def retry_handler(job, exc_type, exc_value, traceback):
job.meta.setdefault('failures', 0)
job.meta['failures'] += 1
# Too many failures
if job.meta['failures'] >= MAX_FAILURES:
logger.warn('job %s: failed too many times times - moving to failed queue' % job.id)
job.save()
return True
# Requeue job and stop it from being moved into the failed queue
logger.warn('job %s: failed %d times - retrying' % (job.id, job.meta['failures']))
for queue in queues:
if queue.name == job.origin:
queue.enqueue_job(job, timeout=job.timeout)
return False
# Can't find queue, which should basically never happen as we only work jobs that match the given queue names and
# queues are transient in rq.
logger.warn('job %s: cannot find queue %s - moving to failed queue' % (job.id, job.origin))
return True
def get_optimum_time(orig_in, dest_in, min_leave_in, max_leave_in, min_dest_in,
max_dest_in, granularity_in, traffic_model_in, tz_in,
orig_to_dest_only_in):
class DirectionTimeOptimizer:
def __init__(self,
origin,
destination,
min_time_to_leave,
max_time_to_leave,
min_time_at_dest,
max_time_at_dest,
orig_to_dest_only,
search_granularity_mins=15,
traffic_model="best_guess"):
self.origin = origin
self.orig_to_dest_only = orig_to_dest_only
self.destination = destination
self.min_time_to_leave = self.round_closest_mins(min_time_to_leave)
self.max_time_to_leave = self.round_closest_mins(max_time_to_leave)
self.search_granularity_mins = int(search_granularity_mins)
self.time_to_leave_segments = int(
(((self.max_time_to_leave - self.min_time_to_leave
).total_seconds() / 60) / self.search_granularity_mins)) + 1
if not orig_to_dest_only:
self.min_time_at_dest = self.round_closest(min_time_at_dest)
self.max_time_at_dest = self.round_closest(max_time_at_dest)
self.time_at_dest_segments = int(
(max_time_at_dest - min_time_at_dest) /
search_granularity_mins) + 1
self.traffic_model = traffic_model
self.origin_leave_time_lookup = {}
def round_closest(self, value, quantization=15):
return int(quantization * round(value / float(quantization)))
def round_closest_mins(self, time_value, quantization=15):
return (time_value +
timedelta(minutes=self.round_closest(time_value.minute) -
time_value.minute))
def get_duration_from_result(self, result):
if (not len(result) > 0 or 'legs' not in result[0]
or not len(result[0]['legs']) > 0
or 'duration_in_traffic' not in result[0]['legs'][0]):
return None
else:
return result[0]['legs'][0]['duration_in_traffic'][
'value'] / 60.0
def calculate_possible_times_to_leave(self):
min_set = [
self.min_time_to_leave +
timedelta(minutes=i * self.search_granularity_mins)
for i in xrange(0, self.time_to_leave_segments)
]
for time_to_leave in min_set:
gmaps_result = gmaps.directions(
self.origin,
self.destination,
mode="driving",
traffic_model=self.traffic_model,
departure_time=time_to_leave)
result = self.get_duration_from_result(gmaps_result)
if result is not None:
self.origin_leave_time_lookup[time_to_leave] = {
'orig_to_dest_time': result,
'summary_name': gmaps_result[0]['summary']
}
if not self.origin_leave_time_lookup == {}:
return True
else:
return False
def calculate_possible_times_to_return(self):
if self.origin_leave_time_lookup == {}:
return False
for leave_time in self.origin_leave_time_lookup.keys():
time_set = [
leave_time +
timedelta(minutes=(i * self.search_granularity_mins) +
self.round_closest(
int(self.origin_leave_time_lookup[leave_time]
['orig_to_dest_time'])) +
self.min_time_at_dest)
for i in xrange(0, self.time_at_dest_segments)
]
for dest_leave_time in time_set:
gmaps_result = gmaps.directions(
self.destination,
self.origin,
mode="driving",
traffic_model=self.traffic_model,
departure_time=dest_leave_time)
result = self.get_duration_from_result(gmaps_result)
if 'dest_return_time_lookup' not in self.origin_leave_time_lookup[
leave_time] and result is not None:
self.origin_leave_time_lookup[leave_time][
'dest_return_time_lookup'] = {}
self.origin_leave_time_lookup[leave_time][
'dest_return_time_lookup'][dest_leave_time] = {
'dest_to_orig_time': result,
'summary_name': gmaps_result[0]['summary']
}
elif 'dest_return_time_lookup' in self.origin_leave_time_lookup[
leave_time] and result is not None:
self.origin_leave_time_lookup[leave_time][
'dest_return_time_lookup'][dest_leave_time] = {
'dest_to_orig_time': result,
'summary_name': gmaps_result[0]['summary']
}
return True
def determine_optimum_times_both_dirs(self):
min_combination = {
'orig_to_dest': None,
'dest_to_orig': None,
'orig_to_dest_summary': None,
'dest_to_orig_summary': None,
'status': 'Success'
}
min_seen = 10000000
for timestamp, item in self.origin_leave_time_lookup.iteritems():
for sub_ts, sub_item in item[
'dest_return_time_lookup'].iteritems():
if (item['orig_to_dest_time'] +
sub_item['dest_to_orig_time']) < min_seen:
min_seen = item['orig_to_dest_time'] + sub_item[
'dest_to_orig_time']
min_combination['orig_to_dest'] = timestamp
min_combination['dest_to_orig'] = sub_ts
min_combination['orig_to_dest_time'] = item[
'orig_to_dest_time']
min_combination['dest_to_orig_time'] = sub_item[
'dest_to_orig_time']
min_combination['orig_to_dest_summary'] = item[
'summary_name']
min_combination['dest_to_orig_summary'] = sub_item[
'summary_name']
return min_combination
def determine_optimum_times_one_dir(self):
min_combination = {
'orig_to_dest': None,
'orig_to_dest_summary': None,
'status': 'Success'
}
min_seen = 10000000
for timestamp, item in self.origin_leave_time_lookup.iteritems():
if item['orig_to_dest_time'] < min_seen:
min_seen = item['orig_to_dest_time']
min_combination['orig_to_dest'] = timestamp
min_combination['orig_to_dest_time'] = item[
'orig_to_dest_time']
min_combination['orig_to_dest_summary'] = item[
'summary_name']
return min_combination
job = get_current_job()
job.meta['status'] = 'create'
job.save()
my_optimizer = DirectionTimeOptimizer(orig_in, dest_in, min_leave_in,
max_leave_in, min_dest_in,
max_dest_in, orig_to_dest_only_in,
granularity_in, traffic_model_in)
job.meta['status'] = 'created'
job.save()
try:
my_optimizer.calculate_possible_times_to_leave()
job.meta['status'] = 'times_to_leave_done'
job.save()
if not orig_to_dest_only_in:
my_optimizer.calculate_possible_times_to_return()
job.meta['status'] = 'times_to_return_done'
job.save()
except googlemaps.exceptions.ApiError as e:
if 'departure_time is in the past' in str(e):
job.meta['status'] = 'api_error_departure_in_the_past'
job.save()
return None
else:
job.meta['status'] = 'api_error_unknown'
job.save()
return None
if not orig_to_dest_only_in:
result = my_optimizer.determine_optimum_times_both_dirs()
else:
result = my_optimizer.determine_optimum_times_one_dir()
job.meta['status'] = 'complete'
job.save()
result['requested'] = ({
'orig_to_dest_only': orig_to_dest_only_in,
'orig_in': orig_in,
'dest_in': dest_in,
'min_leave_in': min_leave_in,
'max_leave_in': max_leave_in,
'min_dest_in': min_dest_in,
'max_dest_in': max_dest_in,
'granularity_in': granularity_in,
'traffic_model_in': traffic_model_in,
'tz_in': tz_in
} if not orig_to_dest_only_in else {
'orig_to_dest_only': orig_to_dest_only_in,
'orig_in': orig_in,
'dest_in': dest_in,
'min_leave_in': min_leave_in,
'max_leave_in': max_leave_in,
'granularity_in': granularity_in,
'traffic_model_in': traffic_model_in,
'tz_in': tz_in
})
return result
def add_meta(job, *exc_info):
job.meta = {'foo': 1}
job.save()
return True
def add_meta(job, *exc_info):
job.meta = {'foo': 1}
job.save()
return True