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https://github.com/iris-hunkeler/gym-class-tracker
A simple tracker for free spots in a gym class implemented in Python using the AWS Serverless Application Model
https://github.com/iris-hunkeler/gym-class-tracker
aws aws-dynamodb aws-lambda python serverless serverless-application-model
Last synced: about 1 month ago
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A simple tracker for free spots in a gym class implemented in Python using the AWS Serverless Application Model
- Host: GitHub
- URL: https://github.com/iris-hunkeler/gym-class-tracker
- Owner: iris-hunkeler
- Created: 2023-04-09T18:01:35.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-18T10:32:03.000Z (about 1 year ago)
- Last Synced: 2023-11-18T11:27:12.381Z (about 1 year ago)
- Topics: aws, aws-dynamodb, aws-lambda, python, serverless, serverless-application-model
- Language: Python
- Homepage:
- Size: 206 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Gym class tracker
The gym class tracker is a very simple application that can setup a "tracker query" to the REST of a Gym for the availability of a class.
The application is built using a simple serverless setup with *Lambda*, *DynamoDB*, *SNS* and *EventBridge Scheduler*.
The "tracker query" and its current state are stored in *DynamoDB*. A *Lambda function* contains the logic to call the gym API and check if the class of interest is currently available to be booked. For any status changes or errors, a notification is sent to a (fixed) user using *SNS*. The Lambda Function is called regularly using an *Event Bridge Scheduler*
## Project structure
This project uses the [AWS Serverless Application Model (SAM)](https://aws.amazon.com/serverless/sam/) and was built using the [AWS Toolkit in VSCode](https://docs.aws.amazon.com/toolkit-for-vscode/latest/userguide/welcome.html).
Folder structure:
- `execute-check` - Code for the Lambda function executing a check against the gym's API.
- `events` - Invocation events that you can use to invoke the function.
- `tests` - Unit tests for the application code.
- `template.yaml` - A template that defines all the application's AWS resources.
- `docs` - Diagrams for documentation
- `proof-of-concept` - First draft of the Lambda function which was originally manually deployed
## Deploy the sample application
To build and deploy the application for the first time, run the following in your shell:
```bash
sam build --use-container
# this will guide you through installation and prompt for the stack name, AWS region and capability to create IAM roles (to create/modifycIAM roles, the `CAPABILITY_IAM` value for `capabilities` must be set). Arguments can be savet to samconfig.toml
sam deploy --guided
```
After the first deployment, these comments can be used to deploy changes:
```bash
sam build
sam deploy --parameter-overrides EmailAddress=[enter email address to send notifications]
```
## Use the SAM CLI to build and test locally
**Build** your application with the `sam build --use-container` command.
```bash
# build: installs dependencies and creates a deployment package in .aws-sam/build
sam build --use-container
```
**Run functions locally** and invoke them with the `sam local invoke` command. Test events are included in the `events` folder in this project.
```bash
sam local invoke ExecuteCheckFunction --event events/empty-call.json
```
## Fetch, tail, and filter Lambda function logs
**Read logfiles** from the deployed Lambda function using the SAM CLI:
```bash
sam logs -n ExecuteCheckFunction --stack-name gym-class-tracker --tail
```
## Create a tracker query
Example for a query in DynamoDB
```
{
"partition-key": {
"S": "query-1"
},
"sort-key": {
"S": "1"
},
"entity-name": {
"S": "check-query"
},
"active-status": {
"S": "1"
},
"availability-status": {
"S": "Unknown"
},
"center-id": {
"S": "23"
},
"course-title": {
"S": "BODYPUMP® 55'"
},
"daytime-id": {
"S": "4"
},
"weekday-id": {
"S": "3"
}
}
```
### Variables
center-id:
* 23 = Zürich Altstetten
daytime-id:
* 1 = morning
* 2 = noon
* 3 = afternoon
* 4 = evening
weekday-id:
* 1 = Monday
* ...
* 7 = Sunday
## Cleanup
**Delete** the deployed application again:
```bash
sam delete gym-class-tracker
```
## Resources
* [AWS SAM developer guide](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/what-is-sam.html): for an introduction to SAM specification, the SAM CLI, and serverless application concepts.
* [AWS Serverless Application Repository main page](https://aws.amazon.com/serverless/serverlessrepo/): Discover, deploy and publish serverless applications
## Improvement options
- [ ] Split `app.py` into different files: separate for DynamoDB, API handling and SNS
- [ ] Add tests for separate areas
- [ ] build additional Lambda CreateTrackerQuery that writes a tracker-query into DynamoDB
- [ ] build API Gateway for CreateTrackerQuery
- [ ] Secure API Gateway
- [ ] Enable different email addresses (maybe move away from SNS for this?)