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# Copyright 2021 Collate
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Models to map profiles definitions
JSON workflows to the profiler
"""
from typing import List
from pydantic import BaseModel, validator
from metadata.orm_profiler.metrics.registry import Metrics
class TimeMetricDef(BaseModel):
"""
Model representing the input of a time metric
"""
name: str # metric name
window: int # time delta in days to apply
class CustomMetricDef(BaseModel):
"""
Model representing the input of a time metric
"""
name: str # metric name
sql: str # custom SQL query to run
class ProfilerDef(BaseModel):
"""
Incoming profiler definition from the
JSON workflow
"""
name: str # Profiler name
metrics: List[str] # names of currently supported Static and Composed metrics
time_metrics: List[TimeMetricDef] = None
custom_metrics: List[CustomMetricDef] = None
# rule_metrics: ...
@validator("metrics", each_item=True)
def valid_metric(cls, value): # cls as per pydantic docs
"""
Validate that the input metrics are correctly named
and can be found in the Registry
"""
if not Metrics.get(value.upper()):
raise ValueError(
f"Metric name {value} is not a proper metric name from the Registry"
)
return value.upper()