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Working with PostgreSQL in Production: Lessons from Real-World Experience

December 20258 min read

Working with production databases is a different experience from practicing SQL in tutorials. At Gorkhali Agents, I've had the opportunity to work directly with PostgreSQL databases that power real applications. Here's what I've learned about handling data in production.

Writing Queries That Actually Work

In production, you can't afford slow queries. I've learned to write efficient SQL using proper joins, filters, and indexing strategies. Here's an example of a query pattern I use frequently:

SELECT 
    u.id,
    u.email,
    o.order_date,
    o.total_amount
FROM users u
INNER JOIN orders o ON u.id = o.user_id
WHERE o.order_date >= CURRENT_DATE - INTERVAL '30 days'
    AND o.status = 'completed'
ORDER BY o.order_date DESC
LIMIT 100;

The key lessons here: always limit results when you don't need everything, filter early with WHERE clauses, and use appropriate JOIN types based on your data needs.

Debugging Data-Related Issues

One of the most challenging parts of my work has been diagnosing production issues. Schema mismatches, inconsistent data, and database connectivity problems are common. I've developed a systematic approach:

  • Check schema first: Verify table structures match what the application expects
  • Validate data types: Type mismatches cause silent failures
  • Review constraints: Foreign key and unique constraints can block inserts unexpectedly
  • Check connection limits: Pool exhaustion is a common issue under load

Understanding DDL vs DML Operations

From my Oracle Database Labs project and production work, I've learned the importance of understanding the difference between DDL (Data Definition Language) and DML (Data Manipulation Language) operations:

-- DDL: Creating and modifying structures
CREATE TABLE audit_log (
    id SERIAL PRIMARY KEY,
    action VARCHAR(50) NOT NULL,
    table_name VARCHAR(100),
    record_id INTEGER,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- DML: Working with data
INSERT INTO audit_log (action, table_name, record_id)
VALUES ('UPDATE', 'users', 42);

DDL operations are typically run during deployments, while DML is what your application does constantly. Understanding this distinction helps when troubleshooting migration issues.

Subqueries and Set Operations

Complex data problems often require combining multiple queries. I frequently use subqueries and set operations:

-- Find users who have orders but no recent activity
SELECT id, email FROM users
WHERE id IN (
    SELECT DISTINCT user_id FROM orders
)
EXCEPT
SELECT id, email FROM users
WHERE last_login > CURRENT_DATE - INTERVAL '90 days';

Key Takeaways

Working with PostgreSQL in production has taught me that database skills go beyond writing queries. You need to understand:

  • How data flows between your application and database
  • The importance of proper indexing and query optimization
  • Schema design principles that prevent future problems
  • Debugging techniques for data-related issues
  • Secure practices for handling production data

These skills directly support my goal of growing into a data engineering role, where understanding databases is fundamental to building reliable data pipelines.