Solved: Cloud Functions vs AWS Lambda

harshada2828

Does Cloud Functions Supports bot layer alike AWS Lambda Functions. If not what is a workaround if we still want to achieve the similar things in GCP?

DamianS

Hello @harshada2828 ,

Okay, I got it right now. AFAIK unfortunately it doesn't offer a direct equivalent to AWS Lambda Layers. Instead, you would typically handle shared libraries or dependencies in Google Cloud Functions using one of the following approaches:

Shared Private Libraries via Cloud Source Repositories: You can create shared libraries as private packages and store them in a source repository such as Google Cloud Source Repositories. Each Cloud Function can then include this shared library as a dependency. This method allows you to maintain a single version of your codebase that all functions can reference. When deploying your Cloud Functions, you can set up your build process (using Cloud Build or another CI/CD tool) to fetch and include these libraries from the source repository.
Vendor the Shared Code: Another common approach is to include the shared libraries directly in your function’s deployment package. This can be done by copying the shared code into each function's directory before deployment. This approach, often referred to as "vendoring", ensures that all functions have access to the same version of the shared code at deployment time. Automation scripts can help manage the process of copying shared code into function directories during deployment.
Google Cloud Artifacts Registry: You can use Google Cloud Artifact Registry to manage and share custom-built packages across your projects. By uploading your shared libraries as packages to the Artifact Registry, you can easily manage versions and dependencies in a similar manner to using a private NPM registry or Python package index. Cloud Functions can be configured to install these packages during the build or deployment process.
Use of Submodules or Git Subtrees: If you're managing your function code in a Git repository, you could leverage Git submodules or Git subtrees to manage shared dependencies. This way, you can maintain a central repository of shared code that is included in each function's repository as a submodule or subtree.

cheers,
DamianS

View solution in original post

DamianS

Hello @harshada2828 ,

Welcome to the Google Cloud Community. What do you mean by bot layer? Any reference to AWS docs or description would be good.
cheers,
DamianS

harshada2828

Does GCP / Cloud Functions have a library concept anything like AWS Lambda Layers? Meaning, if we wanted to reduce code duplication and roll out a common library layer for our Google Cloud Functions, how would we do it?

DamianS

Hello @harshada2828 ,

Okay, I got it right now. AFAIK unfortunately it doesn't offer a direct equivalent to AWS Lambda Layers. Instead, you would typically handle shared libraries or dependencies in Google Cloud Functions using one of the following approaches:

Shared Private Libraries via Cloud Source Repositories: You can create shared libraries as private packages and store them in a source repository such as Google Cloud Source Repositories. Each Cloud Function can then include this shared library as a dependency. This method allows you to maintain a single version of your codebase that all functions can reference. When deploying your Cloud Functions, you can set up your build process (using Cloud Build or another CI/CD tool) to fetch and include these libraries from the source repository.
Vendor the Shared Code: Another common approach is to include the shared libraries directly in your function’s deployment package. This can be done by copying the shared code into each function's directory before deployment. This approach, often referred to as "vendoring", ensures that all functions have access to the same version of the shared code at deployment time. Automation scripts can help manage the process of copying shared code into function directories during deployment.
Google Cloud Artifacts Registry: You can use Google Cloud Artifact Registry to manage and share custom-built packages across your projects. By uploading your shared libraries as packages to the Artifact Registry, you can easily manage versions and dependencies in a similar manner to using a private NPM registry or Python package index. Cloud Functions can be configured to install these packages during the build or deployment process.
Use of Submodules or Git Subtrees: If you're managing your function code in a Git repository, you could leverage Git submodules or Git subtrees to manage shared dependencies. This way, you can maintain a central repository of shared code that is included in each function's repository as a submodule or subtree.

cheers,
DamianS