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https://github.com/ddeutils/ddeutil-workflow
Lightweight workflow orchestration with less dependencies
https://github.com/ddeutils/ddeutil-workflow
data-pipeline data-workflow python utilities workflow-engine
Last synced: 1 day ago
JSON representation
Lightweight workflow orchestration with less dependencies
- Host: GitHub
- URL: https://github.com/ddeutils/ddeutil-workflow
- Owner: ddeutils
- License: mit
- Created: 2024-05-14T15:58:23.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-10-30T02:40:55.000Z (8 days ago)
- Last Synced: 2024-10-30T05:08:43.144Z (8 days ago)
- Topics: data-pipeline, data-workflow, python, utilities, workflow-engine
- Language: Python
- Homepage: https://ddeutils.github.io/ddeutil-workflow/
- Size: 1.02 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# Workflow
[](https://github.com/ddeutils/ddeutil-workflow/actions/workflows/tests.yml)
[](https://codecov.io/gh/ddeutils/ddeutil-workflow)
[](https://pypi.org/project/ddeutil-workflow/)
[](https://pypi.org/project/ddeutil-workflow/)
[](https://github.com/ddeutils/ddeutil-workflow)
[](https://github.com/ddeutils/ddeutil-workflow/blob/main/LICENSE)
[](https://github.com/psf/black)
The **Lightweight workflow orchestration** with less dependencies the was created
for easy to make a simple metadata driven for data workflow orchestration.
It can to use for data operator by a `.yaml` template.
> [!WARNING]
> This package provide only orchestration workload. That mean you should not
> use the workflow stage to process any large volume data which use lot of compute
> resource. :cold_sweat:
In my opinion, I think it should not create duplicate workflow codes if I can
write with dynamic input parameters on the one template workflow that just change
the input parameters per use-case instead.
This way I can handle a lot of logical workflows in our orgs with only metadata
configuration. It called **Metadata Driven Data Workflow**.
**:pushpin: Rules of This Workflow engine**:
1. The Minimum frequency unit of scheduling is **1 minute** :warning:
2. Can not re-run only failed stage and its pending downstream :rotating_light:
3. All parallel tasks inside workflow engine use Multi-Threading
(Python 3.13 unlock GIL :unlock:)
> [!NOTE]
> _Disclaimer_: I inspire the dynamic statement from the [**GitHub Action**](https://github.com/features/actions)
> with `.yml` files and all of config file from several data orchestration framework
> tools from my experience on Data Engineer. :grimacing:
>
> Other workflow that I interest on them and pick some interested feature to this
> package:
>
> - [Google **Workflows**](https://cloud.google.com/workflows)
> - [AWS **Step Functions**](https://aws.amazon.com/step-functions/)
## :round_pushpin: Installation
This project need `ddeutil` and `ddeutil-io` extension namespace packages.
If you want to install this package with application add-ons, you should add
`app` in installation;
| Usecase | Install Optional | Support |
|-------------------|------------------------------------------|--------------------|
| Python & CLI | `pip install ddeutil-workflow` | :heavy_check_mark: |
| FastAPI Server | `pip install ddeutil-workflow[api]` | :heavy_check_mark: |
> I added this feature to the main milestone.
>
> :egg: **Docker Images** supported:
>
> | Docker Image | Python Version | Support |
> |-----------------------------|----------------|---------|
> | ddeutil-workflow:latest | `3.9` | :x: |
> | ddeutil-workflow:python3.10 | `3.10` | :x: |
> | ddeutil-workflow:python3.11 | `3.11` | :x: |
> | ddeutil-workflow:python3.12 | `3.12` | :x: |
> | ddeutil-workflow:python3.12 | `3.13` | :x: |
## :beers: Usage
This is examples that use workflow file for running common Data Engineering
use-case.
> [!IMPORTANT]
> I recommend you to use the `hook` stage for all actions that you want to do
> with workflow activity that you want to orchestrate. Because it able to dynamic
> an input argument with the same hook function that make you use less time to
> maintenance your data workflows.
```yaml
run-py-local:
# Validate model that use to parsing exists for template file
type: ddeutil.workflow.Workflow
on:
# If workflow deploy to schedule, it will running every 5 minutes
# with Asia/Bangkok timezone.
- cronjob: '*/5 * * * *'
timezone: "Asia/Bangkok"
params:
# Incoming execution parameters will validate with this type. It allow
# to set default value or templating.
source-extract: str
run-date: datetime
jobs:
getting-api-data:
stages:
- name: "Retrieve API Data"
id: retrieve-api
uses: tasks/get-api-with-oauth-to-s3@requests
with:
# Arguments of source data that want to retrieve.
method: post
url: https://finances/open-data/currency-pairs/
body:
resource: ${{ params.source-extract }}
# You can able to use filtering like Jinja template but this
# package does not use it.
filter: ${{ params.run-date | fmt(fmt='%Y%m%d') }}
auth:
type: bearer
keys: ${API_ACCESS_REFRESH_TOKEN}
# Arguments of target data that want to landing.
writing_mode: flatten
aws_s3_path: my-data/open-data/${{ params.source-extract }}
# This Authentication code should implement with your custom hook
# function. The template allow you to use environment variable.
aws_access_client_id: ${AWS_ACCESS_CLIENT_ID}
aws_access_client_secret: ${AWS_ACCESS_CLIENT_SECRET}
```
The above workflow template is main executor pipeline that you want to do. If you
want to schedule this workflow, you want to dynamic its parameters change base on
execution time such as `run-date` should change base on that workflow running date.
So, this package provide the `Schedule` template for this action.
```yaml
schedule-run-local-wf:
# Validate model that use to parsing exists for template file
type: ddeutil.workflow.scheduler.Schedule
workflows:
# Map existing workflow that want to deploy with scheduler application.
# It allow you to passing release parameter that dynamic change depend the
# current context of this scheduler application releasing that time.
- name: run-py-local
params:
source-extract: "USD-THB"
asat-dt: "${{ release.logical_date }}"
```
## :cookie: Configuration
The main configuration that use to dynamic changing with your propose of this
application. If any configuration values do not set yet, it will use default value
and do not raise any error to you.
| Environment | Component | Default | Description | Remark |
|:----------------------------------------|:----------|:---------------------------------|--------------------------------------------------------------------------------------------------------------------|--------|
| `WORKFLOW_ROOT_PATH` | Core | . | The root path of the workflow application. | |
| `WORKFLOW_CORE_REGISTRY` | Core | src.ddeutil.workflow,tests.utils | List of importable string for the hook stage. | |
| `WORKFLOW_CORE_REGISTRY_FILTER` | Core | ddeutil.workflow.utils | List of importable string for the filter template. | |
| `WORKFLOW_CORE_PATH_CONF` | Core | conf | The config path that keep all template `.yaml` files. | |
| `WORKFLOW_CORE_TIMEZONE` | Core | Asia/Bangkok | A Timezone string value that will pass to `ZoneInfo` object. | |
| `WORKFLOW_CORE_STAGE_DEFAULT_ID` | Core | true | A flag that enable default stage ID that use for catch an execution output. | |
| `WORKFLOW_CORE_STAGE_RAISE_ERROR` | Core | false | A flag that all stage raise StageException from stage execution. | |
| `WORKFLOW_CORE_JOB_DEFAULT_ID` | Core | false | A flag that enable default job ID that use for catch an execution output. The ID that use will be sequence number. | |
| `WORKFLOW_CORE_JOB_RAISE_ERROR` | Core | true | A flag that all job raise JobException from job strategy execution. | |
| `WORKFLOW_CORE_MAX_NUM_POKING` | Core | 4 | . | |
| `WORKFLOW_CORE_MAX_JOB_PARALLEL` | Core | 2 | The maximum job number that able to run parallel in workflow executor. | |
| `WORKFLOW_CORE_GENERATE_ID_SIMPLE_MODE` | Core | true | A flog that enable generating ID with `md5` algorithm. | |
| `WORKFLOW_LOG_DEBUG_MODE` | Log | true | A flag that enable logging with debug level mode. | |
| `WORKFLOW_LOG_ENABLE_WRITE` | Log | true | A flag that enable logging object saving log to its destination. | |
| `WORKFLOW_APP_MAX_PROCESS` | Schedule | 2 | The maximum process worker number that run in scheduler app module. | |
| `WORKFLOW_APP_MAX_SCHEDULE_PER_PROCESS` | Schedule | 100 | A schedule per process that run parallel. | |
| `WORKFLOW_APP_STOP_BOUNDARY_DELTA` | Schedule | '{"minutes": 5, "seconds": 20}' | A time delta value that use to stop scheduler app in json string format. | |
**API Application**:
| Environment | Component | Default | Description | Remark |
|:--------------------------------------|-----------|---------|------------------------------------------------------------------------------------|--------|
| `WORKFLOW_API_ENABLE_ROUTE_WORKFLOW` | API | true | A flag that enable workflow route to manage execute manually and workflow logging. | |
| `WORKFLOW_API_ENABLE_ROUTE_SCHEDULE` | API | true | A flag that enable run scheduler. | |
## :rocket: Deployment
This package able to run as a application service for receive manual trigger
from the master node via RestAPI or use to be Scheduler background service
like crontab job but via Python API.
### Schedule App
```shell
(venv) $ ddeutil-workflow schedule
```
### API Server
```shell
(venv) $ uvicorn src.ddeutil.workflow.api:app --host 127.0.0.1 --port 80
```
> [!NOTE]
> If this package already deploy, it able to use
> `uvicorn ddeutil.workflow.api:app --host 127.0.0.1 --port 80 --workers 4`
### Docker Container
Create Docker image;
```shell
$ docker build -t ddeutil-workflow:latest -f .container/Dockerfile .
```
Run the above Docker image;
```shell
$ docker run -i ddeutil-workflow:latest
```