|Exam Name||:||Oracle Database 11g Performance Tuning|
|Questions and Answers||:||192 Q & A|
|Updated On||:||April 23, 2019|
|PDF Download Mirror||:||Pass4sure 1Z0-054 Dump|
|Get Full Version||:||Pass4sure 1Z0-054 Full Version|
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**fee compares Oracle FastConnect versus Microsoft Azure express Route in high bandwidth situation (10 Gbps, 500 TB/month) (https://cloud.oracle.com/bare-metal-network/pricing and azure.microsoft.com/en-us/pricing/details/expressroute/).
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Synopsis. Oracle Database 11g liberate 1 (11gR1) facets the brand new SQL performance Analyzer that guarantees to ease tremendously the workload of busy Oracle DBAs since it provides a way to accurately consider comprehensive database workloads for “before versus after” performance alterations. this text – the subsequent in a sequence on new Oracle Database 11g SQL performance improvement elements – discusses a way to use the SQL performance Analyzer to evaluate a SQL workload generated from an Oracle 10gR2 database to observe knowledge efficiency concerns for that workload if it have been done within an Oracle 11g database equipment.
The outdated article in this sequence illustrated the fundamentals of the use of Oracle Database 11g’s new SQL efficiency Analyzer (SPA) tool to:
in this article, I’ll exhibit how the SQL efficiency Analyzer can analyze greater advanced Oracle ambiance changes – peculiarly, the upgrade of the can charge-based mostly optimizer (CBO) between Oracle database releases -- and i’ll reveal the way to switch a SQL workload generated from an Oracle 10gR2 database to an Oracle 11gR1 database. moreover, in view that I concentrated on the PL/SQL interface to measure SQL workload efficiency in the closing article, I’ll focal point on using Oracle 11g’s commercial enterprise supervisor (EM) Database control interface for the SQL performance Analyzer to show how easy it's to analyze a SQL workload with a number of mouse clicks.SQL efficiency evaluation situation: Simulating an Optimizer upgrade
1.) put together for Simulation. I’ve constructed an Oracle 10gR2 database named DB10G the usage of the general “seed” database within an Oracle 10gR2 home. To elevate forward the similar example from the old article, I then developed the, SH.SALES_AGENTS table on that database, loaded it with the identical pattern set of about 420,000 rows, after which constructed the same 5 indexes towards that desk. I used the identical DDL that I offered in listing 1.1 for the outdated article to achieve this, and i utilized the identical DML statements (LoadSalesAgents.sql) to operate an initial load of this table.
2.) prepare for SQL Workload Gathering. next, I’ll create a SQL Workload on the DB10G database. I’ll use the same SQL statements that entry the SH.SALES_AGENTS desk that I used within the outdated article, together with a few additional SQL statements that’ll entry some of the other demo schema tables. The statements that incorporate this SQL workload are shown in GenerateSQLWorkload.sql. note that the closing 4 SQL statements – the ones tagged as LDGN four.1 via LDGN 4.4 - make use of older optimizer tips that are nevertheless attainable in Oracle 9i however not thoroughly supported in Oracle 10g. I’ll use these statements to show how the SQL efficiency Analyzer can simulate performance the usage of can charge-primarily based optimizer settings from different older Oracle database environments. The code shown in list 2.1 captures these SQL statements right into a SQL Tuning Set named STS_SPA_200.
three.) Stage and Export SQL Tuning Set. Now that I’ve achieved the SQL workload and have gathered the resulting statistics into the STS_SPA_200 SQL Tuning Set, I’ll switch that workload and its corresponding execution statistics to my Oracle 11gR1 database. I’ll try this via growing staging tables to cling that counsel, transferring the SQL workload and its efficiency assistance into that desk, after which transporting these records to the targeted Oracle 11g database. listing 2.2 suggests how I captured (or “packed”) the SQL Tuning Set into staging tables with the DBMS_SQLTUNE.PACK_STGTAB_SQLSET technique. I then used Oracle DataPump to export these staging tables right into a DataPump Export dumpset named DumpStagingTable.dmp.
4.) Import, unpack, and put together SQL Tuning Set for test execution. After copying the DataPump dump set into the default DataPump directory for my Oracle 11g database, I’ll need to switch the guidance contained in the staging tables for the SQL Tuning Set into my Oracle 11g goal database for evaluation. list 2.three illustrates how I imported the staging tables from Oracle 10gR2 into the target Oracle 11gR1 database the usage of Oracle DataPump Import and an acceptable parameter file. I then used the DBMS_SQLTUNE.UNPACK_STGTAB_SQLSET manner to “unpack” the SQL Workload saved in those staging tables into my goal Oracle 11gR1 database.
5.) Simulate an Optimizer upgrade From 10.2.0.1 to 188.8.131.52. Now that my preparations are completed, I’m able to flip the SQL efficiency Analyzer unfastened to let it find any substantial performance influences on the SQL statements within the imported SQL Workload if it had been accomplished in opposition t the goal Oracle 11gR1 database. I’ll use the Oracle Database 11g’s commercial enterprise supervisor Database control interface to operate this analysis. figure 2.1 indicates the initial state of the SQL performance Analyzer panel.
figure 2.1. SQL performance Analyzer home Panel
when I choose the Optimizer improve Simulation hyperlink from this panel, enterprise supervisor presents the Optimizer improve Simulation panel. As shown in figure 2.2, I’ve special a task name of SPA_OUS_100, SYS.STS_SPA_200 as the SQL Tuning Set, and a short description of the project. be aware that for this project, I’ve unique the “before” and “after” optimizer versions as 10.2.0.1 and 184.108.40.206, respectively.
determine 2.2. developing an Optimizer improve Simulation task
as soon as I’ve clicked on publish, manage returns to the SQL performance Analyzer home panel, and the job that I’ve submitted ultimately suggests it has accomplished (see figure 2.3 under).
determine 2.3. a success execution of SQL performance Analyzer task SPA_OUS_100
To view the completed assignment, I without problems click on on the project name link, after which commercial enterprise supervisor items a abstract of the a hit project execution as proven below in determine 2.4:
determine 2.four. SQL efficiency Analyzer assignment SPA_OUS_100 Execution particulars
to peer the details of the preliminary run of this task, I’ll click on the corresponding “eyeglasses” icon under the comparison document column close the bottom of this panel, and this displays the outcomes proven in determine 2.5.1:
figure 2.5.1. consequences of Elapsed Time assessment
at last, some entertaining effects – and typically respectable information! The SQL efficiency Analyzer shows that it believes there should be at least some growth in execution time – or at the least, no degradation in execution time - for all 12 SQL statements if they have been accomplished in an Oracle 11gR1 database atmosphere. youngsters, I’m no longer convinced with only evaluating execution instances; I also want to understand more in regards to the change to any of the execution plans. therefore, I reran the same targeted evaluation, but this time I in comparison the estimated adjustments in optimizer prices as neatly. determine 2.5.2 and figure 2.5.three speak for themselves: notwithstanding there are some relatively apparent improvements in execution time, only one commentary had any giant improvements in optimizer can charge,
determine 2.5.2. Elapsed Time evaluation particulars inside SQL commentary
determine 2.5.3. Optimizer charge evaluation particulars within SQL remark
6.) Simulate an Optimizer improve From 9.2.0 to eleven.0.1. Out of curiosity, I’ve also experimented with using the identical SQL Tuning Set (STS_SPA_200) to simulate an improve from the Oracle 9i optimizer to the Oracle 11g optimizer. to accomplish this, I’ve set up a different SQL performance Analyzer task named SPA_OUS_200. This assignment is identical to SPA_OUS_100, except that I’ve chosen a “earlier than” optimizer setting of 9.2.0 as an alternative of 10.2.0.1. A summary of the execution of this new simulated optimizer improve project is proven in figure 2.6, while figure 2.7 suggests the results web page for that equal task:
figure 2.6. SQL performance Analyzer project SPA_OUS_200 Execution
figure 2.7. SQL efficiency Analyzer assignment SPA_OUS_100 effects
Figures 2.eight.1, 2.8.2, and a pair of.eight.three demonstrate the consequences for just one of the SQL statements for which the optimizer upgrade simulation became carried out. observe that while the exact number of buffer gets and the number of rows processed for this remark haven’t changed in the least, Oracle 11g’s SQL efficiency Analyzer is apparently sensible satisfactory to challenge that the 9.2.0 optimizer cost (1001) would had been a little bit bigger (995) if the commentary had been completed below the 11.1.0 optimizer. additionally, observe that the clarify PLAN for the two simulated executions had been a bit diverse for the identical SQL observation.
determine 2.eight.1. SQL performance Analyzer assignment SPA_OUS_200 outcomes (remark 5pkmwau93fg58)
figure 2.eight.2. “earlier than” explain PLAN results for remark 5pkmwau93fg58
determine 2.8.three. “After” clarify PLAN results for remark 5pkmwau93fg58next Steps
The third and ultimate article in this collection on Oracle 11g’s new SQL efficiency Analyzer device set will focus on a way to use this tool to overcome one of the crucial extra frustrating SQL efficiency tuning challenges: the affect of modified database initialization parameters upon SQL observation performance. I’ll additionally delve greater deeply into one of the SQL performance Analyzer’s reporting and analyses alternate options to get a complete photo of what is inflicting a SQL observation to regress, and the way this new device facilitates advent of SQL Plan Baselines for SQL Plan management.References and further analyzing
while I’m hopeful that I’ve given you a radical grounding in the technical aspects of the points I’ve discussed in this article, I’m additionally certain that there can be better documentation attainable because it’s been posted. I for this reason strongly suggest that you just take an in depth seem to be on the corresponding Oracle documentation on these elements to obtain crystal-clear knowing earlier than trying to implement them in a construction environment. Please be aware that I’ve drawn upon right here Oracle Database 11gR1 documentation for the deeper technical details of this text:
B28274-01 Oracle Database 11gR1 efficiency Tuning e-book
B28279-02 Oracle Database 11gR1 New elements e book
B28419-02 Oracle Database 11gR1 PL/SQL programs and kinds Reference
» See All Articles by means of Columnist Jim Czuprynski
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Tuning the performance of Oracle Database 11gR2 is still just as much of an art as it is a science. Jim Czuprynski focuses on the detection and analysis of performance bottlenecks within the database's underlying input/output (I/O) subsystem.
Synopsis. Oracle 11g Release 2 (11gR2) continues the trend toward automated database performance tuning tools that started in Oracle 10g. However, tuning the performance of an Oracle 11gR2 database is still just as much of an art as it is a science. This article - the first in this series - provides a primer that focuses on one of the most challenging arenas of database performance tuning: the detection and analysis of performance bottlenecks within the database's underlying input/output (I/O) subsystem.
As a fledgling Oracle DBA many years ago, one of the first lessons I learned about database performance tuning was that there are only three potential types of bottlenecks to investigate whenever a database application performance issue rears its head:
Memory. An Oracle database instance may simply not have enough memory in the right places to execute all of the SQL statements issued against it, then the database is said to be memory-bound. For example, while there may be sufficient memory, it may have been seriously mis-allocated between the database's Shared Global Area (SGA) and Program Global Area (PGA). Another possibility is that the memory components within the SGA itself have not been divided up properly, and the database buffer cache is significantly undersized for the workload being generated, or the library cache is oversized as compared to the number of SQL statements being issued.
As of Oracle 11g Release 1 (11gR1), however, these memory bottlenecks are easily detected through the automated diagnostic infrastructure that Automatic Database Diagnostic Monitor (ADDM) provides. Oracle 11gR1's Automatic Memory Management (AMM) feature can also be activated to detect and automatically adjust memory allocations as the database instance's workload changes. Finally, if the Oracle DBA decides against using AMM, Oracle 11g Enterprise Manager (EM) provides several elegant GUI-based memory tuning advisors that encompass the instance's complete memory footprint (SGA + PGA) as well as individual advisors for the SGA, PGA, Database Buffer Cache, and the Library Cache; there are also SQL-based memory advisors for the Streams Pool, Large Pool, and Java Pool.
CPU. A database application's SQL statements may be poorly written, or they may be unable to take advantage of efficient execution plans because of missing optimizer statistics. SQL statements may also be efficiently written but may be heavily dependent on complex arithmetic computations or complex set manipulation. In these cases, a database instance may quickly become CPU bound; in other words, there are simply not enough CPU cycles to complete the necessary calculations required to return a query's result set or to process DML statements.
Fortunately, Oracle 11gR1 has already made it simpler than ever to detect, analyze, and resolve these types of "problem" SQL statements. ADDM, in concert with Automatic Workload Repository (AWR) snapshots and reports, are excellent at identifying the top SQL statements that are most degrading to the overall database instance's performance within any specific time frame or workload, and Active Session History (ASH) reports can analyze an application workload at an even deeper granularity. Oracle 11g's SQL Tuning Advisor and SQL Access Advisor respectively offer specific tuning recommendations for individual SQL statements or even entire application workloads. Finally, Oracle 11g's SQL Performance Analyzer (SPA) and SQL Plan Management (SPM) features can be used to execute performance regression analysis for application workloads and even "lock in" a known, better-performing execution plan when upgrading from a prior database version to 11g.
I/O. In my experience, if a database application is experiencing extremely poor performance, it's more likely to stem from the Oracle database instance being either significantly memory-bound or CPU-bound, so it makes sense that Oracle 11g's performance tuning tools are aimed primarily at those classes of root causes. However, it's also possible for a database instance to suffer from poor I/O performance. A database thus becomes I/O bound when it cannot respond quickly enough to requests to either read data from and/or write data to the underlying I/O subsystem.
Unlike CPU-bound or memory bound systems, however, the identification, analysis, isolation, and eventual repair of the root causes for poor I/O response time is significantly more difficulty because of the complex nature of today's storage platforms. So let me attempt to pull back the curtain on I/O performance tuning by explaining it from a completely different perspective: that of the storage administrator.Isolating the Real Problem
Here's the first big secret that storage administrators know intimately, but Oracle DBAs sometimes ignore, when tackling an I/O performance tuning issue: No database truly performs I/O directly and immediately against disk. In fact, even in the simplest case - say, a single disk drive with a native installation of either the NTFS or EXT3 file system - disk I/O is always buffered in the cache memory that's part of each individual disk's I/O controller.
Expanding upon this extremely simple case, I/O performance issues can be much more difficult to accurately diagnose because of various factors, including:
For mid-range and enterprise-level high-performance storage systems, other configuration options may introduce additional complexity:
If the potential complexity of the I/O subsystem doesn't make an Oracle DBA's head spin, here's another factor: I/O performance tuning is often compounded by mixed database application workloads. Only a few years back, Oracle recommended that database application workloads should be isolated at the database level - for example, by creating separate OLTP and DSS databases, each with their own set of storage - and that was certainly helpful when attempting to isolate an I/O performance problem.
In the last few years, however, the dramatic increase in the maturity of Oracle Real Application Cluster (RAC) database software in Oracle Release 10g has made it simpler than ever to deploy RAC database environments in a matter of hours. Oracle RAC certainly adds to the stability and consistency of database applications because the loss of one node or instance no longer means that the Oracle database is completely unavailable. The addition of RAC database services in Oracle 10g also meant it was easier than ever to control and serve up sufficient bandwidth for multiple applications against a single RAC database, but this also means that it's not uncommon today to encounter a single RAC database that's servicing multiple application workloads with dramatically different I/O performance profiles.
The good news is that there are only four basic types of I/O performance profiles that any Oracle DBA need be concerned about:
Here's the second big secret that our storage administrators keenly understand, sometimes better than even experienced Oracle DBAs: Ultimately, all I/O performance tuning revolves around determining what type(s) of application workload(s) the database is actually performing versus what workload the underlying storage has been configured to handle most of the time. So if the underlying storage platform on which the Oracle database's files exist has been tuned primarily for random read/write access, then sequential read/write application workloads (DSS) will most likely perform significantly worse than an OLTP workload; and vice versa, if the storage has been tuned for predominantly sequential read/write access (like a data warehouse), OLTP application workloads are more likely to encounter serious I/O performance bottlenecks.Next Steps
Enough theory! It's time to put at least some of these concepts to work. The next article in this series will delve more deeply into how to:
» See All Articles by Columnist Jim Czuprynski
Oracle has launched Oracle Database 11g, the next version of its main database platform.
Oracle says a number of major customers have successfully completed the Oracle Database 11g beta programme to test out the enhancements to the platform.
The firms, which included Colgate-Palmolive, Fidelity National Information Services, PB (Parsons Brinckerhoff), and several members of the US Independent Oracle Users Group (IOUG), tested out the promised improved management, performance, scalability and security for themselves.
Ari Kaplan, president of IOUG, said, “This latest product launch is based on strategic input from many IOUG members. We believe that this latest Oracle product will be successfully used by our community of enterprise customers.”
Arthur Fleiss, IT architect at Colgate-Palmolive, said, “Three new features in Oracle Database 11g that really jump out are Automatic SQL Tuning, Partition Advisors, and Real Application Testing.
“These three things alone are reason enough to upgrade. They improve the reliability and performance of the database, yet do not take loads of the database administrator’s time to implement.”
Oracle's press release >>
EMC and Oracle expand collaboration plans >>
SAP admits inappropriate downloading of Oracle files >>
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