Esta página destina-se somente a clientes atuais do serviço Amazon Glacier que usam cofres e a API REST original de 2012.

Se você estiver procurando soluções de armazenamento de arquivos do Amazon Glacier, recomendamos usar as classes de armazenamento do Amazon S3, S3 Glacier Instant Retrieval, S3 Glacier Flexible Retrieval e S3 Glacier Deep Archive. Para saber mais sobre essas opções de armazenamento, consulte Classes de armazenamento do Amazon Glacier.

O Amazon Glacier (serviço autônomo original baseado em cofre) não aceitará mais novos clientes a partir de 15 de dezembro de 2025, sem impacto para os clientes existentes. O Amazon Glacier é um serviço independente com suas próprias APIs que armazena dados em cofres e é diferente das classes de armazenamento do Amazon S3 e Amazon S3 Glacier. Seus dados existentes permanecerão seguros e acessíveis no Amazon Glacier indefinidamente. Nenhuma migração é necessária. Para armazenamento de arquivamento de baixo custo e longo prazo, a AWS recomenda as classes de armazenamento do Amazon S3 Glacier, que oferecem uma experiência superior ao cliente com APIs baseadas em bucket do S3, disponibilidade total da Região da AWS, custos mais baixos e integração de serviços da AWS. Se você quiser recursos aprimorados, considere migrar para as classes de armazenamento do Amazon S3 Glacier usando nossas Orientações de soluções da AWS para transferir dados dos cofres do Amazon Glacier para as classes de armazenamento do Amazon S3 Glacier.

Baixando um arquivo grande usando processamento paralelo com Python

Este tópico descreve como baixar um arquivo grande do Amazon S3 Glacier (S3 Glacier) usando processamento paralelo com Python. Essa abordagem permite que você baixe arquivos de qualquer tamanho de forma confiável, dividindo-os em partes menores que podem ser processadas de forma independente.

Visão geral

O script Python fornecido nesse exemplo realiza as seguintes tarefas:

Pré-requisitos

Antes de começar, você deve ter o seguinte:

Exemplo: baixando um arquivo usando processamento paralelo com Python

O script Python a seguir demonstra como fazer o download de um grande arquivo do Amazon Glacier usando processamento paralelo:

import boto3
import time
import json
import jmespath
import re
import concurrent.futures
import os

output_file_path = "output_directory_path"
vault_name = "vault_name"

chunk_size = 1000000000 #1gb - size of chunks for parallel download.
notify_queue_name = 'GlacierJobCompleteNotifyQueue' # SQS queue for Glacier recall notification
chunk_download_queue_name='GlacierChunkReadyNotifyQueue' # SQS queue for chunks
sns_topic_name = 'GlacierRecallJobCompleted' # the SNS topic to be notified when Glacier archive is restored.
chunk_queue_visibility_timeout = 7200 # 2 hours - this may need to be adjusted.
region = 'us-east-1'
archive_id = "archive_id_to_restore"
retrieve_archive = True # set to false if you do not want to restore from Glacier - useful for restarting or parallel processing of the chunk queue.
workers = 12 # the number of parallel worker threads for downloading chunks. 

def setup_queues_and_topic():
    sqs = boto3.client('sqs')
    sns = boto3.client('sns')

    # Create the SNS topic
    topic_response = sns.create_topic(
        Name=sns_topic_name
    )
    topic_arn = topic_response['TopicArn']
    print("Creating the SNS topic " + topic_arn)

    # Create the notification queue
    notify_queue_response = sqs.create_queue(
        QueueName=notify_queue_name,
        Attributes={
            'VisibilityTimeout': '300',  # 5 minutes
            'ReceiveMessageWaitTimeSeconds': '20'  # Enable long polling
        }
    )
    notify_queue_url = notify_queue_response['QueueUrl']
    print("Creating the archive-retrieval notification queue " + notify_queue_url)

    # Create the chunk download queue
    chunk_queue_response = sqs.create_queue(
        QueueName=chunk_download_queue_name,
        Attributes={
            'VisibilityTimeout': str(chunk_queue_visibility_timeout),  # 5 minutes
            'ReceiveMessageWaitTimeSeconds': '0'
        }
    )
    chunk_queue_url = chunk_queue_response['QueueUrl']

    print("Creating the chunk ready notification queue " + chunk_queue_url)


   # Get the ARN for the notification queue
    notify_queue_attributes = sqs.get_queue_attributes(
        QueueUrl=notify_queue_url,
        AttributeNames=['QueueArn']
    )
    notify_queue_arn = notify_queue_attributes['Attributes']['QueueArn']

    # Set up the SNS topic policy on the notification queue
    queue_policy = {
        "Version": "2012-10-17",		 	 	 
        "Statement": [{
            "Sid": "allow-sns-messages",
            "Effect": "Allow",
            "Principal": {"AWS": "*"},
            "Action": "SQS:SendMessage",
            "Resource": notify_queue_arn,
            "Condition": {
                "ArnEquals": {
                    "aws:SourceArn": topic_arn
                }
            }
        }]
    }

    # Set the queue policy
    sqs.set_queue_attributes(
        QueueUrl=notify_queue_url,
        Attributes={
            'Policy': json.dumps(queue_policy)
        }
    )

    # Subscribe the notification queue to the SNS topic
    sns.subscribe(
        TopicArn=topic_arn,
        Protocol='sqs',
        Endpoint=notify_queue_arn
    )

    return {
        'topic_arn': topic_arn,
        'notify_queue_url': notify_queue_url,
        'chunk_queue_url': chunk_queue_url
    }


def split_and_send_chunks(archive_size, job_id,chunk_queue_url):
    ranges = []
    current = 0
    chunk_number = 0

    while current < archive_size:
        chunk_number += 1
        next_range = min(current + chunk_size - 1, archive_size - 1)
        ranges.append((current, next_range, chunk_number))
        current = next_range + 1

    # Send messages to SQS queue
    for start, end, chunk_number in ranges:
        body = {"start": start, "end": end, "job_id": job_id, "chunk_number": chunk_number}
        body = json.dumps(body)
        print("Sending SQS message for range:" + str(body))
        response = sqs.send_message(
            QueueUrl=chunk_queue_url,
            MessageBody=str(body)
        )

def GetJobOutputChunks(job_id, byterange, chunk_number):
    glacier = boto3.client('glacier')
    response = glacier.get_job_output(
        vaultName=vault_name,
        jobId=job_id,
        range=byterange,

    )

    with open(os.path.join(output_file_path,str(chunk_number)+".chunk"), 'wb') as output_file:
        output_file.write(response['body'].read())

    return response

def ReceiveArchiveReadyMessages(notify_queue_url,chunk_queue_url):

    response = sqs.receive_message(
        QueueUrl=notify_queue_url,
        AttributeNames=['All'],
        MaxNumberOfMessages=1,
        WaitTimeSeconds=20,
        MessageAttributeNames=['Message']
    )
    print("Polling archive retrieval job ready queue...")
    # Checking that there is a Messages key before proceeding. No 'Messages' key likely means the queue is empty

    if 'Messages' in response:
        print("Received a message from the archive retrieval job queue")
        jsonresponse = response
        # Loading the string into JSON and checking that ArchiveSizeInBytes key is present before continuing.
        jsonresponse=json.loads(jsonresponse['Messages'][0]['Body'])
        jsonresponse=json.loads(jsonresponse['Message'])
        if 'ArchiveSizeInBytes' in jsonresponse:
            receipt_handle = response['Messages'][0]['ReceiptHandle']    
            if jsonresponse['ArchiveSizeInBytes']:
                archive_size = jsonresponse['ArchiveSizeInBytes']

                print(f'Received message: {response}')      
                if archive_size > chunk_size:
                    split_and_send_chunks(archive_size, jsonresponse['JobId'],chunk_queue_url)

                    sqs.delete_message(
                    QueueUrl=notify_queue_url,
                    ReceiptHandle=receipt_handle)

            else:
                print("No ArchiveSizeInBytes value found in message")
                print(response)

    else:
        print('No messages available in the queue at this time.')

    time.sleep(1)

def ReceiveArchiveChunkMessages(chunk_queue_url):
    response = sqs.receive_message(
        QueueUrl=chunk_queue_url,
        AttributeNames=['All'],
        MaxNumberOfMessages=1,
        WaitTimeSeconds=0,
        MessageAttributeNames=['Message']
    )
    print("Polling archive chunk queue...")
    print(response)
    # Checking that there is a Messages key before proceeding. No 'Messages' key likely means the queue is empty
    if 'Messages' in response:
        jsonresponse = response
        # Loading the string into JSON and checking that ArchiveSizeInBytes key is present before continuing.
        jsonresponse=json.loads(jsonresponse['Messages'][0]['Body'])
        if 'job_id' in jsonresponse: #checking that there is a job id before continuing
            job_id = jsonresponse['job_id']
            byterange = "bytes="+str(jsonresponse['start']) + '-' + str(jsonresponse['end'])
            chunk_number = jsonresponse['chunk_number']
            receipt_handle = response['Messages'][0]['ReceiptHandle']
            if jsonresponse['job_id']:
                print(f'Received message: {response}')
                GetJobOutputChunks(job_id,byterange,chunk_number)
                sqs.delete_message(
                QueueUrl=chunk_queue_url,
                ReceiptHandle=receipt_handle)
    else:
        print('No messages available in the chunk queue at this time.')

def initiate_archive_retrieval(archive_id, topic_arn):
    glacier = boto3.client('glacier')

    job_parameters = {
        "Type": "archive-retrieval",
        "ArchiveId": archive_id,
        "Description": "Archive retrieval job",
        "SNSTopic": topic_arn,
        "Tier": "Bulk"  # You can change this to "Standard" or "Expedited" based on your needs
    }

    try:
        response = glacier.initiate_job(
            vaultName=vault_name,
            jobParameters=job_parameters
        )

        print("Archive retrieval job initiated:")
        print(f"Job ID: {response['jobId']}")
        print(f"Job parameters: {job_parameters}")
        print(f"Complete response: {json.dumps(response, indent=2)}")

        return response['jobId']

    except Exception as e:
        print(f"Error initiating archive retrieval job: {str(e)}")
        raise

def run_async_tasks(chunk_queue_url, workers):
    max_workers = workers  # Set the desired maximum number of concurrent tasks
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        for _ in range(max_workers):
            executor.submit(ReceiveArchiveChunkMessages, chunk_queue_url)

# One time setup of the necessary queues and topics. 
queue_and_topic_atts = setup_queues_and_topic()

topic_arn = queue_and_topic_atts['topic_arn']
notify_queue_url = queue_and_topic_atts['notify_queue_url']
chunk_queue_url = queue_and_topic_atts['chunk_queue_url']

if retrieve_archive:
    print("Retrieving the defined archive... The topic arn we will notify when recalling the archive is: "+topic_arn)
    job_id = initiate_archive_retrieval(archive_id, topic_arn)
else:
    print("Retrieve archive is false, polling queues and downloading only.")

while True:
   ReceiveArchiveReadyMessages(notify_queue_url,chunk_queue_url)
   run_async_tasks(chunk_queue_url,workers)
    

Uso de um script

Para usar esse script, siga estas etapas:

Considerações importantes

Ao usar esse script, lembre-se do seguinte: