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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
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# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import asyncio
from typing import Any, AsyncIterator, Sequence, SupportsAbs
from aiohttp import ClientSession
from aiohttp.client_exceptions import ClientResponseError
from airflow.providers.google.cloud.hooks.bigquery import BigQueryAsyncHook, BigQueryTableAsyncHook
from airflow.triggers.base import BaseTrigger, TriggerEvent
[docs]class BigQueryInsertJobTrigger(BaseTrigger):
"""
BigQueryInsertJobTrigger run on the trigger worker to perform insert operation.
:param conn_id: Reference to google cloud connection id
:param job_id: The ID of the job. It will be suffixed with hash of job configuration
:param project_id: Google Cloud Project where the job is running
:param dataset_id: The dataset ID of the requested table. (templated)
:param table_id: The table ID of the requested table. (templated)
:param poll_interval: polling period in seconds to check for the status. (templated)
: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)
"""
def __init__(
self,
conn_id: str,
job_id: str | None,
project_id: str | None,
dataset_id: str | None = None,
table_id: str | None = None,
poll_interval: float = 4.0,
impersonation_chain: str | Sequence[str] | None = None,
):
super().__init__()
self.log.info("Using the connection %s .", conn_id)
self.conn_id = conn_id
self.job_id = job_id
self._job_conn = None
self.dataset_id = dataset_id
self.project_id = project_id
self.table_id = table_id
self.poll_interval = poll_interval
self.impersonation_chain = impersonation_chain
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BigQueryInsertJobTrigger arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.bigquery.BigQueryInsertJobTrigger",
{
"conn_id": self.conn_id,
"job_id": self.job_id,
"dataset_id": self.dataset_id,
"project_id": self.project_id,
"table_id": self.table_id,
"poll_interval": self.poll_interval,
"impersonation_chain": self.impersonation_chain,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Gets current job execution status and yields a TriggerEvent."""
hook = self._get_async_hook()
try:
while True:
job_status = await hook.get_job_status(job_id=self.job_id, project_id=self.project_id)
if job_status["status"] == "success":
yield TriggerEvent(
{
"job_id": self.job_id,
"status": job_status["status"],
"message": job_status["message"],
}
)
return
elif job_status["status"] == "error":
yield TriggerEvent(job_status)
return
else:
self.log.info(
"Bigquery job status is %s. Sleeping for %s seconds.",
job_status["status"],
self.poll_interval,
)
await asyncio.sleep(self.poll_interval)
except Exception as e:
self.log.exception("Exception occurred while checking for query completion")
yield TriggerEvent({"status": "error", "message": str(e)})
def _get_async_hook(self) -> BigQueryAsyncHook:
return BigQueryAsyncHook(gcp_conn_id=self.conn_id, impersonation_chain=self.impersonation_chain)
[docs]class BigQueryCheckTrigger(BigQueryInsertJobTrigger):
"""BigQueryCheckTrigger run on the trigger worker."""
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BigQueryCheckTrigger arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.bigquery.BigQueryCheckTrigger",
{
"conn_id": self.conn_id,
"job_id": self.job_id,
"dataset_id": self.dataset_id,
"project_id": self.project_id,
"table_id": self.table_id,
"poll_interval": self.poll_interval,
"impersonation_chain": self.impersonation_chain,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Gets current job execution status and yields a TriggerEvent."""
hook = self._get_async_hook()
try:
while True:
# Poll for job execution status
job_status = await hook.get_job_status(job_id=self.job_id, project_id=self.project_id)
if job_status["status"] == "success":
query_results = await hook.get_job_output(job_id=self.job_id, project_id=self.project_id)
records = hook.get_records(query_results)
# If empty list, then no records are available
if not records:
yield TriggerEvent(
{
"status": job_status["status"],
"message": job_status["message"],
"records": None,
}
)
return
else:
# Extract only first record from the query results
first_record = records.pop(0)
yield TriggerEvent(
{
"status": job_status["status"],
"message": job_status["message"],
"records": first_record,
}
)
return
elif job_status["status"] == "error":
yield TriggerEvent({"status": "error", "message": job_status["message"]})
return
else:
self.log.info(
"Bigquery job status is %s. Sleeping for %s seconds.",
job_status["status"],
self.poll_interval,
)
await asyncio.sleep(self.poll_interval)
except Exception as e:
self.log.exception("Exception occurred while checking for query completion")
yield TriggerEvent({"status": "error", "message": str(e)})
[docs]class BigQueryGetDataTrigger(BigQueryInsertJobTrigger):
"""
BigQueryGetDataTrigger run on the trigger worker, inherits from BigQueryInsertJobTrigger class.
:param as_dict: if True returns the result as a list of dictionaries, otherwise as list of lists
(default: False).
"""
def __init__(self, as_dict: bool = False, **kwargs):
super().__init__(**kwargs)
self.as_dict = as_dict
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BigQueryInsertJobTrigger arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.bigquery.BigQueryGetDataTrigger",
{
"conn_id": self.conn_id,
"job_id": self.job_id,
"dataset_id": self.dataset_id,
"project_id": self.project_id,
"table_id": self.table_id,
"poll_interval": self.poll_interval,
"impersonation_chain": self.impersonation_chain,
"as_dict": self.as_dict,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Gets current job execution status and yields a TriggerEvent with response data."""
hook = self._get_async_hook()
try:
while True:
# Poll for job execution status
job_status = await hook.get_job_status(job_id=self.job_id, project_id=self.project_id)
if job_status["status"] == "success":
query_results = await hook.get_job_output(job_id=self.job_id, project_id=self.project_id)
records = hook.get_records(query_results=query_results, as_dict=self.as_dict)
self.log.debug("Response from hook: %s", job_status["status"])
yield TriggerEvent(
{
"status": "success",
"message": job_status["message"],
"records": records,
}
)
return
elif job_status["status"] == "error":
yield TriggerEvent(job_status)
return
else:
self.log.info(
"Bigquery job status is %s. Sleeping for %s seconds.",
job_status["status"],
self.poll_interval,
)
await asyncio.sleep(self.poll_interval)
except Exception as e:
self.log.exception("Exception occurred while checking for query completion")
yield TriggerEvent({"status": "error", "message": str(e)})
[docs]class BigQueryIntervalCheckTrigger(BigQueryInsertJobTrigger):
"""
BigQueryIntervalCheckTrigger run on the trigger worker, inherits from BigQueryInsertJobTrigger class.
:param conn_id: Reference to google cloud connection id
:param first_job_id: The ID of the job 1 performed
:param second_job_id: The ID of the job 2 performed
:param project_id: Google Cloud Project where the job is running
:param dataset_id: The dataset ID of the requested table. (templated)
:param table: table name
:param metrics_thresholds: dictionary of ratios indexed by metrics
:param date_filter_column: column name. (templated)
:param days_back: number of days between ds and the ds we want to check against. (templated)
:param ratio_formula: ration formula. (templated)
:param ignore_zero: boolean value to consider zero or not. (templated)
:param table_id: The table ID of the requested table. (templated)
:param poll_interval: polling period in seconds to check for the status. (templated)
: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)
"""
def __init__(
self,
conn_id: str,
first_job_id: str,
second_job_id: str,
project_id: str | None,
table: str,
metrics_thresholds: dict[str, int],
date_filter_column: str | None = "ds",
days_back: SupportsAbs[int] = -7,
ratio_formula: str = "max_over_min",
ignore_zero: bool = True,
dataset_id: str | None = None,
table_id: str | None = None,
poll_interval: float = 4.0,
impersonation_chain: str | Sequence[str] | None = None,
):
super().__init__(
conn_id=conn_id,
job_id=first_job_id,
project_id=project_id,
dataset_id=dataset_id,
table_id=table_id,
poll_interval=poll_interval,
impersonation_chain=impersonation_chain,
)
self.conn_id = conn_id
self.first_job_id = first_job_id
self.second_job_id = second_job_id
self.project_id = project_id
self.table = table
self.metrics_thresholds = metrics_thresholds
self.date_filter_column = date_filter_column
self.days_back = days_back
self.ratio_formula = ratio_formula
self.ignore_zero = ignore_zero
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BigQueryCheckTrigger arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.bigquery.BigQueryIntervalCheckTrigger",
{
"conn_id": self.conn_id,
"first_job_id": self.first_job_id,
"second_job_id": self.second_job_id,
"project_id": self.project_id,
"table": self.table,
"metrics_thresholds": self.metrics_thresholds,
"date_filter_column": self.date_filter_column,
"days_back": self.days_back,
"ratio_formula": self.ratio_formula,
"ignore_zero": self.ignore_zero,
"impersonation_chain": self.impersonation_chain,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Gets current job execution status and yields a TriggerEvent."""
hook = self._get_async_hook()
try:
while True:
first_job_response_from_hook = await hook.get_job_status(
job_id=self.first_job_id, project_id=self.project_id
)
second_job_response_from_hook = await hook.get_job_status(
job_id=self.second_job_id, project_id=self.project_id
)
if (
first_job_response_from_hook["status"] == "success"
and second_job_response_from_hook["status"] == "success"
):
first_query_results = await hook.get_job_output(
job_id=self.first_job_id, project_id=self.project_id
)
second_query_results = await hook.get_job_output(
job_id=self.second_job_id, project_id=self.project_id
)
first_records = hook.get_records(first_query_results)
second_records = hook.get_records(second_query_results)
# If empty list, then no records are available
if not first_records:
first_job_row: str | None = None
else:
# Extract only first record from the query results
first_job_row = first_records.pop(0)
# If empty list, then no records are available
if not second_records:
second_job_row: str | None = None
else:
# Extract only first record from the query results
second_job_row = second_records.pop(0)
hook.interval_check(
first_job_row,
second_job_row,
self.metrics_thresholds,
self.ignore_zero,
self.ratio_formula,
)
yield TriggerEvent(
{
"status": "success",
"message": "Job completed",
"first_row_data": first_job_row,
"second_row_data": second_job_row,
}
)
return
elif (
first_job_response_from_hook["status"] == "pending"
or second_job_response_from_hook["status"] == "pending"
):
self.log.info("Query is still running...")
self.log.info("Sleeping for %s seconds.", self.poll_interval)
await asyncio.sleep(self.poll_interval)
else:
yield TriggerEvent(
{"status": "error", "message": second_job_response_from_hook["message"], "data": None}
)
return
except Exception as e:
self.log.exception("Exception occurred while checking for query completion")
yield TriggerEvent({"status": "error", "message": str(e)})
[docs]class BigQueryValueCheckTrigger(BigQueryInsertJobTrigger):
"""
BigQueryValueCheckTrigger run on the trigger worker, inherits from BigQueryInsertJobTrigger class.
:param conn_id: Reference to google cloud connection id
:param sql: the sql to be executed
:param pass_value: pass value
:param job_id: The ID of the job
:param project_id: Google Cloud Project where the job is running
:param tolerance: certain metrics for tolerance. (templated)
:param dataset_id: The dataset ID of the requested table. (templated)
:param table_id: The table ID of the requested table. (templated)
:param poll_interval: polling period in seconds to check for the status. (templated)
: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).
"""
def __init__(
self,
conn_id: str,
sql: str,
pass_value: int | float | str,
job_id: str | None,
project_id: str | None,
tolerance: Any = None,
dataset_id: str | None = None,
table_id: str | None = None,
poll_interval: float = 4.0,
impersonation_chain: str | Sequence[str] | None = None,
):
super().__init__(
conn_id=conn_id,
job_id=job_id,
project_id=project_id,
dataset_id=dataset_id,
table_id=table_id,
poll_interval=poll_interval,
impersonation_chain=impersonation_chain,
)
self.sql = sql
self.pass_value = pass_value
self.tolerance = tolerance
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BigQueryValueCheckTrigger arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.bigquery.BigQueryValueCheckTrigger",
{
"conn_id": self.conn_id,
"pass_value": self.pass_value,
"job_id": self.job_id,
"dataset_id": self.dataset_id,
"project_id": self.project_id,
"sql": self.sql,
"table_id": self.table_id,
"tolerance": self.tolerance,
"poll_interval": self.poll_interval,
"impersonation_chain": self.impersonation_chain,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Gets current job execution status and yields a TriggerEvent."""
hook = self._get_async_hook()
try:
while True:
# Poll for job execution status
response_from_hook = await hook.get_job_status(job_id=self.job_id, project_id=self.project_id)
if response_from_hook["status"] == "success":
query_results = await hook.get_job_output(job_id=self.job_id, project_id=self.project_id)
records = hook.get_records(query_results)
records = records.pop(0) if records else None
hook.value_check(self.sql, self.pass_value, records, self.tolerance)
yield TriggerEvent({"status": "success", "message": "Job completed", "records": records})
return
elif response_from_hook["status"] == "pending":
self.log.info("Query is still running...")
self.log.info("Sleeping for %s seconds.", self.poll_interval)
await asyncio.sleep(self.poll_interval)
else:
yield TriggerEvent(
{"status": "error", "message": response_from_hook["message"], "records": None}
)
return
except Exception as e:
self.log.exception("Exception occurred while checking for query completion")
yield TriggerEvent({"status": "error", "message": str(e)})
[docs]class BigQueryTableExistenceTrigger(BaseTrigger):
"""
Initialize the BigQuery Table Existence Trigger with needed parameters.
:param project_id: Google Cloud Project where the job is running
:param dataset_id: The dataset ID of the requested table.
:param table_id: The table ID of the requested table.
:param gcp_conn_id: Reference to google cloud connection id
:param hook_params: params for hook
:param poll_interval: polling period in seconds to check for the status
: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)
"""
def __init__(
self,
project_id: str,
dataset_id: str,
table_id: str,
gcp_conn_id: str,
hook_params: dict[str, Any],
poll_interval: float = 4.0,
impersonation_chain: str | Sequence[str] | None = None,
):
self.dataset_id = dataset_id
self.project_id = project_id
self.table_id = table_id
self.gcp_conn_id: str = gcp_conn_id
self.poll_interval = poll_interval
self.hook_params = hook_params
self.impersonation_chain = impersonation_chain
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BigQueryTableExistenceTrigger arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.bigquery.BigQueryTableExistenceTrigger",
{
"dataset_id": self.dataset_id,
"project_id": self.project_id,
"table_id": self.table_id,
"gcp_conn_id": self.gcp_conn_id,
"poll_interval": self.poll_interval,
"hook_params": self.hook_params,
"impersonation_chain": self.impersonation_chain,
},
)
def _get_async_hook(self) -> BigQueryTableAsyncHook:
return BigQueryTableAsyncHook(
gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Will run until the table exists in the Google Big Query."""
try:
while True:
hook = self._get_async_hook()
response = await self._table_exists(
hook=hook, dataset=self.dataset_id, table_id=self.table_id, project_id=self.project_id
)
if response:
yield TriggerEvent({"status": "success", "message": "success"})
return
await asyncio.sleep(self.poll_interval)
except Exception as e:
self.log.exception("Exception occurred while checking for Table existence")
yield TriggerEvent({"status": "error", "message": str(e)})
async def _table_exists(
self, hook: BigQueryTableAsyncHook, dataset: str, table_id: str, project_id: str
) -> bool:
"""
Create session, make call to BigQueryTableAsyncHook, and check for the table in Google Big Query.
:param hook: BigQueryTableAsyncHook Hook class
:param dataset: The name of the dataset in which to look for the table storage bucket.
:param table_id: The name of the table to check the existence of.
:param project_id: The Google cloud project in which to look for the table.
The connection supplied to the hook must provide
access to the specified project.
"""
async with ClientSession() as session:
try:
client = await hook.get_table_client(
dataset=dataset, table_id=table_id, project_id=project_id, session=session
)
response = await client.get()
return bool(response)
except ClientResponseError as err:
if err.status == 404:
return False
raise err
[docs]class BigQueryTablePartitionExistenceTrigger(BigQueryTableExistenceTrigger):
"""
Initialize the BigQuery Table Partition Existence Trigger with needed parameters.
:param partition_id: The name of the partition to check the existence of.
:param project_id: Google Cloud Project where the job is running
:param dataset_id: The dataset ID of the requested table.
:param table_id: The table ID of the requested table.
:param gcp_conn_id: Reference to google cloud connection id
:param hook_params: params for hook
:param poll_interval: polling period in seconds to check for the status
: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)
"""
def __init__(self, partition_id: str, **kwargs):
super().__init__(**kwargs)
self.partition_id = partition_id
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BigQueryTablePartitionExistenceTrigger arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.bigquery.BigQueryTablePartitionExistenceTrigger",
{
"partition_id": self.partition_id,
"dataset_id": self.dataset_id,
"project_id": self.project_id,
"table_id": self.table_id,
"gcp_conn_id": self.gcp_conn_id,
"poll_interval": self.poll_interval,
"impersonation_chain": self.impersonation_chain,
"hook_params": self.hook_params,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Will run until the table exists in the Google Big Query."""
hook = BigQueryAsyncHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
job_id = None
while True:
if job_id is not None:
job_status = await hook.get_job_status(job_id=job_id, project_id=self.project_id)
if job_status["status"] == "success":
is_partition = await self._partition_exists(
hook=hook, job_id=job_id, project_id=self.project_id
)
if is_partition:
yield TriggerEvent(
{
"status": "success",
"message": f"Partition: {self.partition_id} in table: {self.table_id}",
}
)
return
job_id = None
elif job_status["status"] == "error":
yield TriggerEvent(job_status)
return
self.log.info("Sleeping for %s seconds.", self.poll_interval)
await asyncio.sleep(self.poll_interval)
else:
job_id = await hook.create_job_for_partition_get(self.dataset_id, project_id=self.project_id)
self.log.info("Sleeping for %s seconds.", self.poll_interval)
await asyncio.sleep(self.poll_interval)
async def _partition_exists(self, hook: BigQueryAsyncHook, job_id: str | None, project_id: str):
query_results = await hook.get_job_output(job_id=job_id, project_id=project_id)
records = hook.get_records(query_results)
if records:
records = [row[0] for row in records]
return self.partition_id in records
