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Working with Lambda Functions in Stelvio

Stelvio simplifies AWS Lambda function creation and configuration by automating packaging, dependency management, and secure resource linking, letting you focus on your function's logic. In this guide, we'll explore how to organize your Lambda functions and manage their dependencies using Stelvio.

Understanding Lambda Functions Organization

When creating Lambda functions in Stelvio, you have two main approaches for organizing your code:

  1. Single-file functions
  2. Folder-based functions

Each approach has its own use cases and benefits.

Single-File Lambda Functions

Single-file functions are perfect for simple, focused tasks that don't require additional code files. Here's how to create one:

# functions/simple.py
def handler(event, context):
    return {
        "statusCode": 200,
        "body": "Hello from Lambda!"
    }


# In your infrastructure code
fn = Function(handler="functions/simple.handler")

Key characteristics of single-file functions:

  • One Python file contains all the function code
  • Cannot import from other files in the same directory
  • Perfect for simple, focused tasks
  • Automatically packaged by Stelvio

Folder-Based Lambda Functions

For more complex scenarios where you need to split your code across multiple files, use folder-based functions:

# functions/
# └── users/
#     ├── handler.py         # Main function code
#     ├── database.py        # Database operations
#     └── validation.py      # Input validation

# In your infrastructure code
fn = Function(
    name="my-user-processor",
    folder="functions/users",  # folder of the function
    handler="handler.process"  # Relative to folder directory
)

Key characteristics of folder-based functions:

  • Can split code across multiple files (within its folder)
  • Can import between files in the folder
  • All files in the folder are packaged together
  • Perfect for complex functions with shared code

Function Configuration

You can configure your Lambda functions by specifying different parameters to Function class:

from stelvio.aws.function import Function

fn = Function(
    name="user-processor-config",
    folder="users",  # For folder-based Lambda
    handler="handler.process",  # Handler function relative to folder
    memory=512,  # Memory in MB
    timeout=30,  # Timeout in seconds
)

For simpler cases, when you're happy with defaults, you can just provide the handler:

from stelvio.aws.function import Function

fn = Function(handler="simple.handler")

Linking and Environment Variables

When you link other components to your Lambda function, Stelvio automatically:

  1. Generates the necessary IAM permissions
  2. Creates lambda environment variables for component access
  3. Generates a type-safe component access python file

Here's how it works:

# Create component
from stelvio.aws.dynamo import AttributeType, DynamoTable
from stelvio.aws.function import Function

table = DynamoTable(
    name="users",
    fields={
        "user_id": AttributeType.STRING
    },
    partition_key="user_id"
)

# Link to Lambda
fn = Function(
    handler="users/handler.process",
    links=[table]  # Link the table to the function
)

Stelvio generates a stlv_resources.py file in your Lambda's directory (when you deploy or preview):

# Generated stlv_resources.py
import os
from dataclasses import dataclass
from typing import Final


@dataclass(frozen=True)
class UsersResource:
    @property
    def table_arn(self) -> str:
        return os.getenv("STLV_USERS_TABLE_ARN")

    @property
    def table_name(self) -> str:
        return os.getenv("STLV_USERS_TABLE_NAME")


@dataclass(frozen=True)
class LinkedResources:
    users: Final[UsersResource] = UsersResource()


Resources: Final = LinkedResources()

Generation Timing

The stlv_resources.py file is generated or updated in your function's source directory whenever you run pulumi preview or pulumi up.

You can then use these resources in your Lambda code with full IDE support:

from stlv_resources import Resources


def handler(event, context):
    table_name = Resources.users.table_name
    # Use table_name with boto3...

This provides:

  • Type-safe access to resource properties
  • IDE completion for available resources

Best Practices

  1. Start Simple:

    • Use single-file functions for simple tasks.
    • Move to folder-based organization (using the folder parameter) when your function grows or needs multiple files or specific dependencies.

  2. Dependency Management:

    • Use the requirements parameter to manage dependencies
    • Leverage -r in requirements.txt to share common dependencies.
    • Keep dependencies minimal to reduce package size and cold starts.
    • Stelvio handles the complexities of platform-specific installation and caching.

  3. Resource Access:

    • Use the generated stlv_resources.Resources object for type-safe resource access.
    • Keep your functions focused on business logic
    • Let Stelvio manage IAM permissions through linking

  4. Function Organization:

    • Keep related code together in folder-based functions
    • Use clear file names and structure

Managing Dependencies

Stelvio provides a flexible and automated system for managing Python dependencies for your Lambda functions. It handles finding, installing (using platform-specific settings), caching, and packaging dependencies, ensuring they work correctly in the AWS Lambda environment.

You control dependency handling using the requirements parameter when defining a Function:

from stelvio.aws.function import Function

fn = Function(
    name="my-function",
    handler="functions/users/to/handler.handler_fn",
    requirements=...  # Configure dependencies here
)

Let's explore the different ways you can configure the requirements parameter.

Default Behavior

If you don't specify the requirements parameter (or set it to None), Stelvio automatically looks for a file named requirements.txt relative to your function's code:

  • For Single-File Functions: It looks in the same directory as the handler file.

    Project Structure
    functions/
├── users.py
├── orders.py
└── requirements.txt  # <-- Stelvio finds this automatically

    # Stelvio automatically uses functions/requirements.txt
fn = Function(
    name="users",
    handler="functions/users.get",
    # requirements=None (default)
)

    requirements.txt is shared among lambda functions within the same folder

    In the example above function using orders.py and function using users.py would have same dependencies (defined in requirements.txt)

  • For Folder-Based Functions: It looks inside the function's source folder ( specified by the folder parameter or derived from the handler if using folder::file.handler syntax).

    Project Structure
    functions/
└── my_complex_function/
    ├── handler.py
    ├── utils.py
    └── requirements.txt  # <-- Stelvio finds this automatically

    # Stelvio automatically uses functions/my_complex_function/requirements.txt
fn_folder = Function(
    name="complex-fn-folder",
    folder="functions/my_complex_function", # Specify the folder
    handler="handler.process",              # Handler relative to the folder
    # requirements=None (default)
)

If no requirements.txt file is found in the expected location, Stelvio assumes there are no dependencies to install for that function.

Explicit File Path

You can tell Stelvio to use a specific requirements file by providing its path as a string. The path should be relative to your project's root directory (where yourstlv_app.py).

# Use a shared requirements file (path relative to project root)
fn = Function(
    name="users",
    handler="functions/users.get",
    requirements="common/base_requirements.txt"
)

File Not Found

If you provide a path string and the file does not exist at that location ( relative to the project root), Stelvio will raise an error during deployment.

Inline List

If you prefer, you can provide requirements as a list of strings when defining a function. Each string should be a valid requirement specifier, just like a line in a requirements.txt file.

stlv_app.py
fn = Function(
    name="users",
    handler="functions/users.get",
    requirements=[
        "requests==2.31.0",  # Specific version
        "boto3",  # Latest compatible version
        "pydantic>=2.0,<3.0"  # Version range
    ]
)

You can include any valid pip requirement string, including version specifiers, VCS URLs (git+...), etc.

Disable Dependency Handling

If you want to explicitly prevent Stelvio from looking for or installing any dependencies, even if a default requirements.txt file exists, set requirements to False (or an empty list []). This is useful if your Lambda function doesn't need dependencies specified in requirements.txt file shared in same folder as other functions or if you are managing dependencies through other means (like Lambda Layers).

stlv_app.py
# Disable even if functions/requirements.txt exists
fn = Function(
    name="no-deps-function",
    handler="functions/users.get",
    requirements=False
)

# An empty list also disables dependency handling
fn_other = Function(
    name="other-no-deps",
    handler="functions/users.get",
    requirements=[]
)

How Installation Works

When dependencies need to be installed (i.e., not disabled and requirements are found/provided), Stelvio performs the following steps automatically:

  1. Installer Selection: It prefers uv (if installed and found in PATH) for its speed & global caching, otherwise it falls back to pip.
  2. Platform Targeting: It runs the installer with flags specific to your function's configured architecture (x86_64 or arm64) and Python runtime (e.g., 3.12), ensuring compatibility with the AWS Lambda execution environment. Example flags used internally:
    • --platform manylinux2014_x86_64 (or aarch64)
    • --python-version 3.12
    • --implementation cp (for pip)
    • --only-binary=:all: (to prefer pre-compiled wheels, crucial for Lambda compatibility)
  3. Caching: Dependencies are installed into a local cache directory within your project (.stelvio/lambda_dependencies/). The cache key is intelligently generated based on the requirements content, the target architecture, and the target Python version. The "content" part of the key is derived from a normalized representation of your requirements: Stelvio strips whitespace and comments, sorts the lines, and crucially, resolves any paths in -r or -c flags to be relative to your project root before hashing. This ensures that trivial formatting differences or different ways of specifying the same relative path don't break the cache. Subsequent deployments with identical normalized requirements and configuration will reuse the cache, significantly speeding up the deployment process.
  4. Packaging: The installed dependencies retrieved from the cache are packaged alongside your function code into the final deployment archive (.zip file) uploaded to AWS Lambda.

Sharing Dependencies via -r

If you are using file-based requirements (requirements=None or requirements="path/to/file.txt"), you can leverage pip's standard -r flag within your requirements.txt files to include dependencies from other files. This is useful for sharing common dependencies across multiple functions.

common/base_requirements.txt
# Common libraries used across the project
boto3>=1.34
pydantic>=2.5
functions/orders/requirements.txt
# Include common dependencies (path relative to the directory containing this file)
-r ../../common/base_requirements.txt

# Specific dependencies for this function
stripe>=8.0

order_fn = Function(
    name="order-processor",
    handler="functions/orders::handler.process",
    # Stelvio will find functions/orders/requirements.txt by default
)

Stelvio's caching mechanism is aware of these -r (and -c for constraints) references. If the content of common/base_requirements.txt changes in the example above, the cache key for the order-processor function will also change, correctly triggering a re-installation of its dependencies on the next deployment.

Important Notes

  • Package Size: Be mindful of the total size of your dependencies. Large dependencies increase the size of your Lambda deployment package, which can negatively impact cold start times and potentially hit AWS deployment size limits. Keep your requirements lists focused on what's truly needed.
  • No Usage Analysis: Stelvio installs all packages listed in the resolved requirements file(s); it does not analyze your code to determine which imports are actually used.
  • Binary Compatibility: Stelvio dependencies management works only with pre-compiled binary wheels. If a package (or one of its transitive dependencies) requires compilation during installation and doesn't offer a compatible wheel for the Lambda Linux environment (manylinux...), the installation step might fail. Support for such packages is planned; please raise an issue on the project's repository if this feature is important to you.
  • Cache Management: The dependency cache is stored locally in .stelvio/lambda_dependencies/ (with a separate layers/ subdirectory for layer dependencies). While Stelvio automatically reuses cached dependencies, you might want to clear this directory (rm -rf .stelvio) if you suspect caching issues or want to force a completely clean installation. Stelvio also includes logic to automatically clean up stale cache directories that haven't been used in the most recent deployment.

Sharing Code and Dependencies with Lambda Layers

Lambda Layers provide a mechanism to package libraries, custom runtimes, or other dependencies that you want to share across multiple Lambda functions. Stelvio simplifies the creation and management of layers through the Layer component.

Creating a Layer

You define a layer using the stelvio.aws.layer.Layer component:

from stelvio.aws.layer import Layer
from stelvio.aws.function import Function

# Layer containing shared utility code
utils_layer = Layer(
    name="common-utils",
    code="src/common_utils",  # Path to the directory with your code
)

# Layer containing specific dependencies
libs_layer = Layer(
    name="data-libs",
    requirements="requirements/data_processing.txt" # Path to requirements file
)

# Layer with both code and dependencies
combined_layer = Layer(
    name="shared-logic-and-deps",
    code="src/shared_logic",
    requirements=[  # Or provide requirements inline
        "pandas==2.1.0",
        "numpy>=1.25"
    ],
    runtime="python3.11", # Optional: Specify runtime/architecture if needed
    architecture="arm64"  # Defaults are usually sufficient
)

Key Layer parameters:

  • name: A unique logical name for the layer within your Stelvio application.
  • code: (Optional) Path relative to your project root containing the Python code for the layer. Stelvio packages the directory specified by the last part of this path (e.g., common_utils from src/common_utils) under python/ in the layer archive (resulting in python/common_utils/...). This allows standard Python imports like from common_utils import ....
  • requirements: (Optional) Specifies Python package dependencies. Accepts:
    • A path string (relative to project root) to a requirements.txt file.
    • A list of requirement strings (e.g., ["requests", "boto3"]).
    • None (default): No dependencies are installed for this layer. Unlike functions, there is no automatic lookup for requirements.txt when requirements is None.
  • runtime, architecture: (Optional) Specify the compatible runtime and architecture. Defaults to the project-wide defaults (python3.12, x86_64). Layers must be compatible with the functions that use them (Stelvio performs this check).

Layer Content Required

A Layer must be configured with either the code parameter, the requirements parameter, or both. Defining a layer with neither will result in an error.

How Packaging Works

Stelvio handles the packaging details according to AWS Lambda Layer standards:

  1. Code (code): If a code path is provided (e.g., src/common_utils), Stelvio packages the directory specified by the last part of the path (e.g., common_utils) into a python/ directory within the layer's archive (python/common_utils/...). This structure allows standard Python imports within your Lambda functions (e.g., from common_utils import ...).
  2. Dependencies (requirements): If requirements are specified (as a path string or list), Stelvio uses the same dependency resolution and installation logic as for functions (preferring uv, falling back to pip, targeting the specified runtime/architecture). Dependencies are installed into python/lib/pythonX.Y/site-packages/ within the layer archive.
  3. Caching: Installed layer dependencies are cached separately in .stelvio/lambda_dependencies/layers/ to avoid conflicts with function caches. The cache key considers the requirements content, runtime, and architecture.
  4. Versioning: Stelvio creates a Pulumi AssetArchive from the packaged code and dependencies. Pulumi calculates a hash of this archive. A new AWS LayerVersion resource is created only if this hash changes (meaning the code or resolved dependencies have changed).

Using Layers with Functions

To use one or more layers with a function, pass a list of Layer component instances to the layers parameter of the Function:

# Assume utils_layer and libs_layer are defined as above

data_processor_fn = Function(
    name="data-processor",
    handler="functions/processing.handler",
    layers=[utils_layer, libs_layer] # Attach the layers
)

another_fn = Function(
    name="reporter",
    handler="functions/reporting.handler",
    layers=[utils_layer] # Reuse the utils layer
)

Stelvio automatically retrieves the correct LayerVersion ARN (Amazon Resource Name) for each layer and configures the Lambda function to use them.

Validation and Compatibility Checks

When you define a function with layers, Stelvio performs several validation checks early on (during configuration processing, before deployment):

  • Limit Check: Verifies that no more than 5 layers are attached (the AWS limit).
  • Compatibility Check: Compares the function's effective runtime and architecture (considering defaults if not explicitly set) against those of each attached layer. If any mismatch is found (e.g., attaching an arm64 layer to an x86_64 function, or a python3.13 layer to a python3.12 function), Stelvio raises a clear ValueError, preventing deployment issues.

If a layer's content changes in a subsequent deployment, Stelvio detects the change, creates a new layer version, and updates any functions using that layer to reference the new version.

Precedence

Dependencies included directly in a function's package take precedence over dependencies in layers. If multiple layers provide the same package, the standard AWS layer ordering applies (later layers in the list override earlier ones).

Next Steps

Now that you understand Lambda functions and layers in Stelvio, you might want to explore: