def burp_import(path):
requests = {}
content = reader(path)
matches = re.finditer(burp_regex, content)
for match in matches:
request = parse_request(match.group(4))
url = match.group(1)
requests[url] = request['data']
return requests
def list(self, request):
query, limit, user_agent, ip_address = parse_request(request)
save_query_with_metadata.delay(query, user_agent, ip_address)
queryset = self.get_queryset(query)
tailored_skills = sort_skills(queryset)[:limit]
data = OrderedDict({
"vacancy_name": query,
"number_of_vacancies": len(queryset),
"rated_skills": tailored_skills,
})
return Response(data)
def get_queryset(self):
query, limit, user_agent, ip_address = parse_request(self.request)
save_query_with_metadata.delay(query, user_agent, ip_address)
# SearchVector is currently disabled as it does not work properly on AWS RDS due to lack of
# pg_catalog.english support from the migrations file. Retaled discussions:
# https://stackoverflow.com/q/40032685/10748367
# https://forums.aws.amazon.com/thread.jspa?threadID=143920
# queryset = Vacancy.objects.filter(search_vector=query)
queryset = Vacancy.objects.filter(title__search=query)
tailored_skills = sort_skills(queryset)[:LIMIT_OF_SKILLS]
skills_dict = {
"vacancy_name": query,
"number_of_vacancies": len(queryset),
"rated_skills": tailored_skills,
}
return skills_dict
def burp_import(path):
requests = []
content = reader(path)
matches = re.finditer(burp_regex, content)
for match in matches:
request = parse_request(match.group(4))
headers = request['headers']
if match.group(7) in ('HTML', 'JSON'):
requests.append({
'url': match.group(1),
'method': match.group(2),
'extension': match.group(3),
'headers': headers,
'include': request['data'],
'code': match.group(5),
'length': match.group(6),
'mime': match.group(7)
})
return requests
def request_import(path):
parsed = parse_request(reader(path))
return {parsed['url']: [parsed['data']]}
def request_import(path):
return parse_request(reader(path))