These days with multiple Postgres database environments a commonality, it’s not unheard of to copy data from one to another. Perhaps a production extract is necessary to properly vet data in a staging environment. Maybe the development environment needs to update its data to reflect an especially pernicious and intractable edge case someone observed. In any of these scenarios, we are likely to extract data from multiple tables to import it elsewhere.
In light of recent events where GitLab suffered a massive database loss, this is a great opportunity to examine what happened from a Postgres perspective. Since Simon Riggs over at 2ndQuadrant has already chimed in on improvements Gitlib might consider in their procedures, maybe we should walk the conversation back slightly.
This isn’t the first time Postgres backup tooling has been misused or misunderstood. The topic of backups hits forums and mailing lists rather frequently.
Foreign tables have been a headline feature of Postgres ever since the release of version 9.2. Combined with extensions, they’re the secret sauce that allows Postgres to pull data from other database engines, flat files, REST interfaces, and possibly every gas station, residence, warehouse, farmhouse, hen house, outhouse, and doghouse in the area.
But that kind of power comes at a significant cost. Since the remote data comes from what is essentially a black box, there are a lot of performance optimizations Postgres can’t apply.
Not a lot of people remember what Postgres was like before version 8.4. In many ways, this was the first “modern” release of the database engine. CTEs, Window Functions, column level permissions, in-place upgrade compatible with subsequent versions, collation support, continuous query statistic collection; it was just a smorgasbord of functionality.
Of these, CTEs or Common Table Expressions, probably enjoy the most user-level exposure; for good reason. Before this, there was no way to perform a recursive query in Postgres, which really hurts in certain situations.
There are a smorgasbord of database engines out there. From an outside perspective, Postgres is just another on a steadily growing pile of structured data storage mechanisms. Similarly to programming languages like Rust and Go, it’s the new and shiny database systems like MongoDB that tend to garner the most attention. On the other hand, more established engines like Oracle or MySQL have a vastly larger lead that seems insurmountable. In either case, enthusiasm and support is likely to be better represented in exciting or established installations.
