All platforms use lazy-binding meaning that parts of the binary won't be loaded in memory until the first function call.

All gdal.Geometry methods that return a gdal.Geometry throw an Error on error instead of returning a null object.() now throws an Error if an invalid ring is requested instead of returning a null object.() now throws an Error if an invalid point is requested instead of returning a null object.() now throws an Error if an invalid field is requested instead of returning a null object.Linux uses lazy-binding meaning that parts of the binary won't be loaded in memory until the first function call.Drop support for linking against a shared GDAL 1.x library.now throws an Error on error instead of returning a null geometry.The HDF5 driver is not thread safe on Windowsīreaking changes relative to node-gdal / node-gdal-next It is recommended to retrieve all values such as raster size or no data value or spatial reference before starting any I/O operations or use the new asynchronous getters available in 3.3.2 and later. This includes synchronous getters and setters that might otherwise be instantaneous. If a GDAL operation is running in the background for any given Dataset, all synchronous operations on that same Dataset on the main thread will block the event loop until the background operation is finished. Take a look at ASYNCIO.md which explains this in detail.Īlso be particularly careful when mixing synchronous and asynchronous operations in server code. It is recommended to either increase UV_THREADPOOL_SIZE or to make sure that every dataset has exactly one operation running at any given time. Keep in mind that Node.js/libuv won't be able to detect which async contexts are waiting on each other, so if you launch 16 simultaneous operations on 4 different datasets, there is always a chance that libuv will pick 4 operations on the same dataset to run - which will take all 4 slots on the thread pool. The only way to have multiple parallel operations on the same file is to use multiple dataset objects. Simultaneous operations on the same dataset object are safe too but they won't run in parallel. Simultaneous operations on distinct dataset objects are always safe and can run it parallel. Mixing of synchronous and asynchronous operations Support for worker_threads is planned but it is not a priority project Numerous bugfixes including a number of memory leaks.Built-in support for HDF5, NetCDF, GRIB, WMS, WMTS, WCS and FlatGeobuf (since 3.3).Support for curve geometries (since 3.1).VRT pixel functions support - both with JS functions and ExprTk expressions (since 3.5).

ExprTk.js integration for truly multi-threaded async computing (since 3.5).An alternative gdal_calc.py implementation supporting JS functions (since 3.4).Node.js Streams API for raster datasets (since 3.4).Library versions of GDAL's command-line utilities gdalinfo, gdal_translate, ogr2ogr and gdalwarp (since 3.4).Read-only support for the new Multidimensional Raster Data Model of GDAL 3.1 (since 3.3).scijs/ndarray integration with zero-copy I/O (since 3.3).Built-in networking support with native support for cloud services (since 3.2).

Full native TypeScript support with autocompletion in IDEs that support it (since 3.1).Support for asynchronous IO (since 3.0).This project is a fork of which is a fork of.

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