Open a new terminal with the new Laminar
directory.
/data/Laminar>
Clone the server application repository.
/data/Laminar> git clone https://github.com/StreamingFlow/dispel4py-server.git
Enter the server directory.
/data/Laminar> cd dispel4py-server
To manage permission issues, run:
/data/Laminar/dispel4py-server> chmod +x gradlew
To configure the required fields, navigate to /src/main/resources/application.properties
and fill in the required variables
To run the server
/data/Laminar/dispel4py-server> ./gradlew bootRun
The server will now be running, and ready to receive requests. You should see this sample output
/data/Laminar/dispel4py-server>
...
com.dispel4py.rest.RestApplication: Started RestApplication in 66.905 seconds (JVM running for 69.911)
...
To run the application in a docker container follow these intstructions
Clone the server application repository.
/data/Laminar> git clone https://github.com/Laminar-2/dispel4py-server.git
Enter the server directory.
/data/Laminar> cd dispel4py-server
Run docker compose to load up the server and database. The first time we recommend to use --build flag.
docker compose up --build
Next time you could use:
docker compose up
If you need to rebuild the Docker containers (for instance, after making configuration changes), you can do so by following these steps:
First, bring down the running containers:
docker-compose down
Then, rebuild and start the containers:
docker-compose up --build
By following these steps, you can ensure that the server is properly configured and running efficiently.
You can also prune the data from the docker, if you need to:
docker system prune -a
We recommend to do this step after docker-compose down
and before docker-compose up --build
. But be carreful, this will delete the full registry database information.
The laminar client offers a user-friendly interface for registering and managing Processing Elements (PEs) as well as stream-based dispel4py workflows. For detailed guidance on how to interact with the system, please refer to the user manual available on the project's wiki.
In addition, the laminar execution-engine is a critical component that must be installed, either locally or remotely, to facilitate the serverless execution of workflows.