netcdf-rust

Feature support

The minimum supported Rust version is 1.81. Reads are hardened against malformed input: bounds-checked cursors, cycle detection in B-tree/heap/object-header traversals, recursion-depth limits, and file-size-bounded allocation. The reader crates are exercised by a corpus/property/fuzz suite in addition to reference-oracle tests against the C libraries.

Not yet supported (contributions welcome): writing the szip / lz4 / nbit / scaleoffset filters; typed compound-value writing; appending to or updating an existing file in place; HDF5 virtual datasets; and object-header messages larger than 64 KiB.

Usage

use netcdf_reader::{NcFile, NcSliceInfo, NcSliceInfoElem};

let file = NcFile::open("era5.nc")?;
println!("format: {:?}", file.format());

for var in file.variables()? {
    println!("  var: {} {:?}", var.name(), var.shape());
}

// Read typed data (works for both classic and NetCDF-4)
let temp: ndarray::ArrayD<f32> = file.read_variable("temperature")?;

// Type-promoting read (any numeric type → f64)
let data = file.read_variable_as_f64("temperature")?;

// String variables (classic char arrays and NetCDF-4 NC_STRING)
let names = file.read_variable_as_strings("station_name")?;

// NetCDF-4 user-defined variables (enum, opaque, compound, array, vlen)
let quality = file.read_variable_user_defined("quality")?;

// CF conventions: unpack packed integer data (scale_factor + add_offset)
let unpacked = file.read_variable_unpacked("temperature")?;

// CF conventions: mask fill values + unpack in one call
let clean = file.read_variable_unpacked_masked("temperature")?;

// Hyperslab: read a single time step from a 4D variable
let sel = NcSliceInfo {
    selections: vec![
        NcSliceInfoElem::Index(0),                                        // time=0
        NcSliceInfoElem::Slice { start: 0, end: u64::MAX, step: 1 },     // all levels
        NcSliceInfoElem::Slice { start: 0, end: u64::MAX, step: 1 },     // all lat
        NcSliceInfoElem::Slice { start: 0, end: u64::MAX, step: 1 },     // all lon
    ],
};
let step: ndarray::ArrayD<f32> = file.read_variable_slice("temperature", &sel)?;

// Lazy iteration over time steps
for slice in file.iter_slices::<f32>("temperature", 0)? {
    let data = slice?;
    println!("  step shape: {:?}", data.shape());
}

// In-memory open with custom NC4 cache and read limits
let bytes = std::fs::read("era5.nc")?;
let file = NcFile::from_bytes_with_options(&bytes, netcdf_reader::NcOpenOptions {
    chunk_cache_bytes: 8 * 1024 * 1024,
    chunk_cache_slots: 257,
    max_read_bytes: 1024 * 1024 * 1024,
    ..Default::default()
})?;

Using hdf5-reader directly:

use hdf5_reader::Hdf5File;

let file = Hdf5File::open("data.h5")?;
let ds = file.dataset("/group1/temperature")?;
let data: ndarray::ArrayD<f64> = ds.read_array()?;

// Hyperslab selection
use hdf5_reader::{SliceInfo, SliceInfoElem};
let sel = SliceInfo {
    selections: vec![
        SliceInfoElem::Slice { start: 0, end: 10, step: 1 },
        SliceInfoElem::Index(5),
    ],
};
let slice: ndarray::ArrayD<f64> = ds.read_slice(&sel)?;

// String datasets
let labels = file.dataset("/labels")?.read_strings()?;

Writing

netcdf-writer builds a file from dimensions, variables, and attributes and serializes it to any classic or NetCDF-4 format:

use netcdf_writer::{NcAttrValue, NcFileBuilder, NcWriteOptions};

let mut builder = NcFileBuilder::new();
let time = builder.add_unlimited_dimension("time")?;
let x = builder.add_dimension("x", 3)?;

builder.add_attribute("title", NcAttrValue::Chars("example".into()))?;

let temp = builder.add_variable::<f32>("temp", &[time, x])?;
builder.add_variable_attribute(temp, "units", NcAttrValue::Chars("K".into()))?;
builder.write_variable(temp, &[280.0_f32, 281.0, 282.0, 283.0, 284.0, 285.0])?;

// AutoClassic picks CDF-1/2/5 from the schema; other options force a format.
let (_format, bytes) = builder.to_vec(NcWriteOptions::default())?;
std::fs::write("out.nc", bytes)?;

// NetCDF-4 (HDF5-backed) output:
use netcdf_writer::NcWriteFormat;
let (_format, nc4_bytes) =
    builder.to_vec(NcWriteOptions { format: NcWriteFormat::Nc4 })?;

hdf5-writer offers the same for raw HDF5 via Hdf5Builder / DatasetBuilder / AttributeBuilder.

Features

HDF5

• Superblock v0-v3, including files behind a user block, and object header v1/v2 with checksum verification
• Compact, contiguous, and chunked layouts
• All chunk index types: v1/v2 B-tree, single-chunk, implicit, Fixed Array, Extensible Array
• Deflate, shuffle, Fletcher-32, N-Bit, ScaleOffset, SZIP, and optional LZ4 filters
• Custom filters via FilterRegistry
• Fixed-length strings, HDF5 variable-length strings, and byte-vlen string datasets
• Dense-link resolution, soft-link resolution, optional external-link resolution, committed datatypes, global heap strings, and object references
• SOHM shared-message lookup, fractal heap managed/tiny/huge objects, and external raw data files
• Parallel chunk decoding, chunk caching, and object-header caching
• Range-backed opens via Storage backends (BytesStorage, FileStorage, MmapStorage)

NetCDF

• CDF-1, CDF-2, CDF-5, and NetCDF-4
• Automatic format detection
• In-memory opens that don't copy owned or shared bytes (NcFile::from_vec, from_shared_bytes, from_owner)
• Unified typed reads across formats
• Unified string reads for classic char arrays and NetCDF-4 string variables
• NetCDF-4 user-defined reads for enum, opaque, compound, fixed-size array, and non-string vlen variables, including custom borrowed decoders
• Type promotion to f64, unpacking, masking, and combined CF helpers
• Coordinate-variable lookup plus CF axis/time discovery when cf is enabled
• Exact CF time decoding for standard, proleptic Gregorian, noleap, all_leap, 360_day, and Julian calendars when cf is enabled
• Slice reads, lazy slice iteration, and parallel NC4 slice reads
• Cache and filter configuration through NcOpenOptions, including in-memory and storage-backed opens

Parallel-I/O Compatibility

This project reads files. It does not provide distributed parallel I/O APIs.

• PnetCDF-produced CDF-1, CDF-2, and CDF-5 files are supported as ordinary classic-format NetCDF files. PnetCDF's MPI-IO API surface is not implemented.
• NetCDF-C files created with nc_create_par or Parallel HDF5 are supported when the final file is a normal NetCDF-4/HDF5 file that uses HDF5 features supported by hdf5-reader. Parallel access mode is an open-time API concern, not a persistent file property.
• The Rayon APIs in this crate parallelize local decoding and independent byte range reads inside one process. They are not equivalent to MPI-IO collective or independent access modes.
• PnetCDF subfiling is out of scope for now because it is not an ordinary single-file CDF-1/2/5 dataset.

Feature flags

Minimum supported Rust version: 1.81.

[dependencies]
netcdf-reader = "0.12.1"           # CDF-1/2/5 + NetCDF-4 (default)
netcdf-reader = { version = "0.12.1", default-features = false }  # CDF-1/2/5 only
netcdf-writer = "0.12.1"           # Classic + NetCDF-4 writer (default)
hdf5-writer = "0.12.1"             # Raw HDF5 writer

External Raw Data Files

HDF5 external raw data files are not resolved by default. To allow them for trusted files, opt in with a resolver rooted at the directory that should contain the external data. The filesystem resolver rejects absolute paths and .. components. On Unix, it opens paths relative to the resolver root with openat and O_NOFOLLOW, so symlinks are rejected rather than followed. The same confinement is applied by FilesystemExternalLinkResolver when external links are enabled. On non-Unix platforms, the filesystem resolvers fall back to canonicalize-then-open and attacker-writable resolver roots are out of scope.

use std::path::Path;
use std::sync::Arc;

use hdf5_reader::{FilesystemExternalFileResolver, Hdf5File, OpenOptions};

let path = Path::new("data.h5");
let base_dir = path.parent().unwrap_or_else(|| Path::new("."));
let file = Hdf5File::open_with_options(path, OpenOptions {
    external_file_resolver: Some(Arc::new(FilesystemExternalFileResolver::new(base_dir))),
    ..Default::default()
})?;

Custom filters

Register filters before opening files:

use hdf5_reader::{Hdf5File, OpenOptions};
use hdf5_reader::filters::FilterRegistry;

let mut registry = FilterRegistry::new();
registry.register(32001, Box::new(|_filter, data, _elem_size| {
    // Custom decompression logic
    Ok(data.to_vec())
}));

let file = Hdf5File::open_with_options("data.h5", OpenOptions {
    filter_registry: Some(registry),
    ..Default::default()
})?;

Testing

# Unit tests (no external dependencies)
cargo test --workspace

# Integration tests with generated fixtures
scripts/generate-fixtures.sh
cargo test --workspace

Small compatibility fixtures under testdata/pnetcdf and testdata/parallel exercise standard CDF-1/2/5 and NetCDF-4 files matching layouts produced by PnetCDF and parallel netCDF-C/HDF5 workflows. Reference C generators that use those external parallel libraries live under testdata/external; they are not library dependencies.

For reference comparisons and current benchmark results against georust/netcdf, see docs/benchmark-report.md.

Releasing

See RELEASING.md for the release checklist and the required publish order for all six crates.

Known limitations

• SZIP is not built in (register via FilterRegistry if needed)
• ScaleOffset floating-point E-scale mode is not supported by the HDF5 decoder path

License

MIT OR Apache-2.0