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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 and BloodHound CE (running in browser) 1GB file size limitation.
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| #!/usr/bin/python3 | |
| # Based on https://gist.github.com/deltronzero/7c23bacf97b4b61c7a2f2950ef6f35d8 | |
| # updated to current BloodHound JSON format (compatible with SharpHound 2.x and BloodHound CE) | |
| # 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'] | |
| file_type = 'data' | |
| 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() |
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