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Using Flask + React + ElasticSearch + MySQL to build a real-time searching website with user system.

This repo is to build a website for searching in ElasticSearch. We have a user-friendly UI design and real-time connections to ElasticSearch. The website has user login and register module. Based on various users, we would offer individual recommendations. We have two datasets: ETD dataset and Tobacco dataset.

1. Basically, you should have :

   python@3.6+
   mysql
   elasticsearch@7.3+

2. Git clone this repo into your computer. Add ./config folder and ./config/settings.yaml file to store configs. The settings.yaml is like:

   default:
     mysql:
       host: localhost
       user: root
       password: password
       database: database
     
     baseuri: http://0.0.0.0:3000
     elasticsearch: http://localhost:9200/

3. Installing python packages:

   pip install -r requirements.txt

   Sometimes, the python package dynaconf need further installing supported format. Otherwise, you may not connect to your MySQL. Try this:

   pip install dynaconf[all]

4. Make sure you have Elasticsearch and MySQL running on your computer.

5. Modify your ES config file: elasticsearch.yml . Normally, the path should be /usr/local/etc/elasticsearch/elasticsearch.yml. Add the following settings in this file.

   http.cors.enabled: true
   http.cors.allow-credentials: true
   http.cors.allow-origin: '/https?:\/\/0.0.0.0(:[0-9]+)?/'
   http.cors.allow-headers: X-Requested-With, X-Auth-Token, Content-Type, Content-Length, Authorization, Access-Control-Allow-Headers, Accept%

6. Running website on localhost:

   python app.py

Then, you could open 0.0.0.0:3000 to explore it.

Demo json datasets are listed in directory: /data. Basically, you could use Kibana to import those demo dataset into Elasticsearch and then you could search them on the website. Make sure the indexes you create for two demo data are etd_metadata and tobacco.

This repo contains Dockerfile already.

1. Build images and update imgaes

   docker build --rm -f "Dockerfile" -t [your_dockerhub_username]/fek_test:0.0.0 .

2. Connect to your DockerHub

   docker login

3. Push your images

   docker push bournexu/fek_test:0.0.0

Commands listed below only need to be executed during the first time you deploy your images. Afterwards, the modification in your image will be implemented in your service autometically.

Before you deploy containers on the Kubernetes cluster. Make sure you have already setup the Kubernetes config so that you could access the cluster remotely. You should update ~/.kube/config . Then you can check if you could connect to the cluster.

kubectl get services -n cs5604-minimal
# you will be able to see this
NAME      			TYPE     			CLUSTER-IP      EXTERNAL-IP       PORT(S)          AGE
fek-web-test    ClusterIP     10.1.1.1      	<none>            3000/TCP         15h

Then, let's start to deploy your services.

1. Deploy your images on the Kubernetes cluster. Pull the image from the repository and create a container on the cluster.

   kubectl run my-fek-app --image=[your_dockerhub_username]/fek_test:0.0.0 --port=3000 -n cs5604-minimal

   -n denotes the namespace where you want to deploy your containers. Make sure you have the permission to access that namespace. This application is hosted on port 3000 so I’m opening port 3000 when I run the container, if your application doesn’t require a port just remove the port parameter.

2. Expose the Kubernetes Deployment through a Load Balancer.

   kubectl expose deployment my-fek-app --type=LoadBalancer --port=3000 --target-port=3000 --name=my-fek-app-demo -n cs5604-minimal 

3. Find the external IP of your containers. You could see it in the kubernetes dashbord. Also, you could get it from the terminal.

   kubectl get svc
   # you will see
   NAME     		TYPE           CLUSTER-IP      EXTERNAL-IP     
   my-fek-app  LoadBalancer   10.11.452.237   56.170.30.123

.
├── README.md
├── app.py
├── git_tutorial.md
├── reactivesearch
│   ├── README.md
│   ├── app_test.py
│   ├── package-lock.json
│   ├── package.json
│   ├── public
│   ├── src
│   ├── static
│   └── templates
├── requirements.txt
├── routes
│   ├── __init__.py
│   ├── dbconnect.py
│   ├── login.py
│   └── search.py
├── static
│   ├── __init__.py
│   ├── build
│   ├── css
│   ├── fonts
│   ├── images
│   ├── js
│   ├── prepros-6.config
│   └── scss
└── templates
    ├── __init__.py
    ├── _formhelpers.html
    ├── home.html
    ├── index.html
    ├── login.html
    ├── register.html
    ├── scholarly.jpg
    └── search.html