cnrudd / week12-day03-topmusic

Sample Output

How to Use/Run Instructions

1. Clone or fork & clone repo.

2. run npm i

3. Make database (see sql/schema.sql)

4. Load data from data dir into the new database. See sql/import-data.sql or use MySQLWorkbench GUI.

5. This project uses dotenv to read DB config info from a .env file. To run index.js on your machine, you need to add a .env file to your clone of this repo. It should contain the following keys with values appropriate to your setup:

   • DB_HOST=
   • DB_USER=
   • DB_PASS=
   • DB_NAME=

6. Run app from command line with node index from within the project directory.

Development Instructions

Step 1: Create DB, Import Data

• Create a database called music_rankings_db which will eventually house all of the music data contained within TopSongs.csv

• Within your database create a table called songs and create columns capable of holding all of the data contained within TopSongs.csv properly.

• The columns are:

  • id (the primary key - does not auto increment, already in data) id
  • artist name: artist
  • song name: song
  • year: year
  • raw popularity score, entire world: raw_total
  • raw popularity score, USA: raw_usa
  • raw popularity score, UK: raw_uk
  • raw popularity score, Europe (i.e., non-English speaking countries in Europe): raw_eur
  • raw popularity score, rest of the world: raw_row

• All columns can be NULL, except id

• All of your code should be written and saved within a filed called schema.sql so that you can use this same code later should the need ever arise

• Look into how MySQL Workbench can import and export data files. What file types does it accept? How does it convert the data?

• Import the data from TopSongs.csv into your top5000 table. Google how to do this. it can be done via a SQL query, or via the MySQL Workbench GUI.

Step 2: Code a node app to query the DB.

• With all of your data successfully imported into the database, begin working on a Node console application which will allow a user to query for more specialized pieces of data. For example...

  • A query which returns all data for songs sung by a specific artist
  • A query which returns all artists who appear within the top 5000 more than once
  • A query which returns all data contained within the top 10 in the world
    • Bonus: add more options here to find top 10 by region
  • A query which searches for a specific song in the top 5000 and returns the data for it

• HINT: There are some MySQL queries which could make some of these tasks even easier to accomplish. Feel free to look at MySQL's documentation to find some of them.

Step 3: Time permitting, return search results that include album info

• Modify your schema so that it also contains an albums table with the following columns:

• id (the primary key - does not auto increment, already in data) id

• artist name: artist

• album name: album

• year: year

• raw popularity score, entire world: raw_total

• raw popularity score, USA: raw_usa

• raw popularity score, UK: raw_uk

• raw popularity score, Europe (i.e., non-English speaking countries in Europe): raw_eur

• raw popularity score, rest of the world: raw_row

• All columns can be NULL, except id

• Import the TopAlbums.csv file into the albums table.

• Modify your JS code so that it can return album information along with song information for the queryies above. You can join the tables on matching artist values.