Ah, source control. From Subversion to git and everything in between, we all love to manage our code. The ability to quickly branch from an existing base is incredibly important to exploring and potentially abandoning divergent code paths. One often overlooked Postgres feature is the template database. At first glance, it’s just a way to ensure newly created databases contain some base functionality without having to bootstrap every time, but it’s so much more than that.
Now that we’ve decided to really start embracing horizontal scaling builds, there is a critically important engine-agnostic element we need to examine. Given an existing table, how exactly should we split up the contents across our various nodes during the conversion process? Generally this is done by selecting a specific column and applying some kind of hash or custom distribution mechanism to ensure all node contents are reasonably balanced. But how do we go about figuring that out?
I always advocate breaking up large Postgres tables for a few reasons. Beyond query performance concerns, maintaining one monolithic structure is always more time consuming and consequentially more dangerous. The time required to create a dozen small indexes may be slightly longer than a single larger one, but we can treat the smaller indexes as incremental. If we want to rebuild, add more indexes, or fix any corruption, why advocate an all-or-nothing proposition?
The Postgres developers recently announced the availability of the first public beta for Postgres 9.6. I would be highly remiss to ignore such an opportunity to dig into any interesting functionality listed in the 9.6 release notes. All in all, it’s a pretty exciting series of advancements, and assuming this is a glimpse of what we see when 9.6 drops, I’d say we’re on the right track.
Plentiful Parallelism Probably the most high-profile addition for 9.
Let’s just get the obvious out of the way early: dealing with multiple Terabytes or Petabytes in a database context is something of a nightmare. Distributing it, retrieving it, processing it, aggregating and reporting on it, are all complicated—and perhaps worst of all—non-intuitive. Everything from tooling and maintenance, to usage and input, are either ad-hoc or obfuscated by several special-purpose APIs and wrappers.
One of the reasons a self-scaling database is such a killer app, derives from the failure rate from having so many moving parts.
