Serverless 实战：利用函数计算与对象存储实现 WordCount

MapReduce 在百度百科中的解释是：

MapReduce 是一种编程模型，用于大规模数据集（大于 1TB）的并行运算。"Map（映射）" 和 "Reduce（归约）" 是它们的主要思想，都是从函数式编程语言和矢量编程语言借来的特性。它极大地方便了编程人员在不会分布式并行编程的情况下，将自己的程序运行在分布式系统上。 当前的软件实现是指定一个 Map（映射）函数，用来把一组键值对映射成一组新的键值对，指定并发的 Reduce（归约）函数，用来保证所有映射的键值对中的每一个共享相同的键组。

通过这段描述，我们可以明确知道 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。

本文我们将通过 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，我们使用的是对象存储与云函数的结合。

理论基础

在开始之前，我们根据 MapReduce 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，3 个对象存储的存储桶分别作为输入的存储桶、中间临时缓存的存储桶以及结果存储桶。在开始实践前，我们先在广州区准备 3 个对象存储：

 复制代码

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，我们先对传统的 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：

 复制代码

# -*- coding: utf8 -*-
importdatetime
fromqcloud_cos_v5importCosConfig
fromqcloud_cos_v5importCosS3Client
fromqcloud_cos_v5importCosServiceError
importre
importos
importsys
importlogging
logging.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
defdelete_file_folder(src):
ifos.path.isfile(src):
try:
os.remove(src)
except:
pass
elifos.path.isdir(src):
foriteminos.listdir(src):
itemsrc = os.path.join(src, item)
delete_file_folder(itemsrc)
try:
os.rmdir(src)
except:
pass
defdownload_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)
exceptCosServiceErrorase:
print(e.get_error_code())
print(e.get_error_msg())
return-1
return0
defupload_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))
exceptCosServiceErrorase:
print(e.get_error_code())
print(e.get_error_msg())
return-1
logger.info("Upload data map file [%s] Success"% key)
return0
defdo_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
ifdownload_ret ==0:
inputfile = open(src_file_path,'r')# open local /tmp file
mapfile = open(middle_file_path,'w')# open a new file write stream
forlineininputfile:
line = re.sub('[^a-zA-Z0-9]',' ', line)# replace non-alphabetic/number characters
words = line.split()
forwordinwords:
mapfile.write('%s\t%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)
returnupload_ret
else:
return-1
defmap_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]
returndo_mapping(cos_client, bucket, key, middle_bucket, middle_file_key)
defmain_handler(event, context):
logger.info("start main handler")
if"Records"notinevent.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")
ifres ==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：

 复制代码

# -*- coding: utf8 -*-
fromqcloud_cos_v5importCosConfig
fromqcloud_cos_v5importCosS3Client
fromqcloud_cos_v5importCosServiceError
fromoperatorimportitemgetter
importos
importsys
importdatetime
importlogging
region =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)
defdelete_file_folder(src):
ifos.path.isfile(src):
try:
os.remove(src)
except:
pass
elifos.path.isdir(src):
foriteminos.listdir(src):
itemsrc = os.path.join(src, item)
delete_file_folder(itemsrc)
try:
os.rmdir(src)
except:
pass
defdownload_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)
exceptCosServiceErrorase:
print(e.get_error_code())
print(e.get_error_msg())
return-1
return0
defupload_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))
exceptCosServiceErrorase:
print(e.get_error_code())
print(e.get_error_msg())
return-1
logger.info("Upload data map file [%s] Success"% key)
return0
defqcloud_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)
ifdownload_ret ==0:
map_file = open(src_file_path,'r')
result_file = open(result_file_path,'w')
forlineinmap_file:
line = line.strip()
word, count = line.split('\t',1)
try:
count = int(count)
word2count[word] = word2count.get(word,0) + count
exceptValueError:
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]
forwordcountinsorted_word2count:
res ='%s\t%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)
returnupload_ret
defreduce_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]
returnqcloud_reducer(cos_client, bucket, key, res_bucket, result_key)
defmain_handler(event, context):
logger.info("start main handler")
if"Records"notinevent.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")
ifres ==0:
return"Data reducing SUCCESS"
else:
return"Data reducing FAILED"

部署与测试

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

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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:true

WordCountReducer:
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，成功之后进行基本的测试：

1. 首先准备一个英文文档:

1. 登录腾讯云后台，打开最初建立的存储桶：srcmr，并上传该文件 ;
2. 上传成功之后，稍等片刻就可以看到 Reducer 程序已经在 Mapper 执行之后，产出日志:
   

此时，打开结果存储桶，查看结果:

这样，我们就完成了简单的词频统计功能。

总结

相对来说，Serverless 架构比较适合做大数据处理，在腾讯云官网对 Serverless 应用场景的描述就包含有数据 ETL 处理：

一些数据处理系统中，常常需要周期性 / 计划性地处理庞大的数据量。例如：证券公司每 12 小时统计一次该时段的交易情况并整理出该时段交易量 top 5，每天处理一遍秒杀网站的交易流日志获取因售罄而导致的错误从而分析商品热度和趋势等。云函数近乎无限扩容的能力可以使您轻松地进行大容量数据的计算。我们利用云函数可以对源数据并发执行多个 mapper 和 reducer 函数，在短时间内完成工作；相比传统的工作方式，使用云函数更能避免资源的闲置浪费从而节省资金。

通过本实例，希望读者可以对 Serverless 架构的应用场景有更多的启发，了解到 Serverless 不仅仅在监控告警方面有着很好的表现，在大数据领域也不甘落后。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。