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Seamlessly compress VTK datasets using Zstandard.

Read in VTK datasets 37x faster, write 14x faster, all while using 28% less space versus VTK’s modern XML format.

Read/Write Speedup and Compression Ratios

Read/Write Speedup and Compression Ratios

File Type / Method

Write Speed

Compression Ratio

Notes

Legacy VTK (.vtk)

465 MB/s

0.88

Significant overhead

VTK XML, none

256 MB/s

0.70

Significant overhead

VTK XML, zlib

105 MB/s

2.52

VTK Default

VTK XML, lz4

401 MB/s

1.47

VTK XML, lzma

9.93 MB/s

3.10

VTK HDF (.vtkhdf), lvl0

1733 MB/s

0.93

No compression

VTK HDF (.vtkhdf), lvl4

137 MB/s

2.37

Default compression

pyvista-zstd (.pv), lvl3

711 MB/s

3.02

Threads = 0

pyvista-zstd (.pv), lvl3

1845 MB/s

3.02

Threads = 4

pyvista-zstd (.pv), lvl22

15.8 MB/s

3.79

All threads (-1)

Usage

Install with:

pip install pyvista-zstd

The package is a C++ library with a Python wrapper around it, so an install is either a prebuilt wheel or a compile. Wheels are published for:

Linux

x86_64 and aarch64 (manylinux2014 and manylinux_2_28)

macOS

x86_64 and arm64

Windows

AMD64

Everywhere else – musl-based Linux, Windows on ARM – pip falls back to the source distribution and builds the core during the install, which needs a C++17 compiler on the machine. CMake does not: it comes from the build requirements. A machine without a compiler fails the install rather than producing a package that imports and cannot read anything.

Compatible with all VTK dataset types. Uses PyVista under the hood.

import pyvista_zstd

# create and write out
ds = pv.Sphere()
pyvista_zstd.write(ds, "dataset.pv")

# read in and show these are identical
ds_in = pyvista_zstd.read("dataset.pv")
assert ds == ds_in

For cell arrays where every cell contains the same number of points, pyvista-zstd stores the common cell width in the dataset metadata and omits the redundant offsets array. This applies to triangles, quads, tetrahedra, and any other fixed-width cell topology. Mixed-width cell arrays retain their explicit offsets.

Alternative VTK example

import vtk
import pyvista_zstd

# create dataset using VTK source
sphere_source = vtk.vtkSphereSource()
sphere_source.SetRadius(1.0)
sphere_source.SetThetaResolution(32)
sphere_source.SetPhiResolution(32)
sphere_source.Update()

vtk_ds = sphere_source.GetOutput()

# read back
pyvista_zstd.write(vtk_ds, "sphere.pv")
ds_in = pyvista_zstd.read("sphere.pv")

PyVista Integration

When pyvista-zstd is installed, it automatically registers with PyVista’s reader registry. This means pv.read() handles .pv files directly:

import pyvista as pv

mesh = pv.read("dataset.pv")

No additional imports needed. This works via PyVista’s pyvista.readers entry point group, so the registration happens at install time.

The C library

The reader and writer are a C++ core with a pure C ABI, pvzstd, declared in cpp/include/pvzstd/pvzstd.h. The Python package is one consumer of it, bound with ctypes; a C or C++ consumer – or a WebAssembly build – can link the same library directly, and it depends on nothing but zstd.

find_package(pvzstd CONFIG REQUIRED)
target_link_libraries(my_app PRIVATE pvzstd::pvzstd)

See The C library for the build options, both ways of consuming the CMake package, the ABI-version contract, and a worked example. The on-disk format is specified in doc/format/container-v2.md.

Rational

VTK’s XML writer is flexible and supports most datasets, but its compression is limited to a single thread, has only a subset of compression algorithms, and the XML format adds significant overhead.

To demonstrate this, the following example writes out a single file without compression. This example requires pyvista>=0.47.0 for the compression parameter.

>>> import numpy as np
>>> import pyvista as pv
>>> ugrid = pv.ImageData(dimensions=(200, 200, 200)).to_tetrahedra()
>>> ugrid["pdata"] = np.random.random(ugrid.n_points)
>>> ugrid["cdata"] = np.random.random(ugrid.n_cells)
>>> nbytes = (
...     ugrid.points.nbytes
...     + ugrid.cell_connectivity.nbytes
...     + ugrid.offset.nbytes
...     + ugrid.celltypes.nbytes
...     + ugrid["pdata"].nbytes
...     + ugrid["cdata"].nbytes
... )
>>> print(f"Size in memory: {nbytes / 1024**2:.2f} MB")

Size in memory: 1993.89 MB
Save using VTK XML format

>>> from pathlib import Path
>>> import time
>>> tmp_path = Path("/tmp/ds.vtu")
>>> tstart = time.time()
>>> ugrid.save(tmp_path, compression=None)
>>> print(f"Written without compression in {time.time() - tstart:.2f} seconds")
>>> nbytes_disk = tmp_path.stat().st_size
>>> print(f"  File size:            {nbytes_disk / 1024**2:.2f} MB")
>>> print(f"  Compression Ratio:    {nbytes / nbytes_disk}")
>>> print()

Written without compression in 7.93 seconds
File size:            2858.94 MB
Compression Ratio:    0.6974239255525742

This amounts to around a 43% overhead using VTK’s XML writer. Using the default compression we can get the file size down to 791 MB, but it takes 19 seconds to compress.

>>> tstart = time.time()
>>> ugrid.save(tmp_path, compression='zlib')  # default
>>> print(f"Compressed in {time.time() - tstart:.2f} seconds")
>>> nbytes_disk = tmp_path.stat().st_size
>>> print(f"  File size:            {nbytes_disk / 1024**2:.2f} MB")
>>> print(f"  Compression Ratio:    {nbytes / nbytes_disk}")
>>> print()

Compressed in 18.83 seconds
File size:            791.05 MB
Compression Ratio:    2.5205590295735663

Clearly there’s room for improvement here as this amounts to a compression rate of 105.89 MB/s.

VTK Compression with Zstandard: pyvista-zstd

This library, pyvista-zstd, writes out VTK datasets with minimal overhead and uses Zstandard for compression. Moreover, it’s been implemented with multi-threading support for both read and write operations.

Let’s compress that file again but this time using pyvista-zstd:

>>> import pyvista_zstd
>>> tmp_path = Path("/tmp/ds.pv")
>>> tstart = time.time()
>>> pyvista_zstd.write(ugrid, tmp_path)
>>> print(f"Compressed pyvista_zstd in {time.time() - tstart:.2f} seconds")
>>> nbytes_disk = tmp_path.stat().st_size
>>> print(f"  File size:            {nbytes_disk / 1024**2:.2f} MB")
>>> print(f"  Compression Ratio:    {nbytes / nbytes_disk}")

Compressed pyvista_zstd in 0.92 seconds
Threads:              -1
File size:            660.41 MB
Compression Ratio:    3.019175309922273

This gives us a write performance of 2167 MB/s using the default number of threads and compression level, resulting in a 20x speedup in write performance versus VTK’s XML writer. This speedup is most noticeable for larger files:

Speedup versus VTK’s XML

Speedup versus VTK’s XML

Even when disabling multi-threading we can still achieve excellent performance:

>>> tstart = time.time()
>>> pyvista_zstd.write(ugrid, tmp_path, n_threads=0)
>>> print(f"Compressed pyvista_zstd in {time.time() - tstart:.2f} seconds")
>>> nbytes_disk = tmp_path.stat().st_size
>>> print(f"  File size:            {nbytes_disk / 1024**2:.2f} MB")
>>> print(f"  Compression Ratio:    {nbytes / nbytes_disk}")

Compressed pyvista_zstd in 2.91 seconds
Threads:              0
File size:            660.47 MB
Compression Ratio:    3.0188911592355683

This amounts to a single-core compression rate of 685.18 MB/s, which is in agreement with Zstandard’s benchmarks.

Note that the benefit of threading drops off rapidly past 8 threads, though part of this is due to the performance versus efficiency cores of the CPU used for benchmarking (see below).

Read/Write Speed versus Number of Threads

Read/Write Speed versus Number of Threads

Reading in the dataset is also fast. Comparing with VTK’s XML reader using defaults:

Read VTK XML

>>> print(f"Read VTK XML:")
>>> timeit pv.read("/tmp/ds.vtu")
6.22 s ± 9.21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Read zstd

>>> print(f"Read zstd:")
>>> timeit pyvista_zstd.read("/tmp/ds.pv")
563 ms ± 7.96 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

This is an 11x speedup for this dataset versus VTK’s XML, and it’s still fast even with multi-threading disabled:

>>> timeit pyvista_zstd.read("/tmp/ds.pv", n_threads=0)
1.11 s ± 4.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

This amounts to 1796 MB/s for a single core, which is also in agreement with Zstandard’s benchmarks.

Additionally, you can control Zstandard’s compression level by setting level=. A quick benchmark for this dataset indicates the defaults give a reasonable performance versus size tradeoff:

Read/Write Speed versus Compression Level

Read/Write Speed versus Compression Level

Note that both pyvista-zstd and VTK’s XML default compression give relatively constant compression ratios for this dataset across varying file sizes:

Compression Ratio versus VTK’s XML

Compression Ratio versus VTK’s XML

These benchmarks were performed on an i9-14900KF running the Linux kernel 6.12.41 using zstandard==0.24.0 from PyPI. Storage was a 2TB Samsung 990 Pro without LUKS mounted at /tmp.

Additional Information

The benchmarks/ directory contains additional benchmarks using many datasets, including all applicable datasets in pyvista.examples (see PyVista Dataset Gallery).

Project links

Data verified by PyPI on Aug 29, 2026
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Credits

Author: The PyVista developers

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The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

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Python >=3.10

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