Data Types in Confluent Cloud for Apache Flink¶

Confluent Cloud for Apache Flink® has a rich set of native data types that you can use in SQL statements and queries.

The query planner supports the following SQL types.

Data type definition¶

A data type describes the logical type of a value in a SQL table. You use data types to declare the input and output types of an operation.

The Flink data types are similar to the SQL standard data type terminology, but for efficient handling of scalar expressions, they also contain information about the nullability of a value.

These are examples of SQL data types:

INT
INT NOT NULL
INTERVAL DAY TO SECOND(3)
ROW<fieldOne ARRAY<BOOLEAN>, fieldTwo TIMESTAMP(3)>

The following sections list all pre-defined data types in Flink SQL.

Character strings¶

CHAR¶

Represents a fixed-length character string.

Declaration

CHAR
CHAR(n)

Formats

The following table shows examples of the CHAR type in different formats.

JSON for data type	{"type":"CHAR","nullable":true,"length":8}
CLI/UI format	CHAR(8)
JSON for payload	"Example string"
CLI/UI format for payload	Example string

Declare this type by using CHAR(n), where n is the number of code points. n must have a value between 1 and 2,147,483,647 (both inclusive). If no length is specified, n is equal to 1.

CHAR(0) is not supported for CAST or persistence in catalogs, but it exists in protocols.

VARCHAR / STRING¶

Represents a variable-length character string.

Declaration

VARCHAR
VARCHAR(n)

STRING

Formats

The following table shows examples of the VARCHAR type in different formats.

JSON for data type	{"type":"VARCHAR","nullable":true,"length":8}
CLI/UI format	VARCHAR(800)
JSON for payload	"Example string"
CLI/UI format for payload	Example string

Declare this type by using VARCHAR(n), where n is the maximum number of code points. n must have a value between 1 and 2,147,483,647 (both inclusive). If no length is specified, n is equal to 1.

STRING is equivalent to VARCHAR(2147483647).

VARCHAR(0) is not supported for CAST or persistence in catalogs, but it exists in protocols.

Binary strings¶

BINARY¶

Represents a fixed-length binary string (=a sequence of bytes).

Declaration

BINARY
BINARY(n)

Formats

The following table shows examples of the BINARY type in different formats.

JSON for data type	{"type":"BINARY","nullable":true,"length":1}
CLI/UI format	BINARY(3)
JSON for payload	"x'7f0203'"
CLI/UI format for payload	x'7f0203'

Declare this type by using BINARY(n), where n is the number of bytes. n must have a value between 1 and 2,147,483,647 (both inclusive). If no length is specified, n is equal to 1.

The string representation is hexadecimal format.

BINARY(0) is not supported for CAST or persistence in catalogs, but it exists in protocols.

VARBINARY / BYTES¶

Represents a variable-length binary string (=a sequence of bytes).

Declaration

VARBINARY
VARBINARY(n)

BYTES

Formats

The following table shows examples of the VARBINARY type in different formats.

JSON for data type	{"type":"VARBINARY","nullable":true,"length":1}
CLI/UI format	VARBINARY(800)
JSON for payload	"x'7f0203'"
CLI/UI format for payload	x'7f0203'

Declare this type by using VARBINARY(n) where n is the maximum number of bytes. n must have a value between 1 and 2,147,483,647 (both inclusive). If no length is specified, n is equal to 1.

BYTES is equivalent to VARBINARY(2147483647).

VARCHAR(0) is not supported for CAST or persistence in catalogs, but it exists in protocols.

Exact numerics¶

BIGINT¶

Represents an 8-byte signed integer with values from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807.

Declaration

BIGINT

Formats

The following table shows examples of the BIGINT type in different formats.

JSON for data type	{"type":"BIGINT","nullable":true}
CLI/UI format	BIGINT
JSON for payload	"23"
CLI/UI format for payload	23

DECIMAL¶

Represents a decimal number with fixed precision and scale.

Declaration

DECIMAL
DECIMAL(p)
DECIMAL(p, s)

DEC
DEC(p)
DEC(p, s)

NUMERIC
NUMERIC(p)
NUMERIC(p, s)

Formats

The following table shows examples of the DECIMAL type in different formats.

JSON for data type	{"type":"DECIMAL","nullable":true,"precision":5,"scale":3}
CLI/UI format	DECIMAL(5, 3)
JSON for payload	"12.123"
CLI/UI format for payload	12.123

Declare this type by using DECIMAL(p, s) where p is the number of digits in a number (precision) and s is the number of digits to the right of the decimal point in a number (scale).

p must have a value between 1 and 38 (both inclusive). The default value for p is 10.

s must have a value between 0 and p (both inclusive). The default value for s is 0.

The right side is padded with 0.

The left side must be padded with spaces, like all other values.

NUMERIC(p, s) and DEC(p, s) are synonyms for this type.

INT¶

Represents a 4-byte signed integer with values from -2,147,483,648 to 2,147,483,647.

Declaration

INT

INTEGER

Formats

The following table shows examples of the INT type in different formats.

JSON for data type	{"type":"INT","nullable":true}
CLI/UI format	INT
JSON for payload	"23"
CLI/UI format for payload	23

INTEGER is a synonym for this type.

SMALLINT¶

Represents a 2-byte signed integer with values from -32,768 to 32,767.

Declaration

SMALLINT

Formats

The following table shows examples of the SMALLINT type in different formats.

JSON for data type	{"type":"SMALLINT","nullable":true}
CLI/UI format	SMALLINT
JSON for payload	"23"
CLI/UI format for payload	23

TINYINT¶

Represents a 1-byte signed integer with values from -128 to 127.

Declaration

TINYINT

Formats

The following table shows examples of the TINYINT type in different formats.

JSON for data type	{"type":"TINYINT","nullable":true}
CLI/UI format	TINYINT
JSON for payload	"23"
CLI/UI format for payload	23

Approximate numerics¶

DOUBLE¶

Represents an 8-byte double precision floating point number.

Declaration

DOUBLE

DOUBLE PRECISION

Formats

The following table shows examples of the DOUBLE type in different formats.

JSON for data type	{"type":"DOUBLE","nullable":true}
CLI/UI format	DOUBLE
JSON for payload	"1.1111112120000001E7"
CLI/UI format for payload	1.1111112120000001E7

DOUBLE PRECISION is a synonym for this type.

FLOAT¶

Represents a 4-byte single precision floating point number.

Declaration

FLOAT

Formats

The following table shows examples of the FLOAT type in different formats.

JSON for data type	{"type":"FLOAT","nullable":true}
CLI/UI format	FLOAT
JSON for payload	"1.1111112E7"
CLI/UI format for payload	1.1111112E7

Compared to the SQL standard, this type doesn’t take parameters.

Date and time¶

DATE¶

Represents a date consisting of year-month-day with values ranging from 0000-01-01 to 9999-12-31.

Declaration

DATE

Formats

The following table shows examples of the DATE type in different formats.

JSON for data type	{"type":"DATE","nullable":true}
CLI/UI format	DATE
JSON for payload	"2023-04-06"
CLI/UI format for payload	2023-04-06

Compared to the SQL standard, the range starts at year 0000.

INTERVAL DAY TO SECOND¶

Data type for a group of day-time interval types.

Declaration

INTERVAL DAY
INTERVAL DAY(p1)
INTERVAL DAY(p1) TO HOUR
INTERVAL DAY(p1) TO MINUTE
INTERVAL DAY(p1) TO SECOND(p2)
INTERVAL HOUR
INTERVAL HOUR TO MINUTE
INTERVAL HOUR TO SECOND(p2)
INTERVAL MINUTE
INTERVAL MINUTE TO SECOND(p2)
INTERVAL SECOND
INTERVAL SECOND(p2)

Formats

The following table shows examples of the INTERVAL DAY TO SECOND type in different formats.

JSON for data type	{"type":"INTERVAL_DAY_TIME","nullable":true,"precision":1,"fractionalPrecision":3,"resolution":"DAY_TO_SECOND"}
CLI/UI format	INTERVAL DAY(1) TO SECOND(3)
JSON for payload	"+2 07:33:20.000"
CLI/UI format for payload	+2 07:33:20.000

Declare this type by using the above combinations, where p1 is the number of digits of days (day precision) and p2 is the number of digits of fractional seconds (fractional precision).

p1 must have a value between 1 and 6 (both inclusive). If no p1 is specified, it is equal to 2 by default.

p2 must have a value between 0 and 9 (both inclusive). If no p2 is specified, it is equal to 6 by default.

The type must be parameterized to one of these resolutions with up to nanosecond precision:

Interval of days
Interval of days to hours
Interval of days to minutes
Interval of days to seconds
Interval of hours
Interval of hours to minutes
Interval of hours to seconds
Interval of minutes
Interval of minutes to seconds
Interval of seconds

An interval of day-time consists of +days hours:months:seconds.fractional with values ranging from -999999 23:59:59.999999999 to +999999 23:59:59.999999999. The value representation is the same for all types of resolutions. For example, an interval of seconds of 70 is always represented in an interval-of-days-to-seconds format (with default precisions): +00 00:01:10.000000.

Formatting intervals are tricky, because they have different resolutions:

DAY
DAY_TO_HOUR
DAY_TO_MINUTE
DAY_TO_SECOND
HOUR
HOUR_TO_MINUTE
HOUR_TO_SECOND
MINUTE
MINUTE_TO_SECOND
SECOND

Depending on the resolution, use:

INTERVAL DAY(1)
INTERVAL DAY(1) TO HOUR
INTERVAL DAY(1) TO MINUTE
INTERVAL DAY(1) TO SECOND(3)
INTERVAL HOUR
INTERVAL HOUR TO MINUTE
INTERVAL HOUR TO SECOND(3)
INTERVAL MINUTE
INTERVAL MINUTE TO SECOND(3)
INTERVAL SECOND(3)

INTERVAL YEAR TO MONTH¶

Data type for a group of year-month interval types.

Declaration

INTERVAL YEAR
INTERVAL YEAR(p)
INTERVAL YEAR(p) TO MONTH
INTERVAL MONTH

Formats

The following table shows examples of the INTERVAL YEAR TO MONTH type in different formats.

JSON for data type	{"type":"INTERVAL_YEAR_MONTH","nullable":true,"precision":4,"resolution":"YEAR_TO_MONTH"}
CLI/UI format	INTERVAL YEAR(4) TO MONTH
JSON for payload	"+2000-02"
CLI/UI format for payload	+2000-02

Declare this type by using the above combinations, where p is the number of digits of years (year precision).

p must have a value between 1 and 4 (both inclusive). If no year precision is specified, p is equal to 2.

The type must be parameterized to one of these resolutions:

Interval of years
Interval of years to months
Interval of months

An interval of year-month consists of +years-months with values ranging from -9999-11 to +9999-11.

The value representation is the same for all types of resolutions. For example, an interval of months of 50 is always represented in an interval-of-years-to-months format (with default year precision): +04-02.

Formatting intervals are tricky, because they have different resolutions:

YEAR
YEAR_TO_MONTH
MONTH

Depending on the resolution, use:

INTERVAL YEAR(4)
INTERVAL YEAR(4) TO MONTH
INTERVAL MONTH

TIME¶

Represents a time without timezone consisting of hour:minute:second[.fractional] with up to nanosecond precision and values ranging from 00:00:00.000000000 to 23:59:59.999999999.

Declaration

TIME
TIME(p)

TIME_WITHOUT_TIME_ZONE
TIME_WITHOUT_TIME_ZONE(p)

Formats

The following table shows examples of the TIME type in different formats.

JSON for data type	{"type":"TIME_WITHOUT_TIME_ZONE","nullable":true,"precision":3}
CLI/UI format	TIME(3)
JSON for payload	"10:56:22.541"
CLI/UI format for payload	10:56:22.541

Declare this type by using TIME(p), where p is the number of digits of fractional seconds (precision).

p must have a value between 0 and 9 (both inclusive). If no precision is specified, p is equal to 0.

Compared to the SQL standard, leap seconds (23:59:60 and 23:59:61) are not supported, as the semantics are closer to java.time.LocalTime.

A time with timezone is not provided.

TIME acts like a pure string and isn’t related to a time zone of any kind, including UTC.

TIME WITHOUT TIME ZONE is a synonym for this type.

TIMESTAMP¶

Represents a timestamp without timezone consisting of year-month-day hour:minute:second[.fractional] with up to nanosecond precision and values ranging from 0000-01-01 00:00:00.000000000 to 9999-12-31 23:59:59.999999999.

Declaration

TIMESTAMP
TIMESTAMP(p)

TIMESTAMP WITHOUT TIME ZONE
TIMESTAMP(p) WITHOUT TIME ZONE

Formats

The following table shows examples of the TIMESTAMP type in different formats.

JSON for data type	{"type":"TIMESTAMP_WITHOUT_TIME_ZONE","nullable":true,"precision":3}
CLI/UI format	TIMESTAMP(3)
JSON for payload	"2023-04-06 10:59:32.628"
CLI/UI format for payload	2023-04-06 10:59:32.628

Declare this type by using TIMESTAMP(p), where p is the number of digits of fractional seconds (precision).

p must have a value between 0 and 9 (both inclusive). If no precision is specified, p is equal to 6.

A space separates the date and time parts.

Compared to the SQL standard, leap seconds (23:59:60 and 23:59:61) are not supported, as the semantics are closer to java.time.LocalDateTime.

A conversion from and to BIGINT (a JVM long type) is not supported, as this would imply a timezone, but this type is time-zone free. For more java.time.Instant-like semantics use TIMESTAMP_LTZ.

TIMESTAMP acts like a pure string and isn’t related to a time zone of any kind, including UTC.

TIMESTAMP WITHOUT TIME ZONE is a synonym for this type.

TIMESTAMP_LTZ¶

Represents a timestamp with the local timezone consisting of year-month-day hour:minute:second[.fractional] zone with up to nanosecond precision and values ranging from 0000-01-01 00:00:00.000000000 +14:59 to 9999-12-31 23:59:59.999999999 -14:59.

Declaration

TIMESTAMP_LTZ
TIMESTAMP_LTZ(p)

TIMESTAMP WITH LOCAL TIME ZONE
TIMESTAMP(p) WITH LOCAL TIME ZONE

Formats

The following table shows examples of the TIMESTAMP_LTZ type in different formats.

JSON for data type	{"type":"TIMESTAMP_WITH_LOCAL_TIME_ZONE","nullable":true,"precision":3}
CLI/UI format	TIMESTAMP(3) WITH LOCAL TIME ZONE
JSON for payload	"2023-04-06 11:06:47.224"
CLI/UI format for payload	2023-04-06 11:06:47.224

Declare this type by using TIMESTAMP_LTZ(p), where p is the number of digits of fractional seconds (precision).

p must have a value between 0 and 9 (both inclusive). If no precision is specified, p is equal to 6.

Leap seconds (23:59:60 and 23:59:61) are not supported, as the semantics are closer to java.time.OffsetDateTime.

Compared to TIMESTAMP WITH TIME ZONE, the timezone offset information is not stored physically in every datum. Instead, the type assumes java.time.Instant semantics in the UTC timezone at the edges of the table ecosystem. Every datum is interpreted in the local timezone configured in the current session for computation and visualization.

This type fills the gap between time-zone free and time-zone mandatory timestamp types by allowing the interpretation of UTC timestamps according to the configured session timezone.

TIMESTAMP_LTZ resembles a TIMESTAMP without a timezone, but the string always considers the sessions/query’s timezone. Internally, it is always in the UTC time zone.

If you require the short format, prefer TIMESTAMP_LTZ(3).

TIMESTAMP WITH LOCAL TIME ZONE is a synonym for this type.

TIMESTAMP and TIMESTAMP_LTZ comparison¶

Although TIMESTAMP and TIMESTAMP_LTZ are similarly named, they represent different concepts.

TIMESTAMP_LTZ

TIMESTAMP_LTZ in SQL is similar to the Instant class in Java.
TIMESTAMP_LTZ represents a moment, or a specific point in the UTC timeline.
TIMESTAMP_LTZ stores time as a UTC integer, which can be converted dynamically to every other timezone.
When printing or casting TIMESTAMP_LTZ as a character string, the sql.local-time-zone setting is considered.

TIMESTAMP

TIMESTAMP in SQL is similar to LocalDateTime in Java.
TIMESTAMP has no time zone or offset from UTC, so it can’t represent a moment.
TIMESTAMP stores time as character string, not related to any timezone.

Collection data types¶

ARRAY¶

Represents an array of elements with same subtype.

Declaration

ARRAY<t>
t ARRAY

Formats

The following table shows examples of the ARRAY type in different formats.

JSON for data type	{"type":"ARRAY","nullable":true,"elementType":{"type":"INTEGER","nullable":true}}
CLI/UI format	ARRAY<INT>
JSON for payload	["1", "2", "3", null]
CLI/UI format for payload	[1, 2, 3, NULL]

Declare this type by using ARRAY<t>, where t is the data type of the contained elements.

Compared to the SQL standard, the maximum cardinality of an array cannot be specified and is fixed at 2,147,483,647. Also, any valid type is supported as a subtype.

t ARRAY is a synonym for being closer to the SQL standard. For example, INT ARRAY is equivalent to ARRAY<INT>.

MAP¶

Represents an associative array that maps keys (including NULL) to values (including NULL).

Declaration

MAP<kt, vt>

Formats

The following table shows examples of the MAP type in different formats.

JSON for data type	{"type":"MAP","nullable":true,"keyType":{"type":"INTEGER","nullable":true},"valueType":{"type":"VARCHAR","nullable":true,"length":2147483647}}
CLI/UI format	MAP<STRING>
JSON for payload	[["1", "a"], ["2", "b"], [null, "c"]]
CLI/UI format for payload	{1=a, 2=b, NULL=c}

Declare this type by using MAP<kt, vt> where kt is the data type of the key elements and vt is the data type of the value elements.

A map can’t contain duplicate keys. Each key can map to at most one value.

There is no restriction of element types. It is the responsibility of the user to ensure uniqueness.

The map type is an extension to the SQL standard.

MULTISET¶

Represents a multiset (=bag).

Declaration

MULTISET<t>
t MULTISET

Formats

The following table shows examples of the MULTISET type in different formats.

JSON for data type	{"type":"MULTISET","nullable":true,"elementType":{"type":"INTEGER","nullable":true}}
CLI/UI format	MULTISET<INT>
JSON for payload	[["a", "1"], ["b", "2"], [null, "1"]]
CLI/UI format for payload	{a=1, b=2, NULL=1}

Declare this type by using MULTISET<t> where t is the data type of the contained elements.

Unlike a set, the multiset allows for multiple instances for each of its elements with a common subtype. Each unique value (including NULL) is mapped to some multiplicity.

There is no restriction of element types; it is the responsibility of the user to ensure uniqueness.

t MULTISET is a synonym for being closer to the SQL standard. For example, INT MULTISET is equivalent to MULTISET<INT>.

ROW¶

Represents a sequence of fields.

Declaration

ROW<name0 type0, name1 type1, ...>
ROW<name0 type0 'description0', name1 type1 'description1', ...>

ROW(name0 type0, name1 type1, ...)
ROW(name0 type0 'description0', name1 type1 'description1', ...)

Formats

The following table shows examples of the ROW type in different formats.

JSON for data type	{"type":"ROW","nullable":true,"fields":[{"name":"a","fieldType":{"type":"INTEGER","nullable":true}},{"name":"b","fieldType":{"type":"VARCHAR","nullable":true,"length":2147483647}}]}
CLI/UI format	MULTISET<INT>
JSON for payload	[["a", "1"], ["b", "2"], [null, "1"]]
CLI/UI format for payload	{a=1, b=2, NULL=1}

Declare this type by using ROW<n0 t0 'd0', n1 t1 'd1', ...>, where n is the unique name of a field, t is the logical type of a field, d is the description of a field.

A field consists of a field name, field type, and an optional description. The most specific type of a row of a table is a row type. In this case, each column of the row corresponds to the field of the row type that has the same ordinal position as the column.

To create a table with a row type, use the following syntax:

CREATE TABLE table_with_row_types (
   `Customer` ROW<name STRING, age INT>,
   `Order` ROW<id BIGINT, title STRING>
);

To insert a row into a table with a row type, use the following syntax:

INSERT INTO table_with_row_types VALUES
   (('Alice', 30), (101, 'Book')),
   (('Bob', 25), (102, 'Laptop')),
   (('Charlie', 35), (103, 'Phone')),
   (('Diana', 28), (104, 'Tablet')),
   (('Eve', 22), (105, 'Headphones'));

To work with fields from a row, use dot notation:

SELECT `Customer`.name, `Customer`.age, `Order`.id, `Order`.title
FROM table_with_row_types
WHERE `Customer`.age > 30;

Compared to the SQL standard, an optional field description simplifies the handling with complex structures.

A row type is similar to the STRUCT type known from other non-standard-compliant frameworks.

ROW(...) is a synonym for being closer to the SQL standard. For example, ROW(fieldOne INT, fieldTwo BOOLEAN) is equivalent to ROW<fieldOne INT, fieldTwo BOOLEAN>.

If the fields of the data type contain characters other than [A-Za-z_], use escaping notation. Double backticks escape the backtick character, for example:

ROW<`a-b` INT, b STRING, `weird_col``_umn` STRING>

Rows fields can contain comments, for example:

{"type":"ROW","nullable":true,"fields":[{"name":"a","fieldType":{"type":"INTEGER","nullable":true},"description":"hello"}]}

Format using single quotes. Double single quotes escape single quotes, for example:

ROW<a INT 'This field''s content'>

Other data types¶

BOOLEAN¶

Represents a boolean with a (possibly) three-valued logic of TRUE, FALSE, and UNKNOWN.

Declaration

BOOLEAN

Formats

The following table shows examples of the BOOLEAN type in different formats.

JSON for data type	{"type":"BOOLEAN","nullable":true}
CLI/UI format	NULL
JSON for payload	null
CLI/UI format for payload	NULL

NULL¶

Data type for representing untyped NULL values.

Declaration

NULL

Formats

The following table shows examples of the NULL type in different formats.

JSON for data type	{"type":"NULL"}
CLI/UI format	NULL
JSON for payload	null
CLI/UI format for payload	NULL

The NULL type is an extension to the SQL standard. A NULL type has no other value except NULL, thus, it can be cast to any nullable type similar to JVM semantics.

This type helps in representing unknown types in API calls that use a NULL literal as well as bridging to formats such as JSON or Avro that define such a type as well.

This type is not very useful in practice and is described here only for completeness.

Casting¶

Flink SQL can perform casting between a defined input type and target type. While some casting operations can always succeed regardless of the input value, others can fail at runtime when there’s no way to create a value for the target type. For example, it’s always possible to convert INT to STRING, but you can’t always convert a STRING to INT.

During the planning stage, the query validator rejects queries for invalid type pairs with a ValidationException, for example, when trying to cast a TIMESTAMP to an INTERVAL. Valid type pairs that can fail at runtime are accepted by the query validator, but this requires you to handle cast failures correctly.

In Flink SQL, casting can be performed by using one of these two built-in functions:

CAST: The regular cast function defined by the SQL standard. It can fail the job if the cast operation is fallible and the provided input is not valid. Type inference preserves the nullability of the input type.
TRY_CAST: An extension to the regular cast function that returns NULL if the cast operation fails. Its return type is always nullable.

For example:

-- returns 42 of type INT NOT NULL
SELECT CAST('42' AS INT);

-- returns NULL of type VARCHAR
SELECT CAST(NULL AS VARCHAR);

-- throws an exception and fails the job
SELECT CAST('non-number' AS INT);

-- returns 42 of type INT
SELECT TRY_CAST('42' AS INT);

-- returns NULL of type VARCHAR
SELECT TRY_CAST(NULL AS VARCHAR);

-- returns NULL of type INT
SELECT TRY_CAST('non-number' AS INT);

-- returns 0 of type INT NOT NULL
SELECT COALESCE(TRY_CAST('non-number' AS INT), 0);

The following matrix shows the supported cast pairs, where “Y” means supported, “!” means fallible, and “N” means unsupported:

Input / Target	CHAR¹ / VARCHAR¹ / STRING	BINARY¹ / VARBINARY¹ / BYTES	BOOLEAN	DECIMAL	TINYINT	SMALLINT	INTEGER	BIGINT	FLOAT	DOUBLE	DATE	TIME	TIMESTAMP	TIMESTAMP_LTZ	INTERVAL	ARRAY	MULTISET	MAP	ROW
CHAR / VARCHAR / STRING	Y	!	!	!	!	!	!	!	!	!	!	!	!	!	N	N	N	N	N
BINARY / VARBINARY / BYTES	Y	Y	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N
BOOLEAN	Y	N	Y	Y	Y	Y	Y	Y	Y	Y	N	N	N	N	N	N	N	N	N
DECIMAL	Y	N	N	Y	Y	Y	Y	Y	Y	Y	N	N	N	N	N	N	N	N	N
TINYINT	Y	N	Y	Y	Y	Y	Y	Y	Y	Y	N	N	N²	N²	N	N	N	N	N
SMALLINT	Y	N	Y	Y	Y	Y	Y	Y	Y	Y	N	N	N²	N²	N	N	N	N	N
INTEGER	Y	N	Y	Y	Y	Y	Y	Y	Y	Y	N	N	N²	N²	Y⁵	N	N	N	N
BIGINT	Y	N	Y	Y	Y	Y	Y	Y	Y	Y	N	N	N²	N²	Y⁶	N	N	N	N
FLOAT	Y	N	N	Y	Y	Y	Y	Y	Y	Y	N	N	N	N	N	N	N	N	N
DOUBLE	Y	N	N	Y	Y	Y	Y	Y	Y	Y	N	N	N	N	N	N	N	N	N
DATE	Y	N	N	N	N	N	N	N	N	N	Y	N	Y	Y	N	N	N	N	N
TIME	Y	N	N	N	N	N	N	N	N	N	N	Y	Y	Y	N	N	N	N	N
TIMESTAMP	Y	N	N	N	N	N	N	N	N	N	Y	Y	Y	Y	N	N	N	N	N
TIMESTAMP_LTZ	Y	N	N	N	N	N	N	N	N	N	Y	Y	Y	Y	N	N	N	N	N
INTERVAL	Y	N	N	N	N	N	Y⁵	Y⁶	N	N	N	N	N	N	Y	N	N	N	N
ARRAY	Y	N	N	N	N	N	N	N	N	N	N	N	N	N	N	!³	N	N	N
MULTISET	Y	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N	!³	N	N
MAP	Y	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N	!³	N
ROW	Y	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N	N	!³

Notes:

All the casting to constant length or variable length also trims and pads, according to the type definition.
TO_TIMESTAMP and TO_TIMESTAMP_LTZ must be used instead of CAST/ TRY_CAST.
Supported iff the children type pairs are supported. Fallible iff the children type pairs are fallible.
Supported iff the RAW class and serializer are equals.
Supported iff INTERVAL is a MONTH TO YEAR range.
Supported iff INTERVAL is a DAY TO TIME range.

Note

A cast of a NULL value always returns NULL, regardless of whether the function used is CAST or TRY_CAST.

Data type extraction¶

In many locations in the API, Flink tries to extract data types automatically from class information by using reflection to avoid repetitive manual schema work. But extracting a data type using reflection is not always successful, because logical information might be missing. In these cases, it may be necessary to add additional information close to a class or field declaration for supporting the extraction logic.

The following table lists classes that map implicitly to a data type without requiring further information. Other JVM bridging classes require the @DataTypeHint annotation.

Class	Data Type
boolean	BOOLEAN NOT NULL
byte	TINYINT NOT NULL
byte[]	BYTES
double	DOUBLE NOT NULL
float	FLOAT NOT NULL
int	INT NOT NULL
java.lang.Boolean	BOOLEAN
java.lang.Byte	TINYINT
java.lang.Double	DOUBLE
java.lang.Float	FLOAT
java.lang.Integer	INT
java.lang.Long	BIGINT
java.lang.Short	SMALLINT
java.lang.String	STRING
java.sql.Date	DATE
java.sql.Time	TIME(0)
java.sql.Timestamp	TIMESTAMP(9)
java.time.Duration	INTERVAL SECOND(9)
java.time.Instant	TIMESTAMP_LTZ(9)
java.time.LocalDate	DATE
java.time.LocalTime	TIME(9)
java.time.LocalDateTime	TIMESTAMP(9)
java.time.OffsetDateTime	TIMESTAMP(9) WITH TIME ZONE
java.time.Period	INTERVAL YEAR(4) TO MONTH
java.util.Map<K, V>	MAP<K, V>
short	SMALLINT NOT NULL
structured type T	anonymous structured type T
long	BIGINT NOT NULL
T[]	ARRAY<T>

Data Types in Confluent Cloud for Apache Flink¶

Data type definition¶

Character strings¶

CHAR¶

VARCHAR / STRING¶

Binary strings¶

BINARY¶

VARBINARY / BYTES¶

Exact numerics¶

BIGINT¶

DECIMAL¶

INT¶

SMALLINT¶

TINYINT¶

Approximate numerics¶

DOUBLE¶

FLOAT¶

Date and time¶

DATE¶

INTERVAL DAY TO SECOND¶

INTERVAL YEAR TO MONTH¶

TIME¶

TIMESTAMP¶

TIMESTAMP_LTZ¶

TIMESTAMP and TIMESTAMP_LTZ comparison¶

Collection data types¶

ARRAY¶

MAP¶

MULTISET¶

ROW¶

Other data types¶

BOOLEAN¶

NULL¶

Casting¶

Data type extraction¶

Related content¶