finding projects using a particular plugin

TheMLEngineer

Is there a quick way to find the projects using a particular plugin?

Turribeach

Navigate to the plugin page and select the Usages tab. Then click on Fetch Usages.

TheMLEngineer

Thank you. It works!!

Turribeach

This notebook code is even better, it will run the check for all plugins and output a dataframe which you can then export to CSV. It may take some time to run depending on the number of plugins installed and the size of your install:

from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

import dataiku
import pandas as pd, numpy as np
client = dataiku.api_client()

plugins_list = client.list_plugins()
filter_plugins_list = []

df_plugin_usage = pd.DataFrame(columns=['plugin_id', 'plugin_name', 'plugin_author', 'plugin_version', 'plugin_license', 'plugin_code_env', 'element_kind', 'element_type', 'object_type', 'object_id'])

for plugin in plugins_list:
    plugin_id = plugin['id']
    plugin_author = plugin['meta']['author']
    plugin_license = plugin['meta']['licenseInfo']
    plugin_version = plugin['version']
     
    if filter_plugins_list:
        if plugin_id not in filter_plugins_list:
            continue
    plugin_handle = client.get_plugin(plugin_id)
    plugin_settings = plugin_handle.get_settings().get_raw()
    plugin_name = plugin['meta']['label']
    plugin_code_env = plugin_settings.get('codeEnvName')
    plugin_usages = plugin_handle.list_usages(project_key=None).get_raw()
    for usages in plugin_usages['usages']:
        
        element_kind = usages['elementKind']
        element_type = usages['elementType']
        object_type = usages['objectType']
        project_key = usages['projectKey']
        object_id = usages['objectId']
    
        plugin_usage_record =  pd.DataFrame.from_dict({'plugin_id': [plugin_id], 'plugin_name': [plugin_name], 'plugin_code_env': [plugin_code_env], 'plugin_author': [plugin_author], 'plugin_version': [plugin_version], 'plugin_license': [plugin_license], 'project_key': [project_key], 'element_kind': [element_kind], 'element_type': [element_type], 'object_type': [object_type], 'object_id': [object_id]})
        df_plugin_usage = pd.concat([df_plugin_usage, plugin_usage_record], ignore_index=True)
    print(f'{plugin_id} - {plugin_name} - {plugin_code_env}')

df_plugin_usage.sort_values(by=['plugin_name', 'project_key', 'element_kind', 'element_type', 'object_type', 'object_id'], inplace=True)

import base64

def create_download_link( df, title = "Download CSV file", filename = "plugin_usage.csv"):
    csv = df.to_csv()
    b64 = base64.b64encode(csv.encode())
    payload = b64.decode()
    html = '<a download="{filename}" href="data&colon;text/csv;base64,{payload}" target="_blank">{title}</a>'
    html = html.format(payload=payload,title=title,filename=filename)
    return HTML(html)

create_download_link(df_plugin_usage)