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Presentation cloud control enterprise manager 12c
- 1. Extreme Database Management New Features for Power DBAs
- 2. of DBAs admit doing nothing to address performance issues
- 3. 3 Over 50% of DBAs avoid making changes to production because of negatively impacting performance 90% experienced unplanned downtime resulting from Database changes NOT properly tested CHANGEโAVOID OR EMBRACE
- 4. 4 DATA GROWTH 33% of DBAs handle close to 100 database instances eachโwith data stores expanding by more than 20% per year
- 5. 5
- 7. Unresponsive Database Problem โข How do I diagnose a slow or hung database? โ If the database is unresponsive, I canโt even connect! โ Even if I can connect, I need a diagnosis quickly! โข Should I just bounce the database? โ All in-flight operations will be aborted, mid-tier connections/states will be lost โ All diagnostic information will be lost โ โIf I could only know which blocking session to kill!โ
- 8. Real-Time ADDM โข Real-time analysis of hung or slow database systems โข Holistically identify global resource contentions and deadlocks โข Quantified performance impact โข Precise, actionable recommendations โข Provide cluster-wide analysis for RAC
- 9. Real-Time ADDMโArchitecture Makes a lightweight connection without acquiring additional locks and resources, bypassing the SQL layer through the agent Also attempts to initiate standard JDBC connection Data returned by either connection is analyzed by Real-Time ADDM EM Agent JDBC Connection Diagnostic Connection ADDM Analysis Database Resource Constraints Hangs Memory Issues Resource Limits Reached Deadlocks Top Issues Identified by Real-Time ADDM
- 10. Comparative Performance Analysis โข Performance yesterday was good, today is terrible, what happened? โข What changes were made? โข Is someone running a new batch job? โข RAC instance 1 is running much faster than instance 2, whatโs the difference? โข Is there a workload skew? โข Did someone make configuration changes?
- 12. Compare Period ADDM: Method STEP 1: โข Identify what changed โข DB configurations, workload changes STEP 2: โข Quantify performance differences โข Uses DB Time as basis for measuring performance STEP 3: โข Identify root cause โข Correlate performance differences with changes Did the Buffer cache get smaller? Why is there 10% new SQL? How come Top SQL impact increased by 45%? Read I/O are up by 55%, why? Did a buffer cache reduction cause a read I/O increase?
- 13. Active Session History (ASH) Analytics โข Advanced analysis with graphicalASH report โข Select any time period for analysis โข Analyze performance across many dimensions โข Provides visual filtering for recursive drill- downs โข Different visualizations: Stacked chart or Tree Map โข Collaborate with others using Active Reports
- 14. ASH Analytics Performance Dimensions SQL SQL ID Plan Hash Operation OpCode PL/SQL PL/SQL Top Level PL/SQL Resources Wait Class Wait Event Object Blocking Session Identifiers Instance Services User Session Parallel Process Program Session Type Attributes Consumer Group Module Action Client Trans. ID ASH SQL PL/SQL Resource Consump- tion Session Identifiers Session Attributes
- 16. Advise Act Audit Core Analyze Database Lifecycle Management โข Discover โข Hosts & Applications โข Dependencies and Relationships โข Inventory โข Collect โข Deep configuration data โข Parsed Configuration Files โข Patches installedโข Real-Time Monitoring โ Who/When โข Compliance Score โข Best Practices โข Oracle Recommendations โข Regulatory ( PCI,Cobit) โข Report โข Inventory &Trend โข Automatic Change Reconciliation โข Authorization vs Unauthorized โข Patch Advisories via MOS โข Upgrade Advisories from MOS โข Configuration Policy Violations โข Change/Patch Plans โข Mass deployment โข Schema Synchronization โข Settings, Drift & Policy Actions โข Configuration Changes โข Topology guided Impact Analysis โข Config Comparison for Drift Analysis โข To Gold & Baseline โข 1 to 1, 1 to N โข Target and System โข DB Change Management โข Data Comparison โข Change Plans โข Patch Conflict and PreReq Analysis
- 17. Capture Clone Mask Test NAME SSN SALARY AGUILAR 203-33-3234 40,000 BENSON 323-22-2943 60,000 NAME SSN SALARY SMITH 111-22-3333 60,000 MILLER 112-23-4567 40,000 DB Replay capture files SQL Tuning Set (STS) AWR snapshots Clone prod system Consistent masking across tables, capture files, STS and AWR snapshots Securely replay workload & STS Production Stage Test Secure Database Testing Real Application Testing Integration with Data Masking
- 18. Test Data Management Challenges Error-prone, manual process Producing relationally intact subset is hard but necessary Cannot use sensitive data in test without obfuscation Full production copies for test systems not cost effective
- 19. Data Modeling & Discovery โข Application Data Model (ADM) โ Scans application schemas to model relationships between tables and columns โ Extracts data relationships from Oracle Applications meta-data โ Stores referential relationships stored in repository โ Enables Data Masking & Subsetting operations to be application aware โข Sensitive Data Discovery & Data Masking โ Pattern-based sensitive data scanning โ Import from pre-built mask templates โ Pre-built Data Masking templates for Oracle applications โข Oracle eBusiness Suite โข Oracle FusionApplications
- 20. โข Automatic data extraction rules from Application Data Model โข Understand different table types: transactional vs. reference vs. temp tables โข Subset by percentage of tables or columns (e.g. regions, year) at runtime โข Estimate subset size before execution โข Subset 100 GB ๏จ 20 GB in 12 minutes using export method Data Subsetting Define new Application Data Model Create Data Subset Definition Extract Data Subset Export / Import In-Place Delete TestProduction
- 21. Database Upgrade Automation Plan โข Detect new DB versions in My Oracle Support โข Suggest best upgrade path for patch compatibility โข In-context reference to Upgrade documentation Analyze โข Check DB for upgradeability (space, version, etc.) โข Support upgrade from 10.2.0.4+ to 11.2 Deploy โข Mass deploy binaries to targets and create out-of- place copies โข Upgrade process can be paused/resumed Switch โข Switch instances to new installations โข Easy switchback if needed
- 23. Consolidation Planner Determines candidates for consolidation โข Identifies under and over-utilized server โข Identifies ideal placements โข Works for physical and virtual environments Benefits โข Maximizes server density โข Minimizes resource contention โข Maintains performance commitment โข Satisfies business, compliance, and technical constraints
- 24. Consolidation Planner โข Leverages resource utilization and configuration data from Enterprise Manager repository โ CPU, memory, storage, network โ Over a representative period Administrator specifies servers and constraints for workload migration โ Physical/virtual servers โ Existing/planned servers โ Business/technical constraints Detailed analysis on different scenarios of consolidated workloads
- 25. 27 Copyright ยฉ 2011, Oracle and/or itsaffiliates. All rights reserved. Oracle Exadata Optimized for Database Consolidation Exadata serves as cloud for databases Large memory enables many databases to be consolidated Extreme performance for complex workloads that mix OLTP, DW, batch, reporting I/O and CPU resource management isolates workloads
- 26. Exadata Management Integrated Management of Hardware and Software โข Hardware view โข Schematic of Storage cells, Database Servers (compute nodes) & switches โข Hardware components alerts โข Software/system view โข Performance, availability, usage by databases, services, clusters โข Software alerts from db, cluster, ASM โข Topology view of DB systems/clusters โข Configuration view โข Version summary of all components along with patch recommendations
- 27. Storage Cell Performance โข Drill down from database performance page โข Composite cell health indicators โข Helps triage โข Load imbalance โข ASM problems โข Cell configuration issues โข Cell software or hardware failures โข Network failures
- 28. Storage Cell Management โข Storage Cell monitoring and administration support โ Cell Home page and performance pages โ Actions supported: Start/stop Cell, verify connectivity, setup SSH, execute Cellcli on cells โ Setup IORM for database targets โข Management by Cell Group โ All cells used by a database automatically placed in a group โ Cell Group level administration operations (batch job monitoring)
- 29. Infiniband Network Monitoring โข Infiniband network and switches as EM targets โข Network home page and performance page โ real time and historical โข Network topology view โข Perform admin tasks such as enable/disable port, clear performance/error counters Full monitoring โข Alerts (switch generated and EM generated) โข Performance metrics โข Configuration metrics โ detect and notify configuration changes/best practice violations
- 32. Database Cloud Setup and Resource Monitoring โข Manage zones and underlying resources (databases, Server Pools, VMs) โ Track resource flux, tenants, policy violations, etc โ Drill down into individual resources for deeper monitoring โข Monitor requests and failure rates and identify potential bottlenecks to remediate
- 33. 35 Copyright ยฉ 2011, Oracle and/or itsaffiliates. All rights reserved. Database User Privileges Challenge Self Service works over shared resources SYS and SYSTEM are too powerful to be shared with SSA user SSA user should not be able to change init parameters Solution Create a master account with limited privileges for the SSA user SSA user provides username and password This master account will be the admin account for the database Master Account Privileges Grant DBA role and revoke the following privileges: โข Alter database โข Alter system โข Create any directory โข Drop any directory โข Grant any object privilege โข Grant any privilege โข Grant any role
- 34. 36 Copyright ยฉ 2011, Oracle and/or itsaffiliates. All rights reserved. Locked values shown in read only mode Capturing Provisioning Profiles โข Capture database configuration via Provisioning Profiles โข Lock and Save deployment procedures by using values from profile or by overriding them Database Provisioning Procedures
- 35. 37 Copyright ยฉ 2011, Oracle and/or itsaffiliates. All rights reserved. Catalog of Service Templates 09/05/2011 โข Publish saved deployment procedures as service templates โข Build a large service catalog by changing database versions, configuration, and other params
- 36. Self-Service DB Provisioning by End User Out-of-box console; no additional set up required Supports custom background Rich service catalog: Database service OVM Templates and Assemblies Java applications Additional capabilities: Backup and Restore VM/Database Basic resource monitoring Chargeback information Quota monitoring Cloud APIs RESTFul APIs and CLIs ideal for Cloud integrators
- 37. โBritish Telecom uses Oracle Enterprise Manager to provide database- as-a-service and middleware-as cloud service offerings. We can now deploy a database in 20 minutes whereas in the past it would have taken us a couple of weeks. The business reaps the benefit in decreased costs of hardware, being agile and being able to deliver services quickly to market. BT are excited about the new features in Oracle Enterprise Manager 12c such as customer self-service, templated provisioning, agentless discovery, metering and chargebackโwhich we expect to further help cut costs." Surren Partabh CTO Core Technologies British Telecom Source: BT Deploys Oracle Database as a Service, cutting provisioning time from weeks to minutes http://streaming.oracle.com/ebn/podcasts/media/10957726_BT_110911.mp3
- 38. Metering and Chargeback Charge UserDiscover & Plan Track Usage Benefits Align IT with business goals Drive better decision making Plan for IT budget requirements Quicker return on IT investment Key Features Resource usage metering Historical usage trends Cost allocation and charge plan evaluation Reporting for cloud self-service application
- 39. Database Metering and Chargeback Metrics Metric Metric Type Aggregation Dedicated Base Charge Fixed sum Backup Charge Fixed sum CPU Utilization(SPECintยฎ_rate_base2006) Usage avg CPU Utilization(%)* Usage avg Edition Config n/a Version Config n/a Storage Usage * Config avg MemoryUsage * Config avg Option Config n/a Shared (by Service) Base Charge Fixed sum DB Time Usage sum CPU Time Usage sum CPU Utilization(%) Usage avg CPU Utilization(SPECintยฎ_rate_base2006) Usage avg SQL Executes Usage sum User Transactions Usage sum Disk Read Bytes (Physical) Usage sum Disk Write Bytes (Physical) Usage sum NetworkIO Usage avg