Streamlined, efficient access to configuration values in AWS SSM Parameter Store and SecretsManager.
Project description
aws-parameters ⚙️
Streamlined, efficient access to configuration values in AWS SSM Parameter Store and SecretsManager.
Description
When building applications in AWS, it is common to use SSM Parameter Store and SecretsManager to store configuration values. The aws-parameters
library provides a simple, easy interface to access these values in a way that is fast, efficient, and secure. It is quick to setup and will perform lazy loading for all available parameters or secrets, meaning it will only call the API the first time the value is requested.
The library abstracts the required boilerplate for retrieving SSM Parameters and SecretsManager secrets and provides a simple, fast way to access their values based on a one-time configuration of service-to-parameter mappings.
How it Works
There are 3 basics steps involved in using aws-parameters
:
- Create a JSON object with service-to-parameter mappings
- Pass this JSON object to the
AppConfig
class - Access parameters and secrets through the
params
andsecrets
attributes of theAppConfig
instance
Service-to-Parameter Mappings
A service-to-parameter mapping says which service, SSM Parameter Store (ssm
) or SecretsManager (secretsmanager
), is storing a given parameter.
First, you will need to create a Parameter Mappings object looking something like this:
// param-mappings.json
{
"ssm": [
"/dev/myapp/MyParam1",
"/dev/myapp/MyParam2"
],
"secretsmanager": [
"/dev/myapp/MySecret1",
"/dev/myapp/MySecret2"
]
}
Be aware that aws-parameters
is opinionated about the naming convention for parameters and secrets in that it expects it to describe a path. See Considerations for more details.
Instantiating the AppConfig
class
Next, you can pass this object to the AppConfig
class using several methods to create an instance. See Configuration Methods for more details.
from awsparameters import AppConfig
# load the above JSON object from a file
with open("service-to-parameter-mappings.json", "r") as f:
param_config = json.load(f)
app = AppConfig(mappings_path=param_config)
One of the big advantages of the library is that no API calls are made when you instantiate the AppConfig
class. Instead, it will only make API calls when you access a parameter or secret through the AppConfig.params
or AppConfig.secrets
attributes.
Accessing Parameters and Secrets
Finally you can access parameters and secrets like this:
# access a parameter
MyParam1 = app.params.MyParam1
# access a secret
MySecret1 = app.secrets.MySecret1
This will initiate API calls to AWS to retrieve the values of the parameters and secrets. The values will be cached so that subsequent calls will not require additional API calls.
Advantages
- Fast, simple interface to configuration values that can reduce development overhead when working with SSM Parameter Store and SecretsManager
- Immediate access to available parameters and secrets through intellisense,
map
orlist
methods - Maintain least-privileged permissions to parameters and secrets using path-based access control
- Lazy loading for all available parameters or secrets, meaning it will only make API calls when a value is requested:
- When parameter or secret property is accessed, it first checks if the value has been computed before (cached). If it has, it immediately returns that cached value.
- If the value hasn't been computed before, it fetches the value and then returns it. This means that your Python app is only calling the AWS API when it needs to.
Considerations
- You must use the path convention for naming parameters, but you can choose any separator you want by setting the
path_separator
parameter when creating an instance ofAppConfig
. The default is/
. - Only the latest parameter versions can be fetched.
Quickstart
Install the library using pip.
pip install -i https://test.pypi.org/simple/ aws-parameters
Environment Configuration
Parameter Mappings
The AppConfig
class requires a JSON object with service to parameter mappings in order to know which values it needs to access:
// Parameter Mappings JSON Schema
{
"ssm": [
"parameter_path_and_identifier",
...
],
"secretsmanager": [
"secret_path_and_identifier",
...
]
}
For example:
{
"ssm": [
"/dev/myapp/MyParam1",
"/dev/myapp/MyParam2"
],
"secretsmanager": [
"/dev/myapp/MySecret1",
"/dev/myapp/MySecret2"
]
}
In this example, the path is /dev/myapp
and the identifiers or names are MyParam1
, MyParam2
, MySecret1
, and MySecret2
.
You can store this in a JSON file or as its own SSM Parameter.
Configuration Methods
The current method of providing this JSON object to aws-parameters
is from deployed SSM Parameter mapping (third fastest)
See Methods of Access for more details.
From SSM Parameter
For this method of configuration you would store the parameter mappings as a JSON string using an SSM Parameter in your AWS account. Here is an example using a CloudFormation template:
AWSTemplateFormatVersion: "2010-09-09"
Parameters:
AppName:
Type: String
Description: Name of the application
Stage:
Type: String
Description: Stage of the application
Resources:
ParamMappingsParameter:
Type: AWS::SSM::Parameter
Properties:
Type: String
Name: !Sub '/${Stage}/${AppName}/MyParamMappings'
Description: "Parameter mappings for aws-parameters"
Tier: Standard
Value: !Sub |
{
"ssm": [
"/${Stage}/${AppName}/MyParam1",
"/${Stage}/${AppName}/MyParam2"
],
"secretsmanager": [
"/${Stage}/${AppName}/MySecret1",
"/${Stage}/${AppName}/MySecret2"
]
}
Usage
See Configuration Methods for the different ways to setup the environment and access SSM Parameters and SecretsManager Secrets values.
from awsparameters import AppConfig
# (optional) Create a boto3 session outside the class
session = boto3.Session(region_name=AWS_REGION)
# Retrieve the Parameter Mappings from SSM Parameter Store
mappings_path = "/dev/myapp/MyParamMappings"
# Create the AppConfig object from the mappings path
app = AppConfig(
mappings_path=mappings_path,
boto3_session=session)
To see all the available parameters and secrets by namespace, you can access the map
attribute:
# print all available parameters
app.map
# output
{
"ssm": [
"/dev/myapp/MyParam1",
"/dev/myapp/MyParam2"
],
"secretsmanager": [
"/dev/myapp/MySecret1",
"/dev/myapp/MySecret2"
]
}
Local Development
Follow the steps to set up the deployment environment.
Prerequisites
- Python 3.10
- AWS credentials
Creating a Python Virtual Environment
When developing locally, create a Python virtual environment to manage dependencies:
python3.10 -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install .[dev,test]
Unit Tests
Follow the steps above to create a Python virtual environment. Run tests with the following command.
make test
Authors
Primary Contact: @chrisammon3000
License
This library is licensed under the MIT-0 License. See the LICENSE file.
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