This article explains how PostgreSQL's Foreign Data Wrapper (FDW) and Materialized Views (MV) can be combined to synchronize large‑scale data across databases or clusters, offering alternatives to pg_dump, reducing load, and improving performance for both real‑time and batch scenarios.
This article explains the history and architecture of PostgreSQL's Foreign Data Wrapper (FDW), details the required database objects and callback functions, and provides a step‑by‑step guide for implementing an FDW in Go using cgo, including code examples, build instructions, and performance considerations.
The article summarizes Digoal's 2017 Gdevops talk, detailing PostgreSQL's advanced features such as block-level BRIN indexes, high‑efficiency range queries, asynchronous messaging, data pumps, real sharding, MADlib machine‑learning integration, FDW data federation, and large‑scale use cases in IoT, finance, and geospatial analytics.
This article showcases seven remarkable PostgreSQL extensions—including multi‑master replication, Greenplum MPP OLAP, pg_shard/FDW sharding, PostGIS 3D GIS, GPU‑accelerated PG‑Strom, PipelineDB streaming, and the versatile FDW interface—illustrating how they enable high‑availability, massive analytics, geographic intelligence, and real‑time data processing.
Discover ten practical PostgreSQL techniques—including CTEs, custom .psqlrc settings, pg_stat_statements, FDWs, array handling, materialized views, window functions, pivot shortcuts, PostGIS, and JSONB—to write cleaner queries, improve performance, and leverage advanced database features.
