[BUG] Alembic migration for metrics table uses incorrect server default

MLflow Roadmap Item

This is an MLflow Roadmap item that has been prioritized by the MLflow maintainers. We’re seeking help with the implementation of roadmap items tagged with the help wanted label.

For requirements clarifications and implementation questions, or to request a PR review, please tag @harupy in your communications related to this issue.

System information

• Have I written custom code (as opposed to using a stock example script provided in MLflow): No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): miniconda container - debian buster
• MLflow installed from (source or binary): mflow from pypi
• MLflow version (run mlflow --version): mlflow 1.14.1 trying to upgrade to 1.16 or 1.17
• Python version: Python 3.9.2
• npm version, if running the dev UI: NA
• Exact command to reproduce: mlflow db upgrade <MSSQL connection string >
• Tracking server DB: Azure Microsoft SQL DB

Describe the problem

When I upgrade the database from 1.14.1 to a higher version I get an error. Currently use an Azure MSFT DB. Would like to upgrade to 1.16 or 1.17

Code to reproduce issue

mlflow db upgrade “mssql+pyodbc://_rest_of_conn_string”

Other info / logs

sqlalchemy.exc.ProgrammingError: (pyodbc.ProgrammingError) (‘42000’, ‘[42000] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]Column already has a DEFAULT bound to it. (1781) (SQLExecDirectW)’) [SQL: ALTER TABLE metrics ADD DEFAULT ‘0’ FOR is_nan] (Background on this error at: http://sqlalche.me/e/14/f405)

What component(s), interfaces, languages, and integrations does this bug 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: Local serving, model deployment tools, spark UDFs
• area/server-infra: MLflow server, JavaScript dev server
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, JavaScript, plotting
• 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

[GlamTam]

@marijncv , @harupy. Works like a charm for me! Also kudos for the included SQL tests

[VirajVaitha123]

Facing this issue as I try connect to SQL DB as backend-component too. Any updates on this please?