Author: David Sterling

David Sterling is a PostgreSQL database expert specializing in performance optimization, query tuning, and database administration. With years of experience helping developers and businesses optimize their PostgreSQL deployments, David shares practical insights and best practices on this blog. For inquiries or consulting, contact: [email protected]
Architecture and operations postgresql

Real-Time Well and Field Sensor Data with TimescaleDB: A Deep Dive for Houston Oil & Gas Operations

If you operate upstream assets in the Greater Houston area, you understand the data volume problem: thousands of sensors generating 5,000–10,000 data points per second across wells, compressors, SCADA systems, separators, and field infrastructure. Traditional relational databases buckle under that load. Time-series databases designed for financial markets don’t speak the language of oil and gas […]

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Application patterns and case studies pgvector postgresql

How Houston Oil & Gas Companies Can Use PostgreSQL to Gain a Competitive Edge

Houston is the energy capital of the world. From upstream exploration and drilling to midstream pipeline operations and downstream refining, the Greater Houston area is home to thousands of oil and gas companies managing some of the most complex, data-intensive operations on the planet. In 2026, that data complexity is only growing — and PostgreSQL […]

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Architecture and operations Performance and tuning postgresql

PostgreSQL Connection Pooling in 2026: When to Use PgBouncer vs Built-In Pooling

Connection pooling is one of the fastest ways to fix a “slow” PostgreSQL-backed app without touching a single query. In 2026, most production stacks sit behind some mix of driver-level pooling, PgBouncer, and sometimes pgpool-II—but they’re not interchangeable. This post walks through how PostgreSQL handles connections, where pooling actually helps, and when you should reach […]

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Architecture and operations Performance and tuning postgresql

PostgreSQL Table Bloat Management: VACUUM FULL, pg_repack, and the Coming REPACK CONCURRENTLY

Table bloat is one of PostgreSQL’s most persistent operational challenges. It’s a natural byproduct of how PostgreSQL handles updates and deletes — old row versions (dead tuples) pile up inside heap files, wasting storage, degrading index efficiency, and slowing down queries. Left unchecked, bloat can double or triple the physical size of your busiest tables. […]

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Performance and tuning postgresql

PostgreSQL 18: Unlocking Performance with Async I/O and UUIDv7

PostgreSQL 18, released in late 2025, has introduced several game-changing performance features that are fundamentally altering how we design and scale database-driven applications. Among these, the arrival of native Asynchronous I/O (AIO) and first-class UUIDv7 support stand out as the most impactful for modern, high-concurrency workloads. 1. The AIO Revolution: Eliminating I/O Wait For decades, […]

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postgresql

How to Use PostgreSQL as a Vector Database: A Complete pgvector & pgvectorscale Guide

As the AI landscape evolves in 2026, the question is no longer if you should use a vector database, but where your vectors should live. For many, the answer is increasingly PostgreSQL. With the combination of pgvector for core functionality and pgvectorscale for massive-scale performance, PostgreSQL has transformed from a relational workhorse into a high-performance […]

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