Retry on 503 (#1408)

* add default retry on all client factories, which includes 502 and 503 errors
* update retries to use defaults and ensure that a timeout or deadline is set

.changes/unreleased/Fixes-20241120-163101.yaml

@@ -0,0 +1,7 @@
+kind: Fixes
+body: Fix issue where dbt-bigquery was not retrying in certain retryable scenarios,
+  e.g. 503's
+time: 2024-11-20T16:31:01.60689-05:00
+custom:
+  Author: mikealfare
+  Issue: "682"

dbt/adapters/bigquery/clients.py

@@ -1,10 +1,10 @@
 from google.api_core.client_info import ClientInfo
 from google.api_core.client_options import ClientOptions
-from google.api_core.retry import Retry
 from google.auth.exceptions import DefaultCredentialsError
-from google.cloud.bigquery import Client as BigQueryClient
+from google.cloud.bigquery import Client as BigQueryClient, DEFAULT_RETRY as BQ_DEFAULT_RETRY
 from google.cloud.dataproc_v1 import BatchControllerClient, JobControllerClient
 from google.cloud.storage import Client as StorageClient
+from google.cloud.storage.retry import DEFAULT_RETRY as GCS_DEFAULT_RETRY
 
 from dbt.adapters.events.logging import AdapterLogger
 
@@ -28,23 +28,23 @@ def create_bigquery_client(credentials: BigQueryCredentials) -> BigQueryClient:
         return _create_bigquery_client(credentials)
 
 
-@Retry()  # google decorator. retries on transient errors with exponential backoff
+@GCS_DEFAULT_RETRY
 def create_gcs_client(credentials: BigQueryCredentials) -> StorageClient:
     return StorageClient(
         project=credentials.execution_project,
         credentials=create_google_credentials(credentials),
     )
 
 
-@Retry()  # google decorator. retries on transient errors with exponential backoff
+# dataproc does not appear to have a default retry like BQ and GCS
 def create_dataproc_job_controller_client(credentials: BigQueryCredentials) -> JobControllerClient:
     return JobControllerClient(
         credentials=create_google_credentials(credentials),
         client_options=ClientOptions(api_endpoint=_dataproc_endpoint(credentials)),
     )
 
 
-@Retry()  # google decorator. retries on transient errors with exponential backoff
+# dataproc does not appear to have a default retry like BQ and GCS
 def create_dataproc_batch_controller_client(
     credentials: BigQueryCredentials,
 ) -> BatchControllerClient:
@@ -54,7 +54,7 @@ def create_dataproc_batch_controller_client(
     )
 
 
-@Retry()  # google decorator. retries on transient errors with exponential backoff
+@BQ_DEFAULT_RETRY
 def _create_bigquery_client(credentials: BigQueryCredentials) -> BigQueryClient:
     return BigQueryClient(
         credentials.execution_project,

dbt/adapters/bigquery/retry.py

@@ -2,7 +2,7 @@
 
 from google.api_core.future.polling import DEFAULT_POLLING
 from google.api_core.retry import Retry
-from google.cloud.bigquery.retry import DEFAULT_RETRY, _job_should_retry
+from google.cloud.bigquery.retry import DEFAULT_JOB_RETRY, _job_should_retry
 from requests.exceptions import ConnectionError
 
 from dbt.adapters.contracts.connection import Connection, ConnectionState
@@ -15,14 +15,8 @@
 
 _logger = AdapterLogger("BigQuery")
 
-
-_SECOND = 1.0
-_MINUTE = 60 * _SECOND
-_HOUR = 60 * _MINUTE
-_DAY = 24 * _HOUR
-_DEFAULT_INITIAL_DELAY = _SECOND
-_DEFAULT_MAXIMUM_DELAY = 3 * _SECOND
-_DEFAULT_POLLING_MAXIMUM_DELAY = 10 * _SECOND
+_MINUTE = 60.0
+_DAY = 24 * 60 * 60.0
 
 
 class RetryFactory:
@@ -44,7 +38,7 @@ def create_job_execution_timeout(self, fallback: float = _DAY) -> float:
         )  # keep _DAY here so it's not overridden by passing fallback=None
 
     def create_retry(self, fallback: Optional[float] = None) -> Retry:
-        return DEFAULT_RETRY.with_timeout(self._job_execution_timeout or fallback or _DAY)
+        return DEFAULT_JOB_RETRY.with_timeout(self._job_execution_timeout or fallback or _DAY)
 
     def create_polling(self, model_timeout: Optional[float] = None) -> Retry:
         return DEFAULT_POLLING.with_timeout(model_timeout or self._job_execution_timeout or _DAY)
@@ -53,14 +47,21 @@ def create_reopen_with_deadline(self, connection: Connection) -> Retry:
         """
         This strategy mimics what was accomplished with _retry_and_handle
         """
-        return Retry(
-            predicate=_DeferredException(self._retries),
-            initial=_DEFAULT_INITIAL_DELAY,
-            maximum=_DEFAULT_MAXIMUM_DELAY,
-            deadline=self._job_deadline,
-            on_error=_create_reopen_on_error(connection),
+
+        retry = DEFAULT_JOB_RETRY.with_delay(maximum=3.0).with_predicate(
+            _DeferredException(self._retries)
         )
 
+        # there is no `with_on_error` method, but we want to retain the defaults on `DEFAULT_JOB_RETRY
+        retry._on_error = _create_reopen_on_error(connection)
+
+        # don't override the default deadline to None if the user did not provide one,
+        # the process will never end
+        if deadline := self._job_deadline:
+            return retry.with_deadline(deadline)
+
+        return retry
+
 
 class _DeferredException:
     """

hatch.toml

@@ -8,6 +8,7 @@ packages = ["dbt"]
 packages = ["dbt"]
 
 [envs.default]
+python = "3.9"
 dependencies = [
     "dbt-adapters @ git+https://github.com/dbt-labs/dbt-adapters.git",
     "dbt-common @ git+https://github.com/dbt-labs/dbt-common.git",