Aster Data Database Administration Class Outline

Transcription

1 Aster Data Database Administration Class Outline CoffingDW education has been customized for every customer for the past 20 years. Our classes can be taught either on site or remotely via the internet. Education Contact: Thomas Coffing CoffingDW.com

2 Description: In this course, participants will learn Aster Data from a DBA s perspective with a focus on what s most important to perform Objectives: At the completion of this course, participants will have the knowledge to be able to make strategic decisions regarding their Aster Data environment. Topics: The Aster Data Architecture Administrative Operations Overview Fact and Dimension Tables The AMC in Detail How Aster Processes Data Four Options for Aster Data Table Design Understanding the Event Engine How Joins Work Inside the Aster Engine Temporary and Analytic Tables Backing up the System Configuring the Aster Database Connector Aster Modeling Rules Tera-Tom s Top Tips Indexes SQL-H Workload Management Aster Windows Functions SQL-MapReduce Time and Date Aster Windows Functions

3 The Fundamental SQL Commands That Work on Aster Audience: This course is designed for all users of Aster Data with a special focus for DBAs. Prerequisites: None Duration: 1-2 Days Outline: Chapter 1 The Aster Data Architecture What is Parallel Processing? Aster Data is a Parallel Processing System Each vworker holds a Portion of Every Table The Rows of a Table are Spread Across All vworkers The Aster Data Architecture The Queen Node The Worker Node The Loader Node The Backup Node The Aster Architecture Interconnect Backup and Loader Nodes Do Not use the Interconnect The Aster Architecture has Spare Nodes The Aster Architecture Allows Flexibility based on Need Aster Data Provides Four Fundamental Hardware Strengths Replication Failover

4 Data is Compressed on Data Transfers Aster Utilizes Dual Optimizers Aster Allows a Hybrid of SQL and MapReduce MapReduce History What is MapReduce? What is SQL-MR? Sessionize An Example of SQL-MR Support for Mixed Workload Management and Prioritization Chapter 2 Administrative Operations Overview Cluster Management Cluster Administration Database Administration Bulk Utilities Aster Database Management Console (AMC) Aster Database Event Engine ncluster Command Line Interface (ncli) Aster ncluster Terminal (ACT) ODBC and JDBC Connections to Third Party Tools Aster Database Loader Aster Database Backup Logging Into the AMC Overview of the AMC The Dashboard Tab Overview of the AMC The Processes Tab Overview of the AMC The Nodes Tab Overview of the AMC The Admin Tab Chapter 3 Fact and Dimension Tables Aster Tables are defined as Fact or Dimension when Created Fact Table A More Detailed Look at the Fact Table Distribution

5 Dimension Table are Replicated A Dimension Table is often Replicated across vworkers Aster Data has Fact and Dimension Tables Aster Tables are defined as Fact or Dimension when Created Fact and Dimension Tables can be Hashed by the same Key Distribution Key Rules Aster Data Uses a Hash Formula The Hash Map Determines which vworker will own the Row The Hash Formula, Hash Map and vworker Placing rows on the vworker Placing rows on the vworker Continued A Review of the Hashing Process Like Data Hashes to the Same vworker Distribution Key Data Types Run ANALYZE to COLLECT STATISTICS on a Table Some Examples of ANALYZE What Columns to Analyze Chapter 4 The AMC in Detail Dashboard Tab Processes Tab Processes Tab (Hover over the ID) Processes Tab Detail by Clicking on the ID Processes Tab Query Timeline Processes Tab Sessions Nodes Tab Node Overview Nodes Tab Hardware Stats Nodes Tab Hardware Config Nodes Tab Hardware Config Admin Tab Cluster Management Admin Tab Events

6 Admin Tab Executables In the Admin Tab, we clicked on the Executables Tab. Admin Tab Logs Admin Tab Configuration>Cluster Settings Admin Tab Configuration>Workload>Service Classes Admin Tab Configuration>Roles and Privileges Setting up IP Pools Setting up IP Pools in the AMC Setting up IP Pools in the AMC (continued) Remove Nodes Check Node Hardware Configuration Configure Cluster Settings Cluster Settings Sparkline Graph Scale Units Graph Scaling Internet Access Settings Aster Support Settings QoS Concurrency Threshold Configuration Roles and Privileges View the List of Available AMC User Privileges View the List of Available AMC User Privileges (continued) Create an AMC User Check Current AMC Privileges Edit AMC Privileges Restarting Your Aster Database Procedure for Restarting the Aster Database Procedure for Restarting the Aster Database Activate Aster Database Activate Aster Database (continued) Situations that Require Activation Activate Aster Database: The Procedure

7 Balance Data Balance Process Cluster Management from the Command Line Check Cluster Status Soft Shutdown Soft Startup Free Space Occupied by Defunct Vworkers Passwordless Root SSH Passwordless Root SSH (continued) Passwordless Root SSH (continued) Passwordless Root SSH (continued) Passwordless Root SSH (continued) Passwordless Root SSH (continued) Chapter 5 How Aster Processes Data When a Table is Created, a Table Header is Created Every vworker has the Exact Same Tables All Aster Tables are spread across All vworkers The Table Header and the Data Rows are Stored Separately A vworker Stores the Rows of a Table inside a Data Block To Read Rows, a vworker Moves the Data Block into Memory A Full Table Scan Means All vworkers must Read All Rows The Achilles Heel, or Slowest Process, is Block Transfer Each Table has a Distribution Key A Query Using the Distribution Key uses a Single vworker. As Rows are Added, a Data Block will Eventually Split A Full Table Scan Means All vworkers Read All Blocks Distribution Key Query uses One vworker Each vworker Can Have Many Blocks for a Single Table A Full Table Scan Means All vworkers Read All Blocks Quiz How Many Blocks Move into vworker Memory?

8 Answer How Many Blocks Move into vworker Memory? Quiz How Many Blocks Move Using the Distribution Key? Answer-How Many Blocks Move Using the Distribution Key? Chapter 6 - Four Options for Aster Data Table Design There are Four Options to Aster Table Design Straight up Distribute by Hash Straight up Distribute by Hash - Problems Straight up Distribute by Replication Partition the Table with Logical Partitioning This Partitioned Table Sorts Rows by Month of Order_Date An All vworkers Retrieve By Way of a Single Partition You can Partition a Table by Range or by List A Partitioned By List Example with Three Tactical Queries Aster Data Multi-Level Partitioning Aster Allows for Multi-Level Partitioning SQL Commands for Logical Partitioning as One Table What Partitions are on my Table? What does a Columnar Table look like? A Comparison of Data for Normal Vs. Columnar A Columnar Table is best for Queries with Few Columns Quiz How Many Blocks Move to vworker Memory? Answer How Many Containers Move to vworker Memory? When to use a Columnar Table Chapter 7 Understanding the Event Engine Monitor Events with the Event Engine Monitor Events with the Event Engine Event Engine Overview Manage Event Subscriptions Create or Edit Event Subscriptions

9 Create or Edit Event Subscriptions (continued) Create or Edit Event Subscriptions (continued) Upgrades of Event Engine View Event Subscriptions Supported Events Supported Events (continued) Supported Events (continued) Remediations Automatic Cluster Shutdown on Disk Full Event Automatic Cluster Shutdown on Disk Full Event (continued) Event Engine Best Practices Test the Event Engine Test the Event Engine - Disk Level Events Test the Event Engine - Disk Level Events (continued) Troubleshoot Event Engine Issues Monitor the Aster Database with SNMP Chapter 8 - How Joins Work Inside the Aster Engine Aster Join Quiz Aster Join Quiz Answer The Joining of Two Tables Aster Moves Joining Rows to the Same vworker Because of the Join Rule Dimension Table are Replicated The Two Different Philosophies for Table Join Design What Could You Do If Two Tables Joined 1000 Times a Day? Fact and Dimension Tables can be Hashed by the same Key Joining Two Tables with the same PK/FK Distribution Key A Join With Co-Location A Performance Tuning Technique for Large Joins The Joining of Two Tables with an Additional WHERE Clause Aster Performs Joins Using Three Different Methods

10 The Hash Join The Merge Join Nested Loop Joins Chapter 9 - Temporary and Analytic Tables Aster has Three Types of Data Create a Permanent Table Using Create Table AS (CTAS) Create a Logically Partitioned Table and Populate It Create a Temporary Table with using Create Table AS (CTAS) A Temporary Table in Action A Temporary Table That Uses an Insert/Select Create an Analytic Table Using an Insert/Select Create an Analytic Table Using CREATE TABLE AS (CTAS) Operations that Invalidate an Analytic Table If an Analytic Table is Invalid Tera-Tom History Chapter 10 Backing up the System Manage Backups Add a New Backup Manager to the AMC Start a Backup Backup Manager Commands Backup Manager Commands (continued) Backup Manager Commands (continued) Backup Manager Commands (continued) Monitor and Manage Backups - Starting and Stopping Monitor and Manage Backups - Status and Availability Recovering the Queen Node - Queen Replacement Recovering the Queen Node - Queen Replacement Script Recovering the Queen Node - Queen Replacement (continued) Recovering the Queen Node - Queen Replacement (continued)

11 Queen Replacement Best Practices Chapter 11 - Configuring the Aster Database Connector Set up Host Entries for all Nodes Set up Host Entries for all Nodes (continued) Set up Host Entries for all Nodes (continued) Configure Hosts Configure Hosts (continued) Configure Hosts (continued) Configure Hosts (continued) Setting up the Connector - Networking Setting up the Connector - Client Software Setting up the Connector - Performance load_from_teradata Syntax load_from_teradata Syntax (continued) load_from_teradata Syntax (continued) load_from_teradata Syntax (continued) load_from_teradata Syntax (continued) load_to_teradata Syntax load_to_teradata Syntax (continued) load_to_teradata Syntax (continued) load_to_teradata Syntax (continued) load_to_teradata Syntax (continued) Cancelling load_to_teradata Procedure for Setting START_INSTANCE Procedure for Setting START_INSTANCE (continue) Procedure for Setting START_INSTANCE (continue) Procedure for Setting START_INSTANCE (continue) Troubleshooting the Connector Running Joins in Aster to Teradata Building Remote Views

12 Create Table in Aster Example Chapter 12 Aster Modeling Rules Modeling Rules for Aster Data Three Principles that Govern the Modeling Rules Modeling Rule 1 Dimensionalize your Model A Dimensional Model is called a "Star Schema" To Read a Data Block, a vworker Moves the Block to Memory A Dimensional Model Moves Less Mass into Memory Which Move From Disk to Memory Would You Choose? Vworkers transfer their Fact Table into Memory in Parallel Modeling Rule 2 Use Columnar Which Move From Disk to Memory Would You Choose? Let's Discuss Modeling and Joins at the Simplest Level Let's Discuss Modeling and Joins at the Simplest Level Let's Discuss Joins at the Simplest Level Modeling Rule 3 Distribute your Tables Based on Joins The Two Different Philosophies for Table Join Design Facts are Hashed and most often the Dimension is Replicated Fact and Dimension Tables can be Hashed by the same Key Joining Two Tables with the same PK/FK Primary Index A Join With No Redistribution or Duplication Aster Hates Joining Tables with a Different Distribution Key Aster Hates to Redistribute by Hash to Join Tables Modeling Rule 4 Replicate Dimension Tables Modeling Rule 5 Partition Your Tables Modeling Rule 6 Make Fact Tables Skinny Modeling Rule 6 Make Fact Tables Skinny Example Modeling Rule 7 Index Your Tables The B-Tree Index Which Columns Might You Create an Index?

13 Answer - Which Columns Might You Create an Index? Modeling Rule 8 Denormalize based on Your Environment. Modeling Rule 8 Denormalize based on Your Environment. Chapter 13 Tera-Tom s Top Tips Tera-Tom's Top Tips Tera-Tom's Top Tips # 2 Tera-Tom's Top Tips #3 Tera-Tom's Top Tips # 3 Rewritten Tera-Tom's Top Tips #4 When the GROUP BY Column is NOT the Distribution Key Example of GROUP BY Column is NOT the Distribution Key Tera-Tom's Top Tips #5 Tera-Tom's Top Tips #6 Use EXPLAIN Query Plan and Estimates Explain Plan Showing a Hash Join Explain Plan Showing a Merge Join Explain Plan Showing a Nested Loop Join Chapter 14 - Indexes There are Only Three Types of Scans Guidelines for Indexes An Index Syntax Example The B-Tree Index Which Columns Might You Create an Index? Answer - Which Columns Might You Create an Index? A Visual of an Index (Conceptually) A Query Using an Index Uses All vworkers Multicolumn indexes A NUSI BITMAP Theory A NUSI Bitmap in Action

14 Indexes on Expressions Indexes on Extracts of Dates GiST Indexes Five Operational Tips for Efficient Indexing REINDEX createcompressedindexoncompressedtablebydefault Flag Chapter 15 SQL-H Introduction to SQL-H Configuring SQL-H Aster Aster 5.10 or Earlier SQL-H Configuration Loading from HCatalog -Syntax Loading from HCatalog -Syntax (continued) Loading from HCatalog -Syntax (continued) Displaying the HCatalog Displaying the HCatalog (continued) Using SQL-H to Create Views Displaying the HCatalog (continued) Using SQL-H to Create Views Eliminating Partitions in SQL-H Views Conversions Tips for Working with SQL-H Tips for Working with SQL-H (continued) Troubleshooting SQL-H Troubleshooting SQL-H (Additional Errors You Might See) Chapter 16 Workload Management Introduction to Workload Management Admission Control Managing Concurrency Using ncli Configuring Admission Limits with AMC

15 Resource Management Resource Management (continued) Workload Settings via the Command Line Priority and Weight Resource Allocation Memory Soft Limit Percent Memory Hard Limit Percent Automatic Query Cancellation Workload Policies Setting Up Workload Rules Setting Up Workload Rules (continued) Setting Up Workload Rules (continued) Setting Up Workload Rules (continued) Setting Up Workload Rules (continued) Workload Predicates Workload Predicates (continued Workload Predicates (continued) Monitoring Queries Best Practices Troubleshooting Chapter 17 Aster Windows Functions Cumulative Sum Cumulative Sum - Major and Minor Sort Key(s) The ANSI CSUM Getting a Sequential Number The ANSI OLAP Reset with a PARTITION BY Statement PARTITION BY only Resets a Single OLAP not ALL of them ANSI Moving Sum is Current Row and Preceding n Rows How ANSI Moving SUM Handles the Sort Quiz How is that Total Calculated? Answer to Quiz How is that Total Calculated?

16 Moving SUM every 3-rows vs. a Continuous Sum Moving Average Quiz How is that Total Calculated? Answer to Quiz How is that Total Calculated? Quiz How is that 4th Row Calculated? Answer to Quiz How is that 4th Row Calculated? Partition By Resets an ANSI OLAP Moving Average Using BETWEEN Moving Difference using ANSI Syntax Moving Difference using ANSI Syntax with Partition By RANK Defaults to Ascending Order Getting RANK to Sort in DESC Order You can use Window Functions in Expressions RANK() OVER and PARTITION BY DENSE_RANK() OVER PERCENT_RANK() OVER PERCENT_RANK() OVER with 14 rows in Calculation PERCENT_RANK() OVER with 21 rows in Calculation RANK With ORDER BY SUM() COUNT OVER for a Sequential Number Quiz What caused the COUNT OVER to Reset? Answer to Quiz What caused the COUNT OVER to Reset? The MAX OVER Command MAX OVER with PARTITION BY Reset The MIN OVER Command Quiz Fill in the Blank Answer to Quiz Fill in the Blank The Row_Number Command Quiz How did the Row_Number Reset? Quiz How did the Row_Number Reset? NTILE

17 NTILE Using a Value of 10 NTILE With a Partition CUME_DIST CUME_DIST With a Partition LEAD LEAD With Partitioning LAG LAG with Partitioning FIRST_VALUE FIRST_VALUE After Sorting by the Highest Value FIRST_VALUE with Partitioning LAST_VALUE NTH_VALUE NTH_VALUE With Partition SUM(SUM(n)) Chapter 18 SQL-MapReduce MapReduce History What is MapReduce? What is SQL-MapReduce? SQL-MapReduce Input SQL-MapReduce Output Subtle SQL-MapReduce Processing Aster Data Provides an Analytic Foundation Path Analysis Text Analysis Statistical Analysis Segmentation (Data Mining) Graph Analysis Transformation of Data Sessionize

18 Tokenize SQL-MapReduce Function npath npath SELECT Clause npath ON Clause npath PARTITION BY Expression npath DIMENSION Expression npath ORDER BY Expression npath MODE Clause has Overlapping or NonOverlapping npath PATTERN Clause Pattern Operators Pattern Operators Order of Precedence Matching Patterns Which Repeat npath SYMBOLS Clause npath RESULTS Clause Adding an Aggregate to npath Results Adding an Aggregate to npath Results (Continued) SQL-MapReduce Examples - Use Regular SQL SQL-MapReduce Examples - Create Objects SQL-MapReduce Examples - Subquery SQL-MapReduce Examples - Query as Input SQL-MapReduce Examples - Nesting Functions SQL-MapReduce Examples - Functions in Derived Tables SQL-MapReduce Examples - SMAVG SQL-MapReduce Examples - Pack Function SQL-MapReduce Examples - Pack Function (Continued) SQL-MapReduce Examples - Pivot Columns Workshop: Create This Table Login to your GNOME Terminal Login to your Linux Using the GNOME Terminal Unzip the bank_web_data.zip Use the Function ncluster_loader to Load the Bank Data

19 Run this npath Map Reduce Function on your Table npath in Action Operators at their Simplest Pattern Accumulate Accumulate With All Pages Accumulate npath with a WHERE Clause SQL-MapReduce Examples - Path Generator SQL-MapReduce Examples - Path Generator (Continued) SQL-MapReduce Examples - Path Generator (Continued) SQL-MapReduce Examples - Path Generator (Continued) SQL-MapReduce Examples - Path Generator (Continued) SQL-MapReduce Examples - Linear Regression SQL-MapReduce Examples - Linear Regression (Continued) SQL-MapReduce Examples - Linear Regression (Continued) SQL-MapReduce Examples - Naive Bayes SQL-MapReduce Examples - Naive Bayes (Continued) SQL-MapReduce Examples - Naive Bayes (Continued) SQL-MapReduce Examples - Naive Bayes (Continued) SQL-MapReduce Examples - Naive Bayes (Continued) SQL-MapReduce Examples - Naive Bayes (Continued) SQL-MapReduce Examples - Naive Bayes (Continued) SQL-MapReduce Examples - Naive Bayes (Continued) Join Aster, Teradata and Hadoop Tables; feed into MapReduce Run Both of these Examples Together and Compare Run this npath Map Reduce Function npath in Action Another npath Example Finding Out What Functions You Have Installed Workshop 1 Fill in the x's Answer Workshop 1 - Fill in the x's

20 Workshop 2 Fill in the x's Answer Workshop 2 Fill in the x's Answer Workshop 2 You Could Have Used a GROUP BY Workshop 3 Add to the Query Workshop 3 Answer to Add to the Query Workshop 4 Fill in the x's Answer to Workshop 4 Fill in the x's Workshop 5 Find that Customer Answer to Workshop 5 Find that Customer Workshop 6 Change the MapReduce Function Answer to Workshop 6 Change the MapReduce Function Workshop 7 Build the MapReduce Function Answer to Workshop 7 Build the MapReduce Function Best Answer to Workshop 7 Build the MapReduce Function Workshop 8 Build the Accumulate in the Result Answer to Workshop 8 Build the Accumulate in the Result SQL-MapReduce Examples - Linear Regression (Continued) Workshop 9 Build the Subquery Answer to Workshop 9 Build the Subquery Workshop 10 Do Your First Join Answer to Workshop 10 Do Your First Join Answer to Workshop 10 Do the Join Using a New Syntax Workshop 11 Super Join the Tables Answer to Workshop 11 Super Join the Tables Answer to Workshop 11 Super Join the Tables Workshop 12 Sessionize the Data Answer to Workshop 12 Sessionize the Data Workshop 13 What is this Query Doing? Answer to Workshop 13 What is this Query Doing? Workshop 14 Using ilike Answer to Workshop 14 Using ilike

21 Answer to Workshop 14 Using ilike Workshop 15 What are the First Two Pages Visited? Workshop 15 What are the First Two Pages Visited? Workshop 16 Advanced - First Two Pages Visited? Answer to Workshop 16 Advanced - First Two Pages Visited? Workshop 17 Can You Clean Up the Results? Answer to Workshop 17 Can You Clean Up the Results? Answer to Workshop 17 Format the Date Workshop 18 Build a Churn Table Workshop 18 Run the Query Before Building to Test Workshop 18 A Better Example Answer to Workshop 18 Build a Basic Churn Table Workshop 18 Create the Churn Table with a Better Example Multi-Case The Multi-Case Function The Multi-Case Function in Nexus The Multi-Case Function Mixing and Matching The Multi-Case Function Mixing and Matching SQL-MapReduce Examples - cfilter SQL-MapReduce Examples - cfilter (Continued) SQL-MapReduce Examples - Linear Regression (Continued) SQL-MapReduce Examples - cfilter (Continued) SQL-MapReduce Examples - Linear Regression (Continued) SQL-MapReduce Examples - cfilter (Continued) SQL-MapReduce Examples - cfilter (Continued) SQL-MapReduce Examples - cfilter (Continued) SQL-MapReduce Examples - cfilter (Continued) SQL-MapReduce Examples - cfilter (Continued) SQL-MapReduce Examples - cfilter (Continued) SQL-MapReduce Examples - cfilter (Continued) CFILTER in Action with Bank_Web_Clicks

22 CFILTER in Action CFILTER using Nexus npath Error Chapter 19 Time and Date Date, Time, and Timestamp Keywords Add or Subtract Days from a date The to_char command A Summary of Math Operations on Dates Using a Math Operation to find your Age in Years Find What Day of the week you were Born Date Related Functions The EXTRACT Command EXTRACT from DATES and TIME EXTRACT with DATE and TIME Literals EXTRACT of the Month on Aggregate Queries A Side Title example with Reserved Words as an Alias Implied Extract of Day, Month and Year DATE_PART Function DATE_TRUNC Function DATE_TRUNC Function using TIME Aster NOW() Function Chapter 20 Aster Windows Functions Cumulative Sum Cumulative Sum - Major and Minor Sort Key(s) The ANSI CSUM Getting a Sequential Number The ANSI OLAP Reset with a PARTITION BY Statement PARTITION BY only Resets a Single OLAP not ALL of them ANSI Moving Sum is Current Row and Preceding n Rows How ANSI Moving SUM Handles the Sort

23 Quiz How is that Total Calculated? Answer to Quiz How is that Total Calculated? Moving SUM every 3-rows vs. a Continuous Sum Moving Average Quiz How is that Total Calculated? Answer to Quiz How is that Total Calculated? Quiz How is that 4th Row Calculated? Answer to Quiz How is that 4th Row Calculated? Partition By Resets an ANSI OLAP Moving Average Using BETWEEN Moving Difference using ANSI Syntax Moving Difference using ANSI Syntax with Partition By RANK Defaults to Ascending Order Getting RANK to Sort in DESC Order You can use Window Functions in Expressions RANK() OVER and PARTITION BY DENSE_RANK() OVER PERCENT_RANK() OVER PERCENT_RANK() OVER with 14 rows in Calculation PERCENT_RANK() OVER with 21 rows in Calculation RANK With ORDER BY SUM() COUNT OVER for a Sequential Number Quiz What caused the COUNT OVER to Reset? Answer to Quiz What caused the COUNT OVER to Reset? The MAX OVER Command MAX OVER with PARTITION BY Reset The MIN OVER Command Quiz Fill in the Blank Answer to Quiz Fill in the Blank The Row_Number Command Quiz How did the Row_Number Reset?

24 Quiz How did the Row_Number Reset? NTILE NTILE Using a Value of 10 NTILE With a Partition CUME_DIST CUME_DIST With a Partition LEAD LEAD With Partitioning LAG LAG with Partitioning FIRST_VALUE FIRST_VALUE After Sorting by the Highest Value FIRST_VALUE with Partitioning LAST_VALUE NTH_VALUE NTH_VALUE With Partition SUM(SUM(n)) Chapter 21 The Fundamental SQL Commands That Work on Aster BETWEEN is Inclusive BETWEEN Works for Character Data LIKE uses Wildcards Percent % and Underscore _ LIKE command Underscore is Wildcard for one Character GROUP BY Vs. DISTINCT Good Advice The Five Aggregates of Aster Data GROUP BY when Aggregates and Normal Columns Mix GROUP BY Delivers one row per Group GROUP BY Dept_No or GROUP BY 1 the same thing Limiting Rows and Improving Performance with WHERE WHERE Clause in Aggregation limits unneeded Calculations Keyword HAVING tests Aggregates after they are Totaled

25 Keyword HAVING is like an Extra WHERE Clause for Totals Getting the Average Values per Column Getting the Average Values per Column Average Values per Column for All Columns in a Table A two-table join using Non-ANSI Syntax A two-table join using Non-ANSI Syntax with Table Alias Aliases and Fully Qualifying Columns A two-table join using ANSI Syntax Both Queries have the same Results and Performance Quiz Can You Finish the Join Syntax? Answer to Quiz Can You Finish the Join Syntax? Quiz Can You Find the Error? Answer to Quiz Can You Find the Error? Quiz Which rows from both tables Won t Return? Answer to Quiz Which rows from both tables Won t Return? LEFT OUTER JOIN LEFT OUTER JOIN Brings Back All Rows in the Left Table RIGHT OUTER JOIN RIGHT OUTER JOIN Brings Back All Rows in the RIGHT Table FULL OUTER JOIN FULL OUTER JOIN Brings Back All Rows in All Tables Which Tables are the Left and which are the Right? Answer - Which Tables are the Left and which are the Right? INNER JOIN with Additional AND Clause ANSI INNER JOIN with Additional AND Clause ANSI INNER JOIN with Additional WHERE Clause OUTER JOIN with Additional WHERE Clause OUTER JOIN with Additional AND Clause Results from OUTER JOIN with Additional AND Clause Quiz Why is this considered an INNER JOIN? The DREADED Product Join

26 Result Set of the DREADED Product Join The Horrifying Cartesian Product Join The ANSI Cartesian Join will ERROR Quiz Do these Joins Return the Same Answer Set? Answer Do these Joins Return the Same Answer Set? How would you Join these two tables? How would you Join these two tables? You Can t Yet! An Associative Table is a Bridge that Joins Two Tables Quiz Can you Write the 3-Table Join? Answer to Quiz Can you Write the 3-Table Join? Quiz Can you Write the 3-Table Join to ANSI Syntax? Answer Can you Write the 3-Table Join to ANSI Syntax? Quiz Can you Place the ON Clauses at the End? Answer Can you Place the ON Clauses at the End? The 5-Table Join Logical Insurance Model Quiz - Write a Five Table Join Using ANSI Syntax Answer - Write a Five Table Join Using ANSI Syntax Quiz - Write a Five Table Join Using ANSI Syntax Answer - Write a Five Table Join Using ANSI Syntax Quiz - Write a Five Table Join Using Non-ANSI Syntax Answer - Write a Five Table Join Using Non-ANSI Syntax Quiz Re-Write this putting the ON clauses at the END Answer Re-Write this putting the ON clauses at the END The Nexus Query Chameleon Writes the SQL for Users. An IN List is much like a Subquery An IN List Never has Duplicates Just like a Subquery An IN List Ignores Duplicates The Subquery How a Basic Subquery Works The Final Answer Set from the Subquery Quiz- Answer the Difficult Question

27 Answer to Quiz- Answer the Difficult Question Should you use a Subquery or a Join? Quiz- Write the Subquery Answer to Quiz- Write the Subquery Quiz- Write the More Difficult Subquery Answer to Quiz- Write the More Difficult Subquery Quiz- Write the Subquery with an Aggregate Answer to Quiz- Write the Subquery with an Aggregate Quiz Write the Triple Subquery Answer to Quiz Write the Triple Subquery CHARACTER_LENGTH AND OCTET_LENGTH The TRIM Command trims both Leading and Trailing Spaces Trim and Trailing is Case Sensitive Trim and Trailing works if Case right The SUBSTRING Command How SUBSTRING Works with NO ENDING POSITION Using SUBSTRING to move Backwards How SUBSTRING Works with a Starting Position of -1 How SUBSTRING Works with an Ending Position of 0 An Example using SUBSTRING, TRIM and CHAR Together SUBSTRING and SUBSTR are equal, but use different syntax The POSITION Command finds a Letters Position Concatenation The Basics of CAST (Convert and Store) Some Great CAST (Convert and Store) Examples Some Great CAST (Convert and Store) Examples Combining Searched Case and Valued Case A Trick for getting a Horizontal Case Nested Case Put a CASE in the ORDER BY

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