1. Teradata Basics
After completing this module, you will be able to:
· List and describe the major components of the Teradata
architecture.
· Describe how the components interact to manage incoming
and outgoing data.
· List 5 types of Teradata database objects.

2. Major Components of Teradata
Answer Set
Response
SQL
Request
Parsing Engine
Message Passing Layer
AMPs store and retrieve rows to and from disk.
Parsing Engines (PE)
• Manage sessions for users
• Parse, optimize, and send your request to
the AMPs as execution steps
• Returns answer set response back to client
Message Passing Layer (MPL)
• Allows PEs and AMPs to communicate with
each other
Access Module Processors (AMP)
• Owns and manages its storage
• Performs the steps sent by the PEs
Virtual Disks (Vdisk)
• Space owned by the AMP and is used to
hold user data (rows within tables).
• Maps to physical space in a disk array.
… Parsing Engine
…
…
AMP
Vdisk
AMP
Vdisk
AMP
Vdisk
AMP
Vdisk

3. Teradata Storage Architecture
The Parsing Engine dispatches
request to insert a row.
The Message Passing Layer
insures that a row gets to the
appropriate AMP (Access Module
Processor).
The AMP stores the row on its
associated (logical) disk.
An AMP manages a logical or
virtual disk which is mapped to
multiple physical disks in a disk
array.
Teradata
Parsing
Engine(s)
Message Passing Layer
AMP 1 AMP 2 AMP 3 AMP 4
2 54
18
41
12
90
75
80
32
6
67
25
Records From Client (in random sequence)
2 32 67 12 90 6 54 75 18 25 80 41

4. Teradata Retrieval Architecture
The Parsing Engine dispatches a
request to retrieve one or more
rows.
The Message Passing Layer
insures that the appropriate
AMP(s) are activated.
The AMP(s) locate and retrieve
desired row(s) in parallel access.
Message Passing Layer returns the
retrieved rows to PE.
The PE returns row(s) to
requesting client application.
Teradata
Parsing
Engine(s)
Message Passing Layer
AMP 1 AMP 2 AMP 3 AMP 4
2 54
18
41
12
90
75
80
32
6
67
25
Rows retrieved from table
2 32 67 12 90 6 54 75 18 25 80 41

5. Multiple Tables on Multiple AMPs
EMPLOYEE Table DEPARTMENT Table JOB Table
EMPLOYEE Rows
DEPARTMENT Rows
JOB Rows
Parsing Engine
Message Passing Layer
Row from each table will usually
be stored on each AMP.
Each AMP may have rows from all
tables.
Ideally, each AMP will hold roughly
the same amount of data.
AMP 1 AMP 2 AMP 3 AMP 4
EMPLOYEE Rows
DEPARTMENT Rows
JOB Rows
EMPLOYEE Rows
DEPARTMENT Rows
JOB Rows
EMPLOYEE Rows
DEPARTMENT Rows
JOB Rows

6. Linear Growth and Expandability
Parsing
Engine
AMP
SESSIONS
PARALLEL PROCESSING
DATA
Disk
• Teradata is a linearly
expandable RDBMS.
• Components may be added as
requirements grow.
• Linear scalability allows for
increased workload without
decreased throughput.
• Performance impact of adding
components is shown below.
USERS AMPs DATA Performance
Same Same Same Same
Double Double Same Same
Same Double Double Same
Same Double Same Double
Parsing
Engine
Parsing
Engine
Disk
Disk
AMP
AMP

7. Teradata Objects
Examples of objects within a Teradata database or user include:
Tables – rows and columns of data
Views – predefined subsets of existing tables
Macros – predefined, stored SQL statements
Triggers – SQL statements associated with a table
Stored Procedures – program stored within Teradata
User-Defined Function – function (C program) to provide additional SQL functionality
Join and Hash Indexes – separate index structures stored as objects within a database
Permanent Journals – table used to store before and/or after images for recovery
DATABASE or USER can have a mix
of various objects.
* - require Permanent Space
These objects are created,
maintained, and deleted using SQL.
Object definitions are stored in the
DD/D.
TABLE 1 * TABLE 2 * TABLE 3 *
VIEW 1 VIEW 2
MACRO 1
TRIGGER 1
Stored Procedure 1 *
Join/Hash Index 1 *
Permanent Journal *
UDF 1 *
VIEW 3
These aren't directly accessed by users.

8. The Data Dictionary Directory (DD/D)
The DD/D ...
– is an integrated set of system tables
– contains definitions of and information about all objects in the system
– is entirely maintained by the Teradata Database
– is “data about the data” or “metadata”
– is distributed across all AMPs like all tables
– may be queried by administrators or support staff
– is normally accessed via Teradata supplied views
Examples of DD/D views:
DBC.TablesV – information about all tables
DBC.UsersV – information about all users
DBC.AllRightsV – information about access rights
DBC.AllSpaceV – information about space utilization

9. Structured Query Language (SQL)
SQL is a query language for Relational Database Systems and is used to access Teradata.
– A fourth-generation language
– A set-oriented language
– A non-procedural language (e.g., doesn’t have IF, DO, FOR NEXT, etc. )
SQL consists of:
Data Definition Language (DDL)
– Defines database structures (tables, users, views, macros, triggers, etc.)
CREATE DROP ALTER
Data Manipulation Language (DML)
– Manipulates rows and data values
SELECT INSERT UPDATE DELETE
Data Control Language (DCL)
– Grants and revokes access rights
GRANT REVOKE
Teradata SQL also includes Teradata Extensions to SQL
HELP SHOW EXPLAIN CREATE MACRO

10. CREATE TABLE – Example of DDL
CREATE TABLE Employee
(employee_number INTEGER NOT NULL
,manager_emp_number INTEGER COMPRESS
,dept_number INTEGER COMPRESS
,job_code INTEGER COMPRESS
,last_name CHAR(20) NOT NULL
,first_name VARCHAR (20)
,hire_date DATE FORMAT 'YYYY-MM-DD'
,birth_date DATE FORMAT 'YYYY-MM-DD'
,salary_amount DECIMAL (10,2) COMPRESS 0
)
UNIQUE PRIMARY INDEX (employee_number)
INDEX (dept_number);
Other DDL Examples
CREATE INDEX (job_code) ON Employee ;
DROP INDEX (job_code) ON Employee ;
DROP TABLE Employee ;

11. Views
Views are pre-defined filters of existing tables consisting of specified columns
and/or rows from the table(s).
A single table view:
– is a window into an underlying table
– allows users to read and update a subset of the underlying table
– has no data of its own
EMPLOYEE (Table)
MANAGER
EMPLOYEE EMP DEPT JOB LAST FIRST HIRE BIRTH SALARY
NUMBER NUMBER NUMBER CODE NAME NAME DATE DATE AMOUNT
PK FK FK FK
1006 1019 301 312101 Stein John 861015 631015 3945000
1008 1019 301 312102 Kanieski Carol 870201 680517 3925000
1005 0801 403 431100 Ryan Loretta 861015 650910 4120000
1004 1003 401 412101 Johnson Darlene 861015 560423 4630000
1007 1005 403 432101 Villegas Arnando 870102 470131 5970000
1003 0801 401 411100 Trader James 860731 570619 4785000
Emp403_v (View)
EMP NO DEPT NO LAST NAME FIRST NAME HIRE DATE
1005 403 Villegas Arnando 870102
801 403 Ryan Loretta 861015

12. Multi-Table Views
A multi-table view allows users to access data from multiple tables as if it were in a single
table. Multi-table views (i.e., join views) are used for reading only, not updating.
EMPLOYEE (Table)
MANAGER
EMPLOYEE EMP DEPT JOB LAST FIRST
NUMBER NUMBER NUMBER CODE NAME NAME
PK FK FK FK
1006 1019 301 312101 Stein John
1008 1019 301 312102 Kanieski Carol
1005 0801 403 431100 Ryan Loretta
1004 1003 401 412101 Johnson Darlene
1007 1005 403 432101 Villegas Arnando
1003 0801 401 411100 Trader James
MANAGER
DEPARTMENT (Table)
DEPT DEPARTMENT BUDGET EMP
NUMBER NAME AMOUNT NUMBER
PK FK
501 Marketing Sales 80050000 1017
301 Research & Development 46560000 1019
302 Product Planning 22600000 1016
403 Education 93200000 1005
402 Software Support 30800000 1011
401 Customer Support 98230000 1003
EmpDept_v (View)
Last_Name Department_Name
Stein Research & Development
Kanieski Research & Development
Ryan Education
Johnson Customer Support
Villegas Education
Trader Customer Support
Joined Together
Example of SQL to create a join view:
CREATE VIEW EmpDept_v AS
SELECT Last_Name
,Department_Name
FROM Employee E
INNER JOIN Department D
ON E.dept_number = D.dept_number;

13. Macros
A MACRO is a predefined set of SQL statements which is logically stored in a database.
Macros may be created for frequently occurring queries of sets of operations.
Macros have many features and benefits:
• Simplify end-user access
• Control which operations may be performed by users
• May accept user-provided parameter values
• Are stored in the Teradata Database, thus available to all clients
• Reduces query size, thus reduces LAN/channel traffic
• Are optimized at execution time
• May contain multiple SQL statements
To create a macro:
CREATE MACRO Customer_List AS (SELECT customer_name FROM Customer;);
To execute a macro:
EXEC Customer_List;
To replace a macro:
REPLACE MACRO Customer_List AS
(SELECT customer_name, customer_number FROM Customer;);

14. HELP Command
Databases and Users:
HELP DATABASE Customer_Service;
HELP USER Dave_Jones;
Tables, Views, Macros, etc.:
HELP TABLE Employee;
HELP VIEW Emp_v
HELP MACRO Payroll_3;
HELP COLUMN Employee.*;
Employee.last_name;
HELP INDEX Employee;
HELP TRIGGER Raise_Trigger;
HELP STATISTICS Employee;
HELP CONSTRAINT Employee.over_21;
HELP JOIN INDEX Cust_Order_JI;
HELP SESSION;
This is not an inclusive list of HELP
commands.
Example:
HELP DATABASE Customer_Service;
*** Help information returned. 15 rows.
*** Total elapsed time was 1 second.
Table/View/Macro name Kind Comment
Contact T ?
Customer T ?
Cust_Comp_Orders V ?
Cust_Order_JI I ?
Department T ?
: : :
Orders T ?
Orders_Temp O ?
Orders_HI N ?
Raise_Trigger G ?
Set_Ansidate_on M ?

15. SHOW Command
SHOW commands display how an object was created. Examples include:
Command Returns statement
SHOW TABLE table_name; CREATE TABLE statement …
SHOW VIEW view_name; CREATE VIEW ...
SHOW MACRO macro_name; CREATE MACRO ...
SHOW TRIGGER trigger_name; CREATE TRIGGER …
SHOW PROCEDURE procedure_name; CREATE PROCEDURE …
SHOW JOIN INDEX join_index_name; CREATE JOIN INDEX …
SHOW TABLE Employee;
CREATE SET TABLE PD.Employee, FALLBACK,
NO BEFORE JOURNAL,
NO AFTER JOURNAL,
CHECKSUM = DEFAULT,
DEFAULT MERGEBLOCKRATIO
(
Employee_Number INTEGER NOT NULL,
Emp_Mgr_Number INTEGER COMPRESS,
Dept_Number INTEGER COMPRESS,
Job_Code INTEGER COMPRESS,
Last_Name CHAR(20) CHARACTER SET LATIN NOT CASESPECIFIC,
First_Name VARCHAR(20) CHARACTER SET LATIN NOT CASESPECIFIC,
Salary_Amount DECIMAL(10,2) COMPRESS 0)
UNIQUE PRIMARY INDEX ( Employee_Number )
INDEX ( Dept_Number );

16. EXPLAIN Facility
The EXPLAIN modifier in front of any SQL statement generates an English translation of
the Parser’s plan.
The request is fully parsed and optimized, but not actually executed.
EXPLAIN returns:
• Text showing how a statement will be processed (a plan)
• An estimate of how many rows will be involved
• A relative cost of the request (in units of time)
This information is useful for:
• predicting row counts
• predicting performance
• testing queries before production
• analyzing various approaches to a problem
EXPLAIN SELECT * FROM Employee WHERE Dept_Number = 1018;
:
3) We do an all-AMPs RETRIEVE step from PD.Employee by way of an all-rows scan with a condition of
("PD.Employee.Dept_Number = 1018") into Spool 1 (group_amps), which is built locally on the AMPs.
The size of Spool 1 is estimated with high confidence to be 10 rows (730 bytes). The estimated time for
this step is 0.14 seconds.
4) Finally, we send out an END TRANSACTION step to all AMPs involved in processing the request.
-> The contents of Spool 1 are sent back to the user as the result of statement 1. The total estimated time is
0.14 seconds.

17. Summary
The major components of the Teradata Database are:
Parsing Engines (PE)
• Manage sessions for users
• Parse, optimize, and send your request to the AMPs as execution steps
• Returns answer set response back to client
Message Passing Layer (MPL)
• Allows PEs and AMPs to communicate with each other
Access Module Processors (AMP)
• Owns and manages its storage
• Performs the steps sent by the PEs
Virtual Disks (Vdisk)
• Space owned by the AMP and is used to hold user data (rows within tables).
• Maps to physical space in a disk array.

18. Design and Developed by:
Noor Alam,
DW Architect & Project Manager, BI Solutions
Data edge limited
Mobile: 8801841320998
Email: alambd@gmail.com
Skype: alam.ict