Merge branch 'feature/SIMPL-24642' into 'develop'

SIMPL-24642: consolidate all data services into single code location

See merge request simpl/simpl-open/development/data-services/template-code-location!4

Dockerfile

@@ -1,13 +1,68 @@
 FROM python:3.12-slim-bookworm
 
+# --- Install uv (pinned for reproducibility) ---
+COPY --from=ghcr.io/astral-sh/uv:0.10.8 /uv /uvx /bin/
+
 WORKDIR /app
 
+# Create non-root user with explicit UID/GID 1000
+RUN addgroup --gid 1000 appgroup && \
+    adduser --uid 1000 --gid 1000 --disabled-password --gecos "" appuser
+
+# Install system dependencies:
+#   - git: required to fetch util-services from GitLab (tool.uv.sources)
+#   - build-essential / gcc / g++ / python3-dev / cmake: native extensions
+#     (scrubadub-spacy → spaCy, pycanon, etc.)
+#   - curl: optional healthcheck / runtime tooling
+RUN apt-get update && apt-get upgrade -y \
+    && apt-get install -y --no-install-recommends \
+    build-essential=12.9 \
+    cmake=3.25.1-1 \
+    gcc=4:12.2.0-3 \
+    g++=4:12.2.0-3 \
+    python3-dev=3.11.2-1+b1 \
+    git=1:2.39.5-0+deb12u3 \
+    curl=7.88.1-10+deb12u14 \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -rf /tmp/* \
+    && rm -rf /var/tmp/*
+
+# Pre-own /app so appuser can write to it
+RUN chown -R appuser:appgroup /app
+
+# Copy project metadata and source
 COPY pyproject.toml .
-RUN pip install --no-cache-dir dagster dagster-webserver
+COPY src/ ./src/
 
-COPY src/ src/
-RUN pip install --no-cache-dir .
+# uv environment knobs:
+#   UV_COMPILE_BYTECODE  → compile .pyc files at install time for faster cold start
+#   UV_LINK_MODE=copy    → copy files instead of symlinks (required in Docker layers)
+#   UV_SYSTEM_PYTHON=1   → install into the system Python (no extra venv needed)
+ENV UV_COMPILE_BYTECODE=1
+ENV UV_LINK_MODE=copy
+ENV UV_SYSTEM_PYTHON=1
 
-EXPOSE 3000
+# Install the project and all dependencies, respecting [tool.uv.sources]
+# (git source for util-services and pytorch-cpu index for torch)
+# BuildKit cache mount keeps the uv package cache across builds
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install .
 
-CMD ["dagster", "api", "grpc", "-h", "0.0.0.0", "-p", "3000", "-m", "template-code-location.repository"]
+ENV PYTHONPATH="/app/src"
+
+# Make /app writable for the non-root user (e.g. spaCy model downloads)
+RUN chown -R 1000:1000 /app && chmod -R u+w /app
+
+# Provide a real home directory for appuser
+RUN mkdir -p /home/appuser && chown -R 1000:1000 /home/appuser
+ENV HOME=/home/appuser
+
+USER appuser
+
+# Sanity-check: fail the build early if the dagster CLI is missing
+RUN dagster --version
+
+EXPOSE 4000
+
+CMD ["dagster", "code-server", "start", "-h", "0.0.0.0", "-p", "4000", "-f", "src/template_code_location/repository.py"]

documents/Development Guide.md

@@ -9,64 +9,100 @@ By following a *code-first approach*, developers ensure consistency, traceabilit
 Development must always begin in a local environment. This allows developers to rapidly iterate, test business logic, and validate DAG (Directed Acyclic Graph) structures without impacting production data.
 
 ### 2.1 Project Layout
-To ensure compatibility with the Simpl-Open platform, every Dagster code location must adhere to the following directory structure:
+This repository (`template-code-location`) serves as the **single consolidated code location** for all data services workflows. It imports jobs and ops from three external packages (`data-processing`, `dataframe-level-anonymisation`, and `field-level-pseudo-anonymisation`) which are installed as Git dependencies, and also provides a place for custom template jobs/ops.
+
 ```text
-project-root/
-├── dagster_code_location/
-│   ├── jobs/           # Executable workflows
-│   ├── ops/            # Individual functional units (business logic)
-│   ├── resources/      # External connections (Object storage, APIs, etc...)
-│   └── repository.py   # Central entry point for the code location
-├── tests/              # Unit and integration tests
-├── Dockerfile          # Containerization instructions
-├── pyproject.toml      # Dependency management (Poetry/Pip/UV)
-└── README.md           # Documentation
+template-code-location/
+├── src/
+│   └── template_code_location/
+│       ├── __init__.py
+│       ├── repository.py                  # Unified entry point (all jobs/sensors/resources)
+│       ├── jobs/                           # Custom jobs specific to this code location
+│       │   ├── __init__.py
+│       │   └── jobs.py
+│       └── ops/                            # Custom ops specific to this code location
+│           ├── __init__.py
+│           └── ops.py
+├── tests/                                  # Unit & integration tests
+├── Dockerfile
+├── pyproject.toml                          # Dependencies & external package sources
+└── README.md
 ```
 
-### 2.2 Code Examples (Ops, Jobs, and Definitions)
+### 2.2 External Dependencies (Git Packages)
+
+The heavy-lifting logic lives in separate repositories, pulled in as installable Python packages via `pyproject.toml` and `[tool.uv.sources]`:
+
+| Package | Purpose | Source |
+|---------|---------|--------|
+| `data-processing` | Data cleaning & transformation jobs | Git (branch: `develop`) |
+| `dataframe-level-anonymisation` | k-anonymity, l-diversity, t-closeness | Git (branch: `develop`) |
+| `field-level-pseudo-anonymisation` | Field-level encryption/hashing/redaction | Git (branch: `develop`) |
+| `util-services` | Shared resources, sensors, and logging | Git (tag: `v0.5.0`) |
+
+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.
 
-**1. Defining Ops (ops.py)**  
+**1. Defining Ops (`ops/ops.py`)**  
 Ops are the core units of computation. Keep them focused on a single task.
+
 ```python
 from dagster import op
 
 @op
-def fetch_raw_data() -> list:
-    """Fetches raw data from an external source."""
+def fetch_data() -> list:
+    """Fetches raw data from a source."""
     return [{"id": 1, "value": "A"}, {"id": 2, "value": "B"}]
 
 @op
 def process_data(data: list) -> dict:
-    """Transforms raw data into an aggregated format."""
-    return {"processed_count": len(data), "status": "success"}
+    """Processes raw data and returns a summary."""
+    return {"count": len(data), "status": "success"}
 ```
-**2. Assembling Jobs (jobs.py)**  
+
+**2. Assembling Jobs (`jobs/jobs.py`)**  
 Jobs link ops together to form a dependency graph (workflow).
+
 ```python
 from dagster import job
-from .ops import fetch_raw_data, process_data
+from ..ops.ops import fetch_data, process_data
 
 @job
 def data_processing_job():
-    """A workflow that fetches and processes data."""
-    raw_data = fetch_raw_data()
-    process_data(raw_data)
+    """A simple job that fetches and processes data."""
+    raw = fetch_data()
+    process_data(raw)
 ```
-**3. Registering Definitions (repository.py)**  
-This file acts as the entry point for the Simpl-Open orchestration platform to discover your code.
+
+**3. Registering Definitions (`repository.py`)**  
+This file acts as the entry point for the Simpl-Open orchestration platform to discover your code. It imports jobs from local modules as well as from external packages.
+
 ```python
 from dagster import Definitions
-from .jobs import data_processing_job
+from util_services.resources import s3_resource
+from util_services.sensors import notify_success, notify_failure, notify_canceled
+from util_services.custom_json_logger import simpl_json_logger
+
+# External package jobs
+from data_processing.jobs import remove_duplicates_job_s3, fill_missing_values_job_s3
+from dataframe_level_anonymisation.jobs import k_anonymity_job_s3, l_diversity_job_s3
+from field_level_pseudo_anonymisation.jobs import anonymise_pseudonymise_structured_job_s3
+
+# Local template jobs
+from template_code_location.jobs.jobs import data_processing_job
 
-# The platform will load this Definitions object
 defs = Definitions(
-    jobs=[data_processing_job]
-    # You can also declare schedules, sensors, and resources here
+    jobs=[data_processing_job, remove_duplicates_job_s3, ...],
+    sensors=[notify_success, notify_failure, notify_canceled],
+    resources={"s3": s3_resource.configured({"resource_name": "selfS3"})},
+    loggers={"simpl": simpl_json_logger},
 )
 ```
 
-### 2.3 Best Practices & Constraints
+### 2.4 Best Practices & Constraints
+
 - **Separation of Concerns**: Keep orchestration logic (how ops connect) strictly separate from heavy business logic (which should ideally live in separate Python modules/classes).
 - **Naming Conventions**: Use snake_case for jobs and ops. Code locations should be named based on the domain they represent (e.g., inventory_sync_service).
 - **Dependency Management**: All dependencies must be explicitly declared in pyproject.toml or requirements.txt.

pipeline.variables.sh

@@ -1 +1 @@
-PROJECT_VERSION_NUMBER="0.0.1"
+PROJECT_VERSION_NUMBER="0.1.0"

pyproject.toml

@@ -4,25 +4,35 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "template-code-location"
-version = "0.0.1"
-description = "Template code location for data processings services"
+version = "0.1.0"
+description = "Consolidated code location for all data services workflows"
 requires-python = ">=3.12"
 dependencies = [
+    # Dagster core
     "dagster>=1.8.13",
-    "dagster-webserver>=1.8.13",
-    "dagster-postgres>=0.24.13",
-    "pandas>=3.0",
-    "pyarrow>=23.0",
-    "lxml>=6.0",
-    "xmltodict>=1.0",
-    "rdflib>=7.6",
-    "numpy>=2.4",
-    "great_expectations>=1.16",
-    "pandera>=0.31",
-    "scrapy>=2.15",
-    "BeautifulSoup4>=4.14",
+    # Util services — resolved via [tool.uv.sources] (git)
+    "util-services",
+    # Code location packages — resolved via [tool.uv.sources] (git)
+    "data-processing",
+    "dataframe-level-anonymisation",
+    "field-level-pseudo-anonymisation",
 ]
 
+[tool.uv]
+exclude-dependencies = ["transformers", "spacy-transformers"]
+
+[tool.uv.sources]
+torch = { index = "pytorch-cpu" }
+util-services = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/util-services.git", rev = "v0.5.0" }
+data-processing = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/data-processing.git", branch = "develop" }
+dataframe-level-anonymisation = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/dataframe-level-anonymisation.git", branch = "develop" }
+field-level-pseudo-anonymisation = { git = "https://code.europa.eu/simpl/simpl-open/development/data-services/field-level-pseudo-anonymisation.git", branch = "develop" }
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
 [project.optional-dependencies]
 dev = [
     "pytest>=8.0.0",

src/template-code-location/repository.py

@@ -1,6 +0,0 @@
-from dagster import Definitions
-from .jobs.jobs import data_processing_job
-
-defs = Definitions(
-    jobs=[data_processing_job],
-)

src/template_code_location/repository.py

@@ -0,0 +1,68 @@
+from dagster import Definitions
+from util_services.resources import s3_resource
+from util_services.sensors import (
+    notify_success,
+    notify_failure,
+    notify_canceled
+)
+from util_services.custom_json_logger import simpl_json_logger
+
+# Data processing jobs
+from data_processing.jobs import (
+    remove_duplicates_job_s3,
+    fill_missing_values_job_s3,
+    standardize_categorical_values_job_s3,
+    correct_typos_job_s3,
+    normalize_numeric_min_max_job_s3,
+    normalize_datetime_job_s3,
+    normalize_coordinates_job_s3,
+    add_global_aggregations_job_s3,
+    filter_dataset_job_s3,
+)
+
+# Dataframe-level anonymisation jobs
+from dataframe_level_anonymisation.jobs import (
+    k_anonymity_job_s3,
+    l_diversity_job_s3,
+    t_closeness_job_s3,
+    read_write_semistructured_job_s3,
+)
+
+# Field-level pseudo-anonymisation jobs
+from field_level_pseudo_anonymisation.jobs import (
+    anonymise_pseudonymise_structured_job_s3,
+    depseudonymise_structured_job_s3,
+    anonymise_pseudonymise_unstructured_job_s3,
+    depseudonymise_unstructured_job_s3,
+)
+
+from template_code_location.jobs.jobs import data_processing_job
+
+defs = Definitions(
+    jobs=[
+        data_processing_job,
+        # Data processing
+        remove_duplicates_job_s3,
+        fill_missing_values_job_s3,
+        standardize_categorical_values_job_s3,
+        correct_typos_job_s3,
+        normalize_numeric_min_max_job_s3,
+        normalize_datetime_job_s3,
+        normalize_coordinates_job_s3,
+        add_global_aggregations_job_s3,
+        filter_dataset_job_s3,
+        # Dataframe-level anonymisation
+        k_anonymity_job_s3,
+        l_diversity_job_s3,
+        t_closeness_job_s3,
+        read_write_semistructured_job_s3,
+        # Field-level pseudo-anonymisation
+        anonymise_pseudonymise_structured_job_s3,
+        depseudonymise_structured_job_s3,
+        anonymise_pseudonymise_unstructured_job_s3,
+        depseudonymise_unstructured_job_s3,
+    ],
+    sensors=[notify_success, notify_failure, notify_canceled],
+    resources={"s3": s3_resource.configured({"resource_name": "selfS3"})},
+    loggers={"simpl": simpl_json_logger},
+)