# Using PL/R and PL/Python to find Medians and Quartiles in Postgres

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I’ve recently been exploring options to calculate median and quartiles in my Postgres database. If you’re familiar with quartiles you know how handy they can be. There’s a few different options in the Postgres universe to accomplish this, so I figured I would give them all a whirl and see which was the friendliest (and fastest) on my CPU.

**The Data**

I’m using the “batting” table for Sean Lahman’s baseball database as my proof of concept. The table has just under 100,000 rows. Not too big, but a good test case. For my example here, I’m using the “r” column, which indicates total runs scored for a season.

**The R Method**

PL/R is a popular Postgres extension. If you haven’t checked it out, I would highly recommend it.

Pros: Very fast and simple code. Only one line of actual R code used to build this function.

Cons: R isn’t pre-installed on most systems and PLR isn’t shipped with Postgres. Takes a bit of system config., but not too much.

CREATE OR REPLACE FUNCTION r_quartile(ANYARRAY) RETURNS ANYARRAY AS $$ quantile(arg1, probs = seq(0, 1, 0.25), names = FALSE) $$ LANGUAGE 'plr'; CREATE AGGREGATE quartile (ANYELEMENT) ( sfunc = array_append, stype = ANYARRAY, finalfunc = r_quartile, initcond = '{}');

**The Python Method**

I’m a big fan of Python in general. It’s currently one of my favorite ETL languages. Here I’m using yet another great Postgres extension called PL/Python.

Pros: Python is pre-installed on most Linux and Mac systems making set up a breeze.

Cons: Surprisingly slow! Python ties to pipe all the data into the interpreter and then back out again as a function result. Too much system cost for me!

CREATE TYPE boxplot_values AS ( min numeric, q1 numeric, median numeric, q3 numeric, max numeric ); CREATE OR REPLACE FUNCTION public._final_boxplot(strarr numeric[]) RETURNS boxplot_values AS $BODY$ x = strarr a.sort() i = len(a) return ( a[0], a[i//4], a[i//2], a[i*3//4], a[-1] ) $BODY$ LANGUAGE plpythonu IMMUTABLE COST 100; CREATE AGGREGATE boxplot(numeric) ( SFUNC=array_append, STYPE=numeric[], FINALFUNC=_final_boxplot, INITCOND='{}' );

**The C Method**

Everyone remember C from your CS-101 class in college? Yeah, that’s why no one likes to write it. Fortunately, this is a pre-packaged Postgres extension written in C called Quantile. I’m not going to post the mile-long C code here, but you can see it on the GitHub repo.

Pros: BLAZING FAST! Returned an array faster than native SQL could calculate a median! I ended up putting the R solution into production because PL/R has room for further application, but if I were looking for speed and nothing else, the Quantile extension is the clear winner.

Cons: A third-party extension, so you’re at the mercy of the developers to keep things updated. This particular repo is about four years old and looks to be updated on a regular basis.

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