Timereport slack

Architecture

• AWS API Gateway
• AWS Lambda
• AWS SQS

The project is build with AWS Chalice, allowing us to specify all infrastructure as part of the code. The entrypoint is app.py where all external communication is handled. NOTE: SQS queues are created manually using AWS CLI at Travis.

Important classes and concepts

• Action - Something that you want to do, for example add using the command /timereport add in Slack. In this case Add is the action and is represented by a class in chalicelib/action.py.
• Slack - The public interface of the project is a slack-command /timereport
• Command - When someone calls /timereport in slack, we get a command that is handled by the /command endpoint in app.py
• Interactive - Some actions require confirmation, interactive is the confirmation (or rejection) of a confirmation prompt. When someone confirms a command slack issues an interactive reply and is handled by the /interactive in app.py
• Timereport-api - Another service used to store the worked time, everything stored from this project is stored there. All communication is done over HTTP
• SQS - Amazon provided queue, we're using it to handle tasks in the background. See below for more information

Background tasks

Slack requires all calls to endpoints to be replied to in 3 seconds. This makes it hard for us to do all processing of a command. To be able to handle this we have decided to use SQS as a queue. Below follows the flow for the /command endpoint but /interactive follows an identical flow.

1. User sends the following in slack: /timereport list 2020-09
2. We receive a call to our endpoint /command with data containing the command and metadata about the user
3. We parse and verify the payload, directly in the endpoint and in the is_valid method of the Action
4. If we see any validation errors we reply to Slack and abort the processing, a message will be shown to the user about the error
5. A message is put on the command queue
6. We reply to slack that we received the message and will process it, normally nothing is shown to the user in this step
7. The message on the command queue is received by the command_handler in app.py
8. The Action class will perform the action, loading data from timereport-api and respond to slack

Dev

Install dependencies

Install dev packages

Install pre-commit

MacOS:

$ brew install pre-commit

Install git-commit hook

Pipenv

with pipenv:

pipenv shell
pipenv install --dev

Run unit tests

Go to root of project. Then simply run pytest:

pytest

test coverage

If you want to include test coverage:

pytest --cov=chalicelib

Deploys

Deploys to dev are done automatically on pushes to any branch, this allows easy testing with /timereport-dev command in slack.

Deploys to production are done automatically on tagged releases on the master branch.

to deploy from local environment

Setup awscli with your access_key_id and secret_access_key

Configure

To create the config use the script template-config.py It uses the template config.json.j2.

The script expects environment variables. See the dev pipeline or look in the script. they should never be pushed to github.

Run:

# python template-config.py

You should now have .chalice/config.json with appropriate values

Deploy to dev:

chalice deploy --stage dev

Dependencies

• test-requirements.txt: Packages necessary to run unit tests.
• requirements.txt: Packages that chalice will bundle with the lambda