Presentation on theme: "Optimizing Your WebSphere Applications with Optim and pureQuery Curt Cotner IBM Fellow, CTO for IBM Database Servers."— Presentation transcript:
Optimizing Your WebSphere Applications with Optim and pureQuery Curt Cotner IBM Fellow, CTO for IBM Database Servers
Development Tools for Your Java Database Applications
1. Select table Java Data Access in 5 Simple Steps 2. Name bean & select styles 3. Generate test code 4. Map table to bean 5. Select template SQL CRUD
4 What happened? Generate code from table The code is generated for Bean (aka Java Object) representing the table Interface with associated SQL or Sample Application with Inline SQL Implementation of Interface Optionally a JUnit test case.
5 Generated Test Cases for Your SQL Jump start your migrations!
6 SQL analysisSQL executionSQL validation Developing with pureQuery Unleash SQL from Java SQL content assist
7 SQL analysisSQL execution Developing with pureQuery Unleash SQL from Java SQL validation
8 SQL content assist SQL analysis Developing with pureQuery Unleash SQL from Java SQL execution Run SQL with parameters at design time in the Java program WITHOUT writing a test application
9 SQL executionSQL validation SQL content assist Developing with pureQuery Unleash SQL from Java SQL analysis View explain plans for SQL inside you Java programs
10 pureQuery Outline Speed up problem isolation for developers – even when using frameworks Capture application-SQL-data object correlation (with or without the source code) Trace SQL statements to Java source code for faster problem isolation Enhance impact analysis by identifying specific application code impacted by a database changes Answer “Where used” questions like “Where is this column used within the application?” Use with modern Java frameworks e.g. Hibernate, Spring, iBatis, OpenJPA
11 pureQuery Outline pureQuery Outline View’s 3 categorizations How do you look at the relationship between SQL and Java?
Optim Development Studio Problem determination and isolation –with pureQuery outline go to the source of the problematic SQL Improve Hibernate data access calls –Re-write HQL –Use better performing native SQL With performance metrics Identify the hot spots in your hibernate application Source code correlation Database object correlation Identify and change the HQL
Analyze Use of Sensitive Data See queries accessing sensitive data Optim Development Studio Icon identifies sensitive access Filter to see use of sensitive data View only SQL accessing sensitive data Filter SQL by action
IBM Data Studio pureQuery For DBAs and Applicatio n Developer s (v1.2) - Part 1 15 pureQuery Tools SQL templates and customizations Create your own SQL templates Use templates to write SQL that is frequently reused Use tabs to change the variable names after inserting SQL statement from the template through SQL context assist Use newly created merge template in your java code using SQL assist
16 Java/SQL Development on Steroids Why the 10x Productivity Improvement Hand-code SQL in Java language. The SQL could have input parameters and could return result sets Hand-code an application to test the SQL Hand-code all parameter information to input and retrieve the results Hand-code try- catch and display exceptions caught Execute the application In case the SQL was invalid, investigate the SQL exception thrown Identify cause of the failure by going through database manuals and searching for the error codes sent back in the exception Open an SQL editor or builder from the database vendor, and try to correct the SQL Paste the SQL back into the Java program Repeat steps 5-8 until you are successful
DB2 Connect Recommended Deployment Options Desktop PCs Application servers Web application servers Several options: Personal Edition or DB2 Connect Server or file server Co-locate DB2 Connect on the application server Recommendation: Personal Edition is best for small numbers of end users DB2 Connect server or file server deployment is best for lots of desktops Rationale: drivers now include the key DB2 Connect gateway features (sysplex workload balancing, connection concentrator, XA support, automatic reconnect, etc.) fewer potential points of failure less hardware cost less system administration cost fewer network hops (up to 40% better elapsed time) simplified failover strategy less complex problem determination and monitoring
Open Source Persistence Engine JPA Persistence Engine JDBC API Hibernate, iBATIS, EclipseLink,... JPA for WebSphere, Apache OpenJPA pureQuery API Data Studio pureQuery Plain JDBC Data Web Services, Project Zero, sMash On-ramps to pureQuery JCC driver JDBC.Net Applications.Net applications pureQuery JPA API Open Source Persistence API Web API ADO.Net DB2, Informix, and Oracle now…more coming
Client Optimization Improve Java data access performance– without changing code Captures SQL for Java applications –Custom-developed, framework-based, or packaged applications Bind the SQL for static execution without changing a line of code –New bind tooling included Delivers static SQL execution value to existing DB2 applications –Making response time predictable and stable by locking in the SQL access path pre-execution, rather than re-computing at access time –Limiting user access to tables by granting execute privileges on the query packages rather than access privileges on the table –Aiding forecasting accuracy and capacity planning by capturing additional workload information based on package statistics –Drive down CPU cycles to increase overall capability Choose between dynamic or static execution at deployment time, rather than development time
Optim pureQuery Runtime for z/OS In-house testing shows double-digit reduction in CPU costs over dynamic JDBC IRWW – an OLTP workload, Type 4 driver Cache hit ratio between 70 and 85% 15% - 25% reduction on CPU per txn over dynamic JDBC
Throughput Increase with.NET Same IRWW OLTP application used for the Java tests but in.NET Application access DB2 for z/OS via Windows Application Server (IIS) Throughput during static execution increased by 159% over dynamic SQL execution assuming a 79% statement cache hit ratio
pureQuery -- More Visibility, Productivity, and Control of Application SQL Capture SQL Share, review, and optimize SQL Revise/optimize SQL and validate equivalency without changing the application Bind for static execution to lock in service level or run dynamically Restrict SQL to eliminate SQL injection CaptureReviewOptimizeReviseRestrict
Visualize execution metrics Execute, tune, share, trace, explore SQL Replace SQL without changing the application Position in Database Explorer Visualize application SQL
pureQuery – Stripping Literals from SQL JDBC app INSERT INTO T1 VALUES(‘ABC’,2,’DEF’) INSERT INTO T1 VALUES(:h1,:h2,:h3) pureQuery Runtime conversion pureQuery can identify statements that use no parameter markers, and strip the literals out at runtime significant performance gains: less CPU cost at PREPARE better use of dynamic statement cache
What Is Heterogeneous Batching? Data Server Table 2, operation 2 Table 1, operation 1 Table 1, operation 2 Table 1, operation 3 Table 2, operation 1 Table 3, operation 1 Heterogenous Batching – multiple operations across different tables all execute as one batch Table 1, operation 4
JDBC Batching vs pureQuery Batching JDBC batching used by Hibernate Batcher is currently limited –Cannot batch entities that map to multiple tables Primary and Secondary tables. Inheritance Join and Table per class strategies –Cannot batch different operations against same table Field level updates Insert, update –Cannot batch different entities pureQuery heterogeneous batching plug-in –Can batch entities that map to multiple tables –Can batch different operations against the same table –Can batch different entities into a single batch –Combines insert, deletes, updates into single batch * Preliminary findings based on validation with a test designed to demonstrate heterogeneous batching differences. This is not intended to be a formal benchmark.
OpenJPA and Hibernate -- SQL Query Generation JPA Query Select emp_obj(), dept_obj() SQL Select * from EMP WHERE … Select * from DEPT WHERE … JPA query transform Hibernate and OpenJPA often rewrite queries No database statistics are used – entirely heuristic!!! Can often result in poorly performing queries
Optim Database Performance Manager future directions -- Associate SQL with Java Source Heat ChartDashboardAlertsSLAs In-flight analysis Database: Accounting Statement text schema E2E elapsed occurrences sort time phys. I/O SELECT TIME FROM UNIVERSESAP3132.131323123.321.303 SELECT SALARY FROM PAYMENT …SYSIBM323.422111.332.1 DELETE FROM ACCOUNT WHERE AID = 3… PROC23.343532322.332.1 TOP 3 currently running SQL Statements TOP by DS elapsed DS CPU time Physical I/O Sort time - + SELECT TIME FROM UNIVERSE Stmt text Analyze Application DS user IDKARN Client IP addr / hostnameTPKARN.de.ibm.com Client user IDKARN Client workstation nameTPKARN Client application nameJawaw.exe Client accountingN/A application nameOnline banking application email@example.com packageWest.OLBank classAccount methodTransfer() source line314 Time distribution Force applicationStop SQL sorting Resource usage Query cost estimates18.456 Buffer Pools Data – hit ratio (%)43.4% Data – physical reads / min4323 Index – hit ratio (%)54.2% Index – physical reads / min3214 Statement information X DS Proc USER CPU SYSTEM CPU Unacc wait DS sorting Statement elapsed time Current132.13 sec last day239.40 sec last week 15.60 sec
Toughest issue for Web applications – Problem diagnosis and resolution Web Browser Users Web Server Application Server DB2 Server Business Logic Data Access Logic Persistence Layer DB2 Java Driver JDBC Package EJB Query Language
What’s so Great About DB2 Accounting for CICS Apps? z/OS LPAR CICS AOR1 Txn1 - Pgm1 - Pgm2 CICS AOR2 TxnA - PgmX - PgmY DB2PROD CICS AOR3 Txn1 - Pgm1 - Pgm2 App CPU PLAN Txn1 2.1 TN1PLN TxnA 8.3 TNAPLN DB2 Accounting for CICS apps allows you to study performance data from many perspectives: By transaction (PLAN name) By program (package level accounting) By address space (AOR name) By end user ID (CICS thread reuse) This flexibility makes it very easy to isolate performance problems, perform capacity planning exercises, analyze program changes for performance regression, compare one user’s resource usage to another’s, etc.
JDBC Performance Reporting and Problem Determination – Before pureQuery Application Server DB2 or IDS A1 A2 A5 A3 A6 A4 USER1 User CPU PACKAGE USER1 2.1 JDBC USER1 8.3 JDBC USER1 22.0 JDBC What is visible to the DBA? - IP address of WAS app server - Connection pooling userid for WAS - app is running JDBC or CLI What is not known by the DBA? - which app is running? - which developer wrote the app? - what other SQL does this app issue? - when was the app last changed? - how has CPU changed over time? - etc. Data Access Logic Persistence Layer DB2 Java Driver EJB Query Language
What’s so Great About Optim pureQuery Accounting for WebSphere Applications? z/OS LPAR CICS AOR2 TxnA (PLANA) - PgmX - PgmY App CPU TxnA 2.1 TxnB 8.3 Data Studio and pureQuery provide the same granularity for reporting WebSphere’s DB2 resources that we have with CICS: By transaction (Set Client Application name ) By class name (program - package level accounting) By address space (IP address) By end user ID (DB2 trusted context and DB2 Roles) This flexibility makes it very easy to isolate performance problems, perform capacity planning exercises, analyze program changes for performance regression, compare one user’s resource usage to another’s, etc. Unix or Windows WAS 188.8.131.52 TxnA (Set Client App=TxnA) - ClassX - ClassY
Simplifying Problem Determination Scenario Application Developer Available for each db access –SQL text generated –Access path –Cost estimates –Estimated response time –Elapsed & CPU time –Data transfer (getpages) –Tuning advice Database Administrator Available for each SQL –Application name –Java class name –Java method name –Java object name –Source code line number –Source code context –J-LinQ transaction name –Last compile timestamp Java Profiling pureQuery DRDA Extentions
Using pureQuery to Foster Collaboration and Produce Enterprise-ready Apps Application Server Catalog data for SQL Application Meta data DB2 or IDS Prod A4 A1 A6 A2 A3 A4 A5 A1 A4 A5 Performance Data Warehouse Application Developer Database Administrator A1 A6 A2 A3 A4 A5 Quickly compare unit test perf results to production Use pureQuery app metadata as a way to communicate in terms familiar to both DBA and developer Application Meta data DB2 or IDS Dev System A1 A6 A2A3 A4A5 A1 A4 A5
Customer Job Roles – A Barrier to a “Holistic View” Application Server DB Server Data Access Logic Persistence Layer DB Java Driver JDBC Package EJB Query Language WebSphereConnectionPool BusinessLogic 1 3 5 4 2 Application Developer System Programmer DBA Network Admin
DB2 Performance Expert and Extended Insight Enables early and rapid problem detection to prevent impact to production systems –Identify resource shortages across CPU, memory, and file systems, locking conflicts and deadlocks, and data skew Increase ability to meet service level agreements –Provides optimization and tuning recommendations Supports trend analysis and growth planning –Maintains and analyzes performance warehouse Supports a variety of database workloads –Includes online transaction processing, data warehouse, and enterprise content management –Monitors Workload Management environment Visualizes the end-to-end response time –See where Java database workloads, transactions, and SQL requests are spending their time across the database client, application server, and network Provides in-depth database monitoring, problem isolation, and trend analysis for DB2 for Linux, UNIX, and Windows databases. Add Extended Insight Feature for visibility into where Java database workloads, transactions, and SQL requests are spending their time
Scenario It seems that the first application server has a problem. Double-click to drill-down. In this situation, all applications are equally affected, and the problem seems not to be in the data server.
Scenario - continued Double-click to drill-down and display detail information. Most of the time is spent for „WAS connection pool wait“ time.
Scenario – continued 5 second wait time indicates that the maximum number of allowed connections is not sufficient… … which becomes also evident when comparing the parameters and metrics of this client with other clients.
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