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Logger.exception(f'Could not create SQS queue - queue.') Response = sqs_resource.create_queue(QueueName=queue_name, Sqs_resource = boto3.resource("sqs", region_name=AWS_REGION)ĭef create_queue(queue_name, delay_seconds, visiblity_timeout): import loggingįrom botocore.exceptions import ClientErrorįormat='%(asctime)s: %(levelname)s: %(message)s') These queues can handle an unlimited number of transactions (SendMessage, ReceiveMessage, or DeleteMessage API calls) per second. Standard SQS queues are the default type of queues. To create a standard SQS queue, you need to use the create_queue() method from the Boto3 resource. An SQS queue works like a buffer between the application components that receive data and those components that process the data in your system. Sqs_resource = boto3.resource("sqs", region_name=AWS_REGION) Working Boto3 SQSĪmazon SQS provides an HTTP API over which applications can submit and read messages out of a messaging queue.

Likewise, you can instantiate the Boto3 SQS resource: import boto3 Sqs_client = boto3.client("sqs", region_name=AWS_REGION) Here’s how we can instantiate the Boto3 SQS client to start working with Amazon SQS APIs: import boto3 For more information on the topic, take a look at AWS CLI vs botocore vs Boto3. The resource allows you to use AWS services in a higher-level object-oriented way.For example, you can get access to API response data in JSON format. The client allows you to access the low-level API data.Working with AWS Lambda in Python using Boto3 In general, here’s what you need to have installed:Īlternatively, you can use a Cloud9 IDE. To start interacting with Amazon SQS programmatically and making API calls to manage SQS queues, you have to configure your Python environment. Enable long polling on an existing SQS queue.Read and delete messages from the SQS queue.