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@@ -0,0 +1,640 @@
+# 爬虫入门实战4_高效率的爬虫实现
+
+在现代爬虫开发中，提高爬取效率是一个永恒的话题。本文将深入探讨Python中的多进程、多线程和协程三种并发编程方式，分析它们在爬虫开发中的应用，并通过实际案例比较它们的性能表现。
+
+## 1. 并发编程概述
+
+### 1.1 多进程（Multiprocessing）
+
+多进程是指在操作系统中同时运行多个独立的进程。每个进程都有自己的内存空间和系统资源。
+
+优点：
+- 可以充分利用多核CPU
+- 绕过Python的全局解释器锁（GIL）
+- 进程间内存隔离，更安全
+
+缺点：
+- 进程创建和切换开销大
+- 进程间通信相对复杂
+- 占用较多系统资源
+
+### 1.2 多线程（Multithreading）
+
+多线程是在同一进程内创建多个线程，共享进程的内存空间。
+
+优点：
+- 资源占用相对较少
+- 线程间共享内存，通信方便
+- 适合I/O密集型任务
+
+缺点：
+- 受Python GIL限制，难以充分利用多核CPU
+- 需要考虑线程安全问题
+- 调试相对困难
+
+### 1.3 协程（Coroutine）
+
+协程是一种用户态的轻量级线程，通过协作式多任务实现并发。
+
+优点：
+- 极低的系统开销
+- 高效处理I/O密集型任务
+- 编程模型简单，易于理解
+
+缺点：
+- 不适合CPU密集型任务
+- 需要特定的库支持（如asyncio）
+- 对于长时间运行的I/O操作可能会阻塞事件循环
+
+## 2. 并发编程的演变
+> 最新的3.13已经支持编译一个无gil版本的python，后面可能python真的要起飞🛫了
+
+Python并发编程的演变历程：
+1. 早期：单线程同步编程
+2. Python 2.x：引入threading模块，支持多线程
+3. Python 2.6+：引入multiprocessing模块，支持多进程
+4. Python 3.4+：引入asyncio模块，支持协程
+5. Python 3.5+：引入async/await语法，简化协程编写
+
+这种演变反映了开发者对更高效、更易用的并发编程方式的不懈追求。
+
+## 3. 最简单的基本示例
+
+### 3.1 多进程示例
+
+```python
+import multiprocessing
+import time
+
+def worker(num):
+    print(f"Worker {num} started")
+    time.sleep(2)
+    print(f"Worker {num} finished")
+
+if __name__ == "__main__":
+    processes = []
+    for i in range(5):
+        p = multiprocessing.Process(target=worker, args=(i,))
+        processes.append(p)
+        p.start()
+    
+    for p in processes:
+        p.join()
+    
+    print("All processes completed")
+```
+
+### 3.2 多线程示例
+
+```python
+import threading
+import time
+
+def worker(num):
+    print(f"Thread {num} started")
+    time.sleep(2)
+    print(f"Thread {num} finished")
+
+threads = []
+for i in range(5):
+    t = threading.Thread(target=worker, args=(i,))
+    threads.append(t)
+    t.start()
+
+for t in threads:
+    t.join()
+
+print("All threads completed")
+```
+
+### 3.3 协程示例
+
+```python
+import asyncio
+
+async def worker(num):
+    print(f"Coroutine {num} started")
+    await asyncio.sleep(2)
+    print(f"Coroutine {num} finished")
+
+async def main():
+    tasks = [asyncio.create_task(worker(i)) for i in range(5)]
+    await asyncio.gather(*tasks)
+
+asyncio.run(main())
+print("All coroutines completed")
+```
+
+上述的三个示例，在时间上都是2秒，但是在内存上有所不同，多进程的内存占用最大，多线程次之，协程最小。
+
+## 4. 爬虫中的应用
+
+在爬虫开发中，这三种并发方式各有其适用场景：
+
+- 多进程：适合需要绕过GIL、利用多核CPU的场景，如大规模数据处理。
+- 多线程：适合I/O密集型任务，如同时爬取多个网页。
+- 协程：最适合大量并发I/O操作，如高并发的网络请求。
+
+## 5. 实战对比
+
+我们将使用多进程、多线程和协程三种方式实现同一个爬虫任务：爬取Yahoo Finance的加密货币数据。我们会比较它们的性能表现。
+
+如果不熟悉 Yahoo Finance 的加密货币数据 可以回过头去看 [09_爬虫入门实战2_动态数据提取.md](09_爬虫入门实战2_动态数据提取.md) 章节
+
+
+### 5.1 多进程版本实现
+```python
+# -*- coding: utf-8 -*-
+import csv
+import time
+from typing import Any, Dict, List
+from multiprocessing import Pool, cpu_count
+
+import requests
+from common import SymbolContent, make_req_params_and_headers
+
+HOST = "https://query1.finance.yahoo.com"
+SYMBOL_QUERY_API_URI = "/v1/finance/screener"
+PAGE_SIZE = 100  # 可选配置（25, 50, 100）
+
+
+def parse_symbol_content(quote_item: Dict) -> SymbolContent:
+    """
+    数据提取
+    :param quote_item:
+    :return:
+    """
+    symbol_content = SymbolContent()
+    symbol_content.symbol = quote_item["symbol"]
+    symbol_content.name = quote_item["shortName"]
+    symbol_content.price = quote_item["regularMarketPrice"]["fmt"]
+    symbol_content.change_price = quote_item["regularMarketChange"]["fmt"]
+    symbol_content.change_percent = quote_item["regularMarketChangePercent"]["fmt"]
+    symbol_content.market_price = quote_item["marketCap"]["fmt"]
+    return symbol_content
+
+
+def send_request(page_start: int, page_size: int) -> Dict[str, Any]:
+    """
+    公共的发送请求的函数
+    :param page_start: 分页起始位置
+    :param page_size: 每一页的长度
+    :return:
+    """
+    # print(f"[send_request] page_start:{page_start}")
+    req_url = HOST + SYMBOL_QUERY_API_URI
+    common_params, headers, common_payload_data = make_req_params_and_headers()
+    # 修改分页变动参数
+    common_payload_data["offset"] = page_start
+    common_payload_data["size"] = page_size
+
+    response = requests.post(url=req_url, params=common_params, json=common_payload_data, headers=headers)
+    if response.status_code != 200:
+        raise Exception("发起请求时发生异常，请求发生错误，原因:", response.text)
+    try:
+        response_dict: Dict = response.json()
+        return response_dict
+    except Exception as e:
+        raise e
+
+
+def fetch_currency_data_single(page_start: int) -> List[SymbolContent]:
+    """
+    Fetch currency data for a single page.
+    :param page_start: Page start index.
+    :return: List of SymbolContent for the page.
+    """
+    try:
+        response_dict: Dict = send_request(page_start=page_start, page_size=PAGE_SIZE)
+        symbol_data_list: List[SymbolContent] = [
+            parse_symbol_content(quote) for quote in response_dict["finance"]["result"][0]["quotes"]
+        ]
+        return symbol_data_list
+    except Exception as e:
+        print(f"Error fetching data for page_start={page_start}: {e}")
+        return []
+
+
+def fetch_currency_data_list(max_total_count: int) -> List[SymbolContent]:
+    """
+    Fetch currency data using multiprocessing.
+    :param max_total_count: Maximum total count of currencies.
+    :return: List of all SymbolContent.
+    """
+    with Pool(processes=cpu_count()) as pool:
+        page_starts = list(range(0, max_total_count, PAGE_SIZE))
+        print(f"总共发起: {len(page_starts)} 次网络请求")
+        results = pool.map(fetch_currency_data_single, page_starts)
+
+        # Flatten the list of lists into a single list
+    return [item for sublist in results for item in sublist]
+
+
+def get_max_total_count() -> int:
+    """
+    获取所有币种总数量
+    :return:
+    """
+    print("开始获取最大的币种数量")
+    try:
+        response_dict: Dict = send_request(page_start=0, page_size=PAGE_SIZE)
+        total_num: int = response_dict["finance"]["result"][0]["total"]
+        print(f"获取到 {total_num} 种币种")
+        return total_num
+    except Exception as e:
+        print("错误信息：", e)
+        return 0
+
+
+def save_data_to_csv(save_file_name: str, currency_data_list: List[SymbolContent]) -> None:
+    """
+    保存数据存储到CSV文件中
+    :param save_file_name: 保存的文件名
+    :param currency_data_list:
+    :return:
+    """
+    with open(save_file_name, mode='w', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        # 写入标题行
+        writer.writerow(SymbolContent.get_fields())
+        # 遍历数据列表，并将每个币种的名称写入CSV
+        for symbol in currency_data_list:
+            writer.writerow([symbol.symbol, symbol.name, symbol.price, symbol.change_price, symbol.change_percent,
+                             symbol.market_price])
+
+
+def run_crawler_mp(save_file_name: str) -> None:
+    """
+    爬虫主流程(多进程版本)
+    :param save_file_name:
+    :return:
+    """
+    # step1 获取最大数据总量
+    max_total: int = get_max_total_count()
+    # step2 遍历每一页数据并解析存储到数据容器中
+    data_list: List[SymbolContent] = fetch_currency_data_list(max_total)
+    # step3 将数据容器中的数据保存csv
+    save_data_to_csv(save_file_name, data_list)
+
+
+if __name__ == '__main__':
+    start_time = time.time()
+    save_csv_file_name = f"symbol_data_{int(start_time)}.csv"
+    run_crawler_mp(save_csv_file_name)
+    end_time = time.time()
+    print(f"多进程执行程序耗时: {end_time - start_time} 秒")
+```
+
+### 5.2 多线程版本
+```python
+# -*- coding: utf-8 -*-
+import csv
+import time
+from os import cpu_count
+from typing import Any, Dict, List
+from concurrent.futures import ThreadPoolExecutor
+
+import requests
+from common import SymbolContent, make_req_params_and_headers
+
+HOST = "https://query1.finance.yahoo.com"
+SYMBOL_QUERY_API_URI = "/v1/finance/screener"
+PAGE_SIZE = 100  # 可选配置（25, 50, 100）
+
+
+def parse_symbol_content(quote_item: Dict) -> SymbolContent:
+    """
+    数据提取
+    :param quote_item:
+    :return:
+    """
+    symbol_content = SymbolContent()
+    symbol_content.symbol = quote_item["symbol"]
+    symbol_content.name = quote_item["shortName"]
+    symbol_content.price = quote_item["regularMarketPrice"]["fmt"]
+    symbol_content.change_price = quote_item["regularMarketChange"]["fmt"]
+    symbol_content.change_percent = quote_item["regularMarketChangePercent"]["fmt"]
+    symbol_content.market_price = quote_item["marketCap"]["fmt"]
+    return symbol_content
+
+
+def send_request(page_start: int, page_size: int) -> Dict[str, Any]:
+    """
+    公共的发送请求的函数
+    :param page_start: 分页起始位置
+    :param page_size: 每一页的长度
+    :return:
+    """
+    # print(f"[send_request] page_start:{page_start}")
+    req_url = HOST + SYMBOL_QUERY_API_URI
+    common_params, headers, common_payload_data = make_req_params_and_headers()
+    # 修改分页变动参数
+    common_payload_data["offset"] = page_start
+    common_payload_data["size"] = page_size
+
+    response = requests.post(url=req_url, params=common_params, json=common_payload_data, headers=headers)
+    if response.status_code != 200:
+        raise Exception("发起请求时发生异常，请求发生错误，原因:", response.text)
+    try:
+        response_dict: Dict = response.json()
+        return response_dict
+    except Exception as e:
+        raise e
+
+
+def fetch_currency_data_single(page_start: int) -> List[SymbolContent]:
+    """
+    Fetch currency data for a single page.
+    :param page_start: Page start index.
+    :return: List of SymbolContent for the page.
+    """
+    try:
+        response_dict: Dict = send_request(page_start=page_start, page_size=PAGE_SIZE)
+        return [
+            parse_symbol_content(quote) for quote in response_dict["finance"]["result"][0]["quotes"]
+        ]
+    except Exception as e:
+        print(f"Error fetching data for page_start={page_start}: {e}")
+        return []
+
+
+def fetch_currency_data_list(max_total_count: int) -> List[SymbolContent]:
+    """
+    Fetch currency data using multithreading.
+    :param max_total_count: Maximum total count of currencies.
+    :return: List of all SymbolContent.
+    """
+    with ThreadPoolExecutor(max_workers=cpu_count() * 2) as executor:
+        page_starts = list(range(0, max_total_count, PAGE_SIZE))
+        print(f"总共发起: {len(page_starts)} 次网络请求")
+
+        # 使用 map 方法
+        results = list(executor.map(fetch_currency_data_single, page_starts))
+
+        # 扁平化结果列表
+        return [item for sublist in results for item in sublist]
+
+
+def get_max_total_count() -> int:
+    """
+    获取所有币种总数量
+    :return:
+    """
+    print("开始获取最大的币种数量")
+    try:
+        response_dict: Dict = send_request(page_start=0, page_size=PAGE_SIZE)
+        total_num: int = response_dict["finance"]["result"][0]["total"]
+        print(f"获取到 {total_num} 种币种")
+        return total_num
+    except Exception as e:
+        print("错误信息：", e)
+        return 0
+
+
+def save_data_to_csv(save_file_name: str, currency_data_list: List[SymbolContent]) -> None:
+    """
+    保存数据存储到CSV文件中
+    :param save_file_name: 保存的文件名
+    :param currency_data_list:
+    :return:
+    """
+    with open(save_file_name, mode='w', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        # 写入标题行
+        writer.writerow(SymbolContent.get_fields())
+        # 遍历数据列表，并将每个币种的名称写入CSV
+        for symbol in currency_data_list:
+            writer.writerow([symbol.symbol, symbol.name, symbol.price, symbol.change_price, symbol.change_percent,
+                             symbol.market_price])
+
+
+def run_crawler_mt(save_file_name: str) -> None:
+    """
+    爬虫主流程(多线程版本)
+    :param save_file_name:
+    :return:
+    """
+    # step1 获取最大数据总量
+    max_total: int = get_max_total_count()
+    # step2 遍历每一页数据并解析存储到数据容器中
+    data_list: List[SymbolContent] = fetch_currency_data_list(max_total)
+    # step3 将数据容器中的数据保存csv
+    save_data_to_csv(save_file_name, data_list)
+
+
+if __name__ == '__main__':
+    start_time = time.time()
+    save_csv_file_name = f"symbol_data_{int(start_time)}.csv"
+    run_crawler_mt(save_csv_file_name)
+    end_time = time.time()
+    print(f"多线程执行程序耗时: {end_time - start_time} 秒")
+```
+
+### 5.3 协程版本实现
+```python
+# -*- coding: utf-8 -*-
+import asyncio
+import csv
+import time
+from typing import Any, Dict, List
+
+import aiofiles
+import httpx
+from common import SymbolContent, make_req_params_and_headers
+
+HOST = "https://query1.finance.yahoo.com"
+SYMBOL_QUERY_API_URI = "/v1/finance/screener"
+PAGE_SIZE = 100  # 可选配置（25, 50, 100）
+
+
+def parse_symbol_content(quote_item: Dict) -> SymbolContent:
+    """
+    数据提取
+    :param quote_item:
+    :return:
+    """
+    symbol_content = SymbolContent()
+    symbol_content.symbol = quote_item["symbol"]
+    symbol_content.name = quote_item["shortName"]
+    symbol_content.price = quote_item["regularMarketPrice"]["fmt"]
+    symbol_content.change_price = quote_item["regularMarketChange"]["fmt"]
+    symbol_content.change_percent = quote_item["regularMarketChangePercent"]["fmt"]
+    symbol_content.market_price = quote_item["marketCap"]["fmt"]
+    return symbol_content
+
+
+async def send_request(page_start: int, page_size: int) -> Dict[str, Any]:
+    """
+    公共的发送请求的函数
+    :param page_start: 分页起始位置
+    :param page_size: 每一页的长度
+    :return:
+    """
+    # print(f"[send_request] page_start:{page_start}")
+    req_url = HOST + SYMBOL_QUERY_API_URI
+    common_params, headers, common_payload_data = make_req_params_and_headers()
+    # 修改分页变动参数
+    common_payload_data["offset"] = page_start
+    common_payload_data["size"] = page_size
+
+    async with httpx.AsyncClient() as client:
+        response = await client.post(url=req_url, params=common_params, json=common_payload_data, headers=headers,
+                                     timeout=30)
+    if response.status_code != 200:
+        raise Exception("发起请求时发生异常，请求发生错误，原因:", response.text)
+    try:
+        response_dict: Dict = response.json()
+        return response_dict
+    except Exception as e:
+        raise e
+
+
+async def fetch_currency_data_single(page_start: int) -> List[SymbolContent]:
+    """
+    Fetch currency data for a single page.
+    :param page_start: Page start index.
+    :return: List of SymbolContent for the page.
+    """
+    try:
+        response_dict: Dict = await send_request(page_start=page_start, page_size=PAGE_SIZE)
+        return [
+            parse_symbol_content(quote) for quote in response_dict["finance"]["result"][0]["quotes"]
+        ]
+    except Exception as e:
+        print(f"Error fetching data for page_start={page_start}: {e}")
+        return []
+
+
+async def fetch_currency_data_list(max_total_count: int) -> List[SymbolContent]:
+    """
+    Fetch currency data using asyncio.
+    :param max_total_count: Maximum total count of currencies.
+    :return: List of all SymbolContent.
+    """
+    page_starts = list(range(0, max_total_count, PAGE_SIZE))
+    print(f"总共发起: {len(page_starts)} 次网络请求")
+
+    tasks = [fetch_currency_data_single(page_start) for page_start in page_starts]
+    results = await asyncio.gather(*tasks)
+
+    # 扁平化结果列表
+    return [item for sublist in results for item in sublist]
+
+
+async def get_max_total_count() -> int:
+    """
+    获取所有币种总数量
+    :return:
+    """
+    print("开始获取最大的币种数量")
+    try:
+        response_dict: Dict = await send_request(page_start=0, page_size=PAGE_SIZE)
+        total_num: int = response_dict["finance"]["result"][0]["total"]
+        print(f"获取到 {total_num} 种币种")
+        return total_num
+    except Exception as e:
+        print("错误信息：", e)
+        return 0
+
+
+async def save_data_to_csv(save_file_name: str, currency_data_list: List[SymbolContent]) -> None:
+    """
+    保存数据存储到CSV文件中
+    :param save_file_name: 保存的文件名
+    :param currency_data_list:
+    :return:
+    """
+    async with aiofiles.open(save_file_name, mode='w', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        # 写入标题行
+        await file.write(','.join(SymbolContent.get_fields()) + '\n')
+        # 遍历数据列表，并将每个币种的名称写入CSV
+        for symbol in currency_data_list:
+            await file.write(f"{symbol.symbol},{symbol.name},{symbol.price},{symbol.change_price},{symbol.change_percent},{symbol.market_price}\n")
+
+
+async def run_crawler_async(save_file_name: str) -> None:
+    """
+    爬虫主流程(异步并发版本)
+    :param save_file_name:
+    :return:
+    """
+    # step1 获取最大数据总量
+    max_total: int = await get_max_total_count()
+    # step2 遍历每一页数据并解析存储到数据容器中
+    data_list: List[SymbolContent] = await fetch_currency_data_list(max_total)
+    # step3 将数据容器中的数据保存csv
+    await save_data_to_csv(save_file_name, data_list)
+
+async def main():
+    """
+    主函数
+    :return:
+    """
+    start_time = time.time()
+    save_csv_file_name = f"symbol_data_{int(start_time)}.csv"
+    await run_crawler_async(save_csv_file_name)
+    end_time = time.time()
+    print(f"asyncio调度协程执行程序耗时: {end_time - start_time} 秒")
+
+
+if __name__ == '__main__':
+    asyncio.run(main())
+
+
+```
+
+> 上述源代码路径：[11_爬虫入门实战4_高效率的爬虫实现](https://github.com/NanmiCoder/CrawlerTutorial/tree/main/%E6%BA%90%E4%BB%A3%E7%A0%81/%E7%88%AC%E8%99%AB%E5%85%A5%E9%97%A8/11_%E7%88%AC%E8%99%AB%E5%85%A5%E9%97%A8%E5%AE%9E%E6%88%984_%E9%AB%98%E6%95%88%E7%8E%87%E7%9A%84%E7%88%AC%E8%99%AB%E5%AE%9E%E7%8E%B0)
+
+### 5.4 执行耗时
+
+
+#### 5.4.1 多线程执行耗时
+开始获取最大的币种数量 <br>
+获取到 9967 种币种 <br>
+总共发起: 100 次网络请求 <br>
+多线程执行程序耗时: 7.992658853530884 秒 <br>
+
+
+#### 5.4.2 多进程执行耗时
+开始获取最大的币种数量 <br>
+获取到 9967 种币种 <br>
+总共发起: 100 次网络请求 <br>
+多进程执行程序耗时: 17.447596073150635 秒 <br>
+
+
+#### 5.4.3 协程执行耗时
+开始获取最大的币种数量 <br>
+获取到 9967 种币种 <br>
+总共发起: 100 次网络请求 <br>
+asyncio调度协程执行程序耗时: 4.690491199493408 秒 <br>
+
+
+## 6. 上述代码总结
+> 我比较喜欢使用异步协程，因为编程风格很像同步代码，并且还能带来高效率的表现。
+
+### 6.1. 多线程（run_crawler_multi_thread.py）
+适用场景：适合I/O密集型任务，如网络请求，因为线程在等待I/O操作（如网络响应）时可以让出CPU给其他线程。
+实现逻辑：
+- 使用ThreadPoolExecutor来管理线程池。
+- 将任务（获取单页货币数据）分配给线程池中的线程执行。
+- 使用executor.map来并行处理多个页面的数据获取，这个方法会自动处理任务的分配和结果的收集。
+
+### 6.2. 多进程（run_crawler_multi_process.py）
+适用场景：适合CPU密集型任务，但在这个案例中，它用于处理I/O密集型任务，这通常不是最佳选择，因为进程间通信成本较高。
+实现逻辑：
+- 使用multiprocessing.Pool来创建进程池。
+- 类似于多线程，使用pool.map来并行处理多个页面的数据获取。
+- 进程间的数据传递通过序列化和反序列化实现，这可能会引入额外的开销。
+
+### 6.3. 协程（run_crawler_multi_coroutine.py）
+适用场景：非常适合I/O密集型任务，如网络请求。协程通过事件循环和非阻塞I/O操作提高程序的执行效率。
+实现逻辑：
+- 使用asyncio库来管理协程。
+- 使用httpx.AsyncClient进行异步HTTP请求，这允许在等待网络响应时不阻塞程序的其他部分。
+- 使用asyncio.gather来并发执行多个协程，这些协程分别处理不同页面的数据获取。
+
+总结
+- 多线程和多进程都可以处理并发任务，但在处理大量的网络I/O操作时，它们可能不如协程高效。
+- 协程提供了最高的效率和最佳的资源利用率，特别是在处理网络I/O密集型任务时。
+- 在选择并发策略时，应考虑任务的类型（CPU密集型还是I/O密集型）、系统的资源（如CPU核心数）以及程序的复杂性。
\ No newline at end of file
diff --git "a/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/1_multi_process_demo.py" "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/1_multi_process_demo.py"
new file mode 100644
index 0000000..7dc54c3
--- /dev/null
+++ "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/1_multi_process_demo.py"
@@ -0,0 +1,22 @@
+# -*- coding: utf-8 -*-
+import multiprocessing
+import time
+
+
+def worker(num):
+    print(f"Worker {num} started")
+    time.sleep(2)
+    print(f"Worker {num} finished")
+
+
+if __name__ == "__main__":
+    processes = []
+    for i in range(5):
+        p = multiprocessing.Process(target=worker, args=(i,))
+        processes.append(p)
+        p.start()
+
+    for p in processes:
+        p.join()
+
+    print("All processes completed")
\ No newline at end of file
diff --git "a/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/2_multi_thread_demo.py" "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/2_multi_thread_demo.py"
new file mode 100644
index 0000000..10104b7
--- /dev/null
+++ "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/2_multi_thread_demo.py"
@@ -0,0 +1,18 @@
+import threading
+import time
+
+def worker(num):
+    print(f"Thread {num} started")
+    time.sleep(2)
+    print(f"Thread {num} finished")
+
+threads = []
+for i in range(5):
+    t = threading.Thread(target=worker, args=(i,))
+    threads.append(t)
+    t.start()
+
+for t in threads:
+    t.join()
+
+print("All threads completed")
\ No newline at end of file
diff --git "a/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/3_multi_coroutine_demo.py" "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/3_multi_coroutine_demo.py"
new file mode 100644
index 0000000..ad6ec01
--- /dev/null
+++ "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/3_multi_coroutine_demo.py"
@@ -0,0 +1,14 @@
+# -*- coding: utf-8 -*-
+import asyncio
+
+async def worker(num):
+    print(f"Coroutine {num} started")
+    await asyncio.sleep(2)
+    print(f"Coroutine {num} finished")
+
+async def main():
+    tasks = [asyncio.create_task(worker(i)) for i in range(5)]
+    await asyncio.gather(*tasks)
+
+asyncio.run(main())
+print("All coroutines completed")
\ No newline at end of file
diff --git "a/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/common.py" "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/common.py"
new file mode 100644
index 0000000..dda1f12
--- /dev/null
+++ "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/common.py"
@@ -0,0 +1,73 @@
+# -*- coding: utf-8 -*-
+# @Author  : relakkes@gmail.com
+# @Name    : 程序员阿江-Relakkes
+# @Time    : 2024/4/7 20:54
+# @Desc    : 存放一些公共的函数
+from typing import List
+
+
+class SymbolContent:
+    symbol: str = ""
+    name: str = ""
+    price: str = ""  # 价格（盘中）
+    change_price: str = ""  # 跌涨价格
+    change_percent: str = ""  # 跌涨幅
+    market_price: str = ""  # 市值
+
+    @classmethod
+    def get_fields(cls) -> List[str]:
+        return [key for key in cls.__dict__.keys() if not key.startswith('__') and key != "get_fields"]
+
+    def __str__(self):
+        return f"""
+Symbol: {self.symbol}
+Name: {self.name}
+Price: {self.price}
+Change Price: {self.change_price}        
+Change Percent: {self.change_percent}        
+Market Price: {self.market_price}        
+"""
+
+
+def make_req_params_and_headers():
+    headers = {
+        # cookies是必须的,并且和common_params的crumb参数绑定的。
+        'cookie': 'GUC=AQEBCAFmDYVmOUIdcARM&s=AQAAANxlE2ny&g=Zgw0yA; A1=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI; A3=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI; axids=gam=y-lf5u4KlE2uJWDQYbXyUTkKMC2GVH7OUj~A&dv360=eS1XSElPM3l4RTJ1SHVVV3hNZVBDeG9aTDlDYXdaQ1dPNX5B&ydsp=y-_wiZU4RE2uIAxUbGalyjvJCoR6Le9iVT~A&tbla=y-gt2Wyc1E2uI4nvAYanhnPTMrhn4c3edZ~A; tbla_id=fde33964-c427-4b9c-b849-90a304938e21-tuctb84a272; cmp=t=1712472060&j=0&u=1YNN; gpp=DBABBg~BVoIgACA.QA; gpp_sid=8; A1S=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI&j=WORLD',
+        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36',
+    }
+    common_params = {
+        'crumb': 'UllRf10isbP',
+        'lang': 'en-US',
+        'region': 'US',
+        'formatted': 'true',
+        'corsDomain': 'finance.yahoo.com',
+    }
+    common_payload_data = {
+        'offset': 0,  # 这个是分页其实位置
+        'size': 25,  # 这个是分页数量
+        'sortType': 'DESC',
+        'sortField': 'intradaymarketcap',
+        'quoteType': 'CRYPTOCURRENCY',
+        'query': {
+            'operator': 'and',
+            'operands': [
+                {
+                    'operator': 'eq',
+                    'operands': [
+                        'currency',
+                        'USD',
+                    ],
+                },
+                {
+                    'operator': 'eq',
+                    'operands': [
+                        'exchange',
+                        'CCC',
+                    ],
+                },
+            ],
+        },
+        'userId': '',
+        'userIdType': 'guid',
+    }
+    return common_params, headers, common_payload_data
diff --git "a/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_coroutine.py" "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_coroutine.py"
new file mode 100644
index 0000000..356e960
--- /dev/null
+++ "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_coroutine.py"
@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+import asyncio
+import csv
+import time
+from typing import Any, Dict, List
+
+import aiofiles
+import httpx
+from common import SymbolContent, make_req_params_and_headers
+
+HOST = "https://query1.finance.yahoo.com"
+SYMBOL_QUERY_API_URI = "/v1/finance/screener"
+PAGE_SIZE = 100  # 可选配置（25, 50, 100）
+
+
+def parse_symbol_content(quote_item: Dict) -> SymbolContent:
+    """
+    数据提取
+    :param quote_item:
+    :return:
+    """
+    symbol_content = SymbolContent()
+    symbol_content.symbol = quote_item["symbol"]
+    symbol_content.name = quote_item["shortName"]
+    symbol_content.price = quote_item["regularMarketPrice"]["fmt"]
+    symbol_content.change_price = quote_item["regularMarketChange"]["fmt"]
+    symbol_content.change_percent = quote_item["regularMarketChangePercent"]["fmt"]
+    symbol_content.market_price = quote_item["marketCap"]["fmt"]
+    return symbol_content
+
+
+async def send_request(page_start: int, page_size: int) -> Dict[str, Any]:
+    """
+    公共的发送请求的函数
+    :param page_start: 分页起始位置
+    :param page_size: 每一页的长度
+    :return:
+    """
+    # print(f"[send_request] page_start:{page_start}")
+    req_url = HOST + SYMBOL_QUERY_API_URI
+    common_params, headers, common_payload_data = make_req_params_and_headers()
+    # 修改分页变动参数
+    common_payload_data["offset"] = page_start
+    common_payload_data["size"] = page_size
+
+    async with httpx.AsyncClient() as client:
+        response = await client.post(url=req_url, params=common_params, json=common_payload_data, headers=headers,
+                                     timeout=30)
+    if response.status_code != 200:
+        raise Exception("发起请求时发生异常，请求发生错误，原因:", response.text)
+    try:
+        response_dict: Dict = response.json()
+        return response_dict
+    except Exception as e:
+        raise e
+
+
+async def fetch_currency_data_single(page_start: int) -> List[SymbolContent]:
+    """
+    Fetch currency data for a single page.
+    :param page_start: Page start index.
+    :return: List of SymbolContent for the page.
+    """
+    try:
+        response_dict: Dict = await send_request(page_start=page_start, page_size=PAGE_SIZE)
+        return [
+            parse_symbol_content(quote) for quote in response_dict["finance"]["result"][0]["quotes"]
+        ]
+    except Exception as e:
+        print(f"Error fetching data for page_start={page_start}: {e}")
+        return []
+
+
+async def fetch_currency_data_list(max_total_count: int) -> List[SymbolContent]:
+    """
+    Fetch currency data using asyncio.
+    :param max_total_count: Maximum total count of currencies.
+    :return: List of all SymbolContent.
+    """
+    page_starts = list(range(0, max_total_count, PAGE_SIZE))
+    print(f"总共发起: {len(page_starts)} 次网络请求")
+
+    tasks = [fetch_currency_data_single(page_start) for page_start in page_starts]
+    results = await asyncio.gather(*tasks)
+
+    # 扁平化结果列表
+    return [item for sublist in results for item in sublist]
+
+
+async def get_max_total_count() -> int:
+    """
+    获取所有币种总数量
+    :return:
+    """
+    print("开始获取最大的币种数量")
+    try:
+        response_dict: Dict = await send_request(page_start=0, page_size=PAGE_SIZE)
+        total_num: int = response_dict["finance"]["result"][0]["total"]
+        print(f"获取到 {total_num} 种币种")
+        return total_num
+    except Exception as e:
+        print("错误信息：", e)
+        return 0
+
+
+async def save_data_to_csv(save_file_name: str, currency_data_list: List[SymbolContent]) -> None:
+    """
+    保存数据存储到CSV文件中
+    :param save_file_name: 保存的文件名
+    :param currency_data_list:
+    :return:
+    """
+    async with aiofiles.open(save_file_name, mode='w', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        # 写入标题行
+        await file.write(','.join(SymbolContent.get_fields()) + '\n')
+        # 遍历数据列表，并将每个币种的名称写入CSV
+        for symbol in currency_data_list:
+            await file.write(f"{symbol.symbol},{symbol.name},{symbol.price},{symbol.change_price},{symbol.change_percent},{symbol.market_price}\n")
+
+
+async def run_crawler_async(save_file_name: str) -> None:
+    """
+    爬虫主流程(异步并发版本)
+    :param save_file_name:
+    :return:
+    """
+    # step1 获取最大数据总量
+    max_total: int = await get_max_total_count()
+    # step2 遍历每一页数据并解析存储到数据容器中
+    data_list: List[SymbolContent] = await fetch_currency_data_list(max_total)
+    # step3 将数据容器中的数据保存csv
+    await save_data_to_csv(save_file_name, data_list)
+
+async def main():
+    """
+    主函数
+    :return:
+    """
+    start_time = time.time()
+    save_csv_file_name = f"symbol_data_{int(start_time)}.csv"
+    await run_crawler_async(save_csv_file_name)
+    end_time = time.time()
+    print(f"asyncio调度协程执行程序耗时: {end_time - start_time} 秒")
+
+
+if __name__ == '__main__':
+    asyncio.run(main())
+
diff --git "a/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_process.py" "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_process.py"
new file mode 100644
index 0000000..f3b736d
--- /dev/null
+++ "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_process.py"
@@ -0,0 +1,139 @@
+# -*- coding: utf-8 -*-
+import csv
+import time
+from typing import Any, Dict, List
+from multiprocessing import Pool, cpu_count
+
+import requests
+from common import SymbolContent, make_req_params_and_headers
+
+HOST = "https://query1.finance.yahoo.com"
+SYMBOL_QUERY_API_URI = "/v1/finance/screener"
+PAGE_SIZE = 100  # 可选配置（25, 50, 100）
+
+
+def parse_symbol_content(quote_item: Dict) -> SymbolContent:
+    """
+    数据提取
+    :param quote_item:
+    :return:
+    """
+    symbol_content = SymbolContent()
+    symbol_content.symbol = quote_item["symbol"]
+    symbol_content.name = quote_item["shortName"]
+    symbol_content.price = quote_item["regularMarketPrice"]["fmt"]
+    symbol_content.change_price = quote_item["regularMarketChange"]["fmt"]
+    symbol_content.change_percent = quote_item["regularMarketChangePercent"]["fmt"]
+    symbol_content.market_price = quote_item["marketCap"]["fmt"]
+    return symbol_content
+
+
+def send_request(page_start: int, page_size: int) -> Dict[str, Any]:
+    """
+    公共的发送请求的函数
+    :param page_start: 分页起始位置
+    :param page_size: 每一页的长度
+    :return:
+    """
+    # print(f"[send_request] page_start:{page_start}")
+    req_url = HOST + SYMBOL_QUERY_API_URI
+    common_params, headers, common_payload_data = make_req_params_and_headers()
+    # 修改分页变动参数
+    common_payload_data["offset"] = page_start
+    common_payload_data["size"] = page_size
+
+    response = requests.post(url=req_url, params=common_params, json=common_payload_data, headers=headers)
+    if response.status_code != 200:
+        raise Exception("发起请求时发生异常，请求发生错误，原因:", response.text)
+    try:
+        response_dict: Dict = response.json()
+        return response_dict
+    except Exception as e:
+        raise e
+
+
+def fetch_currency_data_single(page_start: int) -> List[SymbolContent]:
+    """
+    Fetch currency data for a single page.
+    :param page_start: Page start index.
+    :return: List of SymbolContent for the page.
+    """
+    try:
+        response_dict: Dict = send_request(page_start=page_start, page_size=PAGE_SIZE)
+        symbol_data_list: List[SymbolContent] = [
+            parse_symbol_content(quote) for quote in response_dict["finance"]["result"][0]["quotes"]
+        ]
+        return symbol_data_list
+    except Exception as e:
+        print(f"Error fetching data for page_start={page_start}: {e}")
+        return []
+
+
+def fetch_currency_data_list(max_total_count: int) -> List[SymbolContent]:
+    """
+    Fetch currency data using multiprocessing.
+    :param max_total_count: Maximum total count of currencies.
+    :return: List of all SymbolContent.
+    """
+    with Pool(processes=cpu_count()) as pool:
+        page_starts = list(range(0, max_total_count, PAGE_SIZE))
+        print(f"总共发起: {len(page_starts)} 次网络请求")
+        results = pool.map(fetch_currency_data_single, page_starts)
+
+        # Flatten the list of lists into a single list
+    return [item for sublist in results for item in sublist]
+
+
+def get_max_total_count() -> int:
+    """
+    获取所有币种总数量
+    :return:
+    """
+    print("开始获取最大的币种数量")
+    try:
+        response_dict: Dict = send_request(page_start=0, page_size=PAGE_SIZE)
+        total_num: int = response_dict["finance"]["result"][0]["total"]
+        print(f"获取到 {total_num} 种币种")
+        return total_num
+    except Exception as e:
+        print("错误信息：", e)
+        return 0
+
+
+def save_data_to_csv(save_file_name: str, currency_data_list: List[SymbolContent]) -> None:
+    """
+    保存数据存储到CSV文件中
+    :param save_file_name: 保存的文件名
+    :param currency_data_list:
+    :return:
+    """
+    with open(save_file_name, mode='w', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        # 写入标题行
+        writer.writerow(SymbolContent.get_fields())
+        # 遍历数据列表，并将每个币种的名称写入CSV
+        for symbol in currency_data_list:
+            writer.writerow([symbol.symbol, symbol.name, symbol.price, symbol.change_price, symbol.change_percent,
+                             symbol.market_price])
+
+
+def run_crawler_mp(save_file_name: str) -> None:
+    """
+    爬虫主流程(多进程版本)
+    :param save_file_name:
+    :return:
+    """
+    # step1 获取最大数据总量
+    max_total: int = get_max_total_count()
+    # step2 遍历每一页数据并解析存储到数据容器中
+    data_list: List[SymbolContent] = fetch_currency_data_list(max_total)
+    # step3 将数据容器中的数据保存csv
+    save_data_to_csv(save_file_name, data_list)
+
+
+if __name__ == '__main__':
+    start_time = time.time()
+    save_csv_file_name = f"symbol_data_{int(start_time)}.csv"
+    run_crawler_mp(save_csv_file_name)
+    end_time = time.time()
+    print(f"多进程执行程序耗时: {end_time - start_time} 秒")
\ No newline at end of file
diff --git "a/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_thread.py" "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_thread.py"
new file mode 100644
index 0000000..529e0af
--- /dev/null
+++ "b/\346\272\220\344\273\243\347\240\201/\347\210\254\350\231\253\345\205\245\351\227\250/11_\347\210\254\350\231\253\345\205\245\351\227\250\345\256\236\346\210\2304_\351\253\230\346\225\210\347\216\207\347\232\204\347\210\254\350\231\253\345\256\236\347\216\260/run_crawler_multi_thread.py"
@@ -0,0 +1,141 @@
+# -*- coding: utf-8 -*-
+import csv
+import time
+from os import cpu_count
+from typing import Any, Dict, List
+from concurrent.futures import ThreadPoolExecutor
+
+import requests
+from common import SymbolContent, make_req_params_and_headers
+
+HOST = "https://query1.finance.yahoo.com"
+SYMBOL_QUERY_API_URI = "/v1/finance/screener"
+PAGE_SIZE = 100  # 可选配置（25, 50, 100）
+
+
+def parse_symbol_content(quote_item: Dict) -> SymbolContent:
+    """
+    数据提取
+    :param quote_item:
+    :return:
+    """
+    symbol_content = SymbolContent()
+    symbol_content.symbol = quote_item["symbol"]
+    symbol_content.name = quote_item["shortName"]
+    symbol_content.price = quote_item["regularMarketPrice"]["fmt"]
+    symbol_content.change_price = quote_item["regularMarketChange"]["fmt"]
+    symbol_content.change_percent = quote_item["regularMarketChangePercent"]["fmt"]
+    symbol_content.market_price = quote_item["marketCap"]["fmt"]
+    return symbol_content
+
+
+def send_request(page_start: int, page_size: int) -> Dict[str, Any]:
+    """
+    公共的发送请求的函数
+    :param page_start: 分页起始位置
+    :param page_size: 每一页的长度
+    :return:
+    """
+    # print(f"[send_request] page_start:{page_start}")
+    req_url = HOST + SYMBOL_QUERY_API_URI
+    common_params, headers, common_payload_data = make_req_params_and_headers()
+    # 修改分页变动参数
+    common_payload_data["offset"] = page_start
+    common_payload_data["size"] = page_size
+
+    response = requests.post(url=req_url, params=common_params, json=common_payload_data, headers=headers)
+    if response.status_code != 200:
+        raise Exception("发起请求时发生异常，请求发生错误，原因:", response.text)
+    try:
+        response_dict: Dict = response.json()
+        return response_dict
+    except Exception as e:
+        raise e
+
+
+def fetch_currency_data_single(page_start: int) -> List[SymbolContent]:
+    """
+    Fetch currency data for a single page.
+    :param page_start: Page start index.
+    :return: List of SymbolContent for the page.
+    """
+    try:
+        response_dict: Dict = send_request(page_start=page_start, page_size=PAGE_SIZE)
+        return [
+            parse_symbol_content(quote) for quote in response_dict["finance"]["result"][0]["quotes"]
+        ]
+    except Exception as e:
+        print(f"Error fetching data for page_start={page_start}: {e}")
+        return []
+
+
+def fetch_currency_data_list(max_total_count: int) -> List[SymbolContent]:
+    """
+    Fetch currency data using multithreading.
+    :param max_total_count: Maximum total count of currencies.
+    :return: List of all SymbolContent.
+    """
+    with ThreadPoolExecutor(max_workers=cpu_count() * 2) as executor:
+        page_starts = list(range(0, max_total_count, PAGE_SIZE))
+        print(f"总共发起: {len(page_starts)} 次网络请求")
+
+        # 使用 map 方法
+        results = list(executor.map(fetch_currency_data_single, page_starts))
+
+        # 扁平化结果列表
+        return [item for sublist in results for item in sublist]
+
+
+def get_max_total_count() -> int:
+    """
+    获取所有币种总数量
+    :return:
+    """
+    print("开始获取最大的币种数量")
+    try:
+        response_dict: Dict = send_request(page_start=0, page_size=PAGE_SIZE)
+        total_num: int = response_dict["finance"]["result"][0]["total"]
+        print(f"获取到 {total_num} 种币种")
+        return total_num
+    except Exception as e:
+        print("错误信息：", e)
+        return 0
+
+
+def save_data_to_csv(save_file_name: str, currency_data_list: List[SymbolContent]) -> None:
+    """
+    保存数据存储到CSV文件中
+    :param save_file_name: 保存的文件名
+    :param currency_data_list:
+    :return:
+    """
+    with open(save_file_name, mode='w', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        # 写入标题行
+        writer.writerow(SymbolContent.get_fields())
+        # 遍历数据列表，并将每个币种的名称写入CSV
+        for symbol in currency_data_list:
+            writer.writerow([symbol.symbol, symbol.name, symbol.price, symbol.change_price, symbol.change_percent,
+                             symbol.market_price])
+
+
+def run_crawler_mt(save_file_name: str) -> None:
+    """
+    爬虫主流程(多线程版本)
+    :param save_file_name:
+    :return:
+    """
+    # step1 获取最大数据总量
+    max_total: int = get_max_total_count()
+    # step2 遍历每一页数据并解析存储到数据容器中
+    data_list: List[SymbolContent] = fetch_currency_data_list(max_total)
+    # step3 将数据容器中的数据保存csv
+    save_data_to_csv(save_file_name, data_list)
+
+
+if __name__ == '__main__':
+    start_time = time.time()
+    save_csv_file_name = f"symbol_data_{int(start_time)}.csv"
+    run_crawler_mt(save_csv_file_name)
+    end_time = time.time()
+    print(f"多线程执行程序耗时: {end_time - start_time} 秒")
\ No newline at end of file