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🏗️ Python CDK: add schema transformer class #6139
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# | ||
# MIT License | ||
# | ||
# Copyright (c) 2020 Airbyte | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
# | ||
from distutils.util import strtobool | ||
from enum import Flag, auto | ||
from typing import Any, Callable, Dict | ||
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from airbyte_cdk.logger import AirbyteLogger | ||
from jsonschema import Draft7Validator, validators | ||
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logger = AirbyteLogger() | ||
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class TransformConfig(Flag): | ||
""" | ||
Transformer class config. Configs can be combined using bitwise or operator e.g. | ||
``` | ||
TransformConfig.DefaultSchemaNormalization | TransformConfig.CustomSchemaNormalization | ||
``` | ||
""" | ||
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NoTransform = auto() | ||
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DefaultSchemaNormalization = auto() | ||
CustomSchemaNormalization = auto() | ||
# TODO: implement field transformation with user defined object path | ||
FieldTransformation = auto() | ||
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class Transformer: | ||
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""" | ||
Class for transforming object before output. | ||
""" | ||
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_custom_normalizer: Callable[[Any, Dict[str, Any]], Any] = None | ||
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def __init__(self, config: TransformConfig): | ||
""" | ||
Initialize Transformer instance. | ||
:param config Transform config that would be applied to object | ||
""" | ||
if TransformConfig.NoTransform in config and config != TransformConfig.NoTransform: | ||
raise Exception("NoTransform option cannot be combined with other flags.") | ||
self._config = config | ||
all_validators = { | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. piggybacking on jsonschema native validators is a clever idea! There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Thanks, Im proud of it :) |
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key: self.__normalize_and_validate(key, orig_validator) | ||
for key, orig_validator in Draft7Validator.VALIDATORS.items() | ||
# Do not validate field we do not transform for maximum performance. | ||
if key in ["type", "array", "$ref", "properties", "items"] | ||
} | ||
self._normalizer = validators.create(meta_schema=Draft7Validator.META_SCHEMA, validators=all_validators) | ||
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def register(self, normalization_callback: Callable) -> Callable: | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. coming from the point of view of a developer leveraging this class, it's not clear what the contract of the registered normalizer is. What is the input to the callable I provide? is it just a single value? Does it also take in a schema? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. add type annotation and link to docs. |
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""" | ||
Register custom normalization callback. | ||
:param normalization_callback function to be used for value | ||
normalization. Should return normalized value. | ||
:return Same callbeck, this is usefull for using register function as decorator. | ||
""" | ||
if TransformConfig.CustomSchemaNormalization not in self._config: | ||
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raise Exception("Please set TransformConfig.CustomSchemaNormalization config before registering custom normalizer") | ||
self._custom_normalizer = normalization_callback | ||
return normalization_callback | ||
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def __normalize(self, original_item: Any, subschema: Dict[str, Any]) -> Any: | ||
""" | ||
Applies different transform function to object's field according to config. | ||
:param original_item original value of field. | ||
:param subschema part of the jsonschema containing field type/format data. | ||
:return Final field value. | ||
""" | ||
if TransformConfig.DefaultSchemaNormalization in self._config: | ||
original_item = self.default_convert(original_item, subschema) | ||
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if self._custom_normalizer: | ||
original_item = self._custom_normalizer(original_item, subschema) | ||
return original_item | ||
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@staticmethod | ||
def default_convert(original_item: Any, subschema: Dict[str, Any]) -> Any: | ||
""" | ||
Default transform function that is used when TransformConfig.DefaultSchemaNormalization flag set. | ||
:param original_item original value of field. | ||
:param subschema part of the jsonschema containing field type/format data. | ||
:return transformed field value. | ||
""" | ||
target_type = subschema.get("type") | ||
if original_item is None and "null" in target_type: | ||
return None | ||
if isinstance(target_type, list): | ||
# jsonschema type could either be a single string or array of type | ||
# strings. In case if there is some disambigous and more than one | ||
# type (except null) do not do any conversion and return original | ||
# value. If type array has one type and null i.e. {"type": | ||
# ["integer", "null"]}, convert value to specified type. | ||
target_type = [t for t in target_type if t != "null"] | ||
if len(target_type) != 1: | ||
return original_item | ||
target_type = target_type[0] | ||
try: | ||
if target_type == "string": | ||
return str(original_item) | ||
elif target_type == "number": | ||
return float(original_item) | ||
elif target_type == "integer": | ||
return int(original_item) | ||
elif target_type == "boolean": | ||
if isinstance(original_item, str): | ||
return strtobool(original_item) == 1 | ||
return bool(original_item) | ||
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except ValueError: | ||
return original_item | ||
return original_item | ||
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def __normalize_and_validate(self, schema_key: str, original_validator: Callable): | ||
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""" | ||
Traverse through object fields using native jsonschema validator and apply normalization function. | ||
:param schema_key related json schema key that currently being validated/normalized. | ||
:original_validator: native jsonschema validator callback. | ||
""" | ||
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def normalizator(validator_instance: Callable, val: Any, instance: Any, schema: Dict[str, Any]): | ||
""" | ||
Jsonschema validator callable it uses for validating instance. We | ||
override default Draft7Validator to perform value transformation | ||
before validation take place. We do not take any action except | ||
logging warn if object does not conform to json schema, just using | ||
jsonschema algorithm to traverse through object fields. | ||
Look | ||
https://python-jsonschema.readthedocs.io/en/stable/creating/?highlight=validators.create#jsonschema.validators.create | ||
validators parameter for detailed description. | ||
: | ||
""" | ||
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def resolve(subschema): | ||
if "$ref" in subschema: | ||
_, resolved = validator_instance.resolver.resolve(subschema["$ref"]) | ||
return resolved | ||
return subschema | ||
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if schema_key == "type" and instance is not None: | ||
if "object" in val and isinstance(instance, dict): | ||
for k, subschema in schema.get("properties", {}).items(): | ||
if k in instance: | ||
subschema = resolve(subschema) | ||
instance[k] = self.__normalize(instance[k], subschema) | ||
elif "array" in val and isinstance(instance, list): | ||
subschema = schema.get("items", {}) | ||
subschema = resolve(subschema) | ||
for index, item in enumerate(instance): | ||
instance[index] = self.__normalize(item, subschema) | ||
# Running native jsonschema traverse algorithm after field normalization is done. | ||
yield from original_validator(validator_instance, val, instance, schema) | ||
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return normalizator | ||
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def transform(self, instance: Dict[str, Any], schema: Dict[str, Any]): | ||
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""" | ||
Normalize and validate according to config. | ||
:param instance object instance for normalization/transformation. All modification are done by modifing existent object. | ||
:schema object's jsonschema for normalization. | ||
""" | ||
if TransformConfig.NoTransform in self._config: | ||
return | ||
normalizer = self._normalizer(schema) | ||
for e in normalizer.iter_errors(instance): | ||
""" | ||
just calling normalizer.validate() would throw an exception on | ||
first validation occurences and stop processing rest of schema. | ||
""" | ||
logger.warn(e.message) |
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I am conflicted between providing this by default in the source itself and therefore increasing the scope of the source vs. just telling the user they are responsible for using this helper class.
How do you think about this decision?
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In this case it would be a lot of duplicate code like
instead of just
I would like to do this in source code to keep connectors code cleaner. Also connector's developer may forget about get_json_schema caching and this would reduce performance A LOT.