Corresponding Migration Functions

Oct 13, 2025 by talynt.org 6 views

Adding Schema/Metadata Migrations: Enhancing Data Integrity and Compatibility

In the ever-evolving landscape of data management, ensuring the integrity and compatibility of metadata across different schema versions is crucial. As new versions of a schema are introduced, it's essential to migrate existing metadata to the latest version, maintaining data consistency and preventing potential issues. In this article, we'll explore the concept of schema/metadata migrations, discuss the requirements, and outline a possible implementation.

Why Schema/Metadata Migrations are Necessary

As a schema evolves, new fields, data types, or structures may be introduced, while others might be deprecated or removed. This change can lead to compatibility issues, especially when working with legacy data. To address this challenge, we need a mechanism to migrate metadata from older versions to newer versions, ensuring seamless data integration and analysis.

Requirements and Design

To implement schema/metadata migrations, we need to address the following requirements:

Migration Block: Introduce a new block, schema_migrations, to log migration history.
Migration Function: Create a single function, upgrade_metadata, to upgrade metadata from version n to n+1.
Corresponding Migration Functions: Develop a migration function for each schema version, if applicable.
Validation and Testing: Establish functions to validate and test migrations in dataio.
ETL Pipeline Integration: Integrate migration functions into ETL pipelines in Sumo.

Sketch of the Schema Migration Block

The following YAML snippet illustrates the proposed schema migration block:

$schema: ..
version: "0.9.0"
source: "fmu"

schema_migration:
  original_version: "0.8.0"
  - migration_datetime: 2025-....
     migration_tool:
       name: fmu-dataio
       version: 2.5.0
     changes:
       - field: "data.standard_result"
          action: "renamed"
          previous_name: "data.product"
          required_context: false  # If FMU runtime is required
          
tracklog:
  - ...
     event: created
     ...
  - datetime: 2025..
     user:
       id: system
     ...
     event: "schema_migrated"
     migration_details:
       from_version: "0.8.0"
       to_version: "0.9.0"
       migration_id: <uuid>

Implementation in Dataio

To implement schema/metadata migrations in dataio, we'll create functions to perform migrations and validate them. The following Python code snippet demonstrates the migrate_schema function:

from fmu.dataio.migrations import migrate_schema

new_schema = migrate_schema(existing_schema, data_object, "0.9.0")
# Apply all schema migrations between the existing schema version and 0.9.0

Open Questions and Considerations

As we design and implement schema/metadata migrations, we need to address the following open questions and considerations:

Metadata Values during FMU Experiment Runtime: How do we handle metadata values that can only be obtained during FMU experiment runtime? We may need to establish default values and flag when they've been generated by a migration.
Optionality and Consumer End: How do we manage optionality, which adds complexity on the consumer end? We should aim to handle such fields programmatically, regardless of the schema version.

Adding schema/metadata migrations is a crucial step in ensuring data integrity and compatibility across different schema versions. By introducing a new block for migration history, creating a single function for upgrading metadata, and developing corresponding migration functions, we can maintain seamless data integration and analysis. As we move forward, we'll need to address open questions and considerations, such as handling metadata values during FMU experiment runtime and managing optionality on the consumer end.
Schema/Metadata Migrations: Frequently Asked Questions (FAQs)

As we discussed in our previous article, adding schema/metadata migrations is a crucial step in ensuring data integrity and compatibility across different schema versions. In this article, we'll address some frequently asked questions (FAQs) related to schema/metadata migrations, providing clarity and insights into this important topic.

Q: What is the purpose of schema/metadata migrations?

A: The primary purpose of schema/metadata migrations is to ensure that metadata is compatible across different schema versions, preventing potential issues and ensuring seamless data integration and analysis.

Q: Why do we need a new block for migration history?

A: A new block for migration history, such as schema_migrations, is necessary to log migration history, track changes, and provide a clear audit trail of schema updates.

Q: How do we handle metadata values that can only be obtained during FMU experiment runtime?

A: To handle metadata values that can only be obtained during FMU experiment runtime, we may need to establish default values and flag when they've been generated by a migration. This ensures that metadata is consistent and accurate, even in cases where runtime values are not available.

Q: How do we manage optionality on the consumer end?

A: To manage optionality on the consumer end, we should aim to handle such fields programmatically, regardless of the schema version. This ensures that consumers can easily work with different schema versions and metadata formats.

Q: What is the role of the `migrate_schema` function in dataio?

A: The migrate_schema function in dataio is responsible for applying all schema migrations between the existing schema version and the target version. This function ensures that metadata is updated correctly and consistently across different schema versions.

Q: How do we integrate migration functions into ETL pipelines in Sumo?

A: To integrate migration functions into ETL pipelines in Sumo, we need to develop a mechanism to call the migrate_schema function and apply the necessary schema migrations during the ETL process. This ensures that metadata is updated correctly and consistently during data integration and analysis.

Q: What are the benefits of schema/metadata migrations?

A: The benefits of schema/metadata migrations include:

Ensuring data integrity and compatibility across different schema versions
Preventing potential issues and errors during data integration and analysis
Providing a clear audit trail of schema updates and changes
Ensuring seamless data integration and analysis across different schema versions

In this article, we've addressed some frequently asked questions (FAQs) related to schema/metadata migrations, providing clarity and insights into this important topic. By understanding the purpose, benefits, and implementation of schema/metadata migrations, we can ensure that our data is accurate, consistent, and easily integratable across different schema versions.