Revolutionizing the Data Abstraction Layer with IBM Optim pureQuery and DB2

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Revolutionizing the Data Abstraction Layer with IBM Optim pureQuery
Dr. Vladimir Bacvanski, Vice President, InferData, vladimir@inferdata.com
Daniel Galvin, Consultant, Galvin Consulting, dang@galvinconsulting.com
Session Number 2171

What is this revolution about?
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NO SLOW APPS!
NO BAD SQL!
GET CONTROL BACK !

Show of Hands: What Data Access Technology Have You Used?
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Hibernate
EJB Entity Beans
JDBC
JPA
iBatis
SQLJ
What’s most important to you?
Productivity
Performance
Security
Portability

Java Data Access – Two Views of the World
Writing Java code is so easy with this eclipse environment.I wish it was that easy to get the SQL right.
JSP
QoS goals
Spring
Another runaway query! Where are these coming from? JDBC? Hmmm…
Runstats
XML
http
Partition strategy
Stored
Procedures
mashup
SQL
Response Time!
REORG
JSON
JDBC
This ORM doesn’t allow me to leverage all my database’s SQL.
Inconsistent response time? How long will it take me to find the offending application sending bad SQL this time?
JDBC
I can’t believe I got called out last week. I wish I could see how these queries will run in production.
SQLJ
JPA
These ad-hoc queries are dangerous. We need a library of tested SQL interfaces.
Application
Developer
Database Developer& Administrator
Spring
Why does this query take so long?
iBatis, . . .
Sometimes I need POJOs, sometime JSON, sometimes XML, what should I use?
Static SQL? Sounds like another delay to getting my program deployed
Another GRANT request? This security administration is out of control.
Can I examine the SQL “before” the application is deployed?

Meet in the
Middle
Data Mapping Approaches
Application-Centric
Top-Down
Start with Object Domain Model
ORM Mapping
Well supported in dynamic languages and frameworks
Hybrid
Meet in the middle
Can be challenging w/o comprising
Data-Centric
Bottom-UP
Start with Relational Data Model
Not well supported in dynamic languages and frameworks
Top
Down
Persistence Layer
Bottom
Up
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EJB, JPA, and Hibernate vs. The Database

DBA and SQL developer chasm
Where is the SQL coming from?
What is it?
Where is it?
How do we tune it?
How de we manage it?
Performance Concerns:
Some App Server vendors claim (unsurprisingly) that Managed objects performs fine.
There are many user claims of bad Managed object performance is bad on the web.
As always, the truth is in the middle.
And will depend on your app server, application, database, etc ..

“Our top story: Large Customer moves from COBOL to Java to become more agile. In other news, DBA develop amnesia.”
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Introducing pureQuery
A high-performance, data access platform to simplify developing, managing, securing, and optimizing data access.
pureQuery Components:
Simple and intuitive API
Enables SQL access to databases or in-memory Java objects
Facilitates best practices
Optim Development Studio (integrates with RAD/RSA)
Integrated development environment with Java and SQL support
Improve problem isolation and impact analysis
Optim pureQuery Runtime
Flexible static SQL deployment for DB2

Add basic OR mapping and annotated-method style
pureQuery
pureQuery Balances Productivity and Control
Managed objects
Object-relational mapping
Full SQL control
Code all your SQL
JDBC / SQLJ
Use SQL templates, inline only
Spring
templates
iBATIS
Complex OR mapping and persistence management, but loss of controls
Hibernate
Adds container management option
OpenJPA
(EJB3)

Design Phase pureQuery close-up
Jump Start Application Design

Generate SQL and Code from Database Objects
Setup basic DAO Pattern

Existing JDBC to Static

Reroute Dynamic Queries to Static

Code Development Productivity

Code Generation, Content Assist
Database aware, Java SQL Editor

SQL Replacement

Replace Query w/o changing source

SQL Performance Metrics

Find and sort query elapsed time from Java

Oracle Support

Replace Query w/o changing source

Java to SQL Integration

Categorize by Java, SQL, Database , Packages, track back to line of code

Problem Determination

Monitor WebSphere Connection Pool, JDBC Driver, Network
Track back to SQL and line of code in the application

Static SQL

Lock in Access plans, Improve Security, Consistent Performance

SQL Injection Prevention

Lock down SQL for Dynamic

Code Example: JDBC
java.sql.PreparedStatementps = con.prepareStatement(
"SELECT NAME, ADDRESS,PHONE_NUM FROM EMP
WHERE NAME=?");
ps.setString(1, name);
java.sql.ResultSetrs= ps.executeQuery();
names.next();
Employee myEmp = new Employee();
myEmp.setName(rs.getString(1));
myEmp.setHomeAddress(rs.getString(2));
myEmp.setHomePhone(rs.getString(3));
names.close();
class Employee {
String name;
String homeAddress;
String homePhone;
…
}

Code Example: pureQuery
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Employee myEmp= db.queryFirst( "SELECT NAME, ADDRESS, PHONE_NUM FROM EMP WHERE NAME=?", Employee.class, name);
Even simpler, if we have a method getEmployee with a Java annotation or XML file with SQL for the query:
Employee myEmp= getEmployee(name);

Why Should be the Data Specialists be interested in pureQuery?
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Motivations of the Data Specialists

SQL Performance Tuning
Ease of Tuning
Autonomy of Developers
Predictability of an Optimized Data Access Path
Reduction of Costs to satisfy SQL statements
Optimized Access Paths
Reduction of CPU intensive components of SQL Execution
Utilization of Specialty Processors
Capacity Planning
Hardware Utilization
Problem Determination capabilities

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pureQuery Capabilities

Static SQL for Runtime with Dynamic SQL Execution in Development
pureQuery can utilize SELECT INTO from a Java application
With Client Optimization, Static SQL from existing JDBC with no changes to the Application
Homogeneous and Heterogeneous Batching of Statements
Statically bound packages are easy to EXPLAIN and monitor for changes in access path
pureQuery coupled with IBM Optim Performance Monitoring provides E2E Performance Monitoring and Problem Determination
Impact analysis is greatly improved by the static packages and the ability to tie each statement to a method in the application code

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Static vs. Dynamic SQL Execution
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Static vs. Dynamic SQL

Cost of Prepare
CPU cost of Short Prepare on DB2 9 for z/OS – between 400µs and 1ms
CPU cost of Full Prepare on DB2 9 for z/OS – approximately 30 to 50ms. Cost could be much higher and generally increase with complexity.

Trial Results
Estimated CPU Savings
(In DB2 CPU)

How well does it work? – Java applications
In-house testing shows significant performance improvements
IRWW – an OLTP workload, Type 4 driver
Cache hit ratio between 70 and 85%
23 % improvement in throughput using pureQuery over dynamic JDBC
15% - 25% reduction on CPU per transaction over dynamic JDBC
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How well does it work? - .Net applications
Throughput during static execution increased by 159% over dynamic SQL execution assuming a 79% statement cache hit ratio

IRWW – OLTP application
Application accesses DB2 for z/OS

*Any performance data contained in this document were determined in various controlled laboratory environments and are for reference purposes only. Customers should not adapt these performance numbers to their own environments as system performance standards. The results that may be obtained in other operating environments may vary significantly. Users of this document should verify the applicable data for their specific environment.
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Batching SQL Statements
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Homogeneous & Heterogeneous Batch
Homogeneous Batch – all instances in the batch are the same statement and require only 1 line turn
Heterogeneous Batch – allows different SQL statements to be included in batch.
Both Utilize Multi-Row Insert
Heterogeneous Batches may contain 0 to many Homogeneous Batches

Heterogeneous Batch Example
publicint[][] insertAgentAndPolicies(AgentTO agentTO, PolicyTO[] policyTO) {
Data data = DataFactory.getData(ds, pdqProps);
try {
AgentData agentData = DataFactory.getData(AgentData.class, data);
PolicyData policyData = DataFactory.getData(PolicyData.class, data);data.startBatch(HeterogeneousBatchKind.heterogeneousModify__);
agentData.insertAgent(agentTO);
policyData.insertPolicies(policyTO);
returndata.endBatch();
} finally {
data.close();
}
}

Client Optimization
Allows you to bind static SQL packages from existing JDBC code
Avoids the cost of rewriting the application to code to the pureQuery API
Allows Heterogeneous batch with minor changes to the code
None of the productivity advantages are realized. Code is still maintained in JDBC.
End-to-End monitoring lacks some introspective capability into the coding
Creation of the static packages requires that you run the code.
Some overhead at runtime related to resolution of statements to static packages

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Optimize Existing JDBC Applications
Improve performance for DB2 – without changing a line of code
Capture Configure Bind Execute
pureQuery client optimization enables static execution for JDBC applications (custom-developed, framework-based, or packaged)
Existing JDBC Application
Captured SQL-
related
metadata
JDBC Driver w/ pureQuery
Dynamic SQL execution
Static SQL execution
DB2 Data Servers
"The ability to use static SQL with pureQuery is huge. Recently, I worked with a client who could reduce CPU usage by 7 percent thanks to this one feature."
— David Beulke, Pragmatic Solutions Inc.

Design
Develop
Optimize
Govern
Models
Policies
Metadata
Deploy
Operate
IBM Optim pureQuery
Reduce costs
Increase system throughput
Improve developer productivity
Move workload to zIIP and zAAP
Improve quality of service for new and existing Java applications
Improve performance
Lock in access plans
Speed up problem resolution
Reduce development time for new Java applications
Bridge Java and data
Balance productivity and control
Enhance developer and DBA collaboration
Enhance security
Limit user access
Minimize SQL injection risk
Improve audit readiness
Developer
Develop
Code
Debug
Test
Tune, Package
Tester

Why should DBAs care ?
DBAs have little to no visibility of application SQL before deployment, no opportunity for review and optimization
Problem isolation takes days with contemporary environments such as Java, PHP, .NET, etc due to inability to trace SQL to Java application and source code
Constantly increasing Java application workload taxes existing systems – need to fit more work into existing systems
SQL injection represents an increasing risk to data security

Why should Developers care ?
Get data access right the first time !
Get it done faster - Improved productivity
Single environment that spans Java application and database development
Improved problem isolation and resolution

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Control performance
Decide at deployment time how the SQL is executed
Understand and lock down the access plan for SQL
Replace suboptimal SQL without changing the application
Control security
Prevent SQL injection
Prevent execution of unauthorized SQL
Better manage database security
See inside applications that are driving your database
Understand where SQL comes from
Understand when frameworks and ORM’s are getting in the way
Simplify problem determination and troubleshooting
Correlate problem SQL with applications, ORM’s and frameworks
Optim pureQuery Runtime

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How do I start with pureQuery?
Existing applications
Optimize existing JDBC (and .NET!) applications
No code changes needed
Have to go through the client optimization process to get to static SQL
New applications
Use the pureQuery API
Development codes using one API regardless of whether it is deployed dynamically or statically
DBA deploys statically
No need to go through client optimization process
Other
JPA, iBatis, Hibernate

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pureQuery Facilitates Best Practices
Supports both inline SQL and Java annotations (method)
Intuitive interfaces for common data retrieval and manipulation scenarios hides JDBC complexity
Query First
Homogeneous Batch
Reduce network trips to the database
Query Over Java Collections
Heterogeneous Batch
Use custom result handlers to map results to POJO’s, XML, JSON, …
Write high performance Java data access applications, Part 3:
Data Studio pureQuery API best practices
-- VitorRodrigues
http://www.ibm.com/developerworks/db2/library/techarticle/dm-808rodrigues/?S_TACT=105AGX01&S_CMP=LP

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A TypicalApplication Architecture with pureQuery
Presentation Layer
Implements the U/I or network protocols using the business services
Business Service Layer
Never use the pureQuery API directly. Gets data from the Data Access Layer
Data Access Layer
Using the pure-query API to access the database.
Provides a technology neutral API to the data used by the business services
pureQuery
pureQuery makes this layer easy, fast, consistent and traceable
Database
Sometimes additional layers are required
Workflow
Data federation
Workspace

RAD or RSA / Optim Development Studio Data Centric Development Scenario
Write in JavaUsing RAD+WAS FP for Web 2.0
Write in Java with pureQueryUsing Optim Dev. Studio in RAD
Access generated Java data objects from code developed in RAD
WAS Feature Pack for Web 2.0

Demo
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Demo!

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Conclusion: pureQuery Revolutionary Advantages
Excellent performance
Static and dynamic SQL is captured during test and optimized before deployment
Enables lock-in of access path
Great productivity
Excellent tool support through Optim Development Studio
Shell share with Rational tools
Mapping from SQL to Java captured and traceable
Facilitates collaboration between DBA’s and developers
Performance tuning, impact analysis
Better security
Limits SQL injection
Controlled database access

Where to go Next? Resources and more…
Optim Development Studio
http://www.ibm.com/software/data/optim/development-studio/
IBM pureQuery
http://www.ibm.com/software/data/optim/purequery-platform/faq.html
pureQuery Custom Training
InferData, IBM Business Partner http://www.inferdata.com
Course: Developing Database Applications with Optim Development Studio and pureQuery
http://www.inferdata.com/training/data/optim_purequery_training.html
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Web, Blogs
Integrated Data Management (Optim and Data Studio)
http://www.ibm.com/developerworks/spaces/optim
Vladimir’s Blog: On Building Software
http://www.OnBuildingSoftware.com
Twitter:
http://twitter.com/OnSoftware
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