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Aster Data Database Administration Course Outline

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

513-300-0341

[email protected]

CoffingDW.com


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


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


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


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


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


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)





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?


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


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?


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


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)



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)


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)



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_to_teradata Syntax

load_to_teradata Syntax (continued)




Cancelling load_to_teradata

Procedure for Setting START_INSTANCE

Procedure for Setting START_INSTANCE (continue)



Troubleshooting the Connector

Running Joins in Aster to Teradata

Building Remote Views


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


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?


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


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 #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


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


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)




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?


Moving SUM every 3-rows vs. a Continuous Sum

Moving Average



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


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?


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 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


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


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 - Linear Regression

SQL-MapReduce Examples - Linear Regression (Continued)


SQL-MapReduce Examples - Naive Bayes

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



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


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


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


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)











CFILTER in Action with Bank_Web_Clicks


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




Moving SUM every 3-rows vs. a Continuous Sum

Moving Average



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



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




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


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


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


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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