Technical insights, tutorials, and updates about PostgreSQL extensions
Tutorials and technical notes.
Complete guide to building a production-ready PostgreSQL high availability cluster with pgBalancer and 3 PostgreSQL nodes. Includes step-by-step setup, configuration, failover testing, and best practices.
Complete guide to building a PostgreSQL high availability cluster with Patroni and etcd. Includes step-by-step setup, configuration, automatic failover, leader election, and best practices.
Monitor PostgreSQL indexes using pg_stat_insights v3.0.0. Track index usage, detect bloat, identify missing indexes, and optimize performance with 11 specialized views.
NeuronDB adds vector search, ML inference, and RAG capabilities to PostgreSQL. Includes HNSW indexing, GPU acceleration, 10 distance metrics, and pgvector compatibility.
Implement semantic search over text using NeuronDB. Includes examples, SQL queries, and code. Guide to building document search systems, RAG pipelines, and hybrid search.
Agentic AI systems guide. Explains agent architecture, planning, tool use, memory systems, and autonomous task execution. Includes implementation using NeuronDB and NeuronAgent with code examples.
Guide to deploying AI workloads with databases on-premises. Learn about on-premises AI infrastructure, data sovereignty, security, performance, and implementation with NeuronDB. Includes architecture patterns, deployment strategies, and examples.
MCP Server (Model Context Protocol) guide. What it is, how it works, integration with Claude Desktop, known MCP servers, and NeuronMCP implementation. Learn how MCP enables AI assistants to access external tools and resources.
Vector operations, indexing, and similarity search in PostgreSQL with NeuronDB. Guide with SQL queries and results. Learn HNSW indexing, distance metrics, quantization, and performance optimization.
Guide to using PostgreSQL as a vector database. Learn how PostgreSQL with NeuronDB extension works as a vector database with HNSW indexing, similarity search, and production capabilities.
Practical guide to the core RAG architecture patterns: basic, conversational, filtered, adaptive, hypothesis-driven, agent-driven, and graph-based RAG. Learn when to use each and what trade-offs matter in production.
RAG (Retrieval-Augmented Generation) guide with examples, SQL queries, and implementation patterns. Learn how to build RAG systems with document retrieval, context building, LLM integration, and response generation.
PostgreSQL connection pooler with load balancing, automatic failover, and connection management optimization.
PostgreSQL performance monitoring with 52 metrics, 11 views, and insights into query execution, cache efficiency, WAL generation, and JIT compilation.
In distributed database systems, achieving consensus across multiple nodes while maintaining data consistency and preventing split-brain is challenging.
Releases and product updates.