Improve resilience.

Antoine Goutenoir
1 parent d5474086
Showing 1 changed file with 275 additions and 268 deletions Show diff stats
flaskr/controllers/main_controller.py
@@ -240,318 +240,325 @@ def compute():  # process the queue of estimation requests
         _estimation.warnings = _warning_message
         db.session.commit()
-    response = ""
+    try:
+        response = ""
-    count_working = Estimation.query \
-        .filter_by(status=StatusEnum.working) \
-        .count()
+        count_working = Estimation.query \
+            .filter_by(status=StatusEnum.working) \
+            .count()
-    if 0 < count_working:
-        return _respond("Already working on estimation.")
+        if 0 < count_working:
+            return _respond("Already working on estimation.")
-    try:
-        estimation = Estimation.query \
-            .filter_by(status=StatusEnum.pending) \
-            .order_by(Estimation.id.asc()) \
-            .first()
-    except sqlalchemy.orm.exc.NoResultFound:
-        return _respond("No estimation in the queue.")
-    except Exception as e:
-        return _respond("Database error: %s" % (e,))
+        try:
+            estimation = Estimation.query \
+                .filter_by(status=StatusEnum.pending) \
+                .order_by(Estimation.id.asc()) \
+                .first()
+        except sqlalchemy.orm.exc.NoResultFound:
+            return _respond("No estimation in the queue.")
+        except Exception as e:
+            return _respond("Database error: %s" % (e,))
+
+        if not estimation:
+            return _respond("No estimation in the queue.")
+
+        estimation.status = StatusEnum.working
+        db.session.commit()
-    if not estimation:
-        return _respond("No estimation in the queue.")
+        response += u"Processing estimation `%s`...\n" % (
+            estimation.public_id
+        )
-    estimation.status = StatusEnum.working
-    db.session.commit()
+        failed_addresses = []
+        geocoder = CachedGeocoder()
-    response += u"Processing estimation `%s`...\n" % (
-        estimation.public_id
-    )
+        # GEOCODE ORIGINS #########################################################
-    failed_addresses = []
-    geocoder = CachedGeocoder()
+        origins_addresses = estimation.origin_addresses.strip().split("\n")
+        origins = []
-    # GEOCODE ORIGINS #########################################################
+        for i in range(len(origins_addresses)):
-    origins_addresses = estimation.origin_addresses.strip().split("\n")
-    origins = []
+            origin_address = origins_addresses[i].strip()
+            if origin_address in failed_addresses:
+                continue
-    for i in range(len(origins_addresses)):
+            try:
+                origin = geocoder.geocode(origin_address.encode('utf-8'))
+            except geopy.exc.GeopyError as e:
+                response += u"Failed to geocode origin `%s`.\n%s\n" % (
+                    origin_address, e,
+                )
+                _handle_warning(estimation, response)
+                failed_addresses.append(origin_address)
+                continue
-        origin_address = origins_addresses[i].strip()
-        if origin_address in failed_addresses:
-            continue
+            if origin is None:
+                response += u"Failed to geocode origin `%s`.\n" % (
+                    origin_address,
+                )
+                _handle_warning(estimation, response)
+                failed_addresses.append(origin_address)
+                continue
-        try:
-            origin = geocoder.geocode(origin_address.encode('utf-8'))
-        except geopy.exc.GeopyError as e:
-            response += u"Failed to geocode origin `%s`.\n%s\n" % (
-                origin_address, e,
-            )
-            _handle_warning(estimation, response)
-            failed_addresses.append(origin_address)
-            continue
+            origins.append(origin)
-        if origin is None:
-            response += u"Failed to geocode origin `%s`.\n" % (
-                origin_address,
+            response += u"Origin: %s == %s (%f, %f)\n" % (
+                origin_address, origin.address,
+                origin.latitude, origin.longitude,
             )
-            _handle_warning(estimation, response)
-            failed_addresses.append(origin_address)
-            continue
-        origins.append(origin)
+        # GEOCODE DESTINATIONS ####################################################
-        response += u"Origin: %s == %s (%f, %f)\n" % (
-            origin_address, origin.address,
-            origin.latitude, origin.longitude,
-        )
+        destinations_addresses = estimation.destination_addresses.strip().split("\n")
+        destinations = []
-    # GEOCODE DESTINATIONS ####################################################
+        for i in range(len(destinations_addresses)):
-    destinations_addresses = estimation.destination_addresses.strip().split("\n")
-    destinations = []
+            destination_address = destinations_addresses[i].strip()
+            if destination_address in failed_addresses:
+                continue
-    for i in range(len(destinations_addresses)):
+            try:
+                destination = geocoder.geocode(destination_address.encode('utf-8'))
+            except geopy.exc.GeopyError as e:
+                response += u"Failed to geocode destination `%s`.\n%s\n" % (
+                    destination_address, e,
+                )
+                _handle_warning(estimation, response)
+                failed_addresses.append(destination_address)
+                continue
-        destination_address = destinations_addresses[i].strip()
-        if destination_address in failed_addresses:
-            continue
+            if destination is None:
+                response += u"Failed to geocode destination `%s`.\n" % (
+                    destination_address,
+                )
+                _handle_warning(estimation, response)
+                failed_addresses.append(destination_address)
+                continue
-        try:
-            destination = geocoder.geocode(destination_address.encode('utf-8'))
-        except geopy.exc.GeopyError as e:
-            response += u"Failed to geocode destination `%s`.\n%s\n" % (
-                destination_address, e,
-            )
-            _handle_warning(estimation, response)
-            failed_addresses.append(destination_address)
-            continue
+            # print(repr(destination.raw))
-        if destination is None:
-            response += u"Failed to geocode destination `%s`.\n" % (
-                destination_address,
+            destinations.append(destination)
+
+            response += u"Destination: %s == %s (%f, %f)\n" % (
+                destination_address, destination.address,
+                destination.latitude, destination.longitude,
             )
-            _handle_warning(estimation, response)
-            failed_addresses.append(destination_address)
-            continue
-        # print(repr(destination.raw))
+        # GTFO IF NO ORIGINS OR NO DESTINATIONS ###################################
-        destinations.append(destination)
+        if 0 == len(origins):
+            response += u"Failed to geocode all the origin(s).\n"
+            _handle_failure(estimation, response)
+            return _respond(response)
+        if 0 == len(destinations):
+            response += u"Failed to geocode all the destination(s).\n"
+            _handle_failure(estimation, response)
+            return _respond(response)
-        response += u"Destination: %s == %s (%f, %f)\n" % (
-            destination_address, destination.address,
-            destination.latitude, destination.longitude,
-        )
+        # GRAB AND CONFIGURE THE EMISSION MODELS ##################################
-    # GTFO IF NO ORIGINS OR NO DESTINATIONS ###################################
+        emission_models = estimation.get_models()
+        # print(emission_models)
-    if 0 == len(origins):
-        response += u"Failed to geocode all the origin(s).\n"
-        _handle_failure(estimation, response)
-        return _respond(response)
-    if 0 == len(destinations):
-        response += u"Failed to geocode all the destination(s).\n"
-        _handle_failure(estimation, response)
-        return _respond(response)
-
-    # GRAB AND CONFIGURE THE EMISSION MODELS ##################################
-
-    emission_models = estimation.get_models()
-    # print(emission_models)
-
-    extra_config = {
-        'use_train_below_distance': estimation.use_train_below_km,
-        # 'use_train_below_distance': 300,
-    }
-
-    # PREPARE RESULT DICTIONARY THAT WILL BE STORED ###########################
-
-    results = {}
-
-    # UTILITY PRIVATE FUNCTION(S) #############################################
-
-    def get_city_key(_location):
-        # Will this hack hold?  Suspense...
-        return _location.address.split(',')[0]
-
-        # _city_key = _location.address
-        # # if 'address100' in _location.raw['address']:
-        # #     _city_key = _location.raw['address']['address100']
-        # if 'city' in _location.raw['address']:
-        #     _city_key = _location.raw['address']['city']
-        # elif 'state' in _location.raw['address']:
-        #     _city_key = _location.raw['address']['state']
-        # return _city_key
-
-    def compute_one_to_many(
-            _origin,
-            _destinations,
-            _extra_config=None
-    ):
-        _results = {}
-        footprints = {}
-
-        destinations_by_city_key = {}
-
-        cities_sum_foot = {}
-        cities_sum_dist = {}
-        cities_dict_first_model = None
-        for model in emission_models:
-            cities_dict = {}
-            for _destination in _destinations:
-                footprint = model.compute_travel_footprint(
-                    origin_latitude=_origin.latitude,
-                    origin_longitude=_origin.longitude,
-                    destination_latitude=_destination.latitude,
-                    destination_longitude=_destination.longitude,
-                    extra_config=_extra_config,
-                )
+        extra_config = {
+            'use_train_below_distance': estimation.use_train_below_km,
+            # 'use_train_below_distance': 300,
+        }
-                _key = get_city_key(_destination)
-
-                destinations_by_city_key[_key] = _destination
-
-                if _key not in cities_dict:
-                    cities_dict[_key] = {
-                        'city': _key,
-                        'address': _destination.address,
-                        'footprint': 0.0,
-                        'distance': 0.0,
-                        'train_trips': 0,
-                        'plane_trips': 0,
-                    }
-                cities_dict[_key]['footprint'] += footprint['co2eq_kg']
-                cities_dict[_key]['distance'] += footprint['distance_km']
-                cities_dict[_key]['train_trips'] += footprint['train_trips']
-                cities_dict[_key]['plane_trips'] += footprint['plane_trips']
-                if _key not in cities_sum_foot:
-                    cities_sum_foot[_key] = 0.0
-                cities_sum_foot[_key] += footprint['co2eq_kg']
-                if _key not in cities_sum_dist:
-                    cities_sum_dist[_key] = 0.0
-                cities_sum_dist[_key] += footprint['distance_km']
-
-            cities = sorted(cities_dict.values(), key=lambda c: c['footprint'])
-
-            footprints[model.slug] = {
-                'cities': cities,
-            }
+        # PREPARE RESULT DICTIONARY THAT WILL BE STORED ###########################
+
+        results = {}
+
+        # UTILITY PRIVATE FUNCTION(S) #############################################
+
+        def get_city_key(_location):
+            # Will this hack hold?  Suspense...
+            return _location.address.split(',')[0]
+
+            # _city_key = _location.address
+            # # if 'address100' in _location.raw['address']:
+            # #     _city_key = _location.raw['address']['address100']
+            # if 'city' in _location.raw['address']:
+            #     _city_key = _location.raw['address']['city']
+            # elif 'state' in _location.raw['address']:
+            #     _city_key = _location.raw['address']['state']
+            # return _city_key
+
+        def compute_one_to_many(
+                _origin,
+                _destinations,
+                _extra_config=None
+        ):
+            _results = {}
+            footprints = {}
+
+            destinations_by_city_key = {}
+
+            cities_sum_foot = {}
+            cities_sum_dist = {}
+            cities_dict_first_model = None
+            for model in emission_models:
+                cities_dict = {}
+                for _destination in _destinations:
+                    footprint = model.compute_travel_footprint(
+                        origin_latitude=_origin.latitude,
+                        origin_longitude=_origin.longitude,
+                        destination_latitude=_destination.latitude,
+                        destination_longitude=_destination.longitude,
+                        extra_config=_extra_config,
+                    )
-            if cities_dict_first_model is None:
-                cities_dict_first_model = deepcopy(cities_dict)
-
-        _results['footprints'] = footprints
-
-        total_foot = 0.0
-        total_dist = 0.0
-        total_train_trips = 0
-        total_plane_trips = 0
-
-        cities_mean_dict = {}
-        for city in cities_sum_foot.keys():
-            city_mean_foot = 1.0 * cities_sum_foot[city] / len(emission_models)
-            city_mean_dist = 1.0 * cities_sum_dist[city] / len(emission_models)
-            city_train_trips = cities_dict_first_model[city]['train_trips']
-            city_plane_trips = cities_dict_first_model[city]['plane_trips']
-            cities_mean_dict[city] = {
-                'address': destinations_by_city_key[city].address,
-                'city': city,
-                'footprint': city_mean_foot,
-                'distance': city_mean_dist,
-                'train_trips': city_train_trips,
-                'plane_trips': city_plane_trips,
+                    _key = get_city_key(_destination)
+
+                    destinations_by_city_key[_key] = _destination
+
+                    if _key not in cities_dict:
+                        cities_dict[_key] = {
+                            'city': _key,
+                            'address': _destination.address,
+                            'footprint': 0.0,
+                            'distance': 0.0,
+                            'train_trips': 0,
+                            'plane_trips': 0,
+                        }
+                    cities_dict[_key]['footprint'] += footprint['co2eq_kg']
+                    cities_dict[_key]['distance'] += footprint['distance_km']
+                    cities_dict[_key]['train_trips'] += footprint['train_trips']
+                    cities_dict[_key]['plane_trips'] += footprint['plane_trips']
+                    if _key not in cities_sum_foot:
+                        cities_sum_foot[_key] = 0.0
+                    cities_sum_foot[_key] += footprint['co2eq_kg']
+                    if _key not in cities_sum_dist:
+                        cities_sum_dist[_key] = 0.0
+                    cities_sum_dist[_key] += footprint['distance_km']
+
+                cities = sorted(cities_dict.values(), key=lambda c: c['footprint'])
+
+                footprints[model.slug] = {
+                    'cities': cities,
+                }
+
+                if cities_dict_first_model is None:
+                    cities_dict_first_model = deepcopy(cities_dict)
+
+            _results['footprints'] = footprints
+
+            total_foot = 0.0
+            total_dist = 0.0
+            total_train_trips = 0
+            total_plane_trips = 0
+
+            cities_mean_dict = {}
+            for city in cities_sum_foot.keys():
+                city_mean_foot = 1.0 * cities_sum_foot[city] / len(emission_models)
+                city_mean_dist = 1.0 * cities_sum_dist[city] / len(emission_models)
+                city_train_trips = cities_dict_first_model[city]['train_trips']
+                city_plane_trips = cities_dict_first_model[city]['plane_trips']
+                cities_mean_dict[city] = {
+                    'address': destinations_by_city_key[city].address,
+                    'city': city,
+                    'footprint': city_mean_foot,
+                    'distance': city_mean_dist,
+                    'train_trips': city_train_trips,
+                    'plane_trips': city_plane_trips,
+                }
+                total_foot += city_mean_foot
+                total_dist += city_mean_dist
+                total_train_trips += city_train_trips
+                total_plane_trips += city_plane_trips
+
+            cities_mean = [cities_mean_dict[k] for k in cities_mean_dict.keys()]
+            cities_mean = sorted(cities_mean, key=lambda c: c['footprint'])
+
+            _results['mean_footprint'] = {  # DEPRECATED?
+                'cities': cities_mean
             }
-            total_foot += city_mean_foot
-            total_dist += city_mean_dist
-            total_train_trips += city_train_trips
-            total_plane_trips += city_plane_trips
+            _results['cities'] = cities_mean
-        cities_mean = [cities_mean_dict[k] for k in cities_mean_dict.keys()]
-        cities_mean = sorted(cities_mean, key=lambda c: c['footprint'])
+            _results['total'] = total_foot  # DEPRECATED
+            _results['footprint'] = total_foot
-        _results['mean_footprint'] = {  # DEPRECATED?
-            'cities': cities_mean
-        }
-        _results['cities'] = cities_mean
-
-        _results['total'] = total_foot  # DEPRECATED
-        _results['footprint'] = total_foot
-
-        _results['distance'] = total_dist
-        _results['train_trips'] = total_train_trips
-        _results['plane_trips'] = total_plane_trips
-
-        return _results
-
-    # SCENARIO A : One Origin, At Least One Destination #######################
-    #
-    # In this scenario, we compute the sum of each of the travels' footprint,
-    # for each of the Emission Models, and present a mean of all Models.
-    #
-    if 1 == len(origins):
-        estimation.scenario = ScenarioEnum.one_to_many
-        results = compute_one_to_many(
-            _origin=origins[0],
-            _destinations=destinations,
-            _extra_config=extra_config,
-        )
+            _results['distance'] = total_dist
+            _results['train_trips'] = total_train_trips
+            _results['plane_trips'] = total_plane_trips
-    # SCENARIO B : At Least One Origin, One Destination #######################
-    #
-    # Same as A for now.
-    #
-    elif 1 == len(destinations):
-        estimation.scenario = ScenarioEnum.many_to_one
-        results = compute_one_to_many(
-            _origin=destinations[0],
-            _destinations=origins,
-            _extra_config=extra_config,
-        )
+            return _results
-    # SCENARIO C : At Least One Origin, At Least One Destination ##############
-    #
-    # Run Scenario A for each Destination, and expose optimum Destination.
-    #
-    else:
-        estimation.scenario = ScenarioEnum.many_to_many
-        unique_city_keys = []
-        result_cities = []
-        for destination in destinations:
-            city_key = get_city_key(destination)
-
-            if city_key in unique_city_keys:
-                continue
-            else:
-                unique_city_keys.append(city_key)
+        # SCENARIO A : One Origin, At Least One Destination #######################
+        #
+        # In this scenario, we compute the sum of each of the travels' footprint,
+        # for each of the Emission Models, and present a mean of all Models.
+        #
+        if 1 == len(origins):
+            estimation.scenario = ScenarioEnum.one_to_many
+            results = compute_one_to_many(
+                _origin=origins[0],
+                _destinations=destinations,
+                _extra_config=extra_config,
+            )
-            city_results = compute_one_to_many(
-                _origin=destination,
+        # SCENARIO B : At Least One Origin, One Destination #######################
+        #
+        # Same as A for now.
+        #
+        elif 1 == len(destinations):
+            estimation.scenario = ScenarioEnum.many_to_one
+            results = compute_one_to_many(
+                _origin=destinations[0],
                 _destinations=origins,
                 _extra_config=extra_config,
             )
-            city_results['city'] = city_key
-            city_results['address'] = destination.address
-            result_cities.append(city_results)
-        result_cities = sorted(result_cities, key=lambda c: int(c['footprint']))
-        results = {
-            'cities': result_cities,
-        }
+        # SCENARIO C : At Least One Origin, At Least One Destination ##############
+        #
+        # Run Scenario A for each Destination, and expose optimum Destination.
+        #
+        else:
+            estimation.scenario = ScenarioEnum.many_to_many
+            unique_city_keys = []
+            result_cities = []
+            for destination in destinations:
+                city_key = get_city_key(destination)
+
+                if city_key in unique_city_keys:
+                    continue
+                else:
+                    unique_city_keys.append(city_key)
-    # WRITE RESULTS INTO THE DATABASE #########################################
+                city_results = compute_one_to_many(
+                    _origin=destination,
+                    _destinations=origins,
+                    _extra_config=extra_config,
+                )
+                city_results['city'] = city_key
+                city_results['address'] = destination.address
+                result_cities.append(city_results)
+
+            result_cities = sorted(result_cities, key=lambda c: int(c['footprint']))
+            results = {
+                'cities': result_cities,
+            }
+
+        # WRITE RESULTS INTO THE DATABASE #########################################
-    estimation.status = StatusEnum.success
-    estimation.output_yaml = yaml_dump(results)
-    db.session.commit()
+        estimation.status = StatusEnum.success
+        estimation.output_yaml = yaml_dump(results)
+        db.session.commit()
+
+        # FINALLY, RESPOND ########################################################
-    # FINALLY, RESPOND ########################################################
+        response += yaml_dump(results) + "\n"
-    response += yaml_dump(results) + "\n"
+        return _respond(response)
-    return _respond(response)
+    except Exception as e:
+        errmsg = "Computation failed : %s" % e
+        if estimation:
+            _handle_failure(estimation, errmsg)
+        return _respond(errmsg)
 @main.route("/estimation/<public_id>.<extension>")
@@ -563,7 +570,7 @@ def consult_estimation(public_id, extension):
     except sqlalchemy.orm.exc.NoResultFound:
         return abort(404)
     except Exception as e:
-        # TODO: log
+        # TODO: log?
         return abort(500)
     # allowed_formats = ['html']