Browse Chinese-language deep-dive articles on PostgreSQL performance tuning, high availability, and maintenance
Access video course notes covering multi-day PostgreSQL and Greenplum training for database administrators
Find categorized guides for migrating from Oracle to PostgreSQL in a Chinese enterprise context
This repository is a personal technical blog and knowledge archive maintained by a database expert who goes by the handle digoal. The content is written primarily in Chinese and covers PostgreSQL, Greenplum, and related database technologies in depth. Rather than being a software project you install or run, it is a large collection of articles, course notes, and study materials organized as files in a Git repository. The README itself functions as a table of contents, listing hundreds of articles grouped by topic. Categories include application development with PostgreSQL, daily database maintenance, monitoring, backup and recovery, high availability, security and auditing, performance tuning, replication, read/write splitting, horizontal sharding, analytics and parallel processing, database extensions and plugins, version upgrade guides, Oracle migration to PostgreSQL, GIS and spatial data, time-series data, graph search, and more. The repository also includes links to recorded video courses covering multi-day training sessions on PostgreSQL and Greenplum for database administrators and developers, as well as links to courses on AI-assisted database learning, OceanBase, DuckDB, Milvus, and other tools added in more recent entries. The articles appear to be written over many years, with entries organized by year and month in the file structure. The README lists thousands of individual article links. Some articles are philosophical or strategic, discussing how to choose between databases, the future of open-source databases, or how to build a technical community. This kind of repository is used as a long-form reference: someone learning PostgreSQL in a Chinese-speaking context would browse the categorized index to find articles matching a specific topic, then read the corresponding Markdown files. Contributions and corrections are welcomed via GitHub issues or direct contact. The full README is longer than what was shown.
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