Data Type Mappings Between SQL and Handler Languages

A stored procedure or user-defined function you write is called from SQL, and so receives and returns values in SQL data types. However, its underlying handler will use data types from the handler’s language, such as Java, Python, or Scala. At runtime, Snowflake converts between the SQL types and handler types for arguments and return values.

Note that Snowflake makes these conversions the following cases as well:

  • When dynamically constructing a SQL statement that uses a value in a handler variable.

  • When binding a handler variable’s value to a prepared statement.

This topic describes valid mappings between SQL data and types and those from the supported handler languages. Use this content to choose data types when writing a handler.

For information about Snowflake SQL data types, see Summary of Data Types.

SQL-Java Data Type Mappings

The table below shows the type mappings between SQL and Java. These mappings generally apply to both the arguments passed to the procedure or function and the values returned from it. However, there are some exceptions, which are listed in footnotes.

SQL Type

Java Type

Notes

ARRAY

String[]

Formats the elements of the array as strings.

ARRAY

String

Formats the array as a JSON string (e.g. [1, "foo", null]).

BINARY

byte[]

BINARY

String

Encodes the binary string in hexadecimal. 4

BINARY

InputStream

Exposes the BINARY value as a sequence of bytes.

BOOLEAN

boolean

Cannot be null.

BOOLEAN

Boolean

BOOLEAN

String

4

DATE

java.sql.Date

DATE

String

Formats the date as YYYY-MM-DD. 4

FLOAT

double

Cannot be null.

FLOAT

Double

FLOAT

float

Cannot be null. Might result in precision loss.

FLOAT

Float

Might result in precision loss.

FLOAT

String

Might result in precision loss (float -> string conversion is lossy).

GEOGRAPHY

String

Formats the geography as GeoJSON .

GEOGRAPHY

Geography

5

NUMBER

short

Cannot be null. Must fit in the range of short (no fractional part, and integer part cannot exceed the max/min short values).

NUMBER

Short

Must fit in the range of short (no fractional part, and integer part cannot exceed the max/min short values).

NUMBER

int

Cannot be null. Must fit in the range of int (no fractional part, and integer part cannot exceed the max/min int values).

NUMBER

Integer

Must fit in the range of int (no fractional part, and integer part cannot exceed the max/min int values).

NUMBER

long

Cannot be null. Must fit in the range of long (no fractional part, and integer part cannot exceed the max/min long values).

NUMBER

Long

Must fit in the range of long (no fractional part, and integer part cannot exceed the max/min long values).

NUMBER

java.math.BigDecimal

NUMBER

java.math.BigInteger

Must fit into the range of BigInteger (no fractional part).

NUMBER

String

OBJECT

Map<String, String>

The map’s keys are the object’s keys, and the values are formatted as strings.

OBJECT

String

Formats the object as a JSON string (e.g. {"x": 3, "y": true}).

TIME

java.sql.Time

3

TIME

String

Formats the time as HH:MI:SS.SSSSSSSSS where the fractional seconds part depends on the precision of the time. 3

TIMESTAMP_LTZ

java.sql.Timestamp

Must fit in the range of java.sql.Timestamp. 3

TIMESTAMP_LTZ

String

The output format is DY, DD MON YYYY HH24:MI:SS TZHTZM. 1 , 3 , 4

TIMESTAMP_NTZ

java.sql.Timestamp

Must fit in the range of java.sql.Timestamp. Treats the wallclock time as an offset from the Unix epoch (imposing a UTC time zone, effectively). 3

TIMESTAMP_NTZ

String

Treats the wallclock time as an offset from the Unix epoch (imposing a UTC time zone, effectively). The output format is DY, DD MON YYYY HH:MI:SS. 2 , 3 , 4

TIMESTAMP_TZ

java.sql.Timestamp

Must fit in the range of java.sql.Timestamp. 3

TIMESTAMP_TZ

String

The output format is DY, DD MON YYYY HH24:MI:SS TZHTZM. 1 , 3 , 4

VARCHAR

String

VARIANT

Variant

The Variant data type is a class in the Snowpark package. For more information, see Snowpark Package Types Supported for User-Defined Functions. For an example, see Passing a VARIANT Value to an In-line Java UDF.

1(1,2)

The format matches the Internet (RFC) Timestamp Format DY, DD MON YYYY HH24:MI:SS TZHTZM as described in Timestamp Formats. If a timezone offset (the TZHTZM component) is present, it is typically digits (e.g. -0700 indicates 7 hours behind UTC). If the timezone offset is Z (for “Zulu”) rather than digits, that is synonymous with “+0000” (UTC).

2

The format matches the Internet (RFC) Timestamp Format DY, DD MON YYYY HH24:MI:SS as described in Timestamp Formats. If the string is followed by a space and Z (for “Zulu”), that explicitly indicates that the offset is “+0000” (UTC).

3(1,2,3,4,5,6,7,8)

Although Snowflake can store time values with nanosecond precision, the java.sql.time library maintains only millisecond precision. Conversion between Snowflake and Java data types can reduce effective precision to milliseconds.

4(1,2,3,4,5,6)

This type mapping is supported when converting SQL arguments to Java, but not when converting Java return types to SQL types.

5

Java does not have a native Geography data type. The Geography data type referred to here is a class in the Snowpark package. For more information, see Snowpark Package Types Supported for User-Defined Functions.

Notes

Some SQL data types (e.g. NUMBER) are compatible with multiple Java data types (e.g. int, long, etc.). In these cases, you can use any Java data type that has enough capacity to hold the actual values that will be passed. If you pass a SQL value to an incompatible Java data type (or vice versa), Snowflake throws an error.

Snowpark Package Types Supported for User-Defined Functions

In a user-defined function, you can use a specific subset of types that are included in the Snowflake Snowpark Java package. Although these types are designed for use in Snowpark code, a few are also supported for use in UDFs for the convenience they can provide. (For more about Snowpark, see the Snowpark documentation.)

Note

The Snowpark library is a requirement for stored procedures written in Java, Python, and Scala. As a result, you can use Snowpark types there without restriction.

Snowpark types in the following table are supported in UDF code. You should not use other Snowpark types in UDF code; they are not supported there.

Snowpark Type

Snowpark Version Required

Description

Geography

1.2.0 and later

Represents the Snowflake GEOGRAPHY type. For an example that uses the Geography data type, see Passing a GEOGRAPHY Value to an In-line Java UDF.

Variant

1.4.0 and later

Represents Snowflake VARIANT data. For an example that uses the Variant data type, see Passing a VARIANT Value to an In-line Java UDF.

Specifying the Snowpark Package as a UDF Dependency

When developing UDF code that uses the Snowpark package, you’ll need to set up your development environment so that you can compile and run code with Snowpark dependencies. For more, see Setting Up Other Development Environments for Snowpark Java.

When deploying a UDF by executing the CREATE FUNCTION statement, you can specify the Snowpark package as a dependency without uploading the JAR file to a stage (the library is already in Snowflake). To do this, specify the package name and version in the PACKAGES clause. For a syntax example, see Passing a GEOGRAPHY Value to an In-line Java UDF.

SQL-JavaScript Data Type Mappings

The following table shows the Snowflake SQL data types and the corresponding JavaScript data types:

SQL Data Type

JavaScript Data Type

Notes

ARRAY

JSON

BOOLEAN

boolean

DATE

date

REAL, FLOAT, FLOAT8, FLOAT4, DOUBLE, DOUBLE PRECISION

number

TIME

string

TIMESTAMP, TIMESTAMP_LTZ, TIMESTAMP_NTZ, TIMESTAMP_TZ

date or SfDate

When a timestamp is passed as an argument to a stored procedure, the timestamp is converted to a JavaScript date object. In other situations (e.g. when retrieving from ResultSet), a timestamp is converted to an SfDate object. For more details about the SfDate data type, which is not a standard JavaScript data type, see the JavaScript Stored Procedures API.

VARCHAR, CHAR, CHARACTER, STRING, TEXT

string

VARIANT

JSON

Notes

Not all Snowflake SQL data types have a corresponding JavaScript data type. For example, JavaScript does not directly support the INTEGER or NUMBER data types. In these cases, you should convert the SQL data type to an appropriate alternative data type. For example, you can convert a SQL INTEGER into a SQL FLOAT, which can then be converted to a JavaScript value of data type number.

The table below shows appropriate conversions for the incompatible SQL data types:

Incompatible SQL Data Type

Compatible SQL Data Type

BINARY

Uint8Array

INTEGER

FLOAT

NUMBER, NUMERIC, DECIMAL

FLOAT

OBJECT

Uint8Array

When Returning Values

If the return statement in the JavaScript returns a data type different from the stored procedure’s declared return type, the JavaScript value is cast to the SQL data type if possible. For example, if a number is returned, but the stored procedure is declared as returning a string, the number is converted to a string within JavaScript, and then copied to the string returned in the SQL statement. (Keep in mind that some JavaScript programming errors, such as returning the wrong data type, can be hidden by this behavior.)

If no valid cast for the conversion exists, then an error occurs.

When Binding Values

When you bind JavaScript variables to SQL statements, Snowflake converts from the JavaScript data types to the SQL data types. You can bind variables of the following JavaScript data types:

  • number.

  • string.

  • SfDate. (For more details about the SfDate data type, which is not a standard JavaScript data type, see the JavaScript Stored Procedures API.)

For more information about binding, including some examples, see Binding Variables.

You might also find the following topics helpful:

SQL-Python Data Type Mappings

The table below shows the type mappings between SQL and Python. These mappings generally apply to both the arguments passed to the Python handler and the values returned from it.

SQL Type

Python Type

Notes

ARRAY

list

When a Python data type is converted to ARRAY, if there is any embedded Python decimal data, the embedded Python decimal will be converted to a String in the ARRAY.

BINARY

bytes

BOOLEAN

bool

DATE

datetime.date

FLOAT

float

Floating point operations can have small rounding errors, which can accumulate, especially when aggregate functions process large numbers of rows. Rounding errors can vary each time a query is executed if the rows are processed in a different order. For more information, see Numeric Data Types: Float.

GEOGRAPHY

dict

Formats the geography as GeoJSON and then it is converted to a Python dict.

NUMBER

int or decimal.Decimal

If the scale of the NUMBER type is 0 then the int Python type is used. Otherwise decimal.Decimal type is used.

OBJECT

dict

When a Python data type is converted to OBJECT, if there is any embedded Python decimal data, the embedded Python decimal will be converted to a String in the OBJECT.

TIME

datetime.time

Although Snowflake can store time values with nanosecond precision, the Python datetime.time type maintains only millisecond precision. Conversion between Snowflake and Python data types can reduce effective precision to milliseconds.

TIMESTAMP_LTZ

datetime.datetime

Use local timezone to convert internal UTC time to local “naive” datetime. Requires “naive” datetime as return type.

TIMESTAMP_NTZ

datetime.datetime

Directly convert to “naive” datetime. Requires “naive” datetime as return type.

TIMESTAMP_TZ

datetime.datetime

Convert to “aware” datetime with timezone information. Requires “aware” datetime as return type.

VARCHAR

str

VARIANT

dict, list, int, float, str, or bool

Each variant row is converted to a Python type dynamically for arguments and vice versa for return values. The following types are converted to strings rather than native Python types: decimal, binary, date, time, timestamp_ltz, timestamp_ntz, timestamp_tz. When a Python data type is converted to VARIANT, if there is any embedded Python decimal data, the embedded Python decimal will be converted to a String in the VARIANT.

SQL-Scala Data Type Mappings

Snowflake supports the following Scala data types in addition to the Java types listed in SQL-Java Data Type Mappings:

SQL Data Type

Scala Type

Notes

ARRAY

Array[String]

BINARY

Array[Byte]

BOOLEAN

Boolean or Option[Boolean]

DOUBLE

Double or Option[Double]

FLOAT

Float or Option[Float]

NUMBER

The following types are supported:

  • Int or Option[Int]

  • Long or Option[Long]

OBJECT

Map[String, String]

VARCHAR

String

VARIANT

String

Formats the value depending on the type that is represented. Variant null is formatted as the string “null”.

For DATE and TIMESTAMP, use the Java types listed in SQL-Java Data Type Mappings.