Reduce Latency for Lambda Processing Kinesis Logs
A company has an application that runs on AWS Lambda and sends logs to Amazon CloudWatch Logs. An Amazon Kinesis data stream is subscribed to the log groups in CloudWatch Logs. A single consumer Lambda function processes the logs from the data stream and stores the logs in an Amazon S3 bucket. The company’s DevOps team has noticed high latency during the processing and ingestion of some logs. Which combination of steps will reduce the latency? (Choose three.)
Community Insight
The question tests knowledge of Kinesis-Lambda integration performance tuning, specifically the trap of confusing Reserved Concurrency (which limits scale) with Parallelization Factor (which increases it).
This question addresses high latency in an AWS Lambda consumer processing Amazon Kinesis Data Streams logs. The correct steps involve enabling Enhanced Fan-Out and increasing the Parallelization Factor to optimize throughput.
Many candidates incorrectly select C (Reserved Concurrency), believing that reserving capacity helps performance. In reality, Reserved Concurrency sets a ceiling on concurrent executions, which can restrict scaling and potentially increase latency under load if not tuned perfectly, whereas Parallelization Factor directly enables more concurrent invocations.
Community Discussion (6 comments)
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Expert Analysis
Why the Answer Is Correct
To reduce latency in a Kinesis-to-Lambda pipeline, you must maximize the throughput of data delivery and the parallelism of processing. Option A is correct because Enhanced Fan-Out provides dedicated 2 MB/s per shard throughput for consumers, eliminating contention with other consumers and reducing network jitter/latency compared to shared polling. Option B is correct because increasing the Parallelization Factor allows Lambda to invoke multiple instances of your function for a single shard (up to 10 per shard), significantly increasing processing concurrency and reducing the backlog time.Why the Other Options Are Wrong
Option C is incorrect because Reserved Concurrency defines the maximum number of simultaneous executions; while it prevents throttling from other services, it does not inherently reduce latency and can actually limit scaling if set too low. Option D is incorrect because increasing batch size generally increases individual record processing latency due to larger payloads, even if it improves overall throughput efficiency. Option E is incorrect because turning off ReportBatchItemFailures removes visibility into partial failures, making troubleshooting difficult without solving the root cause of latency.Community Comment Notes
Community consensus heavily favored AB, with some users debating F (which was likely a typo or misinterpretation of another option in their source). Comments reference the official AWS documentation confirming that Enhanced Fan-Out and Parallelization Factor are the key levers for Kinesis-Lambda performance optimization.Official Reference
Exam Strategy
When optimizing serverless consumers, always look for options that increase parallelism (Parallelization Factor, Shards) or dedicated resources (Enhanced Fan-Out, Provisioned Concurrency). Avoid options that impose limits (Reserved Concurrency) unless explicitly asked about security or cost caps.
Frequently Asked Questions
Why is Enhanced Fan-Out better than default polling?
Enhanced Fan-Out provides dedicated throughput per consumer, removing contention and reducing latency caused by shared bandwidth and polling intervals.
Does Reserved Concurrency help with latency?
No, Reserved Concurrency limits maximum concurrent executions. It protects against throttling but does not increase processing speed or reduce backlog latency.
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