Issue Detection

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How Issue Detection Works

dba.ai monitors your connected PostgreSQL databases, collecting key metrics and log data to identify potential problems. The system uses a combination of:

1. Metric Analysis: Monitoring performance metrics like CPU usage, memory, I/O, and database-specific metrics
2. Log Analysis: Scanning PostgreSQL logs for errors, warnings, and other signs of trouble
3. Query Performance: Tracking slow queries and identifying optimization opportunities
4. Configuration Analysis: Reviewing your PostgreSQL configuration for potential improvements

When an issue is detected, dba.ai:

1. Analyzes the root cause using AI
2. Classifies the severity of the issue
3. Generates potential solutions
4. Creates a detailed report with actionable recommendations

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Types of Issues Detected

dba.ai can identify and help resolve a wide range of PostgreSQL issues, including:

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Performance Issues

• Slow queries
• Index opportunities
• Connection bottlenecks (coming soon)
• Resource constraints (coming soon)

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Configuration Issues

• Suboptimal PostgreSQL parameters (coming soon)
• Memory allocation problems (coming soon)
• Inefficient work_mem or shared_buffers settings (coming soon)

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Data Management Issues

• Bloat in tables or indexes (coming soon)
• Missing vacuum operations (coming soon)
• Stale statistics (coming soon)

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Security Issues

• Exposed endpoints (coming soon)
• Overly permissive roles (coming soon)
• Missing encryption (coming soon)

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Issue Resolution Workflow

When dba.ai detects an issue, it follows this workflow:

1. Detection: The issue is identified through metrics, logs, or other monitoring
2. Analysis: AI analyzes the issue, considering your specific database context
3. Solution Generation: Multiple potential solutions are developed
4. Recommendation: The best solution is recommended, with explanations for why it’s preferred
5. Implementation: You can approve the fix for automatic implementation or apply it manually

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Automated Pull Requests

For GitHub-connected database repositories, dba.ai can:

1. Generate a fix as code
2. Create a branch in your repository
3. Submit a pull request with the proposed fix
4. Add detailed explanations for reviewers

This allows your team to review the proposed changes using your normal code review process.

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Setting Alert Preferences

You can customize how and when you’re notified about issues:

1. Navigate to Settings > Alert Preferences
2. Configure alert thresholds by severity
3. Choose notification channels (email, Slack, etc.)
4. Set quiet hours if needed

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Issue History and Learning

dba.ai maintains a history of all detected issues and their resolutions, which:

1. Helps track recurring problems
2. Allows the system to learn from previous solutions
3. Provides documentation for audit purposes
4. Informs proactive recommendations

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Advanced Configuration

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Custom Issue Rules

You can create custom rules for your specific environment:

1. Go to Settings > Custom Rules
2. Define trigger conditions
3. Set actions to take when conditions are met
4. Save and activate your rule

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Integration with Monitoring Tools

dba.ai will soon integrate with existing monitoring tools.

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Next Steps

• Learn about pull requests for automated fixes
• Set up GitHub integration for code-based fixes
• Explore the issue dashboard to see your database health