# -*- coding: utf-8 -*-
# @Author : ydf
# @Time : 2019/8/8 0008 14:57
import time
from function_scheduling_distributed_framework import get_publisher
from test_frame.my_patch_frame_config import do_patch_frame_config
do_patch_frame_config()
pb = get_publisher('task1_queue', broker_kind=2)
for i in range(100):
pb.publish({'x': i, 'y': i * 2})
# -*- coding: utf-8 -*-
# @Author : ydf
# @Time : 2019/8/8 0008 14:57
from multiprocessing import Process
import time
from function_scheduling_distributed_framework import get_consumer, get_publisher, AbstractConsumer
from function_scheduling_distributed_framework.consumers.redis_consumer import RedisConsumer
from function_scheduling_distributed_framework.utils import LogManager
from test_frame.my_patch_frame_config import do_patch_frame_config
do_patch_frame_config()
logger = LogManager('complex_example').get_logger_and_add_handlers()
pb2 = get_publisher('task2_queue', broker_kind=2)
def task1(x, y):
logger.info(f'消费此消息 {x} - {y} ,结果是 {x - y}')
for i in range(10):
pb2.publish({'n': x * 100 + i}) # 消费时候发布任务到别的队列或自己的队列。可以边消费边推送。
time.sleep(10) # 模拟做某事需要阻塞10秒种，必须用并发绕过此阻塞。
def task2(n):
logger.info(n)
time.sleep(3)
def multi_processing_consume():
# -*- coding: utf-8 -*-
# @Author : ydf
# @Time : 2019/8/8 0008 14:57
import time
from function_scheduling_distributed_framework import get_publisher, PriorityConsumingControlConfig
pb = get_publisher('queue_test33', broker_kind=2)
# [consumer.publisher_of_same_queue.publish({'a': i, 'b': 2 * i}) for i in range(100)]
for i in range(10000000):
a = i
b = i * 2
async_result = pb.publish({'a': a, 'b': b})
print(f'{a} {b} 之和是 {async_result.result}')