Writing a tile server in Python
Marcos Dione developed a custom Python tile server for OpenStreetMap data, optimizing rendering processes, managing client requests, and addressing challenges with existing frameworks to enhance efficiency and responsiveness.
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Marcos Dione discusses his experience in developing a tile server using Python, focusing on optimizations and the challenges faced during the process. He initially created a tileset based on OpenStreetMap data and modified an existing script, "generate_tiles," to enhance its functionality, including the ability to handle multiple rendering processes and optimize rendering time by avoiding unnecessary tile generation for water areas. Dione's approach involves rendering tiles on a powerful personal computer and syncing them to a server. He prefers creating his own tile server over using existing solutions like renderd due to its limitations in tile storage and update efficiency. The server's architecture includes a master thread and rendering threads that manage tile requests and caching optimizations. Dione also explores the integration of a web frontend to handle client requests, emphasizing the need for a responsive system that can manage long rendering times without causing client timeouts. He initially experimented with frameworks like FastAPI and Flask but found them lacking in handling specific requirements, leading him to consider a custom non-blocking loop implementation. Ultimately, he aims to create a robust tile server that efficiently processes requests while managing client connections and rendering tasks.
- Marcos Dione developed a custom tile server in Python for rendering tiles from OpenStreetMap data.
- The server optimizes rendering by avoiding unnecessary tile generation for water areas and reusing cached data.
- Dione prefers individual tile storage for easier updates and offline access compared to existing solutions.
- He faced challenges with existing web frameworks and opted for a custom non-blocking loop to manage client requests.
- The server architecture includes a master thread and rendering threads to efficiently handle tile rendering and client connections.
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5 commentsRaster tiles get huge at level 12 onwards. For generic usage it is hard to take the entire planet and make it available in a way where any user can use the same file at any zoom level in any location. For me, vector would solve this in a much better way, as we have a lot more control over which features to include and what level of detail to have in specific areas to reduce the overall size of the data.
It is so annoying that they don't give you a way to do this. I built an LLM inference API but am still working on how to cancel an inference job if a client cancels. I really don't want to rewrite my entire server in Twisted so I'll probably end up just building the HTTP responses like you did.
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