Data Types
This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.

Data Types #

Flink SQL has a rich set of native data types available to users.

Data Type #

A data type describes the logical type of a value in the table ecosystem. It can be used to declare input and/or output types of operations.

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

Examples of data types are:

  • INT
  • INT NOT NULL
  • INTERVAL DAY TO SECOND(3)
  • ROW<myField ARRAY<BOOLEAN>, myOtherField TIMESTAMP(3)>

A list of all pre-defined data types can be found below.

Data Types in the Table API #

Users of the JVM-based API work with instances of org.apache.flink.table.types.DataType within the Table API or when defining connectors, catalogs, or user-defined functions.

A DataType instance has two responsibilities:

  • Declaration of a logical type which does not imply a concrete physical representation for transmission or storage but defines the boundaries between JVM-based/Python languages and the table ecosystem.
  • Optional: Giving hints about the physical representation of data to the planner which is useful at the edges to other APIs.

For JVM-based languages, all pre-defined data types are available in org.apache.flink.table.api.DataTypes.

Users of the Python API work with instances of pyflink.table.types.DataType within the Python Table API or when defining Python user-defined functions.

A DataType instance has such a responsibility:

  • Declaration of a logical type which does not imply a concrete physical representation for transmission or storage but defines the boundaries between Python languages and the table ecosystem.

For Python language, those types are available in pyflink.table.types.DataTypes.

It is recommended to add a star import to your table programs for having a fluent API:

import static org.apache.flink.table.api.DataTypes.*;

DataType t = INTERVAL(DAY(), SECOND(3));

It is recommended to add a star import to your table programs for having a fluent API:

import org.apache.flink.table.api.DataTypes._

val t: DataType = INTERVAL(DAY(), SECOND(3))
from pyflink.table.types import DataTypes

t = DataTypes.INTERVAL(DataTypes.DAY(), DataTypes.SECOND(3))

Physical Hints #

Physical hints are required at the edges of the table ecosystem where the SQL-based type system ends and programming-specific data types are required. Hints indicate the data format that an implementation expects.

For example, a data source could express that it produces values for logical TIMESTAMPs using a java.sql.Timestamp class instead of using java.time.LocalDateTime which would be the default. With this information, the runtime is able to convert the produced class into its internal data format. In return, a data sink can declare the data format it consumes from the runtime.

Here are some examples of how to declare a bridging conversion class:

// tell the runtime to not produce or consume java.time.LocalDateTime instances
// but java.sql.Timestamp
DataType t = DataTypes.TIMESTAMP(3).bridgedTo(java.sql.Timestamp.class);

// tell the runtime to not produce or consume boxed integer arrays
// but primitive int arrays
DataType t = DataTypes.ARRAY(DataTypes.INT().notNull()).bridgedTo(int[].class);
// tell the runtime to not produce or consume java.time.LocalDateTime instances
// but java.sql.Timestamp
val t: DataType = DataTypes.TIMESTAMP(3).bridgedTo(classOf[java.sql.Timestamp])

// tell the runtime to not produce or consume boxed integer arrays
// but primitive int arrays
val t: DataType = DataTypes.ARRAY(DataTypes.INT().notNull()).bridgedTo(classOf[Array[Int]])

Attention Please note that physical hints are usually only required if the API is extended. Users of predefined sources/sinks/functions do not need to define such hints. Hints within a table program (e.g. field.cast(TIMESTAMP(3).bridgedTo(Timestamp.class))) are ignored.

List of Data Types #

This section lists all pre-defined data types.

For the JVM-based Table API those types are also available in org.apache.flink.table.api.DataTypes.
For the Python Table API, those types are available in pyflink.table.types.DataTypes.

The default planner supports the following set of SQL types:

Data Type Remarks for Data Type
CHAR
VARCHAR
STRING
BOOLEAN
BINARY
VARBINARY
BYTES
DECIMAL Supports fixed precision and scale.
TINYINT
SMALLINT
INTEGER
BIGINT
FLOAT
DOUBLE
DATE
TIME Supports only a precision of 0.
TIMESTAMP
TIMESTAMP_LTZ
INTERVAL Supports only interval of MONTH and SECOND(3).
ARRAY
MULTISET
MAP
ROW
RAW
Structured types Only exposed in user-defined functions yet.

Character Strings #

CHAR #

Data type of a fixed-length character string.

Declaration

CHAR
CHAR(n)
DataTypes.CHAR(n)

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.String X X Default
byte[] X X Assumes UTF-8 encoding.
org.apache.flink.table.data.StringData X X Internal data structure.
Not supported.

The type can be declared 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.

VARCHAR / STRING #

Data type of a variable-length character string.

Declaration

VARCHAR
VARCHAR(n)

STRING
DataTypes.VARCHAR(n)

DataTypes.STRING()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.String X X Default
byte[] X X Assumes UTF-8 encoding.
org.apache.flink.table.data.StringData X X Internal data structure.
DataTypes.VARCHAR(n)

DataTypes.STRING()

Attention The specified maximum number of code points n in DataTypes.VARCHAR(n) must be 2,147,483,647 currently.

The type can be declared 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 a synonym for VARCHAR(2147483647).

Binary Strings #

BINARY #

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

Declaration

BINARY
BINARY(n)
DataTypes.BINARY(n)

Bridging to JVM Types

Java Type Input Output Remarks
byte[] X X Default
Not supported.

The type can be declared 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.

VARBINARY / BYTES #

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

Declaration

VARBINARY
VARBINARY(n)

BYTES
DataTypes.VARBINARY(n)

DataTypes.BYTES()

Bridging to JVM Types

Java Type Input Output Remarks
byte[] X X Default
DataTypes.VARBINARY(n)

DataTypes.BYTES()

Attention The specified maximum number of bytes n in DataTypes.VARBINARY(n) must be 2,147,483,647 currently.

The type can be declared 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 a synonym for VARBINARY(2147483647).

Exact Numerics #

DECIMAL #

Data type of 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)
DataTypes.DECIMAL(p, s)

Bridging to JVM Types

Java Type Input Output Remarks
java.math.BigDecimal X X Default
org.apache.flink.table.data.DecimalData X X Internal data structure.
DataTypes.DECIMAL(p, s)

Attention The precision and scale specified in DataTypes.DECIMAL(p, s) must be 38 and 18 separately currently.

The type can be declared 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). s must have a value between 0 and p (both inclusive). The default value for p is 10. The default value for s is 0.

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

TINYINT #

Data type of a 1-byte signed integer with values from -128 to 127.

Declaration

TINYINT
DataTypes.TINYINT()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.Byte X X Default
byte X (X) Output only if type is not nullable.
DataTypes.TINYINT()

SMALLINT #

Data type of a 2-byte signed integer with values from -32,768 to 32,767.

Declaration

SMALLINT
DataTypes.SMALLINT()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.Short X X Default
short X (X) Output only if type is not nullable.
DataTypes.SMALLINT()

INT #

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

Declaration

INT

INTEGER
DataTypes.INT()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.Integer X X Default
int X (X) Output only if type is not nullable.
DataTypes.INT()

INTEGER is a synonym for this type.

BIGINT #

Data type of 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
DataTypes.BIGINT()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.Long X X Default
long X (X) Output only if type is not nullable.
DataTypes.BIGINT()

Approximate Numerics #

FLOAT #

Data type of a 4-byte single precision floating point number.

Compared to the SQL standard, the type does not take parameters.

Declaration

FLOAT
DataTypes.FLOAT()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.Float X X Default
float X (X) Output only if type is not nullable.
DataTypes.FLOAT()

DOUBLE #

Data type of an 8-byte double precision floating point number.

Declaration

DOUBLE

DOUBLE PRECISION
DataTypes.DOUBLE()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.Double X X Default
double X (X) Output only if type is not nullable.
DataTypes.DOUBLE()

DOUBLE PRECISION is a synonym for this type.

Date and Time #

DATE #

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

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

Declaration

DATE
DataTypes.DATE()

Bridging to JVM Types

Java Type Input Output Remarks
java.time.LocalDate X X Default
java.sql.Date X X
java.lang.Integer X X Describes the number of days since epoch.
int X (X) Describes the number of days since epoch.
Output only if type is not nullable.
DataTypes.DATE()

TIME #

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

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 time zone is not provided.
Compared to the SQL standard, leap seconds (23:59:60 and 23:59:61) are not supported. A time with time zone is not provided.

Declaration

TIME
TIME(p)
DataTypes.TIME(p)

Bridging to JVM Types

Java Type Input Output Remarks
java.time.LocalTime X X Default
java.sql.Time X X
java.lang.Integer X X Describes the number of milliseconds of the day.
int X (X) Describes the number of milliseconds of the day.
Output only if type is not nullable.
java.lang.Long X X Describes the number of nanoseconds of the day.
long X (X) Describes the number of nanoseconds of the day.
Output only if type is not nullable.
DataTypes.TIME(p)

Attention The precision specified in DataTypes.TIME(p) must be 0 currently.

The type can be declared 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.

TIMESTAMP #

Data type of a timestamp without time zone 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.

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 time zone. However, this type is time zone free. For more java.time.Instant-like semantics use TIMESTAMP_LTZ.

Compared to the SQL standard, leap seconds (23:59:60 and 23:59:61) are not supported.

A conversion from and to BIGINT is not supported as this would imply a time zone. However, this type is time zone free. If you have such a requirement please use TIMESTAMP_LTZ.

Declaration

TIMESTAMP
TIMESTAMP(p)

TIMESTAMP WITHOUT TIME ZONE
TIMESTAMP(p) WITHOUT TIME ZONE
DataTypes.TIMESTAMP(p)

Bridging to JVM Types

Java Type Input Output Remarks
java.time.LocalDateTime X X Default
java.sql.Timestamp X X
org.apache.flink.table.data.TimestampData X X Internal data structure.
DataTypes.TIMESTAMP(p)

Attention The precision specified in DataTypes.TIMESTAMP(p) must be 3 currently.

The type can be declared 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.

TIMESTAMP(p) WITHOUT TIME ZONE is a synonym for this type.

TIMESTAMP WITH TIME ZONE #

Data type of a timestamp with time zone 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.

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.OffsetDateTime.
Compared to the SQL standard, leap seconds (23:59:60 and 23:59:61) are not supported.

Compared to TIMESTAMP_LTZ, the time zone offset information is physically stored in every datum. It is used individually for every computation, visualization, or communication to external systems.

Declaration

TIMESTAMP WITH TIME ZONE
TIMESTAMP(p) WITH TIME ZONE
DataTypes.TIMESTAMP_WITH_TIME_ZONE(p)

Bridging to JVM Types

Java Type Input Output Remarks
java.time.OffsetDateTime X X Default
java.time.ZonedDateTime X Ignores the zone ID.
Not supported.
The type can be declared using TIMESTAMP(p) WITH TIME ZONE 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.

TIMESTAMP_LTZ #

Data type of a timestamp with local time zone 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.

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

Leap seconds (23:59:60 and 23:59:61) are not supported.

Compared to TIMESTAMP WITH TIME ZONE, the time zone offset information is not stored physically in every datum. Every datum is interpreted in the local time zone 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 time zone.

Declaration

TIMESTAMP_LTZ
TIMESTAMP_LTZ(p)

TIMESTAMP WITH LOCAL TIME ZONE
TIMESTAMP(p) WITH LOCAL TIME ZONE
DataTypes.TIMESTAMP_LTZ(p)
DataTypes.TIMESTAMP_WITH_LOCAL_TIME_ZONE(p)

Bridging to JVM Types

Java Type Input Output Remarks
java.time.Instant X X Default
java.lang.Integer X X Describes the number of seconds since epoch.
int X (X) Describes the number of seconds since epoch.
Output only if type is not nullable.
java.lang.Long X X Describes the number of milliseconds since epoch.
long X (X) Describes the number of milliseconds since epoch.
Output only if type is not nullable.
java.sql.Timestamp X X Describes the number of milliseconds since epoch.
org.apache.flink.table.data.TimestampData X X Internal data structure.
DataTypes.TIMESTAMP_LTZ(p)
DataTypes.TIMESTAMP_WITH_LOCAL_TIME_ZONE(p)

Attention The precision specified in DataTypes.TIMESTAMP_LTZ(p) must be 3 currently.

The type can be declared 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.

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

INTERVAL YEAR TO MONTH #

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

The type must be parameterized to one of the following resolutions:

  • interval of years,
  • interval of years to months,
  • or 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.

Declaration

INTERVAL YEAR
INTERVAL YEAR(p)
INTERVAL YEAR(p) TO MONTH
INTERVAL MONTH
DataTypes.INTERVAL(DataTypes.YEAR())
DataTypes.INTERVAL(DataTypes.YEAR(p))
DataTypes.INTERVAL(DataTypes.YEAR(p), DataTypes.MONTH())
DataTypes.INTERVAL(DataTypes.MONTH())

Bridging to JVM Types

Java Type Input Output Remarks
java.time.Period X X Ignores the days part. Default
java.lang.Integer X X Describes the number of months.
int X (X) Describes the number of months.
Output only if type is not nullable.
DataTypes.INTERVAL(DataTypes.YEAR())
DataTypes.INTERVAL(DataTypes.YEAR(p))
DataTypes.INTERVAL(DataTypes.YEAR(p), DataTypes.MONTH())
DataTypes.INTERVAL(DataTypes.MONTH())

The type can be declared 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.

INTERVAL DAY TO SECOND #

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

The type must be parameterized to one of the following 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,
  • or 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.

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)
DataTypes.INTERVAL(DataTypes.DAY())
DataTypes.INTERVAL(DataTypes.DAY(p1))
DataTypes.INTERVAL(DataTypes.DAY(p1), DataTypes.HOUR())
DataTypes.INTERVAL(DataTypes.DAY(p1), DataTypes.MINUTE())
DataTypes.INTERVAL(DataTypes.DAY(p1), DataTypes.SECOND(p2))
DataTypes.INTERVAL(DataTypes.HOUR())
DataTypes.INTERVAL(DataTypes.HOUR(), DataTypes.MINUTE())
DataTypes.INTERVAL(DataTypes.HOUR(), DataTypes.SECOND(p2))
DataTypes.INTERVAL(DataTypes.MINUTE())
DataTypes.INTERVAL(DataTypes.MINUTE(), DataTypes.SECOND(p2))
DataTypes.INTERVAL(DataTypes.SECOND())
DataTypes.INTERVAL(DataTypes.SECOND(p2))

Bridging to JVM Types

Java Type Input Output Remarks
java.time.Duration X X Default
java.lang.Long X X Describes the number of milliseconds.
long X (X) Describes the number of milliseconds.
Output only if type is not nullable.
DataTypes.INTERVAL(DataTypes.DAY())
DataTypes.INTERVAL(DataTypes.DAY(p1))
DataTypes.INTERVAL(DataTypes.DAY(p1), DataTypes.HOUR())
DataTypes.INTERVAL(DataTypes.DAY(p1), DataTypes.MINUTE())
DataTypes.INTERVAL(DataTypes.DAY(p1), DataTypes.SECOND(p2))
DataTypes.INTERVAL(DataTypes.HOUR())
DataTypes.INTERVAL(DataTypes.HOUR(), DataTypes.MINUTE())
DataTypes.INTERVAL(DataTypes.HOUR(), DataTypes.SECOND(p2))
DataTypes.INTERVAL(DataTypes.MINUTE())
DataTypes.INTERVAL(DataTypes.MINUTE(), DataTypes.SECOND(p2))
DataTypes.INTERVAL(DataTypes.SECOND())
DataTypes.INTERVAL(DataTypes.SECOND(p2))

The type can be declared 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). p2 must have a value between 0 and 9 (both inclusive). If no p1 is specified, it is equal to 2 by default. If no p2 is specified, it is equal to 6 by default.

Constructured Data Types #

ARRAY #

Data type of an array of elements with same subtype.

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

Declaration

ARRAY<t>
t ARRAY
DataTypes.ARRAY(t)

Bridging to JVM Types

Java Type Input Output Remarks
t[] (X) (X) Depends on the subtype. Default
java.util.List<t> X X
subclass of java.util.List<t> X
org.apache.flink.table.data.ArrayData X X Internal data structure.
DataTypes.ARRAY(t)

The type can be declared using ARRAY<t> where t is the data type of the contained elements.

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

MAP #

Data type of an associative array that maps keys (including NULL) to values (including NULL). A map cannot 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.

Declaration

MAP<kt, vt>
DataTypes.MAP(kt, vt)

Bridging to JVM Types

Java Type Input Output Remarks
java.util.Map<kt, vt> X X Default
subclass of java.util.Map<kt, vt> X
org.apache.flink.table.data.MapData X X Internal data structure.
DataTypes.MAP(kt, vt)

The type can be declared using MAP<kt, vt> where kt is the data type of the key elements and vt is the data type of the value elements.

MULTISET #

Data type of a multiset (=bag). Unlike a set, it 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.

Declaration

MULTISET<t>
t MULTISET
DataTypes.MULTISET(t)

Bridging to JVM Types

Java Type Input Output Remarks
java.util.Map<t, java.lang.Integer> X X Assigns each value to an integer multiplicity. Default
subclass of java.util.Map<t, java.lang.Integer>> X
org.apache.flink.table.data.MapData X X Internal data structure.
DataTypes.MULTISET(t)

The type can be declared using MULTISET<t> where t is the data type of the contained elements.

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

ROW #

Data type of a sequence of fields.

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.

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.

Declaration

ROW<n0 t0, n1 t1, ...>
ROW<n0 t0 'd0', n1 t1 'd1', ...>

ROW(n0 t0, n1 t1, ...>
ROW(n0 t0 'd0', n1 t1 'd1', ...)
DataTypes.ROW(DataTypes.FIELD(n0, t0), DataTypes.FIELD(n1, t1), ...)
DataTypes.ROW(DataTypes.FIELD(n0, t0, d0), DataTypes.FIELD(n1, t1, d1), ...)

Bridging to JVM Types

Java Type Input Output Remarks
org.apache.flink.types.Row X X Default
org.apache.flink.table.data.RowData X X Internal data structure.
DataTypes.ROW([DataTypes.FIELD(n0, t0), DataTypes.FIELD(n1, t1), ...])
DataTypes.ROW([DataTypes.FIELD(n0, t0, d0), DataTypes.FIELD(n1, t1, d1), ...])

The type can be declared 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.

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

User-Defined Data Types #

Attention User-defined data types are not fully supported yet. They are currently (as of Flink 1.11) only exposed as unregistered structured types in parameters and return types of functions.

A structured type is similar to an object in an object-oriented programming language. It contains zero, one or more attributes. Each attribute consists of a name and a type.

There are two kinds of structured types:

  • Types that are stored in a catalog and are identified by a catalog identifier (like cat.db.MyType). Those are equal to the SQL standard definition of structured types.

  • Anonymously defined, unregistered types (usually reflectively extracted) that are identified by an implementation class (like com.myorg.model.MyType). Those are useful when programmatically defining a table program. They enable reusing existing JVM classes without manually defining the schema of a data type again.

Registered Structured Types #

Currently, registered structured types are not supported. Thus, they cannot be stored in a catalog or referenced in a CREATE TABLE DDL.

Unregistered Structured Types #

Unregistered structured types can be created from regular POJOs (Plain Old Java Objects) using automatic reflective extraction.

The implementation class of a structured type must meet the following requirements:

  • The class must be globally accessible which means it must be declared public, static, and not abstract.
  • The class must offer a default constructor with zero arguments or a full constructor that assigns all fields.
  • All fields of the class must be readable by either public declaration or a getter that follows common coding style such as getField(), isField(), field().
  • All fields of the class must be writable by either public declaration, fully assigning constructor, or a setter that follows common coding style such as setField(...), field(...).
  • All fields must be mapped to a data type either implicitly via reflective extraction or explicitly using the @DataTypeHint annotations.
  • Fields that are declared static or transient are ignored.

The reflective extraction supports arbitrary nesting of fields as long as a field type does not (transitively) refer to itself.

The declared field class (e.g. public int age;) must be contained in the list of supported JVM bridging classes defined for every data type in this document (e.g. java.lang.Integer or int for INT).

For some classes an annotation is required in order to map the class to a data type (e.g. @DataTypeHint("DECIMAL(10, 2)") to assign a fixed precision and scale for java.math.BigDecimal).

Declaration

class User {

    // extract fields automatically
    public int age;
    public String name;

    // enrich the extraction with precision information
    public @DataTypeHint("DECIMAL(10, 2)") BigDecimal totalBalance;

    // enrich the extraction with forcing using RAW types
    public @DataTypeHint("RAW") Class<?> modelClass;
}

DataTypes.of(User.class);

Bridging to JVM Types

Java Type Input Output Remarks
class X X Originating class or subclasses (for input) or
superclasses (for output). Default
org.apache.flink.types.Row X X Represent the structured type as a row.
org.apache.flink.table.data.RowData X X Internal data structure.
case class User(

    // extract fields automatically
    age: Int,
    name: String,

    // enrich the extraction with precision information
    @DataTypeHint("DECIMAL(10, 2)") totalBalance: java.math.BigDecimal,

    // enrich the extraction with forcing using a RAW type
    @DataTypeHint("RAW") modelClass: Class[_]
)

DataTypes.of(classOf[User])

Bridging to JVM Types

Java Type Input Output Remarks
class X X Originating class or subclasses (for input) or
superclasses (for output). Default
org.apache.flink.types.Row X X Represent the structured type as a row.
org.apache.flink.table.data.RowData X X Internal data structure.
Not supported.

Other Data Types #

BOOLEAN #

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

Declaration

BOOLEAN
DataTypes.BOOLEAN()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.Boolean X X Default
boolean X (X) Output only if type is not nullable.
DataTypes.BOOLEAN()

RAW #

Data type of an arbitrary serialized type. This type is a black box within the table ecosystem and is only deserialized at the edges.

The raw type is an extension to the SQL standard.

Declaration

RAW('class', 'snapshot')
DataTypes.RAW(class, serializer)

DataTypes.RAW(class)

Bridging to JVM Types

Java Type Input Output Remarks
class X X Originating class or subclasses (for input) or
superclasses (for output). Default
byte[] X
org.apache.flink.table.data.RawValueData X X Internal data structure.
Not supported.

The type can be declared using RAW('class', 'snapshot') where class is the originating class and snapshot is the serialized TypeSerializerSnapshot in Base64 encoding. Usually, the type string is not declared directly but is generated while persisting the type.

In the API, the RAW type can be declared either by directly supplying a Class + TypeSerializer or by passing Class and letting the framework extract Class + TypeSerializer from there.

NULL #

Data type for representing untyped NULL values.

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 just mentioned here for completeness.

Declaration

NULL
DataTypes.NULL()

Bridging to JVM Types

Java Type Input Output Remarks
java.lang.Object X X Default
any class (X) Any non-primitive type.
Not supported.

CAST 方法 #

Flink Table API 和 Flink SQL 支持从 输入 数据类型 到 目标 数据类型的转换。有的转换 无论输入值是什么都能保证转换成功,而有些转换则会在运行时失败(即不可能转换为 目标 数据类型对应的值)。 例如,将 INT 数据类型的值转换为 STRING 数据类型一定能转换成功,但无法保证将 STRING 数据类型转换为 INT 数据类型。

在生成执行计划时,Flink 的 SQL 检查器会拒绝提交那些不可能直接转换为 目标 数据类型的SQL,并抛出 ValidationException 异常, 例如从 TIMESTAMP 类型转化到 INTERVAL 类型。 然而有些查询即使通过了 SQL 检查器的验证,依旧可能会在运行期间转换失败,这就需要用户正确处理这些失败了。

在 Flink Table API 和 Flink SQL 中,可以用下面两个内置方法来进行转换操作:

  • CAST:定义在 SQL 标准的 CAST 方法。在某些容易发生转换失败的查询场景中,当实际输入数据不合法时,作业便会运行失败。类型推导会保留输入类型的可空性。
  • TRY_CAST:常规 CAST 方法的扩展,当转换失败时返回 NULL。该方法的返回值允许为空。

例如:

CAST('42' AS INT) --- 结果返回数字 42 的 INT 格式(非空)
CAST(NULL AS VARCHAR) --- 结果返回 VARCHAR 类型的空值
CAST('non-number' AS INT) --- 抛出异常,并停止作业

TRY_CAST('42' AS INT) --- 结果返回数字 42 的 INT 格式
TRY_CAST(NULL AS VARCHAR) --- 结果返回 VARCHAR 类型的空值
TRY_CAST('non-number' AS INT) --- 结果返回 INT 类型的空值
COALESCE(TRY_CAST('non-number' AS INT), 0) --- 结果返回数字 0 的 INT 格式(非空)

下表展示了各个类型的转换程度,“Y” 表示支持,"!" 表示转换可能会失败,“N” 表示不支持:

输入类型\目标类型 CHAR¹/
VARCHAR¹/
STRING
BINARY¹/
VARBINARY¹/
BYTES
BOOLEAN DECIMAL TINYINT SMALLINT INTEGER BIGINT FLOAT DOUBLE DATE TIME TIMESTAMP TIMESTAMP_LTZ INTERVAL ARRAY MULTISET MAP ROW STRUCTURED RAW
CHAR/
VARCHAR/
STRING
Y ! ! ! ! ! ! ! ! ! ! ! ! ! N N 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 N N
BOOLEAN Y N Y Y Y Y Y Y Y Y N N 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 N N
TINYINT Y N Y Y Y Y Y Y Y Y N N N N N N N N N
SMALLINT Y N Y Y Y Y Y Y Y Y N N N N N N N N N
INTEGER Y N Y Y Y Y Y Y Y Y N N Y⁵ N N N N N N
BIGINT Y N Y Y Y Y Y Y Y Y N N Y⁶ N N N N N N
FLOAT Y N N Y Y Y Y Y Y Y N N 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 N N
DATE Y N N N N N N N N N Y N Y Y N N 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 N N
TIMESTAMP Y N N N N N N N N N Y Y Y Y N N 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 N N
INTERVAL Y N N N N N Y⁵ Y⁶ N N N N N N Y N N N N N N
ARRAY Y N N N N N N N N N N N N N N N N N N N
MULTISET Y N N N N N N N N N N N N N N N N N N N
MAP Y N N N N N N N N N N N N N N N N N N N
ROW Y N N N N N N N N N N N N N N N N N N N
STRUCTURED Y N N N N N N N N N N N N N N N N N N N
RAW Y ! N N N N N N N N N N N N N N N N N N Y⁴

备注:

  1. 所有转化到具有固长或变长的类型时会根据类型的定义来裁剪或填充数据。
  2. 使用 TO_TIMESTAMP 方法和 TO_TIMESTAMP_LTZ 方法的场景,不要使用 CASTTRY_CAST
  3. 支持转换,当且仅当用其内部数据结构也支持转化时。转换可能会失败,当且仅当用其内部数据结构也可能会转换失败。
  4. 支持转换,当且仅当用使用 RAW 的类和类的序列化器一样。
  5. 支持转换,当且仅当用使用 INTERVAL 做“月”到“年”的转换。
  6. 支持转换,当且仅当用使用 INTERVAL 做“天”到“时间”的转换。

请注意:无论是 CAST 还是 TRY_CAST,当输入为 NULL ,输出也为 NULL

旧版本 CAST 方法 #

用户可以通过将参数 table.exec.legacy-cast-behaviour 设置为 enabled 来启用 1.15 版本之前的 CAST 行为。 在 Flink 1.15 版本此参数默认为 disabled。

如果设置为 enabled,请注意以下问题:

  • 转换为 CHAR/VARCHAR/BINARY/VARBINARY 数据类型时,不再自动修剪(trim)或填充(pad)。
  • 使用 CAST 时不再会因为转化失败而停止作业,只会返回 NULL,但不会像 TRY_CAST 那样推断正确的类型。
  • CHAR/VARCHAR/STRING 的转换结果会有一些细微的差别。
我们 不建议 配置此参数,而是 强烈建议 在新项目中保持这个参数为默认禁用,以使用最新版本的 CAST 方法。 在下一个版本,这个参数会被移除。

Data Type Extraction #

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

The following table lists classes that can be implicitly mapped to a data type without requiring further information.

If you intend to implement classes in Scala, it is recommended to use boxed types (e.g. java.lang.Integer) instead of Scala’s primitives. Scala’s primitives (e.g. Int or Double) are compiled to JVM primitives (e.g. int/double) and result in NOT NULL semantics as shown in the table below. Furthermore, Scala primitives that are used in generics (e.g. java.util.Map[Int, Double]) are erased during compilation and lead to class information similar to java.util.Map[java.lang.Object, java.lang.Object].

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

Other JVM bridging classes mentioned in this document require a @DataTypeHint annotation.

Data type hints can parameterize or replace the default extraction logic of individual function parameters and return types, structured classes, or fields of structured classes. An implementer can choose to what extent the default extraction logic should be modified by declaring a @DataTypeHint annotation.

The @DataTypeHint annotation provides a set of optional hint parameters. Some of those parameters are shown in the following example. More information can be found in the documentation of the annotation class.

import org.apache.flink.table.annotation.DataTypeHint;

class User {

    // defines an INT data type with a default conversion class `java.lang.Integer`
    public @DataTypeHint("INT") Object o;

    // defines a TIMESTAMP data type of millisecond precision with an explicit conversion class
    public @DataTypeHint(value = "TIMESTAMP(3)", bridgedTo = java.sql.Timestamp.class) Object o;

    // enrich the extraction with forcing using a RAW type
    public @DataTypeHint("RAW") Class<?> modelClass;

    // defines that all occurrences of java.math.BigDecimal (also in nested fields) will be
    // extracted as DECIMAL(12, 2)
    public @DataTypeHint(defaultDecimalPrecision = 12, defaultDecimalScale = 2) AccountStatement stmt;

    // defines that whenever a type cannot be mapped to a data type, instead of throwing
    // an exception, always treat it as a RAW type
    public @DataTypeHint(allowRawGlobally = HintFlag.TRUE) ComplexModel model;
}
import org.apache.flink.table.annotation.DataTypeHint

class User {

    // defines an INT data type with a default conversion class `java.lang.Integer`
    @DataTypeHint("INT")
    var o: AnyRef

    // defines a TIMESTAMP data type of millisecond precision with an explicit conversion class
    @DataTypeHint(value = "TIMESTAMP(3)", bridgedTo = java.sql.Timestamp.class)
    var o: AnyRef

    // enrich the extraction with forcing using a RAW type
    @DataTypeHint("RAW")
    var modelClass: Class[_]

    // defines that all occurrences of java.math.BigDecimal (also in nested fields) will be
    // extracted as DECIMAL(12, 2)
    @DataTypeHint(defaultDecimalPrecision = 12, defaultDecimalScale = 2)
    var stmt: AccountStatement

    // defines that whenever a type cannot be mapped to a data type, instead of throwing
    // an exception, always treat it as a RAW type
    @DataTypeHint(allowRawGlobally = HintFlag.TRUE)
    var model: ComplexModel
}
Not supported.

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