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DatabricksKent Marten, Matt Slack, Kaitlin BaumgardnerAnnouncement
Geospatial queries are up to 38X faster with Lakehouse//RT
Summary
Databricks announced that Spatial SQL and spatial joins now run on Lakehouse//RT, delivering up to 38X faster performance under high concurrency compared to traditional cloud data warehouses. The platform supports GEOMETRY and GEOGRAPHY data types, over 100 spatial functions, and high-performance spatial joins, all within the open lakehouse using Apache Spark and Parquet files. Organizations in insurance, agriculture, energy, telecom, retail, and logistics can consolidate geospatial workloads onto a single governed platform, eliminating separate stacks like PostGIS. Benchmarks using SpatialBench show Lakehouse//RT is 3X faster on average and up to 20X faster for single-query concurrency, with no code changes required. The service is available in Beta, allowing users to run existing spatial queries directly on Delta and Iceberg tables without copying data.
Context
Organizations with spatial OLAP and real-time location-based applications often rely on traditional data warehouses and separate stacks like PostGIS, leading to data copies, synchronization pipelines, and governance overhead. Databricks aims to consolidate geospatial workloads onto a single governed platform with Lakehouse//RT.
Approach / What changed
Lakehouse//RT is a real-time data warehouse that delivers millisecond query performance at massive concurrency directly on Delta and Iceberg tables, supporting native GEOMETRY and GEOGRAPHY data types and over 100 spatial functions without requiring code changes.
Takeaways
Lakehouse//RT delivers up to 38X faster spatial queries under high concurrency (50 concurrent users) compared to traditional cloud data warehouses.
Using native GEOMETRY data types instead of ST_GeomFromWKB allows query optimizations and file-skipping for best performance.
Spatial workloads are portable on Databricks; the same queries run on SQL Serverless can run on Lakehouse//RT with no code changes.
FAQ
How much faster is Lakehouse//RT for spatial queries?
In head-to-head benchmarks against a traditional data warehouse, Lakehouse//RT runs spatial queries up to 38X faster under heavy load. On average, benchmark queries are 3.5X faster even when you exclude the largest increase.
Do I need to change my queries?
No. The same spatial functions and spatial joins you run on Databricks SQL Serverless today run on Lakehouse//RT with no code changes. You get the speedup by creating a Lakehouse//RT warehouse and pointing your existing queries at it.
Is Spatial SQL on Lakehouse//RT available now?
Spatial SQL and spatial joins are available on Lakehouse//RT in Beta. To get started, create a Lakehouse//RT warehouse from the SQL Warehouses page and run your existing spatial queries with no code changes.
Source and freshness
- Source
- databricks.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
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