Asynchronous Coroutine Development Guide: Building a High-Performance Recommendation System

With the rapid development of the Internet and mobile Internet, the amount of data has exploded. How to process data efficiently has become an important issue faced by the R&D teams of major companies. Recommendation systems are one of the key application areas and are widely used in many enterprises. Asynchronous coroutines are an important technology for achieving high-performance data processing in high-concurrency scenarios. This article will introduce how to use asynchronous coroutines to build a high-performance recommendation system and provide specific code examples.

1. What is an asynchronous coroutine?

Asynchronous coroutine is a very efficient concurrent programming model. It was originally proposed and implemented by the Python language. It has been borrowed and developed by many languages, such as goroutine in the Go language, SwiftNIO in Swift, etc. Asynchronous coroutines support highly concurrent asynchronous I/O operations by switching at the coroutine level.

Compared with multi-threading, asynchronous coroutines have the following advantages:

1. More efficient: Asynchronous coroutines can implement a very lightweight thread model with very little switching overhead.
2. More flexible: Switching between coroutines does not need to enter the kernel, but is controlled by the program, so the number and scheduling methods of coroutines can be more flexibly controlled.
3. More easy to use: Compared with the multi-threaded lock mechanism, asynchronous coroutines can avoid multi-threading problems such as locks through cooperative scheduling, making the code simpler and easier to use.

2. Asynchronous coroutine application scenarios in recommendation systems

The recommendation system needs to process a large amount of data during the implementation process, such as user behavior logs, item attribute information, etc., and asynchronous Coroutines can achieve high-performance data processing. Specifically, the following application scenarios in the recommendation system are suitable for the use of asynchronous coroutines:

1. User interest feature extraction: Asynchronous reading and processing of user behavior logs are implemented through asynchronous coroutines to extract user interest features , to support personalized recommendations.
2. Item information aggregation: Asynchronous reading and processing of item attribute information is realized through asynchronous coroutines, and various information is aggregated to support comprehensive recommendation of items.
3. Recommendation result sorting: Quick sorting and filtering of recommendation results are implemented through asynchronous coroutines to ensure high throughput and low latency of the recommendation system.

3. Asynchronous Coroutine Development Guide

The following will introduce the development guide for asynchronous coroutines from three aspects: coroutine development process, scheduling mechanism and asynchronous I/O operations.

1. Coroutine development process

In asynchronous coroutines, you need to use a coroutine library to realize the creation, switching and scheduling of coroutines. Currently, the more popular coroutine libraries include asyncio in Python, goroutine in Go, and SwiftNIO in Swift.

Take asyncio in Python as an example to implement a simple asynchronous coroutine program:

import asyncio

async def foo():
    await asyncio.sleep(1)
    print('Hello World!')

loop = asyncio.get_event_loop()
loop.run_until_complete(foo())

In the above program, asyncio.sleep(1) means to let the current coroutine The process sleeps for 1 second to simulate asynchronous I/O operations. The function declared by async def represents an asynchronous function. Use loop.run_until_complete() in the program to run the coroutine, and the output result is Hello World!.

2. Scheduling mechanism

In asynchronous coroutines, the scheduling of coroutines is a very important part. Through collaborative scheduling of asynchronous coroutines, the number and scheduling order of coroutines can be more flexibly controlled to achieve optimal performance.

In asyncio, use the asyncio.gather() method to execute multiple coroutines, for example:

import asyncio

async def foo():
    await asyncio.sleep(1)
    print('foo')

async def bar():
    await asyncio.sleep(2)
    print('bar')

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(foo(), bar()))

In the above program, asyncio.gather( ) can execute multiple coroutines at the same time, and the output results are foo and bar. The time lengths of the two coroutines here are 1 second and 2 seconds respectively, so the output order is foo and bar.

3. Asynchronous I/O operations

In the recommendation system, asynchronous I/O operations need to be used to process a large amount of user behavior logs, item attribute information and other data. Using asynchronous I/O operations in asynchronous coroutines can greatly improve the efficiency of data reading and processing.

In asyncio, use the asyncio.open() method to read files asynchronously, for example:

import asyncio

async def read_file():
    async with aiofiles.open('data.log', 'r') as f:
        async for line in f:
            print(line.strip())

loop = asyncio.get_event_loop()
loop.run_until_complete(read_file())

In the above program, use async with aiofiles. open() to open the file asynchronously, use async for line in f to asynchronously read each line of data in the file. Use loop.run_until_complete() in the program to run the coroutine.

4. Specific code examples

The following is a detailed introduction to the implementation method of asynchronous coroutines in the recommended system.

1. User interest feature extraction

In the recommendation system, user interest feature extraction is a very critical link. User behavior logs are one of the important data in recommendation systems, so asynchronous I/O needs to be used to read and process behavior logs to extract user interest features.

import asyncio
import json

async def extract_feature(data):
    result = {}
    for item in data:
        uid = item.get('uid')
        if uid not in result:
            result[uid] = {'click': 0, 'expose': 0}
        if item.get('type') == 'click':
            result[uid]['click'] += 1
        elif item.get('type') == 'expose':
            result[uid]['expose'] += 1
    return result

async def read_file():
    async with aiofiles.open('data.log', 'r') as f:
        data = []
        async for line in f:
            data.append(json.loads(line))
            if len(data) >= 1000:
                result = await extract_feature(data)
                print(result)
                data = []

        if len(data) > 0:
            result = await extract_feature(data)
            print(result)

loop = asyncio.get_event_loop()
loop.run_until_complete(read_file())

上述程序中，extract_feature() 函数用于从用户行为日志中提取用户兴趣特征，read_file() 函数读取用户行为日志，并调用 extract_feature() 函数进行用户特征提取。在程序中，使用 if len(data) >= 1000 判断每次读取到的数据是否满足处理的条件。

2. 物品信息聚合

在推荐系统中，物品信息的聚合是支持物品的综合推荐的必要环节。物品属性信息是推荐系统中的重要数据之一，因此需要使用异步 I/O 来进行读取和处理。

import asyncio
import json

async def aggregate_info(data):
    result = {}
    for item in data:
        key = item.get('key')
        if key not in result:
            result[key] = []
        result[key].append(item.get('value'))
    return result

async def read_file():
    async with aiofiles.open('data.log', 'r') as f:
        data = []
        async for line in f:
            data.append(json.loads(line))
            if len(data) >= 1000:
                result = await aggregate_info(data)
                print(result)
                data = []

        if len(data) > 0:
            result = await aggregate_info(data)
            print(result)

loop = asyncio.get_event_loop()
loop.run_until_complete(read_file())

上述程序中，aggregate_info() 函数用于从物品属性信息中聚合物品信息，read_file() 函数读取物品属性信息，并调用 aggregate_info() 函数进行信息聚合。在程序中，使用 if len(data) >= 1000 判断每次读取到的数据是否满足处理的条件。

3. 推荐结果排序

在推荐系统中，推荐结果的排序是支持高吞吐量和低延迟的关键环节。通过异步协程进行推荐结果的排序和过滤，可以大大提高推荐系统的性能表现。

import asyncio

async def sort_and_filter(data):
    data.sort(reverse=True)
    result = []
    for item in data:
        if item[1] > 0:
            result.append(item)
    return result[:10]

async def recommend():
    data = [(1, 2), (3, 4), (2, 5), (7, 0), (5, -1), (6, 3), (9, 8)]
    result = await sort_and_filter(data)
    print(result)

loop = asyncio.get_event_loop()
loop.run_until_complete(recommend())

上述程序中，sort_and_filter() 函数用于对推荐结果进行排序和过滤，并只返回前 10 个结果。recommend() 函数用于模拟推荐结果的生成，调用 sort_and_filter() 函数进行结果排序和过滤。在程序中，使用 0 或者 0 以下的值来模拟不需要的结果。

总结

本文介绍了异步协程的基本知识和在推荐系统中的应用，并提供了具体的代码示例。异步协程作为一种高效的并发编程技术，在大数据场景下具有广泛的应用前景。需要注意的是，在实际应用中，需要根据具体的业务需求和技术场景进行针对性的选择和调优，以达到最优的性能表现。

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