Boto3

code for article pfeilbr/boto3-playground

learn boto3, the Amazon Web Services (AWS) SDK for Python

Concepts

Clients provide a low-level interface to AWS whose methods map close to 1:1 with service APIs. All service operations are supported by clients. Clients are generated from a JSON service definition file.

Resources represent an object-oriented interface to Amazon Web Services (AWS). They provide a higher-level abstraction than the raw, low-level calls made by service clients. To use resources, you invoke the resource() method of a Session and pass in a service name:

Session manages state about a particular configuration. By default, a session is created for you when needed. However, it’s possible and recommended that in some scenarios you maintain your own session. Sessions typically store the following:

Credentials
AWS Region
Other configurations related to your profile

Collection provides an iterable interface to a group of resources. A collection seamlessly handles pagination for you, making it possible to easily iterate over all items from all pages of data.

Paginators

Some AWS operations return results that are incomplete and require subsequent requests in order to attain the entire result set. The process of sending subsequent requests to continue where a previous request left off is called pagination. Paginators are a feature of boto3 that act as an abstraction over the process of iterating over an entire result set of a truncated API operation.

Developing

# install virtual env and dependencies
pipenv install

# (optional) install additional pip package
pipenv install <package>

# activate python virtual env (optional)
pipenv shell

# run on change
make dev

# --- running via jupyter notebook ---

# in vscode
# click on `main.ipynb`.  this will automatically start jupyter notebook and connect
# ctrl+enter to run cell
# ctrl+space for intellisense
# see [How to use Pipenv with Jupyter and VSCode](https://towardsdatascience.com/how-to-use-pipenv-with-jupyter-and-vscode-ae0e970df486)

# manually run jupyter notebook
pipenv run jupyter notebook

# access via browser manually (auto opens via above command)
open http://localhost:8888/tree

# convert notebook to python.  generates `main_notebook.py`
jupyter nbconvert --to script main.ipynb --output main_notebook

Notes

for vscode, install Pylance - Visual Studio Marketplace extension