One of the cool things I like most about Postgres, is that it’s probably the most inclusive database software I’ve ever encountered. It’s so full of features and functionality these days, it’s practically middleware. Almost anything plugs into it, and if it doesn’t, there’s usually a way to make it happen.
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SciDB is often used for large analytical data warehouses. They even use Postgres for metadata storage. Despite this, they still haven’t written a foreign data wrapper for back-and-forth interaction.
This week we’ll be covering another method of Postgres partitioning. This is a technique I personally prefer and try to use and advocate at every opportunity. It’s designed to straddle the line between traditional partitioning and standard monolithic table structure by using table inheritance as a convenience factor. The assumption here is that end-user applications either:
Know that partitioning is in use. Only load “current” data and don’t care about partitions.
Most Postgres (PostgreSQL) users who are familiar with partitioning use the method described in the partitioning documentation. This architecture comes in a fairly standard stack:
One empty base table for structure. At least one child table that inherits the base design. A trigger to redirect inserts based on the partitioning scheme. A constraint on each child table to enforce the partition scheme, and help the planner exclude child partitions from inapplicable queries.
What’s a good table to partition? It’s not always a question with an obvious answer. Most often, size and volume determine whether or not a table should be broken into several chunks. However, there’s also cases where business or architecture considerations might use partitioning to preserve a shared table structure, or drive aggregate analysis over a common core. In Postgres (PostgreSQL), this is even more relevant because of how partitioning is handled through inheritance.
In the next few weeks, I’m going to be pushing a long discussion regarding Postgres (PostgreSQL) table partitioning. I’ve covered it in previous articles, but only regarding basic performance considerations. That’s a very limited view of what partitioning can offer; there’s a lot more variance and background that deserves elucidation.
So for the next few articles, the topic of discussion will be partitioning. There’s not really enough of it, and a lot of the techniques used in the field are effectively pulled straight from the documentation.
