Teradata Tutorial for Beginners

Fine Print
2
• Nothing in this presentation constitutes a
commitment to deliver any specific
functionality at any specific time.

What is a Temporal Database
Definitions
4
• Temporal – the ability to store all historic states of a given set of data
(a database row), and as part of the query select a point in time to
reference the data. Examples:
> What was this account balance (share price, inventory level, asset value,
etc) on this date?
> What data went into the calculation on 12/31/05, and what adjustments
were made in 1Q06?
> On this historic date, what was the service level (contract status, customer
value, insurance policy coverage) for said customer?
• Three Types of Temporal Tables
> Valid Time Tables
– When a fact is true in the modeled reality
– User specified times
> Transaction Time Tables
– When a fact is stored in the database
– System maintained time, no user control
> Bitemporal Tables
– Both Transaction Time and Valid Time
• User Defined Time
> User can add time period columns, and take advantage of the added
temporal operators
> Database does not enforce any rules on user defined time columns

Temporal Query
5
Provide a list of members who were reported as covered on
Jan. 15, 2000 in the Feb. 1, 2000 NCQA report, with names as
accurate as our best data shows today.
With Temporal Support
SELECT member.member_id, member.member_nm
FROM edw.member_x_coverage
VALIDTIME AS OF DATE ‘2000-01-15’ AND
TRANSACTIONTIME AS OF DATE ‘2000-01-01’ ,edw.member
WHERE member_x_coverage.member_id =
member.member_id;
Without Temporal Support
select member.member_id
,member.member_nm
from edw.member_x_coverage coverage
,edw.member
where coverage.member_id = member.member_id
and coverage.observation_start_dt <= '2000-02-01'
and (coverage.observation_end_dt > '2000-02-01'
or
coverage.observation_end_dt is NULL)
and coverage.effective_dt <= '2000-01-15'
and (coverage.termination_dt > '2000-01-15'
or
coverage.termination_dt is NULL)

Temporal Update – BiTemporal Table
6
With Temporal Support
UPDATE objectlocation
SET LOCATION = ‘External’
WHERE item_id = 125
AND item_serial_num = 102
Without Temporal Support
INSERT INTO objectlocation
SELECT item_id, item_serial_num, ‘External’, CURRENT_TIME, END(vt), CURRENT_TIME,
‘Until_Closed’
FROM objectlocation
WHERE item_id = 125 AND item_serial_num = 102
AND BEGIN(vt) <= CURRENT_TIME
AND END(vt) > CURRENT_TIME
AND END(tt) = ‘Until_Closed’;
SELECT item_id, item_serial_num, location, BEGIN(vt), CURRENT_TIME, CURRENT_TIME,
‘Until_Closed’
FROM objectlocation
SET END(tt) = CURRENT_TIME
SELECT item_id, item_serial_num, ‘External’, BEGIN(vt), END(vt), CURRENT_TIME,
‘Until_Closed’
FROM objectlocation
AND BEGIN(vt) > CURRENT_TIME
AND END(tt) = ‘Until_Closed’
SET END(tt) = CURRENT_TIME
WHERE item_id =125 AND item_serial_num = 102
AND BEGIN(vt) > CURRENT_TIME
AND END(vt) = ‘Until_Closed’
Current valid time, current transaction time Query
Jeans (125,102) are sold today (2005-08-30)

Moving Current Date in PPI
7
• Description
> Support use of CURRENT_DATE and CURRENT_TIMESTAMP built-in
functions in Partitioning Expression.
> Ability to reconcile the values of these built-in functions to a newer date or
timestamp using ALTER TABLE.
– Optimally reconciles the rows with the newly resolved date or timestamp value.
– Reconciles the PPI expression.
• Benefit
> Users can define with ‘moving’ date and timestamps with ease instead of
manual redefinition of the PPI expression using constants.
– Date based partitioning is typical use for PPI. If a PPI is defined with ‘moving’
current date or current timestamp, the partition that contains the recent data can
be as small as possible for efficient access.
> Required for Temporal semantics feature – provides the ability to define
‘current’ and ‘history’ partitions.

Time Series Expansion Support
8
• Description
> New EXPAND ON clause added to SELECT to expand row with a
period column into multiple rows
– EXPAND ON clause allowed in views and derived tables
> EXPAND ON syntax supports multiple ways to expand rows
• Benefit
> Permits time based analysis on period values
– Allows business questions such as ‘Get the month end average
inventory cost during the last quarter of the year 2006’
– Allows OLAP analysis on period data
> Allows charting of period data in an excel format
> Provides infrastructure for sequenced query semantics on
Temporal tables

Time series Expansion support
9
• What will it do?
> Expand a time period column and produce value equivalent rows
one each for each time granule in the period
– Time granule is user specified
– Permits a period representation of the row to be changed into an event
representation
> Following forms of expansion provided:
– Interval expansion
– By the user specified intervals such as INTERVAL ‘1’ MONTH
– Anchor point expansion
– By the user specified anchored points in a time line
– Anchor period expansion
– By user specified anchored time durations in a time line

Geospatial Enhancements
10
• Description
> Enhancements to the Teradata 13 Geospatial offering drastically
increasing performance, adding functionality and providing
integration points for partner tools
• Benefits
> Increased performance by changing UDF’s to Fast Path System
functions
> Replace the Shape File Generator client tool (org2org) with a
stored procedure for tighter integration with the database and
tools such as ESRI ARCGIS
> Provide geodetic distance methods – SphericalBufferMBR()
> WFS Server provides better tool integration support for MapInfo
and ESRI products

ESRI ArcGIS Connecting to Teradata via
Safe Software FME
11
1. FME connection in
ArcView
2. Connect to Teradata
via TPT API
4. Select Teradata
tables for ArcView
analysis

Projection of Impact Zone
& Storm Path to Google Earth
12
Where do I deploy my
cat management team.

Algorithmic Compression
13
• Description
> Provide the capability that will allow users the option of defining
compression/decompression algorithms that would be
implemented as UDFs and that would be specified and applied to
data at the column level in a row. Initially, Teradata will provide
two compression/decompression algorithms; one set for UNICODE
columns and another set for LATIN columns.
• Benefit
> Data compression is the process by which data is encoded so that
it consumes less physical storage space. This capability reduces
both the overall storage capacity needs and the number of
physical disk I/Os required for a given operation. Additionally,
because less physical data is being operated on there is the
potential to improve query response time as well.
• Considerations
> At some point, compressed data will have to be decompressed
when required. This can cause the use of some extra CPU cycles
but in general, the advantages of compression outweigh the extra
cost of decompression.

Multi-Value Compression For Varchar Columns
14
• Example – Multi-Value Compression for Varchar Column:
CREATE TABLE Customer
(Customer_Account_Number INTEGER
,Customer_Name VARCHAR(150)
COMPRESS (‘Rich’,‘Todd’)
,Customer_Address CHAR(200));

Block Level Compression
15
• Description
> Feature provides the capability to perform compression on whole
data blocks at the file system level before the data blocks are
actually written to storage.
• Benefit
> Block level compression yields benefit by reducing the actual
storage required for storing the data, especially cool/cold data, and
significantly reduce the I/O required to read the data.
• Considerations
> There is a CPU cost to perform the act of compression or
decompression on whole data blocks and is generally considered a
good trade since CPU cost is decreasing while I/O cost remains
high.

User-Defined SQL Operators
16
• Description
> This feature provides the capability that will allow users to define
and encapsulate complex SQL expressions into a User Defined
Function (UDF) database object.
• Benefits
> The use of the SQL UDFs Feature allows users to define their own
functions written using SQL expressions. Previously, the desired
SQL expression would have to be written into the query for each
use or alternatively, an external UDF could have been written in
another programming language to provide the same capability.
> Additionally, SQL UDFs allow one to define functions available in
other databases and with alternative syntax (e.g. ANSI).
• Considerations
> The Teradata SQL UDF feature is a subset of the SQL function
feature described in the ANSI SQL:2003 standard.
> Additionally, this feature does not introduce any changes to the
definition of the Dictionary Tables per se, but will add additional
rows into the DBC.TVM and DBC.UDFInfo tables to indicate the
presence of a SQL UDF.

SQL UDF - Example
17
• The “Months_Between” Function:
CREATE FUNCTION Months_Between
(Date1 DATE,
RETURNS Interval Month (4)
LANGUAGESQL
DETERMINISTIC
CONTAINS SQL
PARAMETER STYLE SQL
RETURN(CAST(Date1 AS DATE)-
Date2 DATE)
CAST(Date2 AS DATE)) MONTH (4);
SELECT MONTHS_BETWEEN ('2008-01-01', '2007-01-01');
MONTHS_BETWEEN ('2008-01-01', '2007-01-01')
---------------------------------------------------
12

Character-Based PPI (CPPI)
19
• Description
> This feature leverages current Teradata Primary Partitioned Index
(PPI) technology and extends this capability to allow the use of
character data (CHAR, VARCHAR, GRAPHIC, VARGRAPHIC) as
table partitioning mechanisms.
• Benefit
> Currently, only an integer datatype is allowed to be used in a PPI
scheme as a partitioning mechanism which facilitates superior
query performance advantage via partition elimination. The
extension of this capability to use character-based datatypes as a
partitioning mechanism will allow for more partitioning options
and in-turn yield similar query performance advantage as the
current PPI technology gleans today.
• Considerations
> As with all Teradata indexes or partitioning database design
choices, the Optimizer will determine the appropriate index/PPI to
use that will provide the best-cost plan for executing the query.
No end-user query modification is required.

Timestamp Partitioning
20
• Description
> Provide the capability that allows users to explicitly specify a time
zone for PPI tables involving DateTime partitioning expressions in
order to make the expressions deterministic (e.g., not dependent
on the session time zone).
> Implement the enhancements that will extend the PPI partition
elimination capability to include timestamp data types in
partitioning expressions.
• Benefit
> Insuring that DateTime partitioning expressions to be
deterministic will eliminate the possibility of any errors that may
occur as a result of incorrect dependence on session time zones.
> The extension of this capability to use timestamp data types as a
partitioning mechanism will allow for more partitioning options
and in-turn yield similar query performance advantage as the
current PPI technology gleans today.
• Considerations
> Enhancements related to deterministic time zone handling will
also be applied to sparse join index search conditions as well.

Fastpath Functions
21
• Description
> The Fastpath Function project combines the extensibility, short
development cycles, and ease-of-use aspects of UDFs with the
high performance and ease-of-use aspects of Teradata system
functions to yield and alternate development path by which
Teradata Engineering software developers may add new Teradata
system functions to the Teradata server.
• Benefit
> The Fastpath Function project will allow Teradata to use a shorter
development cycle to fulfill many customer specific requests for
implementing new system functions that additionally perform in
the same manner as native Teradata system functions.
• Considerations
> Source code and/or libraries used in the development of Teradata
system functions must be solely managed and maintained by
Teradata Engineering. End-users will not be able to develop
Fastpath system functions.

FastExport – Without Spooling
• Description
 > Enhance the FastExport utility to
provide an option that would allow the
utility to execute in a mode that eliminates
the requirement that the query data be
spooled prior to the actual export process.
• Benefit
 > The “direct without spooling” method will
provide the mechanism to extract data from
Teradata table quickly and efficiently with the
main benefit being realized as a performance
gain and minimum resource utilization.
• Considerations
 > The “direct without spooling” method is
not transparent to the user and must be
specified as a discrete option when executing
22

TASM: Additional Workload Definitions
• Description
 > Feature increases the number of
available TASM Workload Definitions
(WDs) to 250 (instead of 40).
• Benefits
 > Complex mixed workloads require the
ability to have a finer degree of granular
control over the parts of the workload.
 Increasing the number of WDs will allow
customers to better
 manage and report on resource usage of their
system to meet either subject area (e.g. by
country, application or division) resource
distribution requirements, or category-of-work
(e.g. high vs. low priority) resource distribution
requirements.
24

TASM: Common Classifications
25
• Description
> This feature provides for capability to have Workload Definition
classification criteria be available for Teradata Workload
Management Category 1, 2 and 3 (Filters, System Throttles and
Workload Definitions) and additionally, extends wildcard support
to Filters and Throttles.
• Benefit
> The implementation of Common Classifications addresses the
differences and delivers consistency between the TDWM categories
(Filters, System Throttles and Workload Definitions), which
improves the Teradata Workload Management user interface and
it’s subsequent usability.
• Considerations
> Consideration should be given to re-evaluating the current
settings for the different categories insofar as common
classification extends the ability to manage a workload in an
easier and simpler fashion.

TASM: Common Classifications
26
• “Who” Criteria
> Account String / Account Name
> Teradata Username / Teradata Profile
> Application Name
> Client Address or Client Name
> QueryBand
• “Where” Criteria (Data Objects)
> Databases
> Tables / Views / Macros
> Stored Procedures
• “What” Criteria
> Statement Type (SELECT, DDL, DML)
> Utility Type
> AMP Limits, Row Count, Final Row Count
> Estimated Processing (CPU time)
> Join Types
– ALL or no joins
– ALL or no product joins
– ALL or no unconstrained product joins

TASM Utility Management
27
• Description
> This feature enhances the TASM utility to augment the existing TD
Utility Management capability to provide controls to be similar to the
workload management of regular SQL requests and to provide for
the automatic selection of the number of sessions used by Teradata
utilities.
• Benefits
> Feature provides for more granular and centralized control of utility
execution and allows deployment to a much wider audience of users
and applications. Additionally, the use of Teradata utility sessions is
moved inside the database and is automated to eliminate the
detailed management of sessions in each job.
• Considerations
> Consideration should be given to a reevaluation of current rule sets
and settings to maximize control of the workload and relative utility
execution.
> Throttling in TASM eliminates need for Tenacity and Sleep.
Execution of queued jobs becomes FIFO. Execution of queued jobs
is immediate when resource available rather than at end of Sleep
time”

TASM Utility Session Configuration Rules
28
• For FastLoad, MultiLoad, and FastExport utilities, the DBS
default for number of AMP sessions is one per AMP.
• On a large system with hundreds or thousands of AMPs, this
default becomes inappropriate.
• Currently, a user can override this default by changing
individual load/export script, or changing the MAXSESS
parameter in the configuration file, or specifying through
runtime parameters (i.e., MAXSESS or –M).
• These overriding methods are inconvenient.
• This feature allows a DBA to define TDWM rules in one central
place that specifies the number of AMP sessions to be used
based a combination of the following criteria:
> Utility Name
> “Who” criteria (user, account, client address, query band, etc.)
> Data size

TASM Utility Session Configuration Rules
29
• Session configuration rules are optional.
• These rules are active when any category of TDWM is enabled.
• In each session configuration rule, the DBA specifies the
criteria and the number of sessions to be used when these
criteria are met.
• For example, for stand alone MultiLoad jobs submitted by user
Charucki, use 10 sessions.
• Session configuration rules also support the Archive/Restore
utility.
• The DBA can define similar rules to specify the number of
HUTPARSE sessions to be used for a specific set of criteria.
• A new internal DBSControl field: DisableTDWMSessionRules
is provided to disable user-defined session configuration rules
and default sessions rules while TDWM is enabled.
• When this field is set, Client and DBS will operate as in
Teradata 13.

Availability, Serviceability, DBA Tasks
Improvements

Fault Isolation
31
• Description
> Remove cases where faults can cause restarts
> Specific cases
– EVL fault isolation
– Unprotected UDFs
– Dictionary cache re-initialization
• Benefits
> Identify and isolate the fault to only the query or session
> Issues in query calculation and qualification will be isolated
> Badly behaving UDFs will have less opportunity to affect the
system
> Faults in the dictionary cache will result in the dictionary cache
being flushed and reloaded rather than affecting the entire system

AMP Fault Isolation
• Description
 > This feature is intended to catch those AMP
errors that currently cause DBS restarts where
the error can be dealt with by taking a snapshot
dump and aborting the transaction that caused
the error
• Benefit
 > This feature can reduce the number of DBS
restarts for customers, thus improving overall
system availability
• What will it do?
 > Current AMP Fault Isolation only avoids a full
database restart for errors when accessing
spool tables
 > The scope of fault isolation will be increased
to cover ERRAMP* or ERRFIL* errors on
permanent tables as well spools
32

Read From Fallback
33
• Description
> In the event of encountering a data block read error, either
unreadable or corrupt data blocks, this feature will leverage the
pre-existing Fallback Table facility to transparently retrieve the
required data block from the fallback copy.
• Benefit
> When fallback is available, feature seriously improves fault
tolerance and system availability. Significantly improves the value of
having fallback and protects non-redundant (RAID 0 or JBOD)
storage media, such as SSD, from data loss without restart/failover.
• Considerations
> Fallback does not need to be instantiated as system-wide property,
because fallback is a table-level attribute, it can be applied
selectively to the largest/most critical customer tables.
> This facility does not in-and-of itself repair bad data blocks, but
allows them to be read from fallback until they can be repaired.

Read From Fallback - Particulars
34
• Reading data blocks from the Fallback copy is transparent to both a
user and/or application. Manual intervention is not required
whatsoever.
• Feature does not require any special or particular locking mechanism.
• A manual process is still required to rebuild the table to repair
unreadable or corrupt data blocks.
• Facility cannot recover from data block errors in the Cylinder Index,
NUSI Secondary Index or Permanent Journals.
• Read errors are fallback recoverable on TD Data Dictionary tables
with the exception of the unhashed system tables such as the WAL
log, Transient Journal and Space Accounting tables.
• Facility applies to SQL Queries with data block read errors, SQL
Insert…Select statements and the Archive utility where the block read
error is on the source table only.

Transparent Cylinder Packing
35
• Description
> Develop a new file system background task that will pro-actively
and transparently monitor and adjust the utilization (high or low)
of user data cylinders and pack/unpack said cylinders accordingly
with the goal of returning them to a more efficiently utilized state.
• Benefit
1. Cylinder Packing will result in cylinders having a higher datablock
to cylinder index ratio making Cylinder Read operations more
effective by reading less unoccupied sectors.
2. Higher cylinder utilization translates into data tables occupying
less cylinders leaving more cylinders available for other purposes.
3. Diminishes the chances that a “mini-cylpack” operation will be
executed and lessens the need for administrators to perform
regularly scheduled Packdisk operations.
• Considerations
> This feature will have several customer tunable parameters in
DBSControl that will allow customers to mange and adjust the
level of impact of the Transparent Cylinder Packing operations.

Merge Data Blocks During Full
Table Modify Operations
36
• Description
> During full table modification operations such as Multiload, Insert
Select and Update or Delete Where, combine adjacent blocks
when small blocks are present.
• Benefit
> Small data blocks increase the I/Os necessary to read a table and
interferes with features such as compression and large cylinders.
> Reduce the instances of small data blocks by combining them
when doing work on those blocks or adjacent ones.

Archive DBQL Rule Table
37
• Description
> Enhance the Teradata Archive utility to include two additional DBC
tables to the DBC database (Dictionary) backup/restore:
– DBC.DBQLRuleTbl
– DBC.DBQLRuleCountTbl
• Benefit
> Inclusion of the additional DBC tables in the DBC Archive/Restore
process will provide a mechanism by which these tables can be
archived/restored and will altogether eliminate the cumbersome
task of having to every time redefine the appropriate DBQL rules
after a Dictionary initialization.
> Implementation of this feature avoids the possibility of any table
synchronicity issues and offers simplicity, convenience, and
integrity when conducting a DBC archive/restore.
• Considerations
> DBC Archive will include these tables automatically in the
Dictionary Archive; no user intervention is required.

Be Aware
Especially if Considering Tech Refresh

Large Cylinder Support
39
• Description
> This feature increases data storage cylinder size, the basic
allocation unit for disk space in the Teradata file system. This also
includes an increase in the Cylinder Index size thus allowing for a
commensurate increase in storing more data blocks per cylinder.
• Benefit
> Eliminates the inefficiency associated with managing a large
number of small cylinders on very large disk drives, allows larger
AMP sizes (~10 TB per AMP), permits the more efficient storage of
Large Objects and provides the foundation for block level
compression by allowing more small blocks on a cylinder.
• Consideration
> This capability is only available starting in Teradata 13.10 and
going forward and requires a System Initialization (SysInit) to be
performed so that large cylinder support can be engaged. It is
anticipated that typically this activity would be performed during
technology refresh opportunities.

Packed Row format for 64-bit
platforms
• Description
 > With the introduction of Teradata 13.10,
data will now be stored on the database in
byte-packed format whereas previously the
data had been stored in byte-aligned format.
• Benefits
 > Translates directly into a 4-7 % disk space
savings insofar as less disk space is required to
store byte-packed data than is required with
byte-aligned data. Additionally, enables data
rows to be accessed using fewer I/Os thus
potentially enhancing the performance of some
workloads.
• Considerations
 > This capability is only available starting in
Teradata 13.10 and going forward and requires a
40

Enhanced Teradata Hashing Algorithm
41
• Description
> Enhance the Teradata Hashing Algorithm to reduce the effects of
irregularities in character data on hash results.
• Benefit
> This enhancement is targeted to reduce the number of hash
collisions for character data stored as either Latin or Unicode,
notably strings that contain primarily numeric data. Reduction in
hash collisions reduces access time per AMP and produces a more
balanced row distribution which in-turn improves parallelism.
Reduced access time and increased parallelism translate directly to
better performance.
• Considerations
> This capability is only available starting in Teradata 13.10 and
going forward and requires a System Initialization (SysInit) to be
performed so that the enhanced hashing algorithm can be
engaged. It is anticipated that typically this activity would be
performed during technology refresh opportunities.

2 >
Teradata Database 13.10
EnterpriseFit
3/18/10Quality/
Support
-ability
• AMP fault isolation
• Parser diagnostic information capture
• Dictionary cache re-initialization
• EVL fault isolation and unprotected UDFs
Performance
• FastExport without spooling
• Character-based PPI
• Timestamp partition elimination
• User Defined Ordered Analytics
• Merge data blocks during full table modify operations
• Statement independence
• TVS Initial suggested temperature tables
Active
Enabl
e
• Restart time reduction
• Read from Fallback
• TASM: Workload Designer
• TASM: Utilities Management
• TASM: Additional Workload Definitions
Ease
of
Use
• Teradata 13.10 Teradata Express Edition
• Domain Specific System Functions
• Moving current date in PPI
• Automatic cylinder packing
4 • Algorithmic Compression for Character Data
• VLC for VARCHAR columns
• Block level compression
• Variable fetch size (JDBC)
• User Defined SQL Operators
• Temporal Processing
• Temporal table support
• Period data type enhancements
• Replication support
• Time series Expansion support
• Archive DBQL rule table
• Enhanced trusted session security
• External Directory support enhancements
• Geospatial enhancements
• Statement Info Parcel Enhancements (JDBC)
• Support for IPv6
• Support unaligned row format for 64-bit platforms
• Enhanced hashing algorithm
• Large cylinder support

43 >
Teradata Developer Exchange
http://developer.teradata.com/
• What is it?
> Portal for technical
insights
– Articles, blogs, podcasts
– Forums, FAQs, “How to”,
etc.
> Community of Teradata
experts
– Customers, Teradata R&D
and PS
> Share software
– Portlets, UDFs, SPs,
scripts, etc.
– Sample applications
• Who can use it?
> Anyone (read only)
> Registered contributors
– Blogs, code, ratings,
articles, etc.

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