70 lines
2.8 KiB
Markdown
70 lines
2.8 KiB
Markdown
## batch processing with python-client
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There are some client libraries for OpenRefine that communicate with the [OpenRefine API](https://github.com/OpenRefine/OpenRefine/wiki/OpenRefine-API). I have prepared a docker container on top of the [Python Library from PaulMakepeace](https://github.com/PaulMakepeace/refine-client-py/) and extended the CLI with some options to create new OpenRefine projects from files.
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### basic usage
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1) start server:
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> docker run -d --name=openrefine-server felixlohmeier/openrefine
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2) start client (prints help screen):
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> docker run --rm --link openrefine-server felixlohmeier/openrefine-client
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3) cleanup:
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> docker stop openrefine-server && docker rm openrefine-server
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### example for customized run commands in interactive mode (e.g. for usage in terminals)
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1) start server in terminal A:
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```docker run --rm --name=openrefine-server -p 80:3333 -v /home/felix/refine:/data:z felixlohmeier/openrefine -i 0.0.0.0 -m 4G -d /data```
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* automatically remove docker container when it exits
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* set name "openrefine" for docker container
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* publish internal port 3333 to host port 80
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* mount host directory /home/felix/refine as working directory
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* make openrefine available in the network
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* increase java heap size to 4 GB
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* set refine workspace to /data
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* OpenRefine should be available at http://localhost
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2) start client in terminal B (prints help screen):
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```docker run --rm --link openrefine-server -v /home/felix/refine:/data:z felixlohmeier/openrefine-client```
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* automatically remove docker container when it exits
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* build up network connection with docker container "openrefine"
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* mount host directory /home/felix/refine as working directory
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* apply history in file /home/felix/refine/history.json to project with id 1234567890123
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### example for customized run commands in detached mode (e.g. for usage in shell scripts)
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1) define variables (bring your own example data)
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> workingdir=/home/felix/refine
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> inputfile=example.csv
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> jsonfile=test.json
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2) start server
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```docker run -d --name=openrefine-server -v ${workingdir}:/data:z felixlohmeier/openrefine -i 0.0.0.0 -m 4G -d /data```
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3) create project (import file)
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```docker run --rm --link openrefine-server -v ${workingdir}:/data:z felixlohmeier/openrefine-client -c $inputfile```
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4) get project id
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```project=($(docker run --rm --link openrefine-server -v ${workingdir}:/data felixlohmeier/openrefine-client --list | cut -c 2-14))```
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5) apply transformations from json file
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```docker run --rm --link openrefine-server -v ${workingdir}:/data felixlohmeier/openrefine-client -f ${jsonfile} ${project}```
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6) export project to file
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```docker run --rm --link openrefine-server -v ${workingdir}:/data felixlohmeier/openrefine-client -E --output=${project}.tsv ${project}```
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7) cleanup
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```docker stop -t=500 openrefine-server && docker rm openrefine-server```
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