python asyncio任务未并行执行

Question

我正在创建异步任务，以便它们可以像这样并行执行

 for symbol in config.symbol_list:
                    tasks.append(asyncio.ensure_future(get_today_Data_async(symbol), loop=loop))
                loop.run_until_complete(asyncio.wait(tasks))

这是我想并行执行的任务

async def get_today_Data_async(symbol):

    periodType = 'day'
    period = 1
    frequencyType = 'minute'
    frequency = '1'
    use_last10_Min = False
    logging.info(f'Updating data {symbol} started...')
    try:
        logging.info(f'thread id - {threading.get_ident()} getting market data {symbol} periodType {periodType} period {period} frequencyType {frequencyType} frequency {frequency}')

        est = pytz.timezone('US/Eastern')
        if use_last10_Min:
            startDate = (datetime.datetime.now()- datetime.timedelta(minutes=10)).astimezone(tz=est).timestamp()
        else:
            startDate =(datetime.datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)).astimezone(tz=est).timestamp()
        endDate = (datetime.datetime.now()+datetime.timedelta(hours=48)).astimezone(tz=est).timestamp()
        endDate = str(endDate).split('.')[0] + '000'
        startDate = str(startDate).split('.')[0] + '000'

        reqDict = {'apikey': '' + config.client_id + '@AMER.OAUTHAP','endDate': endDate, 'frequencyType': frequencyType,'frequency': frequency,
                   'startDate': startDate, 'needExtendedHoursData': usePreMarket}

        header = {'Authorization': 'Bearer ' + config.token['access_token'] + '', 'content-type': 'application/json'}
        logging.info(f"thread id - {threading.get_ident()} datetime check {symbol} {datetime.datetime.now()}   {reqDict}")
        with await tlock:
            resp = requests.get("https://api.tdameritrade.com/v1/marketdata/" + symbol + "/pricehistory", params=reqDict)
        logging.info(f'thread id - {threading.get_ident()} datetime check {symbol} {datetime.datetime.now()} {resp.status_code}')
        if resp.status_code == 200 and not resp.json()['empty']:
            candles = resp.json()['candles']
            logging.info(f"symbol candel {symbol} {frequencyType} {frequency} {period} {get_one_hour(resp.json()['candles'])}")
            if not usePreMarket:
                newcandles = []
                EST = pytz.timezone('us/eastern')
                time_ist_end = datetime.datetime.now(EST).replace(hour=16, minute=00, second=00)
                time_ist_start = time_ist_end.replace(hour=9, minute=30, second=00)
                for x in candles:
                    tmp_date = datetime.datetime.fromtimestamp((x.get('datetime') / 1000), tz=pytz.timezone('US/Eastern'))
                    if tmp_date > time_ist_start and tmp_date < time_ist_end:
                        newcandles.append(x)
                if len(newcandles) > 0:
                    process_price(symbol,newcandles)
            else:
                if len(candles) > 0:
                    process_price(symbol, candles)

        logging.info(f" symbol - {symbol} status code {resp.status_code} resp {resp.text}")

    except Exception as e:
        traceback.print_exc()
        logging.error(f'Error in getting price {e}')
    logging.info(f'Updating data {symbol} completed...')

但是任务按顺序执行，产生以下输出

2020-10-14 20:22:43,293  - root - get_today_Data_async - 398 - INFO - Updating data AAPL started...
2020-10-14 20:22:45,066  - root - get_today_Data_async - 442 - INFO - Updating data AAPL completed...
2020-10-14 20:22:45,066  - root - get_today_Data_async - 398 - INFO - Updating data MSFT started...
2020-10-14 20:22:46,301  - root - get_today_Data_async - 442 - INFO - Updating data MSFT completed...
2020-10-14 20:22:46,301  - root - get_today_Data_async - 398 - INFO - Updating data AMZN started...
2020-10-14 20:22:47,573  - root - get_today_Data_async - 442 - INFO - Updating data AMZN completed...
2020-10-14 20:22:47,573  - root - get_today_Data_async - 398 - INFO - Updating data FB started...
2020-10-14 20:22:48,907  - root - get_today_Data_async - 442 - INFO - Updating data FB completed...
2020-10-14 20:22:48,907  - root - get_today_Data_async - 398 - INFO - Updating data GOOGL started...
2020-10-14 20:22:51,266  - root - get_today_Data_async - 442 - INFO - Updating data GOOGL completed...
2020-10-14 20:22:51,266  - root - get_today_Data_async - 398 - INFO - Updating data GOOG started...
2020-10-14 20:22:52,585  - root - get_today_Data_async - 442 - INFO - Updating data GOOG completed...
2020-10-14 20:22:52,585  - root - get_today_Data_async - 398 - INFO - Updating data JNJ started...
2020-10-14 20:22:54,041  - root - get_today_Data_async - 442 - INFO - Updating data JNJ completed...
2020-10-14 20:22:54,041  - root - get_today_Data_async - 398 - INFO - Updating data PG started...
2020-10-14 20:22:55,275  - root - get_today_Data_async - 442 - INFO - Updating data PG completed...
2020-10-14 20:22:55,275  - root - get_today_Data_async - 398 - INFO - Updating data V started...
2020-10-14 20:22:56,563  - root - get_today_Data_async - 442 - INFO - Updating data V completed..

这意味着任务正在按顺序执行。 大约有 500 个符号。 你能帮我吗，这样我就可以并行执行任务

Answer 1

在python中，理论上在任何给定时间都没有并行执行。

Python 的全局解释器锁 (GIL) 是一种复杂的机制，我不会在这里解释，如果您愿意，可以阅读它，但它会阻止 Python 代码同时在两个不同的线程中运行。

那么为什么仍然使用线程/并行处理？ 在 Python 中，解决 I/O（输入输出）问题是并行处理的经典之选，我将举例说明。 如果你有一个发出 HTTP 请求的代码，因为网络数据传输比 cpu 处理慢得多，为了使你的代码最有效，你宁愿在一个线程上发出请求，而不是让程序卡住并等待响应，继续使用其他线程发出请求，而不是针对每个返回的响应，处理从该响应中获得的输出。

这就是为什么在 Python 中，很多问题可能不应该是多线程的，而在其他语言中它有一些好处。

使用 python 实现真正并行处理的一种方法是使用multiprocessing模块。 但请记住，它会比普通的 Python 执行有更多的 RAM 使用量，因为您在内存中有多个相同的堆栈，而且它不一定会更快，因为打开和关闭进程需要时间。