SQL tuning and the Dynamic Statement Cache

1. SQL tuning and the Dynamic Statement Cache

2. What Will We Talk About? • Some SQL Tuning Fundamentals • Dynamic SQL in More Detail • Introduction to DB2 Statement Caching • Mining the Dynamic Statement Cache • What’s on the Horizon for Statement Caching in DB2 9

3. Performance Tuning MethodologiesGeneral Thoughts • High-level Monitoring is the starting point • High level performance workload metric • System-wide monitoring approaches • Ongoing Monitoring for defined exceptions • Quick Access to a current view of the DB2 subsystem • Quick Access to Accounting and Statistics data • Drilldown using two approaches • First approach • Collect performance data on an ongoing basis • Analyze data for exception conditions • Higher cost for data collection • Second approach • Collect performance data only when indicated by high-level monitoring tool and for a set period of time • Analyze results • Less data for identifying trends over time

4. DB2 TuningWhere Should You Spend Your Time • What Can I tune in DB2 • Where are the biggest problems • Purely an estimate and your experience may vary • Many tuning efforts combine multiple areas • Especially true of SQL and Object Analysis Subsystem 10% SQL 70% Object Analysis 20%

5. Focus on individual SQL statements Do they meet “best practice” coding standards Do they use expected/accepted DB2 access paths Do they deliver desired result set in acceptable time with acceptable resource consumption Developed and tested in controlled environment More predictive in nature Focus on workload dynamics How does concurrent execution affect response time/resource consumption Does this SQL statement/program collide with other transactions Same application Other applications in a shared subsystem Real world unpredictability comes into play More focus on measuring the workload and rapidly reacting Solving the ProblemSQL Analysis Across the Application Life Cycle Atomic SQL SQL Workload

6. Tuning SQL Across the Lifecycle • Early and Often • Definitely not a one-shot deal In Development • Focus on Atomic SQL • Minimal Concern for workload metrics In Test • More focus on workload metrics • Continued focus on atomic SQL In Production • Major emphasis on workload metrics including I/O analysis • Reduced focus on Atomic SQL

7. TABLE SQL Tuning FundamentalsDB2 Optimizer Determines SQL Performance SQL Statement Text DB2 Configuration OPTIMIZER Access Path To the Data Hardware Configuration Catalog Statistics Schema Definitions Tablespace DB04.TS1 Index App1.Index1

8. Direct Row access using ROWID • One Fetch Index Scan using Min, MAX • Unique Matching Index Scan using a • predicate value • Matching Index Scan Only • Non-Matching Index Scan Only • Matching Index Cluster Scan • Matching Random Index Scan • Multiple Matching Index Scan using • AND and OR • Non Matching Cluster Index Scan • Segmented Table Space Scan • Non Segmented Table Space Scan • (in parallel or sequential) • Non-matching Random Index Scan Access Paths To the Data SQL Tuning FundamentalsDB2 Access Paths In order from minimum to maximum resource usage

9. SQL Tuning FundamentalsAccess Path Selection For Static SQL • Access Path Selection occurs during bind step of compile/link-edit application deployment cycle • Options exist to make static run more like dynamic SQL • REOPT (VARS) • Access path determined at run time for those statements with host variables or parameter markers For Dynamic SQL • Access Path Selection occurs at run-time • Options exist to make dynamic SQL run more like static • KEEPDYNAMIC bind option • Holds prepared statements across commits to avoid cost of re-preparing statement • Global Dynamic Statement Cache • Maintains Skeleton of prepared statement in virtual storage

10. Static– Application data access requirements are known and traditional procedural languages are being used Used in most early DB2 applications Simpler programming techniques especially for retrieval Access path determined at bind time – better performance Authorization for execution at the plan (package later) level Qualifiers passed via host variables SQLJ provides for bound static SQL Host variable defined In working storage SQL Tuning ConsiderationsMore on Static SQL

11. Dynamic - Data access requirements are ad hoc in nature and identified on the fly Used for tools like QMF, SPUFI, and others Build and execute SQL on the fly Access path determined at run time User requires authorization to all accessed objects No host variables are allowed – parameter markets used instead Parameter provides placeholder for later substitution SQL Tuning ConsiderationsStatic vs. Dynamic SQL

12. SQL Tuning ConsiderationsStatic vs. Dynamic Usage Over Time Mix of Dynamic and Static SQL has changed • 90% static and 10% in the first 18-20 years of DB2 • Now closer to 50% for each type of SQL • One large BMC banking customer runs 25 million SQL statements a day with 80% of those dynamic • What’s driving dynamic SQL usage • Dynamic SQL offers flexibility that can simplify developing complex applications • New applications being developed on distributed platforms using connections that only support dynamic SQL • DB2 CONNECT, etc. • ERP applications that were implemented with dynamic SQL • SAP, PeopleSoft, Siebel • New applications being developed on distributed platforms • Younger developers are much more familiar with GUI-based programming environments and don’t even sign on to the mainframe • More Java and C++

13. Dynamic SQL Considerations • SQL PREPARE operations required (More on Prepares later) • Prepared dynamic SQL statements by default are not persistent across commit points • Frequently executed dynamic SQL statements may require many prepare operations • Prepare costs vary significantly but can be over 500 μs • Changes in DB2 statistics • Dynamic SQL always uses current catalog statistics for access path selection • Changes in DB2 statistics can cause unpredictable changes in access paths • Some DB2 customers collect catalog statistics to drive maintenance processes • May cause SQL performance to fluctuate unexpectedly • Security is generally more complex with dynamic SQL • Application users generally require authorization to the objects being accessed • Auditing is also affected because statements are developed on the fly • Access path determination is difficult because access path information is not available prior to execution

14. Dynamic SQL Statement Caching Introduction • Goal is to reduce or eliminate SQL Prepare operations required for dynamic SQL statements • Prepare operations have become more costly over time as optimizer has become more sophisticated • Implementation • Three kinds of caching • Local Dynamic Statement Caching • Global Dynamic Statement Caching • Full Caching • Cache prepared SQL statement and statement text for dynamic SQL statements in DBM1address space • Local Statement Cache • Global Dynamic Statement Cache • Controlled by various parameters • Bind options • DSNZPARMs

15. Dynamic SQL Statement CachingAvailable Caching Options • Local Statement Caching • KEEPDYNAMIC(YES) bind option • MAXKEEPD DSNZPARM option controls size • 0 disables keeping executables • Text always held • Virtual Storage Considerations • If you are already VS constrained pre-v8 then consider a dataspace for global cache • MAXKEEPD will also impact VS constraint • V8 the problem is greatly reduced • Still an issue with thread storage • Global Statement Caching • CACHEDYN(YES) ZPARM • V7 and earlier allow data space for this cache • EDMDSPAC to size • V8 moves cache to it’s own pool • EDMSTMTC to size • Removes dataspace option Full Caching - Local and Global caching both active

16. Dynamic Statement CachingImpact on the Prepare Process • Full Prepare • Occurs when SKDS not present in global dynamic SQL cache • Can occur because of PREPARE and EXECUTE IMMEDIATE • Implicitly occurs with EXECUTE IMMEDIATE when using KEEPDYNAMIC(YES) • Short Prepare • SKDS of dynamic SQL statement in the global cache • Copied into local storage for the thread • Avoided Prepare • Only available with full caching option • New EXECUTE statement avoids the prepare because the statement is still in the local cache

17. Taking Advantage of Dynamic Statement Caching Questions to Ask • Are you running dynamic SQL in production environments • Is you said no, there may be some benefit if a development/test environment • Database tools often use dynamic SQL • Might consider for test environments with lots of DBA activity • Is your production dynamic SQL repeated frequently • Products like QMF, SPUFI and other ad hoc query tools probably won’t benefit • Are your bind parameters consistent with dynamic statement caching • Do you have Virtual or Real Storage Constraints • If DBM1 is virtually constrained dynamic statement caching can make the problem worse • EDM Pool and thread storage • If you have real storage constraints any options will probably make the situation worse • If Virtual Storage Constraints but real memory is available • Consider moving the Dynamic Statement Cache to a dataspace

18. Dynamic Statement CachingConditions Where Statements Can Utilize the Cache • Statement text must be 100% the same • Use parameter markers • Additional items must be 100% the same or compatible • Bind rules • Special registers • Authorizations • Others • You may not get any benefit out of the dynamic statement cache at all • Most likely to benefit if you using an ERP or some other application that uses dynamic SQL extensively

19. Turning on the Cache • Global Statement Cache • DSNZPARM CACHEDYN set to Y • For V6 & V7 with no dataspace • Calculate additional space needed for statement cache in the EDMPOOL and set via EDMPOOL ZPARM value • For V6 & V7 with dataspace • Calculate space for the dataspace based on you dynamic SQL workload • Allocated via EDMDSPAC (sets initial value) • Upper limit specified via EDMDSMAX • For DB2 V8 • Set size in bytes of statement cache via EDMSTMTC ZPARM • Default is 5000 bytes • Consider setting EDMBFIT to Y if virtual storage constraint is a problem

20. Turning on the Cache • Local Statement Cache • KEEPDYNAMIC(YES) Bind Parameter • REOPT(ONCE) Bind Parameter • MAXKEEPD ZPARM parameter

21. Dynamic SQL Statement CachingMeasuring Cache Effectiveness Statement Pool Full Failures Should be 0 Increase Statement Pool Size if not Global Cache Hit Ratio Shoot for 90+% Local Cache Hit Ratio Specific for Applications bound with KEEPDYNAMIC(YES) Statement Discarded Shoot for 0 Increase MAXKEEPD

22. Getting Data About Cache Usage • As shown in previous chart • Statistics on Statement Caching in the standard DB2 statistics records • Metrics show details about cache hit ratios and other useful data points that help you evaluate overall performance of your statement caches • For more detail on Global Statement Cache usage the following instrumentation is provided • IFCID 316 – Provides details on statements in the cache • First 60 bytes of SQL text • Includes execution statistics (0 if not being collected) • IFCID 317 can then be used to retrieve the entire SQL statement from the cache once you have identified the statement of interest • EXPLAIN STMTCACHE • V8 feature that exports Dynamic Statement Cache information to the DSN_STATEMENT_CACHE_TABLE • Nearly identical to the detail in IFCID 316 & 317 • Multiple options including ALL, stmt-id, and stmt-token

23. Reviewing Global Statement Cache InformationIFCID 316 Results • First 60 Bytes of SQL Text • Bind Options • Execution Statistics

24. Reviewing Global Statement Cache InformationIFCID 317 Results • Full SQL Text

25. Reviewing Global Statement Cache InformationIFCID 318 • Execution statistics for dynamic SQL statements • Turn on collection with Monitor trace IFCID 318 • Begins collecting statistics and accumulates them for the length of time the monitor trace is on • Stop Monitor trace resets all statistics • 2-4% overhead per dynamic SQL statement stored in the cache • Recommended approach • Run the trace only when actively monitoring the cache • Use EXPLAIN STMTCACHE to externalize data for evaluation

26. Extracting Information from the Dynamic Statement Cache • EXPLAIN STMTCACHE SQL statement • Populates PLAN_TABLE (if defined) • Sets QUERY_NO to UNIQID in DSC • Plan table entry stored in decimal • Remember this is access path currently in use for this statement • Populates DSN_STATEMENT_CACHE_TABLE • Closely resembles the statement cache seen online via IFCID 316 • A few useful fields including Reasons for invalidation if invalidated • Populates DSN_STATEMENT_TABLE (if defined) • COLLID set to ‘DSNDYNAMICSTMTCACHE’ • QUERYNO set to UNIQID from DSC .... .... ...

27. EXPLAIN STMTCACHE Options • EXPLAIN STMTCACHE ALL • Inserts rows in DSN_STATEMENT_CACHE_TABLE for each statement in the Dynamic Statement Cache • Does not populate the PLAN_TABLE or DSN_STATEMNT_TABLE • EXPLAIN STMT_ID :hv or numeric literal • Uniquely identifies the statement the be explained using the unique identifier (UNIQID) associated with the statement and inserts row(s) • Populates DSN_STATEMENT_CACHE_TABLE, PLAN_TABLE, DSN_STATEMMT_TABLE, and DSN_FUNCTION_TABLE • If they exist • SQL Code -248 returned if statement is not found in the cache • EXPLAIN STMT_TOKEN :hv or ‘text literal’ • Token assigned to statements by the application • Insert rows based on the number of statements in the DSC that match the token • Populates same tables as EXPLAIN STMT_ID option

28. Summary • Dynamic SQL is growing in usage • ERP Vendors • Distributed applications • DB2 offers multiple options for reducing the overhead traditionally associated with dynamic SQL • These options include multiple types of statement caching • Local statement caching • Global statement caching • Full statement caching • BMC offers comprehensive solutions that help tune static and dynamic SQL and report on DB2 dynamic statement caching