• Team Members
• Setup
  • 1. Git clone the repository
  • 2. Setup python environment
    • 2.a. Create a venv environment
    • 2.b. Source onto environment
    • 2.c. Install requirements.txt file using pip
  • 3. Add search API data to data folder
  • 3. Setup the SQL and NoSQL databases
  • 4. Setup the Front End
  • 5. Setup the periodic ttl based cacher
• Other information
  • | 5601 | Kibana for ELK |

Team Members

Team 14

Name	Net ID
Advaith Rao	asr209
Ayush Oturkar	ao586
Falgun Malhotra	fm466
Vanshita Gupta	vg422

Setup

1. Git clone the repository

git clone https://github.com/advaithsrao/694Team14Dbms2023.git

2. Setup python environment

NOTE: A quick way to implement this would be with the venv package. You can also setup the environment with conda using the command conda create -n <env_name> python=3.8 | conda activate <env_name>. After this follow step 2.c

2.a. Create a venv environment

python3 -m venv <env_name>

2.b. Source onto environment

source <env_name>/bin/activate

2.c. Install requirements.txt file using pip

pip3 install -r requirements.txt --no-cache-dir

3. Add search API data to data folder

mkdir ./data
#copy data to this folder

3. Setup the SQL and NoSQL databases

sh scripts/setup/main.sh

4. Setup the Front End

python3 UI/ui.py > /outputs/uiLogs.out & 

Once you run this, you should see the search app @ localhost:8000

5. Setup the periodic ttl based cacher

python3 scripts/staleCacheChecker.py > /outputs/cacherLogs.out & 

Other information

Port	Application
8000	Search App
5544	PostgreSQL
9200	Elastic Search (ELK)
5601	Kibana for ELK

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