# instagram-statistics
instagram-statistics is a flow lets us scrape artists data and metrics using ```instaloader``` library.
The flow provides us with the next metrics:
* if the artist has *VERIFIED* label
* current **followers** count,  and it's growth compared to the previous week 
* **posts** count,  and it's growth compared to the previous week
* current **likes** count,  and it's growth compared to the previous week
* current **comments** count,  and it's growth compared to the previous week=stats.get('percentage_change_in_views'),
* **engagement** as *likes and comments per followers* metric 

For scrapping the data ```instaloader``` uses Instagram account. For creating the instance ```instaloader``` INSTAGRAM_NICKNAME and INSTAGRAM_PASSWORD variables written down at ```.env``` file.
```instaloader``` is a side library, so you need to install it separately.
3d party libraries are listed at ```Pipfile``` which lock is using at ```Dockerfile``` while installing it.

###Technical features

It logs in to Instagram account under declared credentials. Locally it uses already existing Cookies. Remotely, e.g. from Jenkins, it requires ```Cookies``` file at the repo. In case it's absent the flow will throw an exception.
Here, Cookies reading is implemented with file requires decoding. The process is implemented at ```import_session(cookies_file, session)``` method.

Also, for getting artists' Instagram username ```ATISTS_TO_TRACK``` table at SnowFlake we use Google Sheets. The same way, to get data from Sheets, ```download_artists_links_from_google_sheets(sheet_id)``` method is implemented. It accesses Google Sheets API with appropriate application with its keys.
For accessing spreadsheets there's client secret json. Its creation is implemented at ```download_artists_links_from_google_sheets()```.
Comments and likes are the total amount scrapped for every single post at the artist's account.

After all the data is scrapped, it is put at a dictionary with artist's username as a key.
And then, using the name, it is loaded to SnowFlake table.

As the initial table we could get the links from is *ARTISTS_TO_TRACK*, having all the links to Spotify, YouTube and SoundCloud also.

#Running the flow

To run the flow the repository contains ```Dockerfile```.
There we have OS installation (Centos8), location set up. 
Then we have *ARG* and *ENV* declarations. To provide them while building the ```Dockerfile``` **--build-arg {var_name}={var_value}** notation for is required.
Required environment variables* are:
* SNOWFLAKE_ROLE
* SNOWFLAKE_WAREHOUSE
* SNOWFLAKE_PASSWORD
* SNOWFLAKE_USER
* SNOWFLAKE_ACCOUNT
* SNOWFLAKE_DATABASE
* SNOWFLAKE_SCHEMA
* INSTAGRAM_PASSWORD
* INSTAGRAM_NICKNAME
* CHROME_SAFE_STORAGE

**CHROME_SAFE_STORAGE** is needed for decoding the Cookies file. It's individual for every computer, the same as Session file stored at Chrome folder.

To build the Docker container the following command from the repository directory:
```docker build -f Dockerfile --build-arg SNOWFLAKE_ROLE=${SNOWFLAKE_ROLE} --build-arg SNOWFLAKE_WAREHOUSE=${SNOWFLAKE_WAREHOUSE} --build-arg SNOWFLAKE_PASSWORD=${SNOWFLAKE_PASSWORD} --build-arg SNOWFLAKE_USER=${SNOWFLAKE_USER} --build-arg SNOWFLAKE_ACCOUNT=${SNOWFLAKE_ACCOUNT} --build-arg SNOWFLAKE_DATABASE=${SNOWFLAKE_DATABASE} --build-arg SNOWFLAKE_SCHEMA=${SNOWFLAKE_SCHEMA} --build-arg INSTAGRAM_NICKNAME=${INSTAGRAM_NICKNAME} --build-arg INSTAGRAM_PASSWORD=${INSTAGRAM_PASSWORD} --build-arg CHROME_SAFE_STORAGE=${CHROME_SAFE_STORAGE} -t myimage .```
*${VAR_NAME}* need to be replaced with *required environment variables.
After this the container starts to build, and the last step written down is ```RUN python3 /instagram-statistics/instagram_with_instaloader.py``` that starts the flow.