process process type
node
modeling nugget score output
Step 1 DB2 Analytics Accelerator Query Acceleration
Step 2 Step 3 In-database transformation and analytics
Without Accelerator: The SPSS source node selects the input data from DB2 z/OS without any
acceleration, then all data preparation is done in SPSS Modeler, followed by the modeling algorithm in
SPSS modeler, then the model is scored and scoring results are inserted into a DB2 z/OS result table
Source tables are accelerator-shadow tables and the accelerator processes the (complex)
select statement
Step 1
Data preparation is pushed down into the accelerator producing an accelerator-only table for the
type node
Step 2
INZA modeling algorithm is called and executed in the accelerator on an accelerator-
only table
Step 3
Data Source Access and Preparation Modeling Scoring
Source data
source process process t
(select) (select) Result data
Data preparation (using AOTs) and SPSS modeling in the
Accelerator
With Accelerator:
Pilot Project with SPSS Modeler

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Results of Pilot Project with SPSS Modeler
source
(select)
process process type
node
modeling nugget score output
Step 1 IDAA Query Acceleration
Step 2 Step 3 In-database transformationand analytics
Source tables are accelerated tables and IDAA processes the (complex) select statement
 Significant acceleration of select statement in stream II: 10x faster
Step 1
Data preparation is pushed down into IDAA producing an accelerator-only table for the type node
Data Preparation in minutes which was not possible before in stream II
Significant acceleration of data preparation in stream I + II + III : 3-240x faster
Step 2
IDAA/INZAmodeling algorithm is called and executed in IDAA on an accelerator-only table
Step 3
Data SourceAccess and Preparation Modelling Scoring
Source data (select) Result data
Total acceleration for stream I + II + III: 3 - 23x faster
• Acceleration of Modelling highly depends on data size.
Your mileage can and will vary !!!

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Batch scoring with accelerated in-database predictive modeling

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Prereqs for SPSS Support with DB2 for z/OS and IDAA/INZA
• IBM SPSS® Modeler 17.0 (or 18.0 now available) running in local mode or against an
SPSS Modeler Server installation.
• DB2 for z/OS Version 10 or later together with DB2 Analytics Accelerator for z/OS
Version 5.1, PTF 2
• IBM SPSS Data Access Pack V7.1 or other compatible ODBC Drivers
• License for DB2 Connect™ for System z®
• SQL generation and optimization enabled in SPSS Modeler
• IBM SPSS Modeler Scoring Adapter for zEnterprise® V17.0
• SPSS Modeler 17.0 Info
https://www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/modeler_mainhelp_clien
t_ddita/clementine/dbmining_zdb2_container.dita?lang=en

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IBM SPSS Modeler 17 (and now 18)
Enabling integration with IBM DB2 Analytics Accelerator
1. Configure ODBC connection to DB2 for z/OS from SPSS Modeler (e.g. MySampleDB)
2. Edit the file odbc-db2-accelerator-names.cfg and associate an accelerator name with the configured
ODBC data source in the format:
• "<DSN>","<ACCELNAME>","<ENCODING>“
• Example (with default encoding UNICODE):
• "MySampleDB","STRIPER“,”UNICODE”
• Default location of the config file:
• Windows: C:Program FilesIBMSPSSModeler17config
• Linux: /opt/ibm/spss/modeler/17.0/config
3. Restart the SPSS Modeler Server and connect to the ODBC data source from the SPSS Modeler Client
• For the MySampleDB data source SPSS will show all accelerator-shadow tables of accelerator
STRIPER
• CURRENT QUERY ACCELERATION register set by SPSS
• If you want to work with tables available in DB2 for z/OS only then create a second ODBC data
source.
• Links:
• https://www.ibm.com/developerworks/community/wikis/home?lang=en#!/wiki/W494c1ca765dc_4cbe_a8cb_dc15fd30847c/page/Use%20of%20IBM%20D
B2%20Analytics%20Accelerator%20in%20SPSS%20Modeler
• http://www-01.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/dbmining_zdb2_enabling.html
Note, encoding EBCDIC is not supported
until PTF2.....Soon to be available!

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Use Data Studio to Load Data / Run Queries

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Invoke INZA Stored Procedure directly from DataStudio

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Example of INZA Stored Procedure Call
 Data Studio or an Application with an embedded call to DB2 Stored Procedure:
CALL INZA.KMEANS(’MYACCEL', 'model=adult_mdl,
intable=TPCH30M.CUSTOMER,
outtable=IWATEST.adult_out,
id=C_CUSTKEY, target=C_NATIONKEY, transform=S,
distance=euclidean, k=3, maxiter=5', ?, '');
Blue = procedure/algorithm to execute
Red = Accelerator to run the procedure on
Green = Algorithm parameters
Orange = Table information

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Example of DB2 SQL Stored Procedure wrapper

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Installation and setup
https://www-
01.ibm.com/support/knowledgecenter/SS4LQ8_5.1.0/com.ibm.datatools.aqt.doc/installma
nual/concept/c_idaa_inst_analytics.html
Installation package can be downloaded from Fix Central
 Described in Knowledge Center

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Retrieving Analytics installation package from Fix Central
Use the following URL:
http://www.933.ibm.com/support/fixcentral/swg/selectFixes?parent=ibm~Information%2BManagement&product=ibm/Information+Management/
Netezza+Applications&release=ANALYTICS_IDAA_3.2&platform=All&function=fixId&fixids=3.2.1.0-IM-Netezza-ANALYTICS-IDAA-
fp105780&includeSupersedes=0

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Additional sources of Information
•IBM DB2 Analytics Accelerator for z/OS V5.1.0 Release Notes:
http://www-
01.ibm.com/support/docview.wss?uid=swg27047096&myns=swgimgmt&mynp=OC
SS4LQ8
•Known issues with IBM Netezza Analytics 3.2.1 for System z
http://www.ibm.com/support/docview.wss?uid=swg27047149

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Accelerator-only tables
Supporting in-database transformation and multi-step
processing
Introduction of Accelerator-only tables (AOT)
to store intermediate or final results of data
transformation or reporting processes
 Accelerate in-database data transformations and data movement processes
 Reduced need of data movement processes to other platforms for data
transformation purposes
 Enables multi-step reporting on the Accelerator
 Saves disk space and CPU cost on z Systems currently used for transformations and
reporting steps
 Allow data preparation steps for data mining and other advanced analytics to
execute on the Accelerator

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Accelerator-only tables – Technical basics
 AOTs are created and dropped using DB2 DDL statements (CREATE; DROP)
• Accelerator must be started
• QUERYACCELERATION behavior may have any value during CREATE/DROP
• Syntax:
 CREATE TABLE MYTABLE (...) IN ACCELERATOR <ACCEL1>;
 DROP TABLE MYTABLE;
 Recommended to create a database in DB2 to be used for the AOTs
• CREATE TABLE MYTABLE (...) IN ACCELERATOR <ACCEL1> IN DATABASE
MYDB;
• Usual authorization necessary to create objects in database
 Queries using AOTs can only run on the Accelerator
• QUERYACCELERATION behavior must be set to ENABLE/ELIGIBLE/ALL
 AOTs can be subject to INSERT/UPDATE/DELETE operations on other accelerated tables
archived tables or AOTs
 Dynamic and static SQL can be used with AOTs

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• Recommended to create a database in DB2 to be used for the AOTs
– CREATE DATABASE MYDB;
– CREATE TABLE MYTABLE (...) IN ACCELERATOR <ACCEL1> IN
DATABASE MYDB;
– DROP TABLE MYTABLE;
– DROP DATABASE MYDB;
– Authorization necessary to create objects in database
• Each single CREATE/DROP statement must be committed
– No other statements are allowed to run in these transactions
• Multiple I/U/D statements in one transaction are only possible if all statements
target AOTs on the same Accelerator
Accelerator-Only tables – Usage Notes

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AOTs in Data Studio
• How do AOTs appear in Data Studio?
• A different icon in front of the table allows distinction to accelerated and
archive tables
• Less operations are possible for AOTs (Load, Switch Acceleration, Storage
Saver …
• The ‘Last Load’ column shows “Operational”

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AoT and special register support in QMF 11.2 for z/OS
Save query results to AOTs
• Available in QMF for z/OS V11.2 (GA 4th of September 2015)
• Full support in TSO client
• Current limitations in other clients, full support in plan for a future fix pack
• Value:
• Save intermediate results temporarily on the Accelerator as part of a multi-step
process
• Persist a query result for later accelerated processing
Global variables
•DSQEC_SAV_ALLOWED – Controls whether users save data to a new table in the database
or in an Accelerator
 0 – Disable Save Data
 1 – Enable Save Data to database tables only
 2 – Enable Save Data to AOT only
 3 – Enable Save Data to either database or AOTs (database default)
 4 – Enable Save Data to either database or AOTs (accelerator default)
•DSQEC_SAV_ACCELNM – Contains the default name of the Accelerator to be used when
creating AOTs from QMF commands (e.g. SAVE DATA)

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QMF V11.2 enhancements to support AoTs (Cont.)
Command with syntax enhancement Description
SAVE DATA AS tabname (ACCELERATOR
accelname
Saves data as accelerator-only table
tabname in accelerator accelname
SAVE DATA AS tabname (SPACE name Saves data as database table tabname in
database and table space specified by
name
IMPORT TABLE tabname FROM
datasetOrFile (ACCELERATOR accelname
Imports table data into accelerator-only
table tabname in accelerator accelname
IMPORT TABLE tabname FROM
datasetOrFile (SPACE name
Imports table data to database table
tabname in database and table space
specified by name
RUN QUERY qname (TABLE tabname
ACCELERATOR accelname
Runs a query and saves the result directly
into accelerator-only table tabname in
accelerator accelname
RUN QUERY qname (TABLE tabname
SPACE name
Runs a query and saves the result directly
into database table tabname in database
and table space specified by name
New syntax enhancement supported in TSO client only so far. For other clients create the AOTs separately
before using the SAVE DATA, RUN QUERY or IMPORT commands.

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Sample QMF procedures for multi-step reporting
• Runs in DB2
• SAVE DATA creates
regular DB2 Table
RUN QUERY BMB.QUERY_ACCEL_NONE
RUN QUERY BMB.DEMO1
SAVE DATAAS BMB.PRICE_PUB
RUN QUERY BMB.DEMO1A
SAVE DATAAS BMB.PRICE_NONPUB
RUN QUERY BMB.DEMO2
RUN QUERY BMB.QUERY_ACCEL_ELIGIBLE
SET GLOBAL (DSQEC_SAV_ALLOWED=4
SET GLOBAL (DSQEC_SAV_ACCELNM=DEMOIDAA
RUN QUERY BMB.DEMO1
SAVE DATAAS BMB.PRICE_PUB_AOT
RUN QUERY BMB.DEMO1A
SAVE DATAAS BMB.PRICE_NONPUB_AOT
RUN QUERY BMB.DEMO3
RUN QUERY BMB.QUERY_ACCEL_ELIGIBLE
SET GLOBAL (DSQEC_SAV_ALLOWED=4
SET GLOBAL (DSQEC_SAV_ACCELNM=DEMOIDAA
RUN QUERY BMB.DEMO1 (TABLE=BMB.PRICE_PUB_AOT
RUN QUERY BMB.DEMO1A (TABLE=BMB.PRICE_NONPUB_AOT
RUN QUERY BMB.DEMO3
 Runs on Accelerator
 SAVE DATA creates AOT
 Multiple SQL statements to
SELECT data and INSERT
data
 Runs on Accelerator
 RUN QUERY creates AOT
 Single SQL statement to
INSERT FROM SELECT
data

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Large Insurance Company
Problem Query - Focus Batch
BEN PERIOD QUERY1
TEST.FOCEXEC.DATA(BEN_Period_Query1)
Additional Notes: Ran off-peak in Job Class R for almost exactly ten hours.
It returned 172,097 rows. 10M+ rows on the largest table referenced.
26.93 CPU minutes, 598.39 clock minutes.
TABLE FILE Example1 /* Part 1 of 3……other parts not shown……. */
SUM
CORP_CD
PROD_CD
TYP_BEN_CD
SPECI_BEN_CD
DEP_AGE_BEN_PRD_CD
BY CORP_CD NOPRINT
BY PROD_CD NOPRINT
BY TYP_BEN_CD NOPRINT
BY SPECI_BEN_CD NOPRINT
WHERE BEN_CAN_DT GT '2016-01-11' AND
(DEP_AGE_BEN_PRD_CD EQ 'C' OR
DEP_AGE_BEN_PRD_CD EQ 'Y')
AND CORP_CD EQ '2'
AND (PROD_CD LT '30' OR PROD_CD GT '60')
ON TABLE HOLD AS TEMP1
END
JOIN CLEAR
JOIN CORP_CD AND PROD_CD AND TYP_BEN_CD AND SPECI_BEN_CD IN TEMP1
TO ALL
CORP_CD AND PROD_CD AND TYP_BEN_CD AND SPECI_BEN_CD IN Example1
END

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Focus Re-write using QMF (Run Query) to Save
Results to AoT
The RUN QUERY command saves the query result faster into an accelerator-only
table than the SAVE DATA AS command, because the RUN QUERY command
runs the query and saves the result into a table using a single INSERT INTO
SELECT FROM SQL statement.
TEST.Ben_Period_Query1 /* example part1 */
SELECT
SUM(CORP_ID),SUM(PROD_CD,SUM(TYP_BEN_CD),SUM(DEP_AGE_BEN_PRD_CD),’CORP_
CD’
FROM (DB2 table)
WHERE BEN_CAN_DT > '2016-02-10' AND DEP_AGE_BEN_PRD_CD = 'C' OR
DEP_AGE_BEN_PRD_CD EQ Y')
AND CORP_CD = '2' AND (PROD_CD LT '30' OR PROD_CD > '60')
GROUP BY CORP_CD,PROD,CD,TYP_BEN_CD,SPECI_BEN_CD
Resulting QMF Example
RUN QUERY TEST.BeneFit_Period_Query1 TABLE TEST.Ben_Period_QRY1AGG_AOT

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Large Insurance Company
Results from using IDAA AoT Support in QMF
Example #1
QMF Batch
QMF Baseline (z13 – DB2):
5 Minutes - Elapsed
Results: 260 Rows
IDAA:
1 Second -
Elapsed (300x
faster)
This QMF PROC runs 24
queries in succession.
We modified the PROC
to take advantage of the
Accelerator Only Tables
(with Common Table
Expressions) feature in
IDAA.
Example #2
FOCUS
BATCH
REWRITE to
QMF
FOCUS
BATCH
Baseline (z13 – DB2):
167.09 Minutes - Elapsed
Results: 171,670 Rows
IDAA:
10 Seconds -
Elapsed (1003x
faster)
Query was rewritten in
SQL/QMF (Insert sub-
select) to take
advantage of
Accelerator Only Table
(1 second) feature in
IDAA.
Last step (data
grouping/consolidation)
was completed in
FOCUS (9 seconds).

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