pyflink的开发模板

PyFlink 提供了多种不同的开发模板，可供开发人员使用。以下是其中的一些模板：

1. Batch job 模板：

from pyflink.dataset import ExecutionEnvironment

exec_env = ExecutionEnvironment.get_execution_environment()

input_path = "path/to/input"
output_path = "path/to/output"

data = exec_env.read_text_file(input_path)

result = data \
    .map(lambda x: x.split(",")) \
    .filter(lambda x: len(x) == 2) \
    .map(lambda x: (x[0], int(x[1]))) \
    .group_by(0) \
    .sum(1)

result.write_as_csv(output_path, "\n", ",")

exec_env.execute("Batch Job Example")

2. Streaming job 模板：

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream import TimeCharacteristic

env = StreamExecutionEnvironment.get_execution_environment()
env.set_stream_time_characteristic(TimeCharacteristic.EventTime)

input_path = "path/to/input"

data = env.read_text_file(input_path)
data = data \
    .map(lambda x: x.split(",")) \
    .filter(lambda x: len(x) == 2) \
    .map(lambda x: (x[0], int(x[1]))) \
    .assign_timestamps_and_watermarks(MyTimestampExtractor())

result = data \
    .key_by(lambda x: x[0]) \
    .time_window(Time.seconds(10)) \
    .reduce(lambda x, y: (x[0], x[1] + y[1]))

result.print()

env.execute("Streaming Job Example")

3. Table job 模板：

from pyflink.table import TableEnvironment

t_env = TableEnvironment.get_batch_environment()

input_path = "path/to/input"
output_path = "path/to/output"

t_env \
    .read_csv(input_path, ['word', 'count'], field_delim=',') \
    .where("count > 10") \
    .group_by("word") \
    .select("word, count(1) as count") \
    .write_csv(output_path, field_delim=',')

t_env.execute("Table Job Example")

以上是 PyFlink 中的三种常用开发模板，可以根据实际情况进行调整和扩展。