Search for AGOL Dependencies given an ItemID or layer URL

dependency-check.py

# This will look for any items that reference a layer or service.
# Given an ItemID, it will find the service URL (or you can specify the URL directly)
# The script will look through every map and app in your org to see if the URL is referenced
# Matching items will be printed out at the end

from arcgis.gis import GIS
import pandas as pd

gis = GIS("home") # or your portal URL; assuming this is run in an AGOL Notebook

# Enter the ItemID of the thing you're interested in.
# Leave the first input blank to search by the service URL instead. Useful if you're looking for a specific sub-layer of the service.
find_id = input('ItemID: ')
find_url = gis.content.get(find_id).url if find_id else input('Service URL: ')
print(find_url)

# Find Maps
webmaps = gis.content.search('', item_type='Web Map', max_items=-1, outside_org=False)
map_list = [m for m in webmaps if str(m.get_data()).find(find_url) > -1]

# Find Apps
apptypes = ['Application', 'Dashboard', 'Story Map', 'Web Experience']
webapps = [item for sublist in [gis.content.search('', item_type=t, max_items=-1, outside_org=False) for t in apptypes] for item in sublist]
app_list = []

for w in webapps:
    try:
        wdata = str(w.get_data())

        criteria = [
            wdata.find(find_url) > -1,
            wdata.find(find_id) > -1,
            any([wdata.find(m.id) > -1 for m in map_list])
        ]
        
        if any(criteria):
            app_list.append(w)
    
    # Some apps don't have data, so we'll just skip them if they throw a TypeError
    except:
        continue
        
# Merge lists into single dataframe
dependencies = pd.concat(
    [
        pd.DataFrame([{'title':a.title, 'id':a.id, 'type':a.type, 'url':f'{gis.url}/home/item.html?id={a.id}', 'data':a.get_data()} for a in app_list]),
        pd.DataFrame([{'title':m.title, 'id':m.id, 'type':m.type, 'url':f'{gis.url}/home/item.html?id={m.id}', 'data':m.get_data()} for m in map_list])
    ]
)

dependencies.reset_index(inplace=True, drop=True)

# Output results; if you're running this in an interactive setting, you could just view the dataframe or look at individual rows
# There are commas in the output, so we use a custom separator here
dependencies.to_csv('dependencies.csv', sep='|', index=False)