# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# 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
# "License"); you may not use this file except in compliance
# 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
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# 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, 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
"""
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,
):
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
[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,
},
)
[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)
[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,
},
)
[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,
"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
:param days_back: number of days between ds and the ds we want to check
against
:param ratio_formula: ration formula
:param ignore_zero: boolean value to consider zero or not
:param table_id: The table ID of the requested table. (templated)
:param poll_interval: polling period in seconds to check for the status
"""
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,
):
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,
)
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,
},
)
[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
: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
"""
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,
):
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,
)
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,
},
)
[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
"""
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,
):
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
[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,
},
)
def _get_async_hook(self) -> BigQueryTableAsyncHook:
return BigQueryTableAsyncHook(gcp_conn_id=self.gcp_conn_id)
[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
"""
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,
"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)
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
