From 6e260256e074d0d113c381055dc9ee1cb3b9f5c1 Mon Sep 17 00:00:00 2001 From: sspaeti Date: Tue, 10 Mar 2026 18:29:24 +0100 Subject: [PATCH 1/3] migrate from pip to uv --- README.md | 6 +- src/pipelines/real-estate/Makefile | 6 +- src/pipelines/real-estate/pyproject.toml | 35 + src/pipelines/real-estate/uv.lock | 2435 ++++++++++++++++++++++ 4 files changed, 2476 insertions(+), 6 deletions(-) create mode 100644 src/pipelines/real-estate/uv.lock diff --git a/README.md b/README.md index 00f2916..1d34e20 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ Next, I might add Rill Developer to the mix to have some fun analyzing the data Please refer to individual component directories for detailed setup and usage instructions. The project is designed to run both locally and on cloud environments, offering flexibility in deployment and testing. ### Prerequisites: -- Python and pip for installing dependencies +- Python and [uv](https://docs.astral.sh/uv/) for installing dependencies - MinIO running for cloud-agnostic S3 storage - Docker Desktop & Kubernetes for running Jupyter Notebooks - Basic understanding of Python and SQL for effective navigation and customization of the project @@ -62,13 +62,13 @@ Please refer to individual component directories for detailed setup and usage in cd src/pipelines/real-estate # installation -pip install -e ".[dev]" +uv sync --all-extras # run minio minio server /tmp/minio/ # startup dagster -dagster dev +uv run dagster dev ``` diff --git a/src/pipelines/real-estate/Makefile b/src/pipelines/real-estate/Makefile index 7730ab7..05f76ef 100644 --- a/src/pipelines/real-estate/Makefile +++ b/src/pipelines/real-estate/Makefile @@ -1,10 +1,10 @@ .DEFAULT_GOAL := up -up: - dagster dev +up: + uv run dagster dev install: - pip install -e ".[dev]" + uv sync --all-extras minio: minio server ~/Documents/minio/ diff --git a/src/pipelines/real-estate/pyproject.toml b/src/pipelines/real-estate/pyproject.toml index 8acf6f9..8ce86f7 100644 --- a/src/pipelines/real-estate/pyproject.toml +++ b/src/pipelines/real-estate/pyproject.toml @@ -1,3 +1,37 @@ +[project] +name = "real-estate" +version = "0.1.0" +requires-python = ">=3.14" +dependencies = [ + "boto3>=1.42.64", + "dagster==1.5.1", + "dagster-aws==0.21.1", + "dagster-pandas==0.21.1", + "dagster-postgres==0.21.1", + "dagster-pyspark==0.21.1", + "dagster-spark==0.21.1", + "dagster-webserver>=1.5.1", + "dagstermill>=0.21.1", + "folium>=0.20.0", + "ijson>=3.5.0", + "koalas>=1.8.2", + "matplotlib>=3.10.8", + "numpy>=2.4.3", + "pandas>=3.0.1", + "pandasql>=0.7.3", + "pyarrow>=23.0.1", + "pyspark>=4.1.1", + "pyyaml>=6.0.3", + "scikit-learn>=1.8.0", + "scipy>=1.17.1", + "seaborn>=0.13.2", +] + +[project.optional-dependencies] +dev 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+0100 Subject: [PATCH 2/3] major upgrade to latest dagster - adding druid and swichting from mino to seaweedsfs (Claude updgrade) --- .claude/settings.local.json | 14 + README.md | 119 +- src/pipelines/real-estate/.tool-versions | 2 +- src/pipelines/real-estate/Makefile | 11 +- .../real-estate/dev-requirements.txt | 48 - src/pipelines/real-estate/docker-compose.yml | 132 + src/pipelines/real-estate/immoscout.html | 129 + src/pipelines/real-estate/pyproject.toml | 58 +- .../real-estate/realestate/__init__.py | 30 +- .../real-estate/realestate/common/__init__.py | 53 +- .../realestate/common/helper_functions.py | 29 +- .../realestate/common/resource_delta_lake.py | 39 +- .../realestate/common/resources.py | 73 +- .../real-estate/realestate/common/solids.py | 9 +- .../realestate/common/solids_druid.py | 50 +- .../realestate/common/solids_filehandle.py | 10 +- .../realestate/common/solids_jupyter.py | 42 +- .../realestate/common/solids_notebook.py | 21 +- .../realestate/common/solids_scraping.py | 280 +- .../realestate/common/solids_spark_delta.py | 536 +-- .../real-estate/realestate/common/types.py | 83 +- .../realestate/common/types_realestate.py | 88 +- .../config_environments/local_base.yaml | 139 +- .../config_pipelines/scrape_realestate.yaml | 46 +- .../real-estate/realestate/pipelines.py | 88 +- .../real-estate/realestate/resources.py | 174 +- .../realestate_tests/pipeline_tests.py | 41 +- .../read_property_json_test.py | 48 +- src/pipelines/real-estate/seaweedfs-s3.json | 14 + src/pipelines/real-estate/setup.cfg | 3 - src/pipelines/real-estate/setup.py | 33 - src/pipelines/real-estate/tox.ini | 29 - src/pipelines/real-estate/uv.lock | 3754 +++++++++++++---- update-prompt.md | 207 + 34 files changed, 4300 insertions(+), 2132 deletions(-) create mode 100644 .claude/settings.local.json delete mode 100644 src/pipelines/real-estate/dev-requirements.txt create mode 100644 src/pipelines/real-estate/immoscout.html create mode 100644 src/pipelines/real-estate/seaweedfs-s3.json delete mode 100644 src/pipelines/real-estate/setup.cfg delete mode 100644 src/pipelines/real-estate/setup.py delete mode 100644 src/pipelines/real-estate/tox.ini create mode 100644 update-prompt.md diff --git a/.claude/settings.local.json b/.claude/settings.local.json new file mode 100644 index 0000000..2c9a331 --- /dev/null +++ b/.claude/settings.local.json @@ -0,0 +1,14 @@ +{ + "permissions": { + "allow": [ + "Bash(cd /home/sspaeti/git/data-engineering/practical-data-engineering/src/pipelines/real-estate && rm -f setup.py setup.cfg dev-requirements.txt tox.ini && rm -rf realestate.egg-info/ real_estate.egg-info/ spark-warehouse/)", + "Bash(rm -f uv.lock && uv lock 2>&1 | tail -30)", + "Bash(uv sync:*)", + "Bash(uv run:*)", + "Bash(pkill -f \"dagster dev\" 2>/dev/null; sleep 1; timeout 15 uv run dagster dev 2>&1 || true)", + "Bash(timeout 15 uv run dagster dev 2>&1; echo \"EXIT: $?\")", + "Read(//tmp/**)", + "Bash(curl -s -A \"Mozilla/5.0 \\(X11; Linux x86_64; rv:128.0\\) Gecko/20100101 Firefox/128.0\" \"https://www.immoscout24.ch/en/real-estate/buy/city-twann?r=0&map=1\" -o /tmp/immo_twann2.html && wc -c /tmp/immo_twann2.html)" + ] + } +} diff --git a/README.md b/README.md index 1d34e20..2157bce 100644 --- a/README.md +++ b/README.md @@ -6,11 +6,11 @@ [![Open Source Logos](https://www.ssp.sh/blog/data-engineering-project-in-twenty-minutes/images/open-source-logos.png)](https://www.ssp.sh/blog/data-engineering-project-in-twenty-minutes/) -This repository containts a practical implementation of a data engineering project that spans across web-scraping real-estates, processing with Spark and Delta Lake, adding data science with Jupyter Notebooks, ingesting data into Apache Druid, visualizing with Apache Superset, and managing workflows with Dagster—all orchestrated on Kubernetes. +This repository containts a practical implementation of a data engineering project that spans across web-scraping real-estates, processing with Spark and Delta Lake, adding data science with Jupyter Notebooks, ingesting data into Apache Druid, visualizing with Apache Superset, and managing workflows with Dagster—all orchestrated on Kubernetes. **Built your own DE project or forked mine? Let me know in the comments; I'd be curious to know more about.** -## 🌟 About This Project +## About This Project This Practical Data Engineering project addresses common data engineering challenges while exploring innovative technologies. It should serve as a learning project but incorporate comprehensive real-world use cases. It's a guide to building a data application that collects real-estate data, enriches it with various metrics, and offers insights through machine learning and data visualization. This application helps you find your dream properties in your area and showcases how to handle a full-fledged data engineering pipeline using modern tools and frameworks. @@ -22,68 +22,127 @@ This Practical Data Engineering project addresses common data engineering challe ### Key Features & Learnings: - Scraping real estate listings with [Beautiful Soup](https://beautiful-soup-4.readthedocs.io/en/latest/index.html) - Change Data Capture (CDC) mechanisms for efficient data updates -- Utilizing [MinIO](https://github.com/minio/minio) as an S3-Gateway for cloud-agnostic storage -- Implementing UPSERTs and ACID transactions with [Delta Lake](https://delta.io/) -- Integrating [Jupyter Notebooks](https://github.com/jupyter/notebook) for data science tasks Visualizing data with [Apache Superset](https://github.com/apache/superset) +- Utilizing [SeaweedFS](https://github.com/seaweedfs/seaweedfs) as an S3-compatible object store for cloud-agnostic storage +- Implementing UPSERTs and ACID transactions with [Delta Lake](https://delta.io/) +- Integrating [Jupyter Notebooks](https://github.com/jupyter/notebook) for data science tasks +- Visualizing data with [Apache Superset](https://github.com/apache/superset) - Orchestrating workflows with [Dagster](https://github.com/dagster-io/dagster/) - Deploying on [Kubernetes](https://github.com/kubernetes/kubernetes) for scalability and cloud-agnostic architecture ### Technologies, Tools, and Frameworks: -This project leverages a vast array of open-source technologies including MinIO, Spark, Delta Lake, Jupyter Notebooks, Apache Druid, Apache Superset, and Dagster—all running on Kubernetes to ensure scalability and cloud-agnostic deployment. +This project leverages a vast array of open-source technologies including SeaweedFS, Delta Lake, Jupyter Notebooks, Apache Druid, Apache Superset, and Dagster—all running on Kubernetes to ensure scalability and cloud-agnostic deployment.

-### 🔄 Project Evolution and Updates +### Project Evolution and Updates -This project started in November 2020 as a project for me to learn and teach about data engineering. I published the entire project in March 2021 (see the initial version on [branch `v1`](https://github.com/sspaeti-com/practical-data-engineering/tree/v1)). Three years later, it's interesting that the tools used in this project are still used today. We always say how fast the Modern Data Stack changes, but if you choose wisely, you see that good tools will stay the time. Today, in `March 2024`, I updated the project to the latest Dagster and representative tools versions. I kept most technologies, except Apache Spark. It was a nightmare to setup locally and to work with Delta Lake SQL APi. I replaced it with [delta-rs](https://github.com/delta-io/delta-rs) direct, which is implemented in Rust and can edit and write Delta Tables directly in Python. +This project started in November 2020 as a project for me to learn and teach about data engineering. I published the entire project in March 2021 (see the initial version on [branch `v1`](https://github.com/sspaeti-com/practical-data-engineering/tree/v1)). Three years later, it's interesting that the tools used in this project are still used today. We always say how fast the Modern Data Stack changes, but if you choose wisely, you see that good tools will stay the time. Today, in `March 2024`, I updated the project to the latest Dagster and representative tools versions. I kept most technologies, except Apache Spark. It was a nightmare to setup locally and to work with Delta Lake SQL APi. I replaced it with [delta-rs](https://github.com/delta-io/delta-rs) direct, which is implemented in Rust and can edit and write Delta Tables directly in Python. -Next, I might add Rill Developer to the mix to have some fun analyzing the data powered by DuckDB. For a more production-ready dashboard, Superset would still be my choice tough. +**March 2026 Update**: Migrated from Dagster 1.5.1 to 1.12.x with modern patterns (`import dagster as dg`, `ConfigurableResource`, `Definitions`). Replaced MinIO with [SeaweedFS](https://github.com/seaweedfs/seaweedfs) for S3-compatible storage. Added Docker Compose setup for Apache Druid. All ops/graphs/jobs modernized while keeping the same pipeline logic. Spark code is kept as commented reference for future reactivation via Dagster Pipes. - -## 🛠 Installation & Usage -Please refer to individual component directories for detailed setup and usage instructions. The project is designed to run both locally and on cloud environments, offering flexibility in deployment and testing. +## Installation & Usage ### Prerequisites: -- Python and [uv](https://docs.astral.sh/uv/) for installing dependencies -- MinIO running for cloud-agnostic S3 storage -- Docker Desktop & Kubernetes for running Jupyter Notebooks -- Basic understanding of Python and SQL for effective navigation and customization of the project +- Python 3.10-3.13 and [uv](https://docs.astral.sh/uv/) for dependency management +- Docker for running SeaweedFS (S3 storage) and optional services +- Basic understanding of Python and SQL ### Quick Start: -1. Clone this repository. -2. Install dependencies -3. Install and start MinIO -4. Explore the data with the provided Jupyter Notebooks and Superset dashboards. ```sh -#change to the pipeline directory +# change to the pipeline directory cd src/pipelines/real-estate -# installation +# install dependencies uv sync --all-extras -# run minio -minio server /tmp/minio/ +# start SeaweedFS (S3-compatible object store) +weed server -s3 -s3.config=seaweedfs-s3.json -dir=/tmp/seaweedfs +# or: make s3 -# startup dagster +# start dagster (in another terminal) uv run dagster dev ``` +Open http://127.0.0.1:3000 in your browser to access the Dagster UI. + +### SeaweedFS (S3 Storage) + +This project uses [SeaweedFS](https://github.com/seaweedfs/seaweedfs) as an S3-compatible object store, replacing the previously used MinIO. SeaweedFS is lightweight and provides full S3 API compatibility on port 8333. + +**Docker (recommended):** +```sh +docker compose up seaweedfs -d +``` + +**Standalone (recommended):** +```sh +# Install via package manager or download from https://github.com/seaweedfs/seaweedfs/releases +# On Arch Linux: +yay -S seaweedfs + +# Run with S3 enabled (from the real-estate directory) +weed server -s3 -s3.config=seaweedfs-s3.json -dir=/tmp/seaweedfs +# or: make s3 +``` + +The `-s3.config` flag points to `seaweedfs-s3.json` which defines the `admin`/`admin` credentials. Without it, SeaweedFS rejects all authenticated requests. + +**Configuration:** + +The pipeline reads S3 credentials from environment variables with these defaults: + +| Variable | Default | Description | +|---|---|---| +| `S3_ACCESS_KEY` | `admin` | S3 access key | +| `S3_SECRET_KEY` | `admin` | S3 secret key | +| `S3_ENDPOINT` | `http://127.0.0.1:8333` | S3 endpoint URL | + +SeaweedFS auto-creates buckets on first write, so no manual bucket setup is needed. + +**Migrating from MinIO:** + +If you have existing data in MinIO, you can copy it to SeaweedFS using any S3-compatible tool: +```sh +# Using aws cli +aws s3 sync s3://real-estate s3://real-estate \ + --source-endpoint-url http://127.0.0.1:9000 \ + --endpoint-url http://127.0.0.1:8333 +``` + +### Apache Druid (Optional) + +The pipeline includes an optional Druid ingestion step for OLAP analytics. The `docker-compose.yml` includes a full Druid cluster (Coordinator, Broker, Historical, MiddleManager, Router) with Zookeeper and a PostgreSQL metadata store. + +```sh +# Start the full Druid stack +docker compose up druid-router -d + +# Druid UI available at http://127.0.0.1:8888 +``` + +Druid is configured to use SeaweedFS for deep storage. The `ingest_druid` op is available in the codebase but not yet wired into the main pipeline graph. To activate it, uncomment the relevant lines in `realestate/pipelines.py`. + +### Running Tests + +```sh +uv run pytest realestate_tests/ -v +``` -## 📈 Visualizing the Pipeline +## Visualizing the Pipeline ![Dagster UI – Practical Data Engineering Pipeline](images/dagster-practical-data-engineering-pipeline.png) -## 📚 Resources & Further Reading +## Resources & Further Reading - [Building a Data Engineering Project in 20 Minutes](https://www.ssp.sh/blog/data-engineering-project-in-twenty-minutes/): Access the full blog post detailing the project's development, challenges, and solutions. -- [DevOps Repositories](https://github.com/sspaeti-com/data-engineering-devops): Explore the setup for Druid, MinIO and other components. +- [DevOps Repositories](https://github.com/sspaeti-com/data-engineering-devops): Explore the setup for Druid, SeaweedFS and other components. - [Business Intelligence Meets Data Engineering with Emerging Technologies](https://www.ssp.sh/blog/business-intelligence-meets-data-engineering/): An earlier post that dives into some of the technologies used in this project. - [Data Engineering Vault](https://vault.ssp.sh/): A collection of resources, tutorials, and guides for data engineering projects. - [Open-Source Data Engineering Projects](https://www.ssp.sh/brain/open-source-data-engineering-projects/): A curated list of open-source data engineering projects to explore. -## 📣 Feedback +## Feedback Your feedback is invaluable to improve this project. If you've built your project based on this repository or have suggestions, please let me know through creating an Issues or a Pull Request directly. --- @@ -97,5 +156,3 @@ Your feedback is invaluable to improve this project. If you've built your projec

- - diff --git a/src/pipelines/real-estate/.tool-versions b/src/pipelines/real-estate/.tool-versions index a7bdacf..e84c824 100644 --- a/src/pipelines/real-estate/.tool-versions +++ b/src/pipelines/real-estate/.tool-versions @@ -1 +1 @@ -python 3.11.7 +python latest diff --git a/src/pipelines/real-estate/Makefile b/src/pipelines/real-estate/Makefile index 05f76ef..f12eb2d 100644 --- a/src/pipelines/real-estate/Makefile +++ b/src/pipelines/real-estate/Makefile @@ -1,15 +1,16 @@ .DEFAULT_GOAL := up -up: +up: ## Start dagster dev server uv run dagster dev -install: +install: ## Install all dependencies uv sync --all-extras -minio: - minio server ~/Documents/minio/ +s3: ## Start SeaweedFS with S3 API + weed server -s3 -s3.config=seaweedfs-s3.json -dir=/tmp/seaweedfs +test: ## Run tests + uv run pytest realestate_tests/ -v help: ## Show all Makefile targets @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' - diff --git a/src/pipelines/real-estate/dev-requirements.txt b/src/pipelines/real-estate/dev-requirements.txt deleted file mode 100644 index 9cc7b71..0000000 --- a/src/pipelines/real-estate/dev-requirements.txt +++ /dev/null @@ -1,48 +0,0 @@ -#dagster dependencies -dagster==1.5.1 -dagster-pandas==0.21.1 -dagstermill -dagster-aws==0.21.1 -dagster-postgres==0.21.1 -dagster-spark==0.21.1 -dagster-pyspark==0.21.1 -dagster-webserver -# general packages -> from: https://github.com/dagster-io/dagster/blob/master/python_modules/dagster/dev-requirements.txt -# astroid>=2.3.3; python_version >= '3.6' -# coverage==4.5.4 # 5.0 release on 12/19 broke everything -# freezegun>=0.3.15 -# grpcio-tools==1.30.0 -# pytest-mock==2.0.0 -# nbsphinx==0.4.2 -# pytest-cov==2.8.1 -# pytest-dependency==0.4.0 -# pytest-runner==5.2 -# recommonmark==0.4.0 -# snapshottest==0.5.1 -# tqdm==4.48.0 # pylint crash 48.1+ -#my additional requirement -#docker-compose==1.23.2 -pyspark -#pyspark>=3.0.0; python_version >= "3.8" -#pyspark>=2.0.2; python_version < "3.8" -#pyspark-stubs==3.0.0.dev8 -pyarrow -pandas -boto3 -pandasql -pyyaml -#requirements for comprehensive-real-estate-data-exploration.ipynb -numpy -pandasql -seaborn -folium -ijson -koalas -scipy -matplotlib -#intel-scikit-learn -scikit-learn -#protobuf==3.20.3 -#markupsafe==2.0.1 -#ipython_genutils -#graphql-ws==0.3.1 diff --git a/src/pipelines/real-estate/docker-compose.yml b/src/pipelines/real-estate/docker-compose.yml index f873528..078fe8d 100644 --- a/src/pipelines/real-estate/docker-compose.yml +++ b/src/pipelines/real-estate/docker-compose.yml @@ -13,7 +13,139 @@ services: networks: - postgres + # --- SeaweedFS (S3-compatible object store, replaces MinIO) --- + + seaweedfs: + image: chrislusf/seaweedfs + container_name: seaweedfs + ports: + - "8333:8333" # S3 API + - "9333:9333" # Master + - "8080:8080" # Volume / Filer + command: "server -s3 -s3.config=/etc/seaweedfs/s3.json -dir=/data" + volumes: + - seaweedfs-data:/data + - ./seaweedfs-s3.json:/etc/seaweedfs/s3.json:ro + networks: + - s3 + + # --- Apache Druid (best-effort, fine-tune later) --- + + druid-zookeeper: + image: zookeeper:3.9 + container_name: druid-zookeeper + ports: + - "2181:2181" + environment: + ZOO_MY_ID: 1 + networks: + - druid + + druid-metadata: + image: postgres:15 + container_name: druid-metadata + ports: + - "5433:5432" + environment: + POSTGRES_PASSWORD: druid + POSTGRES_USER: druid + POSTGRES_DB: druid + networks: + - druid + + druid-coordinator: + image: apache/druid:30.0.1 + container_name: druid-coordinator + ports: + - "8081:8081" + command: coordinator + depends_on: + - druid-zookeeper + - druid-metadata + environment: &druid-env + DRUID_LOG4J: + druid_zk_service_host: druid-zookeeper + druid_metadata_storage_type: postgresql + druid_metadata_storage_connector_connectURI: jdbc:postgresql://druid-metadata:5432/druid + druid_metadata_storage_connector_user: druid + druid_metadata_storage_connector_password: druid + druid_coordinator_balancer_strategy: cachingCost + druid_indexer_runner_javaOptsArray: '["-server", "-Xmx1g", "-Xms1g", "-XX:MaxDirectMemorySize=3g", "-Duser.timezone=UTC", "-Dfile.encoding=UTF-8", "-Djava.util.logging.manager=org.apache.logging.log4j.jul.LogManager"]' + druid_indexer_fork_property_druid_processing_buffer_sizeBytes: "256000000" + druid_storage_type: s3 + druid_storage_bucket: real-estate + druid_storage_baseKey: druid/segments + druid_s3_accessKey: admin + druid_s3_secretKey: admin + druid_s3_endpoint_url: http://seaweedfs:8333 + druid_s3_enablePathStyleAccess: "true" + networks: + - druid + - s3 + + druid-broker: + image: apache/druid:30.0.1 + container_name: druid-broker + ports: + - "8082:8082" + command: broker + depends_on: + - druid-coordinator + environment: *druid-env + networks: + - druid + - s3 + + druid-historical: + image: apache/druid:30.0.1 + container_name: druid-historical + ports: + - "8083:8083" + command: historical + depends_on: + - druid-coordinator + environment: *druid-env + networks: + - druid + - s3 + + druid-middlemanager: + image: apache/druid:30.0.1 + container_name: druid-middlemanager + ports: + - "8091:8091" + command: middleManager + depends_on: + - druid-coordinator + environment: *druid-env + networks: + - druid + - s3 + + druid-router: + image: apache/druid:30.0.1 + container_name: druid-router + ports: + - "8888:8888" + command: router + depends_on: + - druid-coordinator + - druid-broker + environment: *druid-env + networks: + - druid + - s3 + +volumes: + seaweedfs-data: + networks: postgres: driver: bridge name: postgres + druid: + driver: bridge + name: druid + s3: + driver: bridge + name: s3 diff --git a/src/pipelines/real-estate/immoscout.html b/src/pipelines/real-estate/immoscout.html new file mode 100644 index 0000000..c5a869e --- /dev/null +++ b/src/pipelines/real-estate/immoscout.html @@ -0,0 +1,129 @@ + + + + + + + + 6 Properties for sale: Twann (from CHF 600,000.–) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Twann
Twann
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6 results - Properties for sale: Twann

1 / 13
  • Parking space
4.5 rooms, 176m², CHF 2,490,000.–
Strandweg 107, 2513 Twann-Tüscherz

Situation privilégiée ! Belle villa avec accès direct au lac

Située sur les bords du lac de Bienne, cette maison mitoyenne bénéficie d'un emplacement rare avec un accès direct au lac. Parfaitement entretenue, elle offre une belle pièce à vivre avec une grande hauteur sous plafond, ouverte sur la terrasse et le jardin. Son agencement fonctionnel et ses espaces extérieurs en font un lieu idéal pour une famille.Les espaces sont répartis comme suit:Rez-de-chaussée:- Hall d'entrée - Salon / salle à manger avec poêle à bois et accès au jardin et au Lac- Cuisine ouverte entièrement équipée- Salle de douche avec WCEtage:- Couloir avec armoires murales- Chambres à coucher avec salle de douche et WC- Deux chambres à coucher- Salle de bain avec WCSous-sol:- Cave- Local technique / buanderie- Local citernesDeux places de parc complète ce bien. Bon à savoir:- La peinture de l'ensemble des pièces de la maison vient d'être refaite.- Un espace avec une dalle en béton et équipé avec de l'électricité est prévu pour l'installation d'un jacuzzi dans le jardin. Deutsche Version:Privilegierte Lage! Schöne Villa mit direktem Zugang zum SeeDieses Reihenhaus liegt am Ufer des Bielersees und profitiert von einer seltenen Lage mit direktem Zugang zum See. Es ist perfekt gepflegt und bietet einen schönen Wohnbereich mit hohen Decken, der sich zur Terrasse und zum Garten hin öffnet. Seine funktionale Aufteilung und seine Außenbereiche machen es zu einem idealen Ort für eine Familie.Die Räume sind wie folgt aufgeteilt:Erdgeschoss:- Eingangshalle - Wohn-/Esszimmer mit Holzofen und Zugang zum Garten und zum See- Voll ausgestattete offene Küche- Duschbad mit WCObergeschoss:- Flur mit Wandschränken- Schlafzimmer mit Duschbad und WC- Zwei Schlafzimmer- Badezimmer mit WCUntergeschoss:- Keller- Technik-/Waschraum- TankraumZwei Parkplätze vervollständigen dieses Objekt. Gut zu wissen:- Alle Räume des Hauses wurden kürzlich neu gestrichen.- Ein Bereich mit Betonplatte und Stromanschluss ist für die Installation eines Jacuzzis im Garten vorgesehen.

  • Parking space
Mélanie Clerc

Mélanie Clerc

Directrice des ventes Suisse romande

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1 / 18
6.5 rooms, 162m², CHF 825,000.–
Oberi Chros 12, 2513 Twann

Large (Holiday) Apartment with Lake View - Your Oasis on Lake Biel

Mediterranean living feeling with panoramic views of Lake Biel - Your home for every lifestyle! This exceptional terrace apartment combines the best of two worlds: it is ideal as an exclusive holiday home for relaxed downtime as well as a stylish main residence for everyday life. Here you can enjoy peace, nature, and a unique holiday feeling every day - regardless of whether you move in permanently or use the apartment as a retreat for special moments.Inspiring location - Living above the vineyards of Twann - In an elevated, quiet location with an unobstructed view of Lake Biel and surrounded by gentle vineyards above the charming wine village of Twann, this apartment offers an inspiring environment for all those who appreciate nature and deceleration.Plenty of space for your individual needs - The spacious 6.5-room apartment with approximately 162 m² of living space and over 55 m² of balcony and terrace space convinces with a well-thought-out floor plan and flexible usage possibilities - perfect for families, couples, singles, or best agers. Whether as a family nest, stylish retreat, or cozy base for individualists: this apartment adapts to your life plans.Further highlights at a glance: • 5 bright bedrooms with direct access to the balcony or terrace • Spacious living room with chimney corner • Separate, functional kitchen with plenty of storage space • Two well-maintained bathrooms with bathtub and shower • Winter garden with breathtaking lake views • Private laundry room and cellar compartment in the apartment • Barrier-free and wheelchair accessible - suitable for all generations • Two parking spaces in the underground garage (each CHF 25,000.-, obligatory) and lift to the garage Renovation advantage - Co-design according to your wishes - A special plus: next spring, the terrace slabs will be completely renewed and the walls affected by a previous water damage will be professionally renovated.As a future owner, you have the opportunity to determine the design and materials of the new terrace slabs yourself and thus bring in your personal style. The costs for these works will be borne by the current owner - you benefit from a freshly renovated terrace and individually designed outdoor areas.Ready for immediate occupancy - experience high-quality living - Whether as your new home or as a holiday home: this apartment offers a rare combination of space, views, and quality of life. The property is in very good condition and available immediately.Discover the apartment now in the 360° tour: https://my.matterport.com/show/?m=XrxVz6vmom7&amp;lang=de

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1 / 9
  • Balcony / Terrace
  • View
  • Elevator
  • +1
3.5 rooms, 150m², CHF 775,000.–
2513 Twann

HUGE BIELERSEEBLICKWOHNUNG (150 m²)

Living space with panoramic view of Lake Biel - maisonette in TwannIn popular Twann, nestled between vineyards and the edge of the forest, this exceptional maisonette with spectacular views over Lake Biel awaits you.On around 150 m² of living space, spread over two floors, you enjoy plenty of space for individual living ideas and a unique lifestyle.The heart of the apartment is the open living and dining area with cozy fireplace and direct Access to the balcony - a place full of warmth and comfort, ideal for relaxing hours with family and friends. Two modern bathrooms with shower/WC and a separate guest toilet offer additional comfort in everyday life.Especially impressive is the generous outdoor space: The terrace on the second and the balcony on the third floor open up an incomparable view over Lake Biel - a box seat for nature lovers and connoisseurs.The apartment has been renovated continuously in the last 20 years and is in a very good condition. In addition there is the possibility, with little effort to set up a fourth room - perfect for families or people with space requirements.The offer is completed by one or two optionally purchasable indoor parking spaces, which enable convenient parking .A unique home in one of the most beautiful locations on Lake Biel - let yourself be inspired by this special maisonette Living space with panoramic view of Lake Biel - maisonette in TwannIn popular Twann, nestled between vineyards and the edge of the forest, this exceptional maisonette with spectacular views over Lake Biel awaits you.On around 150 m² of living space, spread over two floors, you enjoy plenty of space for individual living ideas and a unique lifestyle.The heart of the apartment is the open living and dining area with cozy fireplace and direct Access to the balcony - a place full of warmth and comfort, ideal for relaxing hours with family and friends. Two modern bathrooms with shower/WC and a separate guest toilet offer additional comfort in everyday life.Especially impressive is the generous outdoor space: The terrace on the second and the balcony on the third floor open up an incomparable view over Lake Biel - a box seat for nature lovers and connoisseurs.The apartment has been renovated continuously in the last 20 years and is in a very good condition. In addition there is the possibility, with little effort to set up a fourth room - perfect for families or people with space requirements.The offer is completed by one or two optionally purchasable indoor parking spaces, which enable convenient parking .A unique home in one of the most beautiful locations on Lake Biel - let this special maisonette inspire you.Interested? Further information, pictures and a virtual tour as well as viewing options can be found at: propertyowner.ch/en/real-estate/25-11008

  • Balcony / Terrace
  • View
  • Elevator
  • +1
Travel time
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  • Aerial view of village, houses, restaurant, buildings, water body, mountains, greenery, train tracks
  • dining room, wooden floor, wooden wall, a large wooden barrel, a table with a cloth, wine glasses on a counter
  • wooden interior, arched windows, white linen tablecloths, wine glasses on table, outdoor view
  • restaurant with tables and chairs, large windows, wooden floor, framed pictures on the wall
  • tables, chairs, umbrella, hanging lamps, plant pot, covered terrace, outdoor area, green plant
  • Commercial kitchen, tiled floor, stainless steel appliances, professional cooking equipment, double oven, extraction hoods, hanging utensils, various storage shelves, preparation tables, sinks, fryers.
  • The interior features a cozy dining area with a round table and chairs, a living area with a couch and armchair, and a kitchen with white cabinets and shelves. The room has a wooden floor and ceiling, with a staircase leading to a balcony or terrace. A te
  • View of balcony/terrace, flowers, wide ocean view, blue sky, hills in the background
  • 2 sinks, bathtub, toilet, shelves, and mirrors
  • Wooden ceiling, desk with chess, wicker chair, wall paintings, wooden cabinet, window
  • white walls, beige tiles, table with 4 chairs, round lamp, picture frame, radiator, dart board on wall
  • Bathroom with bathtub, toilet, sink, mirror, shelves, and toiletries
  • small bedroom with wooden bed, nightstands, window with curtains, painting on the wall, toys on the table
1 / 15
15 rooms, 520m², CHF 1,200,000.–
Chlyne Twann 8, 2513 Douanne
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  • row of terraced houses on a slope with balconies, large windows, solar panels, and views
  • large windows, balcony, dining table and chairs, white kitchen, L-shaped sofa, rug
  • Large dining table, wooden chairs, white kitchen, large windows, hardwood floors, dining area with chairs and table
  • modern kitchen with white cabinets, wooden floors, black countertops, built-in dishwasher
  • wooden bed with mattress, pillows, books on shelf, sliding doors, terrace with stairs and railing
  • empty room, white walls, wooden floor, white sliding doors, large window with trees view
  • minimalist design, glass window, desk with office chair, metal bed frame, gray comforter
  • empty room, wooden floor, white walls, two single beds, wardrobe, white bedding, two gray pillows, two lamps on ceiling
  • white tiled walls, shower cubicle, sink, soap dispenser, faucet, mirror, floor level difference
  • Utility room with washer, dryer, and a sink
  • hill with houses, trees, grass, lake, sky
  • sun loungers, table, plants, trees in background
  • detached house with beautiful lake view and balcony
1 / 14
4.5 rooms, 166m², CHF 1,095,000.–
Mittleri Chros 20, 2513 Twann-Tüscherz

BREATHTAKING VIEW - SOOTHES THE SOUL!

GUARANTEED WOW EFFECT!The picturesque wine village of Twann on the northern shore of Lake Biel has preserved the original charm of a wine and fishing village and impresses with a wealth of scenic attractions that make living and loving worthwhile. This dreamy terraced house is located in the Mittleri Chros above the village center with a spectacular view of Lake Biel and the Alps. For daily needs, the municipality offers the necessary shopping facilities. For larger purchases, you can reach the city of Biel in just 8 km. The school from 1st to 9th grade and kindergarten are located in Twann.The terraced house delights with a generous and well-thought-out floor plan and flexible room division and can be easily reached by lift. The open kitchen is the heart of the living area and has plenty of storage space and a high-quality finish with top-notch appliances. Here you can indulge in your cooking skills and pamper your loved ones with delicacies. The spacious living and dining area can be flexibly furnished and offers ample space. From the living room, you can access the remarkable, south-facing terrace with up to 100m2 of versatile living space and sunshine all day. An absolute highlight is the unobstructed view of Lake Biel and the green surroundings. Further to the interior, you have a large master bedroom with dressing room and en-suite bathroom with double sink and walk-in shower, two rooms that can be separated by a sliding door, and another bathroom with shower cubicle and toilet. Thanks to the large window fronts on the lake side, the rooms have plenty of natural light and each room has direct access to the terrace. In the apartment, you benefit from your own laundry room with washing tower and two storage rooms. In the corridor, there is a large built-in closet for even more storage space. For the green thumb, you have your own garden area with seating on the western side. The whole offer is rounded off by a double garage with lift system and a private basement compartment on the same floor. A wow effect from the very first moment!This gem offers you the following advantages: • Quiet and child-friendly location • Breathtaking views of Lake Biel • Open and high-quality kitchen with cooking island • Spacious living and dining area • Large window fronts with plenty of natural light • 1 bathroom with walk-in rain shower/toilet &amp; double sink • 1 bathroom with shower cubicle &amp; toilet • Large room doors • Underfloor heating • Electric shutters • 100m2 sunny terrace (south-facing) • Water connections • Garden with seating (west) • Laundry room/technical room in the apartment • Private basement compartment on the same floor • 2 garage spaces with lift system and storage niche for more space must be purchased for CHF 70,000 • And much more... Best to arrange a viewing appointment with us and see this exclusive terraced house on site!Do you want to sell your property or land uncomplicatedly through professionals? Then contact us and let us convince you of our many advantages at a non-binding meeting.

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5 rooms, 120m², CHF 600,000.–
Lacheweg, 2513 Twann
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+ + + + diff --git a/src/pipelines/real-estate/pyproject.toml b/src/pipelines/real-estate/pyproject.toml index 8ce86f7..ff07e9d 100644 --- a/src/pipelines/real-estate/pyproject.toml +++ b/src/pipelines/real-estate/pyproject.toml @@ -1,35 +1,38 @@ [project] name = "real-estate" version = "0.1.0" -requires-python = ">=3.14" +requires-python = ">=3.10,<3.14" dependencies = [ - "boto3>=1.42.64", - "dagster==1.5.1", - "dagster-aws==0.21.1", - "dagster-pandas==0.21.1", - "dagster-postgres==0.21.1", - "dagster-pyspark==0.21.1", - "dagster-spark==0.21.1", - "dagster-webserver>=1.5.1", - "dagstermill>=0.21.1", - "folium>=0.20.0", - "ijson>=3.5.0", - "koalas>=1.8.2", - "matplotlib>=3.10.8", - "numpy>=2.4.3", - "pandas>=3.0.1", - "pandasql>=0.7.3", - "pyarrow>=23.0.1", - "pyspark>=4.1.1", - "pyyaml>=6.0.3", - "scikit-learn>=1.8.0", - "scipy>=1.17.1", - "seaborn>=0.13.2", + "dagster>=1.12,<1.13", + "dagster-pandas>=0.28,<0.29", + "dagstermill>=0.28,<0.29", + "notebook", + "dagster-aws>=0.28,<0.29", + "dagster-postgres>=0.28,<0.29", + "dagster-webserver>=1.12,<1.13", + "dagster-deltalake>=0.28,<0.29", + "dagster-deltalake-pandas>=0.28,<0.29", + "pyarrow", + "pandas", + "boto3", + "pandasql", + "pyyaml", + "numpy", + "seaborn", + "folium", + "ijson", + "scipy", + "matplotlib", + "scikit-learn", + "bs4", + "jinja2", + "requests", + "pyspark", ] [project.optional-dependencies] dev = [ - "dagster-webserver", + "dagster-webserver>=1.12,<1.13", "pytest", ] @@ -37,6 +40,13 @@ dev = [ requires = ["setuptools"] build-backend = "setuptools.build_meta" +[tool.setuptools.packages.find] +include = ["realestate*"] + [tool.dagster] module_name = "realestate" +[dependency-groups] +dev = [ + "types-requests>=2.32.4.20260107", +] diff --git a/src/pipelines/real-estate/realestate/__init__.py b/src/pipelines/real-estate/realestate/__init__.py index 52fb6d6..8b5469b 100644 --- a/src/pipelines/real-estate/realestate/__init__.py +++ b/src/pipelines/real-estate/realestate/__init__.py @@ -1,24 +1,8 @@ -from dagster import ( - Definitions, - ScheduleDefinition, - define_asset_job, - load_assets_from_package_module, -) - -from .pipelines import scrape_realestate, resource_def - - +import dagster as dg +from .pipelines import scrape_realestate +from .common import resource_def -# from .common import RESOURCES_LOCAL - -# daily_refresh_schedule = ScheduleDefinition( -# job=define_asset_job(name="all_assets_job"), cron_schedule="0 0 * * *" -# ) - -# print("Loading definitions for environment: ", resources.ENV) - -# defs = Definitions( -# assets=all_assets, -# schedules=[daily_refresh_schedule], -# resources=resource_def[resources.ENV.upper()], -# ) +defs = dg.Definitions( + jobs=[scrape_realestate], + resources=resource_def["local"], +) diff --git a/src/pipelines/real-estate/realestate/common/__init__.py b/src/pipelines/real-estate/realestate/common/__init__.py index c85f2ab..87fbfd6 100644 --- a/src/pipelines/real-estate/realestate/common/__init__.py +++ b/src/pipelines/real-estate/realestate/common/__init__.py @@ -1,51 +1,42 @@ import os -from dagster._config import config_schema + +import dagster as dg from dagster._core.storage.file_manager import LocalFileManager from dagster_aws.s3 import S3Resource -from pandas.io.pytables import config - -from .resources import boto3_connection -from dagster import LocalFileHandle, fs_io_manager, local_file_manager -# from dagster.core.storage.file_cache import fs_file_cache - from dagster_deltalake_pandas import ( DeltaLakePandasIOManager, - DeltaLakePandasTypeHandler, ) from dagster_deltalake import S3Config -MINIO_ACCESS_KEY = os.getenv("MINIO_ACCESS_KEY", "minio") -MINIO_SECRET_KEY = os.getenv("MINIO_SECRET_KEY", "miniostorage") -MINIO_ENDPOINT = os.getenv("MINIO_ENDPOINT", "http://127.0.0.1:9000") -AWS_REGION = os.getenv("AWS_REGION", "us-east-1") # TODO: needed? correct? +from .resources import Boto3Resource, DruidResource + +S3_ACCESS_KEY = os.getenv("S3_ACCESS_KEY", "admin") +S3_SECRET_KEY = os.getenv("S3_SECRET_KEY", "admin") +S3_ENDPOINT = os.getenv("S3_ENDPOINT", "http://127.0.0.1:8333") +AWS_REGION = os.getenv("AWS_REGION", "us-east-1") resource_def = { "local": { "s3": S3Resource( - endpoint_url=MINIO_ENDPOINT, # might be needed to be 127.0.0.1:60599 or similar... + endpoint_url=S3_ENDPOINT, + ), + "boto3": Boto3Resource( + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + endpoint_url=S3_ENDPOINT, ), - # 'druid': druid_db_info_resource, - "boto3": boto3_connection, - # "boto3": boto3_connection( - # config_schema={ - # "aws_access_key_id":MINIO_ACCESS_KEY, - # "aws_secret_access_key":MINIO_SECRET_KEY, - # "endpoint_url":"http://127.0.0.1:9000", - # } - # ), - "fs_io_manager": fs_io_manager, + # "druid": DruidResource(druid_router="http://localhost:8888"), "file_manager": LocalFileManager(base_dir="/tmp/dagster/file_cache"), + "fs_io_manager": dg.FilesystemIOManager(), "io_manager": DeltaLakePandasIOManager( - root_uri="lake/bronze/", # required + root_uri="lake/bronze/", storage_options=S3Config( - bucket="real-estate", # Usually this is defined in the table not as general - access_key_id=MINIO_ACCESS_KEY, - secret_access_key=MINIO_SECRET_KEY, - endpoint=MINIO_ENDPOINT, - # region=AWS_REGION,#not needed for minio - ), # required - # schema="real-estate", # optional, defaults to "public" + bucket="real-estate", + access_key_id=S3_ACCESS_KEY, + secret_access_key=S3_SECRET_KEY, + endpoint=S3_ENDPOINT, + ), ), }, } diff --git a/src/pipelines/real-estate/realestate/common/helper_functions.py b/src/pipelines/real-estate/realestate/common/helper_functions.py index 96e231a..24cf519 100644 --- a/src/pipelines/real-estate/realestate/common/helper_functions.py +++ b/src/pipelines/real-estate/realestate/common/helper_functions.py @@ -3,36 +3,26 @@ import zlib import gzip import json +from typing import Tuple from deltalake import DeltaTable, _internal -from dagster import Tuple import pandas as pd import pyarrow as pa -# def json_zip_writer(j, target_key): -# f = gzip.open(target_key, 'wb') -# f.write(json.dumps(j).encode('utf-8')) -# f.close() - def json_zip_writer(j, file_obj): - # No need to open a file here, file_obj is already a file-like object gzipped_data = gzip.compress(json.dumps(j).encode('utf-8')) file_obj.write(gzipped_data) def json_zip(j): - - # return base64.b64encode(zlib.compress(json.dumps(j).encode('utf-8'))).decode('ascii') return zlib.compress(json.dumps(j).encode('utf-8')) def json_unzip(j, insist=True): - try: - # j = zlib.decompress(base64.b64decode(j)) j = zlib.decompress(j) except: raise RuntimeError("Could not decode/unzip the contents") @@ -44,41 +34,35 @@ def json_unzip(j, insist=True): return j -def rename_spark_dataframe_columns(data_frame, fn): - return data_frame.toDF(*[fn(c) for c in data_frame.columns]) def rename_pandas_dataframe_columns(data_frame, fn): new_columns = [fn(c) for c in data_frame.columns] data_frame.columns = new_columns return data_frame -# Function to read gzipped JSON and return as a Python object (list or dict) + def read_gzipped_json(path): with gzip.open(path, 'rt', encoding='utf-8') as f: return json.load(f) def reading_delta_table(context, s3_path_property) -> Tuple[pd.DataFrame, DeltaTable]: - #TODO: add as resource - MINIO_ACCESS_KEY = os.getenv("MINIO_ACCESS_KEY", "minio") - MINIO_SECRET_KEY = os.getenv("MINIO_SECRET_KEY", "miniostorage") - MINIO_ENDPOINT = os.getenv("MINIO_ENDPOINT", "http://127.0.0.1:9000") + MINIO_ACCESS_KEY = os.getenv("S3_ACCESS_KEY", "admin") + MINIO_SECRET_KEY = os.getenv("S3_SECRET_KEY", "admin") + MINIO_ENDPOINT = os.getenv("S3_ENDPOINT", "http://127.0.0.1:8333") storage_options = { "AWS_ACCESS_KEY_ID": MINIO_ACCESS_KEY, "AWS_SECRET_ACCESS_KEY": MINIO_SECRET_KEY, "AWS_ENDPOINT_URL": MINIO_ENDPOINT, "AWS_ALLOW_HTTP": "true", - # "AWS_REGION": AWS_REGION, #do not use "AWS_S3_ALLOW_UNSAFE_RENAME": "true", } try: dt = DeltaTable(s3_path_property, storage_options=storage_options) - #if table exist go on: df = dt.to_pyarrow_dataset().to_table().to_pandas() - # HACK: just to make it work the first time remove_columns = [ "propertyDetails_images", "propertyDetails_pdfs", @@ -88,7 +72,6 @@ def reading_delta_table(context, s3_path_property) -> Tuple[pd.DataFrame, DeltaT context.log.debug(f"Removing columns: {remove_columns}") df = df.drop(columns=remove_columns, errors="ignore") except _internal.TableNotFoundError: - # If the DeltaTable does not exist, create an empty DataFrame or initialize a new table as needed df = pd.DataFrame(columns=['propertyDetails_propertyId', 'propertyDetails_normalizedPrice']) dt = DeltaTable.create( table_uri=s3_path_property, @@ -102,5 +85,3 @@ def reading_delta_table(context, s3_path_property) -> Tuple[pd.DataFrame, DeltaT context.log.info(f"df type: {type(df)}") context.log.info(f"df columns: {df.columns}") return df, dt - - diff --git a/src/pipelines/real-estate/realestate/common/resource_delta_lake.py b/src/pipelines/real-estate/realestate/common/resource_delta_lake.py index ae13303..f3037e1 100644 --- a/src/pipelines/real-estate/realestate/common/resource_delta_lake.py +++ b/src/pipelines/real-estate/realestate/common/resource_delta_lake.py @@ -1,6 +1,6 @@ +import dagster as dg import pandas as pd from deltalake import DeltaTable, write_deltalake -from delta_spark import merge, TableMerger from .types import S3Coordinate @@ -9,46 +9,35 @@ # https://docs.dagster.io/integrations/deltalake/using-deltalake-with-dagster - -class delta_lake_resource(ConfigurableResource): +class DeltaLakeResource(dg.ConfigurableResource): minio_access_key: str minio_secret_key: str minio_endpoint: str - aws_region: str + aws_region: str = "us-east-1" - def __init__(self): - self.storage_options = { + @property + def storage_options(self): + return { "AWS_ACCESS_KEY_ID": self.minio_access_key, "AWS_SECRET_ACCESS_KEY": self.minio_secret_key, "AWS_ENDPOINT_URL": self.minio_endpoint, "AWS_ALLOW_HTTP": "true", - "AWS_REGION": self.aws_region, #'us-east-1', - "AWS_S3_ALLOW_UNSAFE_RENAME" : "true", + "AWS_REGION": self.aws_region, + "AWS_S3_ALLOW_UNSAFE_RENAME": "true", } def create_table(self, bucket_name: str, folder_path: str) -> DeltaTable: - table_url = f"s3a://{self.minio_access_key}:{self.minio_secret_key}@{self.minio_endpoint}/{bucket_name}/{folder_path}" - dt = DeltaTable(table_url, storage_options=self.storage_options) #.to_pandas() + table_url = f"s3a://{bucket_name}/{folder_path}" + dt = DeltaTable(table_url, storage_options=self.storage_options) return dt - - #def merge_table(self, target_dt: DeltaTable, source : pd.DataFrame, join_condition : str) -> TableMerger: - def merge_table(self, target_dt : S3Coodinate, source : pd.DataFrame, join_condition : str) -> TableMerger: - "delta-rs alternative to merge delta table" - # data = pa.table({"x": [1, 2, 3], "y": [4, 5, 6]}) - # write_deltalake("tmp", data) - # dt = DeltaTable("tmp") - # new_data = pa.table({"x": [2, 3], "deleted": [False, True]}) - + def merge_table(self, target_dt: DeltaTable, source: pd.DataFrame, join_condition: str): + """delta-rs alternative to merge delta table""" ( target_dt.merge( - source=source, # pyarrow.Table, pyarrow.RecordBatch, pyarrow.RecordBatchReader, ds.Dataset, pd.DataFrame] - predicate=join_condition, #'target.x = source.x', + source=source, + predicate=join_condition, source_alias='source', target_alias='target') - # .when_matched_delete( - # predicate="source.deleted = true") .execute() ) - - diff --git a/src/pipelines/real-estate/realestate/common/resources.py b/src/pipelines/real-estate/realestate/common/resources.py index a56bc54..91702cc 100644 --- a/src/pipelines/real-estate/realestate/common/resources.py +++ b/src/pipelines/real-estate/realestate/common/resources.py @@ -1,23 +1,16 @@ -from dagster import resource, Field +import dagster as dg +import requests +from boto3 import session as boto3_session -from boto3 import session - -class Boto3Connector(object): - def __init__(self, aws_access_key_id, aws_secret_access_key, endpoint_url): - self.aws_access_key_id = aws_access_key_id - self.aws_secret_access_key = aws_secret_access_key - self.endpoint_url = endpoint_url - - # def get_session(self): - - # session = session.Session() - # return session +class Boto3Resource(dg.ConfigurableResource): + aws_access_key_id: str + aws_secret_access_key: str + endpoint_url: str def get_client(self): - session = session.Session() - - s3_client = session.client( + sess = boto3_session.Session() + s3_client = sess.client( service_name="s3", aws_access_key_id=self.aws_access_key_id, aws_secret_access_key=self.aws_secret_access_key, @@ -26,51 +19,11 @@ def get_client(self): return s3_client -@resource( - config_schema={ - "aws_access_key_id": Field(str), - "aws_secret_access_key": Field(str), - "endpoint_url": Field(str), - } -) -def boto3_connection(context): - return Boto3Connector( - context.resource_config["aws_access_key_id"], - context.resource_config["aws_secret_access_key"], - context.resource_config["endpoint_url"], - ) - +class DruidResource(dg.ConfigurableResource): + druid_router: str = "http://localhost:8888" -class DruidConnector(object): - def __init__(self, druid_router): # , druid_auth_user, druid_auth_password): - self._druid_router = druid_router - # self._druid_auth_user = druid_auth_user - # self._druid_auth_password = druid_auth_password - - # establish authentificated request-session and return it def get_session(self): - session = requests.Session() - return session - - def get_auth_session(self): - session = requests.Session() - # session.auth = (self._druid_auth_user, self._druid_auth_password) - return session + return requests.Session() def get_router_url(self): - return self._druid_router - - -@resource( - config_schema={ - "druid_router": Field(str), - # 'druid_auth_user': Field(str), - # 'druid_auth_password': Field(str), - } -) -def druid_db_info_resource(context): - return DruidConnector( - context.resource_config["druid_router"], - # context.resource_config['druid_auth_user'], - # context.resource_config['druid_auth_password'], - ) + return self.druid_router diff --git a/src/pipelines/real-estate/realestate/common/solids.py b/src/pipelines/real-estate/realestate/common/solids.py index af5bad0..b04d0cb 100644 --- a/src/pipelines/real-estate/realestate/common/solids.py +++ b/src/pipelines/real-estate/realestate/common/solids.py @@ -1,9 +1,10 @@ -from dagster import op, Optional, Bool, Out +import dagster as dg +from typing import Optional -@op( - out={"foo_output": Out(Optional[float], is_required=False)}, +@dg.op( + out={"foo_output": dg.Out(Optional[float], is_required=False)}, ) -def condition_check_bool(condition: Bool): +def condition_check_bool(condition: bool): if condition: yield 1.0 diff --git a/src/pipelines/real-estate/realestate/common/solids_druid.py b/src/pipelines/real-estate/realestate/common/solids_druid.py index dda46f2..7871949 100644 --- a/src/pipelines/real-estate/realestate/common/solids_druid.py +++ b/src/pipelines/real-estate/realestate/common/solids_druid.py @@ -5,11 +5,8 @@ import requests import json -from dagster import ( - op, - Field, - String, -) +import dagster as dg +from dagster import file_relative_path def _druid_ingest_spec_replacer( @@ -33,28 +30,28 @@ def _druid_ingest_spec_replacer( return spec -@op( - required_resource_keys={'pyspark', 's3', 'druid'}, +@dg.op( + required_resource_keys={'s3', 'druid'}, description='''This ingests data from your input delta table (sitting on s3 bucket) into druid. It will check first the connection to druid cluster then delete all segements in the interval specified before it loads the new parquet files (your delta table)''', config_schema={ - 'status_health_api_postfix': Field( - String, + 'status_health_api_postfix': dg.Field( + str, default_value='status/health', is_required=False, description=('druid api to get healt of druid cluster'), ), - 'datasource_health_api_postfix': Field( - String, + 'datasource_health_api_postfix': dg.Field( + str, default_value='druid/coordinator/v1/datasources', is_required=False, description=('druid api to list datasources in druid cluster'), ), - 'status_index_task_api_postfix': Field( - String, + 'status_index_task_api_postfix': dg.Field( + str, default_value='druid/indexer/v1/task', is_required=False, description=('druid api to ingest a index task to druid cluster'), @@ -67,8 +64,6 @@ def ingest_druid( druid_coordinate: DruidCoordinate, ) -> DruidCoordinate: - # context.log.info(context.resources.druid.druid_router) - context.log.info('router: ' + context.resources.druid.get_router_url()) # @@ -77,7 +72,7 @@ def ingest_druid( druidHealthAPI = ( context.resources.druid.get_router_url() + '/' - + context.solid_config['status_health_api_postfix'] + + context.op_config['status_health_api_postfix'] ) context.log.debug("Druid healt API: " + druidHealthAPI) @@ -89,9 +84,9 @@ def ingest_druid( ) if r.status_code != 200: - raise ValueError("1.2. Druid is unhealthy. Status_Code: {status_code}").format( + raise ValueError("1.2. Druid is unhealthy. Status_Code: {status_code}".format( status_code=r.status_code - ) + )) else: # # submit deletion of segments @@ -101,7 +96,7 @@ def ingest_druid( druidDeleteSegmentAPI = ( context.resources.druid.get_router_url() + '/' - + context.solid_config['datasource_health_api_postfix'] + + context.op_config['datasource_health_api_postfix'] + '/' + druid_coordinate['datasource'] + "/markUnused" @@ -117,9 +112,6 @@ def ingest_druid( ) ) - # TODO: Ingest only latest Parquet-files from delta (delta keeps older versions for time-travel). - # If we're not doing that, we will ingest old versions together agian - # # get ingest spec: # @@ -128,9 +120,9 @@ def ingest_druid( with open(spec_path, 'r') as f: ingestSpec = f.read() except ValueError as error: - raise ValueError("Json at path: '{fpath}' not found. {err}").format( + raise ValueError("Json at path: '{fpath}' not found. {err}".format( fpath=druid_coordinate['PathToJsonIngestSpec'], err=error - ) + )) # # parse json spec # @@ -138,8 +130,9 @@ def ingest_druid( ingestSpecJson = json.loads(ingestSpec) except ValueError as error: raise ValueError( - "Not a valid JSON at path: '{fpath}'. Check the syntaxt: {err}" - ).format(fpath=druid_coordinate['PathToJsonIngestSpec'], err=error) + "Not a valid JSON at path: '{fpath}'. Check the syntaxt: {err}".format( + fpath=druid_coordinate['PathToJsonIngestSpec'], err=error + )) context.log.info( "3. Ingest spec successfully loaded from path '{fpath}'".format(fpath=spec_path) @@ -157,7 +150,7 @@ def ingest_druid( druidTaskAPI = ( context.resources.druid.get_router_url() + '/' - + context.solid_config['status_index_task_api_postfix'] + + context.op_config['status_index_task_api_postfix'] ) r = context.resources.druid.get_session().post( @@ -177,8 +170,5 @@ def ingest_druid( status_code=r.status_code, reason=r.reason ) ) - # TODO: check report status of Druid task, if it failed directly, or if its loading - # - report-API: druid/indexer/v1/task/"+task+"/reports" - # - maybe wait a couple of seconds/minutes, to be sure it stared correctly? # return delta object return druid_coordinate diff --git a/src/pipelines/real-estate/realestate/common/solids_filehandle.py b/src/pipelines/real-estate/realestate/common/solids_filehandle.py index 2412741..ce5ea53 100644 --- a/src/pipelines/real-estate/realestate/common/solids_filehandle.py +++ b/src/pipelines/real-estate/realestate/common/solids_filehandle.py @@ -1,24 +1,16 @@ import json import gzip -from dagster import solid, FileHandle, Failure - -# from realestate.common.solids_scraping import cache_file_from_input - from realestate.common.types_realestate import JsonType def json_to_gzip(json_data: JsonType): - # encoded = json_file.encode('utf-8') - # j = json.dumps(json_data) json_bytes = json_data.encode('utf-8') - return gzip.compress(json_bytes) -# @solid(description='Zipping json file') +# @dg.op(description='Zipping json file') # def json_to_gzip(context, json_path: FileHandle) -> FileHandle: - # jfile = open(json_path, "r") # if jfile.mode == 'r': # json_data = json.dumps(jfile, indent=2) diff --git a/src/pipelines/real-estate/realestate/common/solids_jupyter.py b/src/pipelines/real-estate/realestate/common/solids_jupyter.py index 35cbbfa..fa45fc3 100644 --- a/src/pipelines/real-estate/realestate/common/solids_jupyter.py +++ b/src/pipelines/real-estate/realestate/common/solids_jupyter.py @@ -1,6 +1,6 @@ -from dagster import Out, FileHandle, In, file_relative_path import os +import dagster as dg import dagstermill as dm from realestate.common.types import DeltaCoordinate @@ -10,46 +10,20 @@ def _notebook_path(name): os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "notebooks")), name, ) - # os.path.join(os.path.dirname(os.path.abspath(__file__)), "notebooks", name) -# TODO: add spark as resource and use configs inside notebook. -# TODO: plot pdfs -> copy airline demo notebook -data_exploration = dm.factory.define_dagstermill_op( +data_exploration = dm.define_dagstermill_op( name="data_exploration", notebook_path=_notebook_path("comprehensive-real-estate-data-exploration.ipynb"), ins={ - "delta_path": In( - dagster_type=DeltaCoordinate, description="s3 path to the property-delta-table" - ), - "key": In(dagster_type=str, description="s3 key"), - "secret": In(dagster_type=str, description="s3 secret"), - "endpoint": In(dagster_type=str, description="s3 endpoint"), - # InputDefinition("pyspark", ModeDefinition, description="pyspark resource"), + "delta_path": dg.In(description="s3 path to the property-delta-table"), + "key": dg.In(dagster_type=str, description="s3 key"), + "secret": dg.In(dagster_type=str, description="s3 secret"), + "endpoint": dg.In(dagster_type=str, description="s3 endpoint"), }, outs={ - "plots_pdf_path": Out( - dagster_type=FileHandle, description="The saved PDF plots." + "plots_pdf_path": dg.Out( + dagster_type=dg.Nothing, description="The saved PDF plots." ) }, - io_manager_key="fs_io_manager", - # required_resource_keys={"pyspark"}, ) - - -# from dagster.utils import script_relative_path -# data_exploration = dm.factory.define_dagstermill_op( -# "data_exploration", -# script_relative_path("../notebooks/comprehensive-real-estate-data-exploration.ipynb"), -# input_defs=[ -# InputDefinition("delta_path", str, description="s3 path to the property-delta-table") -# ], -# # config_schema={ -# # 'delta_path': Field( -# # str, -# # default_value="s3a://real-estate/lake/bronze/property", -# # is_required=False, -# # description="s3 path to the property-delta-table", -# # ) -# # }, -# ) diff --git a/src/pipelines/real-estate/realestate/common/solids_notebook.py b/src/pipelines/real-estate/realestate/common/solids_notebook.py index 1553df2..5d79404 100644 --- a/src/pipelines/real-estate/realestate/common/solids_notebook.py +++ b/src/pipelines/real-estate/realestate/common/solids_notebook.py @@ -1,13 +1,16 @@ +import os + + def _notebook_path(name): return os.path.join(os.path.dirname(os.path.abspath(__file__)), "notebooks", name) -# start_solids_marker_3 -def notebook_solid(name, notebook_path, input_defs, output_defs, required_resource_keys): - return define_dagstermill_solid( - name, - _notebook_path(notebook_path), - input_defs, - output_defs, - required_resource_keys=required_resource_keys, - ) \ No newline at end of file +# Legacy solid-based notebook definition (kept as reference) +# def notebook_solid(name, notebook_path, input_defs, output_defs, required_resource_keys): +# return define_dagstermill_solid( +# name, +# _notebook_path(notebook_path), +# input_defs, +# output_defs, +# required_resource_keys=required_resource_keys, +# ) diff --git a/src/pipelines/real-estate/realestate/common/solids_scraping.py b/src/pipelines/real-estate/realestate/common/solids_scraping.py index 5a5bdaf..2232ff9 100644 --- a/src/pipelines/real-estate/realestate/common/solids_scraping.py +++ b/src/pipelines/real-estate/realestate/common/solids_scraping.py @@ -3,166 +3,173 @@ from typing import Any, Generator import requests import re -import os -import datetime from bs4 import BeautifulSoup from datetime import datetime import json from contextlib import closing import io -import gzip -import shutil - -# from io import BytesIO from realestate.common.helper_functions import json_zip_writer - -from dagster import ( - Int, - op, - Field, - String, - FileHandle, - LocalFileHandle, - ExpectationResult, - MetadataValue, - Out, - Output, -) - +import dagster as dg from .types_realestate import PropertyDataFrame, SearchCoordinate, JsonType - -@op( - description="""This will scrape immoscout without hitting the API (light scrape)""", +def _extract_listings_from_json_ld(soup: BeautifulSoup, rent_or_buy: str) -> list[dict]: + """Extract property IDs and prices from JSON-LD structured data in the page. + Returns list of dicts with 'id' and 'price' keys.""" + listings = [] + ld_scripts = soup.find_all("script", {"type": "application/ld+json"}) + for script in ld_scripts: + if not script.string: + continue + try: + data = json.loads(script.string) + except json.JSONDecodeError: + continue + if data.get("@type") != "Product": + continue + # Extract price + price = "" + offers = data.get("offers", []) + if isinstance(offers, list) and offers: + price = str(offers[0].get("price", "")) + elif isinstance(offers, dict): + price = str(offers.get("price", "")) + + # ID not in JSON-LD, will be matched from hrefs + listings.append({"name": data.get("name", ""), "price": price}) + return listings + + +def _extract_property_ids_from_links(soup: BeautifulSoup, rent_or_buy: str) -> list[str]: + """Extract property IDs from hrefs. Handles both relative and absolute URLs.""" + ids = [] + links = soup.find_all("a", href=True) + for link in links: + href = link["href"] + # Match both "/buy/ID" and "https://www.immoscout24.ch/buy/ID" + match = re.search(rf"/{rent_or_buy}/(\d+)", href) + if match: + prop_id = match.group(1) + if prop_id not in ids: + ids.append(prop_id) + return ids + + +@dg.op( + description="""Scrapes immoscout24.ch search results using JSON-LD structured data and link parsing.""", config_schema={ - "immo24_main_url_en": Field( - String, - default_value="https://www.immoscout24.ch/en/", - is_required=False, - description=( - """Main URL to start the search with (propertyType unspecific). - No API will be hit with this request. This is basic scraping.""" - ), - ), - "immo24_search_url_en": Field( - String, + "immo24_search_url_en": dg.Field( + str, default_value="https://www.immoscout24.ch/en/real-estate/", is_required=False, - description=( - """Main URL to start the search with (propertyType specific). - No API will be hit with this request. This is basic scraping.""" - ), + description="Base search URL for immoscout24.", ), }, - required_resource_keys={"fs_io_manager"}, - out=Out(io_manager_key="fs_io_manager"), - + out=dg.Out(io_manager_key="fs_io_manager"), ) def list_props_immo24(context, searchCriteria: SearchCoordinate) -> PropertyDataFrame: - # propertyType : str, rentOrBuys : str, radius : int, cities : str) - listPropertyIds = [] + rent_or_buy = searchCriteria["rentOrBuy"] + city = searchCriteria["city"] + radius = searchCriteria["radius"] + property_type = searchCriteria["propertyType"] - # find max page url = ( context.op_config["immo24_search_url_en"] - + searchCriteria["rentOrBuy"] + + rent_or_buy + "/city-" - + searchCriteria["city"] + + city + "?r=" - + str(searchCriteria["radius"]) - + "&map=1" # only property with prices (otherwise mapping id to price later on does not map) - + "" + + str(radius) + + "&map=1" ) - context.log.info("Search url: {}".format(url)) - html = requests.get(url) - soup = BeautifulSoup(html.text, "html.parser") - buttons = soup.findAll("a") - p = [] - for item in buttons: - if len(item.text) <= 3 & len(item.text) != 0: - p.append(item.text) - if p: - lastPage = int(p.pop()) - else: - lastPage = 1 - context.log.info("Count of Pages found: {}".format(lastPage)) + context.log.info(f"Search url: {url}") - ids = [] - propertyPrice = [] - for i in range(1, lastPage + 1): - url = ( - context.op_config["immo24_main_url_en"] - + searchCriteria["propertyType"] - + "/" - + searchCriteria["rentOrBuy"] - + "/city-" - + searchCriteria["city"] - + "?pn=" # pn= page site - + str(i) - + "&r=" - + str(searchCriteria["radius"]) - + "&se=16" # se=16 means most recent first - + "&map=1" # only property with prices (otherwise mapping id to price later on does not map) - ) - context.log.debug("Range search url: {}".format(url)) - - html = requests.get(url) - soup = BeautifulSoup(html.text, "html.parser") - links = soup.findAll("a", href=True) - hrefs = [item["href"] for item in links] - hrefs_filtered = [href for href in hrefs if href.startswith("/" + searchCriteria['rentOrBuy'] + "/")] - ids += [re.findall("\d+", item)[0] for item in hrefs_filtered] - - # get normalized price without REST-call - h3 = soup.findAll("span") - for h in h3: - text = h.getText() - if "CHF" in text or "EUR" in text: - start = text.find("CHF") + 4 - end = text.find(".â\x80\x94") - - price = text[start:end] + response = requests.get(url) + soup = BeautifulSoup(response.text, "html.parser") - # remove all characters except numbers --> equals price - price = re.sub("\D", "", price) - propertyPrice.append(price) + # Extract property IDs from links + ids = _extract_property_ids_from_links(soup, rent_or_buy) + context.log.info(f"Found {len(ids)} property IDs from links: {ids}") - # merge all ids and prices to dict - dictPropPrice = {} - for j in range(len(ids)): - dictPropPrice[ids[j]] = propertyPrice[j] + # Extract prices from JSON-LD structured data + json_ld_listings = _extract_listings_from_json_ld(soup, rent_or_buy) + context.log.info(f"Found {len(json_ld_listings)} listings from JSON-LD") - propDict = {} - # loop through dict - for id in dictPropPrice: - last_normalized_price = dictPropPrice[id] - - listPropertyIds.append( + # Build ID→price mapping: match by position (JSON-LD and links appear in same order) + dict_prop_price = {} + for i, prop_id in enumerate(ids): + if i < len(json_ld_listings): + dict_prop_price[prop_id] = json_ld_listings[i]["price"] + else: + dict_prop_price[prop_id] = "" + + # Fall back to price parsing if JSON-LD didn't work + if not json_ld_listings and ids: + context.log.info("No JSON-LD data found, falling back to span price parsing") + prices = [] + for span in soup.find_all("span"): + text = span.get_text() + if "CHF" in text or "EUR" in text: + price = re.sub(r"\D", "", text) + if price: + prices.append(price) + for i, prop_id in enumerate(ids): + dict_prop_price[prop_id] = prices[i] if i < len(prices) else "" + + # Handle pagination: check for page links + page_links = soup.find_all("a", href=re.compile(r"[?&]pn=\d+")) + page_numbers = set() + for link in page_links: + match = re.search(r"[?&]pn=(\d+)", link["href"]) + if match: + page_numbers.add(int(match.group(1))) + last_page = max(page_numbers) if page_numbers else 1 + context.log.info(f"Pages found: {last_page}") + + # Scrape remaining pages (page 1 already done) + for page in range(2, last_page + 1): + page_url = url + f"&pn={page}" + context.log.debug(f"Fetching page {page}: {page_url}") + page_response = requests.get(page_url) + page_soup = BeautifulSoup(page_response.text, "html.parser") + + page_ids = _extract_property_ids_from_links(page_soup, rent_or_buy) + page_listings = _extract_listings_from_json_ld(page_soup, rent_or_buy) + + for i, prop_id in enumerate(page_ids): + if prop_id not in dict_prop_price: + price = page_listings[i]["price"] if i < len(page_listings) else "" + dict_prop_price[prop_id] = price + + # Build result list + result = [] + for prop_id, price in dict_prop_price.items(): + result.append( { - "id": id, # name = parition - "fingerprint": str(id) + "-" + str(last_normalized_price), + "id": prop_id, + "fingerprint": f"{prop_id}-{price}", "is_prefix": False, - "rentOrBuy": searchCriteria["rentOrBuy"], - "city": searchCriteria["city"], - "propertyType": searchCriteria["propertyType"], - "radius": searchCriteria["radius"], - "last_normalized_price": str(last_normalized_price), + "rentOrBuy": rent_or_buy, + "city": city, + "propertyType": property_type, + "radius": radius, + "last_normalized_price": str(price), } ) - return listPropertyIds + context.log.info(f"Total properties found: {len(result)}") + return result -@op( +@dg.op( description="Downloads and cache full datasets (JSON) as gzip to avoid re-downloading same properties against API. If file already exists, it will not re-downloaded again.", required_resource_keys={"file_manager"}, config_schema={ - "immo24_api_en": Field( - String, + "immo24_api_en": dg.Field( + str, default_value="https://api.immoscout24.ch/listings/listing/", is_required=False, description=( @@ -171,7 +178,7 @@ def list_props_immo24(context, searchCriteria: SearchCoordinate) -> PropertyData ), ) }, - out={"local_file_handle": Out(LocalFileHandle, io_manager_key="fs_io_manager")}, + out={"local_file_handle": dg.Out(dg.LocalFileHandle, io_manager_key="fs_io_manager")}, ) def cache_properies_from_rest_api( context, properties: PropertyDataFrame @@ -183,16 +190,13 @@ def cache_properies_from_rest_api( for p in properties: context.log.debug(f"Request sent to {context.op_config['immo24_api_en'] + p['id']}") - # Is it possible to do a range instead of each seperately? json_prop = requests.get(context.op_config["immo24_api_en"] + p["id"]).json() - # add metadata if flat, house, detatched-house, etc. if "propertyDetails" not in json_prop: json_prop["propertyDetails"] = {} json_prop["propertyDetails"]["propertyType"] = p["propertyType"] json_prop["propertyDetails"]["isBuyRent"] = p["rentOrBuy"] - # add metadata from search json_prop["propertyDetails"]["propertyId"] = p["id"] json_prop["propertyDetails"]["searchCity"] = p["city"] json_prop["propertyDetails"]["searchRadius"] = p["radius"] @@ -218,32 +222,12 @@ def cache_properies_from_rest_api( """ local_file_manager = context.resources.file_manager - # Create a BytesIO object to hold the gzipped data temporarily with closing(io.BytesIO()) as temp_file_obj: json_zip_writer(property_list, temp_file_obj) - - # Reset the file pointer to the beginning after writing + temp_file_obj.seek(0) - - # Use LocalFileManager to write the gzipped data to a file + file_handle = local_file_manager.write(temp_file_obj, mode="wb", ext="gz") context.log.info(f"File handle written at : {file_handle.path}") - yield Output(value=file_handle, output_name="local_file_handle") - - - -@op(description="Get price from a property via API Call") -def _get_normalized_price(context, id: Int): - api_url = "https://rest-api.immoscout24.ch/v4/en/properties/" - propApi = requests.get(api_url + id).json() - - if "propertyDetails" in propApi: - if "normalizedPrice" in propApi["propertyDetails"]: - last_checked_price = propApi["propertyDetails"]["normalizedPrice"] - else: - last_checked_price = None - else: - last_checked_price = None - - return last_checked_price + yield dg.Output(value=file_handle, output_name="local_file_handle") diff --git a/src/pipelines/real-estate/realestate/common/solids_spark_delta.py b/src/pipelines/real-estate/realestate/common/solids_spark_delta.py index 4e3dd84..8ca0b7e 100644 --- a/src/pipelines/real-estate/realestate/common/solids_spark_delta.py +++ b/src/pipelines/real-estate/realestate/common/solids_spark_delta.py @@ -1,131 +1,75 @@ -# gernal op pyspark execution +# general op pyspark execution -# from dagster_aws.s3.ops import S3Coordinate -from .types import S3Coordinate - -# new types? -# from dagster_aws.s3 import S3Resource, S3FileHandle - -# from pyspark.sql.types import StructType, ArrayType -# from pyspark.sql.functions import col, explode_outer - -from functools import reduce - -from functools import reduce -from pandasql import sqldf from pandas import DataFrame - from realestate.common.types_realestate import PropertyDataFrame from realestate.common.helper_functions import reading_delta_table -from jinja2 import Template import re -import os -from botocore.exceptions import NoCredentialsError import pandas as pd import pandasql as ps import pyarrow as pa -from typing import List -from dagster import ( - LocalFileHandle, - make_python_type_usable_as_dagster_type, - op, - Field, - String, - Bool, - Output, - In, - Out, - check, - FileHandle, - AssetMaterialization, - MetadataValue, -) +import dagster as dg -from realestate.common.types import DeltaCoordinate, SqlTableName +from realestate.common.types import DeltaCoordinate from realestate.common.helper_functions import rename_pandas_dataframe_columns, read_gzipped_json -from dagster import Field, String -from deltalake import DeltaTable - - PARQUET_SPECIAL_CHARACTERS = r"[ ,;{}()\n\t=]" def _get_s3a_path(bucket, path): - # TODO: remove unnessesary slashs if there return "s3a://" + bucket + "/" + path -@op( - required_resource_keys={"pyspark", "s3"}, - description="""Ingest s3 path with zipped jsons -and load it into a Spark Dataframe. -It infers header names but and infer schema. - -It also ensures that the column names are valid parquet column names by -filtering out any of the following characters from column names: - -Characters (within quotations): "`{chars}`" - -""".format( - chars=PARQUET_SPECIAL_CHARACTERS - ), -) -def s3_to_df(context, s3_coordinate: S3Coordinate) -> DataFrame: - context.log.debug( - "AWS_KEY: {access} - Secret: {secret})".format( - access=os.environ["MINIO_ROOT_USER"], secret=os.environ["MINIO_ROOT_PASSWORD"] - ) - ) - # findspark.init(spark_home='/path/to/spark/lib/') - s3_path = _get_s3a_path(s3_coordinate["bucket"], s3_coordinate["key"]) - - context.log.info( - "Reading dataframe from s3 path: {path} (Bucket: {bucket} and Key: {key})".format( - path=s3_path, bucket=s3_coordinate["bucket"], key=s3_coordinate["key"] - ) - ) - - # reading from a folder handles zipped and unzipped jsons automatically - data_frame = context.resources.pyspark.spark_session.read.json(s3_path) - - # df.columns #print columns - - context.log.info("Column FactId removed from df") - - # parquet compat - return rename_spark_dataframe_columns( - data_frame, lambda x: re.sub(PARQUET_SPECIAL_CHARACTERS, "", x) - ) - - -# @op( -# # ins=[In("prop_s3_coordinates", List[S3Coordinate])], -# # out=Out(List[S3Coordinate]), +# @dg.op( # required_resource_keys={"pyspark", "s3"}, -# description="combine multiple s3 coordinates to one dataframe", +# description="""Ingest s3 path with zipped jsons +# and load it into a Spark Dataframe. +# It infers header names but and infer schema. +# +# It also ensures that the column names are valid parquet column names by +# filtering out any of the following characters from column names: +# +# Characters (within quotations): "`{chars}`" +# +# """.format( +# chars=PARQUET_SPECIAL_CHARACTERS +# ), # ) -# # def combine_list_of_dfs(context, prop_s3_coordinates: List[S3Coordinate]): -# def combine_list_of_dfs(context, prop_s3_coordinates): -# dfs = [] -# for p in prop_s3_coordinates: -# dfs.append( -# s3_to_df(_get_s3a_path(p["s3_coordinate_bucket"], p["s3_coordinate_key"])) +# def s3_to_df(context, s3_coordinate: S3Coordinate) -> DataFrame: +# context.log.debug( +# "AWS_KEY: {access} - Secret: {secret})".format( +# access=os.environ["MINIO_ROOT_USER"], secret=os.environ["MINIO_ROOT_PASSWORD"] +# ) +# ) +# s3_path = _get_s3a_path(s3_coordinate["bucket"], s3_coordinate["key"]) +# +# context.log.info( +# "Reading dataframe from s3 path: {path} (Bucket: {bucket} and Key: {key})".format( +# path=s3_path, bucket=s3_coordinate["bucket"], key=s3_coordinate["key"] # ) +# ) +# +# # reading from a folder handles zipped and unzipped jsons automatically +# data_frame = context.resources.pyspark.spark_session.read.json(s3_path) +# +# context.log.info("Column FactId removed from df") +# +# # parquet compat +# return rename_spark_dataframe_columns( +# data_frame, lambda x: re.sub(PARQUET_SPECIAL_CHARACTERS, "", x) +# ) -# return reduce(DataFrame.unionAll, dfs) -@op( +@dg.op( description="""This function is to flatten the nested json properties to a table with flat columns. Renames columns to avoid parquet special characters.""", config_schema={ - "remove_columns": Field( - [String], + "remove_columns": dg.Field( + [str], default_value=[ "propertyDetails_images", "propertyDetails_pdfs", @@ -136,18 +80,17 @@ def s3_to_df(context, s3_coordinate: S3Coordinate) -> DataFrame: description=("unessesary columns to be removed in from the json"), ), }, - out=Out(io_manager_key="fs_io_manager"), + out=dg.Out(io_manager_key="fs_io_manager"), ) -def flatten_json(context, local_file: LocalFileHandle) -> pd.DataFrame: +def flatten_json(context, local_file: dg.LocalFileHandle) -> pd.DataFrame: - # reading from a folder with zipped JSONs + # reading from a folder with zipped JSONs context.log.info(f"Reading from local file: {local_file.path} ...") json_data = read_gzipped_json(local_file.path) # Flatten: Normalize the JSON data df = pd.json_normalize(json_data) - #TODO: Still need to remove FactId? if 'FactId' in df.columns: df.drop('FactId', axis=1, inplace=True) context.log.info("Column FactId removed from df") @@ -166,11 +109,11 @@ def flatten_json(context, local_file: LocalFileHandle) -> pd.DataFrame: return df -# @op( +# @dg.op( # description="""This function is to flatten the nested json properties to a table with flat columns""", # config_schema={ -# "remove_columns": Field( -# [String], +# "remove_columns": dg.Field( +# [str], # default_value=[ # "propertyDetails_images", # "propertyDetails_pdfs", @@ -184,7 +127,7 @@ def flatten_json(context, local_file: LocalFileHandle) -> pd.DataFrame: # ) # def flatten_json(context, df: DataFrame) -> DataFrame: # "Flatten array of structs and structs" - +# # # from pyspark.sql.types import * # # from pyspark.sql.functions import * # # compute Complex Fields (Lists and Structs) in Schema @@ -196,14 +139,14 @@ def flatten_json(context, local_file: LocalFileHandle) -> pd.DataFrame: # and field.name.startswith("propertyDetails") # ] # ) - +# # # print(complex_fields) # while len(complex_fields) != 0: # col_name = list(complex_fields.keys())[0] # context.log.debug( # "Processing :" + col_name + " Type : " + str(type(complex_fields[col_name])) # ) - +# # if col_name in context.op_config["remove_columns"]: # # remove and skip next part # df = df.drop(col_name) @@ -216,12 +159,12 @@ def flatten_json(context, local_file: LocalFileHandle) -> pd.DataFrame: # for k in [n.name for n in complex_fields[col_name]] # ] # df = df.select("*", *expanded).drop(col_name) - +# # # if ArrayType then add the Array Elements as Rows using the explode function # # i.e. explode Arrays # elif type(complex_fields[col_name]) == ArrayType: # df = df.withColumn(col_name, explode_outer(col_name)) - +# # # recompute remaining Complex Fields in Schema # complex_fields = dict( # [ @@ -235,163 +178,110 @@ def flatten_json(context, local_file: LocalFileHandle) -> pd.DataFrame: # "count of rows, in case of no errors, count should stay the same. Count: " # + str(df.count()) # ) - +# # return df -#def sql_solid( -# name, -# sql_statement, -# materialization_strategy, -# # target_delta_table: DeltaCoordinate, -# # src_df: DataFrame, -# table_name=None, -# ins=None, -# # input_defs=[ -# # InputDefinition("target_delta_table", DeltaCoordinate), -# # ], -#): -# """Return a new solid that executes and materializes a SQL select statement. - -# Args: -# name (str): The name of the new solid. -# sql_statement (str): The sql statement to execute which can be MERGE, INSERT, UPDATE. -# materialization_strategy (str): Must be 'delta_table' for now. -# Kwargs: -# input_defs (list[InputDefinition]): 'target_delta_table' (DeltaCoordinate) must be provided, which is -# the table where the sql_statement is running against. -# 'input_dataframe' (DataFrame) must be provided for providing input data for the sql_statement - -# Returns: -# function: -# The new SQL solid. -# """ -# # ins = . opt_list_param(ins, "ins", In) - -# materialization_strategy_output_types = { # pylint:disable=C0103 -# "table": SqlTableName, -# "delta_table": DeltaCoordinate, -# # 'view': String, -# # 'query': SqlAlchemyQueryType, -# # 'subquery': SqlAlchemySubqueryType, -# # 'result_proxy': SqlAlchemyResultProxyType, -# # could also materialize as a Pandas table, as a Spark table, as an intermediate file, etc. -# } - -# if materialization_strategy not in materialization_strategy_output_types: -# raise Exception( -# "Invalid materialization strategy {materialization_strategy}, must " -# "be one of {materialization_strategies}".format( -# materialization_strategy=materialization_strategy, -# materialization_strategies=str( -# list(materialization_strategy_output_types.keys()) -# ), -# ) -# ) - -# output_description = ( -# "The string name of the new table created by the solid" -# if materialization_strategy == "table" -# or materialization_strategy == "delta_table" -# else "The materialized SQL statement. If the materialization_strategy is " -# "'table', this is the string name of the new table created by the solid." -# ) - -# # sql_statement.replace( -# # "delta.``", "delta.{target_delta_path}".format(target_delta_path=target_delta_path) -# # ) - -# description = """This solid executes the following SQL statement: -# {sql_statement}""".format( -# sql_statement=sql_statement -# ) - -# sql_statement = ("{sql_statement};").format(sql_statement=sql_statement) - -# @op( -# name=name, -# ins=ins, -# out=Out( -# materialization_strategy_output_types[materialization_strategy], -# description=output_description, -# ), -# description=description, -# required_resource_keys={"pyspark"}, -# # tags={"kind": "sql", "sql": sql_statement}, -# tags={ -# "kind": "sql", -# "sql": sql_statement, -# }, -# ) -# def _sql_solid(context, **ins): # pylint: disable=unused-argument -# """Inner function defining the new solid. - -# Args: -# context (SolidExecutionContext): Must expose a `spark` resource with an `spark_session` method -# wich can execute SQL against Delta Lake Tables. - -# Returns: -# DeltaCoordinate: -# The Delta Table Coordinates where the SQL statements were running against. -# """ -# if ins["target_delta_table"] is None: -# raise Exception("Input `target_delta_table` not provided.") -# if ins["input_dataframe"] is None: -# raise Exception("Input `input_dataframe` not provided.") -# ## -# ## Handling delta-table -# ## -# target_delta_path = _get_s3a_path( -# ins["target_delta_table"]["s3_coordinate_bucket"], -# ins["target_delta_table"]["s3_coordinate_key"], -# ) -# context.log.info("Target Delta table path: {}".format(target_delta_path)) - -# # prepare colums for merge statement -# insert_columns = "\n, ".join(ins["input_dataframe"].columns) -# update_columns = "\n, ".join( -# ["trg." + c + " = src." + c for c in ins["input_dataframe"].columns] -# ) - -# # Set Delta-table path and columns -# sql_statement_template = Template(sql_statement) -# repl_sql_statement = sql_statement_template.render( -# target_delta_table="delta.`" + target_delta_path + "`", -# update_columns=update_columns, -# insert_columns=insert_columns, -# ) - -# context.log.info( -# "Executing sql statement:\n{sql_statement}".format( -# sql_statement=repl_sql_statement -# ) -# ) - -# ## -# ## Handling spark dataframe -# ## - -# # register input df for spark to be available in spark.sql -# ins["input_dataframe"].createOrReplaceTempView("input_dataframe") - -# context.resources.pyspark.spark_session.sql( -# repl_sql_statement -# ) # text() function removed here (this would validate string as valid SQL, but with Delta-Merge does not work) -# yield AssetMaterialization( -# asset_key=ins["target_delta_table"]["table_name"], -# description="Target Delta table", -# metadata={ -# "delta_table_path": MetadataValue.path(target_delta_path), -# }, -# ) - -# yield Output(value=ins[target_delta_table"], output_name="result") - -# return _sql_solid - -@op(out=Out(io_manager_key="fs_io_manager")) +# def sql_solid( +# name, +# sql_statement, +# materialization_strategy, +# table_name=None, +# ins=None, +# ): +# """Return a new solid that executes and materializes a SQL select statement.""" +# materialization_strategy_output_types = { +# "table": SqlTableName, +# "delta_table": DeltaCoordinate, +# } +# +# if materialization_strategy not in materialization_strategy_output_types: +# raise Exception( +# "Invalid materialization strategy {materialization_strategy}, must " +# "be one of {materialization_strategies}".format( +# materialization_strategy=materialization_strategy, +# materialization_strategies=str( +# list(materialization_strategy_output_types.keys()) +# ), +# ) +# ) +# +# output_description = ( +# "The string name of the new table created by the solid" +# if materialization_strategy == "table" +# or materialization_strategy == "delta_table" +# else "The materialized SQL statement." +# ) +# +# description = """This solid executes the following SQL statement: +# {sql_statement}""".format( +# sql_statement=sql_statement +# ) +# +# sql_statement = ("{sql_statement};").format(sql_statement=sql_statement) +# +# @dg.op( +# name=name, +# ins=ins, +# out=dg.Out( +# materialization_strategy_output_types[materialization_strategy], +# description=output_description, +# ), +# description=description, +# required_resource_keys={"pyspark"}, +# tags={ +# "kind": "sql", +# "sql": sql_statement, +# }, +# ) +# def _sql_solid(context, **ins): +# if ins["target_delta_table"] is None: +# raise Exception("Input `target_delta_table` not provided.") +# if ins["input_dataframe"] is None: +# raise Exception("Input `input_dataframe` not provided.") +# +# target_delta_path = _get_s3a_path( +# ins["target_delta_table"]["s3_coordinate_bucket"], +# ins["target_delta_table"]["s3_coordinate_key"], +# ) +# context.log.info("Target Delta table path: {}".format(target_delta_path)) +# +# insert_columns = "\n, ".join(ins["input_dataframe"].columns) +# update_columns = "\n, ".join( +# ["trg." + c + " = src." + c for c in ins["input_dataframe"].columns] +# ) +# +# sql_statement_template = Template(sql_statement) +# repl_sql_statement = sql_statement_template.render( +# target_delta_table="delta.`" + target_delta_path + "`", +# update_columns=update_columns, +# insert_columns=insert_columns, +# ) +# +# context.log.info( +# "Executing sql statement:\n{sql_statement}".format( +# sql_statement=repl_sql_statement +# ) +# ) +# +# ins["input_dataframe"].createOrReplaceTempView("input_dataframe") +# +# context.resources.pyspark.spark_session.sql(repl_sql_statement) +# yield dg.AssetMaterialization( +# asset_key=ins["target_delta_table"]["table_name"], +# description="Target Delta table", +# metadata={ +# "delta_table_path": dg.MetadataValue.path(target_delta_path), +# }, +# ) +# +# yield dg.Output(value=ins["target_delta_table"], output_name="result") +# +# return _sql_solid + +@dg.op(out=dg.Out(io_manager_key="fs_io_manager")) def merge_property_delta(context, input_dataframe: DataFrame) -> DeltaCoordinate: - + target_delta_table = "s3a://real-estate/lake/bronze/property" target_delta_coordinate = { "s3_coordinate_bucket": "real-estate", "s3_coordinate_key": "lake/bronze/property", "table_name": "property", "database": "core"} @@ -405,7 +295,6 @@ def merge_property_delta(context, input_dataframe: DataFrame) -> DeltaCoordinate ( dt.merge( source=input_dataframe, - # predicate='target.propertyDetails_id = source."propertyDetails_propertyId"', predicate='target.propertyDetails_propertyId = source."propertyDetails_propertyId"', source_alias='source', target_alias='target') @@ -415,7 +304,6 @@ def merge_property_delta(context, input_dataframe: DataFrame) -> DeltaCoordinate ) context.log.info("Merged data into Delta table `property` successfully") - #return delta coordinates for notebooks to read from return target_delta_coordinate @@ -430,23 +318,19 @@ def merge_property_delta(context, input_dataframe: DataFrame) -> DeltaCoordinate # WHEN NOT MATCHED THEN # INSERT * # """ -# # UPDATE SET{{ update_columns }} -# # INSERT ( {{ insert_columns }} ) -# # VALUES ( {{ insert_columns }} ) # , # materialization_strategy="delta_table", -# # table_name="tag", -# ins = {"delta_coordinate": In(dagster_type=DeltaCoordinate), -# "df": In(dagster_type=DataFrame)} +# ins = {"delta_coordinate": dg.In(dagster_type=DeltaCoordinate), +# "df": dg.In(dagster_type=DataFrame)} # ) -@op( +@dg.op( required_resource_keys={"s3"}, description="""This will check if property is already downloaded. If so, check if price or other columns have changed in the meantime, or if date is very old, download again""", - out={"properties": Out(dagster_type=PropertyDataFrame, is_required=False, io_manager_key="fs_io_manager")}, + out={"properties": dg.Out(dagster_type=PropertyDataFrame, is_required=False, io_manager_key="fs_io_manager")}, ) def get_changed_or_new_properties(context, properties: PropertyDataFrame, property_table: pd.DataFrame) -> PropertyDataFrame: @@ -455,7 +339,6 @@ def get_changed_or_new_properties(context, properties: PropertyDataFrame, proper ids: str = ", ".join(ids_tmp) context.log.info("Fetched propertyDetails_id's: [{}]".format(ids)) - # context.log.debug(f"type: property_table: {type(property_table)} and lenght {len(property_table)}") cols_props = ["propertyDetails_propertyId", "fingerprint"] cols_PropertyDataFrame = [ @@ -479,18 +362,11 @@ def get_changed_or_new_properties(context, properties: PropertyDataFrame, proper result_df = ps.sqldf(query, locals()) context.log.info(f"Lenght: property_table: {len(result_df)}") - # get a list selected colum: `property_ids` and its fingerprint existing_props = result_df[["propertyDetails_propertyId", "fingerprint"]].values.tolist() - # Convert dict into pandas dataframe pd_existing_props = pd.DataFrame(existing_props, columns=cols_props) pd_properties = pd.DataFrame(properties, columns=cols_PropertyDataFrame) - # debugging - # context.log.debug(f"pd_existing_props: {pd_existing_props}, type: {type(pd_existing_props)}") - # context.log.debug(f"pd_properties: {pd_properties}") - - # select new or changed once df_changed = ps.sqldf( """ SELECT p.id, p.fingerprint, p.is_prefix, p.rentOrBuy, p.city, p.propertyType, p.radius, p.last_normalized_price @@ -511,50 +387,34 @@ def get_changed_or_new_properties(context, properties: PropertyDataFrame, proper ids_changed = ", ".join(str(e) for e in df_changed["id"].tolist()) context.log.info("changed properties: {}".format(ids_changed)) - yield Output(changed_properties, "properties") + yield dg.Output(changed_properties, "properties") -# @op( +# @dg.op( # required_resource_keys={"pyspark", "s3"}, -# # config={'delta': DeltaType}, -# description="""Creates the delta table on S3 and returns the DeltaCoordinates - -# It will remove existing data on that path and or delte existing delta table.""", +# description="""Creates the delta table on S3 and returns the DeltaCoordinates""", # config_schema={ -# "mergeSchema": Field( -# Bool, +# "mergeSchema": dg.Field( +# bool, # default_value=True, # is_required=False, -# description=( -# "if you want to merge different schema [true/false]. Added columns will be merged automatially by delta" -# ), # ), -# "mode": Field( -# String, +# "mode": dg.Field( +# str, # default_value="overwrite", # is_required=False, -# description=( -# "mode can be set to [overwrite], this way delta data will be overwritten if exists" -# ), # ), -# "partitionBy": Field( -# String, +# "partitionBy": dg.Field( +# str, # default_value="DateTimeDate", # is_required=False, -# description=( -# "column by with delta table (parquet-files) will be partitioned. This column must exist in table" -# ), # ), # }, # ) # def create_delta_table( # context, data_frame: DataFrame, delta_coordinate: DeltaCoordinate # ) -> DeltaCoordinate: -# # TODO: -# # - make paritionBy column optional -# # - add parameter if delete table before creating option - # delta_path = _get_s3a_path( # delta_coordinate["s3_coordinate_bucket"], delta_coordinate["s3_coordinate_key"] # ) @@ -563,32 +423,26 @@ def get_changed_or_new_properties(context, properties: PropertyDataFrame, proper # + delta_coordinate["table_name"] # + " in path: {path} ...".format(path=delta_path) # ) - -# # create database if not exists +# # context.resources.pyspark.spark_session.sql( # "CREATE DATABASE IF NOT EXISTS {}".format(delta_coordinate["database"]) # ) - -# # drop table if exists +# # context.resources.pyspark.spark_session.sql( # "DROP TABLE IF EXISTS {database}.{table_name}".format( # database=delta_coordinate["database"], # table_name=delta_coordinate["table_name"], # ) # ) - -# # drop data_frames and data on delta_path -# # TODO: find if there is a fasater pay to delete on S3? -> databricks has dbutils.fs.rm(delta_path, recurse=True) +# # os.system("hdfs dfs -rm -r -skipTrash " + delta_path) - +# # data_frame.write.format("delta").mode(context.op_config["mode"]).option( # "mergeSchema", context.op_config["mergeSchema"] # ).save(delta_path) -# # .partitionBy(context.op_config['partitionBy']) \ - +# # context.log.info("data_frame written to: " + delta_path) - -# # create delta table +# # context.resources.pyspark.spark_session.sql( # """ # CREATE TABLE IF NOT EXISTS {}.{} @@ -598,25 +452,20 @@ def get_changed_or_new_properties(context, properties: PropertyDataFrame, proper # delta_coordinate["database"], delta_coordinate["table_name"], delta_path # ) # ) - +# # context.log.info("delta table " + delta_coordinate["table_name"] + " created") - -# # TODO: decide if we want to yield Materialization to persist. -# # This way we don't need to pass delta_coordinate down-stream. But maybe we want this on purpose? - -# # TODO: return s3 as well, so we don't need to specify again in ingest-yaml? +# # return delta_coordinate -# @op( +# @dg.op( # required_resource_keys={"pyspark", "s3"}, # description="""Loads given delta coordinates into a spark data frame""", -# # output_defs=[OutputDefinition(name='data_frame', dagster_type=DataFrame, is_optional=False),], # ) # def load_delta_table_to_df( # context, # delta_coordinate: DeltaCoordinate, -# where_conditions: String, +# where_conditions: str, # ) -> DataFrame: # delta_path = _get_s3a_path( # delta_coordinate["s3_coordinate_bucket"], delta_coordinate["s3_coordinate_key"] @@ -632,24 +481,25 @@ def get_changed_or_new_properties(context, properties: PropertyDataFrame, proper # data_frame = context.resources.pyspark.spark_session.read.format("delta").load( # delta_path # ) - +# # return data_frame # -# GENERAL MINOR SPARK FUNCTIONS -def do_prefix_column_names(df, prefix): - check.inst_param(df, "df", DataFrame) - check.str_param(prefix, "prefix") - return rename_spark_dataframe_columns( - df, lambda c: "{prefix}{c}".format(prefix=prefix, c=c) - ) - - -@op -def canonicalize_column_names(_context, data_frame: DataFrame) -> DataFrame: - return rename_spark_dataframe_columns(data_frame, lambda c: c.lower()) - - -def replace_values_spark(data_frame, old, new): - return data_frame.na.replace(old, new) +# GENERAL MINOR SPARK FUNCTIONS (kept as reference) +# +# def do_prefix_column_names(df, prefix): +# check.inst_param(df, "df", DataFrame) +# check.str_param(prefix, "prefix") +# return rename_spark_dataframe_columns( +# df, lambda c: "{prefix}{c}".format(prefix=prefix, c=c) +# ) +# +# +# @dg.op +# def canonicalize_column_names(_context, data_frame: DataFrame) -> DataFrame: +# return rename_spark_dataframe_columns(data_frame, lambda c: c.lower()) +# +# +# def replace_values_spark(data_frame, old, new): +# return data_frame.na.replace(old, new) diff --git a/src/pipelines/real-estate/realestate/common/types.py b/src/pipelines/real-estate/realestate/common/types.py index 8317d6e..8445e78 100644 --- a/src/pipelines/real-estate/realestate/common/types.py +++ b/src/pipelines/real-estate/realestate/common/types.py @@ -1,26 +1,4 @@ -from dagster import Field, String, check, dagster_type_loader, DagsterType -from dagster.core.types.dagster_type import PythonObjectDagsterType, create_string_type - - -# copied from dagster_aws.s3.solids import dict_with_fields - attention, don't activate import from s3.solid, this will require boto3 as S3FileHandle is included there -def dict_with_fields(name, fields): - check.str_param(name, "name") - check.dict_param(fields, "fields", key_type=str) - field_names = set(fields.keys()) - - @dagster_type_loader(fields) - def _input_schema(_context, value): - check.dict_param(value, "value") - check.param_invariant(set(value.keys()) == field_names, "value") - return value - - class _DictWithSchema(PythonObjectDagsterType): - def __init__(self): - super(_DictWithSchema, self).__init__( - python_type=dict, name=name, loader=_input_schema - ) - - return _DictWithSchema() +import dagster as dg ################################################################################# @@ -28,79 +6,62 @@ def __init__(self): ################################################################################# -# Define the type check function def delta_coordinate_type_check(_context, value): - # Check if value is a dictionary if not isinstance(value, dict): return False - - # Check for required keys and types expected_fields = { "database": str, "table_name": str, "s3_coordinate_bucket": str, "s3_coordinate_key": str, } - - # Ensure all fields are present and of the correct type for field, field_type in expected_fields.items(): if field not in value or not isinstance(value[field], field_type): return False - return True -# Define the custom DagsterType -DeltaCoordinate = DagsterType( +DeltaCoordinate = dg.DagsterType( name="DeltaCoordinate", - description="""A dictionary containing details about a delta coordinate, - including 'database', 'table_name', 's3_coordinate_bucket', + description="""A dictionary containing details about a delta coordinate, + including 'database', 'table_name', 's3_coordinate_bucket', and 's3_coordinate_key'.""", type_check_fn=delta_coordinate_type_check, ) -# DeltaCoordinate = dict_with_fields( -# name="DeltaCoordinate", -# fields={ -# "database": Field(String, description="database or schema or delta table"), -# "table_name": Field(String, description="table name of the delta table"), -# "s3_coordinate_bucket": Field(String, description="s3 bucket"), -# "s3_coordinate_key": Field(String, description="s3 delta table path"), -# }, -# ) - -# make_python_type_usable_as_dagster_type(python_type=Dict, dagster_type=DeltaCoordinate) ################################################################################# # Druid Coordinate ################################################################################# -DruidCoordinate = dict_with_fields( + +def druid_coordinate_type_check(_context, value): + if not isinstance(value, dict): + return False + required_keys = {"datasource", "intervalToDelete", "PathToJsonIngestSpec"} + return all(k in value and isinstance(value[k], str) for k in required_keys) + + +DruidCoordinate = dg.DagsterType( name="DruidCoordinate", - fields={ - "datasource": Field(String, description="druid datasource"), - "intervalToDelete": Field( - String, - description="this defines the interval to delete the segment with the format: [YYYY-MM-DDTHH:MM:SS.MMMZ-YYYY-MM-DDTHH:MM:SS.MMMZ]", - ), - "PathToJsonIngestSpec": Field( - String, description="path from project root to druid ingestion spec" - ), - }, + description="A dictionary with 'datasource', 'intervalToDelete', and 'PathToJsonIngestSpec' for Druid ingestion.", + type_check_fn=druid_coordinate_type_check, ) -# make_python_type_usable_as_dagster_type(python_type=Permissive, dagster_type=DruidCoordinate) -SqlTableName = create_string_type( - "SqlTableName", description="The name of a database table" + +SqlTableName = dg.DagsterType( + name="SqlTableName", + description="The name of a database table", + type_check_fn=lambda _context, value: isinstance(value, str), ) + ################################################################################# # S3 Coordinate ################################################################################# -# This Dagster type checks if an object is a dictionary with "bucket" and "key" fields -S3Coordinate = DagsterType( +S3Coordinate = dg.DagsterType( name="S3Coordinate", description="A dictionary with 'bucket' and 'key' to specify an S3 location.", type_check_fn=lambda _context, obj: isinstance(obj, dict) diff --git a/src/pipelines/real-estate/realestate/common/types_realestate.py b/src/pipelines/real-estate/realestate/common/types_realestate.py index 8a6f6a4..ece028e 100644 --- a/src/pipelines/real-estate/realestate/common/types_realestate.py +++ b/src/pipelines/real-estate/realestate/common/types_realestate.py @@ -1,17 +1,8 @@ """Type definitions for the realestate project""" import json -from dagster import ( - Field, - String, - StringSource, - Int, - IntSource, - DagsterType, - usable_as_dagster_type -) +import dagster as dg -from dagster_aws.s3.ops import dict_with_fields def is_json(_, value): try: @@ -21,82 +12,29 @@ def is_json(_, value): return False -JsonType = DagsterType( +JsonType = dg.DagsterType( name="JsonType", description="A valid representation of a JSON, validated with json.loads().", type_check_fn=is_json, ) -SearchCoordinate = dict_with_fields( - "SearchCoordinate", - fields={ - "propertyType": Field( - StringSource, - description="Type of the Property [house, flat, real-estate (flat and house), plot, parking-space, multi-family-residential, office-commerce-industry, agriculture, other-objects]", - ), - "rentOrBuy": Field( - StringSource, - description="Searching for rent or to buy", - ), - "radius": Field( - IntSource, - description="Searched radius", - ), - "city": Field(StringSource, "The city in you want to search for properties"), - }, -) +def search_coordinate_type_check(_context, value): + if not isinstance(value, dict): + return False + required_keys = {"propertyType", "rentOrBuy", "radius", "city"} + return all(k in value for k in required_keys) -@usable_as_dagster_type -class SearchCoordinateClass: - def __init__(self, propertyType, rentOrBuy, radius, city): - self.propertyType = propertyType - self.rentOrBuy = rentOrBuy - self.radius = radius - self.city = city -# Define the fields for the SearchCoordinate type -SearchCoordinateType = SearchCoordinateClass( - propertyType=Field( - String, - description="Type of the Property [house, flat, real-estate (flat and house), plot, parking-space, multi-family-residential, office-commerce-industry, agriculture, other-objects]", - ), - rentOrBuy=Field( - String, - description="Searching for rent or to buy", - ), - radius=Field( - Int, - description="Searched radius", - ), - city=Field(String, description="The city in you want to search for properties"), +SearchCoordinate = dg.DagsterType( + name="SearchCoordinate", + description="A dictionary with 'propertyType', 'rentOrBuy', 'radius', and 'city' for property search.", + type_check_fn=search_coordinate_type_check, ) -PropertyDataFrame = DagsterType( + +PropertyDataFrame = dg.DagsterType( name="PropertyDataFrame", type_check_fn=lambda _, value: isinstance(value, list), description="A List with scraped Properties with id, last_normalized_price and search criterias wich it was found.", ) - -# SimplePropertyDict = dict_with_fields( -# name='SimplePropertyDict', -# fields={ -# 'name': Field(Int, description='this is the unique ID of property'), -# 'fingerprint': Field( -# String, -# description='hash to identify if object need to be downloaded again if already exists', -# ), -# 'is_prefix': Field( -# String, -# description='A boolean that represents whether or not the current partition holds any child partitions', -# ), -# 'rentOrBuy': Field(String, description='If property is for only rent or to buy'), -# 'city': Field(String, description='which city it was searched from'), -# 'propertyType': Field( -# String, -# description='Type of the Property [house, flat, real-estate (flat and house), plot, parking-space, multi-family-residential, office-commerce-industry, agriculture, other-objects])', -# ), -# 'radius': Field(Int, description='searched radius'), -# 'last_normalized_price': Field(Int, description='property price at time of scraping'), -# }, -# ) diff --git a/src/pipelines/real-estate/realestate/config_environments/local_base.yaml b/src/pipelines/real-estate/realestate/config_environments/local_base.yaml index dfd5727..e5ecc5d 100644 --- a/src/pipelines/real-estate/realestate/config_environments/local_base.yaml +++ b/src/pipelines/real-estate/realestate/config_environments/local_base.yaml @@ -1,86 +1,53 @@ -resources: - boto3: - config: - aws_access_key_id: MINIO_ACCESS_KEY - aws_secret_access_key: MINIO_SECRET_KEY - endpoint_url: http://127.0.0.1:9000 - # s3: - # config: - # endpoint_url: http://127.0.0.1:9000 #might be needed to be 127.0.0.1:60599 or similar... - # file_cache: - # config: - # overwrite: false - # target_folder: "/tmp/dagster/file_cache" - #pyspark: - # config: - # spark_conf: - # spark: - # databricks: - # delta: - # schema: - # autoMerge: - # enabled: true #enables schemaEvolution and mergeSchema in MERGE Statement - # # master: "k8s://https://kubernetes.docker.internal:6443" - # # serviceAccount: spark - # delta: - # logStore: - # class: org.apache.spark.sql.delta.storage.S3SingleDriverLogStore #this is used for delta-lake - # executor: - # instances: 1 - # memory: 4g - # hadoop: - # fs: - # s3a: - # endpoint: - # env: "127.0.0.1:9000" - # access: - # key: - # # env: MINIO_ACCESS_KEY - # env: MINIO_ROOT_USER - # secret: - # key: - # # env: MINIO_SECRET_KEY - # env: MINIO_ROOT_PASSWORD - # impl: "org.apache.hadoop.fs.s3a.S3AFileSystem" - # path: - # style: - # access: "true" - # connection: - # ssl: - # enabled: "false" - # aws: - # credentials: - # provider: "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider" - # delta: - # enableFastS3AListFrom: true - # sql: - # extensions: io.delta.sql.DeltaSparkSessionExtension #this is used for delta-lake - # catalog: - # spark_catalog: org.apache.spark.sql.delta.catalog.DeltaCatalog - # # execution: - # # arrow: - # # pyspark: - # # enabled: True #Enable Arrow-based columnar data transfers. Used for simulating SA in SA_simulation_input_nps_categories() - # jars: - # packages: io.delta:delta-spark_2.12:3.1.0,org.apache.hadoop:hadoop-aws:3.3.4,org.apache.hadoop:hadoop-aws:3.3.4 - # # packages: "io.delta:delta-core_2.12:2.4.0,org.apache.hadoop:hadoop-aws:3.3.4,org.apache.hadoop:hadoop-common:3.3.4,com.amazonaws:aws-java-sdk-bundle:1.11.563" - # # packages: "io.delta:delta-core_2.12:2.4.0,org.apache.hadoop:hadoop-aws:3.2.0,com.amazonaws:aws-java-sdk-bundle:1.11.563" - # # repositories: "$SPARK_HOME/jars/delta-core_2.12-0.7.0.jar,$SPARK_HOME/jars/hadoop-aws-3.2.0.jar" - # #use "repositories" if you manually download jars and copy them to $SPARK_HOME/jars (e.g. if proxy doesn't allow) - # #use "packages" if you have internet connection, this way spark will automatically download dependencies with maven - # kubernetes: - # # namespace: default - # # authenticate: - # # driver: - # # serviceAccountName: spark #default - # # container: - # # image: "spark:spark-docker" - # # pyspark: - # # pythonVersion: 3 - # executor: - # request: - # cores: "1" - # limit: - # cores: "4" - # # submit: - # # deployMode: "client" +# Resources are now configured in Python (realestate/common/__init__.py) +# This file is kept for reference of the old Spark/PySpark config. + +# pyspark: +# config: +# spark_conf: +# spark: +# databricks: +# delta: +# schema: +# autoMerge: +# enabled: true +# delta: +# logStore: +# class: org.apache.spark.sql.delta.storage.S3SingleDriverLogStore +# executor: +# instances: 1 +# memory: 4g +# hadoop: +# fs: +# s3a: +# endpoint: +# env: "127.0.0.1:9000" +# access: +# key: +# env: MINIO_ROOT_USER +# secret: +# key: +# env: MINIO_ROOT_PASSWORD +# impl: "org.apache.hadoop.fs.s3a.S3AFileSystem" +# path: +# style: +# access: "true" +# connection: +# ssl: +# enabled: "false" +# aws: +# credentials: +# provider: "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider" +# delta: +# enableFastS3AListFrom: true +# sql: +# extensions: io.delta.sql.DeltaSparkSessionExtension +# catalog: +# spark_catalog: org.apache.spark.sql.delta.catalog.DeltaCatalog +# jars: +# packages: io.delta:delta-spark_2.12:3.1.0,org.apache.hadoop:hadoop-aws:3.3.4,org.apache.hadoop:hadoop-aws:3.3.4 +# kubernetes: +# executor: +# request: +# cores: "1" +# limit: +# cores: "4" diff --git a/src/pipelines/real-estate/realestate/config_pipelines/scrape_realestate.yaml b/src/pipelines/real-estate/realestate/config_pipelines/scrape_realestate.yaml index 50f46d8..82fc064 100644 --- a/src/pipelines/real-estate/realestate/config_pipelines/scrape_realestate.yaml +++ b/src/pipelines/real-estate/realestate/config_pipelines/scrape_realestate.yaml @@ -2,22 +2,26 @@ ops: collect_search_criterias: inputs: search_criterias: - # - city: Bern - # propertyType: real-estate - # radius: 0 - # rentOrBuy: buy - # - city: Solothurn - # propertyType: real-estate - # radius: 0 - # rentOrBuy: buy - - city: twann - propertyType: real-estate - radius: 0 - rentOrBuy: buy - # - city: biel-bienne - # propertyType: real-estate - # radius: 0 - # rentOrBuy: buy + # - value: + # city: Bern + # propertyType: real-estate + # radius: 0 + # rentOrBuy: buy + # - value: + # city: Solothurn + # propertyType: real-estate + # radius: 0 + # rentOrBuy: buy + - value: + city: twann + propertyType: real-estate + radius: 0 + rentOrBuy: buy + # - value: + # city: biel-bienne + # propertyType: real-estate + # radius: 0 + # rentOrBuy: buy # merge_staging_to_delta_table: # ops: # cache_properies_from_rest_api: @@ -32,7 +36,9 @@ ops: # table_name: property data_exploration: inputs: - # delta_path: s3a://real-estate/lake/bronze/property - key: minio - secret: miniostorage - endpoint: http://192.168.46.93:9000 + key: + value: admin + secret: + value: admin + endpoint: + value: http://127.0.0.1:8333 diff --git a/src/pipelines/real-estate/realestate/pipelines.py b/src/pipelines/real-estate/realestate/pipelines.py index fad611a..e13b77f 100644 --- a/src/pipelines/real-estate/realestate/pipelines.py +++ b/src/pipelines/real-estate/realestate/pipelines.py @@ -1,29 +1,8 @@ -from dagster import ( - job, - op, - file_relative_path, - config_from_files, - graph, - In, - Out, - GraphOut, - List, - DynamicOut, - DynamicOutput, - AssetOut, - fs_io_manager, - asset, - SourceAsset, - TableSchema, -) +import dagster as dg from typing import List -import os import pandas as pd -from dagster._utils import dagster_type - -from realestate.common import resource_def from realestate.common.helper_functions import reading_delta_table # from realestate.common.solids_druid import ingest_druid @@ -32,69 +11,45 @@ cache_properies_from_rest_api, ) -# from realestate.common.resources import boto3_connection, druid_db_info_resource -from realestate.common.resources import boto3_connection, druid_db_info_resource - -# from dagster_aws.s3.solids import S3Coordinate from realestate.common.types import DeltaCoordinate from realestate.common.types_realestate import PropertyDataFrame, SearchCoordinate -# from realestate.common.solids_filehandle import json_to_gzip from realestate.common.solids_spark_delta import ( - # upload_to_s3, get_changed_or_new_properties, merge_property_delta, flatten_json, - s3_to_df, ) from realestate.common.solids_jupyter import data_exploration from itertools import chain -# from dagster.core.storage.file_cache import fs_file_cache -# from dagster.core.storage.temp_file_manager import tempfile_resource - - -@op( - description="Reads the Delta Table from S3", out=Out(io_manager_key="fs_io_manager") +@dg.op( + description="Reads the Delta Table from S3", + out=dg.Out(io_manager_key="fs_io_manager"), ) def property_table(context) -> pd.DataFrame: - # should be with SourceAsset, but didnt' work: property_table = SourceAsset(key="s3a://real-estate/test/property") - - # s3_path_property = "s3a://real-estate/test/property" s3_path_property = "s3a://real-estate/lake/bronze/property" - - df, dt = reading_delta_table(context, s3_path_property) - return df - -# @asset -# def property() -> pd.DataFrame: -# return pd.read_json( -# "/Users/sspaeti/Documents/minio_bak/real-estate/src/azure-blob/scrapes/immo24ads__196b0de5-5511-4e0d-ae0f-be5d03bc943e", -# ) + df, _dt = reading_delta_table(context, s3_path_property) + return df -@graph( +@dg.graph( description="Downloads full dataset (JSON) from ImmoScout24, cache it, zip it and and upload it to S3", - # ins={"search_criteria": In(SearchCoordinate)}, #define in function below - # out={"properties": Out(dagster_type=PropertyDataFrame, is_required=False)}, ) def list_changed_properties(search_criteria: SearchCoordinate): - # def list_changed_properties(): return get_changed_or_new_properties( properties=list_props_immo24(searchCriteria=search_criteria), property_table=property_table(), ) -@graph( +@dg.graph( description="""This will take the list of properties, downloads the full dataset (JSON) from ImmoScout24, cache it locally to avoid scraping again in case of error. The cache will be zipped and uploaded to S3. From there the JSON will be flatten and merged (with schemaEvloution=True) into the Delta Table""", - # input_defs=[In(name="properties", dagster_type=PropertyDataFrame)], - out={"delta_coordinate": GraphOut()}, + out={"delta_coordinate": dg.GraphOut()}, ) def merge_staging_to_delta_table_composite(properties: PropertyDataFrame) -> DeltaCoordinate: file_handle = cache_properies_from_rest_api(properties) @@ -104,23 +59,20 @@ def merge_staging_to_delta_table_composite(properties: PropertyDataFrame) -> Del ) -@op( +@dg.op( description="""Collect a List of `PropertyDataFrame` from different cities to a single `PropertyDataFrame` List""", - out={"properties": Out(dagster_type=PropertyDataFrame, io_manager_key="fs_io_manager")}, + out={"properties": dg.Out(dagster_type=PropertyDataFrame, io_manager_key="fs_io_manager")}, ) def collect_properties(properties: List[PropertyDataFrame]) -> List[PropertyDataFrame]: # type: ignore return list(chain.from_iterable(properties)) -@op( +@dg.op( description="Collects Search Coordinates and spawns dynamically Pipelines downstream.", - # ins={"search_criterias": In("search_criterias", List[SearchCoordinate])}, - # out={"sarch_coordinates": DynamicOutput(SearchCoordinate)}, - required_resource_keys={"fs_io_manager"}, - out=DynamicOut(io_manager_key="fs_io_manager"), + out=dg.DynamicOut(io_manager_key="fs_io_manager"), ) -def collect_search_criterias(context, search_criterias: List[SearchCoordinate]): +def collect_search_criterias(context, search_criterias: list): # noqa: ARG001 for search in search_criterias: key = ( "_".join( @@ -135,18 +87,16 @@ def collect_search_criterias(context, search_criterias: List[SearchCoordinate]): .lower() ) - yield DynamicOutput( + yield dg.DynamicOutput( search, mapping_key=key, ) -@job( - resource_defs=resource_def["local"], - config=config_from_files( +@dg.job( + config=dg.config_from_files( [ - file_relative_path(__file__, "config_environments/local_base.yaml"), - file_relative_path(__file__, "config_pipelines/scrape_realestate.yaml"), + dg.file_relative_path(__file__, "config_pipelines/scrape_realestate.yaml"), ] ), ) @@ -158,3 +108,5 @@ def scrape_realestate(): collect_properties(search_criterias.collect()) ) ) + # TODO: wire up ingest_druid after data_exploration when Druid Docker is ready + # ingest_druid(delta_coordinate=..., druid_coordinate=...) diff --git a/src/pipelines/real-estate/realestate/resources.py b/src/pipelines/real-estate/realestate/resources.py index 67b65e0..aba215b 100644 --- a/src/pipelines/real-estate/realestate/resources.py +++ b/src/pipelines/real-estate/realestate/resources.py @@ -1,12 +1,10 @@ -from collections.abc import namedtuple +from collections import namedtuple +import dagster as dg import sqlalchemy -from dagster import Field, IntSource, StringSource, resource -# start_resources_marker_0 DbInfo = namedtuple("DbInfo", "engine url jdbc_url dialect load_table host db_name") -# end_resources_0 def create_redshift_db_url(username, password, hostname, port, db_name, jdbc=True): @@ -47,100 +45,82 @@ def create_postgres_engine(db_url): return sqlalchemy.create_engine(db_url) -@resource( - { - "username": Field(StringSource), - "password": Field(StringSource), - "hostname": Field(StringSource), - "port": Field(IntSource, is_required=False, default_value=5439), - "db_name": Field(StringSource), - "s3_temp_dir": Field(str), - } -) -def redshift_db_info_resource(init_context): - host = init_context.resource_config["hostname"] - db_name = init_context.resource_config["db_name"] - - db_url_jdbc = create_redshift_db_url( - username=init_context.resource_config["username"], - password=init_context.resource_config["password"], - hostname=host, - port=init_context.resource_config["port"], - db_name=db_name, - ) - - db_url = create_redshift_db_url( - username=init_context.resource_config["username"], - password=init_context.resource_config["password"], - hostname=host, - port=init_context.resource_config["port"], - db_name=db_name, - jdbc=False, - ) - - s3_temp_dir = init_context.resource_config["s3_temp_dir"] - - def _do_load(data_frame, table_name): - data_frame.write.format("com.databricks.spark.redshift").option( - "tempdir", s3_temp_dir - ).mode("overwrite").jdbc(db_url_jdbc, table_name) - - return DbInfo( - url=db_url, - jdbc_url=db_url_jdbc, - engine=create_redshift_engine(db_url), - dialect="redshift", - load_table=_do_load, - host=host, - db_name=db_name, - ) - - -# start_resources_marker_1 -@resource( - { - "username": Field(StringSource), - "password": Field(StringSource), - "hostname": Field(StringSource), - "port": Field(IntSource, is_required=False, default_value=5432), - "db_name": Field(StringSource), - } -) -def postgres_db_info_resource(init_context): - host = init_context.resource_config["hostname"] - db_name = init_context.resource_config["db_name"] - - db_url_jdbc = create_postgres_db_url( - username=init_context.resource_config["username"], - password=init_context.resource_config["password"], - hostname=host, - port=init_context.resource_config["port"], - db_name=db_name, - ) - - db_url = create_postgres_db_url( - username=init_context.resource_config["username"], - password=init_context.resource_config["password"], - hostname=host, - port=init_context.resource_config["port"], - db_name=db_name, - jdbc=False, - ) - - def _do_load(data_frame, table_name): - data_frame.write.option("driver", "org.postgresql.Driver").mode("overwrite").jdbc( - db_url_jdbc, table_name +class RedshiftResource(dg.ConfigurableResource): + username: str + password: str + hostname: str + port: int = 5439 + db_name: str + s3_temp_dir: str + + def get_db_info(self) -> DbInfo: + db_url_jdbc = create_redshift_db_url( + username=self.username, + password=self.password, + hostname=self.hostname, + port=self.port, + db_name=self.db_name, ) + db_url = create_redshift_db_url( + username=self.username, + password=self.password, + hostname=self.hostname, + port=self.port, + db_name=self.db_name, + jdbc=False, + ) + + def _do_load(data_frame, table_name): + data_frame.write.format("com.databricks.spark.redshift").option( + "tempdir", self.s3_temp_dir + ).mode("overwrite").jdbc(db_url_jdbc, table_name) + + return DbInfo( + url=db_url, + jdbc_url=db_url_jdbc, + engine=create_redshift_engine(db_url), + dialect="redshift", + load_table=_do_load, + host=self.hostname, + db_name=self.db_name, + ) + - return DbInfo( - url=db_url, - jdbc_url=db_url_jdbc, - engine=create_postgres_engine(db_url), - dialect="postgres", - load_table=_do_load, - host=host, - db_name=db_name, - ) +class PostgresResource(dg.ConfigurableResource): + username: str + password: str + hostname: str + port: int = 5432 + db_name: str + + def get_db_info(self) -> DbInfo: + db_url_jdbc = create_postgres_db_url( + username=self.username, + password=self.password, + hostname=self.hostname, + port=self.port, + db_name=self.db_name, + ) + db_url = create_postgres_db_url( + username=self.username, + password=self.password, + hostname=self.hostname, + port=self.port, + db_name=self.db_name, + jdbc=False, + ) + def _do_load(data_frame, table_name): + data_frame.write.option("driver", "org.postgresql.Driver").mode("overwrite").jdbc( + db_url_jdbc, table_name + ) -# end_resources_marker_1 + return DbInfo( + url=db_url, + jdbc_url=db_url_jdbc, + engine=create_postgres_engine(db_url), + dialect="postgres", + load_table=_do_load, + host=self.hostname, + db_name=self.db_name, + ) diff --git a/src/pipelines/real-estate/realestate_tests/pipeline_tests.py b/src/pipelines/real-estate/realestate_tests/pipeline_tests.py index af002f3..b4102a9 100644 --- a/src/pipelines/real-estate/realestate_tests/pipeline_tests.py +++ b/src/pipelines/real-estate/realestate_tests/pipeline_tests.py @@ -1,51 +1,34 @@ # start with pytest pipeline_test.py -from dagster import execute_solid, file_relative_path, SolidExecutionResult -from dagster.experimental import DynamicOutputDefinition, DynamicOutput +import dagster as dg import yaml -from realestate.common.types_realestate import SearchCoordinate, PropertyDataFrame from realestate.pipelines import collect_search_criterias, collect_properties def read_yaml(path): - with open(file_relative_path(__file__, path)) as file: + with open(dg.file_relative_path(__file__, path)) as file: return yaml.load(file, Loader=yaml.FullLoader) def test_solid_collect_properties_input_list(): input = [[111, 222, 333], [55, 666666, 7777], [99, 999, 9999]] - result = execute_solid(collect_properties, input_values={"properties": input}) + context = dg.build_op_context() + result = collect_properties(context, input) - assert result.success - assert len(result.output_value("properties")) == 9 + assert len(result) == 9 def test_collect_search_criterias(): run_conf = read_yaml('../realestate/config_pipelines/scrape_realestate.yaml') - search_criterias = run_conf['solids']['collect_search_criterias']['inputs']['search_criterias'] - - result = execute_solid( - collect_search_criterias, input_values={"search_criterias": search_criterias} - ) - - ## playing around with the output values: - # print(result.compute_output_events_dict["result"]) - - # assert len(result.compute_output_events_dict["result"]) == 3 - - # assert result.output_values == { - # "result": { - # "bern_buy_real_estate_0": { - # 'city': 'Bern', - # 'propertyType': 'real-estate', - # 'radius': 0, - # 'rentOrBuy': 'buy', - # } - # } - # } - assert result.success + # YAML now wraps each item as {value: {...}}, unwrap for direct invocation + search_criterias = [item['value'] for item in run_conf['ops']['collect_search_criterias']['inputs']['search_criterias']] + + context = dg.build_op_context() + results = list(collect_search_criterias(context, search_criterias)) + + assert len(results) > 0 diff --git a/src/pipelines/real-estate/realestate_tests/read_property_json_test.py b/src/pipelines/real-estate/realestate_tests/read_property_json_test.py index c42e27e..a661bd9 100644 --- a/src/pipelines/real-estate/realestate_tests/read_property_json_test.py +++ b/src/pipelines/real-estate/realestate_tests/read_property_json_test.py @@ -3,17 +3,17 @@ import pandas as pd import json -from dagster import build_op_context, Field, String -from realestate.common.solids_spark_delta import flatten_json_with_pandas +import dagster as dg +from realestate.common.solids_spark_delta import flatten_json from realestate.common.solids_scraping import cache_properies_from_rest_api -context_cache = build_op_context( +context_cache = dg.build_op_context( op_config={ "immo24_api_en": "https://rest-api.immoscout24.ch/v4/en/properties/", } ) -context = build_op_context( +context = dg.build_op_context( op_config={ "remove_columns": [ "propertyDetails_images", @@ -25,33 +25,23 @@ ) -def test_op_cache_properies_from_rest_api(): - properties = [4000830789] +# Note: flatten_json now takes a LocalFileHandle, not raw JSON. +# These tests need rework to provide a LocalFileHandle pointing at gzipped JSON. - assert cache_properies_from_rest_api(context_cache, properties, "test").is_file, "File is not created" +# def test_op_cache_properies_from_rest_api(): +# properties = [4000830789] +# assert cache_properies_from_rest_api(context_cache, properties, "test").is_file, "File is not created" +# def test_op_property_json(): +# with open("property.json", "r") as file: +# json_data = json.load(file) +# # flatten_json now requires a LocalFileHandle, not raw JSON +# pass -def test_op_property_json(): - with open("property.json", "r") as file: - json_data = json.load(file) - result_df = flatten_json_with_pandas(context, json_data) - # assert not result_df.empty, "DataFrame is unexpectedly empty" - # assert flatten_json_with_pandas(context, json_data) == pd.DataFrame() - try: - assert not result_df.empty, "DataFrame is unexpectedly empty" - except AssertionError as e: - print(f"DataFrame is unexpectedly empty: {e}") - raise e # Re-raise the assertion error - - assert result_df.equals(pd.DataFrame()), "DataFrame not as expected" - - -def test_op_with_formated(): - with open("property2.json", "r") as file: - json_data = json.load(file) - - assert flatten_json_with_pandas(context, json_data).equals( - pd.DataFrame() - ), "DataFrame not as expected" +# def test_op_with_formated(): +# with open("property2.json", "r") as file: +# json_data = json.load(file) +# # flatten_json now requires a LocalFileHandle, not raw JSON +# pass diff --git a/src/pipelines/real-estate/seaweedfs-s3.json b/src/pipelines/real-estate/seaweedfs-s3.json new file mode 100644 index 0000000..19b0ed6 --- /dev/null +++ b/src/pipelines/real-estate/seaweedfs-s3.json @@ -0,0 +1,14 @@ +{ + "identities": [ + { + "name": "admin", + "credentials": [ + { + "accessKey": "admin", + "secretKey": "admin" + } + ], + "actions": ["Admin", "Read", "Write", "List", "Tagging"] + } + ] +} diff --git a/src/pipelines/real-estate/setup.cfg b/src/pipelines/real-estate/setup.cfg deleted file mode 100644 index c8f7fe0..0000000 --- a/src/pipelines/real-estate/setup.cfg +++ /dev/null @@ -1,3 +0,0 @@ - -[metadata] -name = realestate diff --git a/src/pipelines/real-estate/setup.py b/src/pipelines/real-estate/setup.py deleted file mode 100644 index e6e5c8d..0000000 --- a/src/pipelines/real-estate/setup.py +++ /dev/null @@ -1,33 +0,0 @@ -from setuptools import find_packages, setup - -setup( - name="realestate", - packages=find_packages(exclude=["realestate_tests"]), - install_requires=[ - "dagster==1.6.8", - "dagster-pandas", - "dagstermill", - "notebook", - "dagster-aws", - "dagster-postgres", - "dagster-webserver", - "dagster-deltalake", - "dagster-deltalake-pandas", - "pyarrow", - "pandas", - "boto3", - "pandasql", - "pyyaml", - "numpy", - "seaborn", - "folium", - "ijson", - "koalas", - "scipy", - "matplotlib", - "scikit-learn", - "bs4", - "pandasql", - ], - extras_require={"dev": ["dagster-webserver", "pytest"]}, -) diff --git a/src/pipelines/real-estate/tox.ini b/src/pipelines/real-estate/tox.ini deleted file mode 100644 index d8265ca..0000000 --- a/src/pipelines/real-estate/tox.ini +++ /dev/null @@ -1,29 +0,0 @@ -[tox] -envlist = py{38}-{unix,windows},pylint -; skipsdist = True - -[testenv] -deps = - -e .[full] - -r dev-requirements.txt - -passenv = CI_* COVERALLS_REPO_TOKEN TOXENV SLUGIFY_USES_TEXT_UNIDECODE AWS_* JAVA_HOME POSTGRES_TEST_DB_HOST -whitelist_externals = - /bin/bash - echo -commands = - echo -e "--- \033[0;32m:pytest: Running tox tests\033[0m" - pytest realestate_tests/example.py - ; !windows: /bin/bash -c '! pip list --exclude-editable | grep -e dagster -e dagit' - ; coverage erase - ; ipython kernel install --name "dagster" --user - ; echo -e "--- \033[0;32m:pytest: Running tox tests\033[0m" - ; pytest -vv - ; pytest realestate_tests/ -v --cov=airline_demo --cov-append --cov-report= -s -W ignore:::psycopg2 -m "not slow" -usedevelop = true - -; [testenv:pylint] -; basepython = -; python3.8 -; commands = -; /bin/bash -c 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b/update-prompt.md @@ -0,0 +1,207 @@ + +when you update dagster, can we do it more sophisticated and update to the very latest verstion, and also use the new introduced `import dagster as dg` and `dg....` notation and use components (where make sense: https://docs.dagster.io/api/dagster/components) + +please check extensive change log of dagster what has change: https://raw.githubusercontent.com/dagster-io/dagster/refs/heads/master/CHANGES.md and analyze my repo and update all required dagster changes needed and make it modern for today while the same pipeline is still working (see @README.md ) + +know that i use "dagster==1.5.1", and latest is 1.12.18 (core) / 0.28.18 (libraries). + +also, as we use spark and this is hard to setup locally, can we use a docker-compose or docker file that we run as part of the dagster pipeline? check docs https://docs.dagster.io/integrations/libraries/spark or here: https://docs.dagster.io/integrations/external-pipelines/pyspark-pipeline (find more if needed) - +I think dagster uses Dagster Pipes (https://docs.dagster.io/integrations/external-pipelines) for this? + +load dagster Skills from here: /home/sspaeti/git/open-source/dagster-skills + +check that the quite complex pipeline still works afterwards. + + +--- + +Claude Code Plan after a couple of iterations: + + +# Dagster Migration Plan: 1.5.1 → 1.12.18 + +## Context +The real-estate pipeline uses dagster 1.5.1 with old-style ops/graphs/jobs, `@resource` decorators, private API imports (`dagster._core`, `dagster.core`), and `from dagster import ...` style. Python 3.14 is incompatible with dagster. The goal is to modernize to dagster 1.12.18, adopt `import dagster as dg`, use `Definitions`, `ConfigurableResource`, and clean up ~300 lines of dead PySpark code — while keeping the working pipeline intact. + +**Note on Spark:** The active pipeline does NOT use Spark — it uses pandas + delta-rs. All PySpark code is commented out or inactive but **kept as valuable reference** for future reactivation. Spark deps stay in pyproject.toml for when Spark is needed again. + +**Note on Druid:** Keep ALL Druid-related code (DruidConnector, DruidCoordinate, ingest_druid op). Update it to modern dagster patterns. Goal: reactivate Druid ingestion as a pipeline step after data science, with Docker-based Druid. + +**Note on Components:** The pipeline uses dynamic fan-out (`.map()/.collect()`) which maps naturally to ops/graphs, not components. We keep ops/graphs but modernize everything else. + +--- + +## Phase 1: Delete Only Build Artifacts + +Remove only build/config files superseded by uv: +- `setup.py`, `setup.cfg`, `dev-requirements.txt`, `tox.ini` +- `realestate.egg-info/`, `spark-warehouse/` directories + +**Keep everything else** — all solids/ops files, resources, types, Druid code, Spark code. Even if not actively used, this code is valuable reference and may be reactivated. Update all kept files to modern dagster patterns. + +## Phase 2: Update `pyproject.toml` + +```toml +[project] +name = "real-estate" +version = "0.1.0" +requires-python = ">=3.10,<3.14" +dependencies = [ + "dagster>=1.12,<1.13", + "dagster-pandas>=0.28,<0.29", + "dagstermill>=0.28,<0.29", + "notebook", + "dagster-aws>=0.28,<0.29", + "dagster-postgres>=0.28,<0.29", + "dagster-webserver>=1.12,<1.13", + "dagster-deltalake>=0.28,<0.29", + "dagster-deltalake-pandas>=0.28,<0.29", + "pyarrow", "pandas", "boto3", "pandasql", "pyyaml", + "numpy", "seaborn", "folium", "ijson", "scipy", + "matplotlib", "scikit-learn", "bs4", "jinja2", "requests", + "pyspark", +] +``` +Removed: `koalas` (archived project). Kept: `pyspark`, but removed `dagster-spark`/`dagster-pyspark` (can re-add when Spark is reactivated via Dagster Pipes). +Then: `rm uv.lock && uv lock && uv sync` + +## Phase 3: Rewrite Types + +### `realestate/common/types.py` +- Replace `dict_with_fields` helper, `PythonObjectDagsterType`, `create_string_type`, `dagster_type_loader` with modern equivalents +- **Keep ALL types**: `DruidCoordinate` (rewrite as `dg.DagsterType` with type_check_fn), `DeltaCoordinate`, `S3Coordinate`, `SqlTableName` +- All types become simple `dg.DagsterType` with validation functions + +### `realestate/common/types_realestate.py` +- Remove `from dagster_aws.s3.ops import dict_with_fields` (broken in 1.12) +- Replace `SearchCoordinate = dict_with_fields(...)` with simple `dg.DagsterType` that checks for required dict keys +- Remove unused `SearchCoordinateClass`/`SearchCoordinateType` +- Keep `PropertyDataFrame`, `JsonType` as `dg.DagsterType` + +**Risk:** Removing `dagster_type_loader` from `SearchCoordinate` may break YAML input hydration. Fallback: keep deprecated `dagster_type_loader` if simple DagsterType doesn't work. + +## Phase 4: Modernize Resources + +### `realestate/common/resources.py` +- Replace `@resource` + `Boto3Connector` with `Boto3Resource(dg.ConfigurableResource)` +- **Keep `DruidConnector`** — convert to `DruidResource(dg.ConfigurableResource)` with `druid_router` field, `get_session()` and `get_router_url()` methods +- Remove old `@resource` decorators, use `ConfigurableResource` for both + +### `realestate/common/__init__.py` +- Remove broken imports: `from dagster._config import config_schema`, `from pandas.io.pytables import config` +- Replace `fs_io_manager` with `dg.FilesystemIOManager()` +- Configure `Boto3Resource` directly in Python (not via YAML) +- Add `DruidResource` to resource_def (commented out or with sensible defaults for Docker Druid) +- Keep `LocalFileManager` from private API (no public replacement exists) + +## Phase 5: Update Ops + +### `realestate/common/solids_scraping.py` +- `import dagster as dg`, replace all decorators/types to `dg.*` +- Remove `required_resource_keys={"fs_io_manager"}` (handled by `io_manager_key`) +- Remove unused `_get_normalized_price` op + +### `realestate/common/solids_spark_delta.py` +- Keep ALL ops (active and inactive/commented): `flatten_json`, `merge_property_delta`, `get_changed_or_new_properties`, `s3_to_df`, `do_prefix_column_names`, `canonicalize_column_names`, `replace_values_spark`, `_get_s3a_path` +- Update active ops to `import dagster as dg` pattern +- Update commented-out Spark ops to modern dagster syntax where practical +- Keep commented code as reference for future Spark reactivation + +### `realestate/common/solids_druid.py` (UPDATE, not delete) +- Switch to `import dagster as dg` +- Fix `context.solid_config` → `context.op_config` (renamed in dagster 1.x) +- Remove `required_resource_keys={'pyspark', ...}` — Druid ingestion doesn't need pyspark; keep `{'s3', 'druid'}` +- Add missing `from dagster import file_relative_path` (used but not imported) +- Update to `@dg.op` pattern + +### `realestate/common/solids_jupyter.py` +- `dm.factory.define_dagstermill_op` → `dm.define_dagstermill_op` +- Switch to `import dagster as dg` +- Change output type from `FileHandle` to `dg.Nothing` (notebook doesn't yield FileHandle) + +### `realestate/common/helper_functions.py` +- Replace `from dagster import Tuple` with `from typing import Tuple` +- Remove `rename_spark_dataframe_columns` (only Spark code used it) + +## Phase 6: Update Pipeline & Definitions + +### `realestate/pipelines.py` +- Switch to `import dagster as dg` +- Remove unused imports: `asset`, `SourceAsset`, `TableSchema`, `AssetOut`, `s3_to_df` +- Remove `from dagster._utils import dagster_type` (private, unused) +- Remove `resource_defs=resource_def["local"]` from `@dg.job` (resources now in Definitions) +- Remove `required_resource_keys={"fs_io_manager"}` from `collect_search_criterias` +- **Add `ingest_druid` as optional step** after `data_exploration` (can be commented out initially, wired up when Druid Docker is ready) + +### `realestate/__init__.py` +```python +import dagster as dg +from .pipelines import scrape_realestate +from .common import resource_def + +defs = dg.Definitions( + jobs=[scrape_realestate], + resources=resource_def["local"], +) +``` + +## Phase 7: Update Config YAML + +### `config_environments/local_base.yaml` +- Remove `resources.boto3.config` section (now configured in Python) +- Remove file from `config_from_files` in pipelines.py if empty + +### `config_pipelines/scrape_realestate.yaml` +- No changes needed (already uses `ops:` key) + +## Phase 8: Add Docker Compose for Druid + +Add Druid services to `docker-compose.yml` based on https://druid.apache.org/docs/latest/tutorials/docker/. This includes: +- Zookeeper, PostgreSQL (metadata store), Druid Coordinator, Broker, Historical, MiddleManager, Router +- Expose Router on port 8888 for API access +- Configure S3 deep storage to point at MinIO/SeaweedFS + +This is a best-effort addition — the user will fine-tune it later. + +## Phase 9: Update Tests + +### `realestate_tests/pipeline_tests.py` +- Replace `execute_solid` with direct op invocation + `dg.build_op_context()` +- Fix `run_conf['solids']` → `run_conf['ops']` + +### `realestate_tests/read_property_json_test.py` +- Fix `flatten_json_with_pandas` → `flatten_json` +- Note: tests need rework since `flatten_json` takes `LocalFileHandle` not raw JSON + +## Phase 10: Verify + +1. `uv sync --all-extras` +2. `uv run python -c "from realestate import defs; print(defs)"` +3. `uv run dagster dev` — verify job appears in UI with correct graph +4. `uv run pytest realestate_tests/ -v` +5. Execute pipeline run with Twann search criteria + +## Files Modified (in order) + +1. `pyproject.toml` +2. `realestate/common/types.py` — keep all types including DruidCoordinate +3. `realestate/common/types_realestate.py` +4. `realestate/common/resources.py` — keep DruidResource + Boto3Resource +5. `realestate/common/__init__.py` +6. `realestate/common/helper_functions.py` +7. `realestate/common/solids_scraping.py` +8. `realestate/common/solids_spark_delta.py` — keep all Spark code, update active ops +9. `realestate/common/solids_druid.py` — update to modern dagster +10. `realestate/common/solids_jupyter.py` +11. `realestate/common/solids.py` — update to `import dagster as dg` +12. `realestate/common/solids_notebook.py` — update to modern dagstermill +13. `realestate/common/solids_filehandle.py` — update imports +14. `realestate/common/resource_delta_lake.py` — fix broken imports +15. `realestate/pipelines.py` — add druid step (commented initially) +16. `realestate/__init__.py` +17. `realestate/resources.py` — update if kept +18. `config_environments/local_base.yaml` +19. `docker-compose.yml` — add Druid services +20. `realestate_tests/pipeline_tests.py` +21. `realestate_tests/read_property_json_test.py` +22. `Makefile` (already done), `README.md` (already done) From 1f3816585f931b7dc038ef0dc8f61599ab50e257 Mon Sep 17 00:00:00 2001 From: sspaeti Date: Tue, 10 Mar 2026 20:06:28 +0100 Subject: [PATCH 3/3] update html parsing --- src/pipelines/real-estate/Makefile | 4 +- .../realestate/common/solids_scraping.py | 39 ++++++++++++++++++- 2 files changed, 39 insertions(+), 4 deletions(-) diff --git a/src/pipelines/real-estate/Makefile b/src/pipelines/real-estate/Makefile index f12eb2d..6004a81 100644 --- a/src/pipelines/real-estate/Makefile +++ b/src/pipelines/real-estate/Makefile @@ -1,6 +1,6 @@ -.DEFAULT_GOAL := up +.DEFAULT_GOAL := run -up: ## Start dagster dev server +run: ## Start dagster dev server uv run dagster dev install: ## Install all dependencies diff --git a/src/pipelines/real-estate/realestate/common/solids_scraping.py b/src/pipelines/real-estate/realestate/common/solids_scraping.py index 2232ff9..80479fb 100644 --- a/src/pipelines/real-estate/realestate/common/solids_scraping.py +++ b/src/pipelines/real-estate/realestate/common/solids_scraping.py @@ -16,6 +16,31 @@ from .types_realestate import PropertyDataFrame, SearchCoordinate, JsonType +# NOTE: This scraper is for EDUCATIONAL PURPOSE ONLY — use at your own risk. +# immoscout24.ch uses bot protection (DataDome) that blocks automated HTTP requests. +# Plain `requests` may get a 403 challenge page instead of real results. +# If you get 0 properties, your requests are likely being blocked. +_session = requests.Session() +_session.headers.update({ + "User-Agent": "Mozilla/5.0 (X11; Linux x86_64; rv:128.0) Gecko/20100101 Firefox/128.0", + "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", + "Accept-Language": "en-US,en;q=0.5", +}) + + +def _is_cloudflare_blocked(soup: BeautifulSoup) -> bool: + """Detect if the response is a Cloudflare/DataDome challenge page.""" + title = soup.find("title") + if title and "captcha" in title.get_text().lower(): + return True + if soup.find("script", src=re.compile(r"captcha-delivery\.com")): + return True + # Cloudflare challenge pages have very little content + if soup.find("p", id="cmsg"): + return True + return False + + def _extract_listings_from_json_ld(soup: BeautifulSoup, rent_or_buy: str) -> list[dict]: """Extract property IDs and prices from JSON-LD structured data in the page. Returns list of dicts with 'id' and 'price' keys.""" @@ -87,9 +112,19 @@ def list_props_immo24(context, searchCriteria: SearchCoordinate) -> PropertyData ) context.log.info(f"Search url: {url}") - response = requests.get(url) + response = _session.get(url) + context.log.info(f"Response status: {response.status_code}, content length: {len(response.text)}") soup = BeautifulSoup(response.text, "html.parser") + if _is_cloudflare_blocked(soup): + title = soup.find("title") + context.log.warning( + f"Cloudflare/bot protection detected! Page title: '{title.get_text() if title else 'N/A'}'. " + f"immoscout24.ch blocks automated requests. Try running from a network without bot protection, " + f"or check if your IP is being rate-limited." + ) + return [] + # Extract property IDs from links ids = _extract_property_ids_from_links(soup, rent_or_buy) context.log.info(f"Found {len(ids)} property IDs from links: {ids}") @@ -133,7 +168,7 @@ def list_props_immo24(context, searchCriteria: SearchCoordinate) -> PropertyData for page in range(2, last_page + 1): page_url = url + f"&pn={page}" context.log.debug(f"Fetching page {page}: {page_url}") - page_response = requests.get(page_url) + page_response = _session.get(page_url) page_soup = BeautifulSoup(page_response.text, "html.parser") page_ids = _extract_property_ids_from_links(page_soup, rent_or_buy)