Azure SQL Default Compatibility Level 170 Implications And Migration Guide

Introduction

This article explores the implications of setting the default compatibility level of Azure SQL Database to 170, particularly focusing on its impact on Entity Framework Core (EF Core) and JSON data handling. The initial motivation for this change stemmed from enhancing the EFCore.CheckConstraints plugin's ability to leverage new regular expression support. However, the decision has evolved to encompass broader changes, notably how complex JSON structures are mapped within Azure SQL databases. Specifically, compatibility level 170 introduces the mapping of JSON data to the native json data type, contrasting with the previous approach of using nvarchar(max). This shift represents a significant change, especially for existing applications relying on EF Core and Azure SQL, as upgrading to EF 10 with this compatibility level will trigger migrations altering existing JSON columns. This article delves into the rationale behind this decision, the potential consequences, and the migration strategies involved. Understanding these nuances is crucial for developers and database administrators to ensure a smooth transition and maintain application functionality. The change in compatibility level is not merely a technical update; it is a strategic move that impacts data storage, performance, and overall application architecture. Therefore, a comprehensive understanding of the implications is essential for making informed decisions about database upgrades and application development.

Background and Motivation

The original impetus for considering a change in the default compatibility level arose from the need to enhance the EFCore.CheckConstraints plugin. This plugin aims to provide more robust constraint checking within EF Core applications, and one specific area of improvement was the integration of new regular expression support in Azure SQL. Regular expressions offer a powerful mechanism for validating data, ensuring that it conforms to predefined patterns and formats. By leveraging regular expression capabilities within database constraints, developers can enforce stricter data integrity rules, reducing the likelihood of invalid data being persisted in the database. This approach aligns with the principle of shifting data validation closer to the data storage layer, which can improve overall application reliability and performance. However, to fully utilize these new regular expression features, the plugin needed a way to detect whether the underlying database supported them. The compatibility level of the Azure SQL database became a key indicator of this support. As discussions progressed, it became clear that the compatibility level could serve a broader purpose beyond just enabling regular expression support. The potential to influence how complex JSON data is handled emerged as a significant consideration. JSON (JavaScript Object Notation) has become a ubiquitous format for data interchange, and many applications rely on storing and manipulating JSON data within relational databases. The decision to use the compatibility level to determine the JSON mapping strategy reflects a desire to optimize both storage and querying of JSON data in Azure SQL databases. This dual motivation—enhancing constraint checking and optimizing JSON data handling—underscores the strategic importance of setting the default compatibility level to 170. The implications of this decision ripple through various aspects of application development and database management, necessitating a thorough understanding of the changes involved.

The Shift in JSON Mapping

A pivotal aspect of setting the Azure SQL default compatibility level to 170 lies in the way complex JSON structures are mapped within the database. Prior to this change, JSON data was typically stored as nvarchar(max), a generic large-string data type. While this approach provided a straightforward way to persist JSON documents, it lacked the specific optimizations and functionalities associated with native JSON support. The nvarchar(max) approach treats JSON data as plain text, meaning that the database engine cannot interpret the internal structure of the JSON document. This limitation impacts query performance, indexing capabilities, and the ability to perform type-specific operations on JSON data. With the introduction of compatibility level 170, Azure SQL gains the ability to map JSON data to the native json data type. This data type is specifically designed for storing and manipulating JSON documents, offering several advantages over the nvarchar(max) approach. First and foremost, the json data type allows the database engine to parse and understand the structure of the JSON document. This understanding enables optimizations for storage and retrieval, leading to improved query performance. For example, the database can create indexes on specific elements within the JSON document, allowing for faster lookups and filtering. Additionally, the json data type provides built-in functions for querying and manipulating JSON data, such as extracting values, updating elements, and validating the JSON structure. These functions can simplify application logic and reduce the need for custom parsing and manipulation code. The shift to the json data type also impacts the way EF Core interacts with JSON data. When compatibility level 170 is active, EF Core can leverage the native JSON support in Azure SQL, providing a more seamless and efficient way to work with JSON data within .NET applications. This integration can lead to cleaner code, improved performance, and a more natural mapping between .NET objects and JSON documents stored in the database. However, this transition also introduces considerations for existing applications, particularly those that have already stored JSON data as nvarchar(max). The implications of migrating existing data to the json data type are discussed in detail in subsequent sections.

Implications for EF Core and Migrations

The decision to map JSON data to the native json data type in Azure SQL compatibility level 170 carries significant implications for applications using Entity Framework Core (EF Core), particularly concerning database migrations. EF Core is an Object-Relational Mapper (ORM) that simplifies database interactions in .NET applications. It allows developers to work with databases using .NET objects, abstracting away much of the complexity of raw SQL queries. Database migrations are a crucial feature of EF Core, enabling developers to evolve their database schema in a controlled and repeatable manner. Migrations track changes to the data model and generate the necessary SQL scripts to update the database schema accordingly. When the default compatibility level is set to 170, and an application using EF Core upgrades to version 10 or later, a migration will be generated to alter existing JSON columns. This migration will change columns that were previously mapped as nvarchar(max) to the json data type. This change is significant because it directly impacts the underlying storage format of JSON data in the database. While this migration is generally beneficial, as it unlocks the performance and functionality advantages of the native json data type, it also introduces potential challenges. One key consideration is the potential for data loss or corruption during the migration process. If the existing data stored in nvarchar(max) columns is not valid JSON, the migration to the json data type may fail or result in data being truncated or discarded. Therefore, it is crucial to ensure that existing JSON data is properly formatted and validated before applying the migration. Another implication is the potential impact on application code. If the application relies on specific behaviors or limitations of the nvarchar(max) data type, such as string-based operations or character encoding, these may need to be adjusted to align with the characteristics of the json data type. For example, if the application performs string comparisons on JSON data, these comparisons may need to be updated to use the JSON-specific functions provided by Azure SQL. Furthermore, the migration to the json data type may affect the performance of existing queries and operations. While the native json data type generally offers performance improvements, there may be specific cases where query plans need to be reevaluated or indexes need to be adjusted to optimize performance. Therefore, thorough testing and performance monitoring are essential after applying the migration. The owned mapping feature in EF Core further complicates the migration process. Owned entities are entities that are conceptually part of another entity and do not have their own identity. When an owned entity contains JSON properties, the mapping to the json data type needs to be handled carefully. The migration process must ensure that the owned entity's JSON properties are correctly migrated to the json data type while maintaining the relationship with the owning entity. In summary, the migration to the json data type in Azure SQL compatibility level 170 is a significant change that requires careful planning and execution. Developers need to be aware of the potential implications for data integrity, application code, and performance. Thorough testing and monitoring are crucial to ensure a smooth transition and to fully realize the benefits of the native json data type.

Migration Strategies and Considerations

Migrating existing Azure SQL databases to compatibility level 170 and transitioning JSON columns from nvarchar(max) to the json data type requires a well-thought-out strategy. This process is not merely a technical upgrade but a strategic shift that impacts data storage, application functionality, and overall system performance. Therefore, a phased approach, coupled with thorough testing and monitoring, is highly recommended. Before initiating the migration, a comprehensive assessment of the existing database and application landscape is essential. This assessment should identify all JSON columns currently stored as nvarchar(max), evaluate the validity and structure of the JSON data, and analyze the application code that interacts with these columns. The goal is to understand the potential impact of the migration and identify any potential compatibility issues or data integrity risks. One of the first steps in the migration process is to validate the existing JSON data. This involves checking whether the data stored in nvarchar(max) columns is well-formed JSON. Invalid JSON data can cause the migration to fail or result in data loss. Azure SQL provides functions and tools for validating JSON data, which can be used to identify and correct any issues before proceeding with the migration. Once the data is validated, the next step is to generate a migration script using EF Core. This script will alter the schema of the database, changing the data type of the JSON columns from nvarchar(max) to json. It is crucial to review this script carefully to ensure that it accurately reflects the intended changes and does not introduce any unintended side effects. The migration script should be executed in a controlled environment, such as a staging or test database, before applying it to the production environment. This allows for thorough testing and validation of the migration process. The testing phase should include functional testing of the application to ensure that it continues to work as expected after the migration. Performance testing is also crucial to verify that the migration does not introduce any performance regressions. In some cases, it may be necessary to adjust indexes or query plans to optimize performance after the migration. For large databases, the migration process can be time-consuming and resource-intensive. It is essential to plan for downtime and to monitor the migration process closely. In some cases, it may be possible to perform the migration online, minimizing downtime. However, this requires careful planning and execution to avoid data inconsistencies. After the migration, ongoing monitoring of the database and application is crucial. This monitoring should include checks for data integrity, performance, and error rates. Any issues that arise should be addressed promptly to ensure the stability and reliability of the system. In addition to the technical aspects of the migration, it is also essential to consider the impact on application code. As mentioned earlier, if the application relies on specific behaviors or limitations of the nvarchar(max) data type, these may need to be adjusted to align with the characteristics of the json data type. This may involve updating queries, data access logic, and data validation routines. Communication and collaboration between database administrators and application developers are crucial throughout the migration process. This ensures that everyone is aware of the changes and potential impacts and that any issues are addressed effectively. In conclusion, migrating Azure SQL databases to compatibility level 170 and transitioning JSON columns from nvarchar(max) to the json data type is a complex process that requires careful planning, execution, and monitoring. A phased approach, coupled with thorough testing and communication, is essential to ensure a smooth transition and to realize the benefits of the native json data type.

Conclusion

Setting the Azure SQL default compatibility level to 170 marks a significant evolution in how JSON data is handled, particularly within the context of EF Core applications. This change, driven by the need to enhance the EFCore.CheckConstraints plugin and optimize JSON data storage, introduces both opportunities and challenges. The shift from storing JSON as nvarchar(max) to utilizing the native json data type promises performance improvements, enhanced query capabilities, and a more seamless integration with EF Core. However, it also necessitates careful consideration of migration strategies, potential data integrity issues, and the impact on existing application code. The migration process, while potentially complex, is crucial for unlocking the full potential of the json data type. A phased approach, encompassing thorough assessment, data validation, controlled migration execution, and rigorous testing, is essential for a successful transition. Furthermore, ongoing monitoring and collaboration between database administrators and application developers are vital for maintaining system stability and performance. The decision to adopt compatibility level 170 should be viewed as a strategic one, aligning with the broader goals of optimizing data storage, improving application performance, and enhancing the overall user experience. By carefully planning and executing the migration, organizations can leverage the benefits of the native json data type while minimizing potential risks. Ultimately, the move to compatibility level 170 represents a step forward in the evolution of Azure SQL Database, providing developers with more powerful tools and capabilities for working with JSON data. Embracing this change requires a commitment to understanding the implications, adopting best practices for migration, and continuously monitoring and optimizing the system. With the right approach, organizations can seamlessly integrate the native json data type into their applications and reap the rewards of improved performance, scalability, and data management.