NULL Handling Differences Between SAS and Relational Databases

Avoid Surprises in Data Analysis Across Platforms

The Challenge

A single NULL can change everything. In hybrid workflows, SAS and databases like Snowflake don’t always treat missing values the same way. If you don’t understand where your NULL value is being processed, you risk reports that look correct but aren’t. This blog explores how those differences arise and how to guard against them.

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What You Need to Know

Relational Databases: In most DBMS platforms, NULL represents an absence of data. NULL values are excluded from analytics by default. They cannot be sorted or evaluated with standard comparison operators or functions.
SAS: When using SAS/ACCESS to read database data, SAS translates database NULL values into missing values. SAS supports 28 types of missing values, which means missing values are included in analytics. They can be sorted and evaluated.
Implication: This difference in handling can significantly affect results depending on whether SAS/ACCESS passes queries to the database for processing or reads the data back into SAS for processing.

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An Example

The sashelp.heart dataset is derived from the Framingham Heart Study, a long-term cardiovascular study. It contains 5,209 observations (one per patient) and 17 columns, including alive/dead status, age, sex, and clinical indicators such as cholesterol status.

The Chol_Status field contains values: High, Borderline, Desirable, and missing.

Goal

Find the proportion of alive and dead patients where cholesterol status is not High.

SAS CODE

proc freq data=sashelp.heart;
    tables Status*Chol_Status;
    where Chol_Status NE 'High';
run;

SAS Output

Alive: 2,267
Dead: 1,151

Snowflake Comparison

When sashelp.heart is copied to Snowflake via implicit passthrough, SAS missing values are translated to NULL . Running the same query in Snowflake produces different results:

PROC FREQ Output	SAS	Snowflake	Difference
Status-Alive	2,267	2,184	83
Status-Dead	1,151	1,082	69
Chol_Status-Missing	152	0	152

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Options for Handling Database NULL Values

1. Modify the WHERE Clause

Explicitly include NULL values to align Snowflake results with SAS:

proc freq data=snowlib.heart;
    tables Status*Chol_Status;
    where Chol_Status in ('Borderline','Desirable') or Chol_Status is NULL;
run;

2. Force SAS Processing

Use the LIBNAME option DIRECT_SQL=NOWHEREto prevent WHERE clauses from being passed to the database. This forces SAS to process the data locally:

libname snowlib snow user=... password=... direct_sql=nowhere;

Spoiler

Caution: This approach requires all data to be read back into SAS, which can have performance implications for large datasets.

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How to Validate Where Processing Occurs

PROC FREQ is one of the SAS procedures that SAS/ACCESS can push down to the database. To confirm enable tracing options:

options sastrace=',,,ds' sastraceloc=saslog nostsuffix sql_ip_trace=(note,source);

The log will show the SQL passed to Snowflake. You can also confirm query execution within Snowflake Monitoring - Query Details.

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Summary

Differences in NULL handling between SAS and DBMS platforms like Snowflake can lead to subtle but significant discrepancies in results. What looks like a small detail — whether missing values are included or excluded — can ripple into compliance reports, clinical trial outcomes, or financial dashboards.

To avoid surprises:

Always check where your query is being processed.
Explicitly handle NULL values in your WHERE clauses.
Use SAS options to validate pushdown behavior.

By understanding these differences and planning accordingly, you can ensure your hybrid workflows remain accurate, reproducible, and trustworthy.

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Resources

NULL Handling Differences Between SAS and Relational Databases

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