Administration and installation

Note

At the moment, iscan-server is only supported on Ubuntu.

Warning

Running analyses via script currently do not work with the docker installation. If you plan on writing/running automated scripts, rather than interacting with the data via the web interface, use the non-Dockerized version.

Much of this documentation is meant to be more technical and is geared towards advanced users interested in setting up their own ISCAN servers, either on a desktop computer for local use or on a dedicated server for communal use. Please see the ISCAN Tutorials section for more information and a walk-through on how to use ISCAN once it has been set up. Please see the troubleshooting section below for a continuously-updated set of solutions to common installation or maintenence issues.

Contents:

• Installation via Docker
  • Prerequisites
  • Installation
  • Development and Production modes
• Use & workflow via Docker
  • Starting and stopping the server
  • Mounted volumes
  • Cleaning
• Installation without using Docker
  • Prerequisites
  • Installation
• Server administration
  • Updating ISCAN and PolyglotDB
  • Getting a tutorial corpus
  • Adding new corpora
  • Creating and Modifying Users
  • Enable running of SPADE scripts
  • Reporting errors and issues
• Apache server configuration
  • Enabling prerequisite Apache modules
  • HTTP server config
  • HTTPS server config
• Troubleshooting common issues
  • Connection refused
  • Neo4j PID error on stopping

Moving parts

The ISCAN server uses several components, which are all managed by Docker (see Installation via Docker and Use & workflow via Docker for more information). The components are:

• A web interface, with which the user can manage and analyze corpora, written in Angular
• A Python API, PolyglotDB, which communicates between the web interface and the back-end web framework, using Django
• A relational database, which manages metadata about Polyglot databases, using PostgreSQL
• Message and job queues for working with asynchronous tasks, using RabbitMQ and Celery