云函数SCF与对象存储实现WordCount算法的过程

本篇文章为大家展示了云函数SCF与对象存储实现WordCount算法的过程，内容简明扼要并且容易理解，绝对能使你眼前一亮，通过这篇文章的详细介绍希望你能有所收获。

将尝试通过 MapReduce 模型实现一个简单的 WordCount 算法，区别于传统使用 Hadoop 等大数据框架，使用云函数 SCF 与对象存储 COS 来实现。

MapReduce 在维基百科中的解释如下：

MapReduce 是 Google 提出的一个软件架构，用于大规模数据集（大于 1TB）的并行运算。概念「Map（映射）」和「Reduce（归纳）」，及他们的主要思想，都是从函数式编程语言借来的，还有从矢量编程语言借来的特性。

通过这段描述，我们知道，MapReduce 是面向大数据并行处理的计算模型、框架和平台，在传统学习中，通常会在 Hadoop 等分布式框架下进行 MapReduce 相关工作，随着云计算的逐渐发展，各个云厂商也都先后推出了在线的 MapReduce 业务。

理论基础

在开始之前，我们根据 MapReduce 的要求，先绘制一个简单的流程图:

在这个结构中，我们需要 2 个云函数分别作 Mapper 和 Reducer；以及 3 个对象存储的存储桶，分别作为输入的存储桶、中间临时缓存存储桶和结果存储桶。在实例前，由于我们的函数即将部署在广州区，因此在广州区建立 3 个存储桶：

对象存储1   ap-guangzhou    srcmr对象存储2   ap-guangzhou    middlestagebucket对象存储3   ap-guangzhou    destcmr

为了让整个 Mapper 和 Reducer 逻辑更加清晰，在开始之前先对传统的 WordCount 结构进行改造，使其更加适合云函数，同时合理分配 Mapper 和 Reducer 的工作：

功能实现

编写 Mapper 相关逻辑，代码如下：

# -*- coding: utf8 -*-import datetimefrom qcloud_cos_v5 import CosConfigfrom qcloud_cos_v5 import CosS3Clientfrom qcloud_cos_v5 import CosServiceErrorimport reimport osimport sysimport logginglogging.basicConfig(level=logging.INFO, stream=sys.stdout)logger = logging.getLogger()logger.setLevel(level=logging.INFO)region = u'ap-guangzhou'  # 根据实际情况，修改地域middle_stage_bucket = 'middlestagebucket'  # 根据实际情况，修改bucket名def delete_file_folder(src):    if os.path.isfile(src):        try:            os.remove(src)        except:            pass    elif os.path.isdir(src):        for item in os.listdir(src):            itemsrc = os.path.join(src, item)            delete_file_folder(itemsrc)        try:            os.rmdir(src)        except:            passdef download_file(cos_client, bucket, key, download_path):    logger.info("Get from [%s] to download file [%s]" % (bucket, key))    try:        response = cos_client.get_object(Bucket=bucket, Key=key, )        response['Body'].get_stream_to_file(download_path)    except CosServiceError as e:        print(e.get_error_code())        print(e.get_error_msg())        return -1    return 0def upload_file(cos_client, bucket, key, local_file_path):    logger.info("Start to upload file to cos")    try:        response = cos_client.put_object_from_local_file(            Bucket=bucket,            LocalFilePath=local_file_path,            Key='{}'.format(key))    except CosServiceError as e:        print(e.get_error_code())        print(e.get_error_msg())        return -1    logger.info("Upload data map file [%s] Success" % key)    return 0def do_mapping(cos_client, bucket, key, middle_stage_bucket, middle_file_key):    src_file_path = u'/tmp/' + key.split('/')[-1]    middle_file_path = u'/tmp/' + u'mapped_' + key.split('/')[-1]    download_ret = download_file(cos_client, bucket, key, src_file_path)  # download src file    if download_ret == 0:        inputfile = open(src_file_path, 'r')  # open local /tmp file        mapfile = open(middle_file_path, 'w')  # open a new file write stream        for line in inputfile:            line = re.sub('[^a-zA-Z0-9]', ' ', line)  # replace non-alphabetic/number characters            words = line.split()            for word in words:                mapfile.write('%st%s' % (word, 1))  # count for 1                mapfile.write('n')        inputfile.close()        mapfile.close()        upload_ret = upload_file(cos_client, middle_stage_bucket, middle_file_key,                                 middle_file_path)  # upload the file's each word        delete_file_folder(src_file_path)        delete_file_folder(middle_file_path)        return upload_ret    else:        return -1def map_caller(event, context, cos_client):    appid = event['Records'][0]['cos']['cosBucket']['appid']    bucket = event['Records'][0]['cos']['cosBucket']['name'] + '-' + appid    key = event['Records'][0]['cos']['cosObject']['key']    key = key.replace('/' + str(appid) + '/' + event['Records'][0]['cos']['cosBucket']['name'] + '/', '', 1)    logger.info("Key is " + key)    middle_bucket = middle_stage_bucket + '-' + appid    middle_file_key = '/' + 'middle_' + key.split('/')[-1]    return do_mapping(cos_client, bucket, key, middle_bucket, middle_file_key)def main_handler(event, context):    logger.info("start main handler")    if "Records" not in event.keys():        return {"errorMsg": "event is not come from cos"}    secret_id = ""     secret_key = ""      config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, )    cos_client = CosS3Client(config)    start_time = datetime.datetime.now()    res = map_caller(event, context, cos_client)    end_time = datetime.datetime.now()    print("data mapping duration: " + str((end_time - start_time).microseconds / 1000) + "ms")    if res == 0:        return "Data mapping SUCCESS"    else:        return "Data mapping FAILED"

同样的方法，建立 reducer.py 文件，编写 Reducer 逻辑，代码如下：

# -*- coding: utf8 -*-from qcloud_cos_v5 import CosConfigfrom qcloud_cos_v5 import CosS3Clientfrom qcloud_cos_v5 import CosServiceErrorfrom operator import itemgetterimport osimport sysimport datetimeimport loggingregion = u'ap-guangzhou'  # 根据实际情况，修改地域result_bucket = u'destmr'  # 根据实际情况，修改bucket名logging.basicConfig(level=logging.INFO, stream=sys.stdout)logger = logging.getLogger()logger.setLevel(level=logging.INFO)def delete_file_folder(src):    if os.path.isfile(src):        try:            os.remove(src)        except:            pass    elif os.path.isdir(src):        for item in os.listdir(src):            itemsrc = os.path.join(src, item)            delete_file_folder(itemsrc)        try:            os.rmdir(src)        except:            passdef download_file(cos_client, bucket, key, download_path):    logger.info("Get from [%s] to download file [%s]" % (bucket, key))    try:        response = cos_client.get_object(Bucket=bucket, Key=key, )        response['Body'].get_stream_to_file(download_path)    except CosServiceError as e:        print(e.get_error_code())        print(e.get_error_msg())        return -1    return 0def upload_file(cos_client, bucket, key, local_file_path):    logger.info("Start to upload file to cos")    try:        response = cos_client.put_object_from_local_file(            Bucket=bucket,            LocalFilePath=local_file_path,            Key='{}'.format(key))    except CosServiceError as e:        print(e.get_error_code())        print(e.get_error_msg())        return -1    logger.info("Upload data map file [%s] Success" % key)    return 0def qcloud_reducer(cos_client, bucket, key, result_bucket, result_key):    word2count = {}    src_file_path = u'/tmp/' + key.split('/')[-1]    result_file_path = u'/tmp/' + u'result_' + key.split('/')[-1]    download_ret = download_file(cos_client, bucket, key, src_file_path)    if download_ret == 0:        map_file = open(src_file_path, 'r')        result_file = open(result_file_path, 'w')        for line in map_file:            line = line.strip()            word, count = line.split('t', 1)            try:                count = int(count)                word2count[word] = word2count.get(word, 0) + count            except ValueError:                logger.error("error value: %s, current line: %s" % (ValueError, line))                continue        map_file.close()        delete_file_folder(src_file_path)    sorted_word2count = sorted(word2count.items(), key=itemgetter(1))[::-1]    for wordcount in sorted_word2count:        res = '%st%s' % (wordcount[0], wordcount[1])        result_file.write(res)        result_file.write('n')    result_file.close()    upload_ret = upload_file(cos_client, result_bucket, result_key, result_file_path)    delete_file_folder(result_file_path)    return upload_retdef reduce_caller(event, context, cos_client):    appid = event['Records'][0]['cos']['cosBucket']['appid']    bucket = event['Records'][0]['cos']['cosBucket']['name'] + '-' + appid    key = event['Records'][0]['cos']['cosObject']['key']    key = key.replace('/' + str(appid) + '/' + event['Records'][0]['cos']['cosBucket']['name'] + '/', '', 1)    logger.info("Key is " + key)    res_bucket = result_bucket + '-' + appid    result_key = '/' + 'result_' + key.split('/')[-1]    return qcloud_reducer(cos_client, bucket, key, res_bucket, result_key)def main_handler(event, context):    logger.info("start main handler")    if "Records" not in event.keys():        return {"errorMsg": "event is not come from cos"}    secret_id = "SecretId"     secret_key = "SecretKey"      config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, )    cos_client = CosS3Client(config)    start_time = datetime.datetime.now()    res = reduce_caller(event, context, cos_client)    end_time = datetime.datetime.now()    print("data reducing duration: " + str((end_time - start_time).microseconds / 1000) + "ms")    if res == 0:        return "Data reducing SUCCESS"    else:        return "Data reducing FAILED"

部署与测试

遵循 Serverless Framework 的 yaml 规范，编写 serveerless.yaml:

WordCountMapper:  component: "@serverless/tencent-scf"  inputs:    name: mapper    codeUri: ./code    handler: index.main_handler    runtime: Python3.6    region: ap-guangzhou    description: 网站监控    memorySize: 64    timeout: 20    events:      - cos:          name: srcmr-1256773370.cos.ap-guangzhou.myqcloud.com          parameters:            bucket: srcmr-1256773370.cos.ap-guangzhou.myqcloud.com            filter:              prefix: ''              suffix: ''            events: cos:ObjectCreated:*            enable: trueWordCountReducer:  component: "@serverless/tencent-scf"  inputs:    name: reducer    codeUri: ./code    handler: index.main_handler    runtime: Python3.6    region: ap-guangzhou    description: 网站监控    memorySize: 64    timeout: 20    events:      - cos:          name: middlestagebucket-1256773370.cos.ap-guangzhou.myqcloud.com          parameters:            bucket: middlestagebucket-1256773370.cos.ap-guangzhou.myqcloud.com            filter:              prefix: ''              suffix: ''            events: cos:ObjectCreated:*            enable: true

完成之后，通过 sls --debug 指令进行部署。部署成功之后，进行基本的测试：

1. 准备一个英文文档：

2. 登录腾讯云后台，打开我们最初建立的存储桶：srcmr，并上传该文件；

3. 上传成功之后，稍等片刻即可看到 Reducer 程序已经在 Mapper 执行之后，产出日志:

此时，我们打开结果存储桶，查看结果:

现在，我们就完成了简单的词频统计功能。

总结

Serverless 架构是适用于大数据处理的。在腾讯云官网，我们也可以看到其关于数据 ETL 处理的场景描述：

本实例中，有一键部署多个函数的操作。在实际生产中，每个项目都不会是单个函数单打独斗的，而是多个函数组合应用，形成一个 Service 体系，所以一键部署多个函数就显得尤为重要。通过本实例，希望读者可以对 Serverless 架构的应用场景有更多的了解，并且能有所启发，将云函数和不同触发器进行组合，应用在自身业务中。

上述内容就是云函数SCF与对象存储实现WordCount算法的过程，你们学到知识或技能了吗？如果还想学到更多技能或者丰富自己的知识储备，欢迎关注行业资讯频道。