25 Commits

Author SHA1 Message Date
ILIA ZAKHARCHUK
e735992048 Merge branch 'develop' into 'main'
Develop -> Main

See merge request simpl/simpl-open/development/data-services/template-code-location!14
2026-06-03 16:45:10 +02:00
ILay
7c67371bb0 add empty changelog 2026-06-03 16:26:01 +02:00
ILay
56c8170585 Remove CHANGELOG.md from MR develop to main 2026-06-03 15:28:09 +02:00
ILIA ZAKHARCHUK
d3b7d17ccd Merge branch 'feature/SIMPL-28408-add-readme' into 'develop'
SIMPL-28408 Add README.md

See merge request simpl/simpl-open/development/data-services/template-code-location!17
2026-06-03 15:14:21 +02:00
ILay
5760f9424a [SIMPL-28408] Added readme.
Changelog: added
2026-06-03 15:05:43 +02:00
ILay
b19182d1d7 Add initial README.md with project overview and usage instructions 2026-06-03 13:38:00 +02:00
Geordi Parrillo
63025e83c8 [SIMPL-27884](https://jira.simplprogramm.eu/browse/SIMPL-27884 Fix CHANGELOG.md
Changelog: fixed
2026-06-03 12:58:58 +02:00
Adrian Lodin
4ece2f3274 Merge branch 'feature/SIMPL-28035' into 'develop'
[SIMPL-28035](https://jira.simplprogramme.eu/browse/SIMPL-28035) Update project version.

See merge request simpl/simpl-open/development/data-services/template-code-location!16
2026-06-03 12:56:53 +03:00
Adrian LODIN
e168eebff3 [SIMPL-28035](https://jira.simplprogramme.eu/browse/SIMPL-28035) Update project version.
Changelog: other
2026-06-03 12:44:13 +03:00
Adrian Lodin
5d691fe3cf Merge branch 'feature/SIMPL-28035' into 'develop'
[SIMPL-28035](https://jira.simplprogramme.eu/browse/SIMPL-28035) Update project version.

See merge request simpl/simpl-open/development/data-services/template-code-location!15
2026-05-29 18:56:25 +03:00
Adrian LODIN
dc0774599d [SIMPL-28035](https://jira.simplprogramme.eu/browse/SIMPL-28035) Update project version.
Changelog: other
2026-05-29 18:45:16 +03:00
Adrian Lodin
9553231c98 Merge branch 'feature/SIMPL-28035' into 'develop'
[SIMPL-28035](https://jira.simplprogramme.eu/browse/SIMPL-28035) Update...

See merge request simpl/simpl-open/development/data-services/template-code-location!13
2026-05-29 17:42:46 +03:00
Adrian Lodin
0701bdb9fd [SIMPL-28035](https://jira.simplprogramme.eu/browse/SIMPL-28035) Update... 2026-05-29 17:42:46 +03:00
Emanuele Graziosi
75408a05ee Merge branch 'develop' into 'main'
[SIMPL-27992](https://jira.simplprogramme.eu/browse/SIMPL-27992) Document...

See merge request simpl/simpl-open/development/data-services/template-code-location!12
2026-05-29 14:34:40 +00:00
Emanuele Graziosi
e684083423 Merge branch 'feature/update-util-services' into 'develop'
Updated util services

See merge request simpl/simpl-open/development/data-services/template-code-location!11
2026-05-29 14:22:48 +00:00
Emanuele Graziosi
4723555e31 [SIMPL-23615](https://jira.simplprogramme.eu/browse/SIMPL-23615) Updated util services
Changelog: changed
2026-05-29 16:13:58 +02:00
Matteo Basile
e55c66b94e Merge branch 'feature/SIMPL-28034' into 'develop'
SIMPL-28034 init

See merge request simpl/simpl-open/development/data-services/template-code-location!10
2026-05-29 16:07:28 +02:00
Emanuele Graziosi
9e3ff7d4ac Updated util services 2026-05-29 15:36:56 +02:00
Adrian Lodin
c8a3657126 Merge branch 'feature/SIMPL-27992' into 'develop'
[SIMPL-27992](https://jira.simplprogramme.eu/browse/SIMPL-27992) Document...

See merge request simpl/simpl-open/development/data-services/template-code-location!9
2026-05-29 16:17:22 +03:00
Adrian LODIN
1b154aa2d7 [SIMPL-28035](https://jira.simplprogramme.eu/browse/SIMPL-28035) Document Workflow Update Process.
Changelog: chenged
2026-05-29 16:04:16 +03:00
Matteo Basile
bfc22e594a SIMPL-28034 init 2026-05-29 12:39:39 +02:00
group_4084_bot_632c4f114295e6a0f087518c8bc95cea
39677b9686 Update NOTICE files 2026-05-29 08:19:24 +00:00
group_4084_bot_6985f47c4ca9e27baaf0248bf4fdff78
ea757b632a Update changelog file 2026-05-29 08:18:46 +00:00
Adrian LODIN
96a8a8e4eb [SIMPL-27992](https://jira.simplprogramme.eu/browse/SIMPL-27992) Update documentation Workflow Deletion Process.
Changelog: changed
2026-05-28 20:05:35 +03:00
Adrian LODIN
81bb3299a4 [SIMPL-27992](https://jira.simplprogramme.eu/browse/SIMPL-27992) Document Workflow Deletion Process.
Changelog: added
2026-05-28 01:10:01 +03:00
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@@ -0,0 +1,165 @@
# Template Code Location
> Purpose: `template-code-location` is the predefined Application Workflow Template for Simpl-Open providers. It offers a standard Dagster code location that providers can download, extend, and package when building new application workflows, reducing onboarding effort and avoiding configuration drift.
---
## Table of Contents
1. [Overview](#overview)
2. [What This Template Includes](#what-this-template-includes)
3. [Repository Layout](#repository-layout)
4. [Prerequisites](#prerequisites)
5. [Quick Start](#quick-start)
6. [Development Guide](#development-guide)
7. [Testing](#testing)
8. [Packaging and Deployment](#packaging-and-deployment)
9. [Documentation](#documentation)
10. [Contributing](#contributing)
---
## Overview
This repository provides a standardized baseline for Dagster code locations in the Simpl-Open ecosystem.
It is intended for providers who need a ready-made starting point for new application workflows. Instead of creating a code location from scratch, a provider can use this template to:
- start from an agreed repository structure
- reuse the standard Dagster packaging model
- follow the same conventions for jobs, ops, resources, and definitions
- integrate existing Simpl-Open workflow packages as dependencies
- add custom workflow logic with minimal setup effort
The template is intentionally simple. It includes a small example job and ops that demonstrate the expected pattern for creating new workflows, while the main Dagster entrypoint already shows how to register both local jobs and jobs imported from external packages.
## What This Template Includes
- A working Dagster code location package under `src/template_code_location`
- A repository entrypoint in `src/template_code_location/repository.py`
- Example local ops in `src/template_code_location/ops/ops.py`
- Example local job in `src/template_code_location/jobs/jobs.py`
- Preconfigured dependencies on shared Simpl-Open workflow packages through `pyproject.toml`
- A Dockerfile for container packaging
- Supporting guidance in the `documents/` folder
This template is not meant to contain business-specific logic by default. Providers are expected to replace or extend the sample workflow with their own application-specific jobs and operations.
## Repository Layout
```text
template-code-location/
├── src/
│ └── template_code_location/
│ ├── repository.py # Unified entry point (all jobs/sensors/resources)
│ ├── jobs/ # Custom jobs specific to this code location
│ │ └── jobs.py
│ └── ops/ # Custom ops specific to this code location
│ └── ops.py
├── tests/ # Unit & integration tests
├── Dockerfile
├── pyproject.toml # Dependencies & external package sources
└── README.md
```
Key files:
- `src/template_code_location/repository.py`: Dagster `Definitions` entrypoint used to register jobs, sensors, resources, and loggers
- `src/template_code_location/jobs/jobs.py`: place for custom workflow definitions
- `src/template_code_location/ops/ops.py`: place for custom operational steps used by local jobs
- `pyproject.toml`: package metadata and Git-based dependencies on shared Simpl-Open packages
- [documents/Development Guide.md](documents/Development%20Guide.md): workflow lifecycle guidance for creating, editing, and deleting workflows
## Prerequisites
Before using this template, make sure the following are available:
- Python 3.12+
- `uv` package manager
- Access to the Simpl-Open Git repositories referenced by `pyproject.toml`
- A local or shared Dagster environment for validation
- Docker, if you plan to build the image locally
Depending on the workflows you build, you may also need access to supporting platform services such as object storage, PostgreSQL, Vault, or Kubernetes.
## Quick Start
### 1. Install dependencies
```powershell
uv sync --dev
```
### 2. Start the Dagster code server locally
```powershell
uv run dagster code-server start -h 0.0.0.0 -p 4000 -f src/template_code_location/repository.py
```
This starts the code location using the repository entrypoint already defined in the template.
### 3. Review the sample workflow
The template ships with:
- `fetch_data` and `process_data` ops in `src/template_code_location/ops/ops.py`
- `data_processing_job` in `src/template_code_location/jobs/jobs.py`
These are example building blocks only. Providers should rename, replace, or extend them to match the target application workflow.
## Development Guide
Workflow creation, update, deletion, and the expected way to work with external code locations are covered in [documents/Development Guide.md](documents/Development%20Guide.md).
Use that guide as the primary reference when customizing this template for a provider-specific workflow.
## Testing
Custom workflows should always be validated before publication.
Recommended checks:
- add unit tests for local jobs and ops
- run `pytest` locally after every workflow change
- verify that the Dagster code server can load `src/template_code_location/repository.py` without import or definition errors
Example command:
```powershell
uv run pytest
```
At the moment this template does not include a `tests/` directory, so providers should add one as part of implementing their own workflows.
## Packaging and Deployment
This repository is designed to be packaged as a Docker image and promoted through the standard Simpl-Open delivery flow.
High-level process:
1. Implement and validate the workflow locally.
2. Commit the changes to a feature branch.
3. Open a pull request for review.
4. Build and publish the container image through CI/CD.
5. Update the deployment configuration to use the new image.
6. Verify the new code location in Dagster.
Use immutable image tags such as semantic versions or commit-based identifiers rather than `latest`.
## Documentation
Additional guidance is available in the repository documents:
- [documents/Development Guide.md](documents/Development%20Guide.md): workflow creation, edit, deletion, and deployment process
- [documents/Output Separation and Non-Overwrite Principles.md](documents/Output%20Separation%20and%20Non-Overwrite%20Principles.md): output handling expectations for workflow implementations
## Contributing
When extending this template:
- keep the repository structure stable so onboarding stays predictable
- preserve the separation between ops, jobs, and repository definitions
- avoid hardcoded credentials and environment-specific values
- update tests and documentation together with workflow changes
This repository should remain a reusable baseline for providers, not a dumping ground for unrelated or one-off implementations.

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@@ -2,7 +2,7 @@
## 1. Goal and Scope
The purpose of this document is to provide a comprehensive guide for participants to create, manage, and update workflows within the Simpl-Open orchestration platform.
The purpose of this document is to provide a comprehensive guide for participants to create, manage, update and delete workflows within the Simpl-Open orchestration platform.
By following a *code-first approach*, developers ensure consistency, traceability, and reliability across all environments.
## 2. Local Development
@@ -43,7 +43,10 @@ The heavy-lifting logic lives in separate repositories, pulled in as installable
These packages expose their jobs and ops which are then imported and registered in `repository.py`.
### 2.3 Code Examples (Ops, Jobs, and Definitions)
The orchestration logic should be modular. Here is a practical example of how to construct a workflow.
The orchestration logic should be modular.
### 2.3.1 Workflow creation
Here is a practical example of how to construct a workflow.
**1. Defining Ops (`ops/ops.py`)**
Ops are the core units of computation. Keep them focused on a single task.
@@ -100,6 +103,52 @@ defs = Definitions(
loggers={"simpl": simpl_json_logger},
)
```
### 2.3.2 Workflow edit
Updating a workflow, means changing the job name, parameters or definition.
Each modification means getting a new version of that workflow. To make the version visible, the name of the job can include a version tag (eg: `data_processing_job_ver_2`).
**1. Change Jobs (`jobs/jobs.py`)**
Change workflow name, parameters or definition in the `jobs.py` file.
**2. Change Ops (`ops/ops.py`)**
If workflow ops are updated, this must be done in `ops.py` file.
**3. Change Tests (`tests/`)**
If jobs or obs are updated, check out their related `tests` and update them accordingly.
**4. Update Registering Definitions (`repository.py`)**
If jobs names are updated, make sure to update the import and definitions.
### 2.3.3 Workflow deletion
Before deleting an existing workflow, first check if is needed or referenced in:
- **Asset Orchestrator**
- Perform an `GET/workflowDefinitions` api call, to get the workflow details.
- **Dagster UI**:
- Navigate to the "Runs". Identify a given workflow using the "Filter" button or navigation "Newer", "Older" buttons.
- Check details and status: open workflow details by clicking on the target, or just check the status value.
- Check logs: open workflow logs by clicking on the uuid.
Here are two practical examples how to delete a workflow:
**Temporary delete workflow**
**1. Clean Registering Definitions (`repository.py`)**
Comment or delete workflow definition from the `repository.py`.
**Permanently delete workflow**
**1. Clean Jobs (`jobs/jobs.py`)**
Delete workflow definition from the `jobs.py` file.
**2. Clean Ops (`ops/ops.py`)**
If workflow links also ops, delete their definition from the `ops.py` file.
**3. Clean Tests (`tests/`)**
If jobs or obs have dedicated unittest, check out the `tests/` folder and delete them.
**4. Clean Scheduler**
If the workflow has any scheduler, check and adjust it accordingly.
### 2.4 Best Practices & Constraints
@@ -145,7 +194,9 @@ The deployment follows these automated steps:
To confirm a successful deployment:
- **Dagster UI**: Navigate to the "Deployment" or "Code Locations" tab. Verify that the loaded image tag matches the latest Git commit.
- **Dagster UI**:
- Navigate to the "Deployment" or "Code Locations" tab. Verify that the loaded image tag matches the latest Git commit.
- Navigate to the "Runs" tab. Identify deleted workflow. Click on it's target to open details. "Pipeline not found" message shows that workflow was deleted successfully. Click on it's uuid to open logs. Logs must be available after deletion.
- **Health Check**: Trigger a "Test Run" of the job in the production environment using a limited data slice.
- **Logs**: Monitor the initialization logs in the Dagster daemon to ensure the code location was loaded without schema or dependency errors.

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@@ -0,0 +1,151 @@
# Dagster Workflow Input/Output Separation and Non-Overwrite Principles
## 1. Objective
The purpose of this document is to describe how Dagster workflows must be configured to ensure that input datasets are **never modified or overwritten** during processing.
The workflow must:
- Read data from a **source location (input)**
- Write processed data to a **separate destination (output)**
- Preserve the original dataset unchanged
---
## 2. Key Principles
To avoid overwriting input datasets, the following principles must always be applied when designing Dagster workflows:
### 2.1 Separation of Input and Output
- Input and output must **always refer to different storage locations**
- This separation can be enforced via:
- Different `file_key` values
- Different buckets or paths
- Different prefixes or folders
### 2.2 Read-Only Input
- Input datasets must be treated as **immutable**
- No operation should write back to the input path
### 2.3 Explicit Output Configuration
- Output destinations must be explicitly configured
- Avoid default or implicit reuse of input configuration
### 2.4 Idempotent Processing
- Workflow execution should not produce side effects on the original dataset
- Re-running the workflow must not alter the source data
---
## 3. Test Setup and Execution
### 3.1 Dataset Configuration
- Prepare an input dataset (e.g. `input.csv`) in a defined location:
- Example: `s3://dagster-workflow-bucket/input.csv`
### 3.2 Workflow Configuration
- Configure Dagster to:
- Read from the input dataset
- Write results to a different location (e.g. `output.csv`)
### 3.3 Execution
- Run the Dagster job
- Ensure that:
- Data is successfully processed
- Output dataset is generated
---
## 4. Verification of No Overwrite
To validate correct behavior:
- Compare input dataset **before and after execution**
- Ensure:
- File content is unchanged
- File timestamp/version is unchanged (if applicable)
- Verify that:
- Output dataset exists in a different location
- Output contains only processed data
---
## 5. Example Simpl-Open Pre-Built Workflow Configuration
In Simpl-Open pre-built workflows, this principle is **already enforced by design**.
Below is an example configuration:
```yaml
ops:
apply_l_diversity:
config:
generalisation_hierarchies:
age: simpl_age
ident:
- Name
k: 2
l: 3
quasi_identifiers:
- age
sensitive_attribute: Disease
supp_level: 50.0
read_structured_from_s3:
config:
bucket_name: dagster-workflow-bucket
file_format: csv
file_key: input.csv
write_df_to_s3:
config:
bucket_name: dagster-workflow-bucket
file_format: csv
file_key: output.csv
```
### Explanation
- `read_structured_from_s3`
- Reads the dataset from `input.csv`
- `write_df_to_s3`
- Writes the processed dataset to `output.csv`
### Key Point
Even when using the **same bucket**, separation is guaranteed by:
- Using a **different `file_key` for output**
This ensures that:
- The input dataset (`input.csv`) is never overwritten
- The output dataset is stored independently (`output.csv`)
---
## 6. Configuration Guidelines
When creating or customizing Dagster workflows, follow these guidelines:
- Always define a **dedicated output path**
- Never reuse the same `file_key` for input and output
- Prefer:
- Different filenames (`input.csv` vs `output.csv`)
- Or structured paths:
- `/input/...`
- `/output/...`
- Validate configuration before execution
---
## 7. Conclusion
The separation between input and output datasets is a **design principle** in Dagster workflows.
Simpl-Open pre-built workflows already implement this approach by:
- Clearly distinguishing input and output configurations
- Ensuring safe, non-destructive data processing
Adhering to these principles guarantees:
- Data integrity
- Reproducibility
- Safe pipeline execution without unintended overwrites

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@@ -1 +1 @@
PROJECT_VERSION_NUMBER="0.1.0"
PROJECT_VERSION_NUMBER="0.2.0"

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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "template-code-location"
version = "0.1.0"
version = "0.2.0"
description = "Consolidated code location for all data services workflows"
requires-python = ">=3.12"
dependencies = [
@@ -18,15 +18,15 @@ dependencies = [
[tool.uv]
exclude-dependencies = ["transformers", "spacy-transformers"]
override-dependencies = [
"util-services @ git+https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git@v0.6.1",
"util-services @ git+https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git@v0.7.0",
]
[tool.uv.sources]
torch = { index = "pytorch-cpu" }
util-services = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git", rev = "v0.6.1" }
data-processing = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/data-processing.git", branch = "0.4.0" }
dataframe-level-anonymisation = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/dataframe-level-anonymisation.git", branch = "0.6.0" }
field-level-pseudo-anonymisation = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/field-level-pseudo-anonymisation.git", branch = "0.7.0" }
util-services = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git", rev = "v0.7.0" }
data-processing = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/data-processing.git", rev = "v0.4.0" }
dataframe-level-anonymisation = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/dataframe-level-anonymisation.git", rev = "v0.6.0" }
field-level-pseudo-anonymisation = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/field-level-pseudo-anonymisation.git", rev = "v0.7.0" }
[[tool.uv.index]]
name = "pytorch-cpu"

279
uv.lock generated
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@@ -15,7 +15,7 @@ resolution-markers = [
]
[manifest]
overrides = [{ name = "util-services", git = "https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git?rev=v0.6.1" }]
overrides = [{ name = "util-services", git = "https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git?rev=v0.7.0" }]
excludes = [
"spacy-transformers",
"transformers",
@@ -292,30 +292,30 @@ wheels = [
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@@ -741,7 +741,7 @@ wheels = [
[[package]]
name = "dagster"
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version = "0.6.1"
source = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git?rev=v0.6.1#81738f2fc3821eb134e7a4d8635c341cf7ce3b53" }
version = "0.7.0"
source = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git?rev=v0.7.0#2b70c4277481948ba6a294e4e3585a5741503b85" }
dependencies = [
{ name = "boto3" },
{ name = "common-logging-python" },