AWStanding

Easily load variables from AWS Parameter store into environment variables.

Why to use AWStanding?

Despite it's built on top of Boto3, it has the following key features that eases the development process:

• Simpler API
• Error Handling
• Pagination handling when needed (Saves you a buch of boilerplate)
• Dynamic Parameters (variables that listen to updates on AWS)
• S3 Integration made easy with Download/Upload methods

Installation

pip install awstanding

I personally recommend using pipenv:

pipenv install awstanding

Quickstart

from awstanding.parameter_store import load_parameters
load_parameters({'/some/path/to/something/stored': 'IMPORTANT_SETTING'})

import os
print(os.environ.get('IMPORTANT_SETTING'))
'super-important-value'

Using with python-decouple

import os
from awstanding.parameter_store import load_parameters
from decouple import config
load_parameters({'/some/path/to/something/stored': 'IMPORTANT_SETTING'})

IMPORTANT_SETTING = config('IMPORTANT_SETTING', default='some-default')
print(IMPORTANT_SETTING)
'super-important-value'

Not allowing missing parameters

from awstanding.parameter_store import load_parameters
# A call like this one:
load_parameters({'/not/defined/parameter': 'IMPORTANT_SETTING'}, allow_invalid=False)

# will raise a ParameterNotFoundException, and you can handle it as follows:
from awstanding.exceptions import ParameterNotFoundException

try:
    load_parameters({'/not/defined/parameter': 'IMPORTANT_SETTING'}, allow_invalid=False)
except ParameterNotFoundException as e:
    # perform any cleanup action..

Performance

Amount of parameters	Missing parameters	AWStanding	SSM client calls
40	0	~3.1s	~15.5s
40	0	~2.4s	~15.3s
40	0	~4.6s	~14.5s
40	0	~2.5s	~15.5s
40	1	~2.1s	error: ParameterNotFound
40	20	~2.2s	error: ParameterNotFound
40	40	~2.1s	error: ParameterNotFound
80	40	~3.5s	error: ParameterNotFound
80	40	~3.9s	(using try..except) ~32.7s

Loading paths

Suppose you have defined these variables in ParameterStore:

'/stripe/price/'
'/stripe/webhook/'  # (Let's not define this one just for demonstration)

You can leverage on the good naming and perform a path variable loading as follows:

import os
from awstanding.parameter_store import load_path

load_path('/stripe', '/spotify')
STRIPE_PRICE = os.environ.get('STRIPE_PRICE', 'fallback_value')
STRIPE_WEBHOOK = os.environ.get('STRIPE_WEBHOOK', 'fallback_value')
SPOTIFY_API_KEY = os.environ.get('SPOTIFY_API_KEY', 'fallback_value')

print(f'price: {STRIPE_PRICE}, webhook: {STRIPE_WEBHOOK}, spotify: {SPOTIFY_API_KEY}')

>>> price: price_1xxxxxxxxxxxxxxxxxxxxxxx, webhook: fallback_value, spotify: fallback_value

Dynamic Parameters

You can define dynamic parameters that uploads themselves each time they are used, so you can update any parameter without re-deploy your service.

from awstanding.parameter_store import DynamicParameter

IMPORTANT_SETTING = DynamicParameter('/test/parameter')

print(IMPORTANT_SETTING)
>>> OriginalValue

# Someone updates /test/parameter on AWS...

print(IMPORTANT_SETTING)
>>> NewValue

Supported operations

Some useful operations are supported by the class itself, emulating built-in str class:

from awstanding.parameter_store import DynamicParameter

IMPORTANT_SETTING = DynamicParameter('/test/parameter')

# Equality comparison
equal = IMPORTANT_SETTING == 'SomeString'

# Length
length = len(IMPORTANT_SETTING)

# Concatenation (Right and Left)
concat = '~' + IMPORTANT_SETTING + '~'

# You can always convert the parameter to string to get full string capabilities:

str_IMPORTANT_SETTING = str(IMPORTANT_SETTING)  # Have in mind this will "freeze" the value, so don't overwrite IMPORTANT_SETTING

S3 Integration

Download files from S3

from awstanding.s3 import Bucket

bucket = Bucket('BUCKET_NAME_HERE')

bucket.download("path/to/file.ext", './some/local/file.ext')

Upload files to S3

from awstanding.s3 import Bucket

bucket = Bucket('BUCKET_NAME_HERE')

bucket.upload('/some/local/file.ext', "some/s3/logical/path.ext")

There's not file type restriction any other that the set by AWS/boto3 itself.