Learn how to create and use Lambda layers to share common Python libraries across multiple Lambda functions. Find out how Lambda layers can help you reduce the size of your deployment packages and simplify your serverless application development.
Table of Contents
Question
A developer is designing a serverless application with two AWS Lambda functions to process photos. One Lambda function stores objects in an Amazon S3 bucket and stores the associated metadata in an Amazon DynamoDB table. The other Lambda function fetches the objects from the S3 bucket by using the metadata from the DynamoDB table. Both Lambda functions use the same Python library to perform complex computations and are approaching the quota for the maximum size of zipped deployment packages.
What should the developer do to reduce the size of the Lambda deployment packages with the LEAST operational overhead?
A. Package each Python library in its own .zip file archive. Deploy each Lambda function with its own copy of the library.
B. Create a Lambda layer with the required Python library. Use the Lambda layer in both Lambda functions.
C. Combine the two Lambda functions into one Lambda function. Deploy the Lambda function as a single .zip file archive.
D. Download the Python library to an S3 bucket. Program the Lambda functions to reference the object URLs.
Answer
B. Create a Lambda layer with the required Python library. Use the Lambda layer in both Lambda functions.
Explanation
The correct answer to the question is B. Create a Lambda layer with the required Python library. Use the Lambda layer in both Lambda functions.
The reason for this answer is that:
- Option B: Creating a Lambda layer with the required Python library and using the Lambda layer in both Lambda functions is the best way to reduce the size of the Lambda deployment packages with the least operational overhead. A Lambda layer is a .zip file archive that contains supplementary code or data that can be imported by Lambda functions at runtime. By using a Lambda layer, you can avoid including the same Python library in each deployment package, which reduces the package size and keeps them organized. You can also update the Python library independent of your function code, and vice versa, which promotes separation of concerns and helps you focus on your function logic. Moreover, using a Lambda layer is simple and convenient, as you only need to create the layer once and then apply it to any number of functions in your account.
- Option A: Packaging each Python library in its own .zip file archive and deploying each Lambda function with its own copy of the library is not an efficient way to reduce the size of the Lambda deployment packages with the least operational overhead. This option does not reduce the package size at all, as you still need to include the same Python library in each deployment package. This option also does not reduce the operational overhead, as you still need to manage and update the Python library for each function separately, which can be tedious and error-prone.
- Option C: Combining the two Lambda functions into one Lambda function and deploying the Lambda function as a single .zip file archive is not a recommended way to reduce the size of the Lambda deployment packages with the least operational overhead. This option may reduce the package size slightly, as you only need to include one copy of the Python library in one deployment package. However, this option also increases the operational overhead, as you need to rewrite your function code to handle both tasks (storing objects in S3 and fetching objects from S3), which can make your code more complex and less modular. Moreover, this option may affect your function performance and scalability, as you need to allocate more memory and time for each invocation, which can increase your costs and risk of timeout.
- Option D: Downloading the Python library to an S3 bucket and programming the Lambda functions to reference the object URLs is not a feasible way to reduce the size of the Lambda deployment packages with the least operational overhead. This option may reduce the package size significantly, as you do not need to include any Python library in your deployment packages. However, this option also introduces many challenges and limitations, such as:
- How to download and install the Python library from S3 to your Lambda execution environment at runtime, which can be slow and unreliable.
- How to ensure that your Lambda functions have permissions to access the S3 bucket and objects that contain the Python library, which can be complicated and insecure.
- How to handle updates or changes to the Python library in S3, which can cause inconsistencies or errors in your function behavior.
- How to deal with dependencies or compatibility issues between the Python library and your function runtime or code, which can be difficult and unpredictable.
Therefore, option D is not a viable choice for reducing the size of the Lambda deployment packages with the least operational overhead.
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