SpatialBench is an open benchmark suite for spatial SQL. The goal is to compare engines on price-performance, since serverless makes raw “x times faster” claims hard to interpret.
I used it to compare Databricks SQL Serverless (Medium) vs Databricks Jobs clusters with Apache Sedona 1.7 across 12 queries (from simple filters to joins, distance joins, multi-way joins, KNN) at SF100 and SF1000 (SF1000 is roughly 500GB uncompressed Parquet).
TLDR apart from one query, Sedona was up to ~6x better on cost per query, and also covered more queries under the same 10 hour timeout guardrails. Some queries didn’t finish or errored on either side, so there is a capability matrix in the post.
I used it to compare Databricks SQL Serverless (Medium) vs Databricks Jobs clusters with Apache Sedona 1.7 across 12 queries (from simple filters to joins, distance joins, multi-way joins, KNN) at SF100 and SF1000 (SF1000 is roughly 500GB uncompressed Parquet).
TLDR apart from one query, Sedona was up to ~6x better on cost per query, and also covered more queries under the same 10 hour timeout guardrails. Some queries didn’t finish or errored on either side, so there is a capability matrix in the post.