Question 1

How to integrate SQLCheck into a CI/CD pipeline like GitHub Actions?

Accepted Answer

You can run SQLCheck as a shell command in your pipeline script to scan SQL files; use the -f flag for file input and set risk levels with -r to fail builds on high-risk issues. However, it lacks native CI integrations, so manual scripting is required.

Question 2

SQLCheck vs other SQL linters like SQLLint or SonarQube?

Accepted Answer

SQLCheck focuses specifically on performance anti-patterns across all SQL dialects with detailed explanations, while tools like SQLLint may emphasize syntax checking or SonarQube offers broader code quality metrics. SQLCheck is best for targeted performance tuning rather than general linting.

Question 3

Does SQLCheck work with stored procedures or dynamic SQL?

Accepted Answer

SQLCheck primarily analyzes static SQL files and may not fully parse complex stored procedures or dynamically generated SQL, as it's designed for common anti-patterns in straightforward queries and schemas.

Question 4

What are common false positives in SQLCheck?

Accepted Answer

False positives can occur with legitimate uses of patterns like SELECT * for quick prototyping or OR in optimized queries, as SQLCheck flags these based on general rules without context-aware analysis.

Question 5

How to run SQLCheck on multiple SQL files in a folder?

Accepted Answer

Use a shell loop or script to iterate over files, such as 'for file in *.sql; do sqlcheck -f $file; done', since the tool processes one file at a time via the -f flag without built-in batch processing.

Question 6

Can SQLCheck detect anti-patterns in NoSQL or graph databases?

Accepted Answer

No, SQLCheck is limited to SQL dialects and does not support NoSQL or graph database queries, as its rule set is tailored for relational SQL anti-patterns only.

SQLCheck

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