You have many Lambda functions that read data from the AWS Kinesis stream.
Your colleague informs you that our application has too many Lambda function invocations, increasing latency for your application.
How can you minimize latency and increase the read throughput of your function efficiently?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A.
Option A is CORRECT as creating a data stream consumer assures a dedicated connection to each shard.
The connection will help maximize throughput and minimize latency.
By default, Amazon states that the Lambda function polls each shard at a base rate of 1 per second.
Option B is incorrect as this option will increase the function execution time that will require longer timeouts and maybe more memory.
This will not be an efficient solution.
Option C is incorrect because the question mentions that there are many Lambda functions.
So it is not feasible to rebuild the Lambda functions.
Option D is incorrect as a dead letter queue will help when your Lambda functions are overwhelmed and are missing processing of the data coming from the stream which is not the case in this scenario.
Reference:
https://docs.aws.amazon.com/lambda/latest/dg/with-kinesis.htmlTo minimize latency and increase the read throughput of your Lambda functions that read data from AWS Kinesis streams, you should consider the following:
A. Create a data stream consumer: AWS provides a Kinesis Client Library (KCL) that enables you to build Amazon Kinesis-enabled applications using AWS Lambda. You can create a data stream consumer using the KCL library to read data from the Kinesis stream and process it in parallel by running multiple instances of the Lambda function. The KCL library manages the state of the stream and the distribution of work among the instances, which can help to reduce latency and increase throughput.
B. Reduce the number of functions and let each function do the work of 2 functions: Reducing the number of Lambda functions can decrease the number of function invocations and thus reduce latency. However, you should ensure that the workload of each function is not too high. One approach is to let each function process multiple records at a time by increasing the batch size configuration, but this may increase the memory requirements of the function.
C. Configure CloudWatch logs to check the latency and rebuild the Lambda functions: CloudWatch can be used to monitor the latency of the Lambda function invocations and identify the causes of latency. By identifying the bottlenecks, you can optimize the code of the Lambda function or adjust the AWS configuration to reduce the latency.
D. Configure a dead letter queue: A dead letter queue can be used to handle the records that the Lambda function is unable to process. By configuring a dead letter queue, you can ensure that these records are not lost and can be retried by the Kinesis stream. However, this option does not directly address the issue of latency.
In summary, options A and B are the most effective ways to minimize latency and increase the read throughput of your Lambda functions that read data from AWS Kinesis streams. Option C and D can be used as complementary measures to monitor and handle the errors in the Lambda function.