Which approach will process the dataset in the LEAST time?
Use a combination of AWS Step Functions and an AWS Lambda function to call the DetectSentiment API operation for each post synchronously.
Use a combination of AWS Step Functions and an AWS Lambda function to call the BatchDetectSentiment API operation with batches of up to 25 posts at a time.
Upload the posts to Amazon S3. Pass the S3 storage path to an AWS Lambda function that calls the StartSentimentDetectionJob API operation.
Use an AWS Lambda function to call the BatchDetectSentiment API operation with the whole dataset.
Explanations:
Calling the DetectSentiment API synchronously for each post will be slow because it processes one post at a time, leading to high latency given the size of the dataset (one million posts). This approach does not leverage batch processing capabilities, resulting in inefficient processing time.
Using BatchDetectSentiment to process up to 25 posts at a time is the most efficient approach. This method reduces the number of API calls, as it allows for parallel processing of multiple posts, significantly speeding up the overall analysis compared to processing posts individually.
While starting a sentiment detection job using StartSentimentDetectionJob can handle larger datasets, this method is typically more suitable for larger text files rather than individual social media posts. Additionally, there might be overhead in job initiation and management that can lead to longer processing times for one million individual posts compared to batch processing.
Calling BatchDetectSentiment with the entire dataset is not supported, as the API limits the number of posts to 25 per call. Attempting to send all posts in one go would result in failure, making this option impractical for the dataset size.
I guess:
Use a combination of AWS Step Functions and an AWS Lambda function to call the BatchDetectSentiment API operation with batches of up to 25 posts at a time.