Introduction to Oracle Database 11g – The Innovation Continues

Introduction to Oracle Database 11g – The Innovation Continues
Pat Shuff Solutions Architect Oracle Corporation BROUG May 17, 2007

Sample Oracle Innovations
Automatic Storage Management Flashback technologies XMLDB Application Express Real Application Clusters

Oracle Database Innovation
As a result of our focus, Oracle leads the industry with a huge number of trend-setting products. Focus on key development areas has lead to a number of industry firsts, from the first commercial relational database, to the first portable tool set and UNIX-based C/S apps, to the first multimedia database architecture. Key Early Differentiators: platform portability, mvrc/row locking, cluster Year Breakthrough gR1 Change Assurance 2005 June 10gR2 Database Vault 2004 Grid Computing 2004 January 10gR1 Self managing db 9iR2 XML Database 2002 Oracle Data Guard 2001 Real Application Clusters 2000 Internet File System 1999 8i Internet Enabled Database 1999 Java Support 1999 XML Support 1997 VLDB Support 1997 Messaging Support 1997 Object Relational Support Support for Multimedia 1995 Data Warehousing Optimizations 1994 Parallel Operations 1992 Active Business Rules 1992 Distributed Transaction Support 1991 Cluster and MPP Support 1989 Mission Critical OLTP Support 1986 Client/Server Support 1983 Platform Portability 1979 Commercial SQL Implementation * Build our core competency internally, rather than buy product (like IBM or Microsoft). Our development expertise translates to better customer competitiveness Audit Vault Database Vault Grid Computing Self Managing Database XML Database Oracle Data Guard Real Application Clusters Flashback Query Virtual Private Database Built in Java VM Partitioning Support Built in Messaging Object Relational Support Multimedia Support Data Warehousing Optimizations Parallel Operations Distributed SQL & Transaction Support Cluster and MPP Support Multi-version Read Consistency Client/Server Support Platform Portability Commercial SQL Implementation 1977 2007 30 years of sustained innovation … … continuing with Oracle Database 11g

Oracle’s Mission Deliver the best information with the highest Quality of Service at the lowest cost Strategy presentations for Server meeting Planning assumptions (the plans should be) : - strategic – high level statements and actions (backed by detail) - long term – 2-5 year horizon (short term actions/milestones) - aggressive – what we think is possible - Oracle-wide – plan for all of Oracle to execute - Action oriented – specific actions to succeed Plans: - Server Technology - Database - Middleware - Systems Management - Collaboration - Other: search, Spatial, OS/Virtualization, etc Presentation: (1 slide each, as much back-up as you want at the end of the presentation, but the intention is that these 8 slides should stand on their own as a credible plan) Mission statement (one “power-packed” sentence) Metrics – what are the measures to determine success vs the mission Revenue – Revenue by year (fy06-fy10) Dollars and growth rates Competitive position – checklist chart – major capabilities by competitor Product differentiators – how we win/ Product Map/Plan - today Components – entire product space (what we have/don’t have) Product Map/Plan – 2010 Same picture – new releases and areas filled in Critical success factors (actions and date) What do we (ST) and the rest of Oracle need to do for us to succeed (these should be specific and measurable)

Oracle’s Mission Deliver the best information with the highest Quality of Service at the lowest cost Oracle Database must be the fastest, most scaleable, most reliable, most secure, easiest to use, for all types of information… Strategy presentations for Server meeting Planning assumptions (the plans should be) : - strategic – high level statements and actions (backed by detail) - long term – 2-5 year horizon (short term actions/milestones) - aggressive – what we think is possible - Oracle-wide – plan for all of Oracle to execute - Action oriented – specific actions to succeed Plans: - Server Technology - Database - Middleware - Systems Management - Collaboration - Other: search, Spatial, OS/Virtualization, etc Presentation: (1 slide each, as much back-up as you want at the end of the presentation, but the intention is that these 8 slides should stand on their own as a credible plan) Mission statement (one “power-packed” sentence) Metrics – what are the measures to determine success vs the mission Revenue – Revenue by year (fy06-fy10) Dollars and growth rates Competitive position – checklist chart – major capabilities by competitor Product differentiators – how we win/ Product Map/Plan - today Components – entire product space (what we have/don’t have) Product Map/Plan – 2010 Same picture – new releases and areas filled in Critical success factors (actions and date) What do we (ST) and the rest of Oracle need to do for us to succeed (these should be specific and measurable)

This is designed to be a standalone slide that can be put in other presentations

The following is intended to outline our general product direction
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

Managing Storage and Data Managing & Using Information
Maintaining Availability, Security and Performance Managing Systems And Managing Change

Managing Storage and Data

Automatic Storage Management
ASM Disk The preferred and best storage manager for Oracle Databases Easier to manage than file systems Performance of raw volumes Built-in to Oracle database Shared storage pool for all databases Free, and widely adopted >65% of 10g RAC deployments on ASM >25% of 10g customers already using ASM Many VLDB over 10TB

Automatic Storage Management
ASM Disk Spreads database files evenly across storage arrays Storage arrays can be easily added or remove transparent data redistribution Data mirrored across arrays Tolerates failure of disks or arrays New ASM features in Oracle 11g: ASM Fast Disk Resync ASM Preferred Mirror Read ASM Rolling Upgrade Larger extent, allocation unit sizes

Data Compression for All Applications
Oracle 9i compresses data only during bulk load; useful for DW and ILM Oracle 11g compresses w/ inserts, updates Typical compression ratio of 2x to 3x Database directly reads compressed data eliminating decompression overhead Strategy: compress db’s 10 largest tables Shrink table data by 50%, increase CPU by 5% Savings cascade to all db copies: test, dev, standby, mirrors, archiving, backup, etc.

Oracle SecureFiles High-Performance Large Objects
High-performance transactional access to large object data documents, medical, CAD, imaging … low-latency, high throughput, concurrent access space-optimized storage Protect your valuable data .. in the db! transactions transparent encryption compression and de-duplication database-quality security, reliability, and scalability Better security, single view and management of data Superset of LOB interfaces – easy migration This is designed to be a standalone slide that can be put in other presentations

SecureFiles Breaks the Performance Barrier!
File Read Performance (MB/second) Innovative technology for high performance large object data Smart buffering, write gathering, intelligent locking Fast bulk data transfers, LOB prefetch Much faster than LOBs with more capabilities File system-like performance with database functionality! LOBs Linux Files SecureFiles File Size (MB)

Database Encryption Encrypt at a column level (10g)
New: encrypt entire tablespaces Redo, undo, backups also encrypted Key management done by database Transparent to applications

Size of the largest data warehouse in Winter Corp Survey
Growing Data Volumes 100 Size of the largest data warehouse in Winter Corp Survey 80 Database Size (TB) 60 245% increase from 2003 to 2005! 40 20 1998 1999 2000 2001 2002 2003 2004 2005 Source: 2005 TopTen Program, November 2005 © Winter Corporation, Waltham, MA, USA

Information Lifecycle Management Optimize storage cost and performance
Less Active Active Historical Archive High Performance Storage Tier Low Cost Storage Tier Online Archive Storage Tier Offline Archive Storage Tier Use Flashback Data Archive for long-term storage of “old” data Use table, index partitioning to separate data into different tiers Use new ILM assistant to establish policies, create scripts

Flashback Data Archive Total Data Recall
Select * from orders AS OF ‘Midnight 31-Dec-2004’ Tamper-proof data archive Efficient storage and retrieval of undo Keep data for months, years, decades! Fast access to even very old data View data, versions of rows as of any time Control data retention time, purging of data Changes Archive Tables User Tablespaces Flashback Data Archive Oracle 11g Database

Flashback Data Archive Total Data Recall
Select * from orders AS OF ‘Midnight 31-Dec-2004’ Access Historical Data –”AS OF” Generate Reports – “ROW VERSIONS” Information Lifecycle Management (ILM) Auditing Data Recovery Enforce Data Retention Policies Changes Archive Tables User Tablespaces Flashback Data Archive Oracle 11g Database

Oracle Partitioning 10 years of innovation
Core functionality Oracle8 Range partitions, global range index Oracle8i Hash and composite range-hash partitioning Oracle9i List partitioning Oracle9i R2 Composite range-list partitioning Oracle 10g Global hash indexes Oracle 10g R2 1M partitions per table

Oracle Partitioning 10 years of innovation
Core functionality Oracle8 Range partitions, global range index Oracle8i Hash and composite range-hash partitioning Oracle9i List partitioning Oracle9i R2 Composite range-list partitioning Oracle 10g Global hash indexes Oracle 10g R2 1M partitions per table Partitioning by reference Virtual column partitioning New composite partitioning: range-range, list-range, list-list, list-hash

New Partitioning Features
New composite partitioning schemes Partition (or index) on virtual (computed) columns Partition advisor Automatic range partition creation Partition by REFERENCE (primary key of parent) Range List Hash 11g 9i 8i

Partitioning by REFERENCE
Table ORDERS RANGE(order_date) Primary key order_id ... ... Jan 2006 Feb 2006 Partitioning key inherited through PK-FK relationship Avoids redundant storage, maintenance of order_date Table LINEITEMS RANGE(order_date) Foreign key order_id ... ... Jan 2006 Feb 2006

Partitioning Advisor Packaged Apps Custom Apps SQL Workload Considers entire query workload to improve query performance Advises on partitioning methods Range (equal-interval), range key and interval Hash, hash key Integrated, non-conflicting advice with Indexes, MVs Partition Analysis Advice New! SQL Advisor SQL Plan Tuning SQL Structure Analysis Access Analysis SQL Profile SQL Advice Indexes & MVs Well-tuned SQL & Schema

Oracle ILM Assistant Manages your ILM environment via a GUI interface
Define lifecycle definitions Manage security & compliance Advises when data needs to be moved, generates scripts Requires Oracle Application Express Supports Oracle Database 9i and up Tool downloaded from OTN (available now!)

Oracle’s Storage Strategy – Sustained Innovation
Oracle storage suite built-out over last decade Each component continuously enhanced to add more value Best of breed in each area Secure Files Oracle Secure Backup, Encryption ASM, RMAN Disk Backup XML DB, ILM, Compression Flashback Data Guard RMAN 8.0 8i 9i 9.2 10g 10.2 11g

Managing and Using Information

Managing All Your Information
Secure Enterprise Search Enterprise Search Solution Oracle Strategy: evolve the Oracle database to manage all enterprise Information Meld db and file metaphors Enable integration of all enterprise information sources Enable rich information retrieval capabilities Provide solutions built on top of the database Uniform management of content and metadata Scalable, secure, highly available, integrated, robust, available on all platforms Content DB Enterprise Content Management XML DB Integrated Native XML Database Text & Ontology Text and Semantic Processing Location & Spatial Location and Proximity Searching Multimedia Multimedia management Relational Characters, Numbers, Dates, LOBs

Oracle XML Support SQL access to XML content and XML access to relational content Flexible native XML storage delivers optimized application performance Repository integration enables document centric access, security and integrity and development Full support for key XML standards including the W3C XQuery recommendation and SQL/XML

Extending Oracle’s XML Lead
Performance Comprehensive XML storage and indexing and efficient end-to-end XML processing Binary XML - Compact and efficient storage representation Complements existing object storage and text storage models XMLtype storage format transparent to developer Single, compact XML representation across client, mid-tier, db Plan to place our Binary XML format into open source XML Path Indexing for schema-less XML documents

Performance Performance improvements in many areas Recursive schema handling Scalable XSL output XQuery and SQL/XML query optimization with schema-less XML XML update optimizations Asynchronous XML index updates

XML application development Enhanced productivity and flexibility Standards Support XQuery 1.0 support XDB as HTTP Server – expose PL/SQL as Web Services Content Repository API for Java (JSR 170) support Support for JCR 1.0, SQL:2007 XLink/Xinclude support XMLDB Repository Triggers

Operational completeness Making XMLDB Mainstream XML type support in streams & logical standby In-place XML schema evolution XML language translation support Asynchronous XMLindex updates Large text node handling

Oracle XMLDB – Sustained Innovation
Binary XML Storage & Indexing XQuery Performance XML Storage & Repository XML API’s

Multimedia and Spatial Enhancements
New spatial features 3-D geometry, surface, and point cloud storage and indexing Spatial web services Spatial routing engine enhancements Scalability, manageability, reliability, usability enhancements Multimedia and medical imaging 3X performance improvement for common image processing operations Large media handling (up to 128 TB) DICOM medical imaging support Java Advanced Imaging (JAI) support 11g provides Complete DICOM support Extract any/all of the 2000 standard attributes (& private) metadata from any DICOM image into XML doc specify which to extract via a XML document, Supports user-defined XML schemas, Support all transfer syntax definitions (binary encoding rules for Implicit/Explicit Little Endian Byte Ordering), Support all service-object pair (SOP classes) – Image (Xray, CT, MRI, utrasound) waveform and structure reports (service classes & information objects combine to form the functional units of DICOM - a service-object pair, or SOP. Since DICOM is an object-oriented standard, the combination is actually called a service-object pair class, or SOP class. The SOP class is the elemental unit of DICOM) Write DICOM objects Build DICOM secondary capture image (JPEG + XML > DICOM), convert Transfer syntax (binary encoding rules - DICOM ILE > DICOM ELE Implicit/Explicit Little Endian), Update DICOM object attributes (DICOM1 > XML1 then XML1 > XML2 then DICOM1 + XML2 > DICOM2) Read/write/compress DICOM images Support any DICOM image content (Single frame, multi-frame and video Extensible), Crop, rotate, scale and convert image content, Compression: JPEG (8bit grayscale/color, 16bit grayscale, 24bit color), JPEG2000, RLE Use XML to specify a set of conformance validation rules that can be applied to a DICOM object - User-defined conformance rules & validation actions - what a well-formed DICOM document should include, which can be more or less than the standard e.g. DICOM doesn’t require a patient name but you can, Rules can be targeted to selected DICOM objects Remove or replace a set of user-specified DICOM attributes from a DICOM object Specify via a XML document which attributes should not be shared to de-identify a DICOM object for HIPAA conformance, User specified attribute set and action: Clear attribute and Overwrite attribute with user-specified string Extensibility, customization, APIs and logging Runtime upgradable, Extensible: Data dictionaries, New DICOM SOP classes (service classes & information objects form the fundamental functional units of DICOM) & more, APIs: PL/SQL object and packages, Java proxy class and mid-tier, BFILE/BLOB/ORDDicom/ORDImage/Stream/File

Text and Semantic Technology
Oracle Text Enterprise Manager support Incremental, online indexing Web services Composite indexes speed text, relational queries User-defined relevance ranking More advanced new multilingual and linguistic support, including Arabic and Hebrew Semantic Technology Query using concepts and terms related to a keyword applications in life sciences, defense, health care, enterprise information integration New in 11g: Improved query/optimizer support Improved bulk loading native, lightweight OWL inferencing Manageability The following sections describe enhancements to Text Manageability, which include: - Improving ease-of-use and self-manageability of the text subsystem by providing Enterprise Manager support. - Exposing the text subsystem as Text services through Java and Web for ease of application development. Improved Support for Advanced Features in More Languages Oracle Database 11g includes multilingual and linguistic support that improves Oracle Text handling of documents in different languages. Two new components are introduced: - A new lexer that automatically detects the language of the document - A new document service that detects the character set and language of a document Oracle Text supports keywords searches in all unicode languages. However, advanced features such as stemming and alternate spelling require lexing support in different languages. The new multilingual and linguistic support make advanced search available in 28 languages including Arabic, Hebrew, and Russian. Incremental Indexing Enhancements This feature introduces three improvements that facilitate large text index creation: - The new ctx_ddl.populate_pending interface - New sync_index enhancements - NOPOPULATE support In large text warehouses, applications cannot afford to have the indexing process running continuously. This feature provides interfaces that let applications create large indexes in a manageable way. Oracle Enterprise Manager Support for Text This feature provides improved Oracle Enterprise Manager support for Oracle Text. DBAs can now administer Oracle Text from Oracle Enterprise Manager. Re-Create Index Online This feature provides the ability to re-create an Oracle Text index without producing any undesirable query results until the application is ready to switch over to the modified index. Users can now re-create an Oracle Text index with new preference values, while preserving base table DML and query capability during the re-creation. Users can See Also: Oracle Text Reference for details Content Management Services Beta Draft Oracle 11g Database New Features 1-51 re-create the index in one operation or can step through each stage of the re-creation manually. 1.6.4 Text Performance and Scalability The following sections describe feature integration with Oracle RAC for scalability and other subsystems such as the optimizer for performance. Composite Index This feature facilitates structured ORDER BY criteria, structured range, or combinations of both. This is accomplished by allowing the specification of FILTER BY and ORDER BY structured column(s) at index creation time. This feature provides better performance for mixed queries involving relational and text predicates required by today's Web applications. More Types of Operations Allowed on Document Sections This feature introduces a new type of document section called SDATA. The content of an SDATA section is typed and not tokenized. SDATA sections support range and equality query operations. The benefit of this feature is faster queries on document metadata by pushing more metadata into the Text index. Text Support for Very Large Number of Partitions Until now, the maximum number of partitions allowed has been 9,999. The limit has been increased to 1,223,054. This increase is of significant benefit to Text users. User-Defined Score User-defined scoring offers users a mechanism to define how the CONTAINS query will score textual content. This mechanism can use the DEFINESCORE or the DEFINEMERGE operator. In some cases, the application would improve from more direct control of how to score documents based on structured values like date. The user-defined score feature allows applications to customize scoring of textual content. New Capabilities for Semantic Data This following sections describe new features and capabilities for semantic data. Improved Performance for Bulk Loading Oracle provides native storage, inference, and querying of semantic data sets often containing hundreds of millions of "triples" (modeled in canonical Resource Description Framework (RDF) <Subject Predicate Object> format). A new bulk loading utility is now introduced that significantly improves the ability to handle large volumes of triples. Query performance on semantic data has been improved, especially for queries returning large result sets, using a query rewrite technique and the Oracle Database optimizer. Support for typed literals in semantic data has been enhanced to include xsd:date and xsd:time. Also, xsd:dateTime with time zone is now supported. Oracle Database provides scalable, secure, integrated, and efficient support to store, inference, and query large semantic data sets described using W3C standards. Performance for loading and querying of semantic data has been improved to enable scaling for large data sets used in the defense and intelligence, life sciences, and geospatial domains. Date and time information, optionally with time zone, can be stored and queried. Support for Storage and Query of Semantic Content Oracle Database 11g extends its semantic capabilities with native, lightweight OWL inferencing that is a practical subset of the OWL-DL standard. Ontologies (sets of terms, associated properties, and the relationships among them) can be stored in the database to enable searching based on relationships described in the ontology using new operators, SEM_RELATED and SEM_DISTANCE. Inferencing support includes efficient and scalable reasoning for a subset of OWL-DL constructs and APIs to generate proofs for inferred triples and to detect inconsistency in semantic data sets. Advanced users can develop custom inferencing rules. Semantic operators can be used for filtering based on semantic relatedness (SEM_RELATED), and the results can be further restricted or ordered using proximity measures (SEM_DISTANCE). A new index type (SEM_INDEXTYPE) allows efficient execution of such queries, enabling scalable performance over large data sets. These new semantic operators enable the Oracle database to query relational data not only through keyword matching but also using concepts and terms related to the keyword. These ontology-assisted queries are based on semantic relationships between the column value data and ontology terms. This enables more complete search results without requiring as much prior knowledge of the data set. The OWL inferencing capability enables discovery of new relationships in RDF and OWL data. This is useful in applications in life sciences, health care, and business enterprise information integration. The W3C has adopted RDF

Fast OLAP via SQL Cubes as Materialized Views
Detail data is stored in tables Applications use SQL queries Summary data is managed in cubes (like materialized view) Databases manages cube refresh as data changes Fast, incremental Cost-based aggregation SQL queries automatically re-written to access the cube Application is unchanged, but updates and queries are faster SQL Query

OLAP for Every Data Warehouse
Any SQL application can leverage OLAP Option performance Cube technology is optimized for business intelligence Excellent performance for ad-hoc query loads Highly optimized incremental refresh and aggregation Content rich calculations are easily queried with SQL Seamless fit with warehouse administration process SQL Query

Oracle Record of OLAP Innovation
Cubes but no SQL access SQL Views over Cubes Transparent SQL Rewrite to Cubes SQL Star Queries Rolap via bitmap indexes and materialized views

Oracle Data Mining Enhanced, Automated, Simplified
On-the-fly Data Mining in SQL Find the top 5 stores that are the furthest below forecast SELECT store_id, loc, sales, forecast FROM (SELECT * FROM (SELECT s.*, PREDICTION (CREATE REGRESSION FOR sales USING *) OVER () as forecast FROM stores) ORDER BY forecast-sales) WHERE ROWNUM < 6; Enhanced data mining in SQL Eases application use of models Data Models as schema objects simpler administration, security Java API standards compliance Automated and embedded data transformations (aka “Supermodels”) eases model building and scoring New predictive analytic data mining procedures Regressions, segmentation, profiling, grouping Regression models SEGMENT - finds hidden segmentations in your data PROFILE - segments your data with respect to a target DETECT – finds odd cases (fraud, intrusion, etc…) GROUP, MAP – find similar records (e.g., customers) PREDICT with EXPLANATION – display rules for predictions PROFILE – find patters among similar records

Enhanced SQL – PIVOT and UNPIVOT
SELECT * FROM sales PIVOT (sum(amount) FOR quarter in ‘Q1’,’Q2’,’Q3’,’Q4’); PROD QUARTER AMOUNT Shoes Q1 2000 Q2 1000 Jeans Q3 500 100 Q4 PROD Q1 Q2 Q3 Q4 Shoes 2000 1000 Null Jeans 600 The PIVOT and UNPIVOT operators are extensions to the table expression in the FROM clause of a SELECT statement. PIVOT spreads values from multiple rows into multiple columns, aggregating data in the process. PIVOT is commonly used to create a result set with more columns and fewer rows than the source data. The PIVOT operator supports multiple pivot columns, multiple aggregates, wildcards, and aliasing. UNPIVOT moves data in the opposite direction from PIVOT. For each input row, UNPIVOT moves values from multiple columns into multiple output rows. The process creates a result set with fewer columns and more rows than the source data. UNPIVOT supports multiple unpivot columns, multiple measure columns, and aliasing. PIVOT can create aggregated cross-tabular output that condenses many rows into a compact result set. For example, input data holding sales of 1 month per row can be pivoted into output holding 12 months per row, with each month in its own column. Another use of PIVOT is to combine multiple input rows into a single output row, enabling inter-row comparison without a table self-join. UNPIVOT reshapes data into a format useful for further relational operations. For example, if a source data set presents 12 months of sales values per row, UNPIVOT can reshape each source row into 12 output rows, each holding 1 month of sales data. The unpivoted results can in turn be manipulated with much simpler and more efficient SQL than required for the source data set. Rotate rows into columns and vice versa Create aggregated cross-tabular result set Use to combine multiple rows to one, enabling inter-row computations without self-join`

ALL Your Data – Sustained Innovation
As a result of our focus, Oracle leads the industry with a huge number of trend-setting products. Focus on key development areas has lead to a number of industry firsts, from the first commercial relational database, to the first portable tool set and UNIX-based C/S apps, to the first multimedia database architecture. Key Early Differentiators: platform portability, mvrc/row locking, cluster Year Breakthrough gR1 Change Assurance 2005 June 10gR2 Database Vault 2004 Grid Computing 2004 January 10gR1 Self managing db 9iR2 XML Database 2002 Oracle Data Guard 2001 Real Application Clusters 2000 Internet File System 1999 8i Internet Enabled Database 1999 Java Support 1999 XML Support 1997 VLDB Support 1997 Messaging Support 1997 Object Relational Support Support for Multimedia 1995 Data Warehousing Optimizations 1994 Parallel Operations 1992 Active Business Rules 1992 Distributed Transaction Support 1991 Cluster and MPP Support 1989 Mission Critical OLTP Support 1986 Client/Server Support 1983 Platform Portability 1979 Commercial SQL Implementation * Build our core competency internally, rather than buy product (like IBM or Microsoft). Our development expertise translates to better customer competitiveness Oracle 11g Secure Files Ontology DICOM Secure Search Binary XML XML Index Oracle 10g ULDB Location Services XQuery Oracle9i XML DB Repository SQL/XML 2007 Oracle8i Text Spatial Media 2004 Oracle8 VLDB LOB’s Object-relational Extensibility 2001 1999 1977

Maintaining Availability, Security and Performance

The Availability Challenge
System Changes Data Changes Planned Downtime Storage Failure Human Error Corruption Site Failure Server Failures Unplanned Downtime Data Failures

Server Scale-Out with Real Application Clusters
Great scalability & availability Pools standard low cost servers, improves server utilization Runs applications unchanged 1000s of production customers Active/passive clustered servers New: 11g fine tunes performance, scaling, fail-over, management Pool of database servers Designed to Tolerate Server Failures

Storage Scale-Out with Automated Storage Management
Dedicated disks for database storage ASM pools storage from modular storage arrays Automatically remirrors when disk or array fails New: 11g fine tunes recovery from corrupt blocks and crashed storage arrays To protect against physical disk or device failures use mirroring or RAID Use redundant hardware in storage array, OR Use Oracle Automated Storage Manager mirroring ASM Mirroring User specifies failure groups (disks that fail together) E.g. disks on SCSI chain or disks in same array ASM mirrors data across failure groups Mirror consistency is automatically recovered after a crash by normal database recovery No additional logging or NVRAM is needed Mirroring across storage arrays can provide an additional degree of protection or permit migration to a new device Pool of Storage for all databases Designed to Tolerate Storage Array Failure

Error Investigation with Flashback
Flashback Query Query all data at point in time select * from Emp AS OF ‘2:00 P.M.’ where … Flashback Versions Query See all versions of a row between times See transactions that changed the row Tx 3 Tx 2 select * from emp VERSIONS BETWEEN ‘2:00 PM’ and ‘3:00 PM’ where … Tx 1

Error Correction with Flashback
Correct errors at any level Flashback Database – restore database to time Flashback Table - restore contents of tables to time Database Customer New: Flashback Transaction – back out transaction and all subsequent conflicting transactions Order

Investigation and Planning
Data Recovery Advisor Investigation and Planning Diagnoses persistent data failures Presents appropriate repair actions Intelligently determines plan for recovery, selecting repair option Data file restore/recovery, media recovery, Flashback database, etc. Validates plan w.r.t. availability of media components required Can automatically apply plan Recovery Uncertainty and confusion during an outage causes delays and errors Reduces Downtime by Eliminating Confusion

Customers Don’t Benefit from Disaster Recovery
Cost – choose no DR, or under-configure DR Rarely used – so little confidence in fail-over Data loss – leads to downstream problems Slow – faster to fix problems than fail-over Limited protection – site failures only

Oracle Data Guard – Practical Disaster Protection
Production Database Standby Database Log Shipping Synchronous or asynchronous log shipping Corruptions don’t propagate Configurable for zero data loss Automatic fail over in seconds to standby (10.2) Uses far less bandwidth than remote mirroring Thousands of production customers The Data Guard Broker fully supports RAC in 10g

Physical Standby Apply Logs Snapshot Standby Perform Testing
Use Standby Database for testing and development Eliminates cost of DR Preserves zero data loss while in test/dev mode But no real time query or fast failover Open Database Back out Changes Snapshot Standby Perform Testing Continuous Redo Shipping

Real-Time Query with Physical Standby
Queries Logical or Physical Standby Database Production Database Continuous Log Shipping Previously available with Logical Standby Available with Physical Standby in 11.1 Handles all data types, very fast, simple operation Eliminates cost of DR: all hardware used for production Continuous Real-Time Query

Benefits of Data Guard for Disaster Recovery
No cost – hardware and software Zero data loss - over long distances Fast Automatic Failover Covers all common failures – not just site failures Works transparently for existing applications Bonus – Big Reduction in Planned Downtime

Reducing Planned Downtime for System Changes
Database Storage Scaling Servers on Demand Add RAC nodes online w/o data movement Scaling storage on demand Add ASM disks online w/ auto data rebalancing Online patching RAC rolling upgrades for complex patches, CPUs Rolling upgrades w/ standby for patch sets, version changes New: simple one-off patches can be applied to a running Oracle instance

Data Security: Oracle Products
Access Control Database Vault Label Security Identity Management Oracle Identity Management Core Platform Security Monitoring Audit Vault EM Configuration Pack Data Protection Advanced Security Secure Backup

Oracle Advanced Security
Transparent Data Encryption Column level encryption (10gR2) Tablespace level encryption For encrypting entire application data Supports foreign keys and range scan LOB encryption Master Key protection in hardware using PKCS #11 Platform integration Log Miner, Logical Standby, Streams, DataPump

Coming soon for Oracle9i, 10g, 11g
Oracle Database Vault Controls privileged users, enforces separation of duties Administrators can’t access application data Site-specific controls limit access by any user Coming soon for Oracle9i, 10g, 11g DBA FIN DBA Fin Realm HR DBA Fin HR Realm HR CREATE … Outside business hours FIN user SELECT … Unexpected IP address Here is a typical database with both Financial and HR application, along with a set of database administrators, one set to manage the database itself, and then another set to manage the individual HR and financial application. One of the problems in this type of situation is that the Database administrator (DBAs) can use their powerful privileges to view or even modify the financial or HR application data if they so desire . For a 7*24 operation, there are many more DBAs, and only one of them has to become curious for the entire security to fall apart. But not with Oracle Database Vault. Database Vault allows you to create a protection zone, a realm, around the set of protected objects such as the entire financial application or some selected sensitive tables. The security office simply creates a Realm around the FIN application and the DBA can no longer be able to use his powerful privileges to access the financial results of the company before the CFO. Not just the database administrators, Application owners tend to have very powerful privileges as well. In a consolidated environment, it’s very likely that you’ll have more than one application and thus several powerful users in the database. In this example, it’s possible for the Financial DBA to look at the HR application data, but not with database vault. Using a Database Vault Realm around the HR application data, the Financial administrator cannot read the HR data. Database Vault Realms, thus eliminate the security risks from server consolidation. Realms can be easily applied to existing application objects with no application changes and minimal performance impact. The DBAs can continue to do their jobs, but they cannot use their powers to look into the data protected with the Database Vault.

Enterprise-wide Audit Solution for Compliance & Security
Oracle Audit Vault Coming soon Console Enterprise-wide Audit Solution for Compliance & Security Collects audit info from multiple sources SDK for customization Monitor security-sensitive activities Tamper-evident repository Alerting policies Monitor Policies Report/Alert Secure Audit Warehouse Collector SDK 9i, 10gR1, 10gR2 Applications Non Oracle DB App Server OS Audit Collectors

Core Database Security Enhancements
Secure configuration by default Password management settings Audit sensitive administrative operations Stronger password verifier PKI / Kerberos authentication for super privileged DBAs

Oracle Database Security
Sustained Innovation Oracle Audit Vault Oracle Database Vault DB Security Evaluation #19 Transparent Data Encryption EM Configuration Scanning Fine Grained Auditing (9i) Secure application roles Client Identifier / Identity propagation Oracle Label Security (2000) Proxy authentication Enterprise User Security Global roles Virtual Private Database (8i) Database Encryption API Strong authentication (PKI, Kerberos, RADIUS) Native Network Encryption (Oracle7) Database Auditing Government customer I like to show this slide to let customers know that Oracle has been working in the security space pretty much since day 1. The very first Oracle customers were in the government space. This close working relationship with customers has enabled Oracle to stay at the forefront of database security technology. As you can see we’ve delivered a great deal of technology over the years. Just recently we completed our 18th independent evaluation of the Oracle database. This was completed under the Common Criteria at EAL4. 1977 2007

Sustain Optimal Performance with Self-Managing Database
Low Impact Integrated Adaptive Auto-Tuning Advisory Schema Storage Backup Memory Apps/SQL RAC Recovery Replication Instrumentation

Automatic SQL Tuning Captures high-load SQL
Nightly Well-tuned SQL SQL Workload Packaged Apps Custom Apps Automatic SQL Tuning SQL Profiles SQL Analysis Report Manually implement Captures high-load SQL Tunes SQL by creating SQL profiles Optionally implements greatly improved SQL plans Reports analysis Runs runs in maintenance window

Automatic Memory Tuning
Automatically adapts to workload changes Maximizes memory use efficiency Adjusts PGA, SGA, o/s memory Single dynamic memory parameter Helps eliminate out-of- memory errors OS Memory OS Memory DB Shared Memory DB Shared Memory DB Process Memory DB Process Memory

Database Result Cache Automatically caches results of queries, query blocks, or pl/sql function calls Cache is shared across statements and sessions on server Significant speed up for read-only / read-mostly data Full consistency and proper semantics Cache refreshed when any underlying table updated query 1 executes Group by Client-Side Query Cache This feature enables caching of query result sets in client memory. The cached result set data is transparently kept consistent with any changes done on the server side. Applications leveraging this feature see improved performance for queries which have a cache hit. Additionally, a query serviced by the cache avoids round trips to the server for sending the query and fetching the results. It also reduces the server CPU that Application Development 1-2 Oracle Database New Features Guide Beta Draft would have been consumed for processing the query, thereby improving server scalbility join join Table 1 Table 2 Table 3

Database Result Cache Automatically caches results of queries, query blocks, or pl/sql function calls Cache is shared across statements and sessions on server Significant speed up for read-only / read-mostly data Full consistency and proper semantics Cache refreshed when any underlying table updated result is cached cached result Group by Client-Side Query Cache This feature enables caching of query result sets in client memory. The cached result set data is transparently kept consistent with any changes done on the server side. Applications leveraging this feature see improved performance for queries which have a cache hit. Additionally, a query serviced by the cache avoids round trips to the server for sending the query and fetching the results. It also reduces the server CPU that Application Development 1-2 Oracle Database New Features Guide Beta Draft would have been consumed for processing the query, thereby improving server scalbility join join Table 1 Table 2 Table 3

query 2 uses cached result transparently
Database Result Cache Automatically caches results of queries, query blocks, or pl/sql function calls Cache is shared across statements and sessions on server Significant speed up for read-only / read-mostly data Full consistency and proper semantics Cache refreshed when any underlying table updated join query 2 uses cached result transparently cached result join Group by Client-Side Query Cache This feature enables caching of query result sets in client memory. The cached result set data is transparently kept consistent with any changes done on the server side. Applications leveraging this feature see improved performance for queries which have a cache hit. Additionally, a query serviced by the cache avoids round trips to the server for sending the query and fetching the results. It also reduces the server CPU that Application Development 1-2 Oracle Database New Features Guide Beta Draft would have been consumed for processing the query, thereby improving server scalbility join Table 4 Group by join Table 1 join Table 2 Table 3 Table 5 Table 5

query 2 uses cached result transparently
Database Result Cache Automatically caches results of queries, query blocks, or pl/sql function calls Cache is shared across statements and sessions on server Significant speed up for read-only / read-mostly data Full consistency and proper semantics Cache refreshed when any underlying table updated Table 1 Table 2 Table 3 join Group by Table 5 Table 4 cached result query 2 uses cached result transparently Client-Side Query Cache This feature enables caching of query result sets in client memory. The cached result set data is transparently kept consistent with any changes done on the server side. Applications leveraging this feature see improved performance for queries which have a cache hit. Additionally, a query serviced by the cache avoids round trips to the server for sending the query and fetching the results. It also reduces the server CPU that Application Development 1-2 Oracle Database New Features Guide Beta Draft would have been consumed for processing the query, thereby improving server scalbility

Other Transparent Performance Improvements
Auto-compiled PL/SQL and Java Compression for all data XML performance improvements Spatial, multimedia and semantic optimizations Streams performance improvements SecureFiles faster than files or LOBs RAC and ASM optimizations

Managing Systems and Managing Change

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Alfred North Whitehead: Principia Mathematica “The art of progress is to preserve order amid change and to preserve change amid order.”

Lifecycle of Change Management
Test Diagnose & Resolve Problems Make Change Realistic Testing Reliable Deployment Set Up Test Environments Provision for Production Diagnose Problems Patches & Workarounds Preserve Order Amid Change

Database Replay Realistic Testing
Recreate actual production database workload Capture production workload incl. concurrency Replay workload in test with production timing Analyze & fix issues before production Middle Tier Capture DB Workload Storage Oracle DB servers Replay DB Workload Production Environment Test (RAC) Environment`

SQL Performance Analyzer
Realistic Testing Test impact of change on SQL performance Capture SQL incl incl. statistics & bind vars Re-execute SQL in test environment Use SQL Tuning Advisor to seed SQL plans Test (RAC) Environment` Production Environment Capture SQL Oracle DB servers Execute SQL Queries Storage

Oracle Enterprise Manager 10g R3
Configuration Management Service Level Management Application Performance Mgmt Lifecycle Management Dashboards Applications Middleware Integrated view of applications and infrastructure Manage service levels, diagnostics and remediation Automate capacity on demand Simplify deployment Database O/S

Automatic provisioning from gold image
Oracle 10g R3 Grid Control 1 Create reference system Stage Gold Image 2 Reliable Deployment Automatic provisioning from gold image Greatly simplify RAC provisioning with automation Single click RAC scale-out and scale-back Provision full RAC and Clusterware systems Configures entire stack Pre-requisite checks & automatic fix-ups 4 3 Scale-out RAC nodes Create production system

Proactive advisories from Oracle Metalink
Patch Automation EM Support Workbench Reliable Deployment Proactive advisories from Oracle Metalink Patches acquired based on configurations and feature usage (new in 11g DB) Stage once in library for multiple deployments Best practice driven patching Rolling patching support for RAC/ASM/Clusterware (new in 10gR3 Grid Control) Proactively search Metalink for relevant patches Apply patch Download available patch Alert DBA

Automatic Diagnostic Workflow
Diagnostic Repository Error! Alert DBA Run Health Checks Auto Incident Creation First-Failure Capture Check Metalink If unknown issue If known issue Package incident & config Use Repair Advisor Apply patch / workaround Use Repair Advisors EM Support Workbench Reduce Time to Problem Resolution

Highest Quality of Service
Performance Scalability Availability Security Lowest Cost Easier to Manage Reduce risk of change

The preceding is intended to outline our general product direction
The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remain at the sole discretion of Oracle.

Introduction to Oracle Database 11g – The Innovation Continues

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