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fix: avoid unnecessary API call in QueryJob.result() when job is already finished #1900
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CC @chalmerlowe |
@@ -1316,37 +1343,6 @@ def test_result_w_empty_schema(self): | |||
self.assertEqual(result.location, "asia-northeast1") | |||
self.assertEqual(result.query_id, "xyz-abc") | |||
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def test_result_invokes_begins(self): |
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As of #967 released in google-cloud-bigquery 3.0.0, the _begin method is no longer used for query jobs.
@@ -1430,40 +1430,6 @@ def _reload_query_results( | |||
timeout=transport_timeout, | |||
) | |||
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def _done_or_raise(self, retry=DEFAULT_RETRY, timeout=None): |
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This was overridden because we wanted result()
from the superclass to call jobs.getQueryResults
, not just jobs.get
(i.e. job.reload()
in Python). Now that we aren't using the superclass for result()
, this method is no longer necessary.
try: | ||
self.reload(retry=retry, timeout=transport_timeout) | ||
except exceptions.GoogleAPIError as exc: | ||
self.set_exception(exc) |
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Thought: We probably should have been calling set_exception
based on the job status. Need to look into this further.
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OK. We are. 😅
self.set_exception(exception) |
Which we call from _set_properties
self._set_future_result() |
Which we call from reload
self._set_properties(api_response) |
# wait for the query to finish. Unlike most methods, | ||
# jobs.getQueryResults hangs as long as it can to ensure we | ||
# know when the query has finished as soon as possible. | ||
self._reload_query_results(retry=retry, timeout=timeout) |
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Uh oh, if jobs.getQueryResults
fails because the job failed it can throw an exception but restart_query_job
will still be False
.
But we don't want restart_query_job = True
because sometimes this can raise an ambiguous exception such as quota exceeded, where we don't know if it's the job quota and it's a failed job or at a higher level (Google Frontend - GFE) where the job might actually still be running and/or succeeded.
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This isn't the worst way to fail, but it'd be nice to do the jobs.get
call above in case of an exception to get a chance at retrying this job if the job failed.
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I am halfway through my review.
Releasing these comments for now.
Will come back to this to finish out my review as soon as possible.
google/cloud/bigquery/job/query.py
Outdated
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do_get_result() | ||
# timeout can be `None` or an object from our superclass |
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Which superclass
are we discussing here?
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google.api_core.future.polling.PollingFuture._DEFAULT_VALUE
introduced in googleapis/python-api-core#462.
I've updated the comments with some more info as well as some things to consider in case we want to have a default value for timeout in future.
google/cloud/bigquery/retry.py
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@@ -39,7 +39,7 @@ | |||
# Allow for a few retries after the API request times out. This relevant for | |||
# rateLimitExceeded errors, which can be raised either by the Google load | |||
# balancer or the BigQuery job server. | |||
_DEFAULT_JOB_DEADLINE = 3.0 * _DEFAULT_RETRY_DEADLINE | |||
_DEFAULT_JOB_DEADLINE = 4.0 * _DEFAULT_RETRY_DEADLINE |
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What is the purpose of using 4.0 here?
Can we get a comment indicating why 4.0?
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Updated to 2.0 * (2.0 * _DEFAULT_RETRY_DEADLINE)
and added some explanation both here and in QueryJob.result()
.
Note: This still only gets us 1 query retry in the face of the problematic ambiguous error codes from jobs.getQueryResults()
but that's better than the nothing that we were actually getting before in some cases. I don't feel comfortable bumping this much further, though maybe 3.0 * 2.0 * _DEFAULT_RETRY_DEADLINE would be slightly less arbitrary at 1 hour?
@@ -970,7 +877,12 @@ def test_result(self): | |||
"rows": [{"f": [{"v": "abc"}]}], | |||
} | |||
conn = make_connection( | |||
query_resource, query_resource_done, job_resource_done, query_page_resource | |||
job_resource, |
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Not sure I am tracking the relationship between the make connection inputs versus the assert_has_calls checks.
Can you explain how these tests are supposed to work?
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make_connection
is a convention in google-cloud-bigquery
unit tests that actually predates our use of the "mock" package. It mocks out the responses to REST API calls, previously with a fake implementation of our "Connection" class from the _http
module and now with a true mock object. For every quest that our test makes, there should be a corresponding response. As with Mock.side_effect, any exceptions in this list will be raised, instead.
I'm guessing your question also relates to "Why this particular set of requests / responses?". I've added some comments explaining why we're expecting this sequence of API calls. I've also updated this test to more explicitly check for a possible cause of customer issue b/332850329.
@@ -1289,7 +1217,18 @@ def test_result_w_retry(self): | |||
) | |||
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connection.api_request.assert_has_calls( | |||
[query_results_call, query_results_call, reload_call] | |||
[ | |||
reload_call, |
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Same thing here. Can I get some clarity on what we are doing and looking for?
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Added some explanation here as well as above in the make_connection()
call.
Co-authored-by: Chalmer Lowe <chalmerlowe@google.com>
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LGTM
tests/unit/job/test_query.py
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job.result() | ||
with freezegun.freeze_time("1970-01-01 00:00:00", tick=False): | ||
job.result(timeout=1.125) |
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Is there are reason we are using such a specific number?
1.125
.
Can I get a comment here to let future me know why we picked this number?
method="GET", | ||
path=f"/projects/{self.PROJECT}/queries/{self.JOB_ID}", | ||
query_params={ | ||
"maxResults": 0, |
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Is maxResults
of 0
synonymous with asking for all results? OR is it really asking for zero results?
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We actually want 0 rows. If we omit this or ask for non-zero number of rows, the jobs.getQueryResults
API can hang when the query has wide rows (many columns).
Co-authored-by: Chalmer Lowe <chalmerlowe@google.com>
…ast-fix-query-retry
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Thanks for all the comments, etc.
Future me thanks you as well.
LGTM, APPROVED.
BEGIN_COMMIT_OVERRIDE
perf: avoid unnecessary API call in
QueryJob.result()
when job is already finished (#1900)fix: retry query jobs that fail even with ambiguous
jobs.getQueryResults
REST errors (#1903, #1900)END_COMMIT_OVERRIDE
Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly:
query_and_wait
for lower-latency small queries python-bigquery-magics#15 since this loop for waiting for the query to finish will be easier to add a progress bar (if we decide that's needed)