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Zorluk: Çok zorServerless and Automated Scaling Architectures for Cost Efficiency

A financial technology company is building a serverless transaction processing system on AWS. The system must ingest ledger transaction entries, process them in the exact order they are received to ensure ledger integrity, and store the results in a database. The volume of transactions is highly unpredictable, scaling from zero to tens of thousands of requests in seconds. Additionally, the system must run a nightly ledger reconciliation job that aggregates all transactions from the past 24 hours, which typically takes 45 minutes to execute.

To minimize idle costs and maximize operational efficiency, which two architectural components should a solutions architect select? (Select two.)

  1. Deploy an Amazon SQS FIFO queue to receive the transaction entries, and configure an AWS Lambda function to process the queue.Cevap
  2. B
    Deploy an Amazon SQS Standard queue to receive the transaction entries, and configure an AWS Lambda function to process the queue.
  3. Use AWS Batch with an AWS Fargate Spot compute environment to run the nightly reconciliation job.Cevap
  4. D
    Create an AWS Lambda function with a custom timeout of 1 hour to execute the nightly reconciliation job.
  5. E
    Store the transaction results in an Amazon DynamoDB table configured in Provisioned Capacity mode with Auto Scaling enabled.

Cevap

The solutions architect should deploy an Amazon SQS FIFO queue to ingest the transaction entries with an AWS Lambda function for ordered processing, and use AWS Batch with an AWS Fargate Spot compute environment to run the nightly reconciliation job.
Deploying an Amazon SQS FIFO queue with AWS Lambda ensures both strict ordering of transaction ledger events and serverless auto-scaling from zero to handle unpredictable spikes cost-effectively. Running the 45-minute nightly job on AWS Batch with AWS Fargate Spot is the most cost-efficient method because it bypasses Lambda's 15-minute limit and runs on discounted spare capacity.

Adım Adım Çözüm

1
Analyze ordering and scaling requirements for transaction ingestion.
Ledger integrity requires strict ordering, which dictates using Amazon SQS FIFO over SQS Standard. High variability of traffic scaling from zero requires a serverless consumer like AWS Lambda.
This establishes the cost-effective and scale-to-zero processing pipeline for the transactions.
2
Analyze execution time requirements for the nightly reconciliation job.
The job takes 45 minutes, exceeding AWS Lambda's 15-minute execution limit.
This rules out AWS Lambda for the batch reconciliation workload.
3
Select a cost-efficient serverless container execution method for long-running batch jobs.
AWS Batch managing a containerized task on AWS Fargate Spot is the most cost-effective solution.
Fargate Spot utilizes spare capacity at a highly discounted rate, and AWS Batch automatically handles scheduling and scaling without persistent compute management overhead.

Anahtar Kavram

Leveraging SQS FIFO for ordered transaction processing and AWS Batch with Fargate Spot for long-running, cost-efficient batch processing.
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Serverless and Automated Scaling Architectures for Cost Efficiency