Data Types for Impala

Overview

Every column in a table has a name and a data type. The data type tells Impala how much physical storage to set aside for the column and the form in which the data is stored. This section includes information about Impala data types, null and default values, and data conversions.

SAS/ACCESS Interface to Impala does not directly support any data types that are not listed below. Any columns that use unsupported data types are read into SAS as character strings.

For more information about Impala data types and to determine which data types are available for your version of Impala, see your Impala documentation.

Supported Impala Data Types

Here are the data types that the Impala engine supports.

Numeric data:

BIGINT

FLOAT

TIMESTAMP

INT

BOOLEAN

SMALLINT

DOUBLE

TINYINT

DECIMAL

Note: When performing calculations on numeric values and when storing numeric values, SAS maintains up to 15 digits of precision. When you read values that contain more than 15 decimal digits of precision from a database into SAS, the values that SAS reads are rounded to meet this condition. When you use a large numeric value in a WHERE clause, this rounding can cause unexpected results, such as not selecting desired rows. For noncomputational purposes, such as storing ID values or credit card numbers, you can read the data in as character data. For more information, see Your Options When Choosing Your Needed Degree of Precision.

Character data:

CHARn

VARCHARn

STRING

Date and time data: TIMESTAMP

To use CHAR and VARCHAR, an Impala 2.0 server and the Cloudera 2.5.22 or higher ODBC client driver are required.

SAS Data Types

SAS has two fundamental data types, character and numeric. SAS character variables (columns) are of a fixed length with a maximum of 32,767 characters. SAS numeric variables are signed eight-byte, floating-point numbers. When SAS numerics are used in conjunction with SAS formats, they can represent a number of data types, including DATE, TIME, and DATETIME. For more information about SAS data types, see SAS Programmer’s Guide: Essentials.

Data Conversion from Impala to SAS

This table shows the default SAS formats that are assigned to SAS variables that are created when SAS/ACCESS reads Impala table columns.

Impala to SAS: Default SAS Formats for Impala Data Types
Impala Data Type	SAS Data Type	Default SAS Format
CHAR	character	$255.
STRING		$32767.
VARCHAR		$32767.
BOOLEAN	numeric	1.
BIGINT		20.
DOUBLE		none
FLOAT		none
INT		11.
SMALLINT		6.
TINYINT		4.

Issues When Converting Data from Impala to SAS

Below are some issues that you might face when you convert data from Impala to SAS:

STRING: Depending on the length of Impala STRING data, the default length that is allocated in SAS (32767 characters) might be too long. Alternatively, SAS might truncate Impala STRING columns that contain more than 32767 characters.
BIGINT: Converting an Impala BIGINT to a SAS numeric can result in loss of precision because the internal SAS eight-byte, floating-point format accurately preserves only 15 digits of precision. An Impala BIGINT preserves up to 19 digits of precision.

Data Conversion from SAS to Impala

This table shows the Impala data types that are assigned when SAS/ACCESS Interface to Impala creates an Impala table.

SAS to Impala: Default Impala Data Types for SAS Formats
SAS Data Type	SAS Format	Impala Data Type
character	$n.	CHAR(n)
	$n.	STRING
	$n.	VARCHAR(n)
numeric	DATETIMEw.p	TIMESTAMP
	DATEw.	DATE
	TIMEw.	TIMESTAMP1
	1. to 2.	TINYINT
	3. to 4.	SMALLINT
	5. to 9.	INT
	10. to 18.	BIGINT
	other numeric formats	DOUBLE
1 Apache Impala does not support time values with no associated date. Time values with no date are passed to Impala with the CURRENT_DATE.

Impala Null Values

Impala has a special value called NULL. An Impala NULL value represents an absence of information and is analogous to a SAS missing value. When SAS/ACCESS Interface to Impala reads an Impala NULL value, it interprets it as a SAS missing value. For more information about how SAS handles NULL values, see Potential Result Set Differences When Processing Null Data.

Last updated: February 3, 2026