您所要求的可以通过创建一个产生结果的生成器来实现apply_async
-调用线程池。
仅供参考,我对这种方法进行了基准测试pandas.read_csv
- 通过指定获得的迭代器chunksize
范围。我创建了 1M 行大小的 csv 文件的八个副本,并指定 chunksize=100_000。
其中四个文件是使用您提供的顺序方法读取的,四个文件是使用mt_gen
下面的函数,使用四个线程池:
- 单线程 ~ 3.68 s
- 多线程 ~ 1.21 s
但这并不意味着它会改善每个硬件和数据设置的结果。
import time
import threading
from multiprocessing.dummy import Pool # dummy uses threads
def _load_sim(x = 10e6):
for _ in range(int(x)):
x -= 1
time.sleep(1)
def gen(start, stop):
for i in range(start, stop):
_load_sim()
print(f'{threading.current_thread().name} yielding {i}')
yield i
def multi_threaded(gens):
combi_g = mt_gen(gens)
for item in combi_g:
print(item)
def mt_gen(gens):
with Pool(N_WORKERS) as pool:
while True:
async_results = [pool.apply_async(next, args=(g,)) for g in gens]
try:
results = [r.get() for r in async_results]
except StopIteration: # needed for Python 3.7+, PEP 479, bpo-32670
return
yield results
if __name__ == '__main__':
N_GENS = 10
N_WORKERS = 4
GEN_LENGTH = 3
gens = [gen(x * GEN_LENGTH, (x + 1) * GEN_LENGTH) for x in range(N_GENS)]
multi_threaded(gens)
Output:
Thread-1 yielding 0
Thread-2 yielding 3
Thread-4 yielding 6
Thread-3 yielding 9
Thread-1 yielding 12
Thread-2 yielding 15
Thread-4 yielding 18
Thread-3 yielding 21
Thread-1 yielding 24
Thread-2 yielding 27
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27]
Thread-3 yielding 7
Thread-1 yielding 10
Thread-2 yielding 4
Thread-4 yielding 1
Thread-3 yielding 13
Thread-1 yielding 16
Thread-4 yielding 22
Thread-2 yielding 19
Thread-3 yielding 25
Thread-1 yielding 28
[1, 4, 7, 10, 13, 16, 19, 22, 25, 28]
Thread-1 yielding 8
Thread-4 yielding 2
Thread-3 yielding 11
Thread-2 yielding 5
Thread-1 yielding 14
Thread-4 yielding 17
Thread-3 yielding 20
Thread-2 yielding 23
Thread-1 yielding 26
Thread-4 yielding 29
[2, 5, 8, 11, 14, 17, 20, 23, 26, 29]
Process finished with exit code 0