在Python中,start()
函數通常與線程(threading模塊)或進程(multiprocessing模塊)相關
concurrent.futures.ThreadPoolExecutor
)來管理線程。線程池會復用已有的線程,并在需要時分配新任務。from concurrent.futures import ThreadPoolExecutor
def task(n):
print(f"Task {n} started")
with ThreadPoolExecutor(max_workers=4) as executor:
for i in range(10):
executor.submit(task, i)
concurrent.futures.ProcessPoolExecutor
)來提高性能。進程池會在多個進程間分配任務,從而利用多核處理器的計算能力。from concurrent.futures import ProcessPoolExecutor
def cpu_intensive_task(n):
# Your CPU-intensive code here
pass
with ProcessPoolExecutor(max_workers=4) as executor:
for i in range(10):
executor.submit(cpu_intensive_task, i)
import threading
def background_task():
while True:
# Your background task code here
pass
background_thread = threading.Thread(target=background_task)
background_thread.daemon = True
background_thread.start()
import threading
semaphore = threading.Semaphore(4)
def limited_concurrency_task():
with semaphore:
# Your task code here
pass
threads = []
for _ in range(10):
t = threading.Thread(target=limited_concurrency_task)
threads.append(t)
t.start()
for t in threads:
t.join()
import threading
event = threading.Event()
def wait_for_event_task():
print("Waiting for event...")
event.wait()
print("Event received, starting task...")
t = threading.Thread(target=wait_for_event_task)
t.start()
# Simulate some work
time.sleep(2)
# Set the event to start the task
event.set()
t.join()
import threading
condition = threading.Condition()
def wait_for_condition_task():
with condition:
print("Waiting for condition...")
condition.wait()
print("Condition met, starting task...")
t = threading.Thread(target=wait_for_condition_task)
t.start()
# Simulate some work
time.sleep(2)
# Notify waiting threads that the condition is met
with condition:
condition.notify_all()
t.join()
總之,根據你的需求選擇合適的方法來實現start()
函數。確保正確地同步和管理線程,以避免競爭條件、死鎖和其他并發問題。