def construct_report(
report: Optional[Union[dict, Report]]) -> Optional[Report]:
if report is None:
return None
elif isinstance(report, Report):
return report
else:
return Report(**report)
def fake_to_report(self) -> Report:
indicators: List[ReportIndicator] = []
report_indicator_column_id = self.indicator.factorId
arithmetics = self.inspection.aggregateArithmetics
if arithmetics is None or len(arithmetics) == 0:
indicators.append(ReportIndicator(
columnId=report_indicator_column_id, name='_SUM_', arithmetic=ReportIndicatorArithmetic.SUMMARY))
else:
if IndicatorAggregateArithmetic.COUNT in arithmetics or IndicatorAggregateArithmetic.COUNT.value in arithmetics:
indicators.append(ReportIndicator(
columnId=report_indicator_column_id, name='_COUNT_', arithmetic=ReportIndicatorArithmetic.COUNT))
if IndicatorAggregateArithmetic.SUM in arithmetics or IndicatorAggregateArithmetic.SUM.value in arithmetics:
indicators.append(ReportIndicator(
columnId=report_indicator_column_id, name='_SUM_', arithmetic=ReportIndicatorArithmetic.SUMMARY))
if IndicatorAggregateArithmetic.AVG in arithmetics or IndicatorAggregateArithmetic.AVG.value in arithmetics:
indicators.append(ReportIndicator(
columnId=report_indicator_column_id, name='_AVG_', arithmetic=ReportIndicatorArithmetic.AVERAGE))
if IndicatorAggregateArithmetic.MAX in arithmetics or IndicatorAggregateArithmetic.MAX.value in arithmetics:
indicators.append(ReportIndicator(
columnId=report_indicator_column_id, name='_MAX_', arithmetic=ReportIndicatorArithmetic.MAXIMUM))
if IndicatorAggregateArithmetic.MIN in arithmetics or IndicatorAggregateArithmetic.MIN.value in arithmetics:
indicators.append(ReportIndicator(
columnId=report_indicator_column_id, name='_MIN_', arithmetic=ReportIndicatorArithmetic.MINIMUM))
dimensions: List[ReportDimension] = []
bucket_on_column = self.find_column_of_measure_on_dimension()
if bucket_on_column is not None:
dimensions.append(ReportDimension(columnId=bucket_on_column.columnId, name='_BUCKET_ON_'))
time_group_column = self.find_column_of_measure_on_time_dimension()
if time_group_column is not None:
fake_column = ArrayHelper(self.subject.dataset.columns) \
.find(lambda x: x.columnId == self.FAKE_TIME_GROUP_COLUMN_ID)
if fake_column is None:
dimensions.append(ReportDimension(columnId=time_group_column.columnId, name='_TIME_GROUP_'))
else:
dimensions.append(ReportDimension(columnId=self.FAKE_TIME_GROUP_COLUMN_ID, name='_TIME_GROUP_'))
if len(dimensions) == 0:
raise IndicatorKernelException('Neither time group nor bucket not found.')
report_filter = self.fake_time_range_to_report()
if report_filter is None:
return Report(indicators=indicators, dimensions=dimensions)
else:
return Report(indicators=indicators, dimensions=dimensions, filters=report_filter)
def fake_to_report(self, subject: Subject) -> Report:
# in subject,
# 1st column is value,
# 2nd column is time group when there is 3rd column, or is measure on when there is no 3rd column,
# 3rd column is measure on
indicators: List[ReportIndicator] = []
arithmetics = self.inspection.aggregateArithmetics
if arithmetics is None or len(arithmetics) == 0:
indicators.append(ReportIndicator(columnId='1', name='_SUM_', arithmetic=ReportIndicatorArithmetic.SUMMARY))
else:
if IndicatorAggregateArithmetic.COUNT in arithmetics or IndicatorAggregateArithmetic.COUNT.value in arithmetics:
indicators.append(
ReportIndicator(columnId='1', name='_COUNT_', arithmetic=ReportIndicatorArithmetic.COUNT))
if IndicatorAggregateArithmetic.SUM in arithmetics or IndicatorAggregateArithmetic.SUM.value in arithmetics:
indicators.append(
ReportIndicator(columnId='1', name='_SUM_', arithmetic=ReportIndicatorArithmetic.SUMMARY))
if IndicatorAggregateArithmetic.AVG in arithmetics or IndicatorAggregateArithmetic.AVG.value in arithmetics:
indicators.append(
ReportIndicator(columnId='1', name='_AVG_', arithmetic=ReportIndicatorArithmetic.AVERAGE))
if IndicatorAggregateArithmetic.MAX in arithmetics or IndicatorAggregateArithmetic.MAX.value in arithmetics:
indicators.append(
ReportIndicator(columnId='1', name='_MAX_', arithmetic=ReportIndicatorArithmetic.MAXIMUM))
if IndicatorAggregateArithmetic.MIN in arithmetics or IndicatorAggregateArithmetic.MIN.value in arithmetics:
indicators.append(
ReportIndicator(columnId='1', name='_MIN_', arithmetic=ReportIndicatorArithmetic.MINIMUM))
dimensions: List[ReportDimension] = []
if len(subject.dataset.columns) == 3:
dimensions.append(ReportDimension(columnId='3', name='_BUCKET_ON_'))
fake_column = ArrayHelper(subject.dataset.columns) \
.find(lambda x: x.columnId == self.FAKE_TIME_GROUP_COLUMN_ID)
if fake_column is None:
dimensions.append(ReportDimension(columnId='2', name='_TIME_GROUP_'))
else:
dimensions.append(ReportDimension(columnId=self.FAKE_TIME_GROUP_COLUMN_ID, name='_TIME_GROUP_'))
elif len(subject.dataset.columns) == 2:
time_group_existing, _, _ = self.has_time_group()
if time_group_existing:
fake_column = ArrayHelper(subject.dataset.columns) \
.find(lambda x: x.columnId == self.FAKE_TIME_GROUP_COLUMN_ID)
if fake_column is None:
dimensions.append(ReportDimension(columnId='2', name='_TIME_GROUP_'))
else:
dimensions.append(ReportDimension(columnId=self.FAKE_TIME_GROUP_COLUMN_ID, name='_TIME_GROUP_'))
else:
dimensions.append(ReportDimension(columnId='2', name='_BUCKET_ON_'))
else:
raise IndicatorKernelException('Neither time group nor bucket on found.')
return Report(indicators=indicators, dimensions=dimensions)
def deserialize(self, row: EntityRow) -> Report:
report = Report(reportId=row.get('report_id'),
name=row.get('name'),
subjectId=row.get('subject_id'),
connectId=row.get('connect_id'),
filters=row.get('filters'),
funnels=row.get('funnels'),
indicators=row.get('indicators'),
dimensions=row.get('dimensions'),
description=row.get('description'),
rect=row.get('rect'),
chart=row.get('chart'),
simulating=row.get('simulating'),
simulateData=row.get('simulate_data'),
simulateThumbnail=row.get('simulate_thumbnail'))
# noinspection PyTypeChecker
report: Report = AuditableShaper.deserialize(row, report)
# noinspection PyTypeChecker
report: Report = UserBasedTupleShaper.deserialize(row, report)
# noinspection PyTypeChecker
report: Report = LastVisitShaper.deserialize(row, report)
return report
def action(factor_id: FactorId) -> Report:
arithmetic = self.achievementIndicator.aggregateArithmetic
if arithmetic is None:
report_indicator = ReportIndicator(
columnId=factor_id,
name='_SUM_',
arithmetic=ReportIndicatorArithmetic.SUMMARY)
elif IndicatorAggregateArithmetic.COUNT == arithmetic or IndicatorAggregateArithmetic.COUNT.value == arithmetic:
report_indicator = ReportIndicator(
columnId=factor_id,
name='_COUNT_',
arithmetic=ReportIndicatorArithmetic.COUNT)
elif IndicatorAggregateArithmetic.SUM == arithmetic or IndicatorAggregateArithmetic.SUM.value == arithmetic:
report_indicator = ReportIndicator(
columnId=factor_id,
name='_SUM_',
arithmetic=ReportIndicatorArithmetic.SUMMARY)
elif IndicatorAggregateArithmetic.AVG == arithmetic or IndicatorAggregateArithmetic.AVG.value == arithmetic:
report_indicator = ReportIndicator(
columnId=factor_id,
name='_AVG_',
arithmetic=ReportIndicatorArithmetic.AVERAGE)
elif IndicatorAggregateArithmetic.MAX == arithmetic or IndicatorAggregateArithmetic.MAX.value == arithmetic:
report_indicator = ReportIndicator(
columnId=factor_id,
name='_MAX_',
arithmetic=ReportIndicatorArithmetic.MAXIMUM)
elif IndicatorAggregateArithmetic.MIN == arithmetic or IndicatorAggregateArithmetic.MIN.value == arithmetic:
report_indicator = ReportIndicator(
columnId=factor_id,
name='_MIN_',
arithmetic=ReportIndicatorArithmetic.MINIMUM)
else:
raise IndicatorKernelException(
f'Indicator aggregate arithmetics[{arithmetic}] is not supported.'
)
return Report(indicators=[report_indicator], dimensions=[])
async def query_dataset(
criteria: SubjectDatasetCriteria,
principal_service: PrincipalService = Depends(get_console_principal)
) -> DataPage:
subject_id = criteria.subjectId
subject_name = criteria.subjectName
indicators = criteria.indicators
conditions = criteria.conditions
if is_blank(subject_id) and is_blank(subject_name):
raise_400(
'At least one of subject id or subject name needs to be provided.')
if indicators is None or len(indicators) == 0:
raise_400('At least one indicator needs to be declared.')
if is_not_blank(subject_id):
subject: Optional[Subject] = get_subject_service(
principal_service).find_by_id(subject_id)
else:
subject: Optional[Subject] = get_subject_service(
principal_service).find_by_name(subject_name)
if subject is None:
raise_400(
f'Subject not found by given criteria[id={subject_id}, name={subject_name}].'
)
subject_column_map: Dict[str, SubjectDatasetColumn] = ArrayHelper(subject.dataset.columns) \
.to_map(lambda x: x.alias, lambda x: x)
def to_report_indicator(
indicator: SubjectDatasetCriteriaIndicator) -> ReportIndicator:
name = indicator.name
dataset_column = subject_column_map.get(name)
if dataset_column is None:
raise_400(f'Cannot find column[name={name}] from subject.')
arithmetic = ReportIndicatorArithmetic.NONE
if indicator.arithmetic == SubjectDatasetCriteriaIndicatorArithmetic.COUNT:
arithmetic = ReportIndicatorArithmetic.COUNT
elif indicator.arithmetic == SubjectDatasetCriteriaIndicatorArithmetic.SUMMARY:
arithmetic = ReportIndicatorArithmetic.SUMMARY
elif indicator.arithmetic == SubjectDatasetCriteriaIndicatorArithmetic.AVERAGE:
arithmetic = ReportIndicatorArithmetic.AVERAGE
elif indicator.arithmetic == SubjectDatasetCriteriaIndicatorArithmetic.MAXIMUM:
arithmetic = ReportIndicatorArithmetic.MAXIMUM
elif indicator.arithmetic == SubjectDatasetCriteriaIndicatorArithmetic.MINIMUM:
arithmetic = ReportIndicatorArithmetic.MINIMUM
elif indicator.arithmetic == SubjectDatasetCriteriaIndicatorArithmetic.NONE or indicator.arithmetic is None:
arithmetic = ReportIndicatorArithmetic.NONE
else:
raise_400(
f'Indicator arithmetic[{indicator.arithmetic}] is not supported.'
)
return ReportIndicator(columnId=dataset_column.columnId,
arithmetic=arithmetic,
name=indicator.alias)
def to_report_joint(joint: ParameterJoint) -> ParameterJoint:
return ParameterJoint(
jointType=ParameterJointType.AND
if joint.jointType is None else joint.jointType,
filters=ArrayHelper(joint.filters).map(to_report_filter).to_list())
def translate_topic_factor(
param: TopicFactorParameter) -> TopicFactorParameter:
factor_id = param.factorId # alias name on subject
dataset_column = subject_column_map.get(factor_id)
if dataset_column is None:
raise_400(f'Cannot find column[name={factor_id}] from subject.')
# topicId is not need here since subject will be build as a sub query
return TopicFactorParameter(kind=ParameterKind.TOPIC,
factorId=dataset_column.columnId)
def translate_constant(param: ConstantParameter) -> ConstantParameter:
# in constant, use alias name from subject columns
# translate is not needed here
return ConstantParameter(kind=ParameterKind.CONSTANT,
value=param.value)
def translate_computed(param: ComputedParameter) -> ComputedParameter:
return ComputedParameter(
kind=ParameterKind.COMPUTED,
conditional=param.conditional,
on=to_report_joint(param.on) if param.on is not None else None,
type=param.type,
parameters=ArrayHelper(
param.parameters).map(translate_parameter).to_list())
def translate_parameter(parameter: Parameter) -> Parameter:
if isinstance(parameter, TopicFactorParameter):
return translate_topic_factor(parameter)
elif isinstance(parameter, ConstantParameter):
return translate_constant(parameter)
elif isinstance(parameter, ComputedParameter):
return translate_computed(parameter)
else:
raise_400(
f'Cannot determine given expression[{parameter.dict()}].')
def to_report_expression(
expression: ParameterExpression) -> ParameterExpression:
return ParameterExpression(left=translate_parameter(expression.left),
operator=expression.operator,
right=None if expression.right is None else
translate_parameter(expression.right))
def to_report_filter(condition: ParameterCondition) -> ParameterCondition:
if isinstance(condition, ParameterExpression):
return to_report_expression(condition)
elif isinstance(condition, ParameterJoint):
return to_report_joint(condition)
else:
raise_400(f'Cannot determine given condition[{condition.dict()}].')
if conditions is None or len(conditions) == 0:
filters = None
else:
filters = ParameterJoint(
jointType=ParameterJointType.AND,
filters=ArrayHelper(conditions).map(to_report_filter).to_list())
# fake a report to query data
report = Report(
indicators=ArrayHelper(indicators).map(to_report_indicator).to_list(),
filters=filters)
page_size = ask_dataset_page_max_rows()
if criteria.pageSize is None or criteria.pageSize < 1 or criteria.pageSize > page_size:
page_size = ask_dataset_page_max_rows()
else:
page_size = criteria.pageSize
pageable = Pageable(pageNumber=1 if criteria.pageNumber is None
or criteria.pageNumber < 1 else criteria.pageNumber,
pageSize=page_size)
return get_report_data_service(subject, report,
principal_service).page(pageable)
def test_single_topic(self):
self.prepare_data()
subject = Subject(dataset=SubjectDataset(columns=[
SubjectDatasetColumn(
columnId='1',
parameter=ComputedParameter(
kind=ParameterKind.COMPUTED,
type=ParameterComputeType.ADD,
parameters=[
TopicFactorParameter(kind=ParameterKind.TOPIC,
topicId='1',
factorId='1'),
ConstantParameter(kind=ParameterKind.CONSTANT,
value='2')
]),
alias='Column1',
arithmetic=SubjectColumnArithmetic.SUMMARY),
SubjectDatasetColumn(
columnId='2',
parameter=TopicFactorParameter(
kind=ParameterKind.TOPIC, topicId='1', factorId='1'),
alias='Column2'),
SubjectDatasetColumn(columnId='3',
parameter=ConstantParameter(
kind=ParameterKind.CONSTANT,
value='{&now}'),
alias='Column3'),
SubjectDatasetColumn(columnId='4',
parameter=ConstantParameter(
kind=ParameterKind.CONSTANT,
value='HELLO WORLD!'),
alias='Column4'),
SubjectDatasetColumn(
columnId='5',
parameter=ComputedParameter(
kind=ParameterKind.COMPUTED,
type=ParameterComputeType.ADD,
parameters=[
ConstantParameter(kind=ParameterKind.CONSTANT,
value='201'),
ConstantParameter(kind=ParameterKind.CONSTANT,
value='102')
]),
alias='Column5'),
SubjectDatasetColumn(columnId='6',
parameter=ComputedParameter(
kind=ParameterKind.COMPUTED,
type=ParameterComputeType.YEAR_OF,
parameters=[
ConstantParameter(
kind=ParameterKind.CONSTANT,
value='2022/03/03'),
]),
alias='Column6'),
SubjectDatasetColumn(
columnId='7',
parameter=TopicFactorParameter(
kind=ParameterKind.TOPIC, topicId='1', factorId='2'),
alias='Column7')
]),
connectId='1',
userId='1',
tenantId='1')
subject_data_service = SubjectDataService(
subject, create_fake_principal_service())
page = subject_data_service.page(Pageable(pageNumber=1, pageSize=100))
print(page)
report = Report(
indicators=[
ReportIndicator(columnId='1',
name='sum_value',
arithmetic=ReportIndicatorArithmetic.SUMMARY)
],
dimensions=[ReportDimension(columnId='7', name='enabled')],
filters=ParameterJoint(
jointType=ParameterJointType.AND,
filters=[
ParameterExpression(
left=TopicFactorParameter(kind=ParameterKind.TOPIC,
factorId='7'),
operator=ParameterExpressionOperator.EQUALS,
right=ConstantParameter(kind=ParameterKind.CONSTANT,
value='true'))
]))
report_data_service = ReportDataService(
subject, report, create_fake_principal_service(), False)
data = report_data_service.find()
print(data)