11g overview

Oracle 11g Introduction James Harding Senior Sales Consultant [email_address]

Agenda Oracle 11gR2 DBMS Advanced Compression Real Application Testing Transparent Data Encryption Data Masking Partitioning Advanced Security Edition Based Redefinition New Indexing Features Analytical & OLAP Features <Insert Picture Here>

Customer Data Management Problems Real World Solutions Modern Enterprise Grids Real Application Clusters Management packs TimesTen In-Memory Database Information Lifecycle Management Partitioning Advanced Compression Data Warehousing Oracle Information Appliances OLAP, Mining, Warehouse Builder Governance, Risk & Compliance Security Options Total Recall Reduce time, cost and risk of change Real Application Testing “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Flashback Archive Data Guard Streams Online Maintenance Data Recovery Advisor High Availability

Flashback Recovery Sustained Innovation… “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Flashback Query Flashback Drop Flashback Database Flashback Data Archive

Error Investigation Using Flashback Flashback Query Query all data at point in time “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Tx 1 Tx 2 Tx 3 select * from Emp AS OF ‘2:00 P.M.’ where … select * from Emp VERSIONS BETWEEN ‘ 2:00 PM’ and ‘3:00 PM’ where … select * from FLASHBACK_TRANSACTION_QUERY where xid = ‘000200030000002D’; Flashback Transaction Query See all changes made by a transaction Flashback Version Query See all versions of a row between times See transactions that changed the row

Flashback Drop Allows users to quickly recover objects after a SQL drop operation Provides self-service recovery for dropped objects Eliminate the need for TSPITR Dropped objects are placed in a Recycle Bin Objects remain in the recycle bin until you permanently drop them with the PURGE command or recover them with the Flashback Drop command. Objects will remain in the recycle bin until there is no room in the tablespace for new rows or updates to existing rows or until the tablespace needs to be extended Purged in the order they were dropped “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Drop table emp; Emp Mistake made Emp Recycle bin Flashback Table emp before drop; Mistake undone

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” <Insert Picture Here> Flashback Data Archive

Flashback Data Archive Long term history- years Automatically stores all changes to selected tables in Flashback Data Archive Archive cannot be modified Old data purged per retention policy View table contents as of any time using Flashback SQL Uses: Change Tracking ILM Long term history Auditing Compliance “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” ORDERS User Tablespaces Flashback Data Archive Archive Tables Oracle Database Changes Total Recall Select * from orders AS OF ‘ Midnight 31-Dec-2004 ’

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” <Insert Picture Here> Data Guard

Unlocking the Value of Standby DBs “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Standby for Online Upgrade, Auto Failover Standby for Testing, Readable Physical Standby for DR and Backup Logical Standby for Realtime Query

Physical Standby with Real-Time Query Read-only queries on physical standby concurrent with redo apply Supports RAC on primary / standby Queries see transactionally consistent results Handles all data types, very fast, operationally simple But not as flexible as logical standby Immediate appeal to the many users of physical standby DR with real time query is unique in the industry – no idle resources “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Physical Standby Database Primary Database Real-time Queries Continuous Redo Shipment and Apply Concurrent Real-Time Query Now supports Incremental backups!

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Data Recovery Advisor

Data Recovery Advisor The Motivation Oracle provides robust tools for data repair: RMAN – physical media loss or corruptions Flashback – logical errors Data Guard – physical or logical problems However, problem diagnosis and choosing the right solution can be error prone and time consuming Errors more likely during emergencies “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Recovery Investigation & Planning

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Data Recovery Advisor Enterprise Manager Support

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Real Application Clusters Automatic Storage Management Direct NFS Optimizer Enhancements Grid and OLTP

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Real Application Clusters

Standard Oracle Architecture “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Instance Database

Shared Disk Architecture “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Database Instance 1 Database Instance 2 Database Instance 3 Table A Table B Table C

RAC – Cache Fusion Protocol Locality Optimized Fusion Protocol (10.2) Oracle detects when most segment accesses are coming from a single instance Optimizes access by that instance Reader Optimized Fusion Protocol Highly read-intensive segments are automatically converted to a reader optimized messaging protocol Improved performance for read-intensive workloads improves any read from disk (not cache) whether short random reads or large table scans Throughput improved up to 70% for internal read-only benchmark Long Query Optimized Fusion Protocol After all modified cache buffers at start of query are written to disk, no more need for RAC communication Direct reads for non-parallel table scans Update Optimized Fusion Protocol Update block in parallel to readers releasing the block “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”

Database Control 11g Tiled Instance Charts “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”

Automatic Database Diagnostics Managers (ADDM) for Real Applications Cluster (RAC) RAC expert in a box Identifies performance problems for the entire RAC cluster database Database-wide analysis of: Global cache interconnect issues Global resource contention, e.g. IO bandwidth, hot blocks Globally high-load SQL Skew in instance response times Runs proactively every hour when taking AWR snapshots (default) ADDM for RAC “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Inst 1 AWR 1 AWR 2 AWR 3 Inst 2 Inst 3 Self-Diagnostic Engine Database-Level ADDM 11g Instance-Level ADDM

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” <Insert Picture Here> Automatic Storage Management

Automatic Storage Management The preferred and best storage manager for Oracle Databases Easier to manage than file systems Performance of raw volumes Built-in to Oracle database Shared storage pool for all databases Free , and widely adopted >65% of 10g RAC deployments on ASM >25% of 10g customers already using ASM Many VLDB over 10TB “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” ASM Disk ASM Disk ASM Disk ASM Disk ASM Disk

Automatic Storage Management Spreads database files evenly across storage arrays Storage arrays can be easily added or remove transparent data redistribution Data mirrored across arrays Tolerates failure of disks or arrays “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” New ASM features in Oracle 11g: ASM Fast Disk Resync ASM Preferred Mirror Read ASM Rolling Upgrade Larger extent, allocation unit sizes ASM Disk ASM Disk ASM Disk ASM Disk ASM Disk

Inside the Oracle Database 11g Optimizer Removing the black magic “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Plans change unexpectedly especially during upgrades Cardinality estimate is wrong so plan goes wrong Gathering Optimizer Statistics takes too long Bind peeking doesn ’ t work when there is a data skew

Inside the Oracle Database 11g Optimizer Removing the black magic “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Plans change unexpectedly especially during upgrades Guaranteed plan stability and controlled plan evolution Controlled statistics publication Cardinality estimate is wrong so plan goes wrong Collect appropriate statistics Eliminate wrong cardinality estimates Gathering Optimizer Statistics takes too long Faster statistics gathering Improved statistics quality Bind peeking doesn ’ t work when there is a data skew Enhanced plan sharing with binds

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” <Insert Picture Here> SQL Plan Management Guaranteed plan stability and controlled plan evolution

Without SQL Plan Management “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” SQL statement is parsed for the first time and a plan is generated Does plan gives good performance? Plan is “ verified by execution ” Something changes in the environment Statistics are re-gathered, DB upgrade or parameter change Changes result in new plan New plan implemented regardless of resulting performance NL NL GB Parse HJ HJ GB Parse Execute Plan Acceptable Execute Plan NOT Acceptable

With SQL Plan Management “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Repeatable SQL populates statement log, plan history and creates a baseline Parse HJ HJ GB Statement log Plan history HJ HJ GB Plan baseline Execute Plan Acceptable

With SQL Plan Management “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Something changes in the environment SQL statement is parsed again and a new plan is generated Statement log Plan history HJ HJ GB Plan baseline NL NL GB Parse GB NL NL New plan is not the same as the baseline – new plan is not executed but marked for verification

With SQL Plan Management “ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” Something changes in the environment SQL statement is parsed again and a new plan is generated New plan is not the same as the baseline – new plan is not executed but marked for verification Execute the known plan baseline “ performance guaranteed by history ” Execute Plan Acceptable Parse HJ HJ GB Statement log Plan history HJ HJ GB Plan baseline GB NL NL

Partitioning Information Lifecycle Management Compression VLDB

Partitioning in Oracle Database 11g

Oracle Partitioning 10 years of innovation Core functionality Oracle8 Range partitions, global range index Oracle8 i Hash and composite range-hash partitioning Oracle9 i List partitioning Oracle9 i R2 Composite range-list partitioning Oracle 10 g Global hash indexes Oracle 10 g R2 1M partitions per table Partitioning by reference Virtual column partitioning Automatic interval partitioning New composite partitioning: range-range, list-range, list-list, list-hash

Partition on virtual (computed) columns New composite partitioning Enhanced Partitioning Range List Hash Range 9i 8i List Range List Hash Range 11g 9i 8i List 11g 11g 11g JAN FEB >5000 1000- 5000 ORDERS RANGE-RANGE Order Date by Order Value USA EUROPE >5000 1000- 5000 ORDERS LIST-RANGE Region by Order Value USA EUROPE Gold Silver ORDERS LIST-LIST Region by Customer Type

The Concept of Partitioning Maintain Consistent Performance as Database Grows SALES SALES Jan Feb SALES Jan Feb Europe USA Large Table Difficult to Manage Partition Divide and Conquer Easier to Manage Improve Performance Composite Partition Higher Performance Match to business needs

Partition for Performance Partition Pruning What was the total sales amount for May 20 and May 21 2010? Select sum(sales_amount) From SALES Where sales_date between to_date( ‘05/20/2010’,’MM/DD/YYYY’) And to_date( ‘05/22/2010’,’MM/DD/YYYY’); 5/20 5/21 5/22 5/19 Sales Table Performs operations only on relevant partitions Dramatically reduces amount of data retrieved from disk Improves query performance and optimizes resource utilization

Partition to Manage Data Growth Compress Data and Lower Storage Costs Distribute partitions across multiple compression tiers Free up storage space and execute queries faster No changes to existing applications Active Data 3x OLTP Compression Read Only Data 10-15x DW Compression Archive Data 15-50x Archive Compression

Partitioning Partition level management On-line addition and removal of partitions Data management operations (loading, index builds) Range, hash, composite range-hash, list, composite range-list Improved availability Localized disk failures, backup and recovery Content is property of Oracle Corp. and is provided for Data Warehousing student education order table Drop Nov 2006 Add Jan 2006 Feb 2006 Mar 2006 Apr 2006 Local Index Other data is not affected

<Insert Picture Here> Virtual Column based Partitioning

Virtual Columns ANSI syntax Look just like regular columns from SQL perspective Support for partitioning, indexes, constraints, statistics, histograms Used by expression evaluation when applicable Oracle Confidential Create table t1 ( first_name varchar2, last_name varchar2, full_name as (first_name || ‘ ‘ || last_name) virtual)

Virtual Columns - Example Base table with all attributes ... CREATE TABLE accounts (acc_no number(10) not null, acc_name varchar2(50) not null, ... 12500 12507 12666 12875 Adams Blake Smith King

Virtual Columns - Example CREATE TABLE accounts (acc_no number(10) not null, acc_name varchar2(50) not null, ... acc_branch number(2) generated always as (to_number(substr(to_char(acc_no),1,2)))‏ 12500 12507 12 12666 12 12875 12 12 Base table with all attributes ... ... is extended with the virtual (derived) column Adams Blake Smith King

Virtual Columns - Example CREATE TABLE accounts (acc_no number(10) not null, acc_name varchar2(50) not null, ... acc_branch number(2) generated always as (to_number(substr(to_char(acc_no),1,2)))‏ partition by list (acc_branch) ... 12500 12507 12 12666 12 12875 12 12 Base table with all attributes ... ... is extended with the virtual (derived) column ... and the virtual column is used as partitioning key 32320 32407 32 32758 32 32980 32 32 ... Hurd Adams Blake Smith King Jones Clark Phillips

<Insert Picture Here> REF Partitioning

Before REF Partitioning Table ORDERS Jan 2006 ... ... Feb 2006 Table LINEITEMS Jan 2006 ... ... Feb 2006 Redundant storage of order_date Redundant maintenance RANGE( order_date ) ‏ Primary key order_id RANGE( order_date ) ‏ Foreign key order_id

REF Partitioning RANGE( order_date ) ‏ Primary key order_id Table ORDERS Jan 2006 ... ... Feb 2006 Table LINEITEMS Jan 2006 ... ... Feb 2006 RANGE( order_date ) ‏ Foreign key order_id PARTITION BY REFERENCE Partitioning key inherited through PK-FK relationship

<Insert Picture Here> Advanced Compression Option

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

Advanced Compression Option New in Oracle Database 11g Compress Large Application Tables Transaction processing, data warehousing Compress All Data Types Structured and unstructured data types Typical Compression of 2-3 X Cascade storage savings throughout data center Content is property of Oracle Corp. and is provided for Data Warehousing student education U p To 3X Compression

Advanced Compression Reduces storage requirements across all tiers Content is property of Oracle Corp. and is provided for Data Warehousing student education 5% Active 35% Less Active 60% Historical $16,600 $22,600 $19,400 Lets use compression factor of 3 $49,800 $67,700 $58,000

OLTP Table Compression Overhead Free Space Uncompressed Compressed Inserts are uncompressed Block usage reaches PCTFREE – triggers Compression Inserts are again uncompressed Block usage reaches PCTFREE – triggers Compression Adaptable, continuous compression Compression automatically triggered when block usage reaches PCTFREE Compression eliminates holes created due to deletions and maximizes contiguous free space in block

Security Access Control Data Protection Monitoring User Management

Oracle Database Vault Compliance and Insider Threats Controls on privileged users Restrict DBA access to application data Provide Separation of Duty Security for database and information consolidation Enforce data access security policies Control who, when, where and how is data accessed Make decision based on IP address, time, auth… Back Ported to Oracle9i R2 Validated with PeopleSoft E-Biz & other Apps validation underway, including 3 rd party Reports Realms Multi-Factor Authorization Separation of Duty Command Rules

Oracle Audit Vault Overview Trust-but-Verify Collect and Consolidate Audit Data Oracle 9i Release 2 and higher Simplify Compliance Reporting Built-in reports Custom reports Detect and Prevent Insider Threats Alert suspicious activity Scale and Security Robust Oracle Database technology Database Vault, Advanced Security Partitioning Lower IT Costs with Audit Policies Centrally manage/provision audit settings 10gR2 10gR1 Oracle 9iR2 (Future) Other Sources, Databases Monitor Policies Reports Security

Database 11g Core Database Security Enhancements Secure Configuration Continuation of Secure By Default initiative started in Oracle9i Password management settings Audit sensitive administrative operations Stronger password verifier Case sensitive passwords Backward compatibility mode Expanded Kerberos support Support principal names up to 2000 characters in length Cross realm support

Fine Grained Access Control for Utl_TCP and its cousins Challenge Oracle Database provides packaged APIs for PL/SQL subprograms to access machines (specified by host and port) using bare TCP/IP and other protocols built on it (SMTP and HTTP) Utl_TCP , Utl_SMTP , Utl_HTTP … If you have Execute on the package, you can access ANY host-port Solution an Access Control List (ACL) specifies a set of users and roles you assign an ACL to a host and port range you may need to explicitly grant this access in 11.1

What is data masking? What The act of anonymizing customer, financial, or company confidential data to create new, legible data which retains the data's properties, such as its width, type, and format. Why To protect confidential data in test environments when the data is used by developers or offshore vendors When customer data is shared with 3 rd parties without revealing personally identifiable information LAST_NAME SSN SALARY AGUILAR 203-33-3234 40,000 BENSON 323-22-2943 60,000 D ’ SOUZA 989-22-2403 80,000 FIORANO 093-44-3823 45,000 LAST_NAME SSN SALARY ANSKEKSL 111—23-1111 40,000 BKJHHEIEDK 111-34-1345 60,000 KDDEHLHESA 111-97-2749 80,000 FPENZXIEK 111-49-3849 45,000

Major features Automatic database referential integrity when masking primary keys Implicit – database enforced Explicit – application enforced Data mask format library View sample data before masking Application masking templates Define once; execute multiple times Enterprise Manager Data Masking Pack Production Staging Mask Test Test Clone Clone

Real Application Testing Database Management Management and Change

Oracle Database 11g Real Application Testing

The Need for Database Replay Businesses want to adopt new technology that adds value Extensive testing and validation is expensive in time and cost Despite expensive testing success rate low Many issues go undetected System availability and performance negatively impacted Cause of low success rate Existing tools provide inadequate testing Simulate synthetic workload instead of replaying actual production workload Provide partial workflow coverage Database Replay makes real-world testing possible

Database Replay Replay actual production database workload in test environment Identify, analyze and fix potential instabilities before making changes to production Capture Workload in Production Capture full production workload with real load, timing & concurrency characteristics Move the captured workload to test system Replay Workload in Test Make the desired changes in test system Replay workload with full production characteristics Honor commit ordering Analyze & Report Errors Data divergence Performance divergence Analysis & Reporting

Database Replay: Supported Changes Changes Unsupported Changes Supported Database Upgrades, Patches Schema, Parameters RAC nodes, Interconnect OS Platforms, OS Upgrades CPU, Memory Storage Etc. Middle Tier Storage Recording of External Client Requests Client Client … Client

Database Replay Workflow … Production (9.2.0.8, 10.2.0.3+) Test (11.1) Capture Replay Analysis & Reporting Process Storage Storage Mid-Tier Replay Driver Clients …

Database Replay Summary Report

Performance Page – Database Replay

Top Activity Page: Database Replay

SQL Performance Analyzer (SPA)

The Need for SQL Performance Analyzer (SPA) Businesses want systems that are performant and meet SLA ’s SQL performance regressions are #1 cause of poor system performance Solution for proactively detecting all SQL regressions resulting from changes not available DBA ’s use ineffective and time-consuming manual process to identify problems SPA automates identification of all SQL performance regressions resulting from changes

Test impact of change on SQL query performance Capture SQL workload in production including statistics & bind variables Re-execute SQL queries in test environment Analyze performance changes – improvements and regressions SQL Performance Analyzer … … Capture SQL Middle Tier Storage Oracle DB Re-execute SQL Queries Production Test Use SQL Tuning Advisor to tune regression Client Client … Client

SQL Performance Analyzer Workflow Storage Production (9iR2+) Test (10.2+) Capture SQL Transport SQL Execute SQL Pre-change Execute SQL Post-change Compare Perf Storage Mid-Tier Clients …

Real Application Testing for Prior Releases Helps Smooth Transition to Newer Releases Database Replay: Capture on older release; Replay on 11.1 and above SQL Performance Analyzer: Execute tests on 10.2 and above † For more details: Note: 560977.1: Real Application Testing for Earlier Releases Feature Upgrade From Upgrade To Database Replay 10 g R2 11 g 9 i R2 11 g SQL Performance Analyzer 10 g R2 10 g R2 or 11 g 10 g R1 10 g R2 or 11 g 9 i R2 10 g R2 or 11 g

Oracle Database 11g Database Manageability

Manageability Evolution Storage Backup Memory Apps/SQL Schema RAC Recovery Replication Auto- Tuning Advisory Instrumentation Low Impact Integrated Adaptive

Identifies cluster-wide critical performance problems Runs automatically when taking AWR snapshots Snapshots are synchronized across cluster Performs database-wide analysis of: Global resources, e.g. IO, global locks High-load SQL, hot blocks Global cache interconnect traffic Network latency issues Skew in instance response times Used by DBAs to analyze cluster performance ADDM for RAC

Enhancements to ADDM Findings Directives can instruct ADDM to suppress findings and reduce noise from known un-actionable findings

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

SQL Access Advisor: Partition Advice Indexes Materialized views Materialized views log SQL Access Advisor Hypothetical SQL cache Filter Options STS Complete Workload Partitioned objects Hash partitions? Interval partitions?

Automatic Memory Management in 11g Unifies system (SGA) and process (PGA) memory management Single dynamic parameter for all database memory – MEMORY_TARGET Automatically adapts to workload changes Maximizes memory utilization Available on: Linux Windows Solaris HPUX AIX O/S Memory O/S Memory PGA SGA PGA SGA

Automatic Diagnostic Repository diag rdbms DB Name SID ADR Base $ORACLE_HOME/log DIAGNOSTIC_DEST ADR Home $ORACLE_BASE ADRCI log.xml alert_SID.log V$DIAG_INFO alert cdump (others) hm incpkg incident metadata incdir_1 incdir_ n … trace Support Workbench BACKGROUND_DUMP_DEST USER_DUMP_DEST CORE_DUMP_DEST

Online Patching of One-off Patches Patch a running Oracle instance with no downtime Many one-off patches can be online patched Subset of RAC online upgradeable patches Great for diagnostic patches Enable, disable and de-install one-off patches with no downtime Integrated with OPatch and inventory Initially available on Linux and Solaris Long term goal is online patching of Critical Patch Updates (CPUs).

Oracle SecureFiles Consolidated Secure Management of Data SecureFiles is a new 11g feature designed to break the performance barrier keeping file data out of databases Next-generation LOBs - faster, and with more capabilities transparent deduplication, compression and encryption leverage the security, reliability, and scalability of database superset of LOB interfaces allows easy migration from LOBs Enables consolidation of file data with associated relational data single security model single view of data single management of data scalable to any level using SMP scale-up, or grid scale-out

Designed from Scratch SecureFiles is a major rearchitecture of how the database handles unstructured (file) data not an incremental improvement to LOBs Entirely new: disk format network protocol versioning and sharing mechanisms caching and locking redo and undo algorithms space and memory management cluster consistency algorithms

11g Statistics & SQL Analytics

11g Statistics & SQL Analytics Ranking functions rank, dense_rank, cume_dist, percent_rank, ntile Window Aggregate functions (moving and cumulative) Avg, sum, min, max, count, variance, stddev, first_value, last_value LAG/LEAD functions Direct inter-row reference using offsets Reporting Aggregate functions Sum, avg, min, max, variance, stddev, count, ratio_to_report Statistical Aggregates Correlation, linear regression family, covariance Linear regression Fitting of an ordinary-least-squares regression line to a set of number pairs. Frequently combined with the COVAR_POP, COVAR_SAMP, and CORR functions Descriptive Statistics DBMS_STAT_FUNCS: summarizes numerical columns of a table and returns count, min, max, range, mean, median, stats_mode, variance, standard deviation, quantile values, +/- n sigma values, top/bottom 5 values Correlations Pearson’s correlation coefficients, Spearman's and Kendall's (both nonparametric). Cross Tabs Enhanced with % statistics: chi squared, phi coefficient, Cramer's V, contingency coefficient, Cohen's kappa Hypothesis Testing Student t-test , F-test, Binomial test, Wilcoxon Signed Ranks test, Chi-square, Mann Whitney test, Kolmogorov-Smirnov test, One-way ANOVA Distribution Fitting Kolmogorov-Smirnov Test, Anderson-Darling Test, Chi-Squared Test, Normal, Uniform, Weibull, Exponential Note: Statistics and SQL Analytics are included in Oracle Database Standard Edition Statistics

Split Lot A/B Offer testing Offer “ A ” to one population and “ B ” to another Over time period “t” calculate median purchase amounts of customers receiving offer A & B Perform t-test to compare If statistically significantly better results achieved from one offer over another, offer everyone higher performing offer

Independent Samples T-Test (Pooled Variances) Query compares the mean of AMOUNT_SOLD between MEN and WOMEN within CUST_INCOME_LEVEL ranges SELECT substr(cust_income_level,1,22) income_level, avg(decode(cust_gender,'M',amount_sold,null)) sold_to_men, avg(decode(cust_gender,'F',amount_sold,null)) sold_to_women, stats_t_test_indep(cust_gender, amount_sold, 'STATISTIC','F') t_observed, stats_t_test_indep(cust_gender, amount_sold) two_sided_p_value FROM sh.customers c, sh.sales s WHERE c.cust_id=s.cust_id GROUP BY rollup(cust_income_level) ORDER BY 1; SQL Worksheet

Oracle OLAP 11g Optimizing BI Solutions with Oracle OLAP

Presentation Agenda Oracle OLAP Overview Enhancing BI Solutions Transparently Delivering Rich Analytics Easily <Insert Picture Here>

Oracle Database Strategy for DW Embedded Analytics Data Mining OLAP Statistics SQL Analytics Bring the analytics to the data Leverage core database infrastructure

Oracle OLAP Leveraging Core Database Infrastructure Single RDBMS-MDDS process Single data storage Single security model Single administration facility Grid-enabled Accessible by any SQL-based tool Connects to all related Oracle data Oracle Database 11 g Data Warehousing Warehouse Builder OLAP Data Mining

Oracle OLAP Goals Improve the delivery of information rich queries by SQL-based business intelligence tools and applications Fast query performance Simplified access to analytic calculations Fast incremental update Leverage existing Oracle Database expertise

Materialized Views Automatic Query Rewrite Most DW/BI customers use Materialized Views (MV) today to improve summary query performance Define appropriate summaries based on query patterns Each summary is typically defined at a particular grain Month, State Qtr, State, Item Month, Continent, Class etc. The SQL Optimizer automatically rewrites queries to access MV ’s whenever possible select month, district, sum(revenue) from sales, time, cust group by month, district rewrite SALES day_id prod_id cust_id chan_id quantity price revenue SALES_YC year_id continent_id quantity revenue Year, Continent SALES_MS month state quantiy revenue Month, State

Materialized Views Challenges in Ad Hoc Query Environments Creating MVs to support ad hoc query patterns is challenging Users expect excellent query response time across any summary Potentially many MVs to manage Practical limitations on size and manageability constrain the number of materialized views SALES day_id prod_id cust_id chan_id quantity revenue SALES_MCC month_id category_id city_id quantiy revenue Month, City, Category SALES_YCC year_id category_id city_id quantiy revenue Year, City, Category SALES_YCC year_id category_id continent_id quantiy revenue Year, Continent, Category SALES_QSI qtr_id item_id state_id quantiy revenue Qtr, State, Item SALES_XXX XXX_id XXX_id XXX_id expense_amount potential_fraud_cost Cust, Time, Prod, Chan Lvls SALES_XXX XXX_id XXX_id XXX_id expense_amount potential_fraud_cost SALES_XXX XXX_id XXX_id XXX_id expense_amount potential_fraud_cost SALES_XXX XXX_id XXX_id XXX_id quantiy revenue SALES_YCT year_id type_id continent_id quantiy revenue Year, District SALES_MS month state quantiy revenue Month, State SALES_YC year_id continent_id quantity revenue Year, Continent

Cube-based Materialized Views Breakthrough Manageability & Performance SALES day_id prod_id cust_id chan_id quantity price revenue TIME day_id month quarter year CUSTOMER cust_id city state country PRODUCT item_id subcategory category type rewrite A single cube provides the equivalent of thousands of summary combinations The 11g SQL Query Optimizer treats OLAP cubes as MV ’s and rewrites queries to access cubes transparently Cube refreshed using standard MV procedures CHANNEL chan_id class SALES CUBE refresh

Cost Based Aggregation Pinpoint Summary Management Improves aggregation speed and storage consumption by pre-computing cells that are most expense to calculate Easy to administer Simplifies SQL queries by presenting data as fully calculated NY 25,000 customers Los Angeles 35 customers Precomputed Computed when queried

Demonstration Transparently Improving Performance of BI Solutions

Easy Analytics Fast Access to Information Rich Results Time-series calculations Calculated Members Financial Models Forecasting Basic Expert system Allocations Regressions Custom functions … and many more Snapshot of some functions

Easy Analytics Optimized Data Access Method Data stored in dense arrays Offset addressing – no joins More powerful analysis Better performance Time Category Hotel Expenses Lunch Food Q1 Q2 Q3 SF West Northeast Market How do Expenses compare this Quarter versus Last Quarter What is an Item ’s Expense contribution to its Category?

BNP Paribas Advanced Time-Series Analyses in Real-Time Large European financial institution Used by traders to help decrease susceptibility to market volatility Replacing FAME Time Series Database F orecasting, A nalysis and M odeling E nvironment Three billion stored facts on RAC Data updated every 2 seconds – processing approximately 1m records daily SQL-based custom application used by 1500 concurrent users

Cube Represented as Star Model Simplifies Access to Analytic Calculations Cube represented as a star schema Single cube view presents data as completely calculated Analytic calculations presented as columns Includes all summaries Automatically managed by OLAP SALES_CUBEVIEW day_id prod_id cust_id chan_id sales profit profit_yrago profit_share_parent TIME_VIEW day_id quarter month year CUSTOMER_VIEW cust_id city state region PRODUCT_VIEW prod_id subcategory category group CHANNEL_VIEW chan_id class total SALES CUBE

The Gallup Organization Healthcare Group Gallup asks over 1 billion questions annually Gallup Healthcare Group Conduct surveys measuring quality of care and patient loyalty Originally developed a reporting infrastructure that delivered static reports to hospitals across the US Enhanced the interactivity and analytic content of solution Support over 1000 concurrent users Response time less than 2 seconds per query Reduced cost and complexity Leveraged Oracle Database investment Integrated OLAP into existing infrastructure (security, navigation, XML/XSL application underpinnings) Lowered application development costs Reduced complexity for users

Empowering Any SQL-Based Tool Leveraging the OLAP Calculation Engine SELECT cu.long_description customer, f.profit_rank_cust_sh_parent, f.profit_share_cust_sh_parent, f.profit_rank_cust_sh_level, f.profit, f.gross_margin FROM time_calendar_view t, product_primary_view p, customer_shipments_view cu, channel_primary_view ch, units_cube_view f WHERE t.level_name = 'CALENDAR_YEAR ‘ AND t.calendar_year = 'CY2006‘ AND p.dim_key = 'TOTAL‘ AND cu.parent = 'TOTAL‘ AND ch.dim_key = 'TOTAL‘ AND t.dim_key = f.TIME AND p.dim_key = f.product AND cu.dim_key = f.customer AND ch.dim_key = f.channel; Application Express on Oracle OLAP

Oracle OLAP 11g Summary Improve the delivery of information rich queries by SQL-based business intelligence tools and applications Fast query performance Simplified access to analytic calculations Fast incremental update Centrally managed by the Oracle Database

Oracle Database 11g Release 1 Upgrade Paths Direct upgrade to 11g is supported from 9.2.0.4 or higher, 10.1.0.2 or higher, and 10.2.0.1 or higher. If you are not at one of these versions you need to perform a “double-hop” upgrade For example: 7.3.4 -> 9.2.0.8 -> 11.1 8.1.7.4->9.2.0.8->11.1

Oracle Database 11g Installation Changes Addition of new products to the install SQL Developer Movement of APEX from companion CD to main CD Warehouse Builder (server-side pieces) Oracle Configuration Management (OCM) New Transparent Gateways Removal of certain products and features from the installation: OEM Java Console Oracle Data Mining Scoring Engine Oracle Workflow i SQL*Plus

<Insert Picture Here> Possible Upgrade Gotchas

Case Sensitive Password By default: Default password profile is enabled Account is locked after 10 failed login attempts In upgrade: Passwords are case insensitive until changed Passwords become case sensitive by ALTER USER On creation: Passwords are case sensitive Review: Scripts Database links with stored passwords Consider backward compatibility parameter, SEC_CASE_SENSITIVE_LOGON

Log files changes Automatic Diagnostic Repository $ORACLE_BASE/diag alert.log xml format $ORACLE_BASE/diag/rdbms/orcl/orcl/alert/log.xml adrci> show alert -tail

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

“ This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”

11g overview

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11g overview