wiring.evaluate_checks(
schema,
frame,
*,
asset_key,
check_granularity=None,
max_failure_examples=None
)
Each failing rule’s check has its rule’s severity, as in dd.asset.
Parameters
schema: type[dy.Schema]-
The schema whose rules the frame is checked against.
frame: pl.DataFrame | pl.LazyFrame-
The frame to check. A pl.LazyFrame is collected once.
asset_key: dg.AssetKey-
The key of the asset the results report on, which the specs name too.
check_granularity: Granularity | None = None-
Pass the value build_asset_check_specs received, or the results name checks the asset does not declare. None uses DAGSTER_DATAFRAMELY_CHECK_GRANULARITY if set, else rule.
max_failure_examples: int | None = None-
A rule’s check metadata has at most this many rows that failed it.
None uses DAGSTER_DATAFRAMELY_MAX_FAILURE_EXAMPLES if set, else 5.
Yields
dg.AssetCheckResult-
The column-schema check’s result, then one result per rule set, in the order of build_asset_check_specs.
Raises
InvalidSettingError-
A setting’s argument or environment variable has a value the setting does not allow.
ReservedColumnError-
A column name is in the reserved dy_ namespace.
InvalidColumnNameError-
A column name has a character Dagster does not allow in an asset check name.
CheckNameCollisionError-
Two rules produce the same asset check name.
InvalidSeverityError-
The schema declares a severity this package cannot read.
ColumnSchemaError-
The frame’s columns or dtypes do not match the schema. It yields the column-schema check’s failing result first.
Examples
import dagster as dg
import dagster_dataframely as dd
from dagster_dataframely.data import OrdersSchema, marketplace_orders
results = dd.wiring.evaluate_checks(
OrdersSchema, marketplace_orders, asset_key=dg.AssetKey("orders")
)
[result.check_name for result in results if not result.passed]
['dy_rule__paid_orders_have_amount',
'dy_rule__line_numbers_are_dense',
'dy_rule__order_id__regex',
'dy_rule__email__check__lowercase',
'dy_rule__amount__min',
'dy_rule__quantity__max',
'dy_rule__priority__is_in']