How to Use PostgreSQL for Everyday Use Cases

Galaxy Glossary

What are the most common PostgreSQL use cases and how do I implement them?

PostgreSQL handles transactional, analytical, and unstructured workloads in one extensible engine.

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Description

Example H2

Example H3

When should I choose PostgreSQL for OLTP?

Pick PostgreSQL when you need ACID-compliant inserts, updates, and deletes that complete in milliseconds. Its MVCC engine prevents read locks, letting hundreds of users place Orders without blocking each other.

Can PostgreSQL power real-time analytics?

Yes. Combine CTEs, window functions, and partial indexes to refresh KPI dashboards every few seconds without ETL.Materialized views cache heavy joins on Orders and OrderItems, cutting query times from seconds to milliseconds.

How do I store semi-structured data?

Use the JSONB type. It indexes key paths so you can filter Orders on dynamic metadata like {"source":"mobile"}. JSONB avoids schema changes while staying queryable.

Is full-text search possible in PostgreSQL?

Absolutely. Create a tsvector column on Products.name to support typo-tolerant searches.GIN indexes keep lookups below 10 ms—no external search engine required.

What about time-series workloads?

Extensions like TimescaleDB add hypertables that partition data by time and space. You can ingest millions of sensor rows per second and still query recent slices quickly.

Best practice: keep one database

Running OLTP and analytics in the same cluster reduces latency and simplifies ops. Use row-oriented tables for hot paths and columnar extensions like Citus for wide scans.

Why How to Use PostgreSQL for Everyday Use Cases is important

How to Use PostgreSQL for Everyday Use Cases Example Usage


-- Identify top 5 customers by lifetime spend
SELECT c.id, c.name, SUM(o.total_amount) AS lifetime_value
FROM Customers c
JOIN Orders o ON o.customer_id = c.id
GROUP BY c.id, c.name
ORDER BY lifetime_value DESC
LIMIT 5;

How to Use PostgreSQL for Everyday Use Cases Syntax


-- Transactional insert
INSERT INTO Orders (customer_id, order_date, total_amount)
VALUES (42, NOW(), 199.99)
RETURNING id;

-- JSONB filter
SELECT id, total_amount
FROM Orders
WHERE metadata ->> 'source' = 'mobile';

-- Full-text search
SELECT id, name
FROM Products
WHERE to_tsvector('english', name) @@ plainto_tsquery('english', 'wireless mouse');

-- Real-time KPI (CTE + window)
WITH daily_sales AS (
  SELECT order_date::date AS day,
         SUM(total_amount) AS revenue
  FROM Orders
  GROUP BY day
)
SELECT day,
       revenue,
       ROUND(100.0 * revenue / SUM(revenue) OVER (), 2) AS pct_total
FROM daily_sales
ORDER BY day DESC;

Common Mistakes

Mistake: Using generic indexes on JSONB columns. Why wrong: B-tree can’t speed up key lookups. Fix: Create a GIN index on (metadata jsonb_path_ops).
Mistake: Running heavy analytical joins on the primary OLTP instance during peak hours. Why wrong: Can cause lock contention. Fix: Use read replicas or materialized views.