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[SIMPL-27992](https://jira.simplprogramme.eu/browse/SIMPL-27992) Document...

See merge request simpl/simpl-open/development/data-services/template-code-location!12
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Emanuele Graziosi
2026-05-29 14:34:40 +00:00
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54
documents/Development Guide.md
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@@ -2,7 +2,7 @@
## 1. Goal and Scope ## 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. By following a *code-first approach*, developers ensure consistency, traceability, and reliability across all environments.
## 2. Local Development ## 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`. These packages expose their jobs and ops which are then imported and registered in `repository.py`.
### 2.3 Code Examples (Ops, Jobs, and Definitions) ### 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`)** **1. Defining Ops (`ops/ops.py`)**
Ops are the core units of computation. Keep them focused on a single task. Ops are the core units of computation. Keep them focused on a single task.
@@ -100,6 +103,49 @@ defs = Definitions(
loggers={"simpl": simpl_json_logger}, 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 its status (if is needed or referenced) in the **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 ### 2.4 Best Practices & Constraints
@@ -145,7 +191,9 @@ The deployment follows these automated steps:
To confirm a successful deployment: 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. - **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. - **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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documents/Output Separation and Non-Overwrite Principles.md Normal file
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# 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

12
pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project] [project]
name = "template-code-location" name = "template-code-location"
version = "0.1.0" version = "0.2.0"
description = "Consolidated code location for all data services workflows" description = "Consolidated code location for all data services workflows"
requires-python = ">=3.12" requires-python = ">=3.12"
dependencies = [ dependencies = [
@@ -18,15 +18,15 @@ dependencies = [
[tool.uv] [tool.uv]
exclude-dependencies = ["transformers", "spacy-transformers"] exclude-dependencies = ["transformers", "spacy-transformers"]
override-dependencies = [ 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] [tool.uv.sources]
torch = { index = "pytorch-cpu" } torch = { index = "pytorch-cpu" }
util-services = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git", rev = "v0.6.1" } 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", branch = "0.4.0" } 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", branch = "0.6.0" } 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", branch = "0.7.0" } 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]] [[tool.uv.index]]
name = "pytorch-cpu" name = "pytorch-cpu"

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uv.lock generated
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@@ -15,7 +15,7 @@ resolution-markers = [
] ]
[manifest] [manifest]
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