def __init__(self, group_strategy, timeseries_unit = 'day', date_filter_attributes=None):
self.filterable_attributes = [DATE, CUSTOM_ATTRIBUTE, TRANSFER_ACCOUNT, USER]
self.timeseries_unit = timeseries_unit
self.date_filter_attributes = date_filter_attributes
self.metrics = []
# Special case query-- this is just used to calculate grouped per_user
if group_strategy:
total_users_grouped_timeseries_query = db.session.query(func.count(User.id).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]).label('date'), group_strategy.group_by_column).group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]))
self.total_users_grouped_timeseries = metric.Metric(
metric_name='total_population_grouped',
query=group_strategy.build_query_group_by_with_join(total_users_grouped_timeseries_query, User),
object_model=User,
stock_filters=[],
timeseries_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[ADD_MISSING_DAYS_TO_TODAY, ACCUMULATE_TIMESERIES])
# Special case query-- this is just used to calculate ungrouped per_user
total_users_timeseries_query = db.session.query(func.count(User.id).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]).label('date')).group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]))
self.total_users_timeseries = metric.Metric(
metric_name='total_population',
query=total_users_timeseries_query,
object_model=User,
stock_filters=[],
timeseries_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[ADD_MISSING_DAYS_TO_TODAY, ACCUMULATE_TIMESERIES])
def __init__(self, group_strategy, timeseries_unit = 'day', token=None, date_filter_attributes=None):
self.filterable_attributes = [DATE, CUSTOM_ATTRIBUTE, TRANSFER_ACCOUNT, CREDIT_TRANSFER, USER]
self.timeseries_unit = timeseries_unit
self.date_filter_attributes = date_filter_attributes
self.metrics = []
total_amount_query = db.session.query(func.sum(CreditTransfer.transfer_amount).label('total'))
self.metrics.append(metric.Metric(
metric_name='total_distributed',
query=total_amount_query,
object_model=CreditTransfer,
stock_filters=[filters.disbursement_filters],
caching_combinatory_strategy=metrics_cache.SUM,
filterable_by=self.filterable_attributes,
bypass_user_filters=True,
))
self.metrics.append(metric.Metric(
metric_name='total_reclaimed',
query=total_amount_query,
object_model=CreditTransfer,
stock_filters=[filters.reclamation_filters],
caching_combinatory_strategy=metrics_cache.SUM,
filterable_by=self.filterable_attributes,
bypass_user_filters=True,
))
self.metrics.append(metric.Metric(
metric_name='total_withdrawn',
query=total_amount_query,
object_model=CreditTransfer,
stock_filters=[filters.withdrawal_filters],
caching_combinatory_strategy=metrics_cache.SUM,
filterable_by=self.filterable_attributes,
bypass_user_filters=True,
))
# Timeseries Metrics
if group_strategy:
transaction_volume_timeseries_query = group_strategy.build_query_group_by_with_join(db.session.query(func.sum(CreditTransfer.transfer_amount).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]).label('date'), group_strategy.group_by_column).group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer])), CreditTransfer)
aggregated_transaction_volume_query = group_strategy.build_query_group_by_with_join(db.session.query(func.sum(CreditTransfer.transfer_amount).label('volume'), group_strategy.group_by_column), CreditTransfer)
else:
transaction_volume_timeseries_query = db.session.query(func.sum(CreditTransfer.transfer_amount).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]).label('date')).group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]))
aggregated_transaction_volume_query = None
total_transaction_volume_query = db.session.query(func.sum(CreditTransfer.transfer_amount).label('volume'))
self.metrics.append(metric.Metric(
metric_name='all_payments_volume',
is_timeseries=True,
query=transaction_volume_timeseries_query,
aggregated_query=aggregated_transaction_volume_query,
total_query=total_transaction_volume_query,
object_model=CreditTransfer,
timeseries_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[FORMAT_TIMESERIES],
aggregated_query_actions=[FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST],
value_type=CURRENCY,
token=token
))
self.metrics.append(metric.Metric(
metric_name='transfer_amount_per_user',
is_timeseries=True,
query=transaction_volume_timeseries_query,
aggregated_query=aggregated_transaction_volume_query,
total_query=total_transaction_volume_query,
object_model=CreditTransfer,
stock_filters=[filters.standard_payment_filters],
timeseries_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[CALCULATE_TIMESERIES_PER_USER, FORMAT_TIMESERIES], # Add per user
aggregated_query_actions=[CALCULATE_AGGREGATE_PER_USER, FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST, CALCULATE_TOTAL_PER_USER],
value_type=CURRENCY,
token=token
))
if group_strategy:
transaction_count_timeseries_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(CreditTransfer.id).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]).label('date'), group_strategy.group_by_column).group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer])), CreditTransfer)
aggregated_transaction_count_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(CreditTransfer.id).label('volume'), group_strategy.group_by_column), CreditTransfer)
else:
transaction_count_timeseries_query = db.session.query(func.count(CreditTransfer.id).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]).label('date')).group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]))
aggregated_transaction_count_query = None
total_transaction_count_query = db.session.query(func.count(CreditTransfer.id).label('volume'))
self.metrics.append(metric.Metric(
metric_name='daily_transaction_count',
is_timeseries=True,
query=transaction_count_timeseries_query,
aggregated_query=aggregated_transaction_count_query,
total_query=total_transaction_count_query,
object_model=CreditTransfer,
stock_filters=[filters.standard_payment_filters],
timeseries_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[FORMAT_TIMESERIES],
aggregated_query_actions=[FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST],
value_type=COUNT
))
self.metrics.append(metric.Metric(
metric_name='trades_per_user',
is_timeseries=True,
query=transaction_count_timeseries_query,
aggregated_query=aggregated_transaction_count_query,
total_query=total_transaction_count_query,
object_model=CreditTransfer,
stock_filters=[filters.standard_payment_filters],
timeseries_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[CALCULATE_TIMESERIES_PER_USER, FORMAT_TIMESERIES], # Add per user
aggregated_query_actions=[CALCULATE_AGGREGATE_PER_USER, FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST, CALCULATE_TOTAL_PER_USER],
value_type=COUNT_AVERAGE,
))
if group_strategy:
active_users_timeseries_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]).label('date'), group_strategy.group_by_column).group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer])), CreditTransfer)
aggregated_active_users_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'), group_strategy.group_by_column), CreditTransfer)
else:
active_users_timeseries_query = db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]).label('date')).group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]))
aggregated_active_users_query = None
total_transaction_volume_query = db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'))
self.metrics.append(metric.Metric(
metric_name='users_who_made_purchase',
is_timeseries=True,
query=active_users_timeseries_query,
aggregated_query=aggregated_active_users_query,
total_query=total_transaction_volume_query,
object_model=CreditTransfer,
#stock_filters=[filters.beneficiary_filters], # NOTE: Do we want this filter?
stock_filters=[],
timeseries_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[FORMAT_TIMESERIES],
aggregated_query_actions=[FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST],
value_type=COUNT,
))
def __init__(self, group_strategy, timeseries_unit = 'day'):
self.filterable_attributes = [DATE, CUSTOM_ATTRIBUTE, TRANSFER_ACCOUNT, USER]
self.timeseries_unit = timeseries_unit
self.metrics = []
total_beneficiaries_query = db.session.query(User)
self.metrics.append(metric.Metric(
metric_name='total_beneficiaries',
query=total_beneficiaries_query,
object_model=User,
stock_filters=[filters.beneficiary_filters],
caching_combinatory_strategy=metrics_cache.COUNT,
filterable_by=self.filterable_attributes))
total_vendors_query = db.session.query(User)
self.metrics.append(metric.Metric(
metric_name='total_vendors',
query=total_vendors_query,
object_model=User,
stock_filters=[filters.vendor_filters],
caching_combinatory_strategy=metrics_cache.COUNT,
filterable_by=self.filterable_attributes))
# Timeseries Metrics
if group_strategy:
users_created_timeseries_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(User.id).label('volume'),
func.date_trunc(self.timeseries_unit, User.created).label('date'), group_strategy.group_by_column).group_by(func.date_trunc(self.timeseries_unit, User.created)), User)
aggregated_users_created_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(User.id).label('volume'), group_strategy.group_by_column), User)
else:
users_created_timeseries_query = db.session.query(func.count(User.id).label('volume'),
func.date_trunc(self.timeseries_unit, User.created).label('date')).group_by(func.date_trunc(self.timeseries_unit, User.created))
aggregated_users_created_query = None
total_users_created_query = db.session.query(func.count(User.id).label('volume'))
self.metrics.append(metric.Metric(
metric_name='users_created',
is_timeseries=True,
query=users_created_timeseries_query,
aggregated_query=aggregated_users_created_query,
total_query=total_users_created_query,
object_model=User,
#stock_filters=[filters.beneficiary_filters], # NOTE: Do we still want this filter?
stock_filters=[],
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[FORMAT_TIMESERIES],
aggregated_query_actions=[FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST],
))
if group_strategy:
active_users_timeseries_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'),
func.date_trunc(self.timeseries_unit, CreditTransfer.created).label('date'), group_strategy.group_by_column).group_by(func.date_trunc(self.timeseries_unit, CreditTransfer.created)), CreditTransfer)
aggregated_active_users_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'), group_strategy.group_by_column), CreditTransfer)
else:
active_users_timeseries_query = db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'),
func.date_trunc(self.timeseries_unit, CreditTransfer.created).label('date')).group_by(func.date_trunc(self.timeseries_unit, CreditTransfer.created))
aggregated_active_users_query = None
total_active_users_query = db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'))
self.metrics.append(metric.Metric(
metric_name='active_users',
is_timeseries=True,
query=active_users_timeseries_query,
aggregated_query=aggregated_active_users_query,
total_query=total_active_users_query,
object_model=CreditTransfer,
#stock_filters=[filters.beneficiary_filters], # NOTE: Do we still want this filter?
stock_filters=[],
caching_combinatory_strategy=metrics_cache.QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[FORMAT_TIMESERIES],
aggregated_query_actions=[FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST],
))
def __init__(self,
group_strategy,
timeseries_unit='day',
date_filter_attributes=None):
self.filterable_attributes = [
DATE, CUSTOM_ATTRIBUTE, TRANSFER_ACCOUNT, USER
]
self.timeseries_unit = timeseries_unit
self.date_filter_attributes = date_filter_attributes
self.metrics = []
# Timeseries Metrics
if group_strategy:
users_created_timeseries_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(User.id).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]).label('date'), group_strategy.group_by_column)\
.group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User])), User)
aggregated_users_created_query = group_strategy\
.build_query_group_by_with_join(db.session.query(func.count(User.id).label('volume'), group_strategy.group_by_column), User)
else:
users_created_timeseries_query = db.session.query(func.count(User.id).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]).label('date'))\
.group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]))
aggregated_users_created_query = None
total_users_created_query = db.session.query(
func.count(User.id).label('volume'))
self.metrics.append(
metric.Metric(
metric_name='users_created',
is_timeseries=True,
query=users_created_timeseries_query,
aggregated_query=aggregated_users_created_query,
total_query=total_users_created_query,
object_model=User,
#stock_filters=[filters.beneficiary_filters], # NOTE: Do we still want this filter?
stock_filters=[],
query_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
aggregated_query_caching_combinatory_strategy=metrics_cache.
SUM_OBJECTS,
total_query_caching_combinatory_strategy=metrics_cache.TALLY,
filterable_by=self.filterable_attributes,
query_actions=[FORMAT_TIMESERIES],
aggregated_query_actions=[FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST],
))
if group_strategy:
active_users_timeseries_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]).label('date'), group_strategy.group_by_column)\
.group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer])), CreditTransfer)
aggregated_active_users_query = group_strategy.build_query_group_by_with_join(
db.session.query(
func.count(func.distinct(
CreditTransfer.sender_user_id)).label('volume'),
group_strategy.group_by_column), CreditTransfer)
else:
active_users_timeseries_query = db.session.query(func.count(func.distinct(CreditTransfer.sender_user_id)).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]).label('date'))\
.group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[CreditTransfer]))
aggregated_active_users_query = None
total_active_users_query = db.session.query(
func.count(func.distinct(
CreditTransfer.sender_user_id)).label('volume'))
self.metrics.append(
metric.Metric(
metric_name='active_users',
is_timeseries=True,
query=active_users_timeseries_query,
aggregated_query=aggregated_active_users_query,
total_query=total_active_users_query,
object_model=CreditTransfer,
#stock_filters=[filters.beneficiary_filters], # NOTE: Do we still want this filter?
stock_filters=[],
query_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
aggregated_query_caching_combinatory_strategy=metrics_cache.
QUERY_ALL,
total_query_caching_combinatory_strategy=metrics_cache.
QUERY_ALL,
filterable_by=self.filterable_attributes,
query_actions=[FORMAT_TIMESERIES],
aggregated_query_actions=[FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST],
))
if group_strategy:
total_users_timeseries_query = group_strategy.build_query_group_by_with_join(db.session.query(func.count(User.id).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]).label('date'), group_strategy.group_by_column)\
.group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User])), User)
else:
total_users_timeseries_query = db.session.query(func.count(User.id).label('volume'),
func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]).label('date'))\
.group_by(func.date_trunc(self.timeseries_unit, self.date_filter_attributes[User]))
self.metrics.append(
metric.Metric(
metric_name='total_population_cumulative',
is_timeseries=True,
query=total_users_timeseries_query,
total_query=total_users_created_query,
aggregated_query=aggregated_active_users_query,
object_model=User,
stock_filters=[],
query_caching_combinatory_strategy=metrics_cache.SUM_OBJECTS,
aggregated_query_caching_combinatory_strategy=metrics_cache.
SUM_OBJECTS,
total_query_caching_combinatory_strategy=metrics_cache.TALLY,
filterable_by=self.filterable_attributes,
aggregated_query_actions=[FORMAT_AGGREGATE_METRICS],
total_query_actions=[GET_FIRST],
query_actions=[
ADD_MISSING_DAYS_TO_TODAY, ACCUMULATE_TIMESERIES,
FORMAT_TIMESERIES
]))