🐛 Source LinkedIn-Ads: Fix for Campaigns/targetingCriteria transformation, coerced Creatives/variables/values to string (#6610)
* fixed targettingCriteria transformation, changed values of creatives/variables to string by default, bumped the version
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@@ -2,6 +2,6 @@
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"sourceDefinitionId": "137ece28-5434-455c-8f34-69dc3782f451",
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"name": "LinkedIn Ads",
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"dockerRepository": "airbyte/source-linkedin-ads",
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"dockerImageTag": "0.1.0",
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"dockerImageTag": "0.1.1",
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"documentationUrl": "https://docs.airbyte.io/integrations/sources/linkedin-ads"
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}
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@@ -538,7 +538,7 @@
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- sourceDefinitionId: 137ece28-5434-455c-8f34-69dc3782f451
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name: LinkedIn Ads
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dockerRepository: airbyte/source-linkedin-ads
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dockerImageTag: 0.1.0
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dockerImageTag: 0.1.1
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documentationUrl: https://docs.airbyte.io/integrations/sources/linkedin-ads
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sourceType: api
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- sourceDefinitionId: b2e713cd-cc36-4c0a-b5bd-b47cb8a0561e
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@@ -5,7 +5,9 @@ FROM base as builder
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WORKDIR /airbyte/integration_code
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# upgrade pip to the latest version
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RUN apk --no-cache upgrade && pip install --upgrade pip
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RUN apk --no-cache upgrade \
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&& pip install --upgrade pip \
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&& apk --no-cache add tzdata build-base
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COPY setup.py ./
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# install necessary packages to a temporary folder
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@@ -17,15 +19,19 @@ WORKDIR /airbyte/integration_code
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# copy all loaded and built libraries to a pure basic image
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COPY --from=builder /install /usr/local
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# add default timezone settings
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COPY --from=builder /usr/share/zoneinfo/Etc/UTC /etc/localtime
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RUN echo "Etc/UTC" > /etc/timezone
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# bash is installed for more convenient debugging.
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RUN apk --no-cache add bash
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# copy payload code only
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COPY main.py ./
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COPY source_linkedin_ads ./source_linkedin_ads
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# set the default Timezone, for use with dependent libraries like: datetime, pendullum, etc.
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ENV TZ "UTC"
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ENV AIRBYTE_ENTRYPOINT "python /airbyte/integration_code/main.py"
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ENTRYPOINT ["python", "/airbyte/integration_code/main.py"]
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LABEL io.airbyte.version=0.1.0
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LABEL io.airbyte.version=0.1.1
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LABEL io.airbyte.name=airbyte/source-linkedin-ads
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@@ -6,7 +6,7 @@
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from setuptools import find_packages, setup
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MAIN_REQUIREMENTS = [
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"airbyte-cdk==0.1.22",
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"airbyte-cdk",
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"pendulum",
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]
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@@ -50,24 +50,7 @@
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"type": ["null", "string"]
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},
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"value": {
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"anyOf": [
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{
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"type": ["null", "string"]
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},
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{
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"type": ["null", "boolean"]
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},
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{
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"type": ["null", "number"]
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},
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{
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"type": ["null", "integer"]
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},
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{
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"type": ["null", "object"],
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"additionalProperties": true
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}
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]
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"type": ["null", "string"]
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}
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}
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}
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@@ -2,6 +2,7 @@
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# Copyright (c) 2021 Airbyte, Inc., all rights reserved.
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#
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import json
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from typing import Any, Dict, Iterable, List, Mapping
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import pendulum as pdm
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@@ -205,42 +206,53 @@ def transform_targeting_criteria(
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}
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"""
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def unnest_dict(nested_dict: Dict) -> Iterable[Dict]:
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"""
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Unnest the nested dict to simplify the normalization
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EXAMPLE OUTPUT:
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[
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{"type": "some_key", "values": "some_values"},
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...,
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{"type": "some_other_key", "values": "some_other_values"}
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]
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"""
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for key, value in nested_dict.items():
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values = []
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if isinstance(value, List):
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if len(value) > 0:
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if isinstance(value[0], str):
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values = value
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elif isinstance(value[0], Dict):
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for v in value:
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values.append(v)
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elif isinstance(value, Dict):
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values.append(value)
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yield {"type": key, "values": values}
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# get the target dict from record
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targeting_criteria = record.get(dict_key)
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# transform `include`
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if "include" in targeting_criteria:
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and_list = targeting_criteria.get("include").get("and")
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for id, and_criteria in enumerate(and_list):
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or_dict = and_criteria.get("or")
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for key, value in or_dict.items():
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values = []
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if isinstance(value, list):
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if isinstance(value[0], str):
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values = value
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elif isinstance(value[0], dict):
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for v in value:
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values.append(v)
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elif isinstance(key, dict):
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values.append(key)
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# Replace the 'or' with {type:value}
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record["targetingCriteria"]["include"]["and"][id]["type"] = key
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record["targetingCriteria"]["include"]["and"][id]["values"] = values
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record["targetingCriteria"]["include"]["and"][id].pop("or")
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updated_include = {"and": []}
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for k in and_list:
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or_dict = k.get("or")
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for j in unnest_dict(or_dict):
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updated_include["and"].append(j)
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# Replace the original 'and' with updated_include
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record["targetingCriteria"]["include"] = updated_include
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# transform `exclude` if present
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if "exclude" in targeting_criteria:
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or_dict = targeting_criteria.get("exclude").get("or")
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updated_exclude = {"or": []}
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for key, value in or_dict.items():
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values = []
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if isinstance(value, list):
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if isinstance(value[0], str):
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values = value
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elif isinstance(value[0], dict):
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for v in value:
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value.append(v)
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elif isinstance(value, dict):
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value.append(value)
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updated_exclude["or"].append({"type": key, "values": values})
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for k in unnest_dict(or_dict):
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updated_exclude["or"].append(k)
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# Replace the original 'or' with updated_exclude
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record["targetingCriteria"]["exclude"] = updated_exclude
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return record
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@@ -282,8 +294,9 @@ def transform_variables(
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for key, params in variables.items():
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record["variables"]["type"] = key
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record["variables"]["values"] = []
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for key, param in params.items():
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record["variables"]["values"].append({"key": key, "value": param})
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for key, value in params.items():
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# convert various datatypes of values into the string
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record["variables"]["values"].append({"key": key, "value": json.dumps(value, ensure_ascii=True)})
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# Clean the nested structure
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record["variables"].pop("data")
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return record
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@@ -23,6 +23,8 @@ input_test_data = [
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},
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{"or": {"urn:li:adTargetingFacet:locations": ["urn:li:geo:103644278"]}},
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{"or": {"urn:li:adTargetingFacet:interfaceLocales": ["urn:li:locale:en_US"]}},
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{"or": {"empty_dict_with_empty_list": []}}, # dict is present, but list is empty
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{"or": {}}, # empty dict
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]
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},
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"exclude": {
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@@ -35,6 +37,7 @@ input_test_data = [
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"facet_test3",
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"facet_test4",
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],
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"empty_list": []
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}
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},
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},
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@@ -52,6 +55,10 @@ input_test_data = [
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"activity": "urn:li:activity:1234",
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"directSponsoredContent": 0,
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"share": "urn:li:share:1234",
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"custom_num_var": 1234,
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"custom_obj_var": {"key": 1234},
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"custom_arr_var": [1, 2, 3, 4],
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"custom_null_var": None,
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}
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}
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},
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@@ -84,6 +91,10 @@ output_test_data = [
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"type": "urn:li:adTargetingFacet:interfaceLocales",
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"values": ["urn:li:locale:en_US"],
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},
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{
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"type": "empty_dict_with_empty_list",
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"values": [],
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},
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]
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},
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"exclude": {
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@@ -96,15 +107,23 @@ output_test_data = [
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"type": "urn:li:adTargetingFacet:facet_Key2",
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"values": ["facet_test3", "facet_test4"],
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},
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{
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"type": "empty_list",
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"values": [],
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},
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]
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},
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},
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"variables": {
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"type": "com.linkedin.ads.SponsoredUpdateCreativeVariables",
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"values": [
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{"key": "activity", "value": "urn:li:activity:1234"},
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{"key": "directSponsoredContent", "value": 0},
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{"key": "share", "value": "urn:li:share:1234"},
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{"key": "activity", "value": '"urn:li:activity:1234"'},
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{"key": "directSponsoredContent", "value": "0"},
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{"key": "share", "value": '"urn:li:share:1234"'},
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{"key": "custom_num_var", "value": "1234"},
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{"key": "custom_obj_var", "value": '{"key": 1234}'},
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{"key": "custom_arr_var", "value": "[1, 2, 3, 4]"},
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{"key": "custom_null_var", "value": "null"},
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],
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},
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"created": "2021-08-21 21:27:55",
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@@ -132,4 +132,5 @@ The complete set of prmissions is:
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| Version | Date | Pull Request | Subject |
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| :------ | :-------- | :-------- | :------ |
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| 0.1.1 | 2021-10-02 | [6610](https://github.com/airbytehq/airbyte/pull/6610) | Fix for `Campaigns/targetingCriteria` transformation, coerced `Creatives/variables/values` to string by default |
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| 0.1.0 | 2021-09-05 | [5285](https://github.com/airbytehq/airbyte/pull/5285) | Initial release of Native LinkedIn Ads connector for Airbyte |
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