Tutorial: Batch-transcoding videos with S3 Batch Operations, Amazon Lambda, and AWS Elemental MediaConvert - Amazon Simple Storage Service
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Tutorial: Batch-transcoding videos with S3 Batch Operations, Amazon Lambda, and AWS Elemental MediaConvert

Video consumers use devices of all shapes, sizes, and vintages to enjoy media content. This wide array of devices presents a challenge for content creators and distributors. Instead of being in a one-size-fits-all format, videos must be converted so that they can span a broad range of sizes, formats, and bitrates. This conversion task is even more challenging when you have a large number of videos that must be converted.

Amazon offers you a method to build a scalable, distributed architecture that does the following:

  • Ingests input videos

  • Processes the videos for playback on a wide range of devices

  • Stores the transcoded media files

  • Delivers the output media files to meet demand

When you have extensive video repositories stored in Amazon S3, you can transcode these videos from their source formats into multiple file types in the size, resolution, and format needed by a particular video player or device. Specifically, S3 Batch Operations provides you with a solution to invoke Amazon Lambda functions for existing input videos in an S3 source bucket. Then, the Lambda functions call AWS Elemental MediaConvert to perform large-scale video transcoding tasks. The converted output media files are stored in an S3 destination bucket.

Objective

In this tutorial, you learn how to set up S3 Batch Operations to invoke a Lambda function for batch-transcoding of videos stored in an S3 source bucket. The Lambda function calls MediaConvert to transcode the videos. The outputs for each video in the S3 source bucket are as follows:

  • An HTTP Live Streaming (HLS) adaptive bitrate stream for playback on devices of multiple sizes and varying bandwidths

  • An MP4 video file

  • Thumbnail images collected at intervals

Prerequisites

Before you start this tutorial, you must have an Amazon S3 source bucket (for example, tutorial-bucket-1) with videos to be transcoded already stored in it.

You can give the bucket another name if you want. For more information about bucket names in Amazon S3, see Bucket naming rules.

For the S3 source bucket, keep the settings related to Block Public Access settings for this bucket set to the defaults (Block all public access is enabled). For more information, see Creating a bucket.

For more information about uploading videos to the S3 source bucket, see Uploading objects. If you're uploading many large video files to S3, you might want to use Amazon S3 Transfer Acceleration to configure fast and secure file transfers. Transfer Acceleration can speed up video uploading to your S3 bucket for long-distance transfer of larger videos. For more information, see Configuring fast, secure file transfers using Amazon S3 Transfer Acceleration.

Step 1: Create an S3 bucket for the output media files

In this step, you create an S3 destination bucket to store the converted output media files. You also create a Cross Origin Resource Sharing (CORS) configuration to allow cross-origin access to the transcoded media files stored in your S3 destination bucket.

Create a bucket for the output media files

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. Choose Create bucket.

  4. For Bucket name, enter a name for your bucket (for example, tutorial-bucket-2).

  5. For Region, choose the Amazon Web Services Region where you want the bucket to reside.

  6. To ensure public access to your output media files, in Block Public Access settings for this bucket, clear Block all public access.

    Warning

    Before you complete this step, review Blocking public access to your Amazon S3 storage to ensure that you understand and accept the risks involved with allowing public access. When you turn off Block Public Access settings to make your bucket public, anyone on the internet can access your bucket. We recommend that you block all public access to your buckets.

    If you don’t want to clear the Block Public Access settings, you can use Amazon CloudFront to deliver the transcoded media files to viewers (end users). For more information, see Tutorial: Hosting on-demand streaming video with Amazon S3, Amazon CloudFront, and Amazon Route 53.

  7. Select the check box next to I acknowledge that the current settings might result in this bucket and the objects within becoming public.

  8. Keep the remaining settings set to the defaults.

  9. Choose Create bucket.

Add a CORS configuration to the S3 output bucket

A JSON CORS configuration defines a way for client web applications (video players in this context) that are loaded in one domain to play transcoded output media files in a different domain.

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. In the Buckets list, choose the name of the bucket that you created earlier (for example, tutorial-bucket-2).

  4. Choose the Permissions tab.

  5. In the Cross-origin resource sharing (CORS) section, choose Edit.

  6. In the CORS configuration text box, copy and paste the following CORS configuration.

    The CORS configuration must be in JSON format. In this example, the AllowedOrigins attribute uses the wildcard character (*) to specify all origins. If you know your specific origin, you can restrict the AllowedOrigins attribute to your specific player URL. For more information about configuring this and other attributes, see CORS configuration.

    [ { "AllowedOrigins": [ "*" ], "AllowedMethods": [ "GET" ], "AllowedHeaders": [ "*" ], "ExposeHeaders": [] } ]
  7. Choose Save changes.

Step 2: Create an IAM role for MediaConvert

To use AWS Elemental MediaConvert to transcode input videos stored in your S3 bucket, you must have an Amazon Identity and Access Management (IAM) service role to grant MediaConvert permissions to read and write video files from and to your S3 source and destination buckets. When you run transcoding jobs, the MediaConvert console uses this role.

To create an IAM role for MediaConvert

  1. Create an IAM role with a role name that you choose (for example, tutorial-mediaconvert-role). To create this role, follow the steps in Create your MediaConvert role in IAM (console) in the AWS Elemental MediaConvert User Guide.

  2. After you create the IAM role for MediaConvert, in the list of Roles, choose the name of the role for MediaConvert that you created (for example, tutorial-mediaconvert-role).

  3. On the Summary page, copy the Role ARN (which starts with arn:aws:iam::), and save the ARN for use later.

    For more information about ARNs, see Amazon Resource Names (ARNs) in the Amazon General Reference.

Step 3: Create an IAM role for your Lambda function

To batch-transcode videos with MediaConvert and S3 Batch Operations, you use a Lambda function to connect these two services to convert videos. This Lambda function must have an IAM role that grants the Lambda function permissions to access MediaConvert and S3 Batch Operations.

Create an IAM role for your Lambda function

  1. Sign in to the Amazon Web Services Management Console and open the IAM console at https://console.amazonaws.cn/iam/.

  2. In the left navigation pane, choose Roles, and then choose Create role.

  3. Choose the Amazon service role type, and then under Common use cases, choose Lambda.

  4. Choose Next: Permissions.

  5. On the Attach permissions policies page, enter AWSLambdaBasicExecutionRole in the Filter policies box. To attach the managed policy AWSLambdaBasicExecutionRole to this role to grant write permissions to Amazon CloudWatch Logs, select the check box next to AWSLambdaBasicExecutionRole.

  6. Choose Next: Tags.

  7. (Optional) Add tags to the managed policy.

  8. Choose Next: Review.

  9. For Role name, enter tutorial-lambda-transcode-role.

  10. Choose Create role.

Embed an inline policy for the IAM role of your Lambda function

To grant permissions to the MediaConvert resource that's needed for the Lambda function to execute, you must use an inline policy.

  1. Sign in to the Amazon Web Services Management Console and open the IAM console at https://console.amazonaws.cn/iam/.

  2. In the left navigation pane, choose Roles.

  3. In the Roles list, choose the name of the IAM role that you created earlier for your Lambda function (for example, tutorial-lambda-transcode-role).

  4. Choose the Permissions tab.

  5. Choose Add inline policy.

  6. Choose the JSON tab, and then copy and paste the following JSON policy.

    In the JSON policy, replace the example ARN value of Resource with the role ARN of the IAM role for MediaConvert that you created in Step 2 (for example, tutorial-mediaconvert-role).

    { "Version": "2012-10-17", "Statement": [ { "Action": [ "logs:CreateLogGroup", "logs:CreateLogStream", "logs:PutLogEvents" ], "Resource": "*", "Effect": "Allow", "Sid": "Logging" }, { "Action": [ "iam:PassRole" ], "Resource": [ "arn:aws:iam::111122223333:role/tutorial-mediaconvert-role" ], "Effect": "Allow", "Sid": "PassRole" }, { "Action": [ "mediaconvert:*" ], "Resource": [ "*" ], "Effect": "Allow", "Sid": "MediaConvertService" }, { "Action": [ "s3:*" ], "Resource": [ "*" ], "Effect": "Allow", "Sid": "S3Service" } ] }
  7. Choose Review Policy.

  8. For Name, enter tutorial-lambda-policy.

  9. Choose Create Policy.

    After you create an inline policy, it is automatically embedded in the IAM role of your Lambda function.

Step 4: Create a Lambda function for video transcoding

In this section of the tutorial, you build a Lambda function using the SDK for Python to integrate with S3 Batch Operations and MediaConvert. To start transcoding the videos already stored in your S3 source bucket, you run an S3 Batch Operations job that directly invokes the Lambda function for each video in the S3 source bucket. Then, the Lambda function submits a transcoding job for each video to MediaConvert.

Write Lambda function code and create a deployment package

  1. On your local machine, create a folder named batch-transcode.

  2. In the batch-transcode folder, create a file with JSON job settings. For example, you can use the settings provided in this section, and name the file job.json.

    A job.json file specifies the following:

    • Which files to transcode

    • How you want to transcode your input videos

    • What output media files you want to create

    • What to name the transcoded files

    • Where to save the transcoded files

    • Which advanced features to apply, and so on

    In this tutorial, we use the following job.json file to create the following outputs for each video in the S3 source bucket:

    • An HTTP Live Streaming (HLS) adaptive bitrate stream for playback on multiple devices of differing sizes and varying bandwidths

    • An MP4 video file

    • Thumbnail images collected at intervals

    This example job.json file uses Quality-Defined Variable Bitrate (QVBR) to optimize video quality. The HLS output is Apple-compliant (audio unmixed from video, segment duration of 6 seconds, and optimized video quality through auto QVBR).

    If you don't want to use the example settings provided here, you can generate a job.json specification based on your use case. To ensure consistency across your outputs, make sure that your input files have similar video and audio configurations. For any input files with different video and audio configurations, create separate automations (unique job.json settings). For more information, see Example AWS Elemental MediaConvert job settings in JSON in the AWS Elemental MediaConvert User Guide.

    { "OutputGroups": [ { "CustomName": "HLS", "Name": "Apple HLS", "Outputs": [ { "ContainerSettings": { "Container": "M3U8", "M3u8Settings": { "AudioFramesPerPes": 4, "PcrControl": "PCR_EVERY_PES_PACKET", "PmtPid": 480, "PrivateMetadataPid": 503, "ProgramNumber": 1, "PatInterval": 0, "PmtInterval": 0, "TimedMetadata": "NONE", "VideoPid": 481, "AudioPids": [ 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492 ] } }, "VideoDescription": { "Width": 640, "ScalingBehavior": "DEFAULT", "Height": 360, "TimecodeInsertion": "DISABLED", "AntiAlias": "ENABLED", "Sharpness": 50, "CodecSettings": { "Codec": "H_264", "H264Settings": { "InterlaceMode": "PROGRESSIVE", "NumberReferenceFrames": 3, "Syntax": "DEFAULT", "Softness": 0, "GopClosedCadence": 1, "GopSize": 2, "Slices": 1, "GopBReference": "DISABLED", "MaxBitrate": 1200000, "SlowPal": "DISABLED", "SpatialAdaptiveQuantization": "ENABLED", "TemporalAdaptiveQuantization": "ENABLED", "FlickerAdaptiveQuantization": "DISABLED", "EntropyEncoding": "CABAC", "FramerateControl": "INITIALIZE_FROM_SOURCE", "RateControlMode": "QVBR", "CodecProfile": "MAIN", "Telecine": "NONE", "MinIInterval": 0, "AdaptiveQuantization": "HIGH", "CodecLevel": "AUTO", "FieldEncoding": "PAFF", "SceneChangeDetect": "TRANSITION_DETECTION", "QualityTuningLevel": "SINGLE_PASS_HQ", "FramerateConversionAlgorithm": "DUPLICATE_DROP", "UnregisteredSeiTimecode": "DISABLED", "GopSizeUnits": "SECONDS", "ParControl": "INITIALIZE_FROM_SOURCE", "NumberBFramesBetweenReferenceFrames": 2, "RepeatPps": "DISABLED" } }, "AfdSignaling": "NONE", "DropFrameTimecode": "ENABLED", "RespondToAfd": "NONE", "ColorMetadata": "INSERT" }, "OutputSettings": { "HlsSettings": { "AudioGroupId": "program_audio", "AudioRenditionSets": "program_audio", "SegmentModifier": "$dt$", "IFrameOnlyManifest": "EXCLUDE" } }, "NameModifier": "_360" }, { "ContainerSettings": { "Container": "M3U8", "M3u8Settings": { "AudioFramesPerPes": 4, "PcrControl": "PCR_EVERY_PES_PACKET", "PmtPid": 480, "PrivateMetadataPid": 503, "ProgramNumber": 1, "PatInterval": 0, "PmtInterval": 0, "TimedMetadata": "NONE", "TimedMetadataPid": 502, "VideoPid": 481, "AudioPids": [ 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492 ] } }, "VideoDescription": { "Width": 960, "ScalingBehavior": "DEFAULT", "Height": 540, "TimecodeInsertion": "DISABLED", "AntiAlias": "ENABLED", "Sharpness": 50, "CodecSettings": { "Codec": "H_264", "H264Settings": { "InterlaceMode": "PROGRESSIVE", "NumberReferenceFrames": 3, "Syntax": "DEFAULT", "Softness": 0, "GopClosedCadence": 1, "GopSize": 2, "Slices": 1, "GopBReference": "DISABLED", "MaxBitrate": 3500000, "SlowPal": "DISABLED", "SpatialAdaptiveQuantization": "ENABLED", "TemporalAdaptiveQuantization": "ENABLED", "FlickerAdaptiveQuantization": "DISABLED", "EntropyEncoding": "CABAC", "FramerateControl": "INITIALIZE_FROM_SOURCE", "RateControlMode": "QVBR", "CodecProfile": "MAIN", "Telecine": "NONE", "MinIInterval": 0, "AdaptiveQuantization": "HIGH", "CodecLevel": "AUTO", "FieldEncoding": "PAFF", "SceneChangeDetect": "TRANSITION_DETECTION", "QualityTuningLevel": "SINGLE_PASS_HQ", "FramerateConversionAlgorithm": "DUPLICATE_DROP", "UnregisteredSeiTimecode": "DISABLED", "GopSizeUnits": "SECONDS", "ParControl": "INITIALIZE_FROM_SOURCE", "NumberBFramesBetweenReferenceFrames": 2, "RepeatPps": "DISABLED" } }, "AfdSignaling": "NONE", "DropFrameTimecode": "ENABLED", "RespondToAfd": "NONE", "ColorMetadata": "INSERT" }, "OutputSettings": { "HlsSettings": { "AudioGroupId": "program_audio", "AudioRenditionSets": "program_audio", "SegmentModifier": "$dt$", "IFrameOnlyManifest": "EXCLUDE" } }, "NameModifier": "_540" }, { "ContainerSettings": { "Container": "M3U8", "M3u8Settings": { "AudioFramesPerPes": 4, "PcrControl": "PCR_EVERY_PES_PACKET", "PmtPid": 480, "PrivateMetadataPid": 503, "ProgramNumber": 1, "PatInterval": 0, "PmtInterval": 0, "TimedMetadata": "NONE", "VideoPid": 481, "AudioPids": [ 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492 ] } }, "VideoDescription": { "Width": 1280, "ScalingBehavior": "DEFAULT", "Height": 720, "TimecodeInsertion": "DISABLED", "AntiAlias": "ENABLED", "Sharpness": 50, "CodecSettings": { "Codec": "H_264", "H264Settings": { "InterlaceMode": "PROGRESSIVE", "NumberReferenceFrames": 3, "Syntax": "DEFAULT", "Softness": 0, "GopClosedCadence": 1, "GopSize": 2, "Slices": 1, "GopBReference": "DISABLED", "MaxBitrate": 5000000, "SlowPal": "DISABLED", "SpatialAdaptiveQuantization": "ENABLED", "TemporalAdaptiveQuantization": "ENABLED", "FlickerAdaptiveQuantization": "DISABLED", "EntropyEncoding": "CABAC", "FramerateControl": "INITIALIZE_FROM_SOURCE", "RateControlMode": "QVBR", "CodecProfile": "MAIN", "Telecine": "NONE", "MinIInterval": 0, "AdaptiveQuantization": "HIGH", "CodecLevel": "AUTO", "FieldEncoding": "PAFF", "SceneChangeDetect": "TRANSITION_DETECTION", "QualityTuningLevel": "SINGLE_PASS_HQ", "FramerateConversionAlgorithm": "DUPLICATE_DROP", "UnregisteredSeiTimecode": "DISABLED", "GopSizeUnits": "SECONDS", "ParControl": "INITIALIZE_FROM_SOURCE", "NumberBFramesBetweenReferenceFrames": 2, "RepeatPps": "DISABLED" } }, "AfdSignaling": "NONE", "DropFrameTimecode": "ENABLED", "RespondToAfd": "NONE", "ColorMetadata": "INSERT" }, "OutputSettings": { "HlsSettings": { "AudioGroupId": "program_audio", "AudioRenditionSets": "program_audio", "SegmentModifier": "$dt$", "IFrameOnlyManifest": "EXCLUDE" } }, "NameModifier": "_720" }, { "ContainerSettings": { "Container": "M3U8", "M3u8Settings": {} }, "AudioDescriptions": [ { "AudioSourceName": "Audio Selector 1", "CodecSettings": { "Codec": "AAC", "AacSettings": { "Bitrate": 96000, "CodingMode": "CODING_MODE_2_0", "SampleRate": 48000 } } } ], "OutputSettings": { "HlsSettings": { "AudioGroupId": "program_audio", "AudioTrackType": "ALTERNATE_AUDIO_AUTO_SELECT_DEFAULT" } }, "NameModifier": "_audio" } ], "OutputGroupSettings": { "Type": "HLS_GROUP_SETTINGS", "HlsGroupSettings": { "ManifestDurationFormat": "INTEGER", "SegmentLength": 6, "TimedMetadataId3Period": 10, "CaptionLanguageSetting": "OMIT", "Destination": "s3://EXAMPLE-BUCKET/HLS/", "DestinationSettings": { "S3Settings": { "AccessControl": { "CannedAcl": "PUBLIC_READ" } } }, "TimedMetadataId3Frame": "PRIV", "CodecSpecification": "RFC_4281", "OutputSelection": "MANIFESTS_AND_SEGMENTS", "ProgramDateTimePeriod": 600, "MinSegmentLength": 0, "DirectoryStructure": "SINGLE_DIRECTORY", "ProgramDateTime": "EXCLUDE", "SegmentControl": "SEGMENTED_FILES", "ManifestCompression": "NONE", "ClientCache": "ENABLED", "StreamInfResolution": "INCLUDE" } } }, { "CustomName": "MP4", "Name": "File Group", "Outputs": [ { "ContainerSettings": { "Container": "MP4", "Mp4Settings": { "CslgAtom": "INCLUDE", "FreeSpaceBox": "EXCLUDE", "MoovPlacement": "PROGRESSIVE_DOWNLOAD" } }, "VideoDescription": { "Width": 1280, "ScalingBehavior": "DEFAULT", "Height": 720, "TimecodeInsertion": "DISABLED", "AntiAlias": "ENABLED", "Sharpness": 100, "CodecSettings": { "Codec": "H_264", "H264Settings": { "InterlaceMode": "PROGRESSIVE", "ParNumerator": 1, "NumberReferenceFrames": 3, "Syntax": "DEFAULT", "Softness": 0, "GopClosedCadence": 1, "HrdBufferInitialFillPercentage": 90, "GopSize": 2, "Slices": 2, "GopBReference": "ENABLED", "HrdBufferSize": 10000000, "MaxBitrate": 5000000, "ParDenominator": 1, "EntropyEncoding": "CABAC", "RateControlMode": "QVBR", "CodecProfile": "HIGH", "MinIInterval": 0, "AdaptiveQuantization": "AUTO", "CodecLevel": "AUTO", "FieldEncoding": "PAFF", "SceneChangeDetect": "ENABLED", "QualityTuningLevel": "SINGLE_PASS_HQ", "UnregisteredSeiTimecode": "DISABLED", "GopSizeUnits": "SECONDS", "ParControl": "SPECIFIED", "NumberBFramesBetweenReferenceFrames": 3, "RepeatPps": "DISABLED", "DynamicSubGop": "ADAPTIVE" } }, "AfdSignaling": "NONE", "DropFrameTimecode": "ENABLED", "RespondToAfd": "NONE", "ColorMetadata": "INSERT" }, "AudioDescriptions": [ { "AudioTypeControl": "FOLLOW_INPUT", "AudioSourceName": "Audio Selector 1", "CodecSettings": { "Codec": "AAC", "AacSettings": { "AudioDescriptionBroadcasterMix": "NORMAL", "Bitrate": 160000, "RateControlMode": "CBR", "CodecProfile": "LC", "CodingMode": "CODING_MODE_2_0", "RawFormat": "NONE", "SampleRate": 48000, "Specification": "MPEG4" } }, "LanguageCodeControl": "FOLLOW_INPUT", "AudioType": 0 } ] } ], "OutputGroupSettings": { "Type": "FILE_GROUP_SETTINGS", "FileGroupSettings": { "Destination": "s3://EXAMPLE-BUCKET/MP4/", "DestinationSettings": { "S3Settings": { "AccessControl": { "CannedAcl": "PUBLIC_READ" } } } } } }, { "CustomName": "Thumbnails", "Name": "File Group", "Outputs": [ { "ContainerSettings": { "Container": "RAW" }, "VideoDescription": { "Width": 1280, "ScalingBehavior": "DEFAULT", "Height": 720, "TimecodeInsertion": "DISABLED", "AntiAlias": "ENABLED", "Sharpness": 50, "CodecSettings": { "Codec": "FRAME_CAPTURE", "FrameCaptureSettings": { "FramerateNumerator": 1, "FramerateDenominator": 5, "MaxCaptures": 500, "Quality": 80 } }, "AfdSignaling": "NONE", "DropFrameTimecode": "ENABLED", "RespondToAfd": "NONE", "ColorMetadata": "INSERT" } } ], "OutputGroupSettings": { "Type": "FILE_GROUP_SETTINGS", "FileGroupSettings": { "Destination": "s3://EXAMPLE-BUCKET/Thumbnails/", "DestinationSettings": { "S3Settings": { "AccessControl": { "CannedAcl": "PUBLIC_READ" } } } } } } ], "AdAvailOffset": 0, "Inputs": [ { "AudioSelectors": { "Audio Selector 1": { "Offset": 0, "DefaultSelection": "DEFAULT", "ProgramSelection": 1 } }, "VideoSelector": { "ColorSpace": "FOLLOW" }, "FilterEnable": "AUTO", "PsiControl": "USE_PSI", "FilterStrength": 0, "DeblockFilter": "DISABLED", "DenoiseFilter": "DISABLED", "TimecodeSource": "EMBEDDED", "FileInput": "s3://EXAMPLE-INPUT-BUCKET/input.mp4" } ] }
  3. In the batch-transcode folder, create a file with a Lambda function. You can use the following Python example and name the file convert.py.

    S3 Batch Operations sends specific task data to a Lambda function and requires result data back. For request and response examples for the Lambda function, information about response and result codes, and example Lambda functions for S3 Batch Operations, see Invoke Amazon Lambda function.

    import json import os from urllib.parse import urlparse import uuid import boto3 """ When you run an S3 Batch Operations job, your job invokes this Lambda function. Specifically, the Lambda function is invoked on each video object listed in the manifest that you specify for the S3 Batch Operations job in Step 5. Input parameter "event": The S3 Batch Operations event as a request for the Lambda function. Input parameter "context": Context about the event. Output: A result structure that Amazon S3 uses to interpret the result of the operation. It is a job response returned back to S3 Batch Operations. """ def handler(event, context): invocation_schema_version = event['invocationSchemaVersion'] invocation_id = event['invocationId'] task_id = event['tasks'][0]['taskId'] source_s3_key = event['tasks'][0]['s3Key'] source_s3_bucket = event['tasks'][0]['s3BucketArn'].split(':::')[-1] source_s3 = 's3://' + source_s3_bucket + '/' + source_s3_key result_list = [] result_code = 'Succeeded' result_string = 'The input video object was converted successfully.' # The type of output group determines which media players can play # the files transcoded by MediaConvert. # For more information, see Creating outputs with AWS Elemental MediaConvert. output_group_type_dict = { 'HLS_GROUP_SETTINGS': 'HlsGroupSettings', 'FILE_GROUP_SETTINGS': 'FileGroupSettings', 'CMAF_GROUP_SETTINGS': 'CmafGroupSettings', 'DASH_ISO_GROUP_SETTINGS': 'DashIsoGroupSettings', 'MS_SMOOTH_GROUP_SETTINGS': 'MsSmoothGroupSettings' } try: job_name = 'Default' with open('job.json') as file: job_settings = json.load(file) job_settings['Inputs'][0]['FileInput'] = source_s3 # The path of each output video is constructed based on the values of # the attributes in each object of OutputGroups in the job.json file. destination_s3 = 's3://{0}/{1}/{2}' \ .format(os.environ['DestinationBucket'], os.path.splitext(os.path.basename(source_s3_key))[0], os.path.splitext(os.path.basename(job_name))[0]) for output_group in job_settings['OutputGroups']: output_group_type = output_group['OutputGroupSettings']['Type'] if output_group_type in output_group_type_dict.keys(): output_group_type = output_group_type_dict[output_group_type] output_group['OutputGroupSettings'][output_group_type]['Destination'] = \ "{0}{1}".format(destination_s3, urlparse(output_group['OutputGroupSettings'][output_group_type]['Destination']).path) else: raise ValueError("Exception: Unknown Output Group Type {}." .format(output_group_type)) job_metadata_dict = { 'assetID': str(uuid.uuid4()), 'application': os.environ['Application'], 'input': source_s3, 'settings': job_name } region = os.environ['AWS_DEFAULT_REGION'] endpoints = boto3.client('mediaconvert', region_name=region) \ .describe_endpoints() client = boto3.client('mediaconvert', region_name=region, endpoint_url=endpoints['Endpoints'][0]['Url'], verify=False) try: client.create_job(Role=os.environ['MediaConvertRole'], UserMetadata=job_metadata_dict, Settings=job_settings) # You can customize error handling based on different error codes that # MediaConvert can return. # For more information, see MediaConvert error codes. # When the result_code is TemporaryFailure, S3 Batch Operations retries # the task before the job is completed. If this is the final retry, # the error message is included in the final report. except Exception as error: result_code = 'TemporaryFailure' raise except Exception as error: if result_code != 'TemporaryFailure': result_code = 'PermanentFailure' result_string = str(error) finally: result_list.append({ 'taskId': task_id, 'resultCode': result_code, 'resultString': result_string, }) return { 'invocationSchemaVersion': invocation_schema_version, 'treatMissingKeyAs': 'PermanentFailure', 'invocationId': invocation_id, 'results': result_list }
  4. To create a deployment package with convert.py and job.json as a .zip file named lambda.zip, in your local terminal, open the batch-transcode folder that you created earlier, and run the following command.

    For macOS users, run the following command:

    zip -r lambda.zip convert.py job.json

    For Windows users, run the following commands:

    powershell Compress-Archive convert.py lambda.zip
    powershell Compress-Archive -update job.json lambda.zip

Create a Lambda function with an execution role (console)

  1. Open the Amazon Lambda console at https://console.amazonaws.cn/lambda/.

  2. In the left navigation pane, choose Functions.

  3. Choose Create function.

  4. Choose Author from scratch.

  5. Under Basic information, do the following:

    1. For Function name, enter tutorial-lambda-convert.

    2. For Runtime, choose Python 3.8 or a later version of Python.

  6. Choose Change default execution role, and under Execution role, choose Use an existing role.

  7. Under Existing role, choose the name of the IAM role that you created for your Lambda function in Step 3 (for example, tutorial-lambda-transcode-role).

  8. For the remaining settings, keep the defaults.

  9. Choose Create function.

Deploy your Lambda function with .zip file archives and configure the Lambda function (console)

  1. In the Code Source section of the page for the Lambda function that you created (for example, tutorial-lambda-convert), choose Upload from and then .zip file.

  2. Choose Upload to select your local .zip file.

  3. Choose the lambda.zip file that you created earlier, and choose Open.

  4. Choose Save.

  5. In the Runtime settings section, choose Edit.

  6. To tell the Lambda runtime which handler method in your Lambda function code to invoke, enter convert.handler in the Handler field.

    When you configure a function in Python, the value of the handler setting is the file name and the name of the handler module, separated by a dot (.). For example, convert.handler calls the handler method defined in the convert.py file.

  7. Choose Save.

  8. On your Lambda function page, choose the Configuration tab. In the left navigation pane on the Configuration tab, choose Environment variables, and then choose Edit.

  9. Choose Add environment variable. Then, enter the specified Key and Value for each of the following environment variables:

    • Key: DestinationBucket Value: tutorial-bucket-2

      This value is the S3 bucket for output media files that you created in Step 1.

    • Key: MediaConvertRole Value: arn:aws:iam::111122223333:role/tutorial-mediaconvert-role

      This value is the ARN of the IAM role for MediaConvert that you created in Step 2. Make sure to replace this ARN with the actual ARN of your IAM role.

    • Key: Application Value: Batch-Transcoding

      This value is the name of the application.

  10. Choose Save.

  11. (Optional) On the Configuration tab, in the General configuration section of the left navigation pane, choose Edit. In the Timeout field, enter 2 min 0 sec. Then, choose Save.

    Timeout is the amount of time that Lambda allows a function to run for an invocation before stopping it. The default is 3 seconds. Pricing is based on the amount of memory configured and the amount of time that your code runs. For more information, see Amazon Lambda pricing.

Step 5: Configure Amazon S3 Inventory for your S3 source bucket

After setting up the transcoding Lambda function, create an S3 Batch Operations job to transcode a set of videos. First, you need a list of input video objects that you want S3 Batch Operations to run the specified transcoding action on. To get a list of input video objects, you can generate an S3 Inventory report for your S3 source bucket (for example, tutorial-bucket-1).

Create and configure a bucket for S3 Inventory reports for input videos

To store an S3 Inventory report that lists the objects of the S3 source bucket, create an S3 Inventory destination bucket, and then configure a bucket policy for the bucket to write inventory files to the S3 source bucket.

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. Choose Create bucket.

  4. For Bucket name, enter a name for your bucket (for example, tutorial-bucket-3).

  5. For Amazon Web Services Region, choose the Amazon Web Services Region where you want the bucket to reside.

    The inventory destination bucket must be in the same Amazon Web Services Region as the source bucket where you are setting up S3 Inventory. The inventory destination bucket can be in a different Amazon Web Services account.

  6. In Block Public Access settings for this bucket, keep the default settings (Block all public access is enabled).

  7. For the remaining settings, keep the defaults.

  8. Choose Create bucket.

  9. In the Buckets list, choose the name of the bucket that you just created (for example, tutorial-bucket-3).

  10. To grant Amazon S3 permission to write data for the inventory reports to the S3 Inventory destination bucket, choose the Permissions tab.

  11. Scroll down to the Bucket policy section, and choose Edit. The Bucket policy page opens.

  12. To grant permissions for S3 Inventory, in the Policy field, paste the following bucket policy.

    Replace the three example values with the following values:

    • The name of the bucket that you created to store the inventory reports (for example, tutorial-bucket-3).

    • The name of the source bucket that stores the input videos (for example, tutorial-bucket-1).

    • The Amazon Web Services account ID that you used to create the S3 video source bucket (for example, 111122223333.

    { "Version":"2012-10-17", "Statement":[ { "Sid":"InventoryAndAnalyticsExamplePolicy", "Effect":"Allow", "Principal": {"Service": "s3.amazonaws.com"}, "Action":"s3:PutObject", "Resource":["arn:aws:s3:::tutorial-bucket-3/*"], "Condition": { "ArnLike": { "aws:SourceArn": "arn:aws:s3:::tutorial-bucket-1" }, "StringEquals": { "aws:SourceAccount": "111122223333", "s3:x-amz-acl": "bucket-owner-full-control" } } } ] }
  13. Choose Save changes.

Configure Amazon S3 Inventory for your S3 video source bucket

To generate a flat file list of video objects and metadata, you must configure S3 Inventory for your S3 video source bucket. These scheduled inventory reports can include all the objects in the bucket or objects grouped by a shared prefix. In this tutorial, the S3 Inventory report includes all the video objects in your S3 source bucket.

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. To configure an S3 Inventory report of the input videos in your S3 source bucket, in the Buckets list, choose the name of the S3 source bucket (for example, tutorial-bucket-1).

  4. Choose the Management tab.

  5. Scroll down to the Inventory configurations section, and choose Create inventory configuration.

  6. For Inventory configuration name, enter a name (for example, tutorial-inventory-config).

  7. Under Inventory scope, choose Current version only for Object versions and keep the other Inventory scope settings set to the defaults for this tutorial.

  8. In the Report details section, for Destination bucket, choose This account.

  9. For Destination, choose Browse S3, and choose the destination bucket that you created earlier to save the inventory reports to (for example, tutorial-bucket-3). Then choose Choose path.

    The inventory destination bucket must be in the same Amazon Web Services Region as the source bucket where you are setting up S3 Inventory. The inventory destination bucket can be in a different Amazon Web Services account.

    Under the Destination bucket field, the Destination bucket permission is added to the inventory destination bucket policy, allowing Amazon S3 to place data in the inventory destination bucket. For more information, see Creating a destination bucket policy.

  10. For Frequency, choose Daily.

  11. For Output format, choose CSV.

  12. For Status, choose Enable.

  13. In the Server-side encryption section, choose Disable for this tutorial.

    For more information, see Configuring inventory using the S3 console and Granting Amazon S3 permission to use your Amazon KMS key for encryption.

  14. In the Additional fields - optional section, select Size, Last modified, and Storage class.

  15. Choose Create.

For more information, see Configuring inventory using the S3 console.

Check the inventory report for your S3 video source bucket

When an inventory report is published, the manifest files are sent to the S3 Inventory destination bucket.

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. In the Buckets list, choose the name of the video source bucket (for example, tutorial-bucket-1).

  4. Choose Management.

  5. To see if your S3 Inventory report is ready so that you can create an S3 Batch Operations job in Step 7, under Inventory configurations, check whether the Create job from manifest button is enabled.

    Note

    It can take up to 48 hours to deliver the first inventory report. If the Create job from manifest button is disabled, the first inventory report has not been delivered. Wait until the first inventory report is delivered and the Create job from manifest button is enabled before you create an S3 Batch Operations job in Step 7.

  6. To check an S3 Inventory report (manifest.json), in the Destination column, choose the name of the inventory destination bucket that you created earlier for storing inventory reports (for example, tutorial-bucket-3).

  7. On the Objects tab, choose the existing folder with the name of your S3 source bucket (for example, tutorial-bucket-1). Then choose the name that you entered in Inventory configuration name when you created the inventory configuration earlier (for example, tutorial-inventory-config).

    You can see a list of folders with the generation dates of the reports as their names.

  8. To check the daily S3 Inventory report for a particular date, choose the folder with the corresponding generation date name, and then choose manifest.json.

  9. To check the details of the inventory report on a specific date, on the manifest.json page, choose Download or Open.

Step 6: Create an IAM role for S3 Batch Operations

To use S3 Batch Operations to do batch-transcoding, you must first create an IAM role to give Amazon S3 permissions to perform S3 Batch Operations.

Create an IAM policy for S3 Batch Operations

You must create an IAM policy that gives S3 Batch Operations permission to read the input manifest, invoke the Lambda function, and write the S3 Batch Operations job completion report.

  1. Sign in to the Amazon Web Services Management Console and open the IAM console at https://console.amazonaws.cn/iam/.

  2. In the left navigation pane, choose Policies.

  3. Choose Create policy.

  4. Choose the JSON tab.

  5. In the JSON text field, paste the following JSON policy.

    In the JSON policy, replace the four example values with the following values:

    • The name of the source bucket that stores your input videos (for example, tutorial-bucket-1).

    • The name of the inventory destination bucket that you created in Step 5 to store manifest.json files (for example, tutorial-bucket-3).

    • The name of the bucket that you created in Step 1 to store output media files (for example, tutorial-bucket-2). In this tutorial, we put job completion reports in the destination bucket for output media files.

    • The role ARN of the Lambda function that you created in Step 4. To find and copy the role ARN of the Lambda function, do the following:

    { "Version": "2012-10-17", "Statement": [ { "Sid": "S3Get", "Effect": "Allow", "Action": [ "s3:GetObject", "s3:GetObjectVersion" ], "Resource": [ "arn:aws:s3:::tutorial-bucket-1/*", "arn:aws:s3:::tutorial-bucket-3/*" ] }, { "Sid": "S3PutJobCompletionReport", "Effect": "Allow", "Action": "s3:PutObject", "Resource": "arn:aws:s3:::tutorial-bucket-2/*" }, { "Sid": "S3BatchOperationsInvokeLambda", "Effect": "Allow", "Action": [ "lambda:InvokeFunction" ], "Resource": [ "arn:aws:lambda:us-west-2:111122223333:function:tutorial-lambda-convert" ] } ] }
  6. Choose Next: Tags.

  7. Choose Next: Review.

  8. In the Name field, enter tutorial-s3batch-policy.

  9. Choose Create policy.

Create an S3 Batch Operations IAM role and attach permissions policies

  1. Sign in to the Amazon Web Services Management Console and open the IAM console at https://console.amazonaws.cn/iam/.

  2. In the left navigation pane, choose Roles, and then choose Create role.

  3. Choose the Amazon Web Service role type, and then choose the S3 service.

  4. Under Select your use case, choose S3 Batch Operations.

  5. Choose Next: Permissions.

  6. Under Attach permissions policies, enter the name of the IAM policy that you created earlier (for example, tutorial-s3batch-policy) in the search box to filter the list of policies. Select the check box next to the name of the policy (for example, tutorial-s3batch-policy).

  7. Choose Next: Tags.

  8. Choose Next: Review.

  9. For Role name, enter tutorial-s3batch-role.

  10. Choose Create role.

    After you create the IAM role for S3 Batch Operations, the following trust policy is automatically attached to the role. This trust policy allows the S3 Batch Operations service principal to assume the IAM role.

    { "Version":"2012-10-17", "Statement":[ { "Effect":"Allow", "Principal":{ "Service":"batchoperations.s3.amazonaws.com" }, "Action":"sts:AssumeRole" } ] }

Step 7: Create and run an S3 Batch Operations job

To create an S3 Batch Operations job to process the input videos in your S3 source bucket, you must specify parameters for this particular job.

Note

Before you start creating an S3 Batch Operations job, make sure that the Create job from manifest button is enabled. For more information, see Check the inventory report for your S3 video source bucket. If the Create job from manifest button is disabled, the first inventory report has not been delivered and you must wait until the button is enabled. After you configure Amazon S3 Inventory for your S3 source bucket in Step 5, it can take up to 48 hours to deliver the first inventory report.

Create an S3 Batch Operations job

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Batch Operations.

  3. Choose Create job.

  4. For Amazon Web Services Region, choose the Region where you want to create your job.

    In this tutorial, to use the S3 Batch Operations job to invoke a Lambda function, you must create the job in the same Region as the S3 video source bucket where the objects referenced in the manifest are located.

  5. In the Manifest section, do the following:

    1. For Manifest format, choose S3 Inventory report (manifest.json).

    2. For Manifest object, choose Browse S3 to find the bucket that you created in Step 5 for storing inventory reports (for example, tutorial-bucket-3). On the Manifest object page, navigate through the object names until you find a manifest.json file for a specific date. This file lists the information about all the videos that you want to batch-transcode. When you've found the manifest.json file that you want to use, choose the option button next to it. Then choose Choose path.

    3. (Optional) For Manifest object version ID - optional, enter the version ID for the manifest object if you want to use a version other than the most recent.

  6. Choose Next.

  7. To use the Lambda function to transcode all the objects listed in the selected manifest.json file, under Operation type, choose Invoke Amazon Lambda function.

  8. In the Invoke Lambda function section, do the following:

    1. Choose Choose from functions in your account.

    2. For Lambda function, choose the Lambda function that you created in Step 4 (for example, tutorial-lambda-convert).

    3. For Lambda function version, keep the default value $LATEST.

  9. Choose Next. The Configure additional options page opens.

  10. In the Additional options section, keep the default settings.

    For more information about these options, see Batch Operations job request elements.

  11. In the Completion report section, for Path to completion report destination, choose Browse S3. Find the bucket that you created for output media files in Step 1 (for example, tutorial-bucket-2). Choose the option button next to that bucket's name. Then choose Choose path.

    For the remaining Completion report settings, keep the defaults. For more information about completion report settings, see Batch Operations job request elements. A completion report maintains a record of the job's details and the operations performed.

  12. In the Permissions section, choose Choose from existing IAM roles. For IAM role, choose the IAM role for your S3 Batch Operations job that you created in Step 6 (for example, tutorial-s3batch-role).

  13. Choose Next.

  14. On the Review page, review the settings. Then choose Create job.

    After S3 finishes reading your S3 Batch Operations job's manifest, it sets the Status of the job to Awaiting your confirmation to run. To see updates to the job's status, refresh the page. You can't run your job until its status is Awaiting your confirmation to run.

Run the S3 Batch Operations job to invoke your Lambda function

Run your Batch Operations job to invoke your Lambda function for video transcoding. If your job fails, you can check your completion report to identify the cause.

To run the S3 Batch Operations job

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Batch Operations.

  3. In the Jobs list, choose the Job ID of the job on the first row, which is the S3 Batch Operations job that you created earlier.

  4. Choose Run job.

  5. Review your job parameters again, and confirm that the value for Total objects listed in manifest is the same as the number of objects in the manifest. Then choose Run job.

    Your S3 Batch Operations job page opens.

  6. After the job starts running, on your job page, under Status, check the progress of your S3 Batch Operations job, such as Status, % Complete, Total succeeded (rate), Total failed (rate), Date terminated, and Reason for termination.

    When the S3 Batch Operations job completes, view the data on your job page to confirm that the job finished as expected.

    If more than 50 percent of an S3 Batch Operations job's object operations fail after more than 1,000 operations have been attempted, the job automatically fails. To check your completion report to identify the cause of the failures, use the following optional procedure.

(Optional) Check your completion report

You can use your completion report to determine which objects failed and the cause of the failures.

To check your completion report for details about failed objects

  1. On the page of your S3 Batch Operations job, scroll down to the Completion report section, and choose the link under Completion report destination.

    The S3 output destination bucket's page opens.

  2. On the Objects tab, choose the folder that has a name ending with the job ID of the S3 Batch Operations job that you created earlier.

  3. Choose results/.

  4. Select the check box next to the .csv file.

  5. To view the job report, choose Open or Download.

(Optional) Monitor each Lambda invocation in the Lambda console

After the S3 Batch Operations job starts running, the job invokes the Lambda function for each input video object. S3 writes logs of each Lambda invocation to CloudWatch Logs. You can use the Lambda console's monitoring dashboard to monitor your Lambda function.

  1. Open the Amazon Lambda console at https://console.amazonaws.cn/lambda/.

  2. In the left navigation pane, choose Functions.

  3. In the Functions list, choose the name of the Lambda function that you created in Step 4 (for example, tutorial-lambda-convert).

  4. Choose the Monitor tab.

  5. Under Metrics, see the runtime metrics for your Lambda function.

  6. Under Logs, view log data for each Lambda invocation through CloudWatch Logs Insights.

    Note

    When you use S3 Batch Operations with a Lambda function, the Lambda function is invoked on each object. If your S3 Batch Operations job is large, it can invoke multiple Lambda functions at the same time, causing a spike in Lambda concurrency.

    Each Amazon Web Services account has a Lambda concurrency quota per Region. For more information, see Amazon Lambda Function Scaling in the Amazon Lambda Developer Guide. A best practice for using Lambda functions with S3 Batch Operations is to set a concurrency limit on the Lambda function itself. Setting a concurrency limit keeps your job from consuming most of your Lambda concurrency and potentially throttling other functions in your account. For more information, see Managing Lambda reserved concurrency in the Amazon Lambda Developer Guide.

(Optional) Monitor each MediaConvert video-transcoding job in the MediaConvert console

A MediaConvert job does the work of transcoding a media file. When your S3 Batch Operations job invokes your Lambda function for each video, each Lambda function invocation creates a MediaConvert transcoding job for each input video.

  1. Sign in to the Amazon Web Services Management Console and open the MediaConvert console at https://console.amazonaws.cn/mediaconvert/.

  2. If the MediaConvert introductory page appears, choose Get started.

  3. From the list of Jobs, view each row to monitor the transcoding task for each input video.

  4. Identify the row of a job that you want to check, and choose the Job ID link to open the job details page.

  5. On the Job summary page, under Outputs, choose the link for the HLS, MP4, or Thumbnails output, depending on what is supported by your browser, to go to the S3 destination bucket for the output media files.

  6. In the corresponding folder (HLS, MP4, or Thumbnails) of your S3 output destination bucket, choose the name of the output media file object.

    The object's detail page opens.

  7. On the object's detail page, under Object overview, choose the link under Object URL to watch the transcoded output media file.

Step 8: Check the output media files from your S3 destination bucket

To check the output media files from your S3 destination bucket

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. In the Buckets list, choose the name of the S3 destination bucket for output media files that you created in Step 1 (for example, tutorial-bucket-2).

  4. On the Objects tab, each input video has a folder that has the name of the input video. Each folder contains the transcoded output media files for an input video.

    To check the output media files for an input video, do the following:

    1. Choose the folder with the name of the input video that you want to check.

    2. Choose the Default/ folder.

    3. Choose the folder for a transcoded format (HLS, MP4, or thumbnails in this tutorial).

    4. Choose the name of the output media file.

    5. To watch the transcoded file, on the object's details page, choose the link under Object URL.

      Output media files in the HLS format are split into short segments. To play these videos, embed the object URL of the .m3u8 file in a compatible player.

Step 9: Clean up

If you transcoded videos using S3 Batch Operations, Lambda, and MediaConvert only as a learning exercise, delete the Amazon resources that you allocated so that you no longer accrue charges.

Delete the S3 Inventory configuration for your S3 source bucket

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. In the Buckets list, choose the name of your source bucket (for example, tutorial-bucket-1).

  4. Choose the Management tab.

  5. In the Inventory configurations section, choose the option button next to the inventory configuration that you created in Step 5 (for example, tutorial-inventory-config).

  6. Choose Delete, and then choose Confirm.

Delete the Lambda function

  1. Open the Amazon Lambda console at https://console.amazonaws.cn/lambda/.

  2. In the left navigation pane, choose Functions.

  3. Select the check box next to the function that you created in Step 4 (for example, tutorial-lambda-convert).

  4. Choose Actions, and then choose Delete.

  5. In the Delete function dialog box, choose Delete.

Delete the CloudWatch log group

  1. Open the CloudWatch console at https://console.amazonaws.cn/cloudwatch/.

  2. In the left navigation pane, choose Logs, and then choose Log groups.

  3. Select the check box next to the log group that has a name ending with the Lambda function that you created in Step 4 (for example, tutorial-lambda-convert).

  4. Choose Actions, and then choose Delete log group(s).

  5. In the Delete log group(s) dialog box, choose Delete.

Delete the IAM roles together with the inline policies for the IAM roles

To delete the IAM roles that you created in Step 2, Step 3, and Step 6, do the following:

  1. Sign in to the Amazon Web Services Management Console and open the IAM console at https://console.amazonaws.cn/iam/.

  2. In the left navigation pane, choose Roles, and then select the check boxes next to the role names that you want to delete.

  3. At the top of the page, choose Delete.

  4. In the confirmation dialog box, enter the required response in the text input field based on the prompt, and choose Delete.

Delete the customer-managed IAM policy

To delete the customer-managed IAM policy that you created in Step 6, do the following:

  1. Sign in to the Amazon Web Services Management Console and open the IAM console at https://console.amazonaws.cn/iam/.

  2. In the left navigation pane, choose Policies.

  3. Choose the option button next to the policy that you created in Step 6 (for example, tutorial-s3batch-policy). You can use the search box to filter the list of policies.

  4. Choose Actions, and then choose Delete.

  5. Confirm that you want to delete this policy by entering its name in the text field, and then choose Delete.

Empty the S3 buckets

To empty the S3 buckets that you created in Prerequisites, Step 1, and Step 5, do the following:

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. In the Buckets list, choose the option button next to the name of the bucket that you want to empty, and then choose Empty.

  4. On the Empty bucket page, confirm that you want to empty the bucket by entering permanently delete in the text field, and then choose Empty.

Delete the S3 buckets

To delete the S3 buckets that you created in Prerequisites, Step 1, and Step 5, do the following:

  1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. In the left navigation pane, choose Buckets.

  3. In the Buckets list, choose the option button next to the name of the bucket that you want to delete.

  4. Choose Delete.

  5. On the Delete bucket page, confirm that you want to delete the bucket by entering the bucket name in the text field, and then choose Delete bucket.

Next steps

After completing this tutorial, you can further explore other relevant use cases: