Introducing Lightweight Versions of GDAL and PDAL

Quansight · Jul 25, 2024

See how Hobu teamed with Quansight to fund the transition to a deferred plugin system in both GDAL and PDAL. The new architecture was implemented in GDAL 3.9.1 and PDAL 2.7.2. (this article was originally published on the Quansight Blog by Isuru Fernando).

The evolution of geospatial data processing has taken a significant step forward with the introduction of lightweight versions of the Geospatial Data Abstraction Library (GDAL) and the Point Data Abstraction Library (PDAL). This new architecture addresses the long-standing issue of dependency bloat, significantly improving solve times, download speeds, and overall package manageability for users. This post delves into the history, technical implementation, and benefits of this transition.

GDAL (Geospatial Data Abstraction Library) is a translator library for raster and vector geospatial data formats. Being a translator library supporting multiple different geospatial data formats, it has a lot of libraries as dependencies. For example The hdf5 for HDF5 package format support. PDAL (Point Data Abstraction Library) is a library built on top of GDAL and has similar support for package formats.

A Little Bit of History

conda-forge was started by a few people, including a couple of oceanographers, who wanted a way to distribute gdal easily. Hence gdal-feedstock is one of the first feedstocks to be made on conda-forge and was the 49th PR on staged-recipes. The initial commit to the gdal-feedstock which builds the conda package, only used a few packages, including hdf4, hdf5, , postgresql, libnetcdf, kealib.

Since then, more dependencies have been added to the gdal conda package and it has now grown to 113 direct and indirect dependencies (numbers based on macOS, JUL 2024). With the huge number of dependencies, the solve times and download times have increased, and images created from these conda packages are unwieldy.

This is where the partnership with Hobu and Quansight comes in to fund the transition to a deferred plugin system in both GDAL and PDAL. The new architecture was implemented in GDAL 3.9.1 and PDAL 2.7.2.

Deferred C++ plugin loading

GDAL RFC 96 enables the support of deferred plugins. Plugins in GDAL support the various raster and vector geospatial data formats. These plugins are usually built into the core library, libgdal.(dylib/so/dll) , but RFC 96 introduced deferred plugins that build these plugins separately such that only the necessary plugin dependencies are needed.

For example, instead of HDF5 being a dependency of libgdal.(dylib/so/dll) , there’s a new gdal_HDF5.(dylib/so/dll) which has an HDF5 dependency and is loaded by the libgdal core library.

This allows us to package the plugins as separate conda packages and therefore the core library can remain small while enabling full functionality of GDAL through these plugins. A nice feature of RFC 96 is that the core libgdal library will output a customizable error message when a plugin fails to load. For example when the hdf5 plugin is in a separate package called libgdal-hdf5, we can introduce an error message that says

You may install it with ‘conda install -c conda-forge libgdal-hdf5’. This concept was first used for libarrow/libparquet dependency since it is a large dependency and especially because gdal supports four different major versions on conda-forge. By separating this dependency, only the plugin needs to be built for the four different arrow/parquet versions as opposed to the core libgdal library being built for the four different versions. The conda package for the plugin was called libgdal-arrow-parquet and depended on the core library conda package libgdal which included the rest of the plugins.

libgdal-core and libgdal

In order to generalize the above strategy to more plugins, we are now introducing a libgdal-core conda package and more plugins as conda packages with all plugins (except arrow/parquet) being installable with libgdal . We also made the python bindings depend on libgdal-core instead of libgdal so that users can select the plugins that they need.

gdal conda packages

– libgdal-core — core C++ library – libgdal — core C++ library and all plugins – gdal — python library without the plugins

gdal plugin conda packages

– libgdal-arrow-parquet : vector.arrow and vector.parquet drivers as a plugin – libgdal-fits : raster.fits driver as a plugin – libgdal-grib : raster.grib driver as a plugin – libgdal-hdf4 : raster.hdf4 driver as a plugin – libgdal-hdf5 : raster.hdf5 driver as a plugin – libgdal-jp2openjpeg :raster.jp2openjpeg driver as a plugin – libgdal-kea : raster.kea driver as a plugin – libgdal-netcdf: raster.netcdf driver as a plugin – libgdal-pdf: raster.pdf driver as a plugin – libgdal-postgisraster: raster.postgisraster driver as a plugin – libgdal-pg: vector.pg driver as a plugin – libgdal-tiledb : raster.tiledb driver as a plugin – libgdal-xls: vector.xls driver as a plugin

libgdal has 113 direct/indirect dependencies, but libgdal-core has only 48 direct/indirect dependencies.

If you are missing plugins with the new split, you can install all the plugins by running:

conda install libgdal To install all the plugins or install individual plugins:

conda install libgdal-hdf5 Currently only the python bindings gdal depend on libgdal-core and in the future more and more downstream packages of libgdal will depend on libgdal-core and individual plugins needed for their usage. Therefore we recommend either installing libgdal or explicitly installing the individual plugins.

We looked at the install times for libgdal vs libgdal-core on Github actions and libgdal-core was faster. We also noticed that libboost-headers was being pulled by libkml which is only needed for development. We split the libkml conda package into libkml and libkml-devel so that end users are not going to end up with the libboost-headers which has thousands of header files.

Note that the timings are from a quick testing on Github actions and not formal benchmarking.

libpdal and libpdal-core Similar to libgdal and libgdal-core , we have introduced libgdal and libgdal-core conda packages. Previously the pdal conda package provided only the C++ library, but now it also provides the python package to match the gdal conda package.

pdal conda packages

– libpdal-core — core C++ library – libpdal — core C++ library and all plugins – pdal-python — python library without the plugins – pdal — python library and all plugins

pdal plugin conda packages

– libpdal-trajectory : filters.trajectory driver as a plugin – libpdal-hdf : readers.hdf driver as a plugin – libpdal-tiledb: readers.tiledb, writers.tiledb driver as a plugin – libpdal-pgpointcloud: readers.pgpointcloud driver as a plugin – libpdal-draco : readers.draco, writers.draco driver as a plugin – libpdal-arrow: readers.arrow, writers.arrow driver as a plugin – libpdal-nitf: readers.nitf driver as a plugin – libpdal-e57: readers.e57, writers.e57 driver as a plugin – libpdal-icebridge: readers.icebridge driver as a plugin – libpdal-cpd : filters.cpd driver as a plugin

The shift to a deferred plugin system in GDAL and PDAL is a pivotal moment in geospatial data processing, offering a more efficient and streamlined approach to handling dependencies. By enabling the separation of core libraries and plugins, users can now enjoy faster installation times and a more manageable set of dependencies tailored to their specific needs. The collaboration between Hobu and Quansight has not only modernized these essential libraries but has also set a new standard for the development and distribution of geospatial tools.

Acknowledgements This work was funded by Hobu, Inc in collaboration with Quansight, Inc.