In my previous post on routing, I used Dijkstra’s algorithm without much discussion of alternatives. The Dijkstra algorithm works for network routing, and for many problems it is the right choice. But pgRouting also ships with pgr_aStar, an implementation of the A* algorithm that can find the same shortest path while exploring fewer edges. The difference comes down to one thing: a heuristic that tells the algorithm which direction to look.
Query optimization is a critical aspect of database performance tuning. While YugabyteDB’s YSQL API provides powerful tools for analyzing query performance through EXPLAIN plans, sometimes we need to experiment with different indexing strategies without the overhead of actually creating the indexes. This is where HypoPG comes in handy.
In my previous post on pgRouting, I showed how to run shortest-path queries directly inside PostgreSQL. That approach works well when your road data is already in Postgres and your network is moderate-sized. But what happens when you need live traffic data, global coverage, or routing at thousands of queries per second? That is where external routing APIs and dedicated routing engines come in.
Finding the nearest X is easy to ask for. Getting it right at scale is another matter.
If your application needs to answer “what is the fastest route between two points,” you might reach for an external routing API like Mapbox Directions. But if your spatial data is already stored in PostgreSQL, the Postgres extension pgRouting lets you run graph-based routing queries right where the data is.