Sql triggers executing SQL, Javascript and Java code

[lvca]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @lvca, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request delivers a powerful new capability to ArcadeDB: database triggers. This feature enables developers to embed business logic, enforce data integrity, manage audit trails, and automate workflows directly within the database schema. By supporting SQL, JavaScript, and Java, it offers flexibility to choose the right tool for the job, from simple data manipulations to complex, high-performance operations, ensuring data consistency and reactivity at the database level.

Highlights

• New Feature: SQL Triggers: Introduced a comprehensive trigger system allowing automatic execution of SQL statements, JavaScript code, or Java classes in response to database record events.
• Multi-Language Support: Triggers can be defined using SQL for simple operations, JavaScript for flexible logic, or Java for maximum performance and type safety.
• Event and Timing Control: Triggers can be configured to fire BEFORE or AFTER CREATE, READ, UPDATE, or DELETE operations on specific document, vertex, or edge types.
• Performance Benchmarking: Included benchmark results demonstrating that Java and SQL triggers offer nearly identical, high performance, while JavaScript triggers introduce a moderate overhead (~27% slower than Java/SQL).
• Comprehensive Documentation: A new SQL-Triggers.md file has been added, detailing syntax, examples, best practices, limitations, and performance considerations for the new trigger system.

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[gemini-code-assist]

Code Review

This pull request introduces a powerful and well-designed trigger mechanism supporting SQL, JavaScript, and Java. The implementation is comprehensive, covering grammar changes, schema management, execution logic, and extensive testing, including benchmarks. The documentation is also very detailed. I've identified a significant performance improvement opportunity for JavaScript triggers and a few areas in the documentation that could be improved for clarity and to promote best practices, particularly around preventing SQL injection in examples.

[codacy-production]

Coverage summary from Codacy

See diff coverage on Codacy

Coverage variation	Diff coverage
✅ +0.07%	✅ 68.94%

Coverage variation details

	Coverable lines	Covered lines	Coverage
Common ancestor commit (4bed2ba)	114154	62172	54.46%
Head commit (53312b8)	114827 (+673)	62616 (+444)	54.53% (+0.07%)

Coverage variation is the difference between the coverage for the head and common ancestor commits of the pull request branch: <coverage of head commit> - <coverage of common ancestor commit>

Diff coverage details

	Coverable lines	Covered lines	Diff coverage
Pull request (#3222)	454	313	68.94%

Diff coverage is the percentage of lines that are covered by tests out of the coverable lines that the pull request added or modified: <covered lines added or modified>/<coverable lines added or modified> * 100%

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