如何解决如何使用 AWS 流式传输上传的视频?
主要任务是保护视频不被下载。
为了实现它,我们决定从 S3 设置视频流。
该项目有一个 PHP API 和一个客户端。 API 生成预签名 URL,指向应将视频上传到 S3 存储桶中的位置。然后,客户端可以通过 CDN URL 请求视频。但是,使用签名的 url,可以从客户端下载视频。
我们找到了一种方法,即使用 AWS Elemental MediaConverter 将视频转换为 MPEG-DASH。 MediaConverter 的作业可以通过 API 创建。然后它应该通过 AWS Elemental MediaPackage 和 CloudFront 进行流式传输。
问题是:
- 如何理解视频上传完成后,启动 MediaConverter Job?
- MPEG-DASH 文件具有 .mpd 清单,但 MediaPackage 需要 .smil 清单。如何从 .mpd 自动生成此文件?
附言如果我哪里有错,请纠正我。
解决方法
如何理解视频上传完成后,启动MediaConverter Job? 可以通过以下工作流程来实现
- 摄取用户将视频上传到 S3 中的 watchfolder 存储桶
- s3:PutItem 事件触发一个 Lambda 函数,该函数调用 MediaConvert 来转换视频。
- 转换后的视频由 MediaConvert 存储在 S3 中
高级说明如下。
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创建一个 Amazon S3 存储桶,用于上传要转换的视频。桶名示例:vod-watchfolder-firstname-lastname
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创建一个 Amazon S3 存储桶,用于存储来自 MediaConvert 的转换视频输出(启用公共读取、静态网站托管和 CORS)
<?xml version="1.0" encoding="UTF-8"?> <CORSConfiguration xmlns="http://s3.amazonaws.com/doc/2006-03-01/"> <CORSRule> <AllowedOrigin>*</AllowedOrigin> <AllowedMethod>GET</AllowedMethod> <MaxAgeSeconds>3000</MaxAgeSeconds> <AllowedHeader>*</AllowedHeader> </CORSRule> </CORSConfiguration>
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创建 IAM 角色以传递给 MediaConvert。使用 IAM 控制台创建新角色。将其命名为 MediaConvertRole 并为角色类型选择 AWS Lambda。使用内联策略向执行 lambda 所需的其他资源授予权限。
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为您的 Lambda 函数创建 IAM 角色。使用 IAM 控制台创建角色。将其命名为 VODLambdaRole 并为角色类型选择 AWS Lambda。将名为 AWSLambdaBasicExecutionRole 的托管策略附加到此角色以授予必要的 CloudWatch Logs 权限。使用内联策略向执行 lambda 所需的其他资源授予权限。
{ "Version": "2012-10-17","Statement": [ { "Action": [ "logs:CreateLogGroup","logs:CreateLogStream","logs:PutLogEvents" ],"Resource": "*","Effect": "Allow","Sid": "Logging" },{ "Action": [ "iam:PassRole" ],"Resource": [ "ARNforMediaConvertRole" ],"Sid": "PassRole" },{ "Action": [ "mediaconvert:*" ],"Resource": [ "*" ],"Sid": "MediaConvertService" },{ "Action": [ "s3:*" ],"Sid": "S3Service" } ] }
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创建一个用于转换视频的 lambda 函数。使用 AWS Lambda 控制台创建一个名为 VODLambdaConvert 的新 Lambda 函数,该函数将处理 API 请求。将提供的 convert.py 示例实现用于您的函数代码。
#!/usr/bin/env python import glob import json import os import uuid import boto3 import datetime import random from urllib.parse import urlparse import logging from botocore.client import ClientError logger = logging.getLogger() logger.setLevel(logging.INFO) S3 = boto3.resource('s3') def handler(event,context): ''' Watchfolder handler - this lambda is triggered when video objects are uploaded to the SourceS3Bucket/inputs folder. It will look for two sets of file inputs: SourceS3Bucket/inputs/SourceS3Key: the input video to be converted SourceS3Bucket/jobs/*.json: job settings for MediaConvert jobs to be run against the input video. If there are no settings files in the jobs folder,then the Default job will be run from the job.json file in lambda environment. Ouput paths stored in outputGroup['OutputGroupSettings']['DashIsoGroupSettings']['Destination'] are constructed from the name of the job settings files as follows: s3://<MediaBucket>/<basename(job settings filename)>/<basename(input)>/<Destination value from job settings file> ''' assetID = str(uuid.uuid4()) sourceS3Bucket = event['Records'][0]['s3']['bucket']['name'] sourceS3Key = event['Records'][0]['s3']['object']['key'] sourceS3 = 's3://'+ sourceS3Bucket + '/' + sourceS3Key destinationS3 = 's3://' + os.environ['DestinationBucket'] mediaConvertRole = os.environ['MediaConvertRole'] application = os.environ['Application'] region = os.environ['AWS_DEFAULT_REGION'] statusCode = 200 jobs = [] job = {} # Use MediaConvert SDK UserMetadata to tag jobs with the assetID # Events from MediaConvert will have the assetID in UserMedata jobMetadata = {} jobMetadata['assetID'] = assetID jobMetadata['application'] = application jobMetadata['input'] = sourceS3 try: # Build a list of jobs to run against the input. Use the settings files in WatchFolder/jobs # if any exist. Otherwise,use the default job. jobInput = {} # Iterates through all the objects in jobs folder of the WatchFolder bucket,doing the pagination for you. Each obj # contains a jobSettings JSON bucket = S3.Bucket(sourceS3Bucket) for obj in bucket.objects.filter(Prefix='jobs/'): if obj.key != "jobs/": jobInput = {} jobInput['filename'] = obj.key logger.info('jobInput: %s',jobInput['filename']) jobInput['settings'] = json.loads(obj.get()['Body'].read()) logger.info(json.dumps(jobInput['settings'])) jobs.append(jobInput) # Use Default job settings in the lambda zip file in the current working directory if not jobs: with open('job.json') as json_data: jobInput['filename'] = 'Default' logger.info('jobInput: %s',jobInput['filename']) jobInput['settings'] = json.load(json_data) logger.info(json.dumps(jobInput['settings'])) jobs.append(jobInput) # get the account-specific mediaconvert endpoint for this region mediaconvert_client = boto3.client('mediaconvert',region_name=region) endpoints = mediaconvert_client.describe_endpoints() # add the account-specific endpoint to the client session client = boto3.client('mediaconvert',region_name=region,endpoint_url=endpoints['Endpoints'][0]['Url'],verify=False) for j in jobs: jobSettings = j['settings'] jobFilename = j['filename'] # Save the name of the settings file in the job userMetadata jobMetadata['settings'] = jobFilename # Update the job settings with the source video from the S3 event jobSettings['Inputs'][0]['FileInput'] = sourceS3 # Update the job settings with the destination paths for converted videos. We want to replace the # destination bucket of the output paths in the job settings,but keep the rest of the # path destinationS3 = 's3://' + os.environ['DestinationBucket'] + '/' \ + os.path.splitext(os.path.basename(sourceS3Key))[0] + '/' \ + os.path.splitext(os.path.basename(jobFilename))[0] for outputGroup in jobSettings['OutputGroups']: logger.info("outputGroup['OutputGroupSettings']['Type'] == %s",outputGroup['OutputGroupSettings']['Type']) if outputGroup['OutputGroupSettings']['Type'] == 'FILE_GROUP_SETTINGS': templateDestination = outputGroup['OutputGroupSettings']['FileGroupSettings']['Destination'] templateDestinationKey = urlparse(templateDestination).path logger.info("templateDestinationKey == %s",templateDestinationKey) outputGroup['OutputGroupSettings']['FileGroupSettings']['Destination'] = destinationS3+templateDestinationKey elif outputGroup['OutputGroupSettings']['Type'] == 'HLS_GROUP_SETTINGS': templateDestination = outputGroup['OutputGroupSettings']['HlsGroupSettings']['Destination'] templateDestinationKey = urlparse(templateDestination).path logger.info("templateDestinationKey == %s",templateDestinationKey) outputGroup['OutputGroupSettings']['HlsGroupSettings']['Destination'] = destinationS3+templateDestinationKey elif outputGroup['OutputGroupSettings']['Type'] == 'DASH_ISO_GROUP_SETTINGS': templateDestination = outputGroup['OutputGroupSettings']['DashIsoGroupSettings']['Destination'] templateDestinationKey = urlparse(templateDestination).path logger.info("templateDestinationKey == %s",templateDestinationKey) outputGroup['OutputGroupSettings']['DashIsoGroupSettings']['Destination'] = destinationS3+templateDestinationKey elif outputGroup['OutputGroupSettings']['Type'] == 'DASH_ISO_GROUP_SETTINGS': templateDestination = outputGroup['OutputGroupSettings']['DashIsoGroupSettings']['Destination'] templateDestinationKey = urlparse(templateDestination).path logger.info("templateDestinationKey == %s",templateDestinationKey) outputGroup['OutputGroupSettings']['DashIsoGroupSettings']['Destination'] = destinationS3+templateDestinationKey elif outputGroup['OutputGroupSettings']['Type'] == 'MS_SMOOTH_GROUP_SETTINGS': templateDestination = outputGroup['OutputGroupSettings']['MsSmoothGroupSettings']['Destination'] templateDestinationKey = urlparse(templateDestination).path logger.info("templateDestinationKey == %s",templateDestinationKey) outputGroup['OutputGroupSettings']['MsSmoothGroupSettings']['Destination'] = destinationS3+templateDestinationKey elif outputGroup['OutputGroupSettings']['Type'] == 'CMAF_GROUP_SETTINGS': templateDestination = outputGroup['OutputGroupSettings']['CmafGroupSettings']['Destination'] templateDestinationKey = urlparse(templateDestination).path logger.info("templateDestinationKey == %s",templateDestinationKey) outputGroup['OutputGroupSettings']['CmafGroupSettings']['Destination'] = destinationS3+templateDestinationKey else: logger.error("Exception: Unknown Output Group Type %s",outputGroup['OutputGroupSettings']['Type']) statusCode = 500 logger.info(json.dumps(jobSettings)) # Convert the video using AWS Elemental MediaConvert job = client.create_job(Role=mediaConvertRole,UserMetadata=jobMetadata,Settings=jobSettings) except Exception as e: logger.error('Exception: %s',e) statusCode = 500 raise finally: return { 'statusCode': statusCode,'body': json.dumps(job,indent=4,sort_keys=True,default=str),'headers': {'Content-Type': 'application/json','Access-Control-Allow-Origin': '*'} }
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确保将您的函数配置为使用您在上一节中创建的 VODLambdaRole IAM 角色。
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为您的 Convert lambda 创建 S3 事件触发器。使用 AWS Lambda 控制台将 putItem 触发器从 vod-watchfolder-firstname-lastname S3 存储桶添加到 VODLambdaConvert lambda。
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测试监视文件夹自动化。您可以使用自己的视频或使用此文件夹中包含的 test.mp4 视频来测试工作流程。
MPEG-DASH 文件具有 .mpd 清单,但 MediaPackage 需要 .smil 清单。如何从 .mpd 自动生成此文件?
- 截至今天,MediaConvert 没有自动生成 smil 文件的功能。因此,您可以考虑将输出更改为 HLS 并摄取到 Mediapackage。或者,手动创建 smil 文件。参考文件如下
- HLS VOD 摄取到 Mediapackage:https://github.com/aws-samples/aws-media-services-simple-vod-workflow/blob/master/13-VODMediaPackage/README-tutorial.md
- 正在创建 smil 文件:https://docs.aws.amazon.com/mediapackage/latest/ug/supported-inputs-vod-smil.html
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