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"""This module contains a Google Cloud Storage to BigQuery operator."""

from __future__ import annotations

import json
from typing import TYPE_CHECKING, Any, Sequence

from google.api_core.exceptions import BadRequest, Conflict
from import (
from import EncryptionConfiguration, Table, TableReference

from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from import BigQueryHook, BigQueryJob
from import GCSHook
from import BigQueryTableLink
from import BigQueryInsertJobTrigger
from airflow.utils.helpers import merge_dicts

    from google.api_core.retry import Retry

    from airflow.utils.context import Context

[docs]class GCSToBigQueryOperator(BaseOperator): """ Loads files from Google Cloud Storage into BigQuery. The schema to be used for the BigQuery table may be specified in one of two ways. You may either directly pass the schema fields in, or you may point the operator to a Google Cloud Storage object name. The object in Google Cloud Storage must be a JSON file with the schema fields in it. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:GCSToBigQueryOperator` :param bucket: The bucket to load from. (templated) :param source_objects: String or List of Google Cloud Storage URIs to load from. (templated) If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI. :param destination_project_dataset_table: The dotted ``(<project>.|<project>:)<dataset>.<table>`` BigQuery table to load data into. If ``<project>`` is not included, project will be the project defined in the connection json. (templated) :param schema_fields: If set, the schema field list as defined here: Should not be set when source_format is 'DATASTORE_BACKUP'. Parameter must be defined if 'schema_object' is null and autodetect is False. :param schema_object: If set, a GCS object path pointing to a .json file that contains the schema for the table. (templated) Parameter must be defined if 'schema_fields' is null and autodetect is False. :param schema_object_bucket: [Optional] If set, the GCS bucket where the schema object template is stored. (templated) (Default: the value of ``bucket``) :param source_format: File format to export. :param compression: [Optional] The compression type of the data source. Possible values include GZIP and NONE. The default value is NONE. This setting is ignored for Google Cloud Bigtable, Google Cloud Datastore backups and Avro formats. :param create_disposition: The create disposition if the table doesn't exist. :param skip_leading_rows: The number of rows at the top of a CSV file that BigQuery will skip when loading the data. When autodetect is on, the behavior is the following: skip_leading_rows unspecified - Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise, data is read starting from the second row. skip_leading_rows is 0 - Instructs autodetect that there are no headers and data should be read starting from the first row. skip_leading_rows = N > 0 - Autodetect skips N-1 rows and tries to detect headers in row N. If headers are not detected, row N is just skipped. Otherwise, row N is used to extract column names for the detected schema. Default value set to None so that autodetect option can detect schema fields. :param write_disposition: The write disposition if the table already exists. :param field_delimiter: The delimiter to use when loading from a CSV. :param max_bad_records: The maximum number of bad records that BigQuery can ignore when running the job. :param quote_character: The value that is used to quote data sections in a CSV file. :param ignore_unknown_values: [Optional] Indicates if BigQuery should allow extra values that are not represented in the table schema. If true, the extra values are ignored. If false, records with extra columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. :param allow_quoted_newlines: Whether to allow quoted newlines (true) or not (false). :param allow_jagged_rows: Accept rows that are missing trailing optional columns. The missing values are treated as nulls. If false, records with missing trailing columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. Only applicable to CSV, ignored for other formats. :param encoding: The character encoding of the data. See: :param max_id_key: If set, the name of a column in the BigQuery table that's to be loaded. This will be used to select the MAX value from BigQuery after the load occurs. The results will be returned by the execute() command, which in turn gets stored in XCom for future operators to use. This can be helpful with incremental loads--during future executions, you can pick up from the max ID. :param schema_update_options: Allows the schema of the destination table to be updated as a side effect of the load job. :param src_fmt_configs: configure optional fields specific to the source format :param external_table: Flag to specify if the destination table should be a BigQuery external table. Default Value is False. :param time_partitioning: configure optional time partitioning fields i.e. partition by field, type and expiration as per API specifications. Note that 'field' is not available in concurrency with dataset.table$partition. :param cluster_fields: Request that the result of this load be stored sorted by one or more columns. BigQuery supports clustering for both partitioned and non-partitioned tables. The order of columns given determines the sort order. Not applicable for external tables. :param autodetect: [Optional] Indicates if we should automatically infer the options and schema for CSV and JSON sources. (Default: ``True``). Parameter must be set to True if 'schema_fields' and 'schema_object' are undefined. It is suggested to set to True if table are create outside of Airflow. If autodetect is None and no schema is provided (neither via schema_fields nor a schema_object), assume the table already exists. :param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys). .. code-block:: python encryption_configuration = { "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key", } :param location: [Optional] The geographic location of the job. Required except for US and EU. See details at :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). :param labels: [Optional] Labels for the BiqQuery table. :param description: [Optional] Description for the BigQuery table. This will only be used if the destination table is newly created. If the table already exists and a value different than the current description is provided, the job will fail. :param deferrable: Run operator in the deferrable mode """
[docs] template_fields: Sequence[str] = ( "bucket", "source_objects", "schema_object", "schema_object_bucket", "destination_project_dataset_table", "impersonation_chain", "src_fmt_configs", )
[docs] template_ext: Sequence[str] = (".sql",)
[docs] ui_color = "#f0eee4"
def __init__( self, *, bucket, source_objects, destination_project_dataset_table, schema_fields=None, schema_object=None, schema_object_bucket=None, source_format="CSV", compression="NONE", create_disposition="CREATE_IF_NEEDED", skip_leading_rows=None, write_disposition="WRITE_EMPTY", field_delimiter=",", max_bad_records=0, quote_character=None, ignore_unknown_values=False, allow_quoted_newlines=False, allow_jagged_rows=False, encoding="UTF-8", max_id_key=None, gcp_conn_id="google_cloud_default", schema_update_options=(), src_fmt_configs=None, external_table=False, time_partitioning=None, cluster_fields=None, autodetect=True, encryption_configuration=None, location=None, impersonation_chain: str | Sequence[str] | None = None, labels=None, description=None, deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False), result_retry: Retry = DEFAULT_RETRY, result_timeout: float | None = None, cancel_on_kill: bool = True, job_id: str | None = None, force_rerun: bool = True, reattach_states: set[str] | None = None, project_id: str = PROVIDE_PROJECT_ID, **kwargs, ) -> None: super().__init__(**kwargs) self.hook: BigQueryHook | None = None self.configuration: dict[str, Any] = {} # GCS config if src_fmt_configs is None: src_fmt_configs = {} if time_partitioning is None: time_partitioning = {} self.bucket = bucket self.source_objects = source_objects self.schema_object = schema_object if schema_object_bucket is None: schema_object_bucket = bucket self.schema_object_bucket = schema_object_bucket # BQ config self.destination_project_dataset_table = destination_project_dataset_table self.project_id = project_id self.schema_fields = schema_fields if source_format.upper() not in ALLOWED_FORMATS: raise ValueError( f"{source_format} is not a valid source format. " f"Please use one of the following types: {ALLOWED_FORMATS}." ) else: self.source_format = source_format.upper() self.compression = compression self.create_disposition = create_disposition self.skip_leading_rows = skip_leading_rows self.write_disposition = write_disposition self.field_delimiter = field_delimiter self.max_bad_records = max_bad_records self.quote_character = quote_character self.ignore_unknown_values = ignore_unknown_values self.allow_quoted_newlines = allow_quoted_newlines self.allow_jagged_rows = allow_jagged_rows self.external_table = external_table self.encoding = encoding self.max_id_key = max_id_key self.gcp_conn_id = gcp_conn_id self.schema_update_options = schema_update_options self.src_fmt_configs = src_fmt_configs self.time_partitioning = time_partitioning self.cluster_fields = cluster_fields self.autodetect = autodetect self.encryption_configuration = encryption_configuration self.location = location self.impersonation_chain = impersonation_chain self.labels = labels self.description = description self.job_id = job_id self.deferrable = deferrable self.result_retry = result_retry self.result_timeout = result_timeout self.force_rerun = force_rerun self.reattach_states: set[str] = reattach_states or set() self.cancel_on_kill = cancel_on_kill self.source_uris: list[str] = [] def _submit_job( self, hook: BigQueryHook, job_id: str, ) -> BigQueryJob: # Submit a new job without waiting for it to complete. return hook.insert_job( configuration=self.configuration, project_id=self.project_id or hook.project_id, location=self.location, job_id=job_id, timeout=self.result_timeout, retry=self.result_retry, nowait=True, ) @staticmethod def _handle_job_error(job: BigQueryJob | UnknownJob) -> None: if job.error_result: raise AirflowException(f"BigQuery job {job.job_id} failed: {job.error_result}")
[docs] def execute(self, context: Context): hook = BigQueryHook( gcp_conn_id=self.gcp_conn_id, location=self.location, impersonation_chain=self.impersonation_chain, ) self.hook = hook self.source_format = self.source_format.upper() job_id = self.hook.generate_job_id( job_id=self.job_id, dag_id=self.dag_id, task_id=self.task_id, logical_date=context["logical_date"], configuration=self.configuration, force_rerun=self.force_rerun, ) self.source_objects = ( self.source_objects if isinstance(self.source_objects, list) else [self.source_objects] ) self.source_uris = [f"gs://{self.bucket}/{source_object}" for source_object in self.source_objects] if not self.schema_fields: # Check for self.autodetect explicitly False. self.autodetect equal to None # entails we do not want to detect schema from files. Instead, it means we # rely on an already existing table's schema if not self.schema_object and self.autodetect is False: raise AirflowException( "Table schema was not found. Neither schema object nor schema fields were specified" ) if self.schema_object and self.source_format != "DATASTORE_BACKUP": gcs_hook = GCSHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.schema_fields = json.loads(, self.schema_object).decode("utf-8") )"Loaded fields from schema object: %s", self.schema_fields) else: self.schema_fields = None if self.external_table:"Creating a new BigQuery table for storing data...") table_obj_api_repr = self._create_external_table() BigQueryTableLink.persist( context=context, task_instance=self, dataset_id=table_obj_api_repr["tableReference"]["datasetId"], project_id=table_obj_api_repr["tableReference"]["projectId"], table_id=table_obj_api_repr["tableReference"]["tableId"], ) if self.max_id_key: max_id = self._find_max_value_in_column() return max_id else:"Using existing BigQuery table for storing data...") self.configuration = self._use_existing_table() try:"Executing: %s", self.configuration) job: BigQueryJob | UnknownJob = self._submit_job(self.hook, job_id) except Conflict: # If the job already exists retrieve it job = self.hook.get_job( project_id=self.project_id or self.hook.project_id, location=self.location, job_id=job_id, ) if job.state in self.reattach_states: # We are reattaching to a job job._begin() self._handle_job_error(job) else: # Same job configuration so we need force_rerun raise AirflowException( f"Job with id: {job_id} already exists and is in {job.state} state. If you " f"want to force rerun it consider setting `force_rerun=True`." f"Or, if you want to reattach in this scenario add {job.state} to `reattach_states`" ) job_types = { LoadJob._JOB_TYPE: ["sourceTable", "destinationTable"], CopyJob._JOB_TYPE: ["sourceTable", "destinationTable"], ExtractJob._JOB_TYPE: ["sourceTable"], QueryJob._JOB_TYPE: ["destinationTable"], } if self.hook.project_id: for job_type, tables_prop in job_types.items(): job_configuration = job.to_api_repr()["configuration"] if job_type in job_configuration: for table_prop in tables_prop: if table_prop in job_configuration[job_type]: table = job_configuration[job_type][table_prop] persist_kwargs = { "context": context, "task_instance": self, "table_id": table, } if not isinstance(table, str): persist_kwargs["table_id"] = table["tableId"] persist_kwargs["dataset_id"] = table["datasetId"] persist_kwargs["project_id"] = table["projectId"] BigQueryTableLink.persist(**persist_kwargs) self.job_id = job.job_id context["ti"].xcom_push(key="job_id", value=self.job_id) if self.deferrable: self.defer( timeout=self.execution_timeout, trigger=BigQueryInsertJobTrigger( conn_id=self.gcp_conn_id, job_id=self.job_id, project_id=self.project_id or self.hook.project_id, location=self.location or self.hook.location, impersonation_chain=self.impersonation_chain, ), method_name="execute_complete", ) else: job.result(timeout=self.result_timeout, retry=self.result_retry) self._handle_job_error(job) if self.max_id_key: return self._find_max_value_in_column()
[docs] def execute_complete(self, context: Context, event: dict[str, Any]): """Return immediately and relies on trigger to throw a success event. Callback for the trigger. Relies on trigger to throw an exception, otherwise it assumes execution was successful. """ if event["status"] == "error": raise AirflowException(event["message"]) "%s completed with response %s ", self.task_id, event["message"], ) return self._find_max_value_in_column()
def _find_max_value_in_column(self): hook = BigQueryHook( gcp_conn_id=self.gcp_conn_id, location=self.location, impersonation_chain=self.impersonation_chain, ) if self.max_id_key:"Selecting the MAX value from BigQuery column %r...", self.max_id_key) select_command = ( f"SELECT MAX({self.max_id_key}) AS max_value " f"FROM {self.destination_project_dataset_table}" ) self.configuration = { "query": { "query": select_command, "useLegacySql": False, "schemaUpdateOptions": [], } } try: job_id = hook.insert_job( configuration=self.configuration, project_id=self.project_id or hook.project_id ) rows = list(hook.get_job(job_id=job_id, location=self.location).result()) except BadRequest as e: if "Unrecognized name:" in e.message: raise AirflowException( f"Could not determine MAX value in column {self.max_id_key} " f"since the default value of 'string_field_n' was set by BQ" ) else: raise AirflowException(e.message) if rows: for row in rows: max_id = row[0] if row[0] else 0 "Loaded BQ data with MAX value of column %s.%s: %s", self.destination_project_dataset_table, self.max_id_key, max_id, ) return str(max_id) else: raise RuntimeError(f"The {select_command} returned no rows!") def _create_external_table(self): external_config_api_repr = { "autodetect": self.autodetect, "sourceFormat": self.source_format, "sourceUris": self.source_uris, "compression": self.compression.upper(), "ignoreUnknownValues": self.ignore_unknown_values, } # if following fields are not specified in src_fmt_configs, # honor the top-level params for backward-compatibility backward_compatibility_configs = { "skipLeadingRows": self.skip_leading_rows, "fieldDelimiter": self.field_delimiter, "quote": self.quote_character, "allowQuotedNewlines": self.allow_quoted_newlines, "allowJaggedRows": self.allow_jagged_rows, "encoding": self.encoding, } src_fmt_to_param_mapping = {"CSV": "csvOptions", "GOOGLE_SHEETS": "googleSheetsOptions"} src_fmt_to_configs_mapping = { "csvOptions": [ "allowJaggedRows", "allowQuotedNewlines", "fieldDelimiter", "skipLeadingRows", "quote", "encoding", "preserveAsciiControlCharacters", ], "googleSheetsOptions": ["skipLeadingRows"], } if self.source_format in src_fmt_to_param_mapping: valid_configs = src_fmt_to_configs_mapping[src_fmt_to_param_mapping[self.source_format]] self.src_fmt_configs = self._validate_src_fmt_configs( self.source_format, self.src_fmt_configs, valid_configs, backward_compatibility_configs ) external_config_api_repr[src_fmt_to_param_mapping[self.source_format]] = self.src_fmt_configs external_config = ExternalConfig.from_api_repr(external_config_api_repr) if self.schema_fields: external_config.schema = [SchemaField.from_api_repr(f) for f in self.schema_fields] if self.max_bad_records: external_config.max_bad_records = self.max_bad_records # build table definition table = Table( table_ref=TableReference.from_string(self.destination_project_dataset_table, self.hook.project_id) ) table.external_data_configuration = external_config if self.labels: table.labels = self.labels if self.description: table.description = self.description if self.encryption_configuration: table.encryption_configuration = EncryptionConfiguration.from_api_repr( self.encryption_configuration ) table_obj_api_repr = table.to_api_repr()"Creating external table: %s", self.destination_project_dataset_table) self.hook.create_empty_table( table_resource=table_obj_api_repr, project_id=self.project_id or self.hook.project_id, location=self.location, exists_ok=True, )"External table created successfully: %s", self.destination_project_dataset_table) return table_obj_api_repr def _use_existing_table(self): destination_project_id, destination_dataset, destination_table = self.hook.split_tablename( table_input=self.destination_project_dataset_table, default_project_id=self.hook.project_id, var_name="destination_project_dataset_table", ) # bigquery also allows you to define how you want a table's schema to change # as a side effect of a load # for more details: # allowed_schema_update_options = ["ALLOW_FIELD_ADDITION", "ALLOW_FIELD_RELAXATION"] if not set(allowed_schema_update_options).issuperset(set(self.schema_update_options)): raise ValueError( f"{self.schema_update_options} contains invalid schema update options. " f"Please only use one or more of the following options: {allowed_schema_update_options}" ) self.configuration = { "load": { "autodetect": self.autodetect, "createDisposition": self.create_disposition, "destinationTable": { "projectId": destination_project_id, "datasetId": destination_dataset, "tableId": destination_table, }, "sourceFormat": self.source_format, "sourceUris": self.source_uris, "writeDisposition": self.write_disposition, "ignoreUnknownValues": self.ignore_unknown_values, }, } self.time_partitioning = self._cleanse_time_partitioning( self.destination_project_dataset_table, self.time_partitioning ) if self.time_partitioning: self.configuration["load"].update({"timePartitioning": self.time_partitioning}) if self.cluster_fields: self.configuration["load"].update({"clustering": {"fields": self.cluster_fields}}) if self.schema_fields: self.configuration["load"]["schema"] = {"fields": self.schema_fields} if self.schema_update_options: if self.write_disposition not in ["WRITE_APPEND", "WRITE_TRUNCATE"]: raise ValueError( "schema_update_options is only " "allowed if write_disposition is " "'WRITE_APPEND' or 'WRITE_TRUNCATE'." ) else: # To provide backward compatibility self.schema_update_options = list(self.schema_update_options or [])"Adding experimental 'schemaUpdateOptions': %s", self.schema_update_options) self.configuration["load"]["schemaUpdateOptions"] = self.schema_update_options if self.max_bad_records: self.configuration["load"]["maxBadRecords"] = self.max_bad_records if self.encryption_configuration: self.configuration["load"]["destinationEncryptionConfiguration"] = self.encryption_configuration if self.labels or self.description: self.configuration["load"].update({"destinationTableProperties": {}}) if self.labels: self.configuration["load"]["destinationTableProperties"]["labels"] = self.labels if self.description: self.configuration["load"]["destinationTableProperties"]["description"] = self.description src_fmt_to_configs_mapping = { "CSV": [ "allowJaggedRows", "allowQuotedNewlines", "autodetect", "fieldDelimiter", "skipLeadingRows", "ignoreUnknownValues", "nullMarker", "quote", "encoding", "preserveAsciiControlCharacters", ], "DATASTORE_BACKUP": ["projectionFields"], "NEWLINE_DELIMITED_JSON": ["autodetect", "ignoreUnknownValues"], "PARQUET": ["autodetect", "ignoreUnknownValues"], "AVRO": ["useAvroLogicalTypes"], } valid_configs = src_fmt_to_configs_mapping[self.source_format] # if following fields are not specified in src_fmt_configs, # honor the top-level params for backward-compatibility backward_compatibility_configs = { "skipLeadingRows": self.skip_leading_rows, "fieldDelimiter": self.field_delimiter, "ignoreUnknownValues": self.ignore_unknown_values, "quote": self.quote_character, "allowQuotedNewlines": self.allow_quoted_newlines, "encoding": self.encoding, } self.src_fmt_configs = self._validate_src_fmt_configs( self.source_format, self.src_fmt_configs, valid_configs, backward_compatibility_configs ) self.configuration["load"].update(self.src_fmt_configs) if self.allow_jagged_rows: self.configuration["load"]["allowJaggedRows"] = self.allow_jagged_rows return self.configuration def _validate_src_fmt_configs( self, source_format: str, src_fmt_configs: dict, valid_configs: list[str], backward_compatibility_configs: dict | None = None, ) -> dict: """ Validate the given src_fmt_configs against a valid configuration for the source format. Adds the backward compatibility config to the src_fmt_configs. :param source_format: File format to export. :param src_fmt_configs: Configure optional fields specific to the source format. :param valid_configs: Valid configuration specific to the source format :param backward_compatibility_configs: The top-level params for backward-compatibility """ if backward_compatibility_configs is None: backward_compatibility_configs = {} for k, v in backward_compatibility_configs.items(): if k not in src_fmt_configs and k in valid_configs: src_fmt_configs[k] = v for k in src_fmt_configs: if k not in valid_configs: raise ValueError(f"{k} is not a valid src_fmt_configs for type {source_format}.") return src_fmt_configs def _cleanse_time_partitioning( self, destination_dataset_table: str | None, time_partitioning_in: dict | None ) -> dict: # if it is a partitioned table ($ is in the table name) add partition load option if time_partitioning_in is None: time_partitioning_in = {} time_partitioning_out = {} if destination_dataset_table and "$" in destination_dataset_table: time_partitioning_out["type"] = "DAY" time_partitioning_out.update(time_partitioning_in) return time_partitioning_out
[docs] def on_kill(self) -> None: if self.job_id and self.cancel_on_kill: self.hook.cancel_job(job_id=self.job_id, location=self.location) # type: ignore[union-attr] else:"Skipping to cancel job: %s.%s", self.location, self.job_id)
[docs] def get_openlineage_facets_on_complete(self, task_instance): """Implement on_complete as we will include final BQ job id.""" from pathlib import Path from openlineage.client.facet import ( ExternalQueryRunFacet, SymlinksDatasetFacet, SymlinksDatasetFacetIdentifiers, ) from import Dataset from import ( get_facets_from_bq_table, get_identity_column_lineage_facet, ) from airflow.providers.openlineage.extractors import OperatorLineage table_object = self.hook.get_client(self.hook.project_id).get_table( self.destination_project_dataset_table ) output_dataset_facets = get_facets_from_bq_table(table_object) input_dataset_facets = { "schema": output_dataset_facets["schema"], } input_datasets = [] for blob in sorted(self.source_objects): additional_facets = {} if "*" in blob: # If wildcard ("*") is used in gcs path, we want the name of dataset to be directory name, # but we create a symlink to the full object path with wildcard. additional_facets = { "symlink": SymlinksDatasetFacet( identifiers=[ SymlinksDatasetFacetIdentifiers( namespace=f"gs://{self.bucket}", name=blob, type="file" ) ] ), } blob = Path(blob).parent.as_posix() if blob == ".": # blob path does not have leading slash, but we need root dataset name to be "/" blob = "/" dataset = Dataset( namespace=f"gs://{self.bucket}", name=blob, facets=merge_dicts(input_dataset_facets, additional_facets), ) input_datasets.append(dataset) output_dataset_facets["columnLineage"] = get_identity_column_lineage_facet( field_names=[ for field in table_object.schema], input_datasets=input_datasets ) output_dataset = Dataset( namespace="bigquery", name=str(table_object.reference), facets=output_dataset_facets, ) run_facets = {} if self.job_id: run_facets = { "externalQuery": ExternalQueryRunFacet(externalQueryId=self.job_id, source="bigquery"), } return OperatorLineage(inputs=input_datasets, outputs=[output_dataset], run_facets=run_facets)

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