🎉 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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One Sort, Many Indexes: Spatial Correlation in GeoParquet

SedonaDB, GeoParquet, and a story that continued at SIGMOD 2019, now in Rust.

A GeoParquet file has one row order and many columns. In September, ORDER BY Is an Index showed that the order is the index, and this post shows that one sort indexes every column that follows the map. Sort 3,036,650 Washington addresses by geometry, and the postcode column gets an index for free. Sort by postcode, and the map gets one. SedonaDB 0.5.0, a database written in Rust, wrote the files and read the footers. The idea comes from HERMIT, which Jia Yu, the original creator of Apache Sedona, co-authored at SIGMOD 2019, one of the top three database conferences alongside VLDB and ICDE.

The title on the left, with Ferris the Rust crab after the subtitle, above three index cards for row group 15: postcode 98101 to 98199, city SEATTLE to SEATTLE, bbox -122.43 to -122.28 and 47.61 to 47.71; on the right a map of Washington State made of addresses, one color per row group, with the Seattle row group ringed in orange

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SedonaDB Ranks Beverly Hills the Second Scariest City in the US. Guess Who Is First.

Beverly Hills has fourteen psychics for 34,979 people. That puts it second on a scare index built from Overture Places: haunted houses, psychics, cemeteries and costume stores per 100,000 residents, for every US city with at least 20,000 people. First place is Salem, Massachusetts, with 45 per 100,000 and a 334-year head start. SedonaDB 0.5.0, a database written in Rust, computed the ranking with one SQL statement that joins the places into city polygons. Then the satellite view: a cloud-free Sentinel-2 image of Salem from 25 October 2026, six days before Halloween, next to one from September, shows how orange the Witch City has turned.

Sentinel-2 true color image of Salem, Massachusetts on 25 October 2026 with large ghost, crystal ball, mask and candy pins marking haunted houses, psychics, costume stores and candy stores, red badges counting places within one block, cemeteries outlined in green with tombstone icons, a scoreboard sticker ranking Salem first and Beverly Hills second, bats and a spider web, and a paper card with the post title and a pumpkin

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Rust Warps, SQL Calls, NumPy Reads: Three Rasters on One Grid

A satellite image and a land cover map of Seattle do not line up pixel for pixel. Each is a raster, a grid of pixels over the ground, and the grids differ: 10 m and 20 m pixels in the image, pixels measured in degrees in the map. Before any comparison, every pixel has to cover the same patch of ground in every file. SedonaDB 0.5.0, a database written in Rust, lines them up with two SQL functions. RS_Resample changes a raster's pixel size, and RS_ReprojectMatch moves a raster onto another raster's grid. NumPy then reads the three rasters as arrays of the same shape.

Sentinel-2 true color image of Seattle, Elliott Bay and Lake Union on 7 August 2026, with the post title on a dark card to the right

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SedonaDB 0.5.0 Release

The Apache Sedona community is excited to announce the release of SedonaDB version 0.5.0!

SedonaDB is the first open-source, single-node analytical database engine that treats spatial data as a first-class citizen. It is developed as a subproject of Apache Sedona. This post also highlights several changes from SedonaDB 0.4.1, which did not receive a separate release post.

Apache Sedona powers large-scale geospatial processing on distributed engines like Spark (SedonaSpark), Flink (SedonaFlink), and Snowflake (SedonaSnow). SedonaDB extends the Sedona ecosystem with a single-node engine optimized for small-to-medium data analytics, delivering simplicity and speed for spatial data that distributed systems and general-purpose databases cannot.

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Ten Billion Pixels, One SQL Statement, 45 Seconds

Where are the trees in Washington State? The answer is spread across 10.4 billion pixels, one byte each. ESA mapped the land cover of the whole planet in 2021, one pixel for every 10 meters of ground, and 8 files of that map cover Washington. SedonaDB 0.5.0, written in Rust, reads the 8 files as one table. One SQL statement joins that table to the 39 counties and returns the tree cover of every county in 45 seconds, on one machine.

Land cover pixels over Puget Sound: dark green tree cover, red built-up land around Seattle and Tacoma, blue water, with white county lines

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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.