High level utilities for a subset of boto3 operations common for AWS serverless development in Python.
Project description
xoto3
xoto3 (pronounced zoto-three) is a layer of useful micro-utilities
for boto3 (the AWS Python library) particularly suitable for
serverless development.
These come from years of experience developing a serverless platform at XOi Technologies, and represent real production code.
Installing
Get started quickly by installing the package with pip install xoto3
Features
Some of the features included:
-
A more general purpose
boto3client API paginator. -
Higher-level abstractions for DynamoDB, including:
- pure data transformation safeguards against various sorts of data that DynamoDB won't accept.
- an multi-item, multi-table composable, retrying, transaction wrapper for TransactWriteItems, allowing arbitrary writes (up to the built in API limitations) to multiple databases to be expressed as pure Python.
- a transactional single-item update that allows you to express your single-item update transformation in pure Python.
- transparent BatchGet and BatchWrite utilities that work around the
many annoyances of
boto3and DynamoDB itself. - composable query interfaces that make writing basic queries against DynamoDB fun.
-
Cloudwatch Insights and Log Groups Query URL formatters.
-
General-purpose AWS Lambda finalization code, to make sure buffered IO gets a chance to flush before your Lambda gets paused.
-
Wrapper for SSM parameter puts and gets, including built-in support for parameter values larger than what SSM will accept by automatically splitting your values and reconstructing them on gets.
Various other utilities are included as well - feel free to poke through the source code.
None of these features "rely" on any of the others, so all of the
power is left in your hands. This is not a framework; just a set of
mostly pure-functional utilities, with a couple of handy wrappers for
boto3 functions that perform IO.
Other Utilities
Some fairly general-purpose utilities are also included. See the readme for more details.
Some highlights:
tree_map- recursively map through a tree of Python builtinslazy- general purpose lazy-loading container- Various serialization utilities (datetimes, decimals, JSON helpers)
pipe_multiprocessing- a Process Pool for places like AWS Lambda where Python's built-in shared memory-dependent Pool does not work.
Development
Getting set up
This repository leverages Poetry >= 2.3. Install it with pipx install "poetry>=2.3,<2.4"
Then simply run poetry install. You'll want to periodically run poetry update as well.
The poetry.lock file is not version-controlled, and shouldn't be (there's an argument to be made about pinning dev dependencies)
Versioning
The version is set in pyproject.toml. Update it with poetry version (major|minor|patch|<x.y.z>).
Testing
You can run all unit tests with poetry run pytest tests.
You can additionally include all the DynamoDB integration tests by setting some environment variables:
XOTO3_INTEGRATION_TEST_DYNAMODB_ID_TABLE_NAME: the name of a
DynamoDB table with a primary key that is a partition key of id
and no range key.
XOTO3_INTEGRATION_TEST_NO_RANGE_KEY_INDEX_HASH_KEY: the name of an
attribute which is the partition key of a GSI with no range key.
If you don't currently have a table viable for testing, you can use the following script to easily create one:
poetry run python ./scripts/create_integration_test_table.py
Development
Writing tests
Any new changes should be accompanied by unit tests. Integration tests should also be included where they are helpful.
Integration tests should make use of environment variables and pytest's ability to skip a test in the event that an environment variable is not set.
Additionally, pytest is configured to run all tests in parallel, so any integration test you write must be independent of any other test, and must not leave behind test data.
Your integration tests should be written alongside the unit tests. We do not keep them in a separate folder.
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