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Certainly! Let’s break down the provided Python code snippet and analyze its functionality, output, time complexity, and space complexity:
### Code Explanation:
1. The code uses the **boto3** library to interact with **AWS Simple Queue Service (SQS)**.
2. It creates an SQS client using `boto3.client(‘sqs’)`.
3. The `get_queue_attributes` method retrieves specific attributes of a specified queue. In this case, it requests the ‘ApproximateNumberOfMessagesNotVisible’ attribute.
4. The ‘ApproximateNumberOfMessagesNotVisible’ metric represents the number of messages that are currently “in flight” (i.e., sent to consumers but not yet deleted or still within their visibility timeout).
### Output Explanation:
The output of this code will be a print statement showing the maximum number of messages in flight at the moment the code is executed. For example:
“`
Maximum number of messages in flight: 5
“`
### Time Complexity:
– The code makes a single API call to AWS SQS (`get_queue_attributes`), which is an O(1) operation. Therefore, the time complexity is constant.
### Space Complexity:
– The memory usage does not depend on the size of the queue or the number of messages in it.
– The code only stores a fixed number of variables regardless of the queue size.
– Thus, the space complexity is also O(1), or constant space.
Please note that the actual execution time and memory usage can vary based on network latency, AWS service load, and other external factors when making the API call. Since the code is not executable here due to the lack of network access and AWS credentials, the explanation above is based on the code’s intent and functionality.
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