It’s no surprise Postgres does more than merely store data; you can’t do everything with SQL. Often, it’s often more beneficial to process data locally without transmitting it to a client interface. Local manipulation can save hours in network traffic alone, let alone client-side allocation and per-row processing. Databases like Postgres are specifically for bulk data operations, so why not take advantage?

All database engines, even Postgres, occasionally present a seemingly intractable problem that will drive everyone insane while attempting to isolate it. All it takes is the perfect storm of situational circumstances, and even a perfectly running stack of software will grind to a screeching halt. It’s situations like this that we must turn to various Postgres forensic tools to track down the issue before management starts firing people in frustration.

“Hey! That row shouldn’t be in that table! How the heck did that get there!? Alright, who wrote the application client filters, because you’re fired!”

Good application developers know never to trust client input, but not all realize that a single app is rarely the only vector into a database. Databases don’t just preserve data with various levels of paranoia, they’re also the central nexus of a constellation of apps, scripts, APIs, GUIs, BMIs, HMOs, and STDs. As such, unless every single one of those share a common ancestor that sanitizes, boils, renders, and formats content before storage, there’s bound to be inconsistencies. That’s just how things work.

Occasionally with a lot of similar-sounding terminology, there’s ample room for misunderstandings. This is especially relevant with overloaded terms like ‘index’, which can be anything from a data lookup to a term used in mutual funds. This is further complicated if a developer’s first experience with databases is with another variant with sufficiently divergent syntax, methodology, or assumptions. To prevent future code refactors born of misunderstandings, let’s build a basic Postgres glossary to act as an executive summary for app devs.

We’ve all had it happen. One day, we run a query or function and we wait for the result. And we wait. And we wait. Eventually, we realize something is wrong and find a DBA and yell at them.

“Hey Postgres dude!” we scream. “The database is slow!”