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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 Conflict
from google.api_core.retry import Retry
from google.cloud.bigquery import DEFAULT_RETRY, CopyJob, ExtractJob, LoadJob, QueryJob
from airflow import AirflowException
from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook, BigQueryJob
from airflow.providers.google.cloud.hooks.gcs import GCSHook
from airflow.providers.google.cloud.links.bigquery import BigQueryTableLink
from airflow.providers.google.cloud.triggers.bigquery import BigQueryInsertJobTrigger
if TYPE_CHECKING:
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:
https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.load
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:
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query.tableDefinitions.(key).csvOptions.encoding
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#externalDataConfiguration.csvOptions.encoding
: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 delegate_to: The account to impersonate using domain-wide delegation of authority,
if any. For this to work, the service account making the request must have
domain-wide delegation enabled.
: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.
:param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
**Example**: ::
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 https://cloud.google.com/bigquery/docs/locations#specifying_your_location
: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",
)
[docs] template_ext: Sequence[str] = (".sql",)
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",
delegate_to=None,
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 = 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,
**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.schema_fields = schema_fields
self.source_format = source_format
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.delegate_to = delegate_to
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
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=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) -> 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,
delegate_to=self.delegate_to,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
self.hook = hook
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]
)
source_uris = [f"gs://{self.bucket}/{source_object}" for source_object in self.source_objects]
if not self.schema_fields:
gcs_hook = GCSHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
if self.schema_object and self.source_format != "DATASTORE_BACKUP":
schema_fields = json.loads(gcs_hook.download(self.bucket, self.schema_object).decode("utf-8"))
self.log.info("Autodetected fields from schema object: %s", schema_fields)
if self.external_table:
self.log.info("Creating a new BigQuery table for storing data...")
project_id, dataset_id, table_id = self.hook.split_tablename(
table_input=self.destination_project_dataset_table,
default_project_id=self.hook.project_id or "",
)
table_resource = {
"tableReference": {
"projectId": project_id,
"datasetId": dataset_id,
"tableId": table_id,
},
"labels": self.labels,
"description": self.description,
"externalDataConfiguration": {
"source_uris": source_uris,
"source_format": self.source_format,
"maxBadRecords": self.max_bad_records,
"autodetect": self.autodetect,
"compression": self.compression,
"csvOptions": {
"fieldDelimeter": self.field_delimiter,
"skipLeadingRows": self.skip_leading_rows,
"quote": self.quote_character,
"allowQuotedNewlines": self.allow_quoted_newlines,
"allowJaggedRows": self.allow_jagged_rows,
},
},
"location": self.location,
"encryptionConfiguration": self.encryption_configuration,
}
table_resource_checked_schema = self._check_schema_fields(table_resource)
table = self.hook.create_empty_table(
table_resource=table_resource_checked_schema,
)
max_id = self._find_max_value_in_column()
BigQueryTableLink.persist(
context=context,
task_instance=self,
dataset_id=table.to_api_repr()["tableReference"]["datasetId"],
project_id=table.to_api_repr()["tableReference"]["projectId"],
table_id=table.to_api_repr()["tableReference"]["tableId"],
)
return max_id
else:
self.log.info("Using existing BigQuery table for storing data...")
destination_project, destination_dataset, destination_table = self.hook.split_tablename(
table_input=self.destination_project_dataset_table,
default_project_id=self.hook.project_id or "",
var_name="destination_project_dataset_table",
)
self.configuration = {
"load": {
"autodetect": self.autodetect,
"createDisposition": self.create_disposition,
"destinationTable": {
"projectId": destination_project,
"datasetId": destination_dataset,
"tableId": destination_table,
},
"destinationTableProperties": {
"description": self.description,
"labels": self.labels,
},
"sourceFormat": self.source_format,
"skipLeadingRows": self.skip_leading_rows,
"sourceUris": source_uris,
"writeDisposition": self.write_disposition,
"ignoreUnknownValues": self.ignore_unknown_values,
"allowQuotedNewlines": self.allow_quoted_newlines,
"encoding": self.encoding,
},
}
self.configuration = self._check_schema_fields(self.configuration)
try:
self.log.info("Executing: %s", self.configuration)
job = self._submit_job(self.hook, job_id)
except Conflict:
# If the job already exists retrieve it
job = self.hook.get_job(
project_id=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,
"project_id": self.hook.project_id,
"table_id": table,
}
if not isinstance(table, str):
persist_kwargs["table_id"] = table["tableId"]
persist_kwargs["dataset_id"] = table["datasetId"]
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.hook.project_id,
),
method_name="execute_complete",
)
else:
job.result(timeout=self.result_timeout, retry=self.result_retry)
max_id = self._find_max_value_in_column()
self._handle_job_error(job)
return max_id
[docs] def execute_complete(self, context: Context, event: dict[str, Any]):
"""
Callback for when the trigger fires - returns immediately.
Relies on trigger to throw an exception, otherwise it assumes execution was
successful.
"""
if event["status"] == "error":
raise AirflowException(event["message"])
self.log.info(
"%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,
delegate_to=self.delegate_to,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
if self.max_id_key:
self.log.info(f"Selecting the MAX value from BigQuery column '{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": [],
}
}
job_id = hook.insert_job(configuration=self.configuration, project_id=hook.project_id)
rows = list(hook.get_job(job_id=job_id, location=self.location).result())
if rows:
for row in rows:
max_id = row[0] if row[0] else 0
self.log.info(
"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 _check_schema_fields(self, table_resource):
"""
Helper method to detect schema fields if they were not specified by user and autodetect=True.
If source_objects were passed, method reads the second row in CSV file. If there is at least one digit
table_resurce is returned without changes so that BigQuery can determine schema_fields in the
next step.
If there are only characters, the first row with fields is used to construct schema_fields argument
with type 'STRING'. Table_resource is updated with new schema_fileds key and returned back to operator
:param table_resource: Configuration or table_resource dictionary
:return: table_resource: Updated table_resource dict with schema_fields
"""
if not self.autodetect and not self.schema_fields:
raise RuntimeError(
"Table schema was not found. Set autodetect=True to "
"automatically set schema fields from source objects or pass "
"schema_fields explicitly"
)
elif not self.schema_fields:
for source_object in self.source_objects:
gcs_hook = GCSHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
blob = gcs_hook.download(
bucket_name=self.schema_object_bucket,
object_name=source_object,
)
fields, values = [item.split(",") for item in blob.decode("utf-8").splitlines()][:2]
import re
if any(re.match(r"[\d\-\\.]+$", value) for value in values):
return table_resource
else:
schema_fields = []
for field in fields:
schema_fields.append({"name": field, "type": "STRING", "mode": "NULLABLE"})
self.schema_fields = schema_fields
if self.external_table:
table_resource["externalDataConfiguration"]["csvOptions"]["skipLeadingRows"] = 1
elif not self.external_table:
table_resource["load"]["skipLeadingRows"] = 1
if self.external_table:
table_resource["schema"] = {"fields": self.schema_fields}
elif not self.external_table:
table_resource["load"]["schema"] = {"fields": self.schema_fields}
return table_resource
[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:
self.log.info("Skipping to cancel job: %s.%s", self.location, self.job_id)
