aggs_for_vecs

This Release
aggs_for_vecs 1.2.1
Date
Status
Stable
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Abstract
Aggregate functions for vectors (arrays) of numbers
Released By
pjungwir
License
The MIT (X11) License
Resources
Special Files
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Extensions

aggs_for_vecs 1.2.1
Aggregate functions for vectors (arrays) of numbers

README

aggs_for_vecs

This is a C-based Postgres extension offering various aggregate functions like min, max, avg, and var_samp that operate on arrays instead of scalars. It treats each array as a "vector" and handles each element independently. So suppose you have 3 rows each with a 4-element array like so:

| id | vals | | -: | :--------- | | 1 | {1,2,3,4} | | 2 | {5,0,-5,0} | | 3 | {3,6,0,9} |

Then SELECT vec_to_min(vals) will pick the minimum item in each array position, giving you {1,0,-5,0}.

Note that the functions here are true aggregate functions. If you want something that provides aggregate-like behavior by computing stats from a single array, take a look at my other extension aggs_for_arrays. You could say that this extension follows a row-based format and the other a column-based.

Details

Functions support arrays of any numeric type: SMALLINT, INTEGER, BIGINT, REAL, or DOUBLE PRECISION (aka FLOAT). They either return an array of the same type (e.g. vec_to_min) or an array of FLOAT (e.g. vec_to_mean).

All input arrays must be the same length, or you get an error. The output array will have the same length as the inputs.

NULLs are ignored, and NULL elements are also skipped. Basically you get the same result as if you could do MIN on the first elements, then MIN on the second elements, etc. If you all inputs are simply NULL, then you'll get a NULL in return. But if the inputs are arrays of NULLs, then you'll get an array of NULLs in return (of the same length).

Note that when input arrays have NULL in some positions but not others, you still get correct results for things like mean. That is, we keep a count for each position separately and divide by the appropriate amount.

Installing

This package installs like any Postgres extension. First say:

make && sudo make install

You will need to have pg_config in your path, but normally that is already the case. You can check with which pg_config. (If you have multiple Postgresses installed, make sure that pg_config points to the right one.)

Then in the database of your choice say:

CREATE EXTENSION aggs_for_vecs;

You can also run tests and benchmarks yourself with make test and make bench, respectively, but first you'll have to set up databases for those to use. If you run the commands and they can't find a database, they'll give you instructions how to make one.

The functions

vec_to_count(ANYARRAY) RETURNS BIGINT[]

Returns the count of non-nulls in each array position.

vec_to_sum(ANYARRAY) RETURNS ANYARRAY

Returns the sum of non-nulls in each array position.

vec_to_min(ANYARRAY) RETURNS ANYARRAY

Returns the minimum in each array position.

vec_to_max(ANYARRAY) RETURNS ANYARRAY

Returns the maximum in each array position.

vec_to_mean(ANYARRAY) RETURNS FLOAT[]

Returns the average (mean) in each array position.

vec_to_var_samp(ANYARRAY) RETURNS FLOAT[]

Returns the sample variance in each array position. The code is very similar to the built-in var_samp function, so if it works there it should work here (or it's a bug).

vec_without_outliers(ANYARRAY, ANYARRAY, ANYARRAY) RETURNS ANYARRAY

This is not an aggregate function, but is useful to trim down the inputs to the other functions here. You pass it three arrays all of the same length and type. The first array has the actual values. The second array gives the minimum amount allowed in each position; the third array, the maximum. The function returns an array where each element is either the input value (if within the min/max) or NULL (if an outlier). You can include NULLs in the min/max arrays to indicate an unbounded limit there, or pass a simple NULL for either to indicate no bounds at all.

hist_2d(x ANYELEMENT, y ANYELEMENT, x_bucket_start ANYELEMENT, y_bucket_start ANYELEMENT, x_bucket_width ANYELEMENT, y_bucket_width ANYELEMENT, x_bucket_count INTEGER, y_bucket_count INTEGER)

Aggregate function that takes a bunch of x and y values, and plots them on a 2-D histogram. The other parameters determine the shape of the histogram (number of buckets on each axis, start of the buckets, width of each bucket).

hist_md(vals ANYARRAY, indexes INTEGER[], bucket_starts ANYARRAY, bucket_widths ANYARRAY, bucket_counts INTEGER[])

Aggregate function to compute an n-dimensional histogram. It takes a vector of values, and it uses indexes to pick one or more elements from that vector and treat them as x, y, z, etc. If you want 2 dimensions, there should be two values for indexes, two for bucket_starts, two for bucket_widths, and two for bucket_counts. Or if you want 3 dimensions, you need three values for each of those.

Since the values in indexes should follow Postgres's convention of 1-indexed arrays, so that if indexes is {1,4}, then we will use vals[1] and vals[4] as the histogram x and y.

Limitations/TODO

  • Tests for floats are pretty good, but we need tests for the other numeric types.
  • Lots of functions are still left to implement:

    • vec_to_min_max
    • vec_to_median
    • vec_to_mode
    • vec_to_percentile
    • vec_to_percentiles
    • vec_to_skewness
    • vec_to_kurtosis

Author

Paul A. Jungwirth

Benchmarks

You can get the same behavior as this extension by using UNNEST to break up the input arrays, and then array_agg to put the results back together. But these benchmarks show that aggs_for_vecs functions are 9-10 times faster:

| function | SQL | aggs_for_vecs | |:------------------|--------------:|----------------:| | vec_to_min | 14150.7 ms | 1468.14 ms | | vec_to_max | 14062.4 ms | 1549.66 ms | | vec_to_mean | 14341.5 ms | 1586.62 ms | | vec_to_var_samp | 14196.7 ms | 1578.92 ms |