I have always been a great fan and avid user of databases. They’re just so versatile, efficient, easy to use, … I found this to be true for all kinds of data, small and large, high-dimensional and low-dimensional, spatial, temporal, you name it. It was only very recently that my data seemed to have outgrown my PostgreSQL database. Not so much in size, but rather in performance.
I’m pretty sure everything I’m writing here is true for most other RDBMS, too, but since I’m currently using PostgreSQL I had a chance to test it and show some hard figures here.
The problem to solve is actually a common one and rather easy to solve conceptually: Take attributes from
table1 and store them in corresponding rows in
table2, using a common
id to join them. The straight-forward (and almost direct) translation into SQL is therefore:
UPDATE schema.table1 a SET attribute = b.attribute FROM schema.table2 b WHERE a.id = b.id;
There’s nothing wrong with that statement and it’s going to do exactly what’s intended. Only, it’s not very clever, and hence not very performant. This obviously only matters if your tables are on the bigger end. In my case
table1 (the one to update) has 576,806 rows while
table2 (the one providing the attribute data) has a whopping 848,664,485 rows. Also I should mention that
table2 contains multiple rows for each corresponding row in
table1. In that concrete case
table2 contains data about point locations (latitude, longitude, timestamp) of people whose attributes (age, gender, etc…) are stored in
table1. And there is this one attribute which is wrongly and inefficiently stored with each point location, while it is only dependent on the person and should hence be stored there.