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Enhance DB Set up

1. Formalize Schema Management with a Migration Tool​

  • Observation: Database schema changes (e.g., CREATE TABLE, ADD COLUMN) are currently documented as checklist items within [TASKS.md](code-assist-path:/Volumes/DevSDD/25-06.ChainAlign/25-06.opsPilot/docs/0_Project_Management/TASKS.md "/Volumes/DevSDD/25-06.ChainAlign/25-06.opsPilot/docs/0_Project_Management/TASKS.md"). While this is great for planning, it relies on developers to apply these changes manually and consistently across all environments (local, staging, production).
  • Suggestion: I recommend adopting a database migration tool. For a Node.js backend, popular choices include knex.js or node-pg-migrate.
  • Why?
    • Version Control for Your Database: Migrations are stored as versioned files (e.g., 20240101120000_create_users_table.js). This puts your database schema under the same version control as your application code.
    • Consistency: It guarantees that every developer's local database and every deployment environment has the exact same schema.
    • Automation: Migrations can be run automatically as part of your deployment pipeline, eliminating manual, error-prone steps.
    • Rollbacks: Most tools provide a simple way to revert a schema change if something goes wrong.

2. Implement a Dedicated Data Access Layer (DAL)​

  • Observation: As the application grows, SQL queries will be needed in various parts of the backend (e.g., fetching S&OP data, managing users, querying vectors). Without a clear structure, this can lead to scattered SQL strings and duplicated logic.
  • Suggestion: Define a formal Data Access Layer (also known as a Repository Pattern). This layer would be the only part of your application responsible for communicating with the database. Your business logic (in "service" files) would call functions on this layer (e.g., userRepository.findById(id)) instead of writing SQL directly.
  • Why?
    • Separation of Concerns: It cleanly separates your business logic from your data persistence logic.
    • Testability: It makes your business logic much easier to unit test, as you can provide a "mock" repository instead of needing a live database connection.
    • Maintainability: If you need to optimize a query or change a table name, you only have to do it in one place: the repository.
    • Flexibility: While you're using PostgreSQL now, having a DAL makes it significantly easier to switch to a different database in the future if needed.

3. Create a Centralized Data Model Document or Diagram​

  • Observation: Details about the database schema are currently spread across various milestones in [TASKS.md](code-assist-path:/Volumes/DevSDD/25-06.ChainAlign/25-06.opsPilot/docs/0_Project_Management/TASKS.md "/Volumes/DevSDD/25-06.ChainAlign/25-06.opsPilot/docs/0_Project_Management/TASKS.md"). This can make it difficult to get a holistic view of the entire data model.
  • Suggestion: Create a single source of truth for your database schema. This could be a markdown file (docs/DataModel.md) or, even better, an Entity-Relationship Diagram (ERD). Tools like dbdiagram.io allow you to generate and share visual ERDs from a simple text-based syntax.
  • Why?
    • Clarity: An ERD provides an immediate, visual understanding of all your tables, their columns, and how they relate to one another.
    • Onboarding: It's an invaluable tool for getting new developers up to speed quickly.
    • Design Discussions: It serves as a focal point for discussions about new features and the data modeling changes they require.

These suggestions are designed to add structure and robustness to your data management practices, ensuring the project remains maintainable and scalable as it evolves from an MVP into a full-featured platform.

Create an Alert Policy for Anomaly Detection:​

  1. In the navigation menu, go to Operations > Monitoring > Alerting.
  2. Click "CREATE POLICY".
  3. Select Metric:
    • Click "SELECT A METRIC".
    • In the "Filter by resource or metric name" search bar, type the name of your log-based metric (e.g., chainalign_app_log_errors).
    • Select your metric under "Log Metrics" (e.g., logging/user/chainalign_app_log_errors).
    • Click "APPLY".
  4. Configure Alert Trigger:
    • Condition type: Select "Metric threshold".
    • Rolling window: Set an appropriate window (e.g., "5 min").
    • Threshold: Instead of a static threshold, you can use "Advanced options" to configure anomaly detection. Look for options like "Forecast" or "Deviation from baseline" if available, or set a dynamic threshold based on historical data.
      • For a simple anomaly, you might set a threshold like "any value is above X for Y minutes". For more advanced anomaly detection, you might need to explore custom metrics and more complex MQL (Monitoring Query Language) queries.
    • Notification Channels: Configure how you want to be notified (e.g., email, PagerDuty, Slack).
  5. Name and save the policy.

docker run -p 8000:8000 -e LANGEXTRACT_API_KEY="AIzaSyBb3JVj1tCaCglYzyEzSEkurIYH690CMJc" langextract-service