python多线程

python的多线程：
使用as_completed()，可以实现优先处理已经完成的任务，若使用pool.map()，则实现的是按请求顺序处理任务

import pandas as pd
from concurrent.futures import ThreadPoolExecutor, as_completed, ProcessPoolExecutor
import math

class Service:
    @staticmethod
    def multi_classifier_predict(id_list, val):
        print("begin！")
        df_group = pd.DataFrame([])
        id_list = list(set(id_list))
        # 分组，一组有10个id
        id_list_ = Service.split_id_block_size(id_list, block_size=10)
        # 创建线程池，然后使用多线程
        with ProcessPoolExecutor(max_workers=4) as pool:
            tasks = [pool.submit(Service().check_map, id, val) for id in id_list_]
            # 优先处理已完成任务
            for future in as_completed(tasks):
                feat_df, id = future.result()
                print(f"id:{id}")
                if feat_df.empty:
                    continue
                df_group = df_group.append(feat_df)
        ls1 = df_group['positionId'].values.tolist()
        ls_origin = set(i for i in id_list)
        print(f'无效id为{ls_origin - set(ls1)}')

        data = df_group.to_dict(orient='records')
        print("success！")
        return {"data": data}
    
	# 分组函数
    @staticmethod
    def split_id_block_size(pids, block_size):
        res_pids = []
        total_len = len(pids)
        nums = math.ceil(total_len / block_size)
        for i in range(nums):
            begin = i * block_size
            end = (i + 1) * block_size
            res_pids.append(pids[begin:end])
        return res_pids
	
	# 需要多线程处理的函数
    @staticmethod
    def check_map(id, val):
        df_res = pd.DataFrame([])
        return df_res, id