Split large SharpHound datasets (JSON files) into smaller files that can more easily be imported into BloodHound. Especially useful due to the Electron memory limitations.

bh_split2.py

#!/usr/bin/python3
# Based on https://gist.github.com/deltronzero/7c23bacf97b4b61c7a2f2950ef6f35d8
# pip install simplejson
import simplejson
import sys

def splitfile(file_name, object_limit):
    print(f"[*] Loading {file_name}")
    with open(file_name) as f:
        data = simplejson.load(f)
        total_objects = data['meta']['count']
        file_type = data['meta']['type']
        print(f"Total Objects: {total_objects}")
        object_count = 0
        file_count = 0

        while object_count < total_objects:
            a = {}
            a[file_type] = data[file_type][object_count:][:object_limit]
            object_count += len(a[file_type])

            a['meta'] = data['meta']
            a['meta']['count'] = object_count

            f_split = file_name.split("\\")[-1].split(".")
            file_name_out = f"{f_split[0]}_{file_count}.{f_split[1]}"

            print(f"[*] Writing {file_name_out} - {object_count} of {total_objects}")
            f_out = open(file_name_out, "w")
            simplejson.dump(a, f_out)
            f_out.close()
            file_count += 1

def main():
    if len(sys.argv) < 2:
        print(sys.argv[0] + " filename.json [object_limit]")
        return

    if len(sys.argv) < 3:
        splitfile(sys.argv[1], 20000)
    else:
        splitfile(sys.argv[1], int(sys.argv[2]))

if __name__ == "__main__":
    main()