Source code for airflow.providers.google.cloud.openlineage.utils

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from __future__ import annotations

from typing import TYPE_CHECKING, Any

from attr import define, field

if TYPE_CHECKING:
    from google.cloud.bigquery.table import Table

    from airflow.providers.common.compat.openlineage.facet import Dataset

from airflow.providers.common.compat.openlineage.facet import (
    ColumnLineageDatasetFacet,
    DocumentationDatasetFacet,
    Fields,
    InputField,
    RunFacet,
    SchemaDatasetFacet,
    SchemaDatasetFacetFields,
)
from airflow.providers.google import __version__ as provider_version

[docs]BIGQUERY_NAMESPACE = "bigquery"
[docs]BIGQUERY_URI = "bigquery"
[docs]def get_facets_from_bq_table(table: Table) -> dict[Any, Any]: """Get facets from BigQuery table object.""" facets = { "schema": SchemaDatasetFacet( fields=[ SchemaDatasetFacetFields( name=field.name, type=field.field_type, description=field.description ) for field in table.schema ] ), "documentation": DocumentationDatasetFacet(description=table.description or ""), } return facets
[docs]def get_identity_column_lineage_facet( field_names: list[str], input_datasets: list[Dataset], ) -> ColumnLineageDatasetFacet: """ Get column lineage facet. Simple lineage will be created, where each source column corresponds to single destination column in each input dataset and there are no transformations made. """ if field_names and not input_datasets: raise ValueError("When providing `field_names` You must provide at least one `input_dataset`.") column_lineage_facet = ColumnLineageDatasetFacet( fields={ field: Fields( inputFields=[ InputField(namespace=dataset.namespace, name=dataset.name, field=field) for dataset in input_datasets ], transformationType="IDENTITY", transformationDescription="identical", ) for field in field_names } ) return column_lineage_facet
@define
[docs]class BigQueryJobRunFacet(RunFacet): """ Facet that represents relevant statistics of bigquery run. This facet is used to provide statistics about bigquery run. :param cached: BigQuery caches query results. Rest of the statistics will not be provided for cached queries. :param billedBytes: How many bytes BigQuery bills for. :param properties: Full property tree of BigQUery run. """
[docs] cached: bool
[docs] billedBytes: int | None = field(default=None)
[docs] properties: str | None = field(default=None)
@staticmethod def _get_schema() -> str: return ( "https://raw.githubusercontent.com/apache/airflow/" f"providers-google/{provider_version}/airflow/providers/google/" "openlineage/BigQueryJobRunFacet.json" )
# TODO: remove BigQueryErrorRunFacet in next release @define
[docs]class BigQueryErrorRunFacet(RunFacet): """ Represents errors that can happen during execution of BigqueryExtractor. :param clientError: represents errors originating in bigquery client :param parserError: represents errors that happened during parsing SQL provided to bigquery """
[docs] clientError: str | None = field(default=None)
[docs] parserError: str | None = field(default=None)
@staticmethod def _get_schema() -> str: return ( "https://raw.githubusercontent.com/apache/airflow/" f"providers-google/{provider_version}/airflow/providers/google/" "openlineage/BigQueryErrorRunFacet.json" )
[docs]def get_from_nullable_chain(source: Any, chain: list[str]) -> Any | None: """ Get object from nested structure of objects, where it's not guaranteed that all keys in the nested structure exist. Intended to replace chain of `dict.get()` statements. Example usage: .. code-block:: python if ( not job._properties.get("statistics") or not job._properties.get("statistics").get("query") or not job._properties.get("statistics").get("query").get("referencedTables") ): return None result = job._properties.get("statistics").get("query").get("referencedTables") becomes: .. code-block:: python result = get_from_nullable_chain(properties, ["statistics", "query", "queryPlan"]) if not result: return None """ # chain.pop modifies passed list, this can be unexpected chain = chain.copy() chain.reverse() try: while chain: while isinstance(source, list) and len(source) == 1: source = source[0] next_key = chain.pop() if isinstance(source, dict): source = source.get(next_key) else: source = getattr(source, next_key) return source except AttributeError: return None

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