def _get_sq_field_for_blender_field(field,
queries,
field_maps,
reference=None):
unmodified_field = find_field_in_modified_field(field)
field_alias = alias_selector(reference_type_alias(field, reference))
# search for the field in each field map to determine which subquery it will be in
for query, field_map in zip(queries, field_maps):
if query is None or unmodified_field not in field_map:
continue
mapped_field = field_map[unmodified_field]
mapped_field_alias = alias_selector(
reference_type_alias(mapped_field, reference))
subquery_field = query[mapped_field_alias]
# case #1 modified fields, ex. day(timestamp) or rollup(dimension)
return field.for_(subquery_field).as_(field_alias)
# Need to copy the metrics if there are references so that the `get_sql` monkey patch does not conflict.
# Given some of them might have nested metrics themselves, the clone process is performed recursively.
definition = field.definition
while isinstance(definition, Field):
definition = definition.definition
# case #2: complex blender fields
return _deepcopy_recursive(definition).as_(field_alias)
def map_blender_field_to_dataset_field(field, field_map, dataset):
field_from_blender = find_field_in_modified_field(field)
if field_from_blender in dataset.fields:
return field
if field_from_blender in field_map:
return field.for_(field_map[field_from_blender])
def make_reference_dimensions(dimensions, ref_dimension, offset_func,
field_transformer, trunc_date):
return [
_replace_reference_dimension(dimension, offset_func, field_transformer,
trunc_date) if
ref_dimension is find_field_in_modified_field(dimension) else dimension
for dimension in dimensions
]
def _map_fields(fields):
"""
TODO describe this
"""
for field in fields:
field_from_blender = find_field_in_modified_field(field)
if field_from_blender in dataset.fields:
yield field
continue
if field_from_blender not in field_map:
continue
yield field.for_(field_map[field_from_blender])
def fetch_data(
database: Database,
queries: List[Type[QueryBuilder]],
dimensions: Iterable[Field],
share_dimensions: Iterable[Field] = (),
reference_groups=(),
) -> Tuple[int, pd.DataFrame]:
queries = [str(query) for query in queries]
# Indicate which dimensions need to be parsed as date types
# For this we create a dictionary with the dimension alias as key and PANDAS_TO_DATETIME_FORMAT as value
pandas_parse_dates = {}
for dimension in dimensions:
unmodified_dimension = find_field_in_modified_field(dimension)
if unmodified_dimension.data_type == DataType.date:
pandas_parse_dates[alias_selector(
unmodified_dimension.alias)] = PANDAS_TO_DATETIME_FORMAT
results = database.fetch_dataframes(*queries,
parse_dates=pandas_parse_dates)
max_rows_returned = 0
for result_df in results:
row_count = len(result_df)
if row_count > max_rows_returned:
max_rows_returned = row_count
if row_count > database.max_result_set_size:
logger.warning('row_count_over_max',
extra={
'row_count': len(result_df),
'database': str(database)
})
# drop all result rows above database.max_result_set_size in place
result_df.drop(result_df.index[database.max_result_set_size:],
inplace=True)
logger.info('max_rows_returned',
extra={
'row_count': max_rows_returned,
'database': str(database)
})
return max_rows_returned, reduce_result_set(results, reference_groups,
dimensions, share_dimensions)
def _get_sq_field_for_blender_field(field, reference=None):
unmodified_field = find_field_in_modified_field(field)
field_alias = alias_selector(reference_alias(field, reference))
# search for the field in each field map to determine which subquery it will be in
for query, field_map in zip(queries, field_maps):
if unmodified_field not in field_map:
continue
mapped_field = field_map[unmodified_field]
mapped_field_alias = alias_selector(
reference_alias(mapped_field, reference))
subquery_field = query[mapped_field_alias]
# case #1 modified fields, ex. day(timestamp) or rollup(dimension)
return field.for_(subquery_field).as_(field_alias)
# Need to copy the metrics if there are references so that the `get_sql` monkey patch does not conflict
definition = copy.deepcopy(field.definition)
# case #2: complex blender fields
return definition.as_(field_alias)
def _blender_join_criteria(base_query, join_query, dimensions, base_field_map,
join_field_map):
"""
Build a criteria for joining this join query to the base query in datset blender queries. This should be a set of
equality conditions like A0=B0 AND A1=B1 AND An=Bn for each mapped dimension between dataset from
`DataSetBlender.dimension_map`.
"""
join_criteria = None
for dimension in dimensions:
dimension = find_field_in_modified_field(dimension)
if not all([dimension in base_field_map, dimension in join_field_map]):
continue
alias0, alias1 = [
alias_selector(field_map[dimension].alias)
for field_map in [base_field_map, join_field_map]
]
next_criteria = base_query[alias0] == join_query[alias1]
join_criteria = (next_criteria if join_criteria is None else
(join_criteria & next_criteria))
return join_criteria
