• Text Based Music Search Engine implemented using Elasticsearch.
  
• This project is developed as a part of the coursework CS185C-Advanced Topics in CS (Big Data) under the supervision of Prof. James Casaletto at San Jose State University.
• The team members are Akhilesh Jichkar and Siddharth Kulkarni.

Elasticsearch is a RESTful powerful indexing and search tool, which is a part of the Elastic Stack.
Elasticsearch is horizontally scalable, which is ideal for music data, whose size may go up to a few GBs.
Elasticsearch is also highly available, has flexible data model, and boasts rapid query execution.
Since the goal is to develop a Music Search Engine, Elasticsearch qualfies as a fast and reliable searching tool.

An Elasticsearch cluster can be installed on all major platforms. In order to ensure availability, this project would be hosted on Elastic cloud.

• Create a new account on Elastic cloud.
  
• Create a New Cluster and keep the default settings intact on the following page. Let the cluster name be Cluster1ES.
  
• Now view the cluster just created. Save the CloudID, username and password for the newly created cluster. You'll need this later.
  
• Wait for the 'Node Status' to change to 'Running'. Now click on the Endpoints -> Elasticsearch to see the properties of the cluster.
  
• The cluster is up and running now.
  

The next step involves creating indices on this Elasticsearch cluster. But before that, we need to take a look into the data we would be using for the project. This is because the Elasticsearch indices depend on the data type of the incoming data.

The data used for this project is from Song Lyrics Dataset on Kaggle. It contains 57650 songs in CSV format. The attributes are Artist Name, Song Name and the Lyrics of the song.

To be ingested by Elasticsearch, the data needs to be converted from CSV to JSON. Since the data is around 70MBs, the online converters won't be able to do the conversion. Therfore, a python script csvtojson.py is written to perform the conversion. The output is LyrJson.zip

PS: Unzip zip file first to get the csv file.
PS: There is an extra comma in Line 2 of the generated JSON file, and it needs to be removed manually.

Optionally, you may want to include images of artist in your search results, artistimages.py shows a rudimentary way to obtain artist images from Wikipedia pages using BeautifulSoup, a Python Web Scraping tool.

The data preprocessing is now complete.

Indices can be thought of as tables in Elasticsearch.
First we need to provide the structure of the table, to do so, first change the username, password and elastic-endpoint cluster address in indices-creation.py and execute it.

There would be compatibilty issues between Python versions. Try executing on Python 2.7.10 or Python 3. Else, Stackoverflow.

Now that the tables are created, we need to feed the data in these indices. Again, change the username, password, and elastic-endpoint cluster address in data-ingestion.py and execute it.

Waiting time: 45 minutes. You could watch an episode of Silicon Valley or you could hit the gym. Your choice!

The data is in the cluster, all properly indexed. Now just launch the web server and you're ready!

Go to root directory of front end folder and run web server.
PHP: php -S localhost:8000
Open browser at http://localhost:8000