1 Oracle 11g Database New Features

1.1.1.1 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 would have been consumed for processing the query, thereby improving server scalability.

1.1.1.2 OCI Client Session NLS Character Set Status Information

This new feature adds client session NLS and character set information to the V$SESSION_CONNECT_INFO and GV$SESSION_CONNECT_INFO views.

This enhancement provides DBAs with access to client-side session character set information for all clients. This information is especially useful in troubleshooting problems.

1.1.1.3 Precompilers: Additional Array INSERT and SELECT Syntax Support by Pro*C/C++ and Pro*COBOL

In this Oracle Database 11g release, there is added support for IBM DB2 array INSERT and array SELECT syntax for Pro*C/C++ and Pro*COBOL.

Adding support for IBM DB2 array INSERT and array SELECT syntax helps with the migration of IBM DB2 installations to Oracle.

1.1.1.4 Precompilers: Dynamic SQL Statement Caching in Pro*C/C++ and Pro*COBOL

Application developers can now precompile Pro*C/C++ and Pro*COBOL applications with the new statement caching option.

The potential benefit of this feature is an increase in performance by enabling statement caching in precompiled programs. The performance improvement is achieved by removing the overhead of parsing the dynamic statements on reuse.

1.1.1.5 Precompilers: Fix Execution Plan in Pro*C/C++ and Pro*COBOL

This new feature focuses on fixing an execution plan for a Pro*C/C++ and Pro*COBOL application by using the outline feature.

By using the outline feature, you can ensure that the performance of the application is not affected when modules are integrated and deployed into different environments.

1.1.1.6 Precompilers: Flexible B Area Length for Pro*COBOL

The length of B Area for the Pro*COBOL application can now be from 8 to 253 columns.

The length of B Area for a Pro*COBOL program was limited to 72 when the format is set to ANSI. COBOL compilers now can support B Area length up to 253. This provides a programmer with the flexibility to type a line that is longer than 72 columns.

1.1.1.7 Precompilers: Implicit Buffered Insert in Pro*C/C++ and Pro*COBOL

Some applications that require periodic insertion of new data use a single INSERT statement within a loop. When queried by a command-line argument, the precompiler buffers the rows being inserted, reducing network round trips.

The benefit is that an implicit buffered INSERT provides performance improvement that is comparable to using the precompiler array interface.

1.1.1.8 Precompilers: Platform Endianness Support by Pro*COBOL

Oracle always stores Unicode data (UTF16) in Big Endian representation. Linux and Windows have Little Endian representation and Solaris has Big Endian representation. With this enhancement, Unicode data can be fetched in Little Endian format on Windows and Linux platforms using a Pro*COBOL application.

When a UTF16 (UCS2) string is fetched in PIC N variable in a Pro*COBOL application running on Little Endian platforms, the application can now be precompiled with the appropriate command-line option to have the correct platform endianness.

1.1.1.9 Precompilers: Scrollable Cursor Support in Pro*COBOL

Pro*COBOL now supports scrollable cursors to fetch data in a nonsequential manner.

A scrollable cursor enables you to access the rows of a database result set in a forward, backward, and random manner. This enables the program to fetch any row in the result set.

1.1.1.10 Precompilers: SQL99 Syntax Support by Pro*C/C++

With this enhancement, the SQL99 syntax for SELECT, INSERT, DELETE, and UPDATE statements and the body of the cursor in a DECLARE CURSOR statement is supported by Pro*C/C++.

Oracle Database supports most of the SQL99 syntax. With this enhancement, Pro*C/C++ also supports the SQL99 syntax that is supported by Oracle Database.

1.1.2.1 Application and Schema Comparison

New application and schema comparison capabilities enable you to identify differences between two selected applications.

You can also identify differences between objects in two different schemas.

1.1.2.2 Drag and Drop Item Layout

The Drag and Drop Layout page enables you to interactively reorder items within a given region, change select item attributes, create new items, and delete existing items.

1.1.2.3 Flash Charts

Oracle Application Express includes support for 18 different types of Flash charts. You can create Flash charts when creating a new page or by defining a Chart region.

This release also includes a utility that enables you to convert existing Scalable Vector Graphics (SVG) charts to Flash.

1.1.2.4 Friendly URL Syntax to Facilitate Bookmarks

By specifying zero as the session ID, you can now make it easier for application users to bookmark pages within an application.

This features makes all public page links consistent, making them easier to bookmark.

1.1.2.5 Improved Web Services

Oracle Application Express now supports more loosely defined Web services types and document styles. Both of these enhancements enable support for Web services created with JDeveloper and Oracle BPEL synchronous processes. You can now interact with Web services over a Secure Sockets Layer (SSL) and work with Web services that require basic authentication.

Finally, this release also enables you to manually create a Web services reference even if the Application Express engine cannot correctly parse a WSDL document.

1.1.2.6 Improved Workspace Management

This release also includes a number of workspace management enhancements. For users requesting new workspaces or requesting additional storage, you can control what sizes display.

You can also request an e-mail of your workspace names. And, finally, you can view a log of login attempts.

1.1.2.7 Microsoft Access Migration

Application Migration enables you to migrate a Microsoft Access application and generate an Oracle Application Express application.

This feature provides an easy way to migrate applications from Microsoft Access to Oracle Express.

1.1.2.8 New Password and Account Controls

You can now specify rules for password expiration, mandate strong passwords (that is, specify the minimum number of and type of characters), require that users change their passwords on first use, and lock accounts.

With this new feature, you can configure account login controls and password policies across a development instance in Oracle Application Express Administration Services. You can also specify controls for a specific account under Account Controls on the Edit User page.

1.1.2.9 Page and Region Caching

You can improve the performance of your application by taking advantage of page and region caching. New Cache attributes have been added to the Page attributes and Region Definition pages.

Page caching works well for static pages and region caching is a good choice for regions such as lists that do not have any conditions or regions containing HTML text.

1.1.2.10 PDF Printing

You can now include the ability to print a report by exporting a report region to PDF. You can also define report queries and print attributes for report regions as Shared Components making them accessible to all pages within an application.

The advantage of this feature is that it provides production of high quality, easily printable reports.

1.1.3.1 Create Rules and Actions for DML Events With Rules Manager

Rules Manager now adds the ability to treat DELETE and UPDATE operations on a table as events that drive the rule evaluation and action execution (INSERT operations are already supported). In addition to pre-commit evaluation of rules based on trigger, this release supports post-transaction evaluation through use of the Change Notification feature of Oracle Database.

Because rules can now be processed during UPDATE and DELETE statements, developers can now model the entire business process for OLTP workloads in addition to the current "append-only" workloads characteristic of data warehouse and sensor-based applications.

1.1.3.2 Enhanced Rules Manager

Rules Manager now includes a PL/SQL API to store reusable and shareable rule conditions in SQL WHERE clause format. Complex rule conditions can be assembled from these shared elements.

In addition, rules can now be enabled and disabled through standard SQL.

Application developers can reuse rule conditions to speed the rule definition process. Since these shared conditions are stored once, changes propagate to all the complex rules referring to them. For example, if a rule component defines a "suspicious bank transaction" as involving an international bank transfer over $10,000, a change to the amount, location, or other condition needs to be made only once and it is updated in all complex rules using "suspicious bank transaction" in their event scenario.

Rules can be turned on and off to facilitate rule testing and control introduction into the system as well as to reflect changes in business conditions.

1.1.3.3 New Capabilities for Domain Index Operations

A new domain index callout updates table and column name metadata for a domain index during an ALTER TABLE ... RENAME or ALTER TABLE ... RENAME COLUMN command to ensure domain index metadata is kept up-to-date.

A partition-specific parameter string provides control over placement of domain index data during partition maintenance operations.

The SQL MERGE statement can now be used with a domain index.

1.1.3.4 Oracle Text Predicates and Aggregate Operators are Included in Rule Conditions

The Rules Manager feature of Oracle Database now supports the use of the Oracle Text CONTAINS operator with the SQL WHERE clause rule conditions.

Rules Manager can now compute aggregate values (SUM, AVG, MIN, MAX, and COUNT) using moving windows semantics. The window size can be based on time or number of events. The aggregate values for a group of events can be computed and tested against predefined or relative thresholds in rule conditions.

Rules can respond to real-world trends identified from a collection of events occurring over a particular length of time or a sliding window of time, such as for Business Activity Monitoring and Business Intelligence applications. For example, you can offer a promotion to a customer if the sum of purchases in the last three days is over $1000.00.

1.1.3.5 Oracle Text Predicates Included in Stored Expressions

The Expression Filter feature of Oracle Database now supports the use of the Oracle Text CONTAINS operator with the SQL WHERE clause stored expressions.

Stored expressions can evaluate text and XML data.

1.1.3.6 System-Managed Partitioning of Extensible Indexes

System-managed domain indexes (both non-partitioned and local partitioned) use Oracle Database to track and maintain the domain index storage tables during DDLs and partition maintenance operations on the base table. System-partitioned tables can be used as index storage tables for local domain indexes. System-managed domain indexes with range partitioning are supported in this release.

System-managed partitioning of domain indexes enhances local domain index manageability and performance.

1.1.4.1 Unicode 5.0 Support

The NLS data files for AL32UTF8 and AL16UTF16 character sets have been updated to match version 5.0 of the Unicode Standard character database.

With this enhancement, Oracle Database conforms to the newest version of the standard.

1.1.5.1 OracleJVM Java/JDK 5.0 Compatibility

OracleJVM supports Sun's JDK 1.5.

This feature provides portability of J2SE applications (stand-alone JDBC or middle-tier) into Oracle Database and the ability to take advantage of the key benefits of J2SE 5.0 such as the "declarative" programming style enabled by Java Annotations.

This feature allows sharing metadata of Java classes loaded by user class loader among different database user sessions.

1.1.5.2 OracleJVM JIT Configuration

The JIT is enabled by default (out-of-the-box) and does not need further configuration. However, it can be disabled or reenabled by setting JAVA_JIT_ENABLED to TRUE or FALSE in the parameter file or dynamically by altering the instance or the session, for example:

ALTER SYSTEM SET JAVA_JIT_ENABLED=true
or
ALTER SESSION SET JAVA_JIT_ENABLED=true

In addition, the OJVM_JIT_PROPERTIES are used to specify the list of properties, for example:

OJVM_JIT_PROPERTIES="key0-value0","key1=value1","keyword0"

Currently, supported properties are:

• DISABLE_OPTIM_* which are various switches to disable optimization passes.

• New dictionary table and Oracle Runtime methods for checking the compilation status of classes or methods.

• New column IS_COMPILED in USER_JAVA_METHODS, ALL_JAVA_METHODS, and DBA_JAVA_METHODS views set to YES or NO.

This feature allows fine-grained control on the behavior of the JIT. In addition, you have the ability to query the status of compiled classes and methods.

1.1.5.3 OracleJVM JIT (Transparent Native Java Compilation Without a C Compiler)

Enabled by default and similar to the JDK JIT, this feature compiles Java in the database natively and transparently without the need of a C compiler.

The JIT runs as an independent session in a dedicated Oracle server process. There is at most one compiler session per database instance and it is Oracle RAC-aware and amortized over all Java sessions.

The compilation session uses profiling feedback data gathered by all Java sessions to compile the most often used methods, so as to get the best overall performance. The executable binary persists in the database.

Java sessions just use the executable binaries of already compiled methods or discover newly compiled methods through an event polling performed by OracleJVM at regular intervals. When there is a compiled code for a given method, future executions of that method use the compiled version.

Conversely, when a Java class is invalidated, the persisted compiled code of the class or method is removed.

This feature brings two major benefits to Java in the database:

• Increased performance of pure Java execution in the database resulting from the transparent compilation of Java into native machine code, similar in behavior to a JDK JIT.

• Ease-of-use. Unlike the Static Native Java compiler (also known as NCOMP or JAccelerator) in previous releases, the new JIT Compiler (when enabled at database configuration level) is activated transparently, without the need of an explicit command, when Java is executed in the database.

In addition, this feature removes the need for a C compiler.

1.1.5.4 OracleJVM Ease-of-Use: JDK-Like Interface

Similar to the JDK VM, this feature provides a command-line interface to OracleJVM through new PL/SQL interfaces, for example:

FUNCTION runjava (cmdline VARCHAR2) RETURN VARCHAR2;
or
FUNCTION runjava_in_current_session (cmdline VARCHAR2) RETURN VARCHAR2;

Analogous to the JDK/JRE Java shell command, the JDK-like interface takes a subset of the JDK/JRE command-line syntax including:

[-options] classname [arguments...]
or
[-options] -jar jarfile [arguments...]

The supported options include:

• {cp|classpath} < ":" separated list of file system directories/JAR/ZIP to search for class files>

• D<name>=<value> set a system property

• Xbootclasspath:<":" separated directories/zip/jar files to search for bootstrap classes and resources>

• Xbootclasspath/a:<":" separated directories/zip/jar files to append to the end of bootstrap class path>

• Xbootclasspath/p:<":" directories/zip/jar files to prepend in front of bootstrap class path>

This feature makes Java easy to use using command-line syntax, which is similar to the JDK JAVA command line. In addition to database resident Java classes, this feature allows the execution in the database runtime environment of file system resident Java class, using the standard classpath syntax. In addition, it allows setting system properties using the standard -D syntax.

1.1.5.5 OracleJVM Ease-of-Use: Property Management Interface

This feature provides DBMS_JAVA functions to emulate JDK System property setting for the duration of the session. The settings are set_property, get_property, remove_property, and show_property.

Java developers can now emulate familiar JDK behavior such as system properties support when using Java in the database.

1.1.5.6 OracleJVM Ease-of-Use: Output Redirect

This feature provides DBMS_JAVA functions and procedures for controlling the redirection of Java output, for example:

{set | remove | enable | disable | query}_output_to_{sql | java | file}

And for enabling or disabling sending output to trace, for example:

trace {enable | disable}_output_to_trc

Java developers can now emulate familiar JDK behavior such as output redirect. In addition, they can now disable sending output to a trace file, which was not possible in previous releases.

1.1.5.7 OracleJVM Ease of Use: Database Resident Jars

This feature allows supporting JAR as a unit of operations (and as a result of support for signed JARs) using new loadjava options, -jarsasdbobjects and -prependjarnames.

When these new options are not used, loadjava explodes the jar into individual classes, while dropjava goes back to the original jar (from which the classes are loaded) and drop the classes each at a time.

Java developers using OracleJVM benefit from the same JDK features and behavior such as JAR support.

1.1.5.8 OracleJVM Ease of Use: Two-Tier Java Session Termination

This feature allows the termination of Java within a database session, with and without retention of system properties, using endsession and endsession_and_related_state.

Java developers can terminate Java in a session while keeping the property settings so as to speed up Java reinitialization within the same database session. This feature also allows immediate termination of Java sessions, without requiring the use of System.exit.

1.1.5.9 OracleJVM Utilities Enhancements

Enhancements to existing OracleJVM utilities (loadjava, dropjava, and ojvmjava) include the deprecation of least-frequently-used options of loadjava and the ability to load Java classes over the web (HTTP URL support). For example:

loadjava -u scott/tiger -r
-v http://my.server.com/this/is/the/path/my.jar

Enhancements to dropjava include:

• list-based deletion

• exception handling

• ability to connect to a different user

• invalid command reporting

ojvmjava has been extended to support the JDK-like command such as runjava and its options. For example:

ojvmjava -u scott/tiger -runjava -d <machine-name-or-ip>:1521:<sid> -t

And, then followed with:

$java -cp .<class>

In addition, a new standalone Class Closure Tool (ojvmtc) is provided for ensuring that a given list of file system resident classes and deployable JARs to be closed as much as possible before loading (for example, loadjava invocation).

The benefits are increased usability and simplified deployment of Java applications in Oracle Database.

1.1.5.10 OracleJVM Manageability: JMX Interface

This feature provides support for OracleJVM with the standard JMX interface.

Support for JMX interface provides:

• Ease-of-use (any standard JMX-enabled GUI or console)

• Runtime monitoring of OracleJVM (memory objects heaps, threads, GC and classes)

• Ability to trigger GC from the console

1.1.6.1 JDBC 4.0 Connection and Statement Enhancements

JDBC 4.0 introduces new methods for:

• Validating a connection.

• Setting and fetching client information on a connection.

• Returning a list of client properties supported by the driver.

• Allowing an application to mark that a statement should not be pooled.

• Allowing a statement pool manager to register a StatementEventListener on the pooled connection.

In addition, it provides a new StatementEventListener interface and a new StatementEvent class.

These JDBC 4.0 enhancements improve the scalability of JDBC applications and provide better control and management of statement objects.

1.1.6.2 JDBC 4.0 Support for New Data Types

This feature provides new standard types including java.sql.XMLType, java.sql.RowID, and java.sql.Nclob to support the National Character Set (NCHAR, NVARCHAR, LONGVARCHAR). In addition, the JDBC 4.0 implementation enhances BLOB and CLOB data type manipulation.

Standard support for new SQL types significantly increases the portability of JDBC applications across databases.

1.1.6.3 JDBC Support for ANYTYPE, ANYDATA, and ANYDATASET

The following support has been added to JDBC in this release:

• ANYTYPE which represents any SQL type including persistent database scalar or object types, and transient unnamed types.

• ANYDATA as a table column type to store heterogeneous values (built-in and user-defined) within the same column, along with the metadata.

• ANYDATASET which represents a set of data instances of the same type, along with the metadata.

The benefits include:

• Mapping any Java types to a general purpose SQL data type (ANYTYPE).

• Persisting Java objects to the same database table column (ANYDATA).

• Mapping any Java collections into ANYDATASET.

1.1.6.4 JDBC Support for Continuous Query Notification

This feature allows Java Applications or Containers to subscribe to SQL queries watch list in the database and be notified when:

• DML or DDL changes affect the objects associated with the queries, or

• DML or DDL changes affect the result set

The notifications are published when the transaction (DML or DDL) commits.

Java Applications and Container prevents unnecessary database querying and implements efficient and consistent result set caching and cache invalidation.

1.1.6.5 JDBC Support for Database Diagnosability

Through this feature, JDBC provides better support for logging, diagnosability and DMS monitoring:

• Logging has been reimplemented to fully utilize java.util.logging. This allows you to selectively log events that occur during the execution of the JDBC driver code such as SQL exceptions, SQL statement executions, or entry or exit from internal JDBC methods.

• JDBC support for performance monitoring uses DMS to capture performance-critical JDBC events including:

  • Connection cache statistics

  • Connection creation

  • Statement cache statistics

  • Query execution

  • Result fetch

  • Oracle RAC events

  • Load balancing statistics

  • New Oracle Database 11g features

• JDBC support for Diagnosability Management consists of an MBean, oracle.jdbc.DiagnosabilityMBean. This MBean provides the means to enable and disable JDBC logging and provides additional statistics about JDBC internals.

This feature simplifies JDBC logging and makes the interpretation of the results easy. Dynamic Monitoring System (DMS) metrics give Java applications the ability to track JDBC events and performance metrics, as well as a programmatic control and configuration of diagnosability features.

1.1.6.6 JDBC Support for Database Startup and Shutdown

You can now perform database startup or shutdown using the following JDBC Oracle Connection methods:

• DBSTARTUP_NO_RESTRICTION

• DBSTARTUP_FORCE

• DBSTARTUP_RESTRICT

• DBSHUTDOWN_CONNECT

• DBSHUTDOWN_TRANSACTIONAL

• DBSHUTDOWN_TRANSACTIONAL_LOCAL

• DBSHUTDOWN_IMMEDIATE

• DBSHUTDOWN_ABORT

• DBSHUTDOWN_FINAL

Note that you must be connected to a dedicated server as SYSOPER or SYSDBA in PRELIM_AUTH mode.

1.1.6.7 JDBC Support for Native Streams AQ Protocol

This feature exposes a new Java package (oracle.jdbc.aq) that leverages the new native JDBC APIs for Streams AQ. This package contains the following classes and interfaces:

• AQQueue

• AQMessage

• AQAgent

• AQDequeueOptions

• AQEnqueueOptions

• AQMessageProperties

• AQRawPayload

• AQObjectPayload

• AQNotificationEvent

• AQNotificationListener

• AQNotificationRegistration

Java and JMS applications run faster (using Native AQ operations as opposed to PL/SQL procedures) when accessing Streams AQ and OJMS queues. In addition, these APIs allow asynchronous notifications of AQ events and exchange rich message payloads including RAW data type, ANYDATA data type, and OBJECT data type.

1.1.6.8 JDBC-Thin Support for Oracle Advanced Security

This feature provides complete JDBC-Thin support for Oracle Advanced Security in terms of encryption, password authentication, strong authentication, and data integrity or checksum.

New security features include Secure Sockets Layer (SSL), Remote Authentication Dial-In User Service (RADIUS) and Kerberos authentication, AES encryption, and SHA1 checksum algorithm. Advanced security support in JDBC-Thin simplifies the development and deployment of secure Java applications.

1.1.6.9 SQLJ: Enhanced Profile Print Option of the Customizer

SQLJ customizer is used during ISO standard code generation. The customizer tailors SQLJ profiles for a particular database implementation and vendor-specific features and data types. The profile print option (-P-print) of SQLJ has been enhanced to print all the customizer options that were passed into the SQLJ translator.

This feature provides information about customizations that SQLJ has received during the precompilation phase to help diagnose any problems that may arise and are specific to different customizations.

1.1.6.10 SQLJ: Outline Generation Option

An application can support SQL execution plan stability by using the outline feature of Oracle. An outline is implemented as a set of optimizer hints that are associated with the SQL statement. If the use of the outline is enabled for the statement, Oracle automatically considers the stored hints and tries to generate an execution plan in accordance with those hints.

A new translation time option is provided in SQLJ to generate and run a SQL file containing the Oracle outline creation commands for all the SQL statements present in a SQLJ application.

This feature helps in ensuring the expected behavior of a database query at development time and helps to better diagnose problems.

1.1.6.11 SQLJ: Support for JDK 1.5

SQLJ applications can now be translated and run under JDK 1.5.

The JDK 1.4 and JDK 1.5 specific language structures outside the #sql region are recognized by the translator.

1.1.6.12 SQLJ: XA Support

The javax.sql.XADataSource interface of JDBC outlines standard functionality of XA data sources. An XA data source is a factory for XA connections. Oracle JDBC implements the XADataSource interface through the OracleXADataSource class. The getConnection() method of the OracleXADataSource class returns an XA connection to the underlying data source.

The benefit of this feature is that these connections can be used in SQLJ applications by creating the appropriate connection contexts in SQLJ.

1.1.7.1 General Visual Studio 2005 Support

This feature provides users of Visual Studio 2005 the same support currently offered for Oracle Develop Tools (ODT) users using Visual Studio 2003. The changes include using different icons and integrating visually with the Visual Studio 2005 look and feel.

This feature makes it easy for customers to continue using ODT when they migrate from Visual Studio 2003 to the Visual Studio 2005 environment.

1.1.7.2 Oracle PL/SQL Debugger in Visual Studio

The Oracle PL/SQL debugger is now integrated directly into Visual Studio. Developers can debug code, set breakpoints, and watch variables exactly as they would for any other language. Three types of debugging are supported:

• Direct Database Debugging

  The developer runs the stored procedure from the Oracle Explorer inside of Visual Studio and enters in the parameters.

• Multi-Tier Application Debugging

  The developer steps from .NET code directly into PL/SQL and back again with the parameters automatically being passed.

• External Application Debugging

  The developer runs any 9.2 or later Oracle application that can be located anywhere (for example, from SQL*Plus). When it calls the stored procedure, the break point in Visual Studio fires and the developer can begin debugging with the stored procedure parameters being passed automatically.

This feature enables developers to seamlessly debug .NET applications that make calls to PL/SQL stored procedures. Developers can debug both .NET code and PL/SQL without having to leave Visual Studio. They can also debug PL/SQL stored procedures that are called from any arbitrary Oracle program, written in any language, and located on the same machine or on a remote machine.

1.1.7.3 Support for Visual Studio 2005 Data Sources Window and Designers

This feature exposes the DDEX interface to enable some Visual Studio 2005 designers as well as integrate with the Data Sources window to enable the new style drag-drop code generation in Visual Studio 2005.

1.1.8.1 Provider-Independent API Using Base Classes and Factories

ODP.NET inherits from the System.Data.Common namespace to provide access through a single API to databases across multiple providers.

ADO.NET developers can build applications that work with multiple vendors' database servers with one set of data access code and with a minimum of vendor-specific code. Developers can now be more productive when building one set of code that works against multiple vendors' databases.

1.1.8.2 Connection String Builder

ODP.NET provides a strongly typed connection string builder class that inherits from DbConnectionStringBuilder. The connection string builder lets developers programmatically create syntactically correct connection strings based on user input, as well as parse and rebuild existing connection strings by using class methods. Connection string builders provide strongly-typed properties that correspond to known key/values pairs.

ODP.NET connection strings are now more secure and more manageable. Because connection string parameters can now be strongly typed, they are less subject to string injection attacks. Connection string parameters can now be referenced in configuration files or as ODP.NET properties, making managing connection information easier.

1.1.8.3 OracleDataAdapter Batch Processing

During updates to Oracle Database, the ODP.NET OracleDataAdapter can batch a set of rows back to the database per round trip. The number of rows per round trip can be set by the developer.

This feature enhances application performance by reducing the number of round trips to the database when applying updates from the DataSet.

1.1.8.4 Row-Level Change Notifications

.NET developers often used client-side cached data. When another user changes the original data on the server, the database sends a change notification message to the client, indicating that the client data is now invalid. Previously, change notifications were registered at the table level. In this release, developers can register their queries at the row level so that they are alerted only when the rows they selected have changed.

.NET clients can now receive more accurate change notification alerts.

1.1.8.5 Schema Discovery

ODP.NET schema discovery allows applications to request and return information about the database schema to the client. Different database schema elements such as tables, columns, and stored procedures are exposed through the GetSchema methods of the OracleConnection class.

ODP.NET can retrieve a set of generic and Oracle-specific schema information.

Schema discovery supplies a common API for all ADO.NET 2.0 drivers to retrieve database schema information to the client application.

1.1.8.6 Data Source Enumeration

The ODP.NET OracleDataSourceEnumerator class provides a list of active Oracle database servers.

Data source enumeration can be retrieved programmatically in a generic manner common to all ADO.NET 2.0 providers.

1.1.8.7 Faster LOB Retrieval

ODP.NET now makes fewer round trips to the database server to retrieve LOB information.

ODP.NET LOB retrieval performance is now faster.

1.1.8.8 Improved Parameter Context Caching in Statement Caching

For statement caching, ODP.NET now caches parameter contexts after the first statement execution.

Many applications execute the same SQL or PL/SQL statement repeatedly. In the first execution of the statement, ODP.NET can now cache parameter contexts so that they can be reused. This improves the performance of subsequent statement executions.

1.1.9.1 Faster Performance

Oracle Provider for OLE DB has implemented a number of new features in its provider to improve query performance for the end user.

1.1.9.2 Improved Metadata Caching

For metadata caching, Oracle Provider for OLE DB now caches the metadata after the first execution when statement caching is turned off. This is useful when a large amount of column metadata is retrieved with a small amount of row data. Oracle Provider for OLE DB has also improved metadata caching when statement caching is turned on.

Many applications execute the same SQL or PL/SQL statement repeatedly. In the first execution of the statement, Oracle Provider for OLE DB can cache a query's metadata so that it can be reused. This improves the performance of subsequent statement executions.

1.1.9.3 Improved Statement Caching

For statement caching, Oracle Provider for OLE DB now caches large fetch arrays and OCI error handles after the first execution.

Many applications execute the same SQL or PL/SQL statement repeatedly. In the first execution of the statement, Oracle Provider for OLE DB can now cache a statement's parse information so that it can be reused. This improves the performance of subsequent statement executions.

1.1.10.1 Database Resident Connection Pooling (DRCP)

Prior to this release, session sharing capabilities were available for multithreaded applications only. With this new feature, different application processes can share sessions within the same host machine as well as across multiple host machines. The pool is maintained on the database server by a new background process.

This feature is useful for applications that cannot be deployed as multithreaded (for example, PHP applications in an Apache Web server environment) and database requests are done in a session state independent manner. These applications no longer have to hold the connections to the database persistently, thereby increasing the scalability of the database.

The pool is managed by the DBA and the OCI and OCCI applications can reuse the sessions by specifying a connection class name within which the application leaves sessions of similar state.

1.1.11.1 XA API Available Within PL/SQL

The functionality available in the XA interface to support transactions involving multiple resource managers, such as databases and queues, is now available from PL/SQL.

Application developers are now able to switch or share transactions across SQL*Plus sessions or processes using PL/SQL.

1.1.11.2 Allow Sequences in PL/SQL Expressions

In the previous release of Oracle Database, when a PL/SQL program needed to get a value from syntax, it used SQL. For example:

DECLARE n NUMBER;
BEGIN
SELECT Seq.Nextval INTO n FROM Dual;
END;

This posed a usability problem for PL/SQL programmers and causes runtime performance and scalability problems for the application particularly because it requires the use of a cursor.

In Oracle Database 11g, it is now possible to simply use Seq.Nextval in a PL/SQL expression. For example:

DECLARE n NUMBER := Seq.Nextval;
BEGIN
...

The same is possible for the CURRVAL pseudocolumn.

This feature brings improved usability for the PL/SQL programmer and improved runtime performance and scalability.

1.1.11.3 Enhancements to the Regular Expression Built-Ins

There is new syntax to access the nth subexpression in REGEXP_SUBSTR and REGEXP_INSTR. This is useful if, for example, the matching string is always surrounded by tags like <string> and </string> but you want to extract just what lies between these tags. REGEXP_SUBSTR returns the subexpression. REGEXP_INSTR returns the position.

The new built-in REGEXP_COUNT returns the number of times the pattern is matched in the input string.

This new functionality is available both in SQL and in PL/SQL.

The benefit is increased functionality for the regular expression built-ins. Earlier applications that needed this functionality had to write relatively elaborate application code which is no longer the case.

1.1.11.4 Functional Completeness of Dynamic SQL for PL/SQL

In Oracle Database 11g, native dynamic SQL now supports statements bigger than 32K characters by allowing a CLOB argument. Also included with this feature:

• DBMS_SQL.PARSE() gains a CLOB overload

• A REF CURSOR can be converted to a DBMS_SQL cursor and vice versa to support interoperability

• DBMS_SQL supports the full range of data types (including collections and object types)

• DBMS_SQL allows bulk binds using user-define collection types

The features for executing dynamic SQL from PL/SQL had some restrictions in Oracle Database 10g. DBMS_SQL was needed for Method 4 scenarios but it could not handle the full range of data types and its cursor representation was not usable by a client to the database. Native dynamic SQL was more convenient for non-Method 4 scenarios, but it did not support statements bigger than 32K. Oracle Database 11g removes these and other restrictions to make the support of dynamic SQL from PL/SQL functionally complete.

1.1.11.5 Method Invocation Scoping Operator

A subtype usually adds specific data attributes to its more generic supertype. Typically, a NOT FINAL member procedure, for example, Show_Attributes(), is implemented at each level in the type hierarchy. It is natural to implement this at one level by reusing the implementation at the parent level and by adding new code just for the attributes of the subtype.

Oracle Database 11g supports the ANSI SQL generalized-invocation syntax to allow the following:

(<type-instance> AS <type-name>).<method-name>.

Without this syntax, programmers were forced to use a cumbersome workaround which relied on using static subprograms rather than member subprograms.

This feature brings the implementation of object-oriented programming in Oracle Database closer to completeness.

1.1.11.6 Named and Mixed Notation for User-Defined PL/SQL Subprogram in a SQL Statement

Consider a schema-level function f() declared as:

FUNCTION f(
p1 IN INTEGER := 1,
p2 IN INTEGER := 2,
...
pn IN INTEGER := 99)
RETURN INTEGER

Beginning in this release, it is now possible to invoke the function in a SQL statement. For example, named notation syntax is:

SELECT f(pn=>3, p2=>2, p1=>1) FROM dual

Or, mixed notation is:

SELECT f(1, pn=>3) FROM dual

In previous releases, attempting named or mixed notation resulted in an error.

The benefit is improved usability when a PL/SQL function, with many default parameters, is invoked in a SQL statement and only one or a small number of the actual parameters need to differ from their default values.

1.1.11.7 New Compound Trigger Type

A compound trigger has a section for each of the BEFORE STATEMENT, BEFORE EACH ROW, AFTER EACH ROW, and AFTER STATEMENT timing points. All of these sections can access a common PL/SQL state. The common state is created when the firing statement starts and destroyed when it completes, even when the firing statement causes an error. A compound trigger can be used to speed performance in an auditing scenario where audit rows can be accumulated in the EACH ROW section and bulk inserted to the audit table in the AFTER STATEMENT section. A similar approach is sometimes required to avoid the mutating table error. The alternative to using compound triggers is to model the common state with an ancillary package. This approach is both cumbersome to program and subject to memory leak when the triggering statement causes an error and the AFTER STATEMENT trigger does not fire.

This feature brings improved usability for the PL/SQL programmer and improved runtime performance and scalability.

1.1.11.8 PL/Scope

PL/Scope allows you to browse PL/SQL source code analogously to the way that Cscope (see http://cscope.sourceforge.net/) allows you to browse C source code. You can search for and display all types of definitions, declarations, assignments and references in the PL/SQL source code.

The PL/SQL compiler can optionally derive the metadata needed to support PL/Scope and store it in the database catalog. The metadata takes into account the nuances of the language, including scoping and overloading. You can generate reports (especially hyperlinked HTML reports) with supplied report generators. This feature is exposed through Oracle SQL Developer's interactive PL/SQL development environment.

PL/Scope supports increased developer productivity, especially for those who need to maintain someone else's code.

1.1.11.9 PL/SQL CONTINUE Statement

Many programming languages allow transfer of control either out of the loop entirely or within a loop back to a new iteration. In previous releases, PL/SQL only allowed transfer of control out of the loop using the EXIT statement. In Oracle Database 11g Release 1 (11.1), the new CONTINUE statement implements the ability to transfer control within a loop back to a new iteration.

Many algorithms require the loop control that this new CONTINUE statement now provides. With this explicit feature, you are no longer required to code several lines of PL/SQL and maintain commonly-adopted style rules.

1.1.11.10 PL/SQL Hierarchical Profiler

PL/SQL Hierarchical Profiler identifies hot spots and performance tuning opportunities in PL/SQL database applications. It reports the dynamic execution program profile organized by subprogram calls. SQL and PL/SQL execution times are accounted for separately. PL/SQL Hierarchical Profiler requires no special source or compile time preparation.

Start and stop procedures in the new package, DBMS_HPROF, control the recording of raw PL/SQL Hierarchical Profiler data. Then other subprograms in this package are used to transform the raw data into schema-level tables to allow the generation of reports. For example, hyperlinked XML or HTML reports.

This feature is part of Oracle SQL Developer's interactive PL/SQL development environment.

Programmer productivity and program performance are both enhanced because PL/SQL Hierarchical Profiler directs tuning effort towards those PL/SQL functions that will benefit most.

1.1.11.11 PL/SQL Inlining Optimization

Modern PL/SQL software includes many procedures frequently called in various programs. The power of the PL/SQL optimizing compiler is increased by incorporating the inlining optimization (replacement of a procedure call with a copy of the procedure body). The revised code executes more quickly because call overhead has been eliminated and because further optimizations are possible when the compiler knows the actual values with which the inlined procedure is called.

Performance gains can be substantial. The compiler does this optimization automatically. However, you may control the optimization in detail when necessary. Many PL/SQL programs execute faster, as a direct consequence of upgrade, simply by recompiling existing units.

1.1.11.12 SIMPLE_INTEGER Data Type

The new PL/SQL SIMPLE_INTEGER data type is a binary integer for use with native compilation which is neither null checked nor overflow checked. The range is all integer values in the inclusive interval [-2147483648..2147483647] and does not include a null value. These rules allow a direct hardware implementation when used in combination with PL/SQL native compilation.

The SIMPLE_INTEGER data type yields significant performance benefits compared to PLS_INTEGER. It can be used when the value will never be null and when overflow check is not needed.

1.1.12.1 Binary XML Support for Java

The new XDK binary XML processors include a binary XML encoder, a binary XML decoder, and a token manager to convert the schema or nonschema-based binary XML stream to and from XML 1.0 text and SAX events. The format is the same as the format used within Oracle XML DB.

This feature allows XML processing applications to leverage a compact, pre-parsed XML format which could save in memory and CPU consumption as well as network bandwidth. With a single format supported by Oracle XML DB and Oracle XDK and the Oracle XML DB support within the binary XML processors, this feature allows the performance optimization across tier XML applications.

1.1.12.2 Binary XML Support for Oracle XML DB

No single approach to persisting XML can solve all of the possible use cases presented by the XML application development paradigm. Binary XML (which is a way of representing the structure of an XML document) adds a third storage model for persisting XML in the database. It complements, rather than replaces, the existing object storage and text storage models.

Binary XML has two significant benefits:

• It enables significant optimizations of XML operations whether or not an XML schema is available.

• It allows the internal representation of XML to be the same regardless of whether the XML is in disk, in memory, or on the wire.

As with other storage mechanisms, the application developer can continue to use XMLType and its associated methods and operators without regard to the details of storage.

1.1.12.3 Configurable DOM Support for Java

The following options are provided for building Document Object Model (DOM):

• static final String ACCESS_MODE = "DOM_ACCESS_MODE";

• static final String READ_ONLY = "READ_ONLY";

• static final String UPDATEABLE = "UPDATEABLE";

• static final String FORWARD_READ = "FORWARD_READ";

• static final String STREAMING = "STREAM_READ";

This feature enables XML applications to achieve maximum efficiency in terms of memory usage and performance by configuring the DOM building with different access patterns such as read-only, forward read, or streaming. This feature prevents light-weight XML applications from having to deal with a heavy DOM object tree.

1.1.12.4 Event-Based XML Pull Parsing for C

XML pull parsing supports the stream-based parsing model which limits memory footprints by not preserving any document states in memory. Compared with SAX, the new pull parsing model only parses events requested by the application and accepts multiple input XML documents.

This feature improves the C XML processing performance and ensures the scalability with the new stream-based XML pull parsing. This feature also limits memory footprints by not preserving any document states in memory and improves the performance of the XML processing by only parsing stream events requested by the application. The pull parsing also makes the XML processing easy-to-use by allowing one parsing process to handle multiple XML documents.

1.1.12.5 High Performance XPath Query for Java

Support JAXP 1.3 APIs for query XML content using XPath with the following extensions:

• Supports variable resolver in both static and dynamic context.

• Allows users to register runtime context.

This feature improves the performance and the usability of Java XPath/XSLT implementation.

1.1.12.6 In-Place XML Schema Evolution

This feature lets users make changes to a registered XML schema without having to unload and reload all of the XML documents that are compliant with the XML schema. Existing documents are not modified during in-place evolution, so only changes that do not invalidate the existing corpus of XML documents are permitted.

This feature enables rapid responses to changes in requirements by allowing certain common changes to XML schemas be made without having to unload and reload data. Organizations can take advantage of the benefits of XML schema-based XML storage in environments where the dynamic nature of the application or the large volume of XML content might make unloading and reloading the entire XML corpus to accommodate a change in the XML schema unrealistic.

The feature greatly reduces the overhead involved in making changes to XML schema in the majority of cases. Evolving an XML schema in cases where there are large number of documents compliant with the XML schema is a very expensive process that can take days to complete. In-place XML schema evolution allows the majority of common changes to be made almost instantaneously, with zero downtime, resulting in significant cost savings for the organization.

1.1.12.7 Mid-Tier XQuery Engine Performance Improvements

XQuery performance improvements have been made in this release.

XQuery performance improvements for the mid-tier speed up applications relying on the mid-tier XQuery services.

1.1.12.8 Pluggable DOM Support for Java

The pluggable DOM splits the DOM implementation into two separate layers, the DOM API layer and the data layer. The API layer consists of a lightweight, transient node fragment that links to underlying data, which can be backed by external storage for scalability. The data has two varieties, internal data and plug-in data. The plug-in data support is through the InfosetReader implementations.

This feature allows XML applications to build DOM based on different forms of XML data through a unified interface. This feature improves portability of the XML applications.

1.1.12.9 Scalable and High Performance XML Validation for C

The stream-based XML Schema validation allows XML applications to validate XML content against XML schemas in XML pull parsing process.

High performance XML processing and its scalability are very critical for enterprise XML applications. This feature improves the C XML processing performance and ensures the scalability with the new stream-based XML Schema validation.

1.1.12.10 Scalable DOM Support for Java

This feature ensures the scalability of Database Object Model (DOM) processing for XML applications with the following support:

• Allows XML applications to process large XML documents by loading and saving DOM nodes in memory and using a page manager for physical binary data management.

• Allows storing DOM updates with references to the source data in Binary XML.

• Allows multiple applications sharing same DOM data source and operating on a lightweight, transient node fragment that links to underlying data, which can be backed by external storage for scalability.

• Supports Binary XML as the data source or the output.

• Supports shadow copy in DOM (cloneNode() or importNode()) which does not require the whole content being deep copied.

Scalability is the key to the success of the enterprise XML applications. This feature ensures the scalability of DOM processing for XML applications.

1.1.12.11 Unified Java API for XML

The new unified Java API for XML allows mid-tier Java programs to leverage all of the benefits of XMLType by providing a disconnected mode of operation that allows XMLType to be used with a session pool model of connection management.

This allows the XMLType object to be disconnected from the database session that was used to create it.

This feature improves Java programmer productivity by allowing a single API to be used to manipulate XML content stored in Oracle XML DB and XML content stored in other sources.

Unified Java API also provides a disconnection mode of operation that allows XMLType to be used in a mid-tier environment where session pooling makes it impractical to hold the database connection that was used to instantiate the XMLType.

1.1.12.12 Unified Java API for XML

This feature allows mid-tier Java programs to leverage all of the benefits of XMLType by providing a disconnected mode of operation that allows XMLType to be used with a session pool model of connection management. This allows the XMLType object to be disconnected from the database session that was used to create it.

This feature improves Java programming productivity by allowing a single API to be used to manipulate the XML content stored in Oracle XML DB and the XML content stored in other sources.

This feature provides a disconnection mode of operation that allows XMLType to be used in a mid-tier environment where session pooling makes it impractical to hold the database connection that was used to instantiate the XMLType.

1.1.12.13 XMLDiff Support for C

The XMLDiff detects the differences between the two XML documents and represents the difference in XML. The XMLPatch takes the differences created by XMLDiff and applies the changes on the target XML document.

This feature allows high performance XML document comparison and user controlled updates. With the support for detecting differences between two XML documents, XML applications can easily control document versions, merge and synchronize the content. The feature also reduces the processor demand by handling only the changed data, reduces bandwidth demands by transmitting only updates, and increases security by splitting sensitive data into separate streams which can then be reassembled on delivery.

1.1.12.14 XMLIndex Enhancements

This feature provides an asynchronous mode for XMLIndex, where the indexing operations take place separately from the INSERT or UPDATE operations. Applications no longer have to wait for indexing to complete (which, for large documents, can be a significant operation) before being able to complete a transaction.

This feature also provides the ability to define the set of XPath expressions that are indexed by a given XMLIndex. The subset can be defined using INCLUDE or EXCLUDE syntax.

Subsetting XPath expressions that are indexed allows organizations to balance index size with disk usage, and to balance performance on insert with flexibility on retrieval.

This feature provides more flexibility for the organizations using XMLIndex. XMLIndex can be used in a wide range of applications. The feature improves application performance by allowing indexing to take place separately from INSERT and UPDATE operations. It also allows organizations to balance performance, flexibility, and disk usage when using XMLIndex.

1.2.1.1 Browser-Based Enterprise Manager Integrated Interface for LogMiner

This feature now makes it possible to use the browser-based Oracle Enterprise Manager Database Control interface for LogMiner. In prior releases, administrators were required to use the standalone Java Console to use LogMiner. The Console was not integrated with the rest of Enterprise Manager and was cumbersome to install. With this new interface, administrators have a task-based, intuitive approach to using LogMiner.

This new feature improves the manageability of LogMiner.

1.2.2.1 Fast-Start Failover for Maximum Performance Mode in a Data Guard Configuration

This feature enables fast-start failover to be used in a Data Guard configuration that is set up in the maximum performance protection mode. Since there is some possibility of data loss when a Data Guard failover occurs in maximum performance mode, administrators can now choose not to do a fast-start failover if the redo loss exposure exceeds a certain amount.

This enhancement allows a larger number of disaster recovery configurations to take advantage of Data Guard's automatic failover feature.

1.2.2.2 Compression of Redo Traffic (Only for Gap Resolution) Over the Network in a Data Guard Configuration

Archived redo logs, if needed to be transmitted to the standby database in an Oracle Data Guard configuration, can now be compressed as they are sent over the network.

This feature improves the network bandwidth utilization in an Oracle Data Guard configuration.

1.2.2.3 Real-Time Query Capability of Physical Standby Database

It is now possible to query a physical standby database while Redo Apply is active.

This new capability increases your return on investment in Data Guard technology because a physical standby database can now be used to offload queries from the primary database in addition to providing data protection.

1.2.2.4 Fast Role Transitions in a Data Guard Configuration

This feature improves the performance of Oracle Data Guard role transitions.

The benefit is increased flexibility and manageability of your disaster recovery configurations using Oracle Data Guard.

1.2.2.5 User Configurable Conditions to Initiate Fast-Start Failover in a Data Guard Configuration

This feature enables the administrator to choose and configure a list of conditions which, if they occur, will initiate fast-start failover.

This feature increases the flexibility and manageability of customers' disaster recovery configurations.

1.2.3.1 Dynamic Setting of Oracle Data Guard SQL Apply Parameters

It is now possible to configure specific SQL Apply parameters without requiring SQL Apply to be restarted.

This ability improves the manageability, uptime, and automation of a Logical Standby configuration.

1.2.3.2 Enhanced Data Guard Broker Based Management Framework

The enhancements for this release include:

• Data Guard Broker improved logging and tracing

• OMF support for Data Guard Broker configuration files

• Data Guard Broker integration with database startup

• Data Guard Broker support for advanced redo transport settings

• Data Guard Broker support of prepared switchovers for Logical Standby

These enhancements make it possible to use Data Guard Broker in a wider variety of disaster recovery configurations.

1.2.3.3 Enhanced Data Guard Management Interface (Using SQL*Plus)

This feature makes it much simpler to use Oracle Data Guard administration-related SQL statements and initialization parameters.

The benefit of this feature is increased flexibility and manageability of disaster recovery configurations using Oracle Data Guard.

1.2.3.4 Histogram for Redo Transport Response Time in a Data Guard Configuration

This feature advises on a suitable value for NET_TIMEOUT based on the response times for SYNC redo transport destinations.

The benefit is an increase in the flexibility and manageability of disaster recovery configurations using Data Guard.

1.2.3.5 Snapshot Standby

A physical standby database can be open temporarily (that is, activated) for read/write activities such as reporting and testing. A physical standby database in such a state can still receive redo data from the primary database, thereby providing data protection for the primary database while still in the reporting database role.

This feature provides the combined benefit of disaster recovery, and reporting and testing using a physical standby database.

1.2.3.6 Strong Authentication for Data Guard Redo Transport

Redo transport network sessions can now be authenticated using SSL. This provides strong authentication and makes the use of remote login password files optional in a Data Guard configuration.

The benefit is increased security of your disaster recovery configurations using Oracle Data Guard.

1.2.3.7 Enhanced DDL Handling in Oracle Data Guard SQL Apply

SQL Apply executes in parallel (based on availability of parallel servers) DDLs that were executed with a parallel hint at the primary database.

1.2.3.8 Enhanced Oracle RAC Switchover Support for Logical Standby Databases

When switching over to a logical standby database where either the primary database or the standby database is using Oracle RAC, the SWITCHOVER command can be used without having to shut down any instance either at the primary or at the logical standby database.

1.2.4.1 Oracle Scheduler Support in Data Guard SQL Apply

It is now possible to use a logical standby database to provide data protection for a primary database in which the DBMS_SCHEDULER package was executed.

This enhancement allows Data Guard SQL Apply (logical standby database) to be used for a wider variety of applications.

1.2.4.2 Fine-Grained Auditing (FGA) Support in Data Guard SQL Apply

It is now possible to use a logical standby database to provide data protection for primary databases using Oracle Fine-Grained Auditing (FGA).

This new support allows Data Guard SQL Apply (logical standby database) to be used for a wider variety of applications. In this case, applications with security features built-in.

1.2.4.3 Support Transparent Data Encryption (TDE) with Data Guard SQL Apply

It is now possible to use a logical standby database to provide data protection for the primary database tables with Transparent Data Encryption (TDE).

This new support allows Data Guard SQL Apply (logical standby database) to be used for a wider variety of applications. In the case, applications with security features built-in.

1.2.4.4 Support XMLType Data Type (Only CLOB) in Data Guard SQL Apply

It is now possible to use a logical standby database to provide data protection for primary database tables with the XMLType data type (only the CLOB representation).

This new support allows Data Guard SQL Apply (logical standby database) to be used in a wider variety of applications.

1.2.4.5 Virtual Private Database (VPD) Support in Data Guard SQL Apply

It is now possible to use a logical standby database to provide data protection for primary database tables with Oracle Virtual Private Database (VPD).

This new support allows Data Guard SQL Apply (logical standby database) to be used for a wider variety of applications. In this case, applications with security features built-in.

1.2.5.1 Flashback Transaction

Flashback transaction is a new feature that can easily back out a transaction and its dependent transactions. This recovery operation utilizes undo data to create and execute the corresponding, compensating transactions that revert the affected data back to its original state.

This feature increases availability during logical recovery by easily and quickly backing out a specific transaction or set of transactions and their dependent transactions, with one command, while the database remains online.

1.2.5.2 SMP Scalable Redo Apply

Redo Apply performance has been significantly improved as a result of some internal optimizations in media recovery. Wait event generation in media recovery has also been enhanced.

This feature enables faster performance of media recovery and also Data Guard Redo Apply (physical standby database), thereby enabling real-time disaster recovery for more voluminous and highly transactional databases.

1.2.5.3 Transportable Databases Between Linux and Windows

This feature enables simple moving of databases between Linux and Windows.

This results in transportable databases between Linux and Windows enabling cross-platform physical standby and simple moving of databases between Linux and Windows.

1.2.6.1 Flashback Data Archive

The historical data store provides the ability to store and track all transactional changes to a record for the duration of its lifetime.

The database maintains every state of the record during its lifetime. You no longer need to build this intelligence into the application. This feature is useful for compliance reporting and audit reports.

1.2.7.1 Automatic Reporting of Corrupt Blocks

During instance recovery, if corrupt blocks are encountered, the DBA_CORRUPTION_LIST is automatically populated.

Block validation occurs at every level of backup, media recovery, and instance recovery.

1.2.7.2 Block Media Recovery Performance Improvements

In prior releases, block media recovery needed to restore original block images from disk or tape backup before applying needed archived logs. In this release, if flashback logging is enabled and contains older, uncorrupted blocks of the corrupt blocks in question, then these blocks will be used, speeding up the recovery operation.

The benefit is a reduction in the time it takes for block media recovery by restoring block images from flashback logs instead of from disk or tape backups.

1.2.7.3 Creation and Recovery of Long-Term Backups Improved

Long-term backups created with the KEEP option only back up the archive logs needed to make the backup consistent. No further archive log backups are retained.

This feature reduces the archive log backup storage needed for online, long-term KEEP backups by only backing up the archive logs needed to recover the long-term backup.

1.2.7.4 End-to-End Redo Validation

This feature enhances redo validation.

End-to-end redo validation provides foreground redo checksum at generation time. This improves CPU and cache efficiency for the checksum computation.

1.2.7.5 Merge Catalogs

The new IMPORT CATALOG command allows one catalog schema to be merged into another, either the whole schema or just the metadata for specific database IDs.

This feature simplifies catalog management by allowing separate catalog schemas, created at different versions, to be merged into a single catalog schema.

1.2.7.6 Parallel Backup and Restore for Very Large Files

Backups of large data files now use multiple parallel server processes to efficiently distribute the workload for each file. This is especially useful for very large files.

This features improves the performance backups of large data files by parallelizing the workload for each file.

1.2.8.1 Data Recovery Advisor

Data Recovery Advisor is a built-in tool that automatically diagnoses data failures and reports the appropriate repair options.

Data Recovery Advisor ensures the correct repair is performed for a failure and decreases the amount of time to recover from a failure.

1.2.8.2 Enhance LogMiner to Support Automatic Data Repair

This new feature adds support for Automatic Data Repair (ADR).

Automatic Data Repair (ADR) requires the ability to lookup metadata when the database is not available. This project enhances the existing functionality in LogMiner to support ADR.

1.2.8.3 Integrated Protection From Various Data Corruptions

Through the creation of a new initialization parameter, DB_ULTRA_SAFE={off, data_only, data_and_index}, this capability provides an integrated mechanism to offer protection from various possible data corruptions that may impact Oracle Database. This parameter controls the setting of other related parameters such as DB_BLOCK_CHECKING, DB_BLOCK_CHECKSUM, and DB_LOST_WRITE_PROTECT, and also controls other data protection behavior within Oracle Database (for example, requiring ASM to do sequential mirror writes).

This feature improves the data protection capabilities of Oracle Database by offering an integrated mechanism to control various levels of protection from data corruptions. By making it possible to detect data corruptions in a timely manner, this feature also provides critical high availability benefits for Oracle Database.

1.2.8.4 Lost-Write Detection Using a Physical Standby Database

Lost-write is a serious form of data corruption that can adversely impact a database. Lost-write occurs when an I/O subsystem acknowledges the completion of a block write in the database while, in fact, the write did not occur in the persistent storage. This arises mostly because of hardware or software malfunctioning. This feature enables the use of the physical standby database to detect data corruptions that could arise as a result of lost-writes on the primary database.

This feature allows for more comprehensive data corruption detection using a physical standby database, helping you get increased value from your disaster recovery investment and maintain the uptime requirements of your business.

1.2.9.1 DDL With the WAIT Option

Data definition language (DDL) commands require exclusive locks on internal structures. If DDL commands are issued, these locks may not be available causing the statement to immediately fail even though the DDL could have possibly succeeded sub-seconds later. This can be resolved by setting the WAIT option in the DDL_LOCK_TIMEOUT initialization parameter.

The WAIT option is the new default. The wait time is specified instance-wide (in the initialization parameter file) and can be modified on a session level.

The WAIT option gives you more flexibility to define grace periods for such commands to succeed instead of raising an error right away, thus requiring additional application logic to handle such errors.

1.2.9.2 Enhanced ADD COLUMN Functionality

Default values of columns are maintained in the data dictionary for columns specified as NOT NULL.

Adding new columns with DEFAULT values and NOT NULL constraint no longer requires the default value to be stored in all existing records. This not only enables a schema modification in sub-seconds and independent of the existing data volume, it also consumes no space.

1.2.9.3 Finer Grained Dependencies

In previous releases, metadata recorded mutual dependencies between objects with the granularity of the whole object. For example, PL/SQL unit P depends on PL/SQL unit Q or that view V depends on table T. This means that dependent objects were sometimes invalidated when there was no logical requirement to do so. For example, if view V depends only on columns C1, C2, and C3 in table T and a new column, C99, is added, the validity of view V is not logically affected. Nevertheless, in earlier releases, V was invalidated by the addition of column C99.

Oracle Database 11g records dependency metatdata at a finer level of granularity so that the addition of C99 does not invalidate view V. Similarly, if procedure P depends only on elements E1 and E2 in package PKG, then if element E99 is added to PKG, procedure P is not invalidated. (In Oracle Database 10g, this change to PKG would invalidate procedure P.)

By reducing the consequential invalidation of dependent objects in response to changes in the objects they depend upon, application availability is increased. The benefit is felt both in the development environment and when a live application is parsed or upgraded. The benefit occurs when an Oracle Database patchset is applied because changes to schema objects are required to be compatible and, therefore, not cause consequential invalidations.

1.2.9.4 Invisible Indexes

An invisible index is an alternative to making an index unusable or even to drop it. An invisible index is maintained for any DML operation but is not used by the optimizer unless you explicitly specify the index with a hint.

Applications often have to be modified without being able to bring the complete application offline. Create invisible indexes temporarily for specialized non-standard operations, such as online application upgrades, without affecting the behavior of any existing application. Furthermore, invisible indexes can be used to test the removal of an index without dropping it right away, thus enabling a grace period for testing in production environments.

1.2.9.5 Materialized View Logging Control

In this release, Oracle Database has added session-level control for materialized view logs. The capture of changes for materialized views (materialized view logs) can be disabled for an individual session while logging continues for changes made by other sessions.

This feature reduces downtime for application upgrade.

1.2.9.6 Minimize Dependent PL/SQL Recompilation After Online Table Redefinition

This feature minimizes the need to recompile dependent PL/SQL packages after an online table redefinition. If the redefinition does not logically affect the PL/SQL packages, recompilation is not needed. This optimization is on by default.

This feature reduces the time and effort to manually recompile dependent PL/SQL after an online table redefinition. This also includes views, synonyms, and other table dependent objects (with the exception of triggers) that are not logically affected by the redefinition.

1.2.9.7 Online Index Creation and Rebuild Enhancements

In highly concurrent environments, the requirement of acquiring a DML-blocking lock at the beginning and end of an online index creation and rebuild could lead to spikes of waiting DML operations and, therefore, a short drop and spike of system usage. While this is not an overall problem for the database, this anomaly in system usage could trigger operating system alarm levels. This feature eliminates the need for DML-blocking locks when creating or rebuilding an online index.

Online index creation and rebuild prior to this release required a DML-blocking lock at the beginning and end of the rebuild for a short period of time. This meant that there would be two points at which DML activity came to a halt. This DML-blocking lock is no longer required, making these online index operations fully transparent.

1.2.9.8 Online Redefinition for Tables with Materialized View Logs

In previous releases, a table could not be redefined if it had a log or materialized views defined.

With this feature, this restriction has been lifted.

You can now clone the materialized view log onto the interim just like triggers, indexes, and other similar dependent objects. At the end of the redefinition, ROWID logs are invalidated. Initially, all dependent materialized views need to do a complete refresh.

1.2.9.9 Read-Only Tables

Oracle Database 11g introduces new ALTER TABLE syntax. For example:

ALTER TABLE <name> READ ONLY
and
ALTER TABLE <name> READ WRITE

The operating system sets the precedent to make a file read-only even for its owner. Earlier, a table could be made read-only (by granting only SELECT on it) to users other than the owner of the table. With this feature, the owner too can be prevented from doing unintended DML to a table.

1.2.10.1 Archive Log Management Improvements

This feature provides the following enhancements:

• Ensure that archive logs are deleted only when not needed by required components (for example, Data Guard, Streams, and Flashback).

• In a Data Guard environment, allow all standby destinations to be considered where logs are applied (instead of just mandatory destinations), before marking archive logs to be deleted. This configuration is specified using CONFIGURE ARCHIVELOG DELETION POLICY TO APPLIED ON ALL STANDBY.

• Allow optional archive log destination to be utilized in the event that the flash recovery area is inaccessible during backup. Archive logs in this optional destination can be deleted using BACKUP .. DELETE INPUT or DELETE ARCHIVELOG.

This feature simplifies archive log management when used by multiple components. It also increases availability when backing up archive logs, when an archive log in the flash recovery area is missing or inaccessible. In this case, the backup will failover to an optional archive log destination to continue backing up the archive logs.

1.2.10.2 Fast Incremental Backups on Physical Standby Database

You can enable block change tracking on a physical standby database. RMAN uses the change tracking file, on incremental backups, to quickly identify the changed blocks since the last incremental backup and to read and write just those blocks.

This feature enables faster incremental backups on a physical standby database than in previous releases.

1.2.10.3 Improved Backup Compression Performance

In this release, when creating compressed backup sets, performance will be noticeably faster than in previous releases.

The benefit is faster backup compression performance.

1.2.10.4 Improved Integration with Data Guard

Improved integration with Data Guard includes the following:

• Persistent configurations can be set for each primary and standby database. You now have the ability to modify database-specific configurations in the catalog without having to connect to the specific primary or standby database.

• Allow conversion of a backup control file to a standby control file and vice versa. This eliminates the need to explicitly create a standby control file from the primary database.

• Share backups among primary and standby databases in a storage area network (SAN) by supporting restore to or from different mount points.

• Define which backups are available to particular databases, for restore operations, based on the network topology.

This feature simplifies backup and recovery operations across all primary and physical standby databases when using a catalog.

1.2.10.5 Network-Aware DUPLICATE Command

A clone database on a remote site can now be easily created directly over the network with the enhanced DUPLICATE command without existing backups. ASM-to-ASM DUPLICATE over the network is also supported.

This feature eliminates the need to copy or move backups to the remote site before executing the DUPLICATE command. This reduces DBA time and effort, and eliminates storage for the additional copy at the remote site.

1.2.10.6 Optimized Undo Backup

During backup, undo that is not needed for recovery of the backup (for example, for transactions that have already been committed), is not backed up. In past releases, all undo was backed up. This new feature is enabled with CONFIGURE BACKUP UNDO OPTIMIZATION. If you wish to preserve active undo within the UNDO_RETENTION period in the backup (for example, for future flashback query or flashback table operations), an option is available to only eliminate the inactive undo beyond the UNDO_RETENTION period.

The benefit is reduced overall backup time and storage by not backing up undo that applies to transactions that have already been committed by the time the backup starts.

1.2.10.7 Read-Only Transported Tablespaces Backup

Transported tablespaces can be backed up, whether in read-only or read/write mode. This feature removes the restriction in previous releases, that transported tablespaces must be enabled for read/write before making a backup.

The feature allows transported tablespaces to be immediately backed up, without having to be made read/write first.

1.3.1.1 Compressed Dump File Sets

Data Pump provides the ability to compress the metadata of a dump file set, which can shrink the dump files by 10-15%.

In this release, Data Pump adds the ability to compress the entire dump file set, including data and metadata.

This feature lets you specify compression for metadata, row data, or the entire dump file set.

1.3.1.2 Data Pump API Enhancements

The Data Pump API has been enhanced to allow individual worker processes to be restarted after a failure without terminating the Data Pump job. This enhancement improves availability when using the parallel capabilities of Data Pump.

The new DATA_REMAP API allows you to specify transformations to be applied to data during export or import. This can be useful when you need to obfuscate sensitive data while retaining its data type, structure, or format which may be needed when moving data from a production system to a test environment.

1.3.1.3 Database Upgrade/Downgrade Enhancements

Oracle Database 11g upgrade now includes enhanced information gathering and reporting facilities. The pre-upgrade information utility has improved resource estimations, and the post-upgrade status tool has improved error message generation and display.

Over the past three database releases, improvements have been made to simplify both manual upgrades and upgrades done using the Database Upgrade Assistant (DBUA). This feature further reduces the complexity of database upgrades and gives DBAs more information they can use to determine whether their database upgrade was successful.

1.3.1.4 Direct Path API Multiple Subtype Support

Prior to Oracle Database 11g, only one object type could be loaded into an object column or object table when using the Direct Path API.

This feature extends that support to allow loading of multiple subtypes into a column object or object table using Direct Path INSERT, thereby providing more robust support for objects and for XML schema data. Data Pump now supports the export and import of XML data.

1.3.1.5 Encrypted Dump File Sets

In this release, Data Pump adds the ability to encrypt the dump file set, including data and metadata. You can choose to encrypt part or all of the dump file set by specifying encryption for encrypted columns, metadata, row data, all, or none.

Customers want sensitive data residing in dump files to be secure.

1.3.1.6 Enhancements to Metadata API

The Metadata API heterogeneous object types have been enhanced to support all object types currently supported by original Export (exp). In particular, XML Schemas and schema-based tables are now supported.

Data Pump also supports editable XML. You can extract metadata from the dictionary, manipulate it, and then convert the metadata to DDL so that the object can be re-created on the same database or on another database.

The TABLE_EXPORT heterogeneous object type is enhanced to properly support AQ tables. This allows AQ tables to be exported. All dependent objects for the table are also exported.

The Metadata API provides the ability to transform a local index defined on a single partition into an index on a nonpartitioned table.

1.3.1.7 Oracle Data Pump External Table Enhancement

In previous releases of Data Pump, a row error in an external table caused the entire table load to abort, requiring you to reload the table.

This feature ensures that a row error does not cause a table load to abort, thus saving load time.

1.3.1.8 Single Partition Transportable for Oracle Data Pump

Until this release, the Transportable Tablespaces mechanism for Data Pump could be used to specify only the physical tablespaces to be exported. This feature adds a partition mode, which can be used to move one or more partitions or subpartitions of a table without having to move the entire table or exchange out the partition or subpartition.

In addition, partitions can be imported to the target database either as part of an existing table or as a separate table for each partition.

1.3.1.9 SQL*Plus BLOB Support

Queries in SQL*Plus now support BLOB columns.

This feature allows you to verify the contents of BLOB columns.

1.4.1.1 Change Data Purge Enhancements

In earlier releases, purging the change table would always remove all consumed change data from the change table. The change data purge enhancements feature introduces a date range-based model to purge change data.

This feature increases the flexibility of purging change data from the change tables. Users can specify a date range for which data should be purged.

1.4.1.2 Change Subscription Enhancements

In this release, the change data subscription window can now be moved forward and backward.

This feature makes it easier to maintain a subscription window to change data. You are given more control over the definition of the subscription window.

1.4.1.3 Enabling or Disabling Synchronous Change Data Capture

This feature adds an interface to enable users to turn synchronous Change Data Capture (CDC) off once it has been started, and turn it on when needed.

With this feature, users have more flexibility and control of the use of synchronous CDC to meet their needs.

1.4.1.4 Enhanced DML Tracking

The enhanced DML tracking feature enables change capture for all DML operations, including direct path loads and partition maintenance operations. A separate record in the change table indicates that such an operation has taken place. This feature also extends Change Data Capture support for data types including CLOB, NCLOB, and BLOB in the asynchronous modes.

Change Data Capture formerly did not always capture direct path load operations or implicit data changes as the result of partition maintenance operations. The enhanced DML tracking feature relieves this restriction.

1.4.1.5 Enhanced Materialized View Catalog Views

New catalog views display the partition change tracking (PCT) information for a given materialized view by showing which sections of the materialized views data are fresh or stale.

This is a critical piece of information for the user to be able to view the partition staleness information of the materialized view. It affects the usability and maintainability of the materialized view.

1.4.1.6 Query Rewrite Enhancement to Support Queries Containing Inline Views

In this release, query rewrite has been enhanced to support queries containing inline views.

With this feature, more queries are eligible for query rewrite thus improving system throughput and performance.

1.4.1.7 Query Rewrite Support of Queries on Remote Tables

In this release, query rewrite has been enhanced to support queries when the tables are remote.

With this feature, more queries are eligible for query rewrite thus improving system throughput and performance.

1.4.1.8 Refresh Performance Improvements

Refresh operations on materialized views are now faster with the following improvements:

• Refresh statement combinations (merge and delete)

• Removal of unnecessary refresh hint

• Index creation for UNION ALL MV

• PCT refresh possible for UNION ALL MV

With this feature, the time required to refresh materialized views is reduced.

1.4.2.1 Composite List-Hash Partitioning

Composite list-hash partitioning enables hash subpartitioning of a list-partitioned object to, for example, enable partition-wise joins.

Composite list-hash partitioning provides an additional modeling strategy to optimally map a business need to the partitioning of an object.

1.4.2.2 Composite List-List Partitioning

Composite list-list partitioning enables logical list partitioning along two dimensions. For example, list partition by country_id and list subpartition by sales_channel.

Composite list-list partitioning provides an additional modeling strategy to optimally map a business need to the partitioning of an object.

1.4.2.3 Composite List-Range Partitioning

Composite list-range partitioning enables logical range subpartitioning within a given list partitioning strategy. For example, list partition by country_id and range subpartition by order_date.

Composite list-range partitioning provides an additional modeling strategy to optimally map a business need to the partitioning of an object.

1.4.2.4 Composite Range-Range Partitioning

Composite range-range partitioning enables logical range partitioning along two dimensions. For example, partition by order_date and range subpartition by shipping_date.

Composite range-range partitioning provides an additional modeling strategy to optimally map a business need to the partitioning of an object.

1.4.2.5 Interval Partitioning

This new partitioning strategy fully automates the partition creation for range. In other words, new partitions will be created when they are needed. By defining the interval criteria, the database knows when to create new partitions for new or modified data.

Managing the creation of new partitions can be a cumbersome and highly repetitive task. This is especially true for predictable additions of partitions covering small ranges, such as adding new daily partitions. Interval partitioning automates this operation by creating partitions on-demand.

1.4.2.6 Metadata for SQL Built-In Operators and Functions

In this release, metadata for all Oracle SQL built-in operations and functions has been made accessible through fixed tables in the dictionary.

This feature enables all third party tools to leverage any built-in Oracle SQL operation or function without having to maintain the function usage metadata redundantly in the application layer.

1.4.2.7 OLTP Table Compression

The Oracle compression feature allows heap tables to be stored in a compressed format, resulting in significant savings in disk storage, I/O, and redo logs. This feature extends the compression feature to OLTP environments so that compression works seamlessly for all DMLs.

Oracle's heap segment compression (HSC) has been well received in data warehousing environments. With this feature compression is extended to OLTP environments so that compression works well with all DMLs including conventional INSERT, UPDATE, and DELETE.

1.4.2.8 Reference Partitioning

The partitioning key is resolved through an existing parent-child relationship, enforced by active primary key or foreign key constraints.

The benefit of this feature is that tables with a parent-child relationship can be logically equi-partitioned by inheriting the partition key from the parent table without duplicating the key columns. The logical dependency will also automatically cascade partition maintenance operations, thus making application development easier and less error-prone.

1.4.2.9 SQL PIVOT and UNPIVOT Operators

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.

1.4.2.10 System Partitioning

System partitioning enables application-controlled partitioning for arbitrary tables and indexes. The database simply provides the ability to break down an object into partitions without any data placement rules. All aspects of data placement and retrieval are controlled by the application.

System partitioning provides the well-known benefits of partitioning (scalability, availability, and manageability), but the partitioning and actual data placement are controlled by the application.

1.4.2.11 Virtual Columns

Virtual columns are defined by evaluating an expression the results of which become the metadata of the columns for tables. Virtual columns can be defined at table creation or modification time.

Virtual columns enable application developers to define computations and transformations as the column (metadata) definition of tables without space consumption. This makes application development easier and less error-prone, as well as enhances query optimization by providing additional statistics to the optimizer for these virtual columns.

1.4.2.12 Virtual Column-Based Partitioning

In Oracle Database 11g, you can now partition key columns defined on virtual columns of a table.

Frequently, business requirements to logically partition objects does not match existing columns in a one-to-one manner. Oracle partitioning has been enhanced to allow a partitioning strategy being defined on virtual columns, thus enabling a more comprehensive match of the business requirements.

1.4.3.1 Cost-Based Aggregation of OLAP Cubes

Cost-based aggregation of cubes provides a statistics-based approach to determining the best data within the cube to preaggregate and store for both efficient updates of the cube and efficient query.

Cost-based aggregation provides the DBA with an easy-to-use method of optimizing the cube as a general purpose summary management solution. The DBA needs to indicate only the amount of resources that should be applied to the task of optimizing summary data. Cost-based aggregation automatically determines and executes the best summary management plan to support ad-hoc query and reporting of the cube. This results in shorter refresh times and better query times.

1.4.3.2 Cube Organized Materialized Views

Cube organized materialized views represent the cube to SQL-based applications as materialized views that can be used for both refresh and query rewrite. Cube organized views are metadata objects that access data stored or calculated with the OLAP cube; they do not replicate data from the cube into new storage.

Cube organized materialized views can improve the ease and efficiency of dimension and cube updates and improve query performance.

Cube organized materialized views allow the database administrator to manage OLAP dimension and cube updates using the materialized view refresh system of Oracle Database, thus leveraging existing DBA skills and incremental data loading using materialized view log tables.

Cube organized materialized views can improve query performance by allowing SQL-based applications querying detail relational tables to automatically and transparently access summary data in the OLAP cube.

1.4.3.3 Fine-Grained Partitioning of OLAP Cubes

Scalability improvements in cube partitioning increase the number of partitions that can be efficiently maintained in a cube.

Fine-grained partitioning of OLAP cubes provide support for larger cubes and more efficient cube update processing. Fine-grained partitioning allows the cube to be partitioned by more members of a dimension (as compared to Oracle Database 10g Release 2). Support for many more partitions can be used to create larger cubes (for example, retaining more time periods). Smaller partitions can be processed more quickly during a cube update.

1.4.3.4 OLAP Security Enhancements

Security of OLAP cubes and dimensions is enhanced to include support for Oracle Database object privileges (SELECT, INSERT, UPDATE and DELETE) on analytic workspaces, cubes and dimensions. For example, GRANT SELECT ON sales_cube.

Fine-grained access control to OLAP dimensions and cubes, which provides support for granting access privileges (SELECT, INSERT, UPDATE and DELETE) on data within a dimension and cube, is also supported in OLAP 11g and Analytic Workspace Manager.

For the general OLAP or database user, this collection of features enhances the integration of multidimensional data types with standard operational features of the database with the goal of making multidimensional data types more secure and easier to manage.

1.4.3.5 SQL Optimizer Support for OLAP Option Cubes and Dimensions

The SQL Optimizer adds support for OLAP cube and dimension statistics and for pushing joins between cubes and dimensions into the OLAP cube. Optimizations also include efficient joins between relational tables and dimension and cube views.

SQL Optimizer support for OLAP cubes and dimensions provides general performance improvements and support for a wider variety of SQL (including counts and joins) to be efficiently executed against the cube. The main benefit is that a wider variety of SQL-based tools and applications will be able to easily query the cube.

1.4.3.6 Storage and Access Improvements of Compressed Cubes

This feature provides internal scalability and performance improvements of compressed cubes.

This feature supports larger compressed cubes and broadens the applicability of compressed cubes to include a wider variety of data sets.

1.4.3.7 Unified OLAP API Metadata Change Management

Unified OLAP API metadata change management involves initial deployment of Analytic Workspace (AW) dimension objects, dependency tracking for these objects, and merging new versions of AWs and AW metadata with site-specific customizations.

Unified OLAP API metadata change management allows you to make site-specific modifications to the dimensional model. This feature is primarily in support of Oracle applications.

1.4.4.1 Automated and Embedded Data Transformations for Mining (Supermodels)

This new feature in Oracle Database 11g can:

• Perform automatic data preparation for data mining models to reduce the burden on the user and provide an improved out-of-the-box experience (supermodels).

• Encapsulate the transformations with the model metadata to simplify model deployment (the transformations are automatically deployed with the model itself).

• Allow advanced users to specify some amount of transformations to the model build routine, which simplifies deployment for the advanced user as well.

This feature adds support to automate data preparation, the most difficult part of the data mining process. Another benefit is the simplification and automation of the overall data preparation process which simplifies model building and model scoring activities.

1.4.4.2 Data Mining Schema Objects

In Oracle Database 10g Release 2, Data Mining models were not defined in the database at the same class level as other DBMS objects. In Oracle Database 11g, models and the Data Mining environment are enhanced as follows:

• Eliminate the DMSYS schema, migrating necessary information to SYS

• Enhance existing catalog views to be Data Mining model aware

• Add new catalog views to expose Data Mining model metadata

• Add specific privileges for building, viewing, and scoring Data Mining models

Data Mining models are now more similar to other database objects. This allows you to assign access privileges, start and stop Data Mining models from Oracle Enterprise Manager and, in general, manage Data Mining models as full database objects.

1.4.4.3 Java API for Oracle Data Mining (JSR-73 and JSR-247)

JSR-247 provides algorithms and functions to meet new features provided in Oracle Data Mining for Oracle Database 11g; for example, Multiple Linear and Logistic Regression. Doing so maintains equivalent functionality between the SQL and PL/SQL APIs and Java API.

This feature provides enhancements for the Java API for Oracle Data Mining conforming to the Java Data Mining standard (JSR-73 and JSR-247).

1.4.4.4 Multivariate Linear Regression

Multivariate Linear Regression (MLinR) enables data analysts to build classic statistical models of the relationships between a range of input attributes and a target (numerical) output attribute. Additionally, MLinR finds the strength of the relationships between variables. Oracle Data Mining MLinR supports a large number of input attributes. This is a differentiator as most MLinR implementations cannot do this well. MLinR models can score data using the PREDICTION operators introduced in Oracle Database 10g Release 2. Several model diagnostics, both at global and attribute level, can be reported.

Oracle Database 11g augments the statistical capabilities available in Oracle Database by adding support for Multivariate Linear Regression in Oracle Data Mining.

1.4.4.5 Multivariate Logistic Regression

Multivariate Linear Regression (MLogR) enables data analysts to build classic statistical models of the relationships between a range of input attributes and a target (categorical) output attribute. Additionally, MLogR finds the strength of the relationships between variables. The Oracle Data Mining Multivariate Linear Regression (MLinR) supports a large number of input attributes. This is a differentiator as most MLogR implementations cannot do this well. MLogR models can score data using the PREDICTION operators introduced in Oracle Database 10g Release 2. Several model diagnostics, both at global and attribute level, can be reported.

Oracle Database 11g Release 1 (11.1) augments the statistical capabilities available in the database by adding support for Multivariate Logistic Regression in Oracle Data Mining.

1.4.4.6 Predictive Analytics: PROFILE

The Predictive Analytics (PA) package, introduced in Oracle Database 10g Release 2, provided a very simple interface for business users to leverage the power of Data Mining without the requisite statistical background. Also, in Oracle Database 10g Release 2, functions were provided to PREDICT (classification and regression) and to EXPLAIN (attribute importance). In Oracle Database 11g, a new Predictive Analytics (PA) method is added to develop profiles of a specified target attribute. PROFILE uses the decision tree algorithm.

PROFILE Predictive Analytics automatically sorts through the records and finds patterns and relationships of similar records (or customers) given a specified target attribute which designates a business problem. For example, PROFILE can be used to find the various profiles of customers who churn versus those who remain loyal. Similarly, PROFILE can be used to find the profiles of highly valued customers, customers who commit fraud, patients who contract a disease, and so on.

1.4.4.7 SQL Prediction Enhancements

SQL built-in functions for Data Mining scoring (for example, PREDICTION) were added in Oracle Database 10g Release 2. Oracle Database 11g is enhanced to:

• Allow cost matrix identification in the PREDICTION family to push down cost computations and provide a simple, best prediction by cost.

• Enable on-the-fly model builds (requiring no user intervention) with associated data preparation, as well as a mechanism for model inspection for the more advanced user).

• Enhance our *_DETAILS routines to support more algorithms and provide more details than the current form.

SQL Prediction enhancements allow for rapid, on-the-fly Data Mining model creation and tuning. This enables Data Mining to be more easily and seamlessly integrated into applications.

1.5.1.1 Enhanced Oracle RAC Monitoring and Diagnostics in Enterprise Manager

This feature improves Oracle RAC performance monitoring and diagnostics through the GUI interface in Enterprise Manager.

This enhanced GUI interface eases management of Oracle RAC.

1.5.1.2 Enhanced Oracle Real Application Clusters Configuration Assistants

This feature updates the graphical configuration utilities (DBCA, DBUA and NetCA) for all features of Oracle RAC.

The benefit is a reduction in the management costs of cluster database environments through wizard-driven GUI management tools.

1.5.1.3 OCI Runtime Connection Load Balancing

In Oracle RAC environments, session pools to services use service metrics to load balance sessions across different instances at runtime. OCI and OCCI applications have this feature enabled by default provided the necessary events are turned on.

Applications using OCI Session Pools have the best possible throughput for the resources provided. No application changes are required. Load balancing works toward the goal specified for the service. If an instance becomes hung or unresponsive, connections are directed to other instances that can provide better service.

1.5.1.4 Parallel Execution for Oracle Real Application Clusters

Parallel execution is now aware of the service definition and automatically takes on the appropriate PARALLEL_INSTANCE_GROUPS setting, thus making the explicit setting of PARALLEL_INSTANCE_GROUPS for a services-controlled information grid environment unnecessary.

Oracle RAC services and the control of eligible nodes for parallel execution are now integrated and controlled by the services setup, making parallel execution fully transparent in an Oracle RAC services-controlled environment.

1.5.1.5 Support for Distributed Transactions in an Oracle RAC Environment

An XA transaction can now span Oracle RAC instances. Individual branches have the ability to execute on different instances in the cluster.

This feature allows any application using XA to take full advantage of the Oracle RAC environment, thus enhancing high availability and scalability of the application.

1.6.1.1 Fast Bulk Data Transfers

Oracle Database now uses a larger session data unit (SDU) by default. Optimizations to the Network Session SDU size result in significant performance gains for large data transfers.

This feature also takes advantage of zero copy and vectored I/O at the network level.

Fast bulk data transfers result in significant performance improvements for large data transfers.

1.6.1.2 LOB Prefetch

OCI now allows programs to set the amounts of prefetching of LOB data for optimized information retrieval, especially of smaller data sizes.

Business applications that often access smaller LOBs perform faster, because there are fewer round trips to the server for data.

1.6.1.3 SecureFiles

This feature introduces a completely reengineered large object (LOB) data type to dramatically improve performance, manageability, and ease of application development. The new implementation also offers advanced, next-generation functionality such as intelligent compression and transparent encryption.

This feature significantly strengthens the native content management capabilities of Oracle Database.

1.6.1.4 SecureFiles: Compression

This feature allows you to explicitly compress SecureFiles to gain disk, I/O, and redo logging savings.

The benefits of this feature are:

• Reduced costs due to the most efficient utilization of space.

• Improved performance of SecureFiles as compression reduces I/O and redo logging (at some CPU expense).

1.6.1.5 SecureFiles: Deduplication

Oracle Database can now automatically detect duplicate SecureFiles LOB data and conserve space by storing only one copy.

This feature implements disk storage, I/O, and redo logging savings for SecureFiles.

1.6.1.6 SecureFiles: Encryption

This feature introduces a new encryption facility for SecureFiles. The encrypted data is now stored in-place and is available for random reads and writes.

The benefit of this feature is enhanced data security.

1.6.2.1 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 keyword 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.

1.6.2.2 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.

1.6.2.3 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.

1.6.2.4 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 re-create the index in one operation or can step through each stage of the re-creation manually.

1.6.3.1 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.

1.6.3.2 More Types of Operations Allowed on Document Sections

This feature introduces a new type of document section called Structured/Sort Data (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.

1.6.3.3 Text Support for Very Large Number of Partitions

Until now, the maximum number of partitions allowed has been 9,999. This limit has been increased to 1,223,054.

This increase is of significant benefit to Text users.

1.6.3.4 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.

1.6.4.1 Content Repository Management (JSR-170) Support for Java

This feature supports JSR-170 1.0 defined operations on both unstructured and semi-structured content and provides a comprehensive Oracle-proprietary content management platform, which includes:

• API extensions for access control, based on the DAV Access Control model

• Support for type definition and evolution, based on Oracle XML DB support for XML Schema

• Support for Oracle XML DB repository events

This feature provides a set of simple, consistent, and standard-based APIs to traverse, query, access, and operate on data in Oracle XML DB when building mid-tier content-centric applications. The feature streamlines the development of internet applications and encourages customer uptake and partner integration on Oracle XML DB.

1.6.5.1 DAV ACL Support

The DAV ACL standard is an industry standard for defining security in a WebDAV environment. Support for DAV ACL allows the XML DB repository to interact directly with other tools that understand the DAV ACL standard.

This feature enables interoperability between the Oracle XML DB repository and programs and tools that implement the DAV ACL standard. It enables the implementation of document-level security using industry standards, leading to reduced costs and increased productivity. Finally, it allows organizations to adopt authoring and security products that best meet their business needs.

1.6.5.2 Enable the XDB HTTP Server for SOA

This feature allows Oracle Database to be treated as simply another service provider in a service-oriented architecture (SOA) environment. The database is now capable of exposing PL/SQL packages, procedures, and functions as Web services. The database can also execute dynamic XQuery and SQL queries.

These enhancements reduce complexity and cost by allowing the database HTTP server to expose native Web services in an SOA environment without requiring any additional Oracle or third-party software components. The feature provides performance enhancements over previous architectures in cases where Oracle mid-tier software is already providing SOA capabilities on top of an Oracle Database.

1.6.5.3 Large Text Node Handling

In earlier releases, nodes with an Oracle XML DB document were limited to 64K of text. In most cases this was not a problem, because 64K translates to approximately 32 pages of text. However, when an XML document has non-XML documents embedded inside it, this limitation can be a significant problem.

For example, a text node might contain an embedded HTML or RTF document such as an abstract or Web page. Or an XML document might contain embedded binary content, such as JPEG images, Word Documents, or PDF files.

The XML standard does not allow an XML document to contain binary content, so the content of the embedded documents has to be encoded into a character-based format using algorithms such as base64, binHex, or UUENCODE. The resulting in text node can easily exceed 64K.

This feature allows Oracle XML DB to handle this class of XML document by eliminating the current 64K limit on the size of a text node and by providing a streaming API that makes it possible to manipulate nodes that contain large amounts of text in a memory-efficient manner.

1.6.5.4 Oracle XML DB Repository Triggers

This feature provides a series of events related to repository operations. Developers can attach code to these events in the same way that they can attach code to database triggers. This code can then perform application logic based on repository events in the same way that triggers allow application code to be executed based on operations on database triggers.

This feature provides a framework for developing intelligent content management systems based on the Oracle XML DB repository. It allows businesses to reduce costs and ensure compliance by allowing business logic to be associated with document-centric applications that make use of the Oracle XML DB repository. Organizations can ensure that they remain in compliance with document and content management regulations in legislation such as Sarbens/Oxley.

1.6.5.5 Recursive Schema Handling

This feature allows Oracle XML DB to deliver improved performance execution of XPath-based operations on XML documents that contain highly recursive structures. It enables XPath Rewrite in cases where all of the predicates operate on nodes that can occur at an arbitrary depth within the recursive structure.

The ability to easily describe and manipulate highly recursive structures is a key benefit of the XML paradigm. This project results in significant performance improvements when XML schema-based documents of this nature are stored and manipulated using Oracle XML DB.

1.6.5.6 Repository Performance Improvements

Performance is improved in the Oracle XML DB repository for complex folder-restricted queries.

This feature improves the response time for certain kinds of SQL operations on the Oracle XML DB repository.

1.6.5.7 Scalable XSL Output

Until now, the XSLT capabilities of XML DB could not be used to generate HTML or Text formatted documents. This feature allows XSLT processing to write directly to a CLOB or Output stream, thus removing this limitation and allowing the XML DB XSLT capabilities to be used in a much wider set of cases without requiring any additional infrastructure.

This feature helps organizations to reduce cost and complexity by generating large HTML and Text formatted documents directly from the database without requiring additional software components.

1.6.5.8 SQL/XML Standards Compliance and Performance Optimizations

This feature implements the XMLExists and XMLCast operators, which are part of the SQL:2005 standard. In addition, this feature:

• Adds support for the * operator with XMLForest(*).

• Adds support for complex storage parameters in the STORE AS CLOB clause of XMLType column DLL.

• Adds enhancements to the XMLAgg operator. The XMLAgg operator is used very commonly when generating XML from relation tables, or reconstructing XML from the internal object representation. XMLAgg is required whenever an element occurs multiple times within the document being generated. The proposed optimizations result in a significant reduction in the resources required to aggregate the target nodes, leading to noticeable performance improvements for these operations.

This feature provides significant performance improvements related to common XML generation operations. Reduced coding and maintenance costs result from a reduction in the amount of code required to perform common XML-centric tasks. Better support for relevant SQL standards enable the development of more portable application code.

1.6.5.9 Support for More Types of Links in XDB

This feature adds support for a variety of links to Oracle XML DB, for example:

• Symbolic links to local XDB paths

• URL links

• Weak links (these store the OID, as do hard links, but do not affect persistence)

• Fixed links (these links can never be updated or deleted once created except by DDL operations)

• Pathname cache for fixed links

• Links as attributes

• The ability to globally disallow hard links to folders

• Preferred links

This variety of links enhances the XML Content Repository.

1.6.5.10 XLink Support in XML DB

Oracle Database 11g provides the XML equivalent of referential integrity between XML documents using well understood W3C standards. XBRL validation, XLink validation, and compound document support are all enabled.

This feature allows organizations to use the W3C XLink recommendation to define additional rules about the relationships between documents. XLink is a popular W3C standard that allows integrity rules and other relationships be specified in an XML-centric manner. The XLink specification is used by a number of important XML standards, including XBRL, the eXtensible Business Reporting Language, which is being adopted by a number of regulatory agencies worldwide, including the FDIC and SEC in the USA.

Support of XLink also offers organizations additional flexibility on how XML documents are stored in the database. For example, they can now store a Book as a set of Chapters, providing more granularity and flexibility.

An XLink-aware repository ensures that the rules specified in the XLink language are enforced while the content is stored in the Oracle XML DB repository, making the use of the XML language a more compelling story.

1.6.5.11 XML DB Operational Completeness

Oracle Streams now enables the propagation and management of data, transactions, and events in a data stream either within a database or from one database to another. The stream routes published information to subscribed destinations. The result provides greater functionality and flexibility than traditional solutions for capturing and managing events and for sharing the events with other databases and applications. As users' needs change, they can implement a new capability of Oracle Streams, without sacrificing existing capabilities.

Oracle Streams and Logical Standby can now be used in conjunction with the XMLType data type.

Oracle Streams delivers a common infrastructure that can meet the complex information sharing needs of today's organizations. These capabilities are now being extended so that they can be used in conjunction with all of the storage paradigms supported by the Oracle XMLType data type.

1.6.5.12 XML Query Optimization with Schemaless or Weakly-Typed Schema-Based XML

This feature enables cost-based optimizations of XQuery and XPath operations in cases where the underlying storage of some or all of the XML content is CLOB or CSX (binary XML). This results in significant performance improvements for queries over this kind of structure.

This feature allows organizations that cannot or do not want to leverage the schema-based storage model to improve the performance of their applications.

1.6.5.13 XML Translation Support

Operations on some classes of XML documents are now language-aware. As a result, the information returned by an operation on an XML document will be based on the users language preferences. This enhancement can significantly reduce the amount of code required to develop applications that are independent of a users language preferences. Until now, this capability was available only for documents stored using the Binary XML storage model.

This feature reduces costs associated with developing applications that are independent of the target users preferred language.

1.6.5.14 XML Update Performance Optimizations

This feature provides significant optimizations in the way in which UPDATE, DELETE, and INSERT operations on existing documents are implemented. The changes, which are totally transparent to the application, result in significant reductions on the amount of code that is executed internally and the amount of I/O and log that is generated when the structure of an existing document is updated.

The performance of update operations on XML content stored in the database is greatly improved.

1.6.5.15 XMLIndex

XMLIndex addresses the serious limitations inherent in the design and operation of the CTXSYS.CTXXPATH index. Among its benefits, XMLIndex:

• Does not require any prior knowledge of shape or content of the XML.

• Does not require any particular storage model for the XMLType.

• Can resolve any Xpath expression without requiring a subsequent filter of the result set identified by the index.

• Is transactional.

• Can support date and numeric range searching.

• Can support text-based searching of textnodes and attribute values through the ora:contains() XPath extension function.

This feature allows an organization to efficiently index XML documents without requiring explicit prior knowledge of shape or content of the XML that will be indexed. It provides full support for extremely efficient XPath-based searching of the indexed XML content.

1.6.5.16 XQuery and SQL/XML Performance Improvements

This feature implements performance improvements in a number of areas for SQL/XML and XQuery in the database.

XQuery and SQL/XML enhancements provide customers with performance gains when they upgrade to the current release.

1.6.5.17 XQuery Standards Conformance

Oracle XQuery is in compliance with the final version of the W3C XQuery Standards and the JSR 225 XQJ standard.

Oracle XQuery standards compliance allows customers to build portable applications based on industry standards.

1.6.5.18 XSLT Performance Improvements

This feature optimizes some classes of XSLT transformation by rewriting the operation into an equivalent XQuery expression and then executing the XQuery. The XQuery then becomes the subject of XQuery optimizations, which can result in set-based rather than iterative execution. This in turn can improve performance significantly.

XSLT transformation is commonly used to generate one format of XML from another format of XML. This feature provides significant performance improvements for some classes of XML to XML transformation.

1.7.1.1 Automatic Inter-Component Deadlock Detection

Automatic inter-component deadlock detection automatically executes deadlock detection when such conditions are created or persist in the database.

This feature also provides the necessary infrastructure for other features in Oracle Database 11g that minimize database and application upgrade downtime.

1.7.1.2 Hang Manager

The Hang Manager automatically detects, analyzes, and dumps diagnostic information for hangs in Oracle Database environments, including Oracle RAC and ASM databases. V$ views are provided to allow users to see hangs in the system and their current status.

This feature simplifies database management by enabling you to more quickly resolve database hangs.

1.7.1.3 Incident Packaging Service (IPS)

Providing appropriate information to Oracle Support or Development is a tedious and sometimes time-consuming task for users. IPS provides a facility that extracts diagnostic and test case data associated with product exceptions (incidents) from Automatic Diagnostic Repository (ADR) and packages the data for transport to Oracle.

IPS also provides mechanisms to automatically generate test cases, such as SQL test cases, so that support and development personnel can easily reproduce the problems in-house for analysis and resolution.

1.7.1.4 SQL Test Case Builder

SQL Test Case Builder is a PL/SQL package that gathers all of the information at the customer site that is needed to reproduce a problem on a different system.

Obtaining a reproducible test case is the single most important factor in the speed of incident resolution. The SQL Test Case Builder gathers as much information as possible related to a SQL incident and packages it in a way that allows the problem to be reproduced.

1.7.2.1 Automatic Diagnostic Repository

Automatic Diagnostic Repository (ADR) is a new system-managed repository for storing and organizing trace files and other error diagnostic data. ADR provides a comprehensive view of all the serious errors encountered by the database and maintains all relevant data needed for problem diagnostics and their eventual resolution.

Automatic Diagnostic Repository (ADR) provides a uniform and consistent mechanism to store, format, and locate all database diagnostic information. Customers can now correlate errors across various components such as Oracle RAC, Oracle Clusterware, OCI, Net, processes, and so forth. ADR automatically generates incidents for serious errors and provides incident management functionality. As a result, ADR significantly reduces time to problem resolution time for customers.

1.7.2.2 Enhanced Network Diagnosability

This feature enhances network diagnosability by consolidating Oracle Net diagnostics for various clients with the database infrastructure and presenting the diagnostic information in a standardized readable format. The network diagnostics information for Oracle products such as clients, application server, and database is stored in the Automatic Diagnostic Repository (ADR).

Oracle Net can now use the same diagnostic infrastructure as Oracle Database. This feature helps users diagnose Oracle Net related problems more easily than in earlier releases.

1.7.2.3 OCI Diagnosability

The following enhancements have been added in Oracle Database 11g to ease problem diagnosis in OCI applications:

• Record appropriate application, process, network, or other state information and dump it to log files when necessary.

• Add correlation information between client and server to diagnose problems easily.

The support for enhanced diagnosability mechanisms in OCI results in faster resolution of customer problems.

1.7.3.1 Improved Block Corruption Detection

In earlier releases, RMAN-detected block corruptions were recorded in V$DATABASE_BLOCK_CORRUPTION. In Oracle Database 11g, live block corruptions are also recorded in that view. This view is automatically updated when corruptions are repaired; for example, using block media recovery or data file recovery.

This feature shortens the time to discover block corruptions.

1.7.3.2 SQL Repair Advisor

When a problematic SQL statement is identified, the SQL Repair Advisor reloads the SQL and recompiles it in a more detailed diagnostic mode. The SQL Repair Advisor gathers more information on the incident and provides a workaround or recommendations to the DBA.

The SQL Repair Advisor is a new tool that helps DBAs diagnose problematic SQL statements and automatically provides workarounds for them.

1.7.4.1 Automatic Health Monitoring

Automatic Health Monitoring proactively checks the health of the database and identifies any issues affecting the database. Problems are detected even before users run into them and before they cause widespread damage. The results of Automatic Health Monitoring are reflected in a simple meter (the Health Meter) designed to allow the DBA to quickly gauge the health of the database. With the help of Automatic Health Monitoring, a DBA can get a comprehensive picture of the current health of the database as well as help on how to remedy the problems affecting the database.

Automatic Health Monitoring proactively detects problems early in their life-cycle and notifies the DBA of the user impact and recommended action to take. Depending on the component or situation, Automatic Health Monitoring can either quarantine the offending component/resource or provide a fix to a problem. This feature helps businesses minimize their downtime and plan ahead for outages.

1.7.4.2 Automatic Quarantine of Corrupt Undo Segments

This feature automatically quarantines corrupt undo segments and prevents future transactions from using the same undo segments.

This enhancement limits corruption propagation and thus limits the damage caused on the system.

1.7.4.3 Fast ANALYZE: Finding Table-Index Corruptions

ANALYZE VALIDATE CASCADE operations are extremely expensive for large indexes. This feature introduces a hashing scheme for validating mismatch conditions.

The hashing is significantly faster than traditional cascade validation. The hash is designed so that both full table scans and index fast full scans produce the same hash value.

Many customers use ANALYZE TABLE ... VALIDATE STRUCTURE CASCADE statements to detect incompatibilities or corruptions between tables and indexes. ANALYZE operations in earlier releases took a long time for large tables and often could not complete in the diagnostics or maintenance window. With this feature, these ANALYZE operations now run much faster.

1.7.4.4 Holistic Undo Health Monitoring

This feature performs a number of health checks to determine the holistic health of the transactional engine. It proactively examines data and index blocks for transactional consistency and validates undo segments for possible corruption. Any corruption detected results in a proactive dump of all the redo related to the block.

This feature protects user data from software and hardware malfunction and helps prevent corruption propagation.

1.7.5.1 Support Workbench

The Support Workbench provides an easy-to-use interface that presents database health-related incidents on the system to the DBAs in a timely manner along with information on how to manage the incidents. It also assists DBAs in viewing diagnostic information from multiple Oracle products (such as Net, clients, ASM, Oracle RAC, etc.), running health checks, packaging incident data to Oracle Support, and managing incidents.

The Support Workbench significantly reduces problem resolution time for customers by providing a simple workflow interface to view and diagnose incident data and package it for Oracle Support.

1.8.1.1 Event Notification Grouping and Management

Event notifications can be grouped by a time interval, enabling a single batch notification for the group rather than multiple individual event notifications. Notifications may be retained for user-specified durations if the client is unavailable.

Notification configuration and management is more flexible.

1.8.1.2 Oracle Scheduler for Streams Jobs

Jobs scheduled for propagation and notification for Streams are now managed with the DBMS_SCHEDULER package.

This conversion to the standard Oracle Scheduler simplifies administration of Streams.

1.8.1.3 Scalability for Streams Notifications

The notification server (EMON) is now a multiprocess server, consisting of 5 processes.

This feature enables large numbers of simultaneous notifications system-wide.

1.8.2.1 Messaging Gateway Service-Based Failover

Messaging Gateway agents can now be started by the Oracle Scheduler. The scheduler uses the Oracle RAC service feature, through which users can specify a primary instance and associated machine to host the Messaging Gateway agent. When the primary instance fails, the service fails over to another instance. The Messaging Gateway is started on the machine hosting that instance and connects to the instance on that node.

With this feature, you can now use Oracle RAC services to control where the Messaging Gateway will fail over, allowing for high availability without the need to make every database instance ready to run the Messaging Gateway agent.

1.8.2.2 Multiple Messaging Gateway Agents in Oracle RAC Environments

This feature provides support for running multiple Messaging Gateway agents on a single database. Users can now scale the messaging workload with the number of instances, by balancing the workload across multiple Oracle RAC instances.

With this feature, you can now build messaging solutions with greater throughput that scales across multiple Oracle RAC instances.

1.8.2.3 Simplified Messaging Gateway Propagation Job Configuration

A new enhanced PL/SQL API consolidates the propagation subscriber and the propagation schedule into a new propagation job.

It is now easier and less confusing to create and schedule a propagation job for a Messaging Gateway. A new API provides a more intuitive means for configuring these jobs.

1.8.2.4 Database Gateways: Performance Improvements

With this feature, you experience significant improvement when loading large volumes of data from the non-Oracle systems. Performance of the gateways also improves in general.

This feature improves the performance of Database Gateways.

1.8.2.5 Database Gateway for Adabas

Oracle Database 11g now provides a Database Gateway for Adabas.

Database Gateway for Adabas allows you to integrate data from Adabas using standard Oracle SQL.

1.8.2.6 Database Gateway for IMS

Oracle Database 11g provides a Database Gateway for IMS.

Database Gateway for IMS enables a customer to integrate data from IMS using standard Oracle SQL.

1.8.2.7 Database Gateway for VSAM

Oracle Database 11g provides a Database Gateway for VSAM.

Database Gateway for VSAM allows a customer to integrate data from VSAM using standard Oracle SQL.

1.8.3.1 Continuous Query Notification Enhancements

Continuous Query Notification has been enhanced to provide notifications when the result set for a query changes on the server. These notifications can be registered at the row-level, rather than just at the table-level. Thus, alerts will only be sent when changes to the specific rows selected have been modified.

Applications, such as Radio Frequency Identification (RFID), can now receive notifications directly from the database rather than build this functionality into the application.

1.8.4.1 Additional Data Type Support for Streams

Streams provides support the XMLType data type and for Transparent Data Encryption (TDE).

Data can be shared more easily between applications and databases.

1.9.1.1 Spatial: 3-D Geometry, Surface, and Point Cloud Storage and Indexing Functions

Oracle Spatial provides native storage, querying, and retrieval for three-dimensional (3-D) data including points, lines, surfaces, triangulated irregular networks (TINs - an alternative to rasters), and point clouds. Spatial R-tree indexing now supports 3-D data; SQL operators and analysis functions for 3-D data are also provided.

Oracle Spatial supports very large 3-D data sets such as urban models, point clouds, and terrain models with open, secure, scalable, and improved data management for urban planning and design, government, homeland security, military, oil and gas exploration, transportation engineering, gaming and simulation, geoengineering, and LIDAR-based map production.

1.9.1.2 Spatial Web Services

Oracle Web Services now support the following XML-based geospatial Web Services standards on a variety of client technologies and platforms:

• OGC OpenLS 1.1

• Web Feature Service 1.0

• Web Feature Service - Transactional 1.0

• Catalogue Service 2.0

Oracle Database and Oracle Application Server provides security for these Web services standards including authorization, authentication, and transport confidentiality and integrity. Java and PL/SQL client APIs are provided.

Oracle Spatial Web Services are location-ready and tightly integrated with Oracle Database and Oracle Application Server to provide a secure, transactional, service-oriented architecture platform to access and incorporate any published services for routing, geocoding, directory, catalog, feature, and mapping.

1.9.1.3 Spatial Routing Engine Enhancements

The Oracle Spatial routing engine now generates driving directions in German, French, Spanish, and Italian, and in turn-specific geometries.

The Oracle Spatial routing engine provides driving directions in various Western European languages and turning diagrams for linear asset management and travel and transport applications.

1.9.1.4 Spatial Network Data Model Enhancements

The Oracle Spatial network data model supports multiple concurrent Application Server requests with its thread-safe Java API and supports an XML interface for Web services queries.

New analysis and modeling capabilities include database-level handling of user or application-specific attributes. These features enable the network data model to manage nonconnectivity-related information. You can also selectively extract a subset of the network using SQL-like filters. Other enhancements include path arithmetic support such as path addition, subtraction, intersection, comparison, and duration modeling.

The Oracle Spatial network data model provides Java and XML APIs for Service Oriented Architectures and high query volume applications such as field service scheduling and logistics.

New analysis and modeling capabilities are provided for applications that analyze very large networks with complex attributes and relationships such as utilities, logistics, transportation, life sciences, and web information management.

1.9.1.5 Load-On-Demand for Very Large Networks in Oracle Spatial Network Data Model

Oracle Spatial now supports load-on-demand for partitioned networks, which eliminates physical memory as a limitation for in-memory analysis. Large networks can be partitioned into manageable subnetworks and incrementally loaded into memory as needed for performance and scalable analysis. Partitioning utilities are available for partitioning large spatial networks.

Oracle Spatial network data model can analyze very large networks without loading the entire network into memory. This ability benefits applications such as utility and street networks.

1.9.1.6 Spatial GeoRaster: Manageability, Reliability, Usability Enhancements

GeoRaster DML triggers are created and monitored by the system automatically. New tools automate GeoRaster upgrade, migration, and use. GeoRaster has support for Workspace Manager long transactions and fine-grained Label Security.

More metadata and data types are now supported. GeoRaster supports a generic polynomial georeferencing model, which includes direct linear transportation and rapid positioning capability georeferencing for nonrectified airborne photos and satellite images. It also supports bitmap masks, multiple NODATA values and value ranges, and has limited data type support.

Advanced operations have been added for advanced mosaic, GeoRaster object or layer union, GeoRaster templates, and related functions. GeoRaster also fully supports load and export of GeoTIFF geometadata and DigitalGlobe RPC file format.

Additional raster processing capabilities include GeoTIFF geometadata, loading, and exporting.

Oracle Spatial GeoRaster supports more data types and file formats, editing of image and grid data with long transaction versioning, fine-grained security, and new management tools and functionality.

1.9.1.7 Enhanced Workspace Manager Performance and Data Storage Options

User-specified optimizer hints on Workspace Manager operations are now supported. Partition operations (such as ADD, DELETE, MERGE, and EXCHANGE) are supported on a versioned table. New online operations on versioned tables include ALTER INDEX and ALTER TABLE operations, including moving a table to another tablespace and changing the percent free space. Row-level locking is implemented for merge and refresh operations. Compression is optional for the RemoveWorkspace operation.

This release of Workspace Manager supports greater tuning of queries for workspace operations and more physical storage options. Merge and refresh operations can be performed faster by executing them in parallel and making garbage collection optional if the workspace is removed.

1.9.1.8 Enhanced Workspace Manager Security and Administration

Oracle Label Security policies can now be set and altered on a versioned table. Changes to tables involved in a referential integrity constraint can be merged as a group. The name of the workspace that creates and retires a history row is recorded.

Workspace Manager allows Oracle Label Security policies to be applied and altered for long transactions. Changes to groups of tables can be merged as a set. Changes to data can be tracked by project.

1.9.1.9 Improved Workspace Manager Valid Time Usability

In this release, the unique ROWID for history rows with a Valid Time is available to query. Initial Valid Time timestamp values can be specified when a table is versioned. Valid Time timestamps can be presented as TIMESTAMP WITH TIME ZONE scalar type as an alternative to using the WM_PERIOD object type. Changes to Valid Time timestamps are now allowed for an Oracle Spatial feature stored in a parent table of a referential integrity constraint.

Valid Time, also known as effective dating, is a capability of Workspace Manager that allows versioned data to be stored with an associated time range that indicates when the data is valid. This feature includes several usability enhancements. A specific historical change with Valid Time can be presented to a user. Valid Time timestamp values can be initialized with user-defined values when a table is versioned and timestamps can be used as simple scalar values in a Microsoft .Net environment. Valid Time timestamps can be updated anytime an Oracle Spatial feature participates in a referential integrity constraint.

1.9.1.10 Oracle Multimedia Performance and Scalability

Earlier versions of Oracle interMedia (now called Oracle Multimedia) object types (ORDImage, ORDAudio, ORDVideo, and ORDDoc) allowed storage of electronic media objects of up to 4 Gbytes. In Oracle Database 11g, this media size limit is extended to the BLOB size limit, between 8 and 128 terabytes.

In addition, Oracle Multimedia uses less memory for scale-down and thumbnail generation functions, raising the practical limit for images to be processed in this manner.

Applications in the security, media and entertainment, medical, and life sciences markets are generating more detailed and higher fidelity digital media. Advances in capture technology and reductions in the cost of storage present the need to organize more and larger digital images, audio, and video. In the past, individual digital pictures, audio, and video rarely exceeded a gigabyte. With Oracle Database 11g, Oracle Multimedia supports emerging applications that may require tens of gigabytes for a single multimedia file.

1.9.2.1 Multimedia DICOM Medical Imaging Format Support

Oracle Multimedia now fully supports the format universally recognized as the standard for medical imaging, Digital Imaging and Communications in Medicine (DICOM) version 3. Applications can now use Multimedia JAVA and PL/SQL APIs to store, manage, and manipulate DICOM content.

It is now quite reasonable to build large archives of medical content such as single frame and multiframe images, waveforms, slices of 3-D volumes, video segments, and structured reports that are managed and secured using powerful Oracle Database tools. Complete DICOM metadata support makes it very easy to index and search DICOM content for research. Central storage of DICOM content makes telemedicine practical. Incorporating DICOM content in a database makes it feasible to build electronic health care records applications using powerful application development tools from Oracle or other third-party vendors.

1.9.2.2 ORDDicom Object Type

A new Multimedia object type, ORDDicom, has been defined to hold the data associated with DICOM content such as single or multiframe images, waveforms, or structured reports, and to implement the methods to manipulate the DICOM content. A Java proxy class, OrdDicom, is also defined. It provides access to the ORDDicom database object through JDBC in a Java application. For applications that already store DICOM content directly in BLOB or BFILE, a relational interface is also provided.

By presenting DICOM content stored in a database as objects, Oracle enables both rapid application development and easy, secure management of large medical archives.

1.9.2.3 DICOM Metadata Extraction

In the initial release of Oracle Multimedia DICOM support, the most important metadata tags associated with DICOM content could be extracted into an XML document that could then be indexed and searched to find DICOM content that matched certain conditions. With Oracle Database 11g, that support is extended to offer complete and extensible metadata extraction. Customers can use either the supplied DICOM mapping document to extract metadata into an XML document, or they can create their own mapping documents to include support for private tags or subsets of the standard DICOM metadata tags. The extracted metadata can then be stored in a table to facilitate DICOM content searching based on standard or private DICOM attributes.

With this significant enhancement to the DICOM metadata extraction ability, customers can build large archives of DICOM content that will prove invaluable for research. By customizing DICOM mapping documents, customers can easily create highly specialized indexes to DICOM content such as single and multiframe images, waveforms, and structured reports based on both standard and private metadata tags.

1.9.2.4 DICOM Conformance Validation

Given DICOM content and a set of user-specified conformance rules, Oracle Multimedia can verify that the DICOM content adheres to the conformance rules.

DICOM content is produced by many modalities. Most content conforms to the DICOM standard, but inevitably some does not. It is worthwhile to be able to identify DICOM content that does not conform to the standard. Validating DICOM content for conformance can ensure the consistency of a DICOM archive. It enables a database to accept DICOM content from multiple sources and to verify the integrity of the DICOM metadata.

1.9.2.5 DICOM Image Processing

Oracle Multimedia includes the ORDDICOM data type to natively support imagery produced by medical imaging devices. This release adds methods and functions to copy and convert images from DICOM to JPEG, GIF, PNG, TIFF, and other formats and to generate scaled versions and thumbnails.

In order to view medical images stored in the DICOM format in Web applications, the image must be reformatted and converted into a browser-ready format. Oracle Database 11g provides methods to reformat and deliver DICOM images to applications that require popular industry-standard image formats.

1.9.2.6 Making DICOM Content Anonymous

Oracle Database 11g offers a makeAnonymous() method which can be used to ensure that users of a DICOM medical archive see only the metadata that they are authorized to see.

Rules for making DICOM content anonymous can be customized in an anonymity document that specifies the set of attributes that should be made anonymous and how each should be obfuscated.

Clinicians need to be able to view all the metadata included in DICOM content for a patient being treated. Researchers need to be able to see the same DICOM content but, due to privacy regulations, must not be able to see any personally identifying information from that same DICOM content used.

By providing anonymity services in the database, Oracle Multimedia allows appropriate access for different classes of users of DICOM archives regardless of the application used to access the data.

1.9.2.7 Creation of DICOM Content

This release of Oracle Multimedia includes the ability to generate new DICOM content by combining digital images with an XML representation of the associated DICOM metadata. The result of this operation, well-formed and validated DICOM content, can be stored in a table in the database or delivered to a DICOM viewer.

Storing and retrieving paper- or film-based medical images is expensive and prone to error. However, many old images must be retained for a very long time and might be useful for both research and clinical purposes. By storing scanned images with the known metadata about those images in DICOM format, non-DICOM images become useful again and both storage and retrieval costs are reduced. New DICOM format content can also be generated to correct metadata errors in the original DICOM content using this technique.

1.9.2.8 Runtime Updatable DICOM Data Model

The runtime behavior of Oracle Multimedia DICOM support is determined by a set of user-configurable documents. This set of documents is collectively managed by a DICOM data model repository. An administrator can modify this data model repository to configure DICOM features for a particular database instance. With this design, a customer can upgrade DICOM features at any time, without interfering with an active DICOM archive.

Hospitals need their DICOM archives to be operational 24x7x365. They cannot afford to take down the system to update to a new version of the DICOM standard, to incorporate private tags for a new piece of equipment, to change their DICOM conformance rules, or to modify the set of tags they extract from DICOM content or the XML document they produce from the extracted tags. The data model repository implemented by this feature means that users can update their systems to accommodate new equipment and standards without interrupting the operation of the archive.

1.9.3.1 RFID Identity Code Data Type for EPC, DoD, and Custom Sensor Tags

A new Identity Code object type is implemented in Oracle Database to support custom, Electronic Product Code (EPC), and Department of Defense standard tag formats. Metadata is used to define rules for encoding and decoding any tag information into Identity Code objects. All EPC standard tag types and metadata standards are supported.

With this new feature, application developers can define and process identity code tags faster and with less programming in Oracle Database 11g.

1.9.4.1 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 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.

1.9.4.2 Support for Storage and Query of Semantic Content

Oracle Database 11g extends its semantic capabilities with native, lightweight Web Ontology Language (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 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, RDFS, and OWL as standards to represent semantic data.

1.10.1.1 ASM Fast Mirror Resync

A new SQL statement, ALTER DISKGROUP ... DISK ONLINE, can be executed after a failed disk has been repaired. The command first brings the disk online for writes so that no new writes are missed. Subsequently, it initiates a copy of all extents marked as stale on a disk from their redundant copies.

This feature significantly reduces the time it takes to repair a failed diskgroup, potentially from hours to minutes. The repair time is proportional to the number of extents that have been written to or modified since the failure.

1.10.1.2 ASM Manageability Enhancements

The new storage administration features for ASM manageability include the following:

• New attributes for disk group compatibility

  To enable some of the new ASM features, you can use two new disk group compatibility attributes, compatible.rdbms and compatible.asm. These attributes specify the minimum software version that is required to use disk groups for the database and for ASM, respectively. This feature enables heterogeneous environments with disk groups from both Oracle Database 10g and Oracle Database 11g. By default, both attributes are set to 10.1. You must advance these attributes to take advantage of the new features.

• New ASM command-line utility (ASMCMD) commands and options

  ASMCMD allows ASM disk identification, disk bad block repair, and backup and restore operations in your ASM environment for faster recovery.

• ASM fast rebalance

  Rebalance operations that occur while a disk group is in RESTRICTED mode eliminate the lock and unlock extent map messaging between ASM instances in Oracle RAC environments, thus improving overall rebalance throughput.

This collection of ASM management features simplifies and automates storage management for Oracle databases.

1.10.1.3 ASM Preferred Mirror Read

When ASM failure groups are defined, ASM can now read from the extent that is closest to it, rather than always reading the primary copy. A new initialization parameter, ASM_PREFERRED_READ_FAILURE_GROUPS, lets the ASM administrator specify a list of failure group names that contain the preferred read disks for each node in a cluster.

In an extended cluster configuration, reading from a local copy provides a great performance advantage. Every node can read from its local diskgroup (failure group), resulting in higher efficiency and performance and reduced network traffic.

1.10.1.4 ASM Rolling Upgrade

Rolling upgrade is the ability of clustered software to function when one or more of the nodes in the cluster are at different software versions. The various versions of the software can still communicate with each other and provide a single system image. The rolling upgrade capability will be available when upgrading from Oracle Database 11g Release 1 (11.1).

This feature allows independent nodes of an ASM cluster to be migrated or patched without affecting the availability of the database. Rolling upgrade provides higher uptime and graceful migration to new releases.

1.10.1.5 ASM Scalability and Performance Enhancements

This feature increases the maximum data file size that Oracle can support to 128 TB. ASM supports file sizes greater than 128 TB in any redundancy mode. This provides near unlimited capacity for future growth. The ASM file size limits are:

• External redundancy - 140 PB

• Normal redundancy - 42 PB

• High redundancy - 15 PB

Customers can also increase the allocation unit size for a disk group in powers of 2 up to 64 MB.

These improvements reduce database startup time and memory requirements, and allow support for larger ASM files, making it feasible to implement several hundred TB or even PB Oracle databases on ASM. Larger allocation units provide better sequential read performance.

1.10.1.6 Convert Single-Instance ASM to Clustered ASM

This feature provides support within Enterprise Manager to convert a non-clustered ASM database to a clustered ASM database by implicitly configuring ASM on all nodes. It also extends the single-instance to Oracle RAC conversion utility to support standby databases.

Simplifying the conversion makes it easier for customers to migrate their databases and achieve the benefits of scalability and high availability provided by Oracle RAC.

1.10.1.7 New SYSASM Privilege for ASM Administration

This feature introduces the new SYSASM privilege to allow for separation of database management and storage management responsibilities.

The SYSASM privilege allows an administrator to manage the disk groups that can be shared by multiple databases. The SYSASM privilege provides a clear separation of duties from the SYSDBA privilege.

1.10.2.1 Database Replay

Before making changes, such as hardware or software upgrades, companies typically conduct extensive testing to validate the changes. However, when the change is made on the production system, problems are often encountered because the testing was not performed on a realistic workload. Tools are available in the market to construct "synthetic workloads" involving multiple users. However, they are not able to adequately simulate the complexities of a real-world workload, such as interactions between concurrent activities and unpredictability of the workload profile.

The Database Replay feature addresses this need by enabling users to perform real-world testing by capturing the actual database workload on the production system and replaying it on the test system. It also provides analysis and reporting to highlight potential problems (for example, errors encountered and divergence in performance) and recommend ways to remedy the problems.

1.10.2.2 SQL Performance Analyzer

SQL performance regression is always a concern during system changes such as database upgrades, initialization parameter changes, and addition or dropping of indexes. The SQL Performance Analyzer feature alleviates this concern by providing an easy way to assess the impact of a change on the performance of SQL statements by comparing and contrasting their response times before and after the change. SQL Performance Analyzer allows you to capture the SQL workload from the source system, such as the production database, and to replay it on the test system where the change has been applied.

Any differences in response time of SQL statements, execution plan regressions, and so forth, are reported and precise recommendations on how to tune the under-performing SQL statements are provided.

1.10.3.1 Advanced Replication Cutover

In previous releases, Advanced Replication was a trigger-based method of replication. With Oracle Database 11g, this feature is a cutover of existing functionality to Web-based Enterprise Manager Database Control.

This feature provides support for existing Advanced Replication customers by providing the ability to manage Advanced Replication from 11g Enterprise Manager Database Control.

1.10.3.2 ASM Enhancements

This feature provides several enhanced ASM features for the Enterprise Manager user interface.

Enterprise Manager has been enhanced for ASM File Access Control, OCR and Voting Disk in ASM, Disk Resync, ASM Rolling Migrations, Disk Zones, ASM Manageability and Infrastructure, ACL Enhancements, and Security Classes for Fusion Security.

1.10.3.3 Change Manager - Sync and Propagate

This feature helps users deploy an application to multiple sites and to upgrade an earlier version of an application to the current version at multiple sites.

Users can now propagate metadata definitions, and some data, from a database or baseline to multiple database targets.

1.10.3.4 Database Cloning Enhancements

This feature provides the following improvements in Enterprise Manager database cloning pages:

• Removes the requirement for source and destination stage area.

• Supports cloning from a generic RMAN backup set.

• Enhances the job summary user interface.

These improvements facilitate database cloning from Enterprise Manager using new RMAN features.

1.10.3.5 Database Configuration (ECM) Collection Enhancements

This new feature provides enhancements to Database Configuration (ECM collections) maintenance.

The benefit is an improved configuration collection framework.

1.10.3.6 Database Home Page and Performance Page Improvements

This feature provides new charts, changes the layout, includes more details, and makes other improvements to the Database Home page and Performance page.

Enhancements to the Database Home page and Performance page lead to a better utility and ease-of-use.

1.10.3.7 DBCA Enhancements

The following enhancements have been added to DBCA:

• Added support to configure new database options

  The following options in Oracle Database 11g can be configured using Database Configuration Assistant:

  • Oracle Application Express

  • Oracle Database Vault

  • Oracle Warehouse Builder

• Automatic memory management

  This is a new initialization parameter in Oracle Database 11g to automate the memory allocation. By default, Database Configuration Assistant now uses MEMORY_TARGET instead of specifying individual values for SGA_TARGET and PGA_AGGREGATE_TARGET. The memory management page of Database Configuration Assistant has new option to select automatic memory management.

• Oracle Data Mining

  In Oracle Database 11g, the data mining schema are created when you run the catproc.sql script as the SYS user. You no longer configure this option through the Database Features screen of Database Configuration Assistant.

• ORACLE_BASE and Diagnostic Destination configuration

  The directory that you specify when you are prompted for ORACLE_BASE by Oracle Universal Installer is stored in the Oracle home inventory. Database Configuration Assistant uses this value to derive the default database locations and the DIAGNOSTIC_DEST parameter. The diagnostic destination location contains all ADR directories (diagnostic files such as Alert logs and so on). Starting with Oracle Database 11g Release 1 (11.1), the initialization parameter settings for background dump, user dump, and core dump destinations are replaced by the Diagnostic Destination.

• Secure database configuration

  Oracle Database 11g has new defaults for audit and password profiles. Database Configuration Assistant has a new screen to enable the new security settings during the database creation and existing database configuration.

• Switching a database from Database Control to Grid Control configuration

  In previous releases, Database Configuration Assistant contained functionality to configure a database either with Database Control or with Grid Control. You could configure a database either while creating it or later. However, reconfiguring a database from Database Control to Grid Control required significant manual effort. With Oracle Database 11g, Database Configuration Assistant provides the Enterprise Manager Configuration plug-in, which automates the process to switch configuration of a database from Database Control to Grid Control.

1.10.3.8 DBUA Enhancements

The following enhancements have been added to DBUA:

• Express Edition upgrade

  For single-instance databases, Oracle Database Upgrade Assistant configuration utility enables you to upgrade from Oracle Database Express Edition (Oracle Database XE) to Oracle Database 11g. The XE database files reside under the path ORACLE_BASE/oradata/XE. These files must be copied to a new location as the user may remove the XE Home after upgrade.

• Moving datafiles into ASM, SAN, and other file systems

  You can move datafiles to ASM, OFS, or other storage devices, such as Storage Area Networks (SAN) and Network Area Storage (NAS), as part of the upgrade. If you move the database files during the upgrade, you can benefit from the typical downtime for this tablespace by rebalancing disks and moving files to a better storage device, such as SAN, NAS, or ASM.

• ORACLE_BASE and Diagnostic Destination configuration

  The directory that you specify when you are prompted for ORACLE_BASE by Oracle Universal Installer is stored in the Oracle home inventory. Database Upgrade Assistant uses this value to derive the default database locations and the DIAGNOSTIC_DEST parameter. The diagnostic destination location contains all ADR directories (diagnostic files such as Alert logs and so on). This diagnostic destination directory is required while upgrading an earlier Oracle Database release to Oracle Database 11g release of the database. If the ORACLE_BASE directory already exists, Oracle Database Upgrade Assistant automatically retrieves this information and populates its path. Starting with Oracle Database 11g Release 1 (11.1), the initialization parameter settings for background dump, user dump, and core dump destinations are replaced by the Diagnostic Destination.

• Command-line option to AUTOEXTEND system files

  The command line option AUTOEXTEND facilitates auto extending of the data files as a part of the upgrade. This option autoextends the data files during the upgrade and turns the autoextend back to its original settings after the upgrade. This option is useful if there is enough room on the disk, and if you do not need to add new datafiles or manually increase the size of the files.

These features allow XE customers to upgrade to Standard Edition or Express Edition database for support and scalability.

These features also provide the ability to move to better storage devices as part of upgrade. You can move datafiles to be in compliance with OFA standards.

1.10.3.9 Enhanced Adaptive Metric Thresholds

The Adaptive Metric Thresholds feature simplifies and improves the selection of alert thresholds for database performance metrics. Key enhancements include:

• Full integration with AWR baselines as the source for metric statistics.

• Quick configuration option offers one-click starter set of thresholds based on OLTP or Data Warehouse system profiles.

• Automated time group selection for SYSTEM moving window baseline.

• Guided discovery of candidate alert metrics based on response levels to known problem.

• Improved metric charting and analysis.

This feature helps DBAs configure high quality, baseline-driven performance alert thresholds with minimal effort.

1.10.3.10 Fusion Requirements

This feature fulfills the requirements for Fusion in Enterprise Manager, including:

• Group copy of tnsnames.ora

• Monitor and validate init.ora

• Monitor database session trace options

• View database trace files

• Archive and purge trace and alert files

This feature allows Oracle Application Server to use Enterprise Manager for many monitoring and configuration-related tasks.

1.10.3.11 Management of Oracle Text Indexes

This feature provides a user interface to support management of Oracle Text indexes.

Oracle Enterprise Manager provides an interface for configuration, maintenance, and administration of Oracle Text, including:

• Viewing details of Oracle Text indexes

• Synchronizing Oracle Text indexes

• Optimizing Oracle Text indexes

• Rebuilding indexes

• Editing Oracle Text indexes

• Querying log analysis

1.10.3.12 Migrate Database to ASM - Enhancements in Enterprise Manager

The Enterprise Manager pages for Migrate Database to ASM have been enhanced to include notification when a job is suspended and support for tablespace level migration.

This feature makes it easier to use Enterprise Manager to migrate non-ASM databases to ASM.

1.10.3.13 Storage / Audit Report and Metric Enhancement

Enterprise Manager has been enhanced to provide better storage and audit reports and metrics.

This feature facilitates monitoring in the storage area and audit area.

1.10.3.14 Storage: Schema, Security, and Configuration Enhancements

Enterprise Manager has been enhanced in the following areas: export, import, Data Pump, storage administration, schema administration, OLAP, summary management, and partition maintenance.

This feature improves Enterprise Manager support in several areas.

1.10.3.15 Wait Activity Detail Enhancement

Until now, Oracle Enterprise Manager displayed the Wait Activity Drilldown detail in a chart format.

This feature provides more detailed Wait Activity information for the client, service, module, and action pages in Oracle Enterprise Manager.

1.10.3.16 Workspace Manager

The Oracle Enterprise Manager interface for the Workspace Manager features has been enhanced to include support for compress workspace, manage system parameters, privilege management, FindRICSet, and others.

This feature facilitates the administration of Workspace Manager from Oracle Enterprise Manager.

1.10.4.1 ADDM for Oracle Real Application Clusters

ADDM has been enhanced to provide comprehensive cluster-wide performance diagnostic and tuning advice. A special mode of ADDM analyzes an Oracle RAC database and reports on issues that are affecting the entire cluster as well as those that are affecting individual instances.

This feature is particularly helpful in tuning global resources such as I/O and interconnect traffic and makes the tuning of Oracle RAC databases easier and more precise.

1.10.5.1 Auto-Task

In Oracle Database 11g, Auto-Task can run hundreds or thousands of jobs in the manageability windows. This feature adds the necessary scheduler infrastructure to automatically manage this very high manageability job load.

This feature provides Auto-Task the necessary infrastructure to handle large numbers of jobs.

1.10.5.2 Automatic Maintenance Tasks Management

This feature provides out-of-the-box management of resource distribution (CPU and I/O) among the various database maintenance tasks such as Automatic Optimizer Statistics Collection, Automatic Segment Advisor, and others. The CPU is automatically managed. I/O is managed only if the I/O Resource Manager is enabled.

This feature ensures that work during maintenance operations is not affected and that user activity gets the necessary resources to complete.

1.10.5.3 Automatic Workload Repository (AWR) Baselines

Automatic Workload Repository (AWR) baselines enable accurate performance comparison by providing the ability to tag a certain period of time as a period of interest against which comparisons can be made at some time in the future. All the related performance statistics for this period are maintained and are available for use for any performance analysis with another time period.

AWR baselines provide powerful capabilities for defining dynamic and future baselines and considerably simplify the process of creating and managing performance data for comparison purposes.

1.10.5.4 Database Statistics Model Consolidation

In this release, the V$SYSSTAT and V$SYS_TIME_MODEL infrastructures have been consolidated into one advanced component.

This feature also implements a model to break out wait and general statistics into divisions that are usable for automated and manual tuning of the system.

1.10.5.5 Enhanced Active Session History

The Active Session History infrastructure has been enhanced to allow for improved database performance diagnosis and monitoring.

This feature includes enhancements to row source information for query progress monitoring and time model statistics.

1.10.5.6 Enhanced Advisor Framework

In this release, the Advisor Framework has been enhanced to support Health Monitor features.

The Advisor Framework also supports AWR import and export features.

1.10.5.7 Lightweight Jobs

Lightweight jobs have lower creation overhead and much less redo. You can now create large numbers of short, frequently executing jobs and still maintain performance.

1.10.5.8 Scheduling External Jobs on Multiple Nodes

With this new feature, you can create external jobs (for example, shell scripts) and provide a list of target machines where the jobs should be executed. For example, you can schedule a shell script x.sh to execute on machines A, B, and C. The target machines need not necessarily have an Oracle database installed. The jobs are managed from a central location.

This feature enables you to schedule an external job on multiple nodes while managing it from a central location.

1.10.5.9 Simplified Initialization Parameter Management

Several enhancements in this release make the management of server parameter file and initialization parameter values easier. Some of these enhancements are:

• More fault tolerant SPFILE

• Easier recovery from loss of SPFILE

• Preventing users from setting invalid parameter values in the SPFILE

• Redesigned, more intuitive Enterprise Manager initialization parameter management interface

This feature improves database manageability by making it easier to manage the server parameter file and changes to initialization parameter values.

1.10.6.1 I/O Calibration

The DBA can assess the I/O capability of the database's storage system by using the PL/SQL function DBMS_RESOURCE_MANAGER.CALIBRATE_IO(). This routine issues a very I/O intensive read-only workload to the database's files to assess the maximum IOPS (I/O requests per second) and MBPS (megabytes of I/O per second) that can be sustained. This data can be reexamined at any time using the PL/SQL function DBMS_RESOURCE_MANAGER.CALIBRATION_RESULTS().

The calibration should be performed when the database is idle and during off-peak hours to minimize the heavy I/O workload from interfering with the regular workload and vice versa.

I/O calibration can be used to understand the performance of the storage subsystem and figure out whether I/O performance problems stem from the database or the storage subsystem. Unlike various external I/O calibration tools, this tool uses the Oracle code stack and issues I/O randomly rather than sequentially. The results, therefore, much more closely match the actual database performance.

1.10.6.2 I/O Statistics

I/O Statistics provide consistent and complete statistics from all database clients by:

• I/O type (single and multi block, read and write)

• Component

• Data file

• Consumer group

The I/O statistics collected are used in AWR for monitoring, diagnostics, and tuning. The information is also used by the I/O Resource Manager.

1.10.6.3 Per Session I/O Limits

In previous versions of Oracle, the DBA could specify the maximum amount of time a session could run before some action was taken; for example, the call was aborted, the session was killed, or the session was migrated to a new consumer group. This feature is configured on a per consumer group basis in a resource plan.

In Oracle Database 11g, DBAs can also specify the maximum number of I/O requests or the maximum megabytes of I/O that a session can issue before the same set of actions are taken.

This feature is used for two purposes. The first is to automatically identify runaway queries. The second is to move sessions executing long-running calls to lower-priority consumer groups.

1.10.6.4 Resource Manager Statistics in AWR

AWR contains new historical views for Resource Manager statistics. These new views, DBA_HIST_RSRC_PLAN and DBA_HIST_RSRC_CONSUMER_GROUP, contain the historical version of the statistics in the views V$RESOURCE_PLAN and V$RESOURCE_CONSUMER_GROUP. AWR also contains per-minute metrics for resource consumption and Resource Manager-induced waits in the view V$RSRCMGRMETRIC.

This feature makes it easier to view historical Resource Manager statistics.

1.10.6.5 Resource Plans

This feature provides sophisticated new built-in resource plans:

• Mixed-workload plan

  Provides resource management for a mixed environment consisting of OLTP and DSS/batch jobs.

• Data Warehouse plan

  Provides resource management for a data warehousing environment.

• Maintenance Plan

  Provides resource management for the maintenance window.

The resource plans are shipped with the database and provide resource management directives that should bring immediate benefits in most customers situations.

1.10.7.1 Enhanced Optimal Flexible Architecture (OFA) Support

The database installation process has been redesigned to be based on the ORACLE_BASE environment variable. Until now, setting this variable has been optional and the only required variable has been ORACLE_HOME. With this feature, ORACLE_BASE is the only required input, and the ORACLE_HOME setting will be derived from ORACLE_BASE. This change streamlines how Oracle software installation is organized, thereby making ongoing management easier.

This feature improves manageability by making default Oracle Database installations more compliant with Optimal Flexible Architecture (OFA) specifications.

1.10.8.1 AUM Default Setting and Migration Support

Automatic Undo Management (AUM) is now enabled by default. A PL/SQL procedure is provided to help properly size the Undo tablespace for each individual environment.

This feature facilitates seamless migration to AUM from databases being upgraded to the new release so that they can start taking advantage of all the benefits of AUM.

1.10.8.2 Enhanced Statistics Collection for Partitioned Objects

An improved statistics collection process for partitioned objects avoids having to regather statistics on partitions that have not been touched by using a summary instead.

Partitioned objects tend to become larger and larger, and statistics collection, and particularly global statistics collection, can become increasingly time and resource intensive. This feature significantly improves the speed and accuracy of statistics collection for partitioned objects.

1.10.8.3 Simplified Temp Space Management

This feature demystifies the management of temp space. It allows a DBA to easily determine how much temp space is being used, who is using it, and whether more is needed.

This feature helps reduce errors due to inadequate temp space configuration and identifies application design issues such as runaway queries.

1.10.9.1 Automatic SQL Tuning with Self-Learning Capabilities

This feature takes automatic SQL tuning to the next level by adding self-learning capabilities to it.

Oracle Database now automatically detects high-load SQL statements and then tunes them automatically as needed in the maintenance window by creating appropriate SQL Profiles. It also issues proactive advice to create new access structures such as indexes that will significantly improve the performance of the high-load SQL statements.

1.10.9.2 Enhanced Optimizer Statistics Maintenance

The publication of statistics is now decoupled from the collection of statistics.

Changing optimizer statistics can have an adverse affect on execution plans. By separating the statistics collection from making them fully available to the optimizer, application developers and DBAs have the opportunity to assess the effects of the new statistics on execution plans.

1.10.9.3 Multi-Column Statistics

Multi-column statistics can be now collected for any arbitrary set of columns in a single table. This enhancement improves the costing accuracy of many SQL constructs, such as multi-column filter conditions and joins involving more than one column.

Extending the type of optimizer statistics gathered helps the optimizer choose the best plan with improved accuracy.

1.10.9.4 Partition Advisor

The SQL Access Advisor has been enhanced to include advice on how to partition tables, materialized views, and indexes in order to improve performance of SQL statements.

Oracle Database offers a wide variety of partitioning options whose proper use requires expertise and time. This feature makes it easy for all users to use partitioning in a way that is most suitable for their environments by giving intelligent and accurate advice on exactly how to partition a particular object for optimal performance.

1.10.9.5 SQL Plan Management

The SQL plan management feature enables the optimizer to maintain a history of execution plans for a SQL statement. Using the execution plan history, the optimizer is able to detect a new plan representing a plan change for a SQL statement. When the optimizer detects a new plan, it stores the new plan and marks it for performance evaluation and uses the old (currently known good) plan. The optimizer uses the new plan only after its performance is verified to be better than that of the old plan.

Execution plan change for a SQL statement can cause severe performance degradation on a system. Execution plan changes occur due to system changes such as a new optimizer version, refresh of optimizer statistics, and optimizer parameter changes. This new SQL plan management feature prevents performance regressions caused by execution plan changes.

1.10.10.1 Automated Alerts for Streams

Alerts are generated automatically if a Streams process becomes disabled. Alerts can also be generated based on user-defined thresholds for key Streams metrics.

Alerts provide the Streams administrator with immediate information on critical Streams components.

1.10.10.2 Divergence Detection and Resolution

The DBMS_COMPARISON package enables comparison of table data between databases and provides the ability to resolve any data discrepancies.

This feature can be used to validate the consistency of data within a single database or across databases.

1.10.10.3 Split and Merge of Streams

Administrators can split a stream in cases where a replica is unavailable and can allow that stream to replicate at a future point in time. After the replica using the split stream catches up, the stream can be merged into a single stream again.

Use of this feature minimizes the impact of unavailable replicas in a Streams replication.

1.10.10.4 Streams Message Tracking

This feature provides a simple way for an administrator to track the path an LCR traverses between processes and across databases within Streams. Streams message tracking is initiated by way of a procedure for a particular session. Subsequent LCR activity generated in that session is marked. As the LCRs move through the stream, the actions on those LCRs are traced, and the information is visible in runtime views.

Streams message tracking allows administrators to easily diagnose problems in a Streams configuration.

1.10.10.5 Streams Performance Advisor

This feature provides an advisor to diagnose configuration and performance issues while using Streams. The Streams performance advisor reports on performance of all Streams components and provide suggestions for performance tuning.

The Streams performance advisor provides valuable information on setting up and managing a Streams configuration.

1.10.10.6 Streams Topology

This feature provides a PL/SQL package and set of data dictionary views for discovery of the Streams topology. Using a single schema with database links and privileges to participating databases, the topology discoverer traverses the various components of Streams (capture, propagation sender/receiver, and apply) in each database to identify correlated components and assign a Streams identifier. Database views provide the administrator with details of the Streams topology.

Administrators can check the Streams set up quickly using the Streams topology views. In addition, the Streams advisors are used in combination with this feature to manage and monitor Streams.

1.10.10.7 Synchronous Streams Capture

This feature makes it possible to capture changes to database tables as part of a user transaction.

Synchronous Streams Capture minimizes the resource requirements to replicate a small subset of database tables.

1.11.1.1 Direct NFS Client

This features implements NFS client as part of Oracle Database kernel.

The primary benefits of this feature are improved performance and manageability. The Direct NFS Client improves I/O performance by incorporating Oracle-specific optimizations and eliminating the additional overhead traditionally found in operating system kernel mode implementation of NFS protocol. In addition, it simplifies configuration by eliminating the need to manually tune most of the NFS client parameters.

1.11.1.2 Enhanced Partition Pruning Capabilities

Partition pruning now uses bloom filtering instead of subquery pruning. While subquery pruning was activated on a cost-based decision and consumed internal (recursive) resources, pruning based on bloom filtering is activated all the time without consuming additional resources.

The performance of partition pruning has been enhanced. Furthermore, partition pruning will be automatically activated for every join with a partitioned object.

1.11.1.3 Intelligent Cursor Sharing

This feature helps determine whether a cursor will be shared. For example, instead of blindly reusing an existing cursor with its plan, the optimizer takes ranges of safe values into account when evaluating cursor sharability.

Cursor sharing has been enhanced to a more sophisticated sharing model. This enables you to leverage cursor sharing more commonly while preserving bind variable specific plan optimizations for shared statements.

1.11.1.4 NLS Performance Improvements

Oracle Database 11g includes incremental performance enhancements for NLS-related features.

This feature provides better performance for customers using NLS features.

1.11.2.1 Binding and Defining Multiple Buffers in OCI

This feature allows OCI applications to specify noncontiguous buffers for reading or writing in the define or bind calls.

Applications with disperse data sources with frequent read/writes often have data in noncontiguous memory. With this feature, these applications perform faster and use less memory because the overhead of memory copying to a contiguous chunk is eliminated.

1.11.2.2 Bitmap Join Index Support for Index-Organized Tables

A bitmap join index on index-organized tables (IOTs) provides access to rows of one IOT or heap table based on columns of another IOT or heap table. Bitmap join index support for IOTs can avoid actual joins of tables and reduce the volume of data to be joined by performing restrictions in advance. Bitmap join indexes are space efficient and can speed up queries through bitwise operations.

This feature provides bitmap join index support for index-organized tables (IOTs) to improve performance.

1.11.2.3 Cost-Based Optimizer Enhancements

The cost bases of various SQL execution steps and internal transformations are now determined in a more accurate manner.

Enhancements to the optimizer's internal costing mechanisms improve the accuracy of execution plan generation, thus generating more optimal plans.

1.11.2.4 Network/OCI Fusion

This feature increases the throughput and decreases the overhead of data transfer through OCI/Network layers.

This enhancement improves the performance of SQL statements as well as statements involving large amounts of data transfer.

1.11.2.5 OCI Descriptor Array Allocation

OCI descriptors are opaque data structures meant only for interpretation by OCI but used by applications to signify data such as OCIDateTime and OCILobLocator.

Until now, descriptors could only be allocated one at a time. This feature allows allocating and freeing an array of descriptors simultaneously.

Applications using many OCI resources can allocate these resources more easily with the new Descriptor Array Allocation interface, providing less code maintenance and less CPU overhead for allocating and freeing memory.

1.11.2.6 OCI Implicit ROWID Fetching

This feature provides the ability to implicitly fetch the ROWID of the rows fetched as the result of a SELECT...FOR UPDATE query into memory defined at position 0 while doing array fetches. Users need not alter the query to fetch the ROWID along with other columns explicitly.

Applications taking advantage of array fetches can now update individual rows as needed, giving more flexibility to high performance applications.

1.11.2.7 PL/SQL Function Result Cache

New in Oracle Database 11g is the ability to mark a PL/SQL function to indicate that its result should be cached to allow lookup, rather than recalculation, on the next access when the same parameter values are called. This function result cache saves significant space and time. Oracle does this transparently using the input value as the lookup key. The cache is system-wide so that all distinct sessions invoking the function benefit. If the result for a given set of values changes, you can use constructs to invalidate the cache entry so that it is properly recalculated on the next access. This feature is especially useful when the function returns a value that is calculated from data selected from schema-level tables. For such uses, the invalidation constructs are simple and declarative.

Concurrent, multi-user applications that use this feature experience better response times. Applications that implement a session-private scheme consume significantly less memory by using this feature and, therefore, experience improved scalability.

1.11.2.8 PL/SQL Native Compilation Without Needing a Third-Party C Compiler

On many platforms, the PL/SQL compiler may now generate processor-specific native code directly from the PL/SQL source code without needing to use a third-party C compiler. As in Oracle Database 10g, the generated code will be stored canonically in the database catalog. When a unit is needed, the Oracle executable loads the code directly into memory from the catalog, without first staging the unit through a .DLL or .so file.

The execution speed of natively compiled PL/SQL programs will never be slower than in Oracle Database 10g and may be improved in some cases by as much as an order of magnitude.

On platforms with this feature, Oracle Database 10g parameters, plsql_native_library_dir and plsql_native_library_subdir_count, will no longer be needed, and need not be set. If upgrading from Oracle Database 10g, any existing files in those directories may safely be deleted.

The benefit of PL/SQL native compilation is automatically available with Oracle Database 11g. No third-party software (neither a C compiler nor a DLL loader) is needed.

Note that this feature may not be available on all platforms in the first release of Oracle 11g. If this feature is not available on a particular platform, Oracle Database 10g implementation of native compilation will be unchanged on that platform. Check your platform-specific documentation to verify that this feature is available on your platform.

1.11.2.9 Query Result Cache

A separate shared memory pool is now used for storing and retrieving cached results. Query retrieval from the query result cache is faster than rerunning the query. Frequently executed queries will see performance improvements when using the query result cache.

The new query result cache enables explicit caching of results in database memory. Subsequent queries using the cached results will experience significant performance improvements.

1.11.3.1 ODBC Performance Improvement

The Oracle ODBC Driver has been enhanced with the following options that can be set using Setup DDL on Windows or through odbc.ini on Linux and Solaris:

• Statement caching

• Removal of optimizer hints for catalog functions

• Support for column binding for NUMBER columns as FLOAT

ODBC applications will perform faster, with little or no code change.

1.12.1.1 Kerberos Cross Realm Support

This feature updates the Oracle Kerberos implementation to support cross realm authentication. Now a Kerberos principal in one realm can provide authentication to a principal in another realm.

The benefit of this feature is that it makes Kerberos a more viable alternative for customers seeking strong authentication without the overhead of managing certificates and public key infrastructure (PKI).

1.12.1.2 SYSDBA Strong Authentication

All connections to the database can now use strong authentication, including those made as SYSDBA and SYSOPER.

Oracle has supported strong authentication through PKI, Kerberos, and Radius since Oracle8i for all connections to the database except those connections made as SYSDBA or SYSOPER. This feature completes Oracle support for strong authentication by extending the strong authentication feature to connections as SYSDBA and SYSOPER.

1.12.1.3 Tablespace Encryption

Tablespace encryption is an enhancement to the Oracle Advanced Security Transparent Data Encryption solution. Using tablespace encryption, customers can encrypt an entire tablespace, encrypting all data within the tablespace. When the database accesses the tablespace, the relevant data blocks are transparently decrypted for the application.

Transparent Data Encryption tablespace encryption provides an alternative to Transparent Data Encryption column encryption by enabling encryption of an entire tablespace. This eliminates the need for granular analysis of applications to determine which columns to encrypt, especially for applications with a large number of columns containing personally identifiable information (PII). Using tablespace encryption, customers can encrypt entire tables, eliminating the need to identify which columns contain personally identifiable information (PII). Customers who have small amounts of data to encrypt can continue to use the Transparent Data Encryption column encryption solution.

1.12.1.4 Hardware-Based Master Key Protection

This feature allows the Transparent Data Encryption (TDE) master key to be stored in an external Hardware Security Module (HSM) for even stronger security.

This feature provides even stronger security for the Transparent Data Encryption (TDE) master key for customers who are concerned about storing the master key on the operating system.

1.12.2.1 Audit By Default

This feature automatically configures the database for auditing and turns on auditing for specific events such as database connections.

Auditing is an important part of the security layer. Oracle has turned on some auditing settings by default to help customers better track connections to the database.

1.12.2.2 Built-in Password Complexity Checker

This feature builds into the database the password complexity routine that is documented in the Oracle Database Security Guide.

This built-in functionality can be easily turned on to guarantee that complex passwords are used when setting or resetting a password.

1.12.2.3 Built-in User Profile

This feature makes it easier to associate a database user with a profile by providing a build-in profile.

Customers can use a built-in profile to enforce password expiration and reuse policies.

1.12.2.4 Fine-Grained Access Control on Network Call-outs from the Database

The packages UTL_TCP, UTL_INADDR, UTL_HTTP, UTL_SMTP, and UTL_MAIL allow Oracle users to make network callouts from the database using raw TCP or using higher level protocols built on raw TCP. Until now, when the granularity of a privilege is simply execute on each package, there has been no way to allow a user access to just a list of specified internet hosts. The new package DBMS_NETWORK_ACL_ADMIN allows fine-grained control using ACLs implemented by XML DB.

This feature enables the DBA to carefully control which internet hosts Oracle users can access using the supplied PL/SQL packages.

1.12.2.5 Improve Security for Network Administration, Registration, and Operation

This feature provides secure service registration with the listener. Listener administration is secure by default. The new model provides an easy, optional, and manageable process administration and registration.

This security improvement prevents unauthorized use of remote and local operations.

1.12.2.6 Parameters for Enhanced Security of Database Communication

This feature provides the following enhancements:

• Reporting bad packets received on the database from protocol errors.

• Terminating or resuming server execution on receiving bad packets.

• Configuring the maximum number of authentication attempts.

• Controlling the display of the database version banner.

• Configuring banners for unauthorized access and auditing user actions.

This feature helps to keep data safe and secure.

1.12.2.7 Support Nonanonymous LDAP Access for Net Naming

This feature provides support for nonanonymous LDAP lookup for Network naming. For more secure LDAP access, users can specify that clients must identify themselves before a name lookup.

This feature results in improved security between LDAP and Net naming.

1.12.3.1 Integrated Database Security Manageability

Functionality previously provided in Enterprise Security Manager and Oracle Policy Manager has been integrated into Enterprise Manager. In addition, manageability for new features such as Transparent Data Encryption has been added to Enterprise Manager.

Over the years, Oracle has had numerous tools for managing database security. Now all of the functionality in those tools has been integrated into Enterprise Manager, dramatically simplifying security management in the database.

1.12.3.2 Virtual Private Catalog

This enhancement allows the catalog administrator to grant visibility of certain registered databases in the catalog to certain RMAN users.

This feature improves catalog security by allowing an RMAN user to view only his or her authorized, registered databases within a catalog. An RMAN user is no longer able to see all registered databases.

1.12.4.1 Standards-Based Password Algorithm

This feature modifies the verifier or hash used to store Oracle passwords.

This feature provides stronger protection for stored database passwords based on industry standard algorithms and provides enhanced security for password-based authentication by enabling usage of mixed case in passwords.

1.13.1.1 Active Directory Security Enhancements

This feature enhances security through better integration with the default Active Directory configuration, adding support for OS authentication with Active Directory.

This feature improves Net Naming support for Microsoft Active Directory.

1.13.1.2 Volume Shadow Copy Service (VSS) Writer

The Oracle VSS writer allows Oracle Database to participate in VSS-initiated backup and recovery on Windows 2003 and above. VSS is a Windows infrastructure composed of three participants:

• Requestors - management applications that initiate backup and recovery

• Writers - applications that own the data

• Providers - storage/hardware applications that coordinate the reading and writing of the data to appropriate storage

Several leading backup and storage vendors currently provide requestor and provider applications. With the Oracle VSS writer, customers can reliably back up and recover Oracle data in a VSS environment, which interoperates with these vendors' components.

This feature provides full interoperability with Volume Shadow Copy Service (VSS) infrastructure on Windows 2003 and above.