Go Geospatial

Open-source Go projects categorized as Geospatial

Top 11 Go Geospatial Projects

  • Tile38

    Real-time Geospatial and Geofencing

    Project mention: Show HN: TG – Fast geometry library in C | news.ycombinator.com | 2023-09-22

    [2] https://github.com/tidwall/tile38

  • buntdb

    BuntDB is an embeddable, in-memory key/value database for Go with custom indexing and geospatial support

    Project mention: PostgreSQL: No More Vacuum, No More Bloat | news.ycombinator.com | 2023-07-15

    Experimental format to help readability of a long rant:


    According to the OP, there's a "terrifying tale of VACUUM in PostgreSQL," dating back to "a historical artifact that traces its roots back to the Berkeley Postgres project." (1986?)


    Maybe the whole idea of "use X, it has been battle-tested for [TIME], is robust, all the bugs have been and keep being fixed," etc., should not really be that attractive or realistic for at least a large subset of projects.


    In the case of Postgres, on top of piles of "historic code" and cruft, there's the fact that each user of Postgres installs and runs a huge software artifact with hundreds or even thousands of features and dependencies, of which every particular user may only use a tiny subset.


    In Kleppmann's DDOA [1], after explaining why the declarative SQL language is "better," he writes: "in databases, declarative query languages like SQL turned out to be much better than imperative query APIs." I find this footnote to the paragraph a bit ironic: "IMS and CODASYL both used imperative query APIs. Applications typically used COBOL code to iterate over records in the database, one record at a time." So, SQL was better than CODASYL and COBOL in a number of ways... big surprise?

    Postgres' own PL/pgSQL [2] is a language that (I imagine) most people would rather NOT use: hence a bunch of alternatives, including PL/v8, on its own a huge mass of additional complexity. SQL is definitely "COBOLESQUE" itself.


    Could we come up with something more minimal than SQL and looking less like COBOL? (Hopefully also getting rid of ORMs in the process). Also, I have found inspiring to see some people creating databases for themselves. Perhaps not a bad idea for small applications? For instance, I found BuntDB [3], which the developer seems to be using to run his own business [4]. Also, HYTRADBOI? :-) [5].


    A usual objection to use anything other than a stablished relational DB is "creating a database is too difficult for the average programmer." How about debugging PostgreSQL issues, developing new storage engines for it, or even building expertise on how to set up the instances properly and keep it alive and performant? Is that easier?

    I personally feel more capable of implementing a small, well-tested, problem-specific, small implementation of a B-Tree than learning how to develop Postgres extensions, become an expert in its configuration and internals, or debug its many issues.

    Another common opinion is "SQL is easy to use for non-programmers." But every person that knows SQL had to learn it somehow. I'm 100% confident that anyone able to learn SQL should be able to learn a simple, domain-specific, programming language designed for querying DBs. And how many of these people that are not able to program imperatively would be able to read a SQL EXPLAIN output and fix deficient queries? If they can, that supports even more the idea that they should be able to learn something different than SQL.


    1: https://dataintensive.net/

    2: https://www.postgresql.org/docs/7.3/plpgsql-examples.html

    3: https://github.com/tidwall/buntdb

    4: https://tile38.com/

    5: https://www.hytradboi.com/

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  • orb

    Types and utilities for working with 2d geometry in Golang

  • go-geom

    Package geom implements efficient geometry types for geospatial applications.

  • rtreego

    an R-Tree library for Go

  • h3-go

    Go bindings for H3, a hierarchical hexagonal geospatial indexing system

  • polyline

    Package polyline implements a Google Maps Encoding Polyline encoder and decoder.

  • LearnThisRepo.com

    Learn 300+ open source libraries for free using AI. LearnThisRepo lets you learn 300+ open source repos including Postgres, Langchain, VS Code, and more by chatting with them using AI!

  • go-kml

    Package kml provides convenience methods for creating and writing KML documents.

  • go-gpx

    Package gpx provides convenience types for reading and writing GPX files.

  • web-mercator-projection

    A Go project to explore the math to calculate and present data in a map using the `Web Mercator Projection`

  • go-h3geo-dist

    H3-geo distributed cells

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020). The latest post mention was on 2023-09-22.

Go Geospatial related posts


What are some of the best open-source Geospatial projects in Go? This list will help you:

Project Stars
1 Tile38 8,841
2 buntdb 4,329
3 orb 796
4 go-geom 762
5 rtreego 589
6 h3-go 269
7 polyline 96
8 go-kml 79
9 go-gpx 29
10 web-mercator-projection 5
11 go-h3geo-dist 2
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