How to use SeaSchema to generate SQL migration scripts?

SeaSchema can discover your database schema and export it to raw SQL or SeaQuery statements using the writer module. First, connect to your database with the discovery module, then use the writer to convert the Schema struct into SQL strings, which you can save as migration files. This is ideal for automating schema synchronization in CI/CD pipelines.

SeaSchema vs Diesel for schema management in Rust?

SeaSchema focuses solely on schema discovery and representation, providing type-safe definitions and multi-database support for introspection. In contrast, Diesel is a full ORM with query building, migrations, and model execution, but it's more tightly coupled to specific databases. Choose SeaSchema for tooling that needs lightweight schema manipulation; use Diesel for applications requiring integrated query execution and migrations.

Does SeaSchema support JSON export of database schemas?

Yes, SeaSchema offers optional JSON serialization via the with-serde feature. Enable it in your Cargo.toml to serialize schema definitions like TableDef into JSON format, useful for documentation, configuration storage, or inter-process communication. This leverages Rust's Serde library for flexible data handling.

Can SeaSchema discover schemas from a remote PostgreSQL database?

Absolutely, SeaSchema's discovery module connects to live databases, including remote PostgreSQL instances, using standard connection strings. It queries the information_schema to introspect tables, columns, indexes, and foreign keys, returning a structured Schema object. Ensure your network and authentication settings allow the connection for seamless operation.

What's the performance impact of SeaSchema on large databases?

Schema discovery involves querying database metadata, which can be resource-intensive for databases with hundreds of tables. SeaSchema optimizes this by fetching data in batches, but performance may vary based on network latency and database size. For large-scale use, consider caching discovered schemas or running introspection during off-peak hours.

How to handle custom or unsupported data types in SeaSchema?

SeaSchema provides generic type definitions for common SQL types, but unsupported custom types might not map accurately. You can extend the ColumnInfo struct or use the expression field for complex cases, though this requires manual intervention. Check the documentation for type mapping details and consider contributing to the project for broader support.

Open-Awesome

sea-schema

Apache-2.0Rust0.18.1

A Rust library for SQL schema definition and discovery across MySQL, PostgreSQL, and SQLite databases.

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What is sea-schema?

SeaSchema is a Rust library that provides type definitions and utilities for managing SQL database schemas. It enables developers to programmatically represent, discover, and export database structures like tables, columns, indexes, and foreign keys from live MySQL, PostgreSQL, or SQLite databases. This bridges the gap between database metadata and Rust applications, facilitating tasks like code generation and migration scripting.

Target Audience

Rust developers building database tooling, ORM layers, or applications that require programmatic interaction with SQL database schemas, such as those working on migration systems, schema introspection tools, or code generators.

Value Proposition

Developers choose SeaSchema for its uniform, type-safe API that works across multiple SQL databases (MySQL, PostgreSQL, SQLite), offering precise schema discovery and export capabilities. Its integration with SeaQuery for generating SQL statements and optional Serde support for JSON serialization provides flexibility for building robust database-related tooling.

Overview

🌿 SQL schema definition and discovery

Use Cases

Best For

Automatically discovering and introspecting database schemas from live MySQL, PostgreSQL, or SQLite databases in Rust applications.
Generating SeaQuery statements or raw SQL for database migrations and schema synchronization.
Building ORM layers or database abstraction tools that require type-safe representation of SQL schemas.
Creating code generation tools that produce Rust code from existing database structures.
Exporting database schema definitions to JSON format for documentation or configuration purposes using the optional Serde integration.
Developing database administration or monitoring tools that need to programmatically analyze table structures, indexes, and foreign keys.

Not Ideal For

Teams using non-Rust programming languages or frameworks
Applications that only need basic CRUD operations without schema introspection or migration tooling
Projects requiring support for databases outside MySQL, PostgreSQL, or SQLite, such as Oracle or NoSQL systems
Developers seeking a full-featured ORM with built-in query execution and model lifecycle management

Pros & Cons

Pros

Multi-Database Uniformity

Provides a consistent API for schema discovery across MySQL, PostgreSQL, and SQLite, as demonstrated in the README with examples for each database type, reducing vendor-specific code.

Type-Safe Definitions

Uses comprehensive Rust structs like TableDef and ColumnInfo that closely map to SQL schema elements, ensuring compile-time safety and accurate representation, as shown in the detailed film_actor table example.

Schema Discovery and Export

Enables programmatic introspection of live databases and exports schemas to SeaQuery statements or raw SQL, facilitating migration scripts and code generation, evidenced by the discovery module and writer utilities.

Optional Serde Integration

Supports JSON serialization/deserialization via the with-serde feature, allowing easy storage or sharing of schema definitions, as mentioned in the architecture section.

Cons

Limited Database Support

Only covers MySQL, PostgreSQL, and SQLite, excluding other common SQL databases like SQL Server or Oracle, which limits its use in heterogeneous environments.

Dependency on SeaQuery for Export

Full schema export to SeaQuery statements requires the SeaQuery library, adding an extra dependency and complexity if not already part of the project's toolchain.

Overhead for Simple Use Cases

The detailed type system and discovery process may introduce unnecessary complexity for applications that only need basic schema access without advanced manipulation or code generation.

Frequently Asked Questions

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