docs: rewrite documentations, fix typos and introduce spell-check to avoid typos

Author: Lee-W

.github/pull_request_template.md

@@ -31,7 +31,7 @@ Steps to reproduce the behavior:
 <!--A clear and concise description of what you expected to happen-->
 
 ## Related Issue
-<!--If applicable, refernce to the issue related to this pull request.-->
+<!--If applicable, reference to the issue related to this pull request.-->
 
 ## Additional context
 <!--Add any other context or screenshots about the pull request here.-->

.pre-commit-config.yaml

@@ -0,0 +1,25 @@
+default_install_hook_types:
+  - pre-commit
+
+default_stages:
+  - pre-commit
+
+repos:
+- repo: https://github.com/codespell-project/codespell
+  rev: v2.4.1
+  hooks:
+    - id: codespell
+      name: Run codespell to check for common misspellings in files
+      # config section is within pyproject.toml
+      language: python
+      types: [ text ]
+      args:
+
+        - --ignore-words=spelling_wordlist.txt
+        - --write-changes
+      exclude: >
+          (?x)
+          uv.lock$|
+          ^dags/fixtures/data_questionnaire.csv$
+      additional_dependencies:
+        - tomli

contrib/README.md

@@ -2,7 +2,7 @@
 
 ## Upload KKTIX
 
-![](../docs/kktix.png)
+![kktix](../docs/images/kktix.png)
 
 1. Navigate to KKTIX's attendees page
 2. Download the CSV

dags/ods/kktix_ticket_orders/udfs/kktix_api.py

@@ -46,7 +46,7 @@ def main(**context):
 def _extract(year: int, timestamp: float) -> list[dict]:
     """
     get data from KKTIX's API
-    1. condition_filter_callb: use this callbacl to filter out unwanted event!
+    1. condition_filter_callb: use this callback to filter out unwanted event!
     2. right now schedule_interval_seconds is a hardcoded value!
     """
     event_raw_data_array: list[dict] = []

docs/CONTRIBUTING.md

@@ -2,74 +2,108 @@
 
 ## How to Contribute to this Project
 
-1. Clone this repository:
+### 1. Clone this repository:
 
-    ```bash
-    git clone https://github.com/pycontw/pycon-etl
-    ```
+```bash
+git clone https://github.com/pycontw/pycon-etl
+```
 
-2. Create a new branch:
+### 2. Create a new branch:
 
-    ```bash
-    git checkout -b <branch-name>
-    ```
+Please checkout your branch from the latest master branch before doing any code change.
 
-3. Make your changes.
+```bash
+# Checkout to the master branch
+git checkout master
 
-    > **NOTICE:** We are still using Airflow v1, so please read the official document [Apache Airflow v1.10.15 Documentation](https://airflow.apache.org/docs/apache-airflow/1.10.15/) to ensure your changes are compatible with our current version.
+# Ensure that's you're on the latest master branch
+git pull origin master
 
-    If your task uses an external service, add the connection and variable in the Airflow UI.
+# Create a new branch
+git checkout -b <branch-name>
+```
 
-4. Test your changes in your local environment:
+### 3. Make your changes.
 
-    - Ensure the DAG file is loaded successfully.
-    - Verify that the task runs successfully.
-    - Confirm that your code is correctly formatted and linted.
-    - Check that all necessary dependencies are included in `requirements.txt`.
+If your task uses an external service, add the connection and variable in the Airflow UI.
 
-5. Push your branch:
+### 4. Test your changes in your local environment:
 
-    ```bash
-    git push origin <branch-name>
-    ```
+- Ensure that the dag files are loaded successfully.
+- Verify that the tasks run without errors.
+- Confirm that your code is properly formatted and linted. See [Convention](#convention) section for more details.
+- Check that all necessary dependencies are included in the `pyproject.toml` file.
+  - Airflow dependencies are managed by [uv].
+- Ensure that all required documentation is provided.
 
-6. Create a Pull Request (PR).
+### 5. Push your branch:
 
-7. Wait for the review and merge.
+```bash
+git push origin <branch-name>
+```
 
-8. Write any necessary documentation.
+### 6. Create a Pull Request (PR).
 
-## Release Management
+If additional steps are required after merging and deploying (e.g., add new connections or variables), please list them in the PR description.
 
-Please use [GitLab Flow](https://about.gitlab.com/topics/version-control/what-is-gitlab-flow/); otherwise, you cannot pass Docker Hub CI.
+### 7. Wait for the review and merge.
 
-## Dependency Management
+## Convention
 
-Airflow dependencies are managed by [uv]. For more information, refer to the [Airflow Installation Documentation](https://airflow.apache.org/docs/apache-airflow/1.10.15/installation.html).
+### Airflow Dags
+- Please refer to [「大數據之路：阿里巴巴大數據實戰」 讀書心得](https://medium.com/@davidtnfsh/%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B9%8B%E8%B7%AF-%E9%98%BF%E9%87%8C%E5%B7%B4%E5%B7%B4%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%AE%9E%E8%B7%B5-%E8%AE%80%E6%9B%B8%E5%BF%83%E5%BE%97-54e795c2b8c) for naming guidelines.
+- Table name convention:
+  ![img](https://miro.medium.com/max/1400/1*bppuEKMnL9gFnvoRHUO8CQ.png)
 
-## Code Convention
+### Code Formatting
+Please run `make format` to ensure your code is properly formatted before committing; otherwise, the CI will fail.
 
-### Airflow DAG
+### Commit Message
+It is recommended to use [Commitizen](https://commitizen-tools.github.io/commitizen/).
 
-- Please refer to [this article](https://medium.com/@davidtnfsh/%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B9%8B%E8%B7%AF-%E9%98%BF%E9%87%8C%E5%B7%B4%E5%B7%B4%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%AE%9E%E8%B7%B5-%E8%AE%80%E6%9B%B8%E5%BF%83%E5%BE%97-54e795c2b8c) for naming guidelines.
+## Release Management (CI/CD)
+We use [Python CI] and [Docker Image CI] to ensure our code quality meets specific standards and that Docker images can be published automatically.
 
-  - Examples:
-    1. `ods/opening_crawler`: Crawlers written by @Rain. These openings can be used for the recruitment board, which was implemented by @tai271828 and @stacy.
-    2. `ods/survey_cake`: A manually triggered uploader that uploads questionnaires to BigQuery. The uploader should be invoked after we receive the SurveyCake questionnaire.
+When a pull request is created, [Python CI] checks whether the code quality is satisfactory. At the same time, we build a `cache` image using `Dockerfile` and a `test` image with `Dockerfile.test`, which are then pushed to the [GCP Artifact Registry].
 
-- Table name convention:
-  ![img](https://miro.medium.com/max/1400/1*bppuEKMnL9gFnvoRHUO8CQ.png)
+After a pull request is merged into the `master` branch, the two image tags mentioned above are created, along with a new `staging` tag for the image generated from `Dockerfile`.
 
-### Format
+Once we verify that the `staging` image functions correctly, we merge the `master` branch into the `prod` branch through the following commands.
 
-Please use `make format` to format your code before committing, otherwise, the CI will fail.
+<!--TODO: This is not ideal. The "master" and "prod" branches should be protected and should not allow human pushes. We should create a GitHub action for this..-->
 
-### Commit Message
+```bash
+git checkout prod
+git pull origin prod
 
-It is recommended to use [Commitizen](https://commitizen-tools.github.io/commitizen/).
+git merge origin/master
+
+git pull origin prod
+```
 
-### CI/CD
+This triggers the [Docker Image CI] again to update the `cache`, `test`, and `staging` images, as well as to create a `latest` image that we will later use for deploying to our production instance. See the [Deployment Guide](./DEPLOYMENT.md) for the following steps.
 
-Please check the [.github/workflows](.github/workflows) directory for details.
+```mermaid
+---
+config:
+  theme: 'base'
+  gitGraph:
+    mainBranchName: 'prod'
+    tagLabelFontSize: '25px'
+    branchLabelFontSize: '20px'
+---
+      gitGraph
+        commit id:"latest features" tag:"latest"
+        branch master
+        commit id:"staging features" tag:"staging"
+        checkout prod
+        commit id:"prod config"
+        checkout master
+        branch feature-1
+        commit id: "new features" tag:"cache" tag:"test"
+```
 
-[uv]: https://docs.astral.sh/uv/
+[uv]: https://docs.astral.sh/uv/
+[Python CI]: https://github.com/pycontw/pycon-etl/actions/workflows/python.yml
+[Docker Image CI]: https://github.com/pycontw/pycon-etl/actions/workflows/dockerimage.yml
+[GCP Artifact Registry]: https://cloud.google.com/artifact-registry/

docs/DEPLOYMENT.md

@@ -1,21 +1,51 @@
 # Deployment Guide
 
-1. Login to the data team's server:
-    1. Run: `gcloud compute ssh --zone "asia-east1-b" "data-team" --project "pycontw-225217"`
-    2. Services:
-        * ETL: `/srv/pycon-etl`
-        * Metabase is located here: `/mnt/disks/data-team-additional-disk/pycontw-infra-scripts/data_team/metabase_server`
+## Start Deploying
 
-2. Pull the latest codebase to this server: `git pull`
+### 1. Login to the data team's GCE server
 
-3. Add credentials to the `.env.production` file (only needs to be done once).
+```bash
+gcloud compute ssh --zone "asia-east1-b" "data-team" --project "pycontw-225217"
+```
+
+* Location of the Services:
+    * ETL (airflow): `/srv/pycon-etl`
+    * Metabase: `/mnt/disks/data-team-additional-disk/pycontw-infra-scripts/data_team/metabase_server`
+
+### 2. Pull the latest codebase and image to this server
+
+```bash
+git checkout prod
+git pull origin prod
+
+docker pull asia-east1-docker.pkg.dev/pycontw-225217/data-team/pycon-etl:latest
+```
 
-4. Start the services:
+### 3. Add credentials to the `.env.production` file (only needs to be done once).
+
+### 4. Restart the services:
 
 ```bash
 # Start production services
 docker-compose -f ./docker-compose.yml up
 
 # Stop production services
 # docker-compose -f ./docker-compose.yml down
-```
+```
+
+### 5. Check whether the services are up
+
+```bash
+# For Airflow, the following services should be included: 
+# * airflow-api-server
+# * airflow-dag-processor
+# * airflow-scheduler
+# * airflow-triggerer
+docker ps
+
+# Check the resource usage if needed
+docker stats
+```
+
+### 6. Login to the service
+For security reasons, our Airflow instance is not publicly accessible. You will need an authorized GCP account to perform port forwarding for the webserver and an authorized Airflow account to access it.

docs/MAINTENANCE.md

@@ -2,23 +2,30 @@
 
 ## Disk Space
 
+<!--TODO: we probably can make this check a dag-->
+
 Currently, the disk space is limited, so please check the disk space before running any ETL jobs.
 
 This section will be deprecated if we no longer encounter out-of-disk issues.
 
-1. Find the largest folders:
-    ```bash
-    du -a /var/lib/docker/overlay2 | sort -n -r | head -n 20
-    ```
-2. Show the folder size:
-    ```bash
-    du -hs xxxx
-    ```
-3. Delete the large folders identified.
-4. Check disk space:
-    ```bash
-    df -h
-    ```
+### 1. Find the largest folders:
+
+```bash
+du -a /var/lib/docker/overlay2 | sort -n -r | head -n 20
+```
+
+### 2. Show the folder size:
+
+```bash
+du -hs 
+```
+
+### 3. Delete the large folders identified.
+### 4. Check disk space:
+
+```bash
+df -h
+```
 
 ## Token Expiration