Description
I know BigQuery jobs are asynchronous by default. However, I am struggling to make my datapipeline async end-to-end.
Looking at this JS example, I thought it would be the most Pythonic to make a BigQuery job awaitable. However, I can't get that to work in Python i.e. errors when await client.query(query). Looking at the source code, I don't see which method returns an awaitable object.
I have little experience in writing async Python code and found this example that wraps jobs in a async def coroutine.
class BQApi(object):
def __init__(self):
self.api = bigquery.Client.from_service_account_json(BQ_CONFIG["credentials"])
async def exec_query(self, query, **kwargs) -> bigquery.table.RowIterator:
job = self.api.query(query, **kwargs)
task = asyncio.create_task(self.coroutine_job(job))
return await task
@staticmethod
async def coroutine_job(job):
return job.result()
The google.api_core.operation.Operation shows how to use add_done_callback to asynchronously wait for long-running operations. I have tried that, but the following yields AttributeError: 'QueryJob' object has no attribute '_condition' :
from concurrent.futures import ThreadPoolExecutor, as_completed
query1 = 'SELECT 1'
query2 = 'SELECT 2'
def my_callback(future):
result = future.result()
operations = [bq.query(query1), bq.query(query2)]
[operation.add_done_callback(my_callback) for operation in operations]
results2 = []
for future in as_completed(operations):
results2.append(list(future.result()))
Given that jobs are already asynchronous, would it make sense to add a method that returns an awaitable?
Or am I missing something and is there an Pythonic way to use the BigQuery client with the async/await pattern?