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

Data Types #

In Apache Flink’s Python DataStream API, a data type describes the type of a value in the DataStream ecosystem. It can be used to declare input and output types of operations and informs the system how to serailize elements.

Pickle Serialization #

If the type has not been declared, data would be serialized or deserialized using Pickle. For example, the program below specifies no data types.

from pyflink.datastream import StreamExecutionEnvironment


def processing():
    env = StreamExecutionEnvironment.get_execution_environment()
    env.set_parallelism(1)
    env.from_collection(collection=[(1, 'aaa'), (2, 'bbb')]) \
        .map(lambda record: (record[0]+1, record[1].upper())) \
        .print()  # note: print to stdout on the worker machine

    env.execute()


if __name__ == '__main__':
    processing()

However, types need to be specified when:

  • Passing Python records to Java operations.
  • Improve serialization and deserialization performance.

Passing Python records to Java operations #

Since Java operators or functions can not identify Python data, types need to be provided to help to convert Python types to Java types for processing. For example, types need to be provided if you want to output data using the StreamingFileSink which is implemented in Java.

from pyflink.common.serialization import Encoder
from pyflink.common.typeinfo import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.connectors import StreamingFileSink


def streaming_file_sink():
    env = StreamExecutionEnvironment.get_execution_environment()
    env.set_parallelism(1)
    env.from_collection(collection=[(1, 'aaa'), (2, 'bbb')]) \
        .map(lambda record: (record[0] + 1, record[1].upper()),
             output_type=Types.ROW([Types.INT(), Types.STRING()])) \
        .add_sink(StreamingFileSink
                  .for_row_format('/tmp/output', Encoder.simple_string_encoder())
                  .build())

    env.execute()


if __name__ == '__main__':
    streaming_file_sink()

Improve serialization and deserialization performance #

Even though data can be serialized and deserialized through Pickle, performance will be better if types are provided. Explicit types allow PyFlink to use efficient serializers when moving records through the pipeline.

Supported Data Types #

You can use pyflink.common.typeinfo.Types to define types in Python DataStream API. The table below shows the types supported now and how to define them:

PyFlink Type Python Type Java Type
Types.BOOLEAN() bool java.lang.Boolean
Types.BYTE() int java.lang.Byte
Types.SHORT() int java.lang.Short
Types.INT() int java.lang.Integer
Types.LONG() int java.lang.Long
Types.FLOAT() float java.lang.Float
Types.DOUBLE() float java.lang.Double
Types.CHAR() str java.lang.Character
Types.STRING() str java.lang.String
Types.BIG_INT() int java.math.BigInteger
Types.BIG_DEC() decimal.Decimal java.math.BigDecimal
Types.INSTANT() pyflink.common.time.Instant java.time.Instant
Types.TUPLE() tuple org.apache.flink.api.java.tuple.Tuple0 ~ org.apache.flink.api.java.tuple.Tuple25
Types.ROW() pyflink.common.Row org.apache.flink.types.Row
Types.ROW_NAMED() pyflink.common.Row org.apache.flink.types.Row
Types.MAP() dict java.util.Map
Types.PICKLED_BYTE_ARRAY() The actual unpickled Python object byte[]
Types.SQL_DATE() datetime.date java.sql.Date
Types.SQL_TIME() datetime.time java.sql.Time
Types.SQL_TIMESTAMP() datetime.datetime java.sql.Timestamp
Types.LIST() list of Python object java.util.List

The table below shows the array types supported:

PyFlink Array Type Python Type Java Type
Types.PRIMITIVE_ARRAY(Types.BYTE()) bytes byte[]
Types.PRIMITIVE_ARRAY(Types.BOOLEAN()) list of bool boolean[]
Types.PRIMITIVE_ARRAY(Types.SHORT()) list of int short[]
Types.PRIMITIVE_ARRAY(Types.INT()) list of int int[]
Types.PRIMITIVE_ARRAY(Types.LONG()) list of int long[]
Types.PRIMITIVE_ARRAY(Types.FLOAT()) list of float float[]
Types.PRIMITIVE_ARRAY(Types.DOUBLE()) list of float double[]
Types.PRIMITIVE_ARRAY(Types.CHAR()) list of str char[]
Types.BASIC_ARRAY(Types.BYTE()) list of int java.lang.Byte[]
Types.BASIC_ARRAY(Types.BOOLEAN()) list of bool java.lang.Boolean[]
Types.BASIC_ARRAY(Types.SHORT()) list of int java.lang.Short[]
Types.BASIC_ARRAY(Types.INT()) list of int java.lang.Integer[]
Types.BASIC_ARRAY(Types.LONG()) list of int java.lang.Long[]
Types.BASIC_ARRAY(Types.FLOAT()) list of float java.lang.Float[]
Types.BASIC_ARRAY(Types.DOUBLE()) list of float java.lang.Double[]
Types.BASIC_ARRAY(Types.CHAR()) list of str java.lang.Character[]
Types.BASIC_ARRAY(Types.STRING()) list of str java.lang.String[]
Types.OBJECT_ARRAY() list of Python object Array