Merging develop into master

.gitignore

@@ -1,3 +1,5 @@
+.env
+dataplatform/.env
 /dataplatform/tests/HDFD_VENV_3.7
 /dataplatform/tests/HDFD_VENV_3.7
 /dataplatform/multiple_stacks/runtime/
@@ -639,3 +641,5 @@ fabric.propertyDataList
 /python-utils/src/main/python/gmail/credentials.json
 /python-utils/src/main/python/gmail/token.pickle
 *.png
+
+*.airflow.env

airflow_dags/README.md

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+# Airflow
+[Installazione](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html)
+
+## Docker registry
+- stack `dataplatform/multiple_stacks/registry.yaml`
+- contact locally in the cluster at 127.0.0.0:5000
+- Starting from a Docker file in a cluster machine
+  - `docker image build --tag 127.0.0.0:5000/IMAGE_NAME:VERSION -f PATH_DOCKERFILE .`
+  - `docker push 127.0.0.0:5000/IMAGE_NAME:VERSION`
+- From any other cluster machine
+  - `docker pull 127.0.0.0:5000/IMAGE_NAME:VERSION`
+
+- Clean data in the registry (enter in the container)
+  - `registry garbage-collect -m /etc/docker/registry/config.yml`
+
+## Airflow
+Start example:
+- In the directory of dags that is `:${NFSPATH}/dataplatform_config/airflow_data/dags`
+  - create a directory for each project and put the file for generate the DAG (in a subdirectory)
+  - example of a files are in __abds-bigdata__ project `\cimice\src\main\resources` and `\ingestion-weather\src\main\resources`
+- [DAG](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html): A DAG (Directed Acyclic Graph) is the core concept of Airflow, collecting Tasks together, organized with dependencies and relationships to say how they should run.
+  - [scheduling options](https://airflow.apache.org/docs/apache-airflow/1.10.1/scheduler.html)
+- Our dags are always of one task, that is a [Docker Operator](https://airflow.apache.org/docs/apache-airflow-providers-docker/1.0.2/_api/airflow/providers/docker/operators/docker/index.html)
+  - have a name has to be made of alphanumeric characters, dashes, dots and underscores exclusively
+- In particular, we use a specialization that is the [Docker swarm operator](https://airflow.apache.org/docs/apache-airflow-providers-docker/stable/_api/airflow/providers/docker/operators/docker_swarm/index.html#airflow.providers.docker.operators.docker_swarm.DockerSwarmOperator),
+  that can be useful for put constraints in where spawn docker containers.
+### DockerSwarmOperator Airflow (some things)
+- last line returned by the docker container is in XComs
+  - If want logs all the things on the standard output: `xcom_all=True`
+- constraints in cpus and memory usage
+- **auto_remove**=True, the docker rm
+- **mounts**=[] Use volumes "source", "target", "type", "read_only"
+- **command**="Command to be run in the container", overwrites the cmd, add a space at the end to tell that is not a template
+- **mount_tmp_dir**=False, not mount a temporary directory
+- **container_name**=similar to task name 
+- **placement**
+- **network_mode** and **networks** use BIG-dataplatform-network
+- For extra things refer to the official documentation
+
+### Trigger a dag from python application
+This is made in **abds-bigdata** project `ingestion-weather` module, 
+through the `python-service-interaction-utils/src/main/python/airflow_interaction.py` service.
+
+### Common errors in the deploy
+- not pass files that are in .gitignore in the build of the container
+- use service names and internal ports when refer to other services (not use exposed ports)
+- set the link to services in the config to the new clusters (e.g., hdfs) 
+- Errors in dag import: enter inside airflow-scheduler container and launch `airflow scheduler`
+
+### Possible updates
+- configure an smpt server, for [send mails on failure](https://stackoverflow.com/questions/58736009/email-on-failure-retry-with-airflow-in-docker-container)