使用Flink与Python进行实时数据处理的基本步骤

pip install apache-flink

from pyflink.datastream import StreamExecutionEnvironment

env = StreamExecutionEnvironment.get_execution_environment()

from pyflink.flinkkafkaconnector import FlinkKafkaConsumer
from pyflink.common.serialization import SimpleStringSchema

properties = {
    'bootstrap.servers': 'localhost:9092',
    'group.id': 'test-group',
    'auto.offset.reset': 'latest'
}
consumer = FlinkKafkaConsumer(
    topic='test',
    properties=properties,
    deserialization_schema=SimpleStringSchema()
)
stream = env.add_source(consumer)

from pyflink.datastream.functions import MapFunction

class MyMapFunction(MapFunction):
    def map(self, value):
        return value.upper()

stream = stream.map(MyMapFunction())

from pyflink.datastream import FlinkKafkaProducer

producer_properties = {
    'bootstrap.servers': 'localhost:9092'
}
producer = FlinkKafkaProducer(
    topic='output',
    properties=producer_properties,
    serialization_schema=SimpleStringSchema()
)
stream.add_sink(producer)

env.execute('my_flink_job')