Mlflow db store raise mlflow exception with correct message argument

[WeichenXu123]

Signed-off-by: Weichen Xu weichen.xu@databricks.com

What changes are proposed in this pull request?

Mlflow db store raise mlflow exception with correct message argument

How is this patch tested?

Manually test:
(1) start server first: mlflow server --backend-store-uri sqlite:///mydb.sqlite --default-artifact-root=local-artifacts --host 0.0.0.0 -w 1
(2) run mlflow code:

import mlflow
mlflow.set_tracking_uri('http://localhost:5000/')
mlflow.create_experiment("HelloWorld", artifact_location="local")
mlflow.create_experiment("HelloWorld", artifact_location="local")

the second run "create_experiment" will fail immediately and raise error like:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/weichen.xu/work/mlflow/mlflow/tracking/fluent.py", line 1023, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location, tags)
  File "/Users/weichen.xu/work/mlflow/mlflow/tracking/client.py", line 507, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location, tags)
  File "/Users/weichen.xu/work/mlflow/mlflow/tracking/_tracking_service/client.py", line 176, in create_experiment
    return self.store.create_experiment(
  File "/Users/weichen.xu/work/mlflow/mlflow/store/tracking/rest_store.py", line 99, in create_experiment
    response_proto = self._call_endpoint(CreateExperiment, req_body)
  File "/Users/weichen.xu/work/mlflow/mlflow/store/tracking/rest_store.py", line 56, in _call_endpoint
    return call_endpoint(self.get_host_creds(), endpoint, method, json_body, response_proto)
  File "/Users/weichen.xu/work/mlflow/mlflow/utils/rest_utils.py", line 256, in call_endpoint
    response = verify_rest_response(response, endpoint)
  File "/Users/weichen.xu/work/mlflow/mlflow/utils/rest_utils.py", line 185, in verify_rest_response
    raise RestException(json.loads(response.text))
mlflow.exceptions.RestException: RESOURCE_ALREADY_EXISTS: (sqlite3.IntegrityError) UNIQUE constraint failed: experiments.name
[SQL: INSERT INTO experiments (name, artifact_location, lifecycle_stage) VALUES (?, ?, ?)]
[parameters: ('HelloWorld', 'local', 'active')]
(Background on this error at: https://sqlalche.me/e/14/gkpj)

Does this PR change the documentation?

• No. You can skip the rest of this section.
• Yes. Make sure the changed pages / sections render correctly by following the steps below.

1. Check the status of the ci/circleci: build_doc check. If it's successful, proceed to the
   next step, otherwise fix it.
2. Click Details on the right to open the job page of CircleCI.
3. Click the Artifacts tab.
4. Click docs/build/html/index.html.
5. Find the changed pages / sections and make sure they render correctly.

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

[harupy]

If we do this, I'd define from_exception class method.

[WeichenXu123]

Some tests failed in CI but passed in my local machine. I added this for debugging for now.

[harupy]

why do we need f"Wrong exception {exception_context.exception.message} raised."? How does the pytest error report look like when this line fails?

[WeichenXu123]

I am thinking we should catch all SQLAlchemyError and mark it as "BAD_REQUEST".
Thoughts ? @harupy @dbczumar

I checked the sub-classes of SQLAlchemyError, when these errors happens we should not retry.