🎉 Apache Sedona 1.9.1 已正式发布！🗺️ 新增 Geography SQL 函数、Box2D 与 Box3D 类型、栅格 Python UDF 等。查看发布说明 →

The official source for Apache Sedona news, technical insights, release updates, and best practices in large-scale spatial data management.

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Australia or the Ocean? It's 2026 and Nobody Agrees Which Way a Polygon Goes

Draw a line around Australia and close it. On a globe that ring has two sides, and both are real polygons: the Australian mainland, 7.6 million km², and everything else on Earth, about 502 million km². The coastline is the same in both. The only thing that says which one you meant is the direction you drew it.

So which direction means "the land"? It depends on whom you ask. The standards have disagreed for decades, and two of them call their opposite answers by the same name.

Two globes with the same ring around Australia: drawn counterclockwise, the land inside is filled, 7.6 million square kilometers; drawn clockwise, everything else on Earth is filled, 502 million square kilometers

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The Serialization Tax, and How Sedona Stopped Paying It

Transform a building footprint to a projected coordinate system, buffer it by ten meters, then calculate its area. Three spatial operations, written as one SQL expression. There is also work hidden between those operations: converting each intermediate geometry to bytes, only for the next function to turn it back into an object.

Apache Sedona avoids those conversions by passing objects directly between functions that support it. The mechanism is small, and its effect depends on the workload. The measurements below show where it matters, where other work dominates, and what interrupts it.

Two evaluation paths for geometry and raster functions: the naive path encodes and decodes intermediate results; Sedona passes the objects directly to the next function

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ORDER BY Is an Index. Does It Have to Be?

SedonaDB, GeoParquet, and a story that started at VLDB 2016, now in Rust.

Parquet has stored a minimum and a maximum for every column in every row group since September 2013. PostgreSQL 9.5 added BRIN in January 2016: one small summary per range of blocks, cheap to update, useful when the data is clustered. In the same year the original creators of Apache Sedona, Jia Yu and Mohamed Sarwat, published Hippo at VLDB, the top database conference: a sparse index 25 to 30 times smaller than a B+-tree, built for data that is not sorted.

Ten years later, data skipping is how every lakehouse engine reads a file, and the question from 2016 is still open. Below: 3.82 million Washington buildings in one GeoParquet file, written three ways. Sorted by geometry, sorted by another column, and shuffled.

The title over three maps of Washington State made of building centroids, one color per row group; sorted by geometry the map is 77 solid patches, sorted by name it is confetti, shuffled it blends into one color

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GROUP BY, But for Pixels

Many raster analyses need one result for each polygon. Examples include mean elevation by canton, forest cover by district, and flood depth by parcel. A raster stores values in a grid of pixels. A polygon defines an area, also called a zone.

Zonal statistics summarize the raster pixels inside each zone. A spatial join matches data by location. Apache Sedona performs both steps in one query. This example reads 7,006,027 elevation pixels from Amazon S3 and summarizes all 26 Swiss cantons in 56 seconds.

Map of Switzerland. Each canton is shaded by its mean elevation. Low cantons are light blue. Valais and Graubünden are dark blue.

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One More Dimension: Apache Sedona Enters the 3D World

In a flat spatial query the world is only its noon shadow on the ground. Two objects whose shadows touch can be four hundred feet apart, and from a flat predicate the answer is the same for both.

The questions put to spatial engines have stopped being flat. Drones under FAA ceilings, aircraft over towers, apartments above restaurants, mineral deposits below mining leases: each one comes down to whether two volumes meet, and until now the SQL answer was the shadow version. Sedona 1.9.1 adds the missing axis. There is a Box3D type with constructors and accessors, 3D predicates and distances, a 3D extent aggregate, and query plans that account for the third axis.

The cover: an isometric drawing of a San Diego neighborhood, its street grid on the ground and every building rendered as an extruded box, beside the post title

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Find the Middle of the Mississippi

The Mississippi wanders. Between the two edges of a single Sentinel-2 scene it travels 147.6 km to cover 103 km of ground, and its width swings from 131 m to almost 3 km. Every one of those numbers came out of raw pixels: Apache Sedona's raster operators built the river polygon, and ST_ApproximateMedialAxis drew the line down the middle of it.

A Sentinel-2 view of the Mississippi's meanders near Greenville, Mississippi, with the detected river polygon in cyan and its computed centerline in orange; a stats panel lists 148 km of centerline, sinuosity 1.43, widths from 131 to 2,975 m, and the 5,429 water bodies found in the scene

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Seven Lines of NumPy, 121 Million Pixels

Those deep-green grids are California's Central Valley — some of the most intensively farmed land on Earth, not as a camera sees it but as the NDVI vegetation index does: computed, pixel by pixel, by seven lines of NumPy. Sedona ran those seven lines on 484 satellite tiles in parallel, straight out of a public S3 bucket, and wrote the answer back as Cloud Optimized GeoTIFFs. That's the new move in Sedona 1.9.1: a plain Python UDF can take raster columns and return rasters. If the raster function you need isn't built in — your index, your QA rule, your model — write it.

Five NDVI tiles computed by the seven-line Python UDF — deep-green field grids, blue canals, sandy rangeland, a city — above the UDF's code on a card

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Index a Million Rasters Without Reading a Pixel

Every raster team has the bucket. Thousands of GeoTIFFs accumulated from surveys, vendors, and pipelines — and no catalog. Which files cover this area? Which CRS are they in? Which ones were never converted to Cloud Optimized GeoTIFF? Until now, answering meant crawling the bucket with one-off scripts — or pulling rasters down just to inspect them.

Real Sentinel-2 scenes above the metadata card Sedona actually read — index a million rasters, read zero pixels

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Your Map Has Hotspots. Prove It.

Anyone can look at a map and see clumps. The interesting questions are the ones eyeballing can't answer: is that clustering statistically real? Exactly where is it significant? And which points genuinely don't belong?

Your map has hotspots — prove it: a field of grid cells with one glowing, statistically significant hotspot, and Sedona's spatial statistics toolkit named beneath