Efficient Processing of AWS Kinesis Streams with Lambda Functions

Answer - A,B and C.

There is a good article which lists down ways in which you can get better performance of your shards and processing data with AWS Lambda.

For more information on this article, please refer to the below URL.

When processing data from AWS Kinesis streams using AWS Lambda functions, there are several factors to consider to ensure maximum efficiency. The following are the three answers that can be used to achieve this:

1. Number of Shards for the Kinesis stream: A Kinesis stream is made up of multiple shards, and each shard can support up to 1000 writes per second or up to 2MB of data per second. By increasing the number of shards, you can increase the overall capacity of the stream, allowing more Lambda functions to process the data concurrently.

2. Batch size for AWS Lambda: The batch size parameter controls the number of records that are passed to each instance of a Lambda function. By increasing the batch size, you can reduce the number of Lambda function invocations needed to process a given number of records, thereby reducing the overall cost and increasing efficiency.

3. Memory for AWS Lambda: The amount of memory allocated to a Lambda function affects its performance, as it determines the amount of CPU and network resources available to the function. By allocating more memory, you can increase the processing power of the Lambda function, allowing it to process records more quickly.

Note that the number of shard iterators for the Kinesis stream is not relevant to maximizing processing efficiency. Shard iterators are used to read data from specific positions within a shard, and the number of iterators needed will depend on the specific use case.