Abstract. NetCDF (Network Common Data Form) is a self-describing, portable and platform-independent format for array-oriented scientific data which has become a community standard for sharing measurements and analysis results in the fields of oceanography, meteorology, and space domain. The volume of scientific data is continuously increasing at a very fast rate and poses challenges for efficient storage and sharing of these data. The object storage paradigm that appeared with cloud infrastructures, can help with data storage and parallel access issues. The availability of ample network bandwidth within cloud infrastructures allows for the utilization of large amounts of data. Processing data where the data is located is preferable as it can result in substantial resource savings. However, for some use cases downloading data from the cloud is required and results still have to be fetched once processing tasks have been executed on the cloud. However, networks bandwidth and quality can exhibit significant variations depending on the available resources in different use cases: networks can range from fiber-optic and copper connections to satellite connections with poor reception in degraded conditions on boats, among other scenarios. Therefore, it is crucial for formats and software libraries to be specifically designed to optimize access to data by minimizing the transfer to only what is strictly necessary. By design, the NetCDF data format offers such capabilities. A netCDF file is composed of a pool of chunks. Each chunk is a small unit of data that is independent and may be compressed. These units of data are read or written in a single I/O operation. In this article, we reuse the notion of sub-chunk introduced in the kerchunk library (Sterzinger et al., 2021): we refer to a sub-chunk as a sub-part of a chunk. The sub-chunking strategies help reducing the amount of data transferred by splitting netCDF chunks in even smaller unit of data. Kerchunk limits the sub-chunking capability to uncompressed chunks. Our approach goes further: it allows the sub-chunking of compressed chunks by indexing their content. In this context, a new approach has emerged in the form of a library that indexes the content of netCDF-4. This indexing enables the retrieval of sub-chunks without the need to read and decompress the entire chunk. This approach targets access patterns such as time series in netCDF-4 datasets formatted with large chunks and it has the advantage of not requiring the entire file to be reformatted. This report provides a performance assessment of netCDF-4 datasets for various use cases and conditions: POSIX (see POSIX) and S3 local filesystems, as well as a simulated degraded network connection. The results of this assessment may provide guidance on the most suitable and most efficient library for reading netCDF data in different situations. Another outcome of this study is the impact of the libraries used to access the data: while extensive existing literature compares different file formats performance (open, read and write), the impact of specific standard libraries remains poorly studied. This study evaluates the performance of four Python libraries (netcdf4-python, xarray, h5py, and a custom chunk-indexing library) for reading parts of the datasets through fsspec or s3fs. To complete the study, a comparison with the cloud-oriented formats Zarr and ncZarr is conducted. Results show that the h5py library provides high efficiency for accessing netCDF-4 formatted files and performance close to Zarr on a S3 server. These results suggest that it may not be necessary to convert netCDF-4 datasets to Zarr when moving to the cloud. This would avoid reformatting petabytes of data and costly adaptation of scientific software.
Penard et al. (Fri,) studied this question.