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README.MD

About the Project

A global charity search engine.

  • All data from official open data sources.
  • Scripts to fully automate processing source files.

Test it out at: http://charitysearch.juliuszelf.nl

Background info on the concept at: http://juliushuijnk.nl/project/charitius/

Built With

  • Scripts to process source files (Python3, Bash, Make)
  • Docker
  • Flask
  • Elastic Search

Getting Started

Prerequisites

  • Docker installed
  • Python 3.6 installed
  • Unzip, Git

Installation

Install Python libraries

The scripts that manipulate the CSV files, use python libraries.

  • You can install the required python libraries globally with python3.6 -m pip install -r requirements.txt.

Create data to search

  • Type make in the root folder. It will download source files, process to .json file, and split it into parts that are uploadable to ES.

Create docker-compose.override.yml file

During development

During development we want to make use of a Kibana container, but in production we don't want to make that available, since it costs RAM memory to run, but is not used. Therefore the production docker-compose file is different from production (live) version.

  • We extend the docker-compose.yml file with the docker-compose.override.yml. During development let the override yml file hold the content of docker-compose.dev.yml by creating a symlink: ln -s docker-compose.dev.yml docker-compose.override.yml

For production (live)

  • Make symlink connecting the prod file: ln -s docker-compose.prod.yml docker-compose.override.yml

Start containers

  • If you want to track with google analytics, add environmental variable via export ANALYTICS=UA-XXXXXXX-X, where you replace the UA part with your google analytics id.
  • Type docker-compose up -d, it will take a minute or two for Elastic Search to fully start.
  • Verify container are running with docker ps, it should show es01 and flask. During development also kib01.

Upload charity data

  • Once ES is fully started, assuming it's still empty, do bash scripts/upload.sh to upload file(s) to elastic search.
  • Verify the expected number of records ('documents') using: curl -X GET "localhost:9200/_cat/count/chars?pretty"
  • Verify the expected structure of data using: curl -X GET "localhost:9200/chars/?pretty"

Usage

Play around with search

Go to http://localhost:5601 for Kibana interface where you can search using the console.

More info on how to do search queries in the Kibana console:

https://www.elastic.co/guide/en/kibana/7.4/console-kibana.html

Search page

For development the website is available via http://localhost:5000, it contains the search bar.

For production (live) the website is on port 80 (default port).

Replace data & update code

On live server you might need to re-upload the data to ES, since the mapping of the fields has changed. In this case you can:

  • Remove all data with: curl -X DELETE "localhost:9200/chars?pretty"
  • You might als need to recreate all json file with: rm data/*/*json*
  • (And perhaps remove even more files)
  • Use make to create new json files to upload if required.
  • Upload to ES via bash scripts/upload-all.sh

After you did a git pull or otherwise updated the Flask code, you might need to do docker restart flask01 in order to let the changes go into effect.

If you made changes to for instance required python libraries you need to rebuild your docker image. First remove and stop the flask01 container via docker stop flask01 and docker rm flask01. Then find correct image via docker images, and remove that image via docker image rm <name image>. Then you can do docker-compose up -d again, and it will build the new image and start containers.

Debugging with Flask outside container

To (more easily) set breakpoints and debug Flask, you can run it outside the docker container on your local developer computer.

Install the dependencies, if you do this globally type python3 -m pip install -r requirements.txt inside the flask-app folder.

Then you start it with python3 app.py outside from the flask-app folder. The 'outside' part makes flask look for Elastic Search via 0.0.0.0 and not the address from inside the docker network.

Roadmap

1.0 A working search engine [ DONE ]

Most basic working search engine.

  • Name, City, Country, Website, sourceURL, sourceDate.

Beyond 1.0

Iterate:

  • Datasets from more countries.
  • Improved scripts to upload to ES replacing old files.
  • Improved ways to search, for instance with categories.

I use the todo.taskpaper file to keep track of my TODO's.

You may read the todo.taskpaper file, but it's probably (mostly) in Dutch and could be outdated. This README.md file should make sense and be up te date.

Contributing

If you'd like to help, great! You can do so by:

  • Suggesting code or feature improvements.
  • Suggest good open data sources.

There is no fixed process yet. If you have suggestions, leave an issue over here, or contact me directly.

License

Distributed under the GNU GPLv3 license. In short: Use it freely, share improvements with me, no warranties.

See LICENSE.md for more information.

Easy to comprehend page on license:

https://choosealicense.com/licenses/gpl-3.0/

Contact

Julius Huijnk | @Juliu on Twitter | http://www.juliushuijnk.nl

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