Sleep is the crank that turns the engine, and mine has long since snapped at the axle.
I don’t blog much anymore, a thing I realized once it became obvious even to me. I didn’t wax nostalgic about turning 40. I didn’t say goodbye to the home where I’d spent the last six years. I didn’t gush about the Porsche Cayman I recently purchased to fulfill an old childhood dream. No espousing about Keto, either recipes or studies.
Ever since my previous foray into building a server, I’v been trolling Lab Gopher for an upgrade. My preference would have been for a Dell PowerEdge R720xd 3.5-inch format since it could hold 12 full-size hard disks. But those are relatively rare and deals were scarce.
Instead, I stumbled across a Dell PowerEdge R720 2.5-inch format with an additional drive cage. So while 2.5-inch drives were lower capacity, I could use 16 of them if necessary.
It’s about the time for year-end performance reviews. While I’m always afraid I’ll narrowly avoid being fired for gross incompetence, that’s not usually how it goes. But that meeting did remind me about a bit of restructuring I plan to impose for 2017 that should vastly improve database availability across our organization. Many of the techniques to accomplish that—while Postgres tools in our case—are not Postgres-specific concepts.
Much of database fabric design comes down to compromise.
On a higher level, Postgres has a bevy of libraries, interfaces, and clients for accessing a database instance. From language APIs to GUIs like pgAdmin, or SaaS entries like JackDB, every flavor of interaction is covered. And yet, that’s only a small part of the story. For those who dare to tread into the watery depths, there’s also the world of dark incantations that is the command-line.
While most are aware of psql, the Postgres command-line client for accessing databases, there are far more creatures lurking in the black oblivion which deserve more visibility.
Postgres is a great tool for most databases. Larger installations however, pretty much require horizontal scaling; addressing multi-TB tables relies on multiple parallel storage streams thanks to the laws of physics. It’s how all immense data stores work, and for a long time, Postgres really had no equivalent that wasn’t a home-grown shard management wrapper. To that end, we’ve been considering Postgres-XL as a way to fill that role. At first, everything was going well.
