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Best Practices

Optimize your Query Builder usage with these proven best practices for performance, security, and maintainability.

Query Design Principles​

1. Start Specific, Then Expand​

Begin with the most restrictive filters and gradually broaden your criteria:

✅ Good Practice

1. Filter by date range (last 30 days)
2. Filter by status (active only)
3. Then add additional criteria

❌ Poor Practice

1. Select all fields
2. No initial filters
3. Apply aggregations on entire dataset

2. Use Meaningful Names​

Choose descriptive, consistent naming conventions:

✅ Good Names

  • weekly-sales-summary-2024
  • pending-approvals-finance
  • customer-feedback-q1

❌ Poor Names

  • query1
  • test
  • temp-report

3. Document Your Queries​

Always include comprehensive descriptions:

Name: quarterly-revenue-analysis
Description: 
  Purpose: Analyzes revenue trends for executive dashboard
  Filters: Completed orders only, current quarter
  Updates: Refreshed daily at 6 AM
  Used by: Finance team, Executive dashboard
  Note: Excludes cancelled and refunded orders

Performance Optimization​

Field Selection Strategy​

Minimize Selected Fields​

Only select fields you actually need:

✅ Optimized Selection

Selected Fields:
- order_id
- customer_name
- order_total
- order_date

❌ Inefficient Selection

Selected Fields:
- Select All (45 fields)

Filter Optimization​

Use Indexed Fields​

Prioritize filtering on indexed fields:

Commonly Indexed Fields:

  • submission_id
  • created_date
  • status
  • workflow_id

Apply Most Restrictive Filters First​

Order filters from most to least restrictive:

1. Date range (reduces by 90%)
2. Status = "Approved" (reduces by 50%)
3. Department contains "Sales" (reduces by 20%)

Aggregation Best Practices​

Aggregate After Filtering​

Always apply filters before aggregations:

✅ Efficient

1. Apply filters → 1,000 records
2. Calculate aggregations

❌ Inefficient

1. Calculate aggregations → 1,000,000 records
2. Apply filters

Limit Complex Aggregations​

Avoid unnecessary calculations:

  • Use COUNT instead of COUNT DISTINCT when possible
  • Aggregate only required fields
  • Consider pre-calculated fields for common metrics

Security Best Practices​

API Token Management​

Token Rotation Schedule​

Implement regular token rotation:

Development: Every 30 days
Staging: Every 60 days
Production: Every 90 days

Token Storage​

✅ Secure Storage

  • Environment variables
  • Secure key management systems
  • Encrypted configuration files

❌ Insecure Storage

  • Hard-coded in source code
  • Plain text files
  • Client-side JavaScript

Data Access Control​

Principle of Least Privilege​

Grant minimum necessary access:

  1. Create role-specific queries
  2. Limit field exposure
  3. Apply row-level security
  4. Audit access regularly

Sensitive Data Handling​

Protect sensitive information:

Sensitive Fields:
- SSN → Mask or exclude
- Credit Card → Never include
- Passwords → Always exclude
- Personal Health Info → Restrict access

Naming Conventions​

Query Naming Standards​

Follow consistent naming patterns:

Pattern: {frequency}-{subject}-{type}-{version}
Examples:
- daily-sales-report-v1
- weekly-inventory-summary-v2
- monthly-customer-analysis-v1

Field Naming Consistency​

Maintain consistent field references:

Standardized Names:
- Use: created_date (not creation_date, date_created)
- Use: customer_id (not cust_id, customer_identifier)
- Use: total_amount (not amount_total, total)

Query Maintenance​

Version Control Strategy​

When to Create New Versions​

Create a new version when:

  • Changing core filter logic
  • Modifying aggregation methods
  • Altering field selections significantly
  • Breaking changes to API consumers

Version Documentation​

Document version changes:

Version 1.0: Initial query
Version 1.1: Added department filter
Version 2.0: Changed aggregation logic (breaking change)
Version 2.1: Performance optimizations

Query Lifecycle Management​

Regular Review Schedule​

Implement periodic reviews:

  • Weekly: Check failed queries
  • Monthly: Review performance metrics
  • Quarterly: Audit unused queries
  • Annually: Complete query inventory

Deprecation Process​

  1. Mark query as deprecated
  2. Notify API consumers (30 days notice)
  3. Provide migration path
  4. Archive after grace period

Testing and Validation​

Query Testing Checklist​

Before deploying queries:

  • Test with sample data
  • Verify filter logic
  • Validate aggregation results
  • Check edge cases (nulls, empty sets)
  • Test API endpoint
  • Verify permissions
  • Document expected results

Data Validation​

Cross-Reference Results​

Validate query accuracy:

  1. Compare with known good data
  2. Verify aggregation totals
  3. Check for data completeness
  4. Test boundary conditions

Monitor Data Quality​

Track data quality metrics:

Quality Checks:
- Null value percentage
- Data type consistency
- Range validations
- Duplicate detection

Documentation Standards​

Query Documentation Template​

Use standardized documentation:

# Query: [Query Name]

## Purpose
[Brief description of what this query does]

## Business Context
[Why this query exists and who uses it]

## Technical Details
- Workflow: [Source workflow]
- Update Frequency: [How often it runs]
- Average Response Time: [Performance metric]
- Data Volume: [Typical record count]

## Filters
- [List all applied filters]

## Aggregations
- [List all aggregations]

## API Usage
- Endpoint: /api/query/[query-name]
- Authentication: Bearer token required
- Rate Limit: 1000 req/hour

## Dependencies
- [List any dependent systems or queries]

## Change Log
- [Date]: [Change description]

Integration Best Practices​

API Consumer Guidelines​

For systems consuming query APIs:

Implement Retry Logic​

Handle transient failures:

const fetchWithRetry = async (url, options, maxRetries = 3) => {
  for (let i = 0; i < maxRetries; i++) {
    try {
      const response = await fetch(url, options);
      if (response.ok) return response;
      if (response.status === 429) {
        await sleep(Math.pow(2, i) * 1000); // Exponential backoff
        continue;
      }
      throw new Error(`HTTP ${response.status}`);
    } catch (error) {
      if (i === maxRetries - 1) throw error;
    }
  }
};

Cache Responses​

Reduce API calls with intelligent caching:

Cache Strategy:
- Static data: 24 hours
- Frequently changing: 5 minutes
- Real-time critical: No cache

Monitoring and Alerts​

Key Metrics to Monitor​

Track these essential metrics:

  1. Query Performance

    • Response time
    • Records processed
    • Error rate

  2. API Usage

    • Request volume
    • Unique consumers
    • Rate limit hits

  3. Data Quality

    • Missing data alerts
    • Unexpected value ranges
    • Schema changes

Alert Configuration​

Set up proactive alerts:

Alert Triggers:
- Response time > 5 seconds
- Error rate > 1%
- No data returned (when expected)
- API rate limit exceeded
- Authentication failures > 10/hour

Common Pitfalls to Avoid​

1. Over-Aggregation​

Don't aggregate unnecessarily - it impacts performance

2. Unfiltered Queries​

Always apply appropriate filters to limit data volume

3. Selecting All Fields​

Only select fields you need to reduce payload size

4. Ignoring Null Values​

Account for null values in filters and aggregations

5. Hard-Coding Values​

Use parameters instead of hard-coded filter values

6. Missing Error Handling​

Always implement proper error handling in API consumers

7. Insufficient Testing​

Test with various data scenarios before production

8. Poor Naming​

Use descriptive names that indicate query purpose

Next Steps​