analysis/webservice/algorithms_spark/Matchup.py [261:292]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        except Exception as e:
            self.log.exception(e)
            raise NexusProcessingException(reason="An unknown error occurred while computing matches", code=500)

        end = datetime.utcnow()

        self.log.debug("Building and saving results")
        args = {
            "primary": primary_ds_name,
            "matchup": secondary_ds_names,
            "startTime": start_time,
            "endTime": end_time,
            "bbox": request.get_argument('b'),
            "timeTolerance": time_tolerance,
            "radiusTolerance": float(radius_tolerance),
            "platforms": platforms,
            "parameter": parameter_s
        }

        if depth_min is not None:
            args["depthMin"] = float(depth_min)

        if depth_max is not None:
            args["depthMax"] = float(depth_max)

        total_keys = len(list(spark_result.keys()))
        total_values = sum(len(v) for v in spark_result.values())
        details = {
            "timeToComplete": int((end - start).total_seconds()),
            "numSecondaryMatched": total_values,
            "numPrimaryMatched": total_keys
        }
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



analysis/webservice/algorithms_spark/MatchupDoms.py [240:271]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        except Exception as e:
            self.log.exception(e)
            raise NexusProcessingException(reason="An unknown error occurred while computing matches", code=500)

        end = datetime.utcnow()

        self.log.debug("Building and saving results")
        args = {
            "primary": primary_ds_name,
            "matchup": secondary_ds_names,
            "startTime": start_time,
            "endTime": end_time,
            "bbox": request.get_argument('b'),
            "timeTolerance": time_tolerance,
            "radiusTolerance": float(radius_tolerance),
            "platforms": platforms,
            "parameter": parameter_s
        }

        if depth_min is not None:
            args["depthMin"] = float(depth_min)

        if depth_max is not None:
            args["depthMax"] = float(depth_max)

        total_keys = len(list(spark_result.keys()))
        total_values = sum(len(v) for v in spark_result.values())
        details = {
            "timeToComplete": int((end - start).total_seconds()),
            "numSecondaryMatched": total_values,
            "numPrimaryMatched": total_keys
        }
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -