Sas Analytics Accelerator User Manual
SAS®Analytics Accelerator 1.3
for Teradata
Guide
SAS®Documentation
The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2011. SAS®Analytics Accelerator 1.3 for
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SAS®Analytics Accelerator 1.3 for Teradata: Guide
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Contents
Chapter 1. SAS Analytics Accelerator 1.3 for Teradata: Guide . . . . . . . . . . . . . 1
Subject Index 71
Syntax Index 73
iv
Chapter 1
SAS Analytics Accelerator 1.3 for Teradata: Guide
Contents
What’s New in SAS Analytics Accelerator 1.3 for Teradata . . . . . . . . . . . . . . . . . . 3
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Overview of In-Database Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Teradata and the SAS System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
SQL Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
SAS In-Database Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
SAS In-Database Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Base SAS In-Database Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
SAS/STAT In-Database Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
SAS/ETS In-Database Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
SAS Enterprise Miner In-Database Procedures . . . . . . . . . . . . . . . . . . . . . 10
Limitations of In-Database Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 10
Performance and Numerical Accuracy Issues with In-Database Computing . . . . . . . . . 11
Enabling and Controlling In-Database Computing . . . . . . . . . . . . . . . . . . . . . . . 11
LIBNAME Statement Options for In-Database Computing . . . . . . . . . . . . . . . . . . 14
Using SAS/ACCESS Software with Teradata: Some Cautionary Notes . . . . . . . . . . . . 19
Indeterminate Row Order in a DBMS Table . . . . . . . . . . . . . . . . . . . . . . . 28
SAS/ACCESS Data Set Options for Teradata . . . . . . . . . . . . . . . . . . . . . . . . . 31
Base SAS Data Set Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Deploying and Using SAS Formats in Teradata . . . . . . . . . . . . . . . . . . . . . . . . 37
Using SAS Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
How It Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Formats That SAS Supplies in the Teradata EDW . . . . . . . . . . . . . . . . . . . . 41
User-Defined Formats in the Teradata EDW . . . . . . . . . . . . . . . . . . . . . . . 41
Overview of the Publishing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Using SAS Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
BY Groups and In-Database Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Support for Teradata Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Variables in the DATA= Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Numeric Computations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
BY Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Conditions That Prevent In-Database Processing . . . . . . . . . . . . . . . . . . . . . . . 49
2 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
LIBNAME Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Data Set and Variable Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
BY Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Compatibility of the DATA= and OUT= Data Sets . . . . . . . . . . . . . . . . . . . 54
Other Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
In-Database Computing for the DATA= Data Set by the CANCORR, FACTOR, PRINCOMP,
REG, and VARCLUS Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
ODS Tables for the CANCORR, FACTOR, PRINCOMP, REG, and VARCLUS Procedures . 56
In-Database Computing for the OUT= Data Set by the CANCORR, FACTOR, PRINCOMP,
and SCORE Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
SAS/STAT Procedure Options, Statements, and Features Affected by In-Database Computing 57
CANCORR Procedure Options, Statements, and Features Affected by In-Database
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
FACTOR Procedure Options, Statements, and Features Affected by In-Database Com-
puting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
PRINCOMP Procedure Options, Statements, and Features Affected by In-Database
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
REG Procedure Options, Statements, and Features Affected by In-Database Computing 59
SCORE Procedure Options, Statements, and Features Affected by In-Database Com-
puting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
VARCLUS Procedure Options, Statements, and Features Affected by In-Database
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
SAS/ETS Procedure Options, Statements, and Features Affected by In-Database Computing 62
TIMESERIES Procedure Options, Statements, and Features Affected by In-Database
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Base SAS Procedure Options, Statements, and Features Affected by In-Database Computing 62
CORR Procedure Options, Statements, and Features Affected by In-Database Computing 62
FREQ Procedure Options, Statements, and Features Affected by In-Database Computing 63
SAS Enterprise Miner Procedure Options, Statements, and Features Affected by In-Database
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
DMDB Procedure Options, Statements, and Features Affected by In-Database Com-
puting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
DMINE Procedure Options, Statements, and Features Affected by In-Database Com-
puting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
DMREG Procedure Options, Statements, and Features Affected by In-Database Com-
puting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Miscellaneous Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
What’s New in SAS Analytics Accelerator 1.3 for Teradata F 3
What’s New in SAS Analytics Accelerator 1.3 for Teradata
Overview
This release contains the first implementation of in-database functionality for SAS®Enterprise Miner™.
SAS Analytics Accelerator 1.3 for Teradata supports three SAS Enterprise Miner procedures: DMDB,
DMINE, and DMREG.
Overview of In-Database Computing
SAS applications are often built to work with large volumes of data in environments that demand rigorous IT
security and management. When the data are stored in an external database, such as a Teradata database, the
transfer of large data sets to the computers that run the SAS System can cause a performance bottleneck and
possible unwanted security and resource management consequences for local data storage. SAS Analytics
Accelerator 1.3 for Teradata addresses these challenges by moving computational tasks closer to the data
and by improving the integration between the SAS System and the database management system (DBMS).
At present, there are in-database versions of six SAS/STAT procedures, one SAS/ETS procedure, and three
SAS Enterprise Miner procedures. The SAS/STAT in-database procedures include CANCORR, FACTOR,
PRINCOMP, REG, SCORE, and VARCLUS; the SAS/ETS procedure is TIMESERIES; and the SAS Enterprise Miner procedures are DMDB, DMINE, and DMREG.
There are also five Base SAS in-database procedures (CORR, FREQ, MEANS, RANK, and SUMMARY),
but Base SAS in-database procedures, with the exception of the CORR procedure, do not require the installation of SAS Analytics Accelerator 1.3 for Teradata. Table 1.1 lists the software licenses that are required
to take advantage of in-database computing.
4 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
Table 1.1 Software Licensing Requirements for In-Database Computing
Procedures Licenses Required
SAS/STAT procedures Base SAS 9.2M3
(CANCORR, FACTOR, PRINCOMP, SAS/ACCESS 9.2 (SAS/ACCESS Interface to Teradata)
REG, VARCLUS, and SCORE) SAS/STAT 9.22
SAS Analytics Accelerator 1.3 for Teradata
SAS/ETS procedure Base SAS 9.2M3
(TIMESERIES) SAS/ACCESS 9.2 (SAS/ACCESS Interface to Teradata)
SAS/ETS 9.22
SAS Analytics Accelerator 1.3 for Teradata
SAS Enterprise Miner procedures
(DMDB, DMINE, and DMREG) Base SAS 9.2M3
SAS/ACCESS 9.2 (SAS/ACCESS Interface to Teradata)
SAS Enterprise Miner 6.2
SAS Analytics Accelerator 1.3 for Teradata
Base SAS statistical procedures Base SAS 9.2M3
(FREQ, MEANS, RANK, and SUMMARY) SAS/ACCESS 9.2 (SAS/ACCESS Interface to Teradata)
BASE SAS statistical procedures Base SAS 9.2M3
(CORR) SAS/ACCESS 9.2 (SAS/ACCESS Interface to Teradata)
SAS Analytics Accelerator 1.3 for Teradata
All SAS Analytics Accelerator 1.3 for Teradata in-database functions require Teradata 13.00.00.15 or higher.
Installation instructions for SAS Analytics Accelerator 1.3 for Teradata are published in the section “Configuring SAS Analytics Accelerator for Teradata” in the Configuration Guide for SAS 9.2 Foundation for
UNIX Environments and the Configuration Guide for SAS 9.2 Foundation for Microsoft Windows.
In SAS 9.2M3, SAS/STAT 9.22, and SAS/ETS 9.22 in-database computing is available only for the Teradata
DBMS. Future releases might provide in-database computing for other DBMSs.
Teradata and the SAS System
In a conventional environment where in-database computing is not possible, a large amount of data is stored
in the Teradata database. When a SAS procedure executes, it must read the data through the SAS/ACCESS
engine. This movement of data from the database to the SAS workspace server causes a performance
penalty. In an environment where in-database computing is possible, such as that provided by SAS Analytics
Accelerator 1.3 for Teradata, when a comparable Base SAS, SAS/STAT, SAS/ETS, or SAS Enterprise Miner
in-database procedure executes, some of the procedure’s computations are performed within Teradata, and
only the results of those computations, rather than the raw data, are passed to the SAS workspace server.
The benefit is a reduction in data movement between the database and the SAS workspace server. The cost
SQL Generation F 5
of this reduction in data movement is the difference in processing time due to using Teradata versus the SAS
System to process the data. The trade-off in processing time versus data movement time depends on several
dynamic factors, including client and server workloads, network speed and workload, and data size. The net
benefit from using in-database procedures varies based on the particular environment. In general, the net
benefit increases as the number of rows in a database table increases.
SQL Generation
When performing in-database modeling, a SAS/STAT, SAS/ETS, or SAS Enterprise Miner procedure dynamically generates SQL code, which is based on the procedure options and statements. It then submits the
SQL code directly to the database. The code can be standard SQL that can be interpreted by any database,
or it can be tuned specifically for Teradata. The choice for the type of SQL code is determined by the complexity of the required analysis. The code can include SAS formats that are executed in Teradata. The query
returns result sets that are used by the in-database procedure to complete the analysis before supplying the
results to one of the following: the SAS output listing, the ODS listing, ODS Graphics, or output tables.
The end result is usually the same as when you use Base SAS tables rather than Teradata tables. However,
with in-database computing the relational database software is responsible for optimizing and executing the
query. When the table to be analyzed is very large, the in-database computing approach can result in significantly lower total elapsed processing time and in reduced data movement between the SAS System and the
Teradata database.
SAS In-Database Functions
When a SAS/STAT, SAS/ETS, or SAS Enterprise Miner procedure dynamically creates and submits SQL
code, this code includes references to new user-defined functions (UDFs), developed by SAS, which are
installed on the Teradata system. These UDFs are the key to performing advanced statistical computations efficiently in-database. For example, SAS has developed a Teradata UDF called SAS_TACORR for
computing uncorrected sum of squares and crossproducts (SSCP) matrices (also known as X’X matrices)
and related statistics in-database. The SAS/STAT procedures CANCORR, FACTOR, PRICOMP, REG,
and VARCLUS have been modified to take advantage of the SAS_TACORR UDF, which enables them
to perform advanced statistical computations on a DBMS table without downloading the entire table with
SAS/ACCESS software.
To better understand how the SAS_TACORR UDF enhances computing efficiency, consider the following.
The SSCP matrix is a condensed representation of the relationships between variables. The size of the
SSCP matrix is determined by the number of variables that are used by the procedure and does not depend
on the number of rows. For example, a data set that contains 100 numeric columns and 5 million rows of
data produces an SSCP matrix that contains, nominally, 100x100 cells. However, a SAS/STAT in-database
procedure needs only the lower triangle of 100x50 cells. Thus, the data that is transferred from Teradata to
SAS is reduced from 500,000,000 raw data cells to 5,000 SSCP data cells, which is a reduction of 99.999
percent. As the example illustrates, the benefit of computing the SSCP matrix in-database versus passing
the raw data to the SAS System increases greatly as the number of rows increases. Large data applications
benefit the most from in-database processing.
6 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
The SAS/STAT in-database procedure constructs SQL code that uses the SAS in-database function to create the SSCP matrix. This function is not intended for general-purpose SQL queries, but it is useful for
SAS/STAT in-database procedures. The elements of the SSCP are created and transferred to the SAS System. After the crossproducts have been transferred to the SAS System, the database is available to continue
processing queries while the SAS/STAT procedure continues the analysis.
SAS In-Database Procedures
Several SAS/STAT, SAS/ETS, SAS Enterprise Miner, and Base SAS procedures have been modified to
move critical data-intensive operations into the database. Such operations include basic summarization and
exploratory data analysis. These SAS procedures are commonly found in many SAS programs and represent
a large opportunity to improve efficiency when working with relational databases. Current changes are
optimized for the Teradata relational database. The modifications to the SAS/STAT, SAS/ETS, and SAS
Enterprise Miner in-database procedures, as well as the Base SAS CORR procedure, require the installation
of the SAS Analytics Accelerator 1.3 for Teradata and are documented here.
Base SAS In-Database Procedures
Base SAS currently offers in-database implementations of the CORR, FREQ, MEANS, RANK, and SUMMARY procedures. The Base SAS in-database procedures work differently from the SAS/STAT, SAS/ETS,
and SAS Enterprise Miner in-database procedures and do not require the installation of the SAS Analytics
Accelerator for Teradata (with the exception of the CORR procedure). The Base SAS in-database procedures are included in the Base SAS software and are documented in Base SAS Procedures Guide. However,
some notes about the options that affect the Base SAS in-database procedures are included in this document
for convenience.
SAS/STAT In-Database Procedures
In-database versions of the CANCORR, FACTOR, PRINCOMP, REG, SCORE, and VARCLUS procedures
are available in SAS/STAT 9.22. Each procedure generates SQL code which, except for the SCORE procedure, calls the SAS in-database function for SSCP creation. These procedures are commonly used in
exploratory data analysis and regression model building. The procedure syntax and output for in-database
computing is identical to the syntax and output for conventional SAS processing in most cases. In those
cases, existing procedure steps in SAS programs are able to run in-database without modification.
However, some issues with in-database processing can cause the output to differ because of numerical precision issues and BY processing issues. These issues are discussed in the section “Conditions That Prevent
In-Database Processing.” Together with the in-database Base SAS procedures, the SAS/STAT procedures
can be used for basic tasks that are often executed at the beginning of larger analytical tasks. Use of these
techniques can greatly reduce the total data transfer between relational databases and the SAS System.
SAS/STAT In-Database Procedures F 7
The CANCORR Procedure
The CANCORR procedure performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
Canonical correlation is a generalization of multiple correlation for analyzing the relationship between two
sets of variables. In multiple correlation, you examine the relationship between a linear combination of a
set of explanatory variables, X, and a single response variable, Y. In canonical correlation, you examine
the relationship between linear combinations of the set of X variables and linear combinations of a set of
Y variables. These linear combinations are called canonical variables or canonical variates. Either set of
variables can be considered explanatory or response variables, since the statistical model is symmetric in
the two sets of variables. Simple and multiple correlation are special cases of canonical correlation in which
one or both sets contain a single variable.
The SSCP matrix is computed in-database and is transferred to the SAS System for further processing. The
options in the CANCORR procedure that are affected by in-database processing are detailed in the section
“CANCORR Procedure Options Affected by In-Database Computing.”
The FACTOR Procedure
The FACTOR procedure performs a variety of common factor and component analyses and rotations. The
methods for factor extraction are principal component analysis, principal factor analysis, iterated principal
factor analysis, unweighted least squares factor analysis, maximum likelihood (canonical) factor analysis,
alpha factor analysis, image component analysis, and Harris component analysis. A variety of methods for
prior communality estimation are also available.
The SSCP matrix is computed in-database and is transferred to the SAS System for further processing. The
options in the FACTOR procedure that are affected by in-database processing are detailed in the section
“FACTOR Procedure Options Affected by In-Database Computing.”
The PRINCOMP Procedure
The PRINCOMP procedure computes a principal component transformation of the SSCP matrix into orthogonal components. The first component accounts for the maximum amount of variation; the second
accounts for the next largest amount of variation; and so on. This procedure is typically used in exploratory
data analysis and visualization. Scatter plots that are generated from the principal component dimensions
often reveal interesting relationships among the data points.
Principal component analysis (PCA) reduces the dimensionality of data for predictive modeling by replacing
the variables in the original data with fewer principal component terms in the final model. The SSCP matrix
is computed in-database and is transferred to the SAS System for further processing. The options in the
PRINCOMP procedure that are affected by in-database processing are detailed in the section “PRINCOMP
Procedure Options Affected by In-Database Computing.”
8 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
The REG Procedure
The REG procedure computes a model in which a dependent variable is modeled as a linear equation of
multiple independent variables by a least squares function. PROC REG has numerous model-fitting options,
which include many model selection variations and hypothesis tests. Regression analysis is often used to
identify a subset of independent variables that have unique and significant relationships with the dependent
variable. In this case, PROC REG is used for its exploratory analysis, in addition to its extensive model
fitting and reporting functions. You might want to use the REG procedure to compute candidate models in
the database. A full SSCP matrix is computed in Teradata and is transferred to the SAS System, in which
the model fitting occurs.
PROC REG includes an option that uses a CORR or SSCP matrix as input and creates a CORR or SSCP matrix as output. For both in-database processing and matrix input, any option that requires access to individual
rows of data is disabled. The disabled options include the general class of residual analysis, plot options,
and confidence intervals. Complete details of which options and statements are affected by in-database
processing are provided in the section “REG Procedure Options Affected by In-Database Computing.”
The SCORE Procedure
Many statistical procedures create output data sets (using the OUTEST= or OUTSTAT= options) that contain coefficients. The SCORE procedure can apply the coefficients in those data sets to a raw data set and
compute new variables that are generically called scores. Each new score variable is formed as a linear
combination of the raw data and the scoring coefficients. That is, for each observation in the raw data set,
PROC SCORE multiplies the value of a variable in the raw data set by the matching scoring coefficient
from the data set of scoring coefficients. This multiplication process is repeated for each variable in the
VAR statement. The resulting products are then summed to produce the value of the new score variable. In
other words, PROC SCORE performs a matrix multiplication on the two data sets.
For example, you can use the SCORE procedure to produce output in the form of a table in Teradata that
contains predicted values or residuals from a model that is estimated using the REG procedure in-database.
The SCORE procedure dynamically generates SQL code for the given model. PROC SCORE then submits
the SQL code to the database, which produces a Teradata table without having to extract any rows of data into
the SAS System. Details of which options in the SCORE procedure are affected by in-database processing
are provided in the section “SCORE Procedure Options Affected by In-Database Computing.”
PROC SCORE cannot be used for scoring nonlinear models.
The following SAS procedures produce output data sets that contain scoring coefficients that can be used
by PROC SCORE:
ACECLUS procedure
CALIS procedure
CANCORR procedure
CANDISC procedure
DISCRIM procedure
SAS/ETS In-Database Procedures F 9
FACTOR procedure
PRINCOMP procedure
TCALIS procedure
VARCLUS procedure
ORTHOREG procedure
QUANTREG procedure
REG procedure
ROBUSTREG procedure
You can also use a DATA step or the IML procedure to output any coefficients you want in a data set to use
with PROC SCORE.
The VARCLUS Procedure
The VARCLUS procedure groups variables into clusters that are based on their correlations. The full SSCP
matrix is created, and then clusters are chosen to maximize the variance that is associated with the first
principal component within the cluster. An iterative process assigns variables to clusters to maximize the
sum across clusters of the variance of the original variables, which is explained by the centroid cluster
measure.
Similar to principle components, variable clustering is another technique that is used for exploratory data
analysis and for variable reduction, which reduces the number of terms that are used in successive analyses.
The SSCP matrix is computed in-database and is then transferred to the SAS System for further processing.
Details of which options in the VARCLUS procedure are affected by in-database processing are provided in
the section “VARCLUS Procedure Options Affected by In-Database Computing.”
SAS/ETS In-Database Procedures
The TIMESERIES procedure analyzes time-stamped transactional data with respect to time and accumulates
the data into a time series format. The procedure can perform trend and seasonal analysis on the transactions.
After the transactional data are accumulated, time domain analysis and frequency domain analysis can be
performed on the accumulated time series.
Details of which options in the TIMESERIES procedure are affected by in-database processing are provided
in the section “TIMESERIES Procedure Options Affected by In-Database Computing.”
10 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
SAS Enterprise Miner In-Database Procedures
The DMDB Procedure
SAS Enterprise Miner architecture is based on the creation of a data mining database (DMDB) that is a
snapshot of the original data. PROC DMDB creates this DMDB from the input data source. It also compiles
and computes metadata information about the input data based on variable roles and stores it in a metadata
catalog. The DMDB and the associated metadata catalog facilitate subsequent data mining activities. They
are both designed for processing and storage efficiencies.
The DMINE Procedure
Many data mining databases have hundreds of potential model inputs. The DMINE procedure enables you
to quickly identify the input variables that are useful for predicting the target variables based on a linear
models framework. The procedure facilitates ordinary least squares or logistic regression methods.
The DMREG Procedure
The DMREG procedure enables you to fit both linear and logistic regression models. Linear regression
attempts to predict the value of a continuous target as a linear function of one or more independent inputs.
Logistic regression attempts to predict the probability that a categorical (binary, ordinal, or nominal) target
will acquire the event of interest as a function of one or more independent inputs. The procedure supports
forward, backward, and stepwise selection methods. It also enables you to score data sets or generate SAS
DATA step code to score a data set.
Limitations of In-Database Procedures
Some features of SAS procedures do not execute in-database. The ability to perform in-database processing
depends on the specific type of analysis that is provided by the statements and options of a procedure. These
limitations are considered reasonable for the use case of accessing and exploring data. After a subset of data
has been selected and a model form has been established, a data sample can be defined by using PROC SQL.
This subset of data can then be accessed directly by the SAS procedure to execute an analysis that includes
functions that do not execute in-database. Use of the SAS in-database procedures reduces the amount of
data that is transferred and makes use of the resources of the Teradata system.
Performance and Numerical Accuracy Issues with In-Database Computing F 11
Performance and Numerical Accuracy Issues with In-Database Computing
Consider the following issues when performing in-database computations:
In-database computation can be either faster or slower than computation that uses SAS/ACCESS
software, depending on client performance, network performance, server performance, and the type
of computations involved.
In-database computation tends to be slow when the number of variables in the analysis is large or
when the number of BY groups is large.
Additional overhead is involved with in-database computation, and therefore more memory is required
on the client for in-database computation compared to out-of-database computation.
The SSCP matrix is computed with less accuracy in-database when the variables have small coeffi-
cients of variation.
Enabling and Controlling In-Database Computing
You control in-database computing with options in the LIBNAME statement and the OPTIONS statement.
SQLGENERATION=NONE | DBMS | ALL
specifies the type of in-database computing to be performed. The SQLGENERATION= option can
be specified as either a LIBNAME statement option or as a system option in an OPTIONS statement.
The value used for the SQLGENERATION= option depends on whether the option is specified in the
LIBNAME statement, an OPTIONS statement, or both as follows:
If the SQLGENERATION= option is specified in the LIBNAME statement for a data set, then
with respect to that data set, any value of the SQLGENERATION= option that is specified in
any OPTIONS statement is ignored.
If the SQLGENERATION= option is not specified in a LIBNAME statement for a data set, then
with respect to that data set, the value of the SQLGENERATION= option that is specified in the
most recent OPTIONS statement is used.
If the SQLGENERATION= option is not specified in either the LIBNAME statement for a data
set or in an OPTIONS statement, then by default with respect to that data set, SQLGENERATION=DBMS.
You can specify different values of the SQLGENERATION= option for the DATA= and OUT= data
sets by using different LIBNAME statements for the two data sets.
12 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
The values of the SQLGENERATION option are interpreted as follows:
NONE specifies that no in-database computation be performed. In this case, a SAS/STAT
procedure uses SAS/ACCESS to copy the raw data from Teradata to the SAS Work
directory, and the procedure performs all computations within the SAS System.
DBMS specifies that in-database computation be performed for data sets that are DBMS
tables if no system options, LIBNAME options, data set attributes, or procedure
options are incompatible with in-database computation. DBMS is the default
value, and it automatically enables in-database processing.
When the value is DBMS, the procedure uses in-database processing (when possible) and uses conventional SAS processing when the specific procedure statements
and options do not support in-database processing. For example, a REG procedure
statement that contains a residual option computes the SSCP matrix in-database,
but transfers the detail data to the SAS System to create residual analysis and output.
If the procedure attempts to perform a computation in-database but the SQL command fails, the procedure tries to perform that computation out-of-database.
ALL specifies that in-database computation be performed whenever possible.
In-database computation is not applicable to data sets that contain summary statistics, such as OUTEST= and OUTSTAT= data sets, or DATA= data sets for which the TYPE= attribute is CORR, EST,
SSCP, FACTOR, and so on. (See the “Special SAS Data Sets” chapter in SAS/STAT User’s Guide for
the complete list of TYPE= attributes.) Because the data in these tables are already summaries, no
additional summarization can be performed in-database. The SQLGENERATION= option is ignored
with respect to those data sets for which in-database computation is not applicable.
The following system options, which are specified in an OPTIONS statement, also control in-database
computing for SAS/STAT, SAS/ETS, and SAS Enterprise Miner procedures:
MSGLEVEL=N | I
specifies the level of messages to be printed to the log. N is the default value.
N specifies that the following messages be printed in the log:
I specifies that everything printed by MSGLEVEL=N be printed plus the following
a confirmatory note that states that SQL is used for in-database computations,
when that is the case
error messages if anything goes wrong with the SQL commands submitted for
in-database computations
note that states whether SQL is used, when there are SQL error messages
notes:
note that explains why SQL is not used for in-database computations, if that
is the case (however, no note is printed when OPTIONS SQLGENERATION=NONE is specified)
note that explains that the TYPE= attribute is not stored in DBMS tables when
you try to create a special SAS data set (that is, a data set that has a non-blank
value of the TYPE= attribute) as a DBMS table
SQL_IP_TRACE=NOTE | SOURCE | ALL
specifies the level of information to be included in the trace output. You can specify more than one
value by enclosing the values in parentheses.
NOTE specifies that the trace output include the following information:
all original messages from the DBMS (ordinarily, the SAS System intercepts
certain confusing messages from the DBMS and replaces them with messages
that can be more easily understood by SAS customers)
note that indicates whether SQL is used for in-database computations, even
when SQLGENERATION=NONE is specified. By default, a note is printed
when SQL is used, but no note is printed when SQL is not used. If you specify
SQL_IP_TRACE=NOTE, a note is always printed.
notes that state the name of the data set engine, the name of the DBMS
database, and so on
notes that explain what kinds of SAS passwords apply to the data set, because
the drivers used for in-database computation do not support SAS passwords
note that indicates that no system options or data set attributes prevent using
SQL for in-database computation, when that is the case (this note does not
rule out the possibility that some procedure options might prevent in-database
computation)
Enabling and Controlling In-Database Computing F 13
SOURCE specifies that trace output include the major SQL commands that are submitted
for in-database computation, but not minor SQL commands such as those used to
obtain metadata.
ALL specifies that trace output include the output for NOTE and SOURCE, plus all
SQL commands that are submitted (including those used to obtain metadata and to
verify that UDFs are published), plus additional details.
SQLMAPPUTTO=NONE | SAS_PUT
is not applicable to the SAS/STAT, SAS/ETS, or SAS Enterprise Miner procedures but is included
here for completeness.
The Base SAS procedures use implicit pass-through to send SQL commands to Teradata. With implicit pass-through, the SQL commands are written in the SAS dialect of SQL and then the SAS
System translates those commands to the Teradata dialect of SQL. The SAS dialect of SQL uses the
PUT function to format variables. The Teradata dialect of SQL uses the SAS_PUT UDF to format
variables. The SQLMAPPUTTO=SAS_PUT option specifies that the PUT function in SAS SQL be
translated (mapped) to the SAS_PUT function in Teradata SQL.
The SAS/STAT, SAS/ETS, and SAS Enterprise Miner procedures write SQL commands directly in the
Teradata dialect of SQL instead of using implicit pass-through. Therefore, this option has no effect
on the SAS/STAT, SAS/ETS, and SAS Enterprise Miner procedures. See the section “SQLMAPPUTTO= System Option” in SAS/ACCESS for Relational Databases: Reference.
FMTERR | NOFMTERR
specifies whether to refrain from using unpublished formats.
If a procedure needs to format a BY variable but that format has not been published (that is, not
installed on the Teradata server in SYSLIB or the current database), the procedure does not perform
14 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
in-database computation unless you specify NOFMTERR. NOFMTERR tells the procedure to refrain
from using unpublished formats and to use the unformatted values of the BY variables instead. If you
specify NOFMTERR and a Teradata error occurs when the procedure tests a format, the procedure
uses the unformatted values of the BY variables to define BY groups.
For each BY variable that has a format that cannot be used, a message is printed to that effect. That
message is an error if FMTERR is specified or a note if NOFMTERR is specified. In addition, a note
states whether SQL is used.
For information about publishing formats, see the section “Deploying and Using SAS Formats in
Teradata” on page 37 in this document or the section “Deploying and Using SAS Formats in Teradata”
in SAS/ACCESS 9.2 for Relational Databases: Reference.
DBFMTIGNORE | NODBFMTIGNORE
specifies which type of formats be used for DBMS columns.
Ordinarily, SAS formats control how data values are displayed rather than how data values are stored.
However, SAS/ACCESS software works differently from other SAS products with respect to formats.
NODBFMTIGNORE (the default) causes SAS/ACCESS software, when it creates a DBMS table, to
use the SAS formats assigned to SAS variables to decide which DBMS data types to assign to the
corresponding DBMS columns. For example, the following SAS statements create a DBMS table
named pi_table which contains a column named pi with a DBMS data type of decimal (3,2) with a
total of 3 digits with 2 decimal places:
data dbms.pi_table;
pi = 22/7;
format pi 3.2;
run;
The value that SAS/ACCESS software inserts in the column pi is 3.14 rather than 3.142857142 . ..
(that is, the actual numeric value of the column is truncated to the two decimal places that are specified
in the SAS format). Computations performed with the DBMS column pi suffer from loss of precision
and possibly numeric overflow.
If you specify OPTIONS DBFMTIGNORE, SAS/ACCESS software creates numeric DBMS columns
with the data type DOUBLE PRECISION. This data type is the same as the default data type that the
SAS System uses for numeric variables in ordinary SAS data sets.
The DBFMTIGNORE option does not apply to datetime or character formats.
See the section “Using SAS/ACCESS Software with Teradata: Some Cautionary Notes” for more details.
LIBNAME Statement Options for In-Database Computing
To perform in-database computations, the SAS/STAT, SAS/ETS, and SAS Enterprise Miner procedures
connect to the DBMS by using the credentials (username, password, and so on) in the LIBNAME statement for the DATA= data set. This connection is separate from any connection made by SAS/ACCESS
LIBNAME Statement Options for In-Database Computing F 15
software. Therefore, some SAS/ACCESS options such as the CONNECTION= option do not work with the
SAS/STAT, SAS/ETS, and SAS Enterprise Miner in-database procedures. Also, LIBNAME concatenation
cannot be used for in-database computation because the procedure cannot determine which credentials to
use for the connection.
Although the DATABASE= and SCHEMA= options are aliases as data set options, they are not aliases in
a LIBNAME statement. Table 1.2 describes the effects of the DATABASE= and SCHEMA= options in a
LIBNAME statement.
Table 1.2 DATABASE= and SCHEMA= Options for Teradata
Options
specified in
LIBNAME
statement
Neither
DATABASE=
nor SCHEMA=
DATABASE=
but not
SCHEMA=
SCHEMA= but
not
DATABASE=
Both
DATABASE=
and SCHEMA=
Option that
specifies name
of database
where the SAS
System
Option that
specifies name of
database where
OUT= table is
stored**
Option that specifies
name of database where
Teradata looks for
UDFs***
searches for
DATA= table*
USER= USER= USER=
DATABASE= DATABASE= DATABASE=
SCHEMA= SCHEMA= USER=
SCHEMA= SCHEMA= DATABASE=
* The DBMS
user for the
DATA= table
must have the
SELECT
privilege for the
named
database.
** The DBMS user
for the DATA=
table must have the
CREATE TABLE,
SELECT, and
INSERT privileges
for the named
database.
*** If Teradata does not
find the UDF in the
named database, then
Teradata looks in
SYSLIB. The DBMS
user for the DATA= table
must have the EXECUTE
FUNCTION privilege for
the database where the
UDF is found.
Table 1.3 describes whether SAS/ACCESS LIBNAME options for Teradata work correctly with the DATA=
16 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
and OUT= options for the SAS/STAT and SAS/ETS in-database procedures. The cell entries in the DATA=
and OUT= columns have the following values:
Yes—indicates that the option works for in-database computing
No—indicates that the option does not work for in-database computing
N/A (not applicable)—indicates that the option does not apply to in-database computing
Some cell entries in the DATA= and OUT= columns have superscripts that are intended to convey additional
information. The meanings of the superscripts are as follows:
A superscript of 1 (Yes1or No1) indicates an option that is recommended for statistical applications
and correctly affects in-database processing as expected.
A superscript of 2 (Yes2or No2) indicates an option that correctly affects in-database processing as
expected.
A superscript of 3 (Yes3or No3) indicates an option that causes the procedure to use SAS/ACCESS
for out-of-database computing.
A superscript of 4 (Yes4or No4) indicates an option that neither works for in-database computing nor
prevents in-database computing, but is unlikely to cause a serious error. See the Comment column for
more information.
Table 1.3 SAS/ACCESS LIBNAME Options for Teradata
LIBNAME option DATA= OUT= Comments
ACCESS=READONLY Yes
2
Yes
2
For an OUT= data set, you
correctly receive a
SAS/ACCESS error message.
BULKLOAD= N/A N/A For out-of-database I/O.
CAST= N/A N/A For out-of-database I/O.
CAST_OVERHEAD_MAXPERCENT= N/A N/A For out-of-database I/O.
CONNECTION= No
4
No
4
In-database SAS/STAT and
SAS/ETS procedures use
separate connections from
SAS/ACCESS software and
therefore work as if
CONNECTION=UNIQUE is
specified.
LIBNAME Statement Options for In-Database Computing F 17
LIBNAME option DATA= OUT= Comments
CONNECTION_GROUP= No
3
No
3
In-database SAS/STAT and
SAS/ETS procedures use
separate connections from
SAS/ACCESS software.
DBCOMMIT= N/A N/A For out-of-database I/O.
DBCONINIT= No
3
No
3
In-database SAS/STAT and
SAS/ETS procedures use
separate connections from
SAS/ACCESS software.
DBCONTERM= No
3
No
3
In-database SAS/STAT and
SAS/ETS procedures use
separate connections from
SAS/ACCESS software.
DBCREATE_TABLE_OPTS= N/A Yes
2
DBINDEX= N/A N/A For out-of-database I/O.
DBLIBINIT= N/A N/A Executed when LIBNAME
connection is made.
DBLIBTERM= N/A N/A Executed when LIBNAME is
disconnected.
DBMSTEMP=YES No
3
No
3
In-database SAS/STAT and
SAS/ETS procedures use
separate connections from
SAS/ACCESS software.
Temporary tables can be
accessed only within the
connection in which they are
created, so DBMSTEMP=YES
causes Teradata errors that say
the table does not exist. The
default value NO works.
DBPROMPT= N/A N/A Executed when LIBNAME
connection is made.
DBSASLABEL= Yes
1
N/A DBSASLABEL=NONE is
recommended for correct
procedure output.
18 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
LIBNAME option DATA= OUT= Comments
DBSLICEPARM= N/A N/A For out-of-database I/O.
DEFER=NO No
4
No
4
In-database SAS/STAT and
SAS/ETS procedures use
separate connections from
SAS/ACCESS software and
therefore work as if
DEFER=YES is specified.
DIRECT_EXE=DELETE N/A N/A
DIRECT_SQL= N/A N/A PROC SQL only.
ERRLIMIT= N/A N/A For out-of-database I/O.
LOGDB= N/A N/A For out-of-database I/O.
MODE= N/A N/A Applies to explicit pass-through
in PROC SQL.
MULTISTMT= N/A N/A For out-of-database I/O.
MULTI_DATASRC_OPT= N/A N/A For out-of-database I/O with
PROC SQL.
PASSWORD= Yes
2
Yes
2
PREFETCH= N/A N/A For out-of-database I/O.
PRESERVE_COL_NAMES=NO No
PRESERVE_NAMES=NO No
PRESERVE_TAB_NAMES=NO No
READ_ISOLATION_LEVEL= No
READ_LOCK_TYPE= No
READ_MODE_WAIT= No
3
3
3
4
4
4
No
No
No
No
No
No
3
3
3
4
4
4
The default value YES works.
The default value YES works.
The default value YES works.
The option is ignored.
The option is ignored.
The option is ignored.
REREAD_EXPOSURE= N/A N/A
SERVER= Yes
2
Yes
2
Same as TDPID=.
SPOOL= N/A N/A For out-of-database I/O.
Using SAS/ACCESS Software with Teradata: Some Cautionary Notes F 19
LIBNAME option DATA= OUT= Comments
SQL_FUNCTIONS= N/A N/A For PROC SQL.
SQL_FUNCTIONS_COPY= N/A N/A For PROC SQL.
SQLGENERATION= Yes
2
Yes
2
If SQLGENERATION= is
specified in the LIBNAME
statement, any value specified in
an OPTIONS statement is
ignored.
TDPID= Yes
UPDATE_ISOLATION_LEVEL= No
UPDATE_LOCK_TYPE= No
UPDATE_MODE_WAIT= No
USER= Yes
2
4
4
4
2
Yes
No
No
No
Yes
2
4
4
4
2
Same as SERVER=.
The option is ignored.
The option is ignored.
The option is ignored.
UTILCONN_TRANSIENT= N/A N/A In-database SAS/STAT and
SAS/ETS procedures use
separate connections from
SAS/ACCESS software.
Using SAS/ACCESS Software with Teradata: Some Cautionary Notes
See the chapter “SAS/ACCESS Interface to Teradata” in SAS/ACCESS for Relational Databases: Reference
for the complete documentation regarding the use of SAS/ACCESS software with Teradata.
When a SAS/STAT or SAS/ETS procedure performs in-database computation, SAS/ACCESS soft-
ware is not used for downloading the rows of the DATA= data set from the DBMS or for uploading
the rows of the OUT= data set to the DBMS. Rather, SAS/ACCESS software is used as follows:
– When the SAS System parses the statements of a SAS/STAT or SAS/ETS procedure,
SAS/ACCESS software looks up the variable names in the DATA= data set. Or, when you do
not specify an explicit variable list, SAS/ACCESS software obtains the names of the variables
from the DBMS.
– SAS/ACCESS software tells the procedure which SAS data types and SAS formats are assigned
to the variables in both the DATA= and OUT= data sets when those data sets are DBMS tables.
20 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
– When a SAS/STAT or SAS/ETS procedure attempts to create an OUT= data set, SAS/ACCESS
software generates the CREATE TABLE command and sends it to the DBMS.
Because SAS/ACCESS software is used for these purposes, numerous SAS/ACCESS options affect
in-database computation as described in this and subsequent sections. In addition, SAS/ACCESS
options are important for using the DATA step or out-of-database procedures to create DBMS tables,
and these options can have important consequences for subsequent analyses that are performed using
in-database procedures.
In statistical applications, you should exercise extreme caution with the BULKLOAD=YES option
in the LIBNAME statement. The BULKLOAD=YES option omits duplicate rows. It is not unusual
for statistical data sets to have duplicate rows, and omitting them produces incorrect answers. This
behavior affects the DATA step and procedures that do not perform in-database computation. This
behavior does not affect OUT= data sets that are created in-database by the PRINCOMP or SCORE
procedures.
You should usually specify the following SAS/ACCESS options regardless of whether you are per-
forming in-database computations:
– Either specify OPTIONS NOLABEL or specify the DBSASLABEL=NONE option in a LIB-
NAME statement to prevent SAS/ACCESS software from assigning default labels and thus
adding spurious variable labels to procedure listings, ODS tables, and output SAS data sets.
(Teradata tables do not store SAS variable labels. Variable labels must be specified in the PROC
step using a LABEL or ATTRIB statement. However, when SAS/ACCESS software creates a
SAS data set from a DBMS table, it assigns the variable names as labels by default.)
– Specify DBFMTIGNORE in an OPTIONS statement to prevent SAS/ACCESS software from
assigning data types to output variables based on their SAS formats. Without the DBFMTIGNORE option, output data sets in the DBMS (including ODS tables) might suffer from loss of
precision or numeric overflow, causing loss of data. See the subsection “LIBNAME Statement
Data Conversions” in the section “Data Types for Teradata” in SAS/ACCESS 9.2 for Relational
Databases: Reference.
When SAS/ACCESS software reads a Teradata table or creates an ordinary SAS data set from a
Teradata table, it assigns formats to the SAS variables based on the Teradata data types. These formats
are often inappropriate and can interfere with the proper operation of SAS/STAT, SAS/ETS, and Base
SAS procedures such as PRINT, PRINCOMP, and TRANSREG, especially for character variables.
When using such a data set, you might want to specify a FORMAT statement to remove the formats,
such as the following:
FORMAT _CHARACTER_;
If you use a RENAME= data set option, SAS/ACCESS software assigns the old variable names as
variable labels.
Most SAS data set options are not stored in Teradata tables. In particular, the TYPE= data set option is
not stored in Teradata tables. For example, if you run the CORR procedure to create a TYPE=CORR
data set as a Teradata table, the data set TYPE= is lost and you must specify the TYPE= data set
option every time you use that table.
Using SAS/ACCESS Software with Teradata: Some Cautionary Notes F 21
Teradata has nulls instead of missing values. SAS software has 28 kinds of numeric missing values,
but SAS/ACCESS software converts all of them to null when it creates a Teradata table from SAS
input. SAS character missing values are the same as blank strings, but Teradata nulls are different from
blank strings. When SAS/ACCESS software creates a Teradata table from SAS input, blank strings
are converted to nulls. Conversely, when a Teradata table is converted to a SAS data set, numeric
nulls are converted to ordinary SAS numeric missing values, and character nulls are converted to
blank strings.
Comparisons with Teradata nulls work differently from comparisons with SAS missing values. Tera-
data uses three-valued logic, so a comparison with a null produces an “unknown” logical value. In the
SAS System, numeric missing values are regarded as less than any nonmissing value, and character
missing values are (equal to) blank strings. Comparisons between two SAS missing values that the
SAS System considers equal might yield either a null or a true value on Teradata, depending on how
the SAS System translates the syntax.
When the SAS System copies a column from a DBMS table to a new DBMS table, the new DBMS
data type can differ from that of the original table. This is true for both in-database and out-of-database
processing and is a consequence of the manner in which SAS/ACCESS software handles DBMS data
types and SAS formats. See the subsection “LIBNAME Statement Data Conversions” in the section
“Data Types for Teradata” in SAS/ACCESS 9.2 for Relational Databases: Reference. The change in
data types happens in a DATA step with a SET or MERGE statement and in procedure statements that
copy variables from the DATA= data set to the OUT= data set.
You can exercise limited control over data types in output tables by using FORMAT or ATTRIB
statements to assign formats. For out-of-database processing, you can use the DBTYPE= data set
option to specify data types for output tables. However, the DBTYPE= data set option is not supported
for in-database computation by the SAS/STAT and SAS/ETS procedures.
SAS/ACCESS software does not change the following data types:
– BYTE
– CHAR
– DATE
– FLOAT (also known as REAL or DOUBLE PRECISION)
– TIME
– TIMESTAMP
22 F Chapter 1: SAS Analytics Accelerator 1.3 for Teradata: Guide
Table 1.4 shows how data types are changed when the SAS System copies a Teradata column.
Table 1.4 Data Type Changes
Original Data Type New Data Type
Byte Data Types
VarByte(n) Byte(n)
Character Data Types
VarChar(n) Char(n)
Numeric Data Types
BYTEINT SMALLINT
SMALLINT INTEGER
INTEGER DECIMAL(11)
DECIMAL INTEGER
DECIMAL(1) BYTEINT
DECIMAL(1,0) BYTEINT
DECIMAL(1,1) DECIMAL(2,1)
DECIMAL(2) SMALLINT
DECIMAL(2,0) SMALLINT
DECIMAL(2,1) DECIMAL(3,1)
DECIMAL(2,2) DECIMAL(3,2)
DECIMAL(3) SMALLINT
DECIMAL(3,0) SMALLINT
DECIMAL(3,1) DECIMAL(4,1)
DECIMAL(3,2) DECIMAL(4,2)
DECIMAL(3,3) DECIMAL(4,3)
DECIMAL(4) INTEGER
DECIMAL(4,0) INTEGER
DECIMAL(4,1) DECIMAL(5,1)
DECIMAL(4,2) DECIMAL(5,2)
DECIMAL(4,3) DECIMAL(5,3)
DECIMAL(4,4) DECIMAL(5,4)
DECIMAL(5) INTEGER
DECIMAL(5,0) INTEGER
DECIMAL(5,1) DECIMAL(6,1)
DECIMAL(5,2) DECIMAL(6,2)
DECIMAL(5,3) DECIMAL(6,3)
DECIMAL(5,4) DECIMAL(6,4)
DECIMAL(5,5) DECIMAL(6,5)
DECIMAL(6) INTEGER
DECIMAL(6,0) INTEGER
DECIMAL(6,1) DECIMAL(7,1)
DECIMAL(6,2) DECIMAL(7,2)
DECIMAL(6,3) DECIMAL(7,3)
DECIMAL(6,4) DECIMAL(7,4)
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