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# Hastic server
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Implementation of basic pattern recognition and unsupervised learning for anomaly detection.
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Implementation of analytic unit for Hastic.
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see [REST API](REST.md)
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## Build & run
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Server needs Grafana's API key (http://<your_grafana_url>/org/apikeys) to query data from Grafana datasources.
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API key role can be any of:
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- Viewer
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- Editor
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- Admin
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### Docker installation
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Example of running hastic-server in Docker:
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```
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docker build -t hastic-server .
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docker run -d --name hastic-server -p 80:8000 -e HASTIC_API_KEY=<your_grafana_api_key> hastic-server
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```
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### Linux installation
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#### Environment variables
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You can export following environment variables for hastic-server to use:
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- HASTIC_API_KEY - (required) API-key of your Grafana instance
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- HASTIC_PORT - (optional) port you want to run server on, default: 8000
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#### Dependencies
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- python3 with:
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- pip
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- pandas
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- seglearn
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- scipy
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- tsfresh
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- nodejs >= 9
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Example of running hastic-server on Debian / Ubuntu host:
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```
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$ git clone https://github.com/hastic/hastic-server.git
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$ export HASTIC_API_KEY=<your_grafana_api_key>
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$ export HASTIC_PORT=<port_you_want_to_run_server_on>
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# apt-get install python3 \
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python3-pip \
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gnupg \
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curl \
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make \
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g++ \
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git
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$ pip3 install pandas
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$ pip3 install seglearn
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$ pip3 install scipy
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$ pip3 install tsfresh
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$ curl -sL https://deb.nodesource.com/setup_9.x | bash -
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# apt-get update && apt-get install -y nodejs
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$ cd hastic-server/server
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$ npm install && npm run build
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$ npm start
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```
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