Course Catalog  > Data Science and Big Data  > Teradata
TP2827

Teradata Database Administration Training

This class teaches everything a Teradata DBA needs to know.  This class covers everything from Teradata DBA fundamentals, Teradata Architecture, and Teradata Internals.

Request Information
Request On-Site or Customized Course Info
Course Details

Duration

3 days

Prerequisites

No prior knowledge is presumed.

Target Audience

  • Teradata DBAs
  • Anyone who has interest in learning more about how to perform the role of a Teradata DBA.

Skills Gained

  • Knowledge to perform DBA job at a very high level.
Course Outline
  • Introduction and Good Advice
    • What is Parallel Processing?
    • Start Small and Think Big
    • Give your Enterprise the Tools they need
    • Model the Business with ERwin
    • Educate the Business on the Business by Sharing the Model
    • Load Your Models and have the SQL Built Automatically
    • Five Brilliant Pieces of Teradata (1 of 5) is MPP
    • Five Brilliant Pieces (2 of 5) are Tactical Queries
    • Five Brilliant Pieces (3 of 5) Is a Traffic System
    • Five Brilliant Pieces (4 of 5) Is Viewpoint
    • Five Brilliant Pieces (5 of 5) Are Data Processing Options
    • Support Large Queries, but Monitor them closely
    • Experiment and Improve Loading Data Strategies
    • Compress Your Data with Multi-Value Compression
    • Separate your Production System from Your Test System
  • Teradata Architecture Fundamentals the DBA must know
    • Parallel Architecture
    • The Teradata Architecture
    • All Teradata Tables are spread across ALL AMPS
    • Teradata Systems can Add AMPs for Linear Scalability
    • AMPs and Parsing Engines (PE’s) live inside SMP Nodes
    • Each Node is Attached via a Network to a Disk Farm
    • Two SMP Nodes Connected Become One MPP System
    • There are Many Nodes in a Teradata Cabinet
    • This is the Visual You Want to Understand Teradata
    • Responsibilities of the DBA
  • The Primary Index is the Axis of all Teradata Systems
    • The Primary Index is defined when the table is CREATED
    • A Unique Primary Index (UPI)
    • Primary Index in the WHERE Clause - Single-AMP Retrieve
    • A Non-Unique Primary Index (NUPI)
    • Primary Index in the WHERE Clause - Single-AMP Retrieve
    • Primary Index in the WHERE Clause - Single-AMP Retrieve
    • A Full Table Scan is likely on a table with NO Primary Index
    • What happens when you forget the Primary Index?
    • Why create a table with No Primary Index (NoPI)?
  • A DBA’s best friend - The Data Dictionary
    • The Data Dictionary Resides in User DBC
    • The DBC.DBCInfoV View
    • Querying the Data Dictionary
    • Using the Keyword USER
    • Restricted Views have an X at the End of their Name
    • The V is New with Teradata V12
    • The V and the Restricted X are Now Often Combined
    • A Recap of What We Have Learned So Far
    • The DBC.DatabasesV View
    • The DBC.Users View
    • The DBC.Tables View
    • Using DBC.Tables to find out about Fallback
    • The DBC.Indices View
    • The DBC.Columns View
    • Clever Queries for the DBC.ColumnsV View
    • New V14 - The DBC.PartitioningConstraintsV View
    • The DBC.AccountInfo View
    • The DBC.AMPUsage View
    • Clearing Out the DBC.AMPUsage Data
    • The DBC.AllTempTables
    • The DBC.Triggers
    • The DBC.All_RI_ChildrenV
    • DBC.SessionInfoV Information
    • DBC.LogonOffV
    • AllRoleRights, AllRightsV, UserRightsV and UserGrantedRightsV
    • The DBC.Profiles View
    • RoleMembers, RoleInfo, UserRoleRights and ProfileInfoVX,
    • Understanding that Space is based on a Per-AMP Basis
    • Total Space for a Single Database or User
    • Using the Data Dictionary to see the Space for Everyone
    • Finding the Perm Percent Used
    • Finding the Perm Percent Used with a HAVING Clause
    • Finding the Perm Percent Left with a HAVING Clause
    • Creating a Macro for Perm Percent Used with a Dynamic %
    • Orphaned Spool Files That Need to be deleted
    • Finding Table Sizes
    • Finding Skew in the Tables in a Database
    • Finding Skew in a Table
    • Display the Distribution of a Column per AMP
    • Your Users and Databases
    • DBC Tables used in the Collect Statistics Process
    • The DBC Table DBC.Next
    • DBA Advice - ClearPeakDisk to Reset Peak Space
    • DBA Advice – Clean out these Tables Periodically
    • The DBC.AssociationV View
    • The DBC.JournalsV View
    • DBC.Databases2V is for Unresolved Reference Constraints
    • The DBC.All_RI_ChildrenV for Inconsistent RI
    • The DBC.ShowColChecksV View
    • The DBC.ShowTblChecksV View
    • The DBC.PartitioningConstraintsV View
    • The DBC.AccessLogV View
    • The DBC.AccessLogV View for Today’s Queries
    • The DBC.AccessLogV View Denials for Today
    • DBC.DBQLRulesV
    • DBC.QryLogV
    • DBC.QryLogSummaryV
    • ResUsage Macros
    • Executing the ResUsage Macro DBC.Resnode
    • The DBC.IdCol Table
  • How Teradata Tracks Objects
    • Teradata Assigns each Object a Unique Numeric ID
    • The Table ID
    • The Table ID in Greater Detail
    • Looking at the TableID inside the actual Cylinders
    • A More Detailed View of TableID inside the actual Cylinders
    • The Blocks Below are All Associated with the Same Table
    • Bits, Bytes and More
    • Cylinder Sizes
  • Creating Users and Databases
    • Creating Users and Databases
    • Password Security Meanings
    • Now we have Two Users in the Teradata System
    • A Grant Statement so others Create a Database or User
    • And so the Teradata Hierarchy Begins
    • Creating a Database
    • Users are Given Passwords While Database are Not
    • Teradata Administrator Can CREATE Users
    • The Modify User Statement
    • A Clever Way to Reset a User Password
    • Accounts and their Associated Priorities
    • Creating a User with Multiple Account Priorities
    • Self-Nicing to change Account Priorities
    • Account String Expansion (ASE)
    • The DBC.AccountInfo View
    • The DBC.AMPUsage View
    • Account String Expansion (ASE) in Action
    • The DBC.AMPUsage View
  • Profiles
    • Profiles
    • Getting Started for Profile Creation
    • Creating A Profile and a User
    • Password Security
    • Password Security Meanings
    • Creating A Profile and then Modifying a User
    • The DBC.ProfilesVX View
    • The DBC.ProfilesV View
    • The DBC.AccountInfoVX View
    • ProfileInfoVX, RoleMembers, RoleInfo and UserRoleRights
    • Teradata Administrator Can CREATE Profiles
    • Dropping a Profile
    • The Effects of Dropping a Profile
  • Roles
    • Getting Started for Role Creation
    • Create A Role and then Assign that Role It’s Access Rights
    • Create a User and Assign them a Default Role
    • A Role vs. a Profile
    • Granting a Role to a Current User
    • Active Roles
    • Setting Your Active Role to ALL
    • Roles and Valid Objects
    • Roles and Invalid Commands
    • Nesting of Roles
    • Nesting of Roles in Action
    • GRANT WITH ADMIN OPTION Command
    • REVOKE ADMIN OPTION FOR Command
    • RoleMembers, RoleInfo, UserRoleRights and ProfileInfoVX,
    • DBC Tables for AllRoleRights, AllRightsV, UserRightsV and UserGrantedRightsV
  • Access Rights
    • The Objects That Require Access Rights
    • Objects and Available Access Rights
    • There are Three Types of Access Rights
    • A Dinner Invitation of Access Rights
    • One of the Problems with Access Rights
    • The Rights for SysDBA and TeraTom
    • The GRANT Statement
    • Create A Role and then Assign that Role It’s Access Rights
    • GRANT to PUBLIC
    • GRANT To ALL DBC
    • GRANT Using the ALL Keyword
    • GRANT Database Strategy for Users, Views and Tables
    • Inheriting Access Rights
    • GRANT at the Column Level
    • GRANT for the Ability to CREATE Secondary Indexes
    • Access Rights to CREATE Triggers
    • The REVOKE Command
    • DBC Tables for AllRoleRights, AllRightsV, UserRightsV and UserGrantedRightsV
    • The GIVE Statement
    • A DROP User can be Better than a GIVE Statement
    • Removing a Level in the Teradata Hierarchy
  • Collect Statistics
    • The Teradata Parsing Engine (Optimizer) is Cost Based
    • The Purpose of Collect Statistics
    • When Teradata Collects Statistics it creates a Histogram
    • The Interval of the Collect Statistics Histogram
    • What to COLLECT STATISTICS On?
    • Why Collect Statistics?
    • How do you know if Statistics were collected on a Table?
    • A Huge Hint that No Statistics Have Been Collected
    • The Basic Syntax for COLLECT STATISTICS
    • The New Teradata V14 Way to Collect Statistics
    • COLLECT STATISTICS Directly From another Table
    • Where Does Teradata Keep the Collected Statistics?
    • The Official Syntax for COLLECT STATISTICS
    • How to Recollect STATISTICS on a Table
    • Teradata Always Does a Random AMP Sample
    • Random Sample is Kept in the Table Header in FSG Cache
    • Multiple Random AMP Samplings
    • How a Random AMP gets a Table Row count
    • Random AMP Estimates for NUSI Secondary Indexes
    • USI Random AMP Samples are Not Considered
    • There’s No Random AMP Estimate for Non-Indexed Columns
    • A Summary of the PE Plan if No Statistics Were Collected
    • Stale Statistics Detection and Extrapolation
    • Extrapolation for Future Dates
    • How to Copy a Table with Data and the Statistics
    • How to Copy a Table with NO Data and the Statistics
    • When to COLLECT STATISTICS Using only a SAMPLE
    • How to Collect Statistics on a PPI Table on the Partition
    • Teradata V12 and V13 Statistics Enhancements
    • Teradata V14 Statistics Enhancements
    • Teradata V14 Summary Statistics
    • Teradata V14 MaxValueLength
    • Teradata V14 MaxIntervals
    • Teradata V14 Sample N Percent
    • Teradata V14.10 Statistics Collection Improvements
    • Teradata V14.10 AutoStats feature
    • Teradata Statistics Wizard
  • Locking
    • The Four Major Locks of Teradata
    • The Read Lock
    • The Read Lock and Joins
    • The Write Lock
    • The Exclusive Lock
    • The Three Levels of Locking
    • Locking at the Row Hash Level
    • Locking at the Table Level
    • Locking at the Database Level
    • The Ongoing Battle between Read and Write Locks
    • Compatibility between Read Locks
    • Why Read Locks Wait on Write Locks
    • Why Write Locks Wait on Read Locks
    • The Access Lock is Different from the Other Locks
    • What is the Purpose of an Access Lock?
    • Locking Modifiers - Locking Row, Table or Database
    • All Views should consider the Locking for Access Statement
    • What is a Dead Lock or a Deadly Embrace?
    • Pseudo Tables are designed to minimize Dead Locks
    • Pseudo Tables are referenced in the Explain Plan
    • Incompatible Locks Wait on each Other
    • The Checksum Lock of Teradata
    • The Nowait Option for Locking
    • The Automatic Locking for Access Button inside Nexus
    • Viewpoint Lock Viewer
    • Viewpoint Lock Viewer Lets You Configure Your View
    • What is a Host Utility (HUT) Lock?
  • Protection Features
    • A List of the Protection Features
    • Transient Journal Protects the Transaction Integrity
    • The Transient Journal in Action
    • A Single Transaction could Involve All AMPs
    • The Secret to turning off the Transient Journal
    • The Transient Journal’s Write Ahead Logging (WAL)
    • A Node with 40 AMPs and 40 Dedicated FSG Caches
    • The Transient Journal’s Write Ahead Logging (WAL)
    • Fallback to Protect against an AMP Failure
    • Fallback Clusters
    • AMPs in a Cluster are Physically Separated
    • The Reason AMPs in a Cluster are Physically Separated
    • The Price you pay for Fallback
    • How to Create a Table with Fallback
    • How to Create a Table with No Fallback
    • How to Alter a Table to Add or Drop Fallback
    • What is a Virtual Disk?
    • Why do AMPs each have Four Physical Disks?
    • Is a Mirror just like Looking into a Mirror?
    • RAID 1 Mirroring – Redundant Array of Independent Disks
    • What does RAID Protect?
    • How Does RAID Fail?
    • Do RAID and Fallback have a Connection?
    • What is a Clique?
    • If a Node goes down the AMPs migrate within the Clique?
    • Does Teradata Reset during a Node Failure?
    • Four Node Cliques
    • Migrating AMPs in Four Node Cliques
    • The Hot Spare Node
    • The Hot Spare Node in Action
    • With a Hot Spare a Second Teradata Reset isn’t Needed
    • A Node, It’s AMPs and their Disks
    • How Cliques are Physically Defined
    • Cliques are cabled so Migrating AMPs can access their Disks
    • The Permanent Journal
    • Difference Between the Transient and the Permanent Journal
    • Difference Between the Before and After Permanent Journal
    • Full System Backup compared to an After Journal
    • How Full System Backups work with the After Journal
    • The Many Different Permanent Journal Options
    • Where is the Permanent Journal Stored?
    • Using Common Sense about Journal Locations
    • After Journals are Never stored in the Same Node or Clique
    • What is a Dual After Journal?
    • What is a Dual Before Journal?
    • What is a Journal?
    • Creating a Table with Fallback and a Before and After Journal
    • Does Fallback Affect a Permanent Journal?
    • Permanent Journal Rules
    • How to Create Database with a Permanent Journal
    • Creating Tables under different Journal Circumstances
    • Permanent Journal’s Three Main Areas
    • The Current Journal consists of the Active and Saved Areas
    • Permanent Journal Commands
    • Deleting a Permanent Journal
    • Some Great Advice for Maintaining the Permanent Journals
    • Recovery Using the Permanent Journals
    • The Journals View in DBC (DBC.Journals)
    • Archive Recovery Console (ARC)
    • Reasons You Might Utilize ARC
    • ARC raising the BAR (Backup Archive Restore)
    • ARC Commands in Alphabetical Order
  • The Cold, Hard Teradata Facts
    • What is Parallel Processing?
    • The Basics of a Single Computer
    • Teradata Parallel Processes Data
    • Parallel Architecture
    • The Teradata Architecture
    • All Teradata Tables are spread across ALL AMPS
    • Teradata Systems can Add AMPs for Linear Scalability
    • Understand that Teradata can scale to incredible size
    • AMPs and Parsing Engines (PEs) live inside SMP Nodes
    • Each Node is attached via a Network to a Disk Farm
    • Two SMP Nodes Connected Become One MPP System
    • There are Many Nodes in a Teradata Cabinet
    • Inside a Teradata Node
    • The Boardless BYNET and the Physical BYNET
    • The Parsing Engine
    • The AMPs Responsibilities
    • Teradata Parallel Processing
    • Each Table has a Primary Index that is Unique or Non-Unique
    • The Hash Map Determines which AMP will own the Row
    • A Unique Primary Index Spreads the Data Evenly
    • The AMP Adds a Uniqueness Value to Create the Row-ID
    • Each AMP Sorts Their Tables by the Row-ID
    • A Non-Unique Primary Index Skews the Data
    • Comparing the Same Table with Different Primary Indexes
    • Unique Primary Index Queries are a Single AMP Retrieve
    • A Non-Unique Primary Index is also a Single AMP Retrieve
    • Teradata has a No Primary Index Table called a NoPI Table
    • There are Normal Tables and then there are Partitioned Tables
    • A Visual of One Year of Data with Range_N Per Month
    • Partitioning is designed to eliminate the Full Table Scan
    • A Partition # and Row-ID = Row Key
    • An AMP Stores its Rows Sorted in only Two Different Ways
    • AMPs Moves Their Data Blocks into Memory to Read/Write
    • The Most Taxing thing for an AMP is Moving Blocks into Memory
    • Rows are Stored in Data Blocks which are stored in Cylinders
    • Rows for an AMP Stored Inside a Data Block in a Cylinder
    • An AMP’s Master Index is Used to Find the Right Cylinder
    • The Row Reference Array (RRA) Does the Binary Search
    • A Block Splits into Two Blocks at Maximum Block Size
    • Data Blocks Maximum Block Size has Changed (V14.10)
    • The New Block Split with Teradata V14.10
    • The Block Split with Even More Detail in Teradata V14.10
    • There is One Master Index and Thousands of Cylinder Indexes
    • Each Table has a 48-bit TableID
  • How Teradata Tracks Objects
    • Teradata Assigns each Object a Unique Numeric ID
    • The Table ID
    • The Table ID in Greater Detail
    • Looking at the TableID inside the actual Cylinders
    • A More Detailed View of TableID inside the actual Cylinders
    • The Blocks Below are All Associated with the Same Table
    • Bits, Bytes, and More
    • Cylinder Sizes
  • AMP Worker Tasks
    • Teradata is a Message Passing System
    • The Parsing Engine Parses the SQL and comes up with a Plan
    • What is an AMP Worker Task (AWT)?
    • Each AMP has 80 AMP Worker Tasks (AWTs)
    • Each Query Takes Up One or More AMP Worker Tasks
    • An All-AMP Query Usually Won’t Use More Than 4 AWTs
    • There are 24 AWTs Reserved for Internal Work
    • How Utilities Use AWTs
    • Monitoring AMP Worker Tasks with ResAMPCpuByGroup
  • Deep Dive Overhead for each Row
    • Why Go Deep inside the Overhead of a Row?
    • A Row Layout in Teradata
    • Row Length
    • Row ID
    • How The Row Hash is created for Each Row
    • Unique Primary Indexes have Even Distribution
    • The AMP adds a Uniqueness Value to Its Rows
    • The Row-Hash is 32-bits and so is the Uniqueness Value
    • Non-Unique Primary Indexes have Skewed Data
    • Flag Byte
    • Presence Byte
    • Presence Byte is used to show Null Values in each Row
    • A Close-up look at the Presence Byte for Nulls
    • What Happens when we need more than One Presence Byte?
  • Compression
    • Important Information about Compression
    • Presence Bytes are also used for Compression
    • Why One Byte (8 bits) can represent up to 255 Values
    • Now that you Understand that 8 Bits can Represent 0 – 255
    • The Next Important Concept in Compression
    • The Last Major Concept in Compression
    • The Cost Vs. the Savings
    • The Cost List of Compression
    • A Deeper Dive Into NULL Values
    • Using the DBC Tables in a Compression Experiment
    • A Compression Test
    • We then moved all Eight Tables to another Database
    • Compression Reports with Nexus and SmartCompress
    • We Then Created Two Global Temporary Tables
    • We Then Created and Executed our Macro
    • Report Comparing Compressed and NonCompressed Tables
  • Data Stored in the Row
    • The Varchar Offset
    • The Fixed Columns
    • Compressible Columns
    • VARCHAR Columns
    • Teradata’s Maximum Row Size
  • How Data Rows are Stored in Blocks
    • Why Go Deep inside Data Blocks?
    • In The Beginning a Table is created
    • Every AMP has the Exact Same Tables
    • Rows are Stored in Blocks
    • Each Table Header and Data Block have the Same TableID
    • AMPs Moves Their Data Blocks into Memory to Read/Write
    • AMPs can Read/Write their Rows once they are in FSG Cache
    • Every Data Block Starts with a Data Block Header
    • Every Data Block Ends with a Data Block Trailer
    • Each Block has a Row Reference Array (RRA)
    • The Row Reference Array (RRA) is in Descending Order
    • A Binary Search is always done through the RRA
    • A Binary Search is a quick Search among thousands of Rows
    • The Ref Array Pointer in the Row Layout in Teradata
    • How Blocks of Data Begin in Teradata
    • How Blocks of Data Grow in Teradata
    • Did You Notice the Row Reference Array (RRA)?
    • A Great Picture of a Single AMP’s Data Block with Details
    • Data Blocks Grow until they Reach Maximum Block Size
    • The Block Split
    • The Block Split with Even More Detail
    • The Block Split Showing Two Blocks with Greater Detail
    • Blocks Continue to Split as Tables Grow Larger
  • Disk Cylinders and the Master Index
    • Disks have Cylinders which hold Data Blocks
    • Rows are Stored in Data Blocks which are stored in Cylinders
    • A Real World View of Rows inside a Data Block in a Cylinder
    • A Top down View of Cylinders
    • There are Hot, Warm, and Cold Cylinders
    • Cylinders are used for Perm, Spool, Temp, and Journals
    • Synchronized Scan (Sync Scan)
    • EXPLAIN Using a Synchronized Scan
    • Intelligent Memory (Teradata V14.10)
    • Teradata V14.10 Intelligent Memory Gives Data a Temperature
    • Data deemed VeryHot stays in each AMP's Intelligent Memory
    • Intelligent Memory Stays in Memory
    • Each AMP has Their Own Master Index
    • Each Cylinder on an AMP has a Cylinder Index
    • A More Detailed Illustration of the Master Index
    • A Real-World View of the Master Index
    • An Even More Realistic View of an AMP’s Master Index
    • The Cylinder Index
    • An Even More Realistic View of a Cylinder Index
    • How a Query using the Primary Index works
    • How the AMPs Do a Full Table Scan
    • How An AMP Reads Using a Primary Index
    • Teradata Assigns each Object a Unique Numeric ID
    • The Table ID
    • Looking at the TableID inside the actual Cylinders
    • A More Detailed View of TableID inside the actual Cylinders
    • An Even More Realistic View of a Cylinder Index
    • Bits, Bytes, and More
    • Cylinder Sizes
    • How TVS Monitors and Migrates Tables
    • How TVS Monitors and Migrates Partitioned (PPI) Tables
    • A Summary of the Master and Cylinder Index
    • Chapter 24 - Teradata Virtual Storage (TVS)
    • Solid State Drives (SSD) Vs. Hard Disk Drives (HDD)
    • Teradata Uses Two Types of Disks
    • Traditional Teradata Without Teradata Virtual Storage (TVS)
    • Teradata With TVS in a Conceptual Diagram
    • What TVS is Responsible for Doing
    • The Benefits of Teradata Virtual Storage (TVS)
    • What is a Clique?
    • If a Node goes down the AMPs migrate within the Clique?
    • Teradata Virtual Storage (TVS) Manages within a Clique
    • Before TVS vs. TVS Today with Teradata V13.10
    • TVS Operates in Two Different Modes
    • TVS Knows the Disks and Which Cylinders are the Fastest
    • A Concept Called Recency
    • Data Placement and Migration
  • Teradata Writes and Blocks
    • A Teradata Write
    • Teradata Insert (Option 1 of 3) has enough space for the Insert
    • Teradata Insert (Option 2 of 3) is a Defragment to make Space
    • Teradata Insert (Option 3 of 3) is to Get a Bigger Block
    • Checksum Determines if a New Block is Needed
    • A Reminder of How Rows are Sorted with Block Utilities
    • A Reminder of How Rows are Sorted with SQL Inserts
    • When a Block Reaches Maximum Size, it Splits into Two
    • A Block Split Always Sorts the Rows Perfectly Once Again
    • In Teradata V14.10 the Maximum Block Size is 1 Megabyte
    • Cylinder Sizes
    • Teradata V14 Large Cylinders
    • Blocking Terms for Teradata V14 and Below
    • Blocking Terms for Teradata V14.10 and Beyond
    • Block Sizes and Filling of Cylinders
    • Space Fragmentation
    • What is a Defrag?
    • What Happens When a Cylinder is Full?
    • What is a Mini-Cylpack?
    • What is a Mini-Cylpack Vs. a Pack Disk?
    • A Pack Disk Picture
    • New Teradata 13.10 Auto Cylinder Pack Feature
    • A Pack Disk Honors the Free Space Percent
    • Free Space Percent
    • The Free Space Percent can be set in Three Ways
    • Why Would I Want Bigger or Smaller Block Sizes?
    • How Does Teradata Manage Space?
    • How Can Many Big Blocks Become Many Small Blocks?
    • Merge Datablocks (13.10 Feature)
    • Merge Datablocks Details
    • Setting Merge Datablocks in DBS Control or at Table Level
    • How Have Customers Previously Handled Shrinking Blocks?
  • Access Logging
    • Access Logging
    • Security for the DBA
    • The Tables and Views Associated with Access Logging
    • Begin Logging Options
    • Begin Logging, View Rules, See Log Data, and End Logging
    • The DBC.AccessLogV View
    • The DBC.AccessLogV View for Today’s Queries
    • The DBC.AccessLogV View Denials For Today
    • Controlling the Log Files
  • DBQL Query Logging
    • DBQL Query Logging
    • The Tables and Views Associated with DBQL
    • There are Seven Major Tables to Store DBQL Entries
    • The Views for the Major DBQL Tables
    • Begin Query Logging Default Information
    • Begin Query Logging WITH Options
    • Begin Query Logging LIMIT Options
    • SUMMARY and THRESHOLD have Additional Options
    • Replace Query Logging Statement
    • An Inside Look at the View DBC.DBQLRulesV
    • The Columns in the View DBC.DBQLRulesV
    • Begin Logging, View Rules, See Log Data and End Logging
    • DBC.DBQLRulesV
    • DBC.QryLogV
    • DBC.QryLogSummaryV
  • ResUsage
    • ResUsage
    • Major Tables to Store ResUsage Entries
    • The ResUsage Views
    • ResUsage Macro Information
    • ResUsage Macros
    • Executing the ResUsage Macro DBC.Resnode
    • DBC.Resnode Major Column Explanation
    • ResAMPCpuByGroup
    • ResCpuByAMP
    • ResCpuByGroup
    • ResCpuByNode
    • ResCpuByPE
    • ResHostByGroup
    • ResHostByLink
    • ResHostTotal
    • ResHostTotalDay
    • ResHostTotalHour
    • ResIvprMigrate
    • ResIvprMigrateHour
    • ResLdvByGroup
    • ResLdvByNode
    • ResMemByGroup
    • ResMemMgmtByNode
    • ResNetByGroup
    • ResNetByNode
    • ResPeCpuByGroup
    • ResPeCpuByGroup
    • ResScpuDayTotal
    • ResScpuSec
    • ResSvprDetailReadTotal
    • ResSvprPreReadBySec
    • ResSvprQLenAvg
    • ResSvprQLenAvgByVproc
    • ResSvprQLenAvgByVproc
    • ResSvprQLenMaxHour
    • ResSvprReadByVprocSec
    • ResSvprReadByVprocSec
    • ResSvprReadTotal
    • ResSvprReadTotal
    • ResSvprWriteTotal
    • ResSvprWriteTotalHour
    • ResSyncScan