Zstandard bindings for Python
================
This project provides Python bindings for interfacing with the
Zstandard <http://www.zstd.net>_ compression library. A C extension
and CFFI interface is provided.
The primary goal of the extension is to provide a Pythonic interface to the underlying C API. This means exposing most of the features and flexibility of the C API while not sacrificing usability or safety that Python provides.
The canonical home for this project is https://github.com/indygreg/python-zstandard.
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The project is officially in beta state. The author is reasonably satisfied with the current API and that functionality works as advertised. There may be some backwards incompatible changes before 1.0. Though the author does not intend to make any major changes to the Python API.
There is continuous integration for Python versions 2.6, 2.7, and 3.3+ on Linux x86x64 and Windows x86 and x8664. The author is reasonably confident the extension is stable and works as advertised on these platforms.
The author is reasonably confident in the current state of what's
implemented on the ZstdCompressor and ZstdDecompressor types.
Those APIs likely won't change significantly. Some low-level behavior
(such as naming and types expected by arguments) may change.
There will likely be arguments added to control the input and output buffer sizes (currently, certain operations read and write in chunk sizes using zstd's preferred defaults).
There should be an API that accepts an object that conforms to the buffer interface and returns an iterator over compressed or decompressed output.
The author is on the fence as to whether to support the extremely low level compression and decompression APIs. It could be useful to support compression without the framing headers. But the author doesn't believe it a high priority at this time.
The CFFI bindings are half-baked and need to be finished.
This extension is designed to run with Python 2.6, 2.7, 3.3, 3.4, and 3.5 on common platforms (Linux, Windows, and OS X). Only x86_64 is currently well-tested as an architecture.
This package is uploaded to PyPI at https://pypi.python.org/pypi/zstandard. So, to install this package::
$ pip install zstandard
Binary wheels are made available for some platforms. If you need to
install from a source distribution, all you should need is a working C
compiler and the Python development headers/libraries. On many Linux
distributions, you can install a python-dev or python-devel
package to provide these dependencies.
Packages are also uploaded to Anaconda Cloud at
https://anaconda.org/indygreg/zstandard. See that URL for how to install
this package with conda.
Very crude and non-scientific benchmarking (most benchmarks fall in this category because proper benchmarking is hard) show that the Python bindings perform within 10% of the native C implementation.
The following table compares the performance of compressing and decompressing
a 1.1 GB tar file comprised of the files in a Firefox source checkout. Values
obtained with the zstd program are on the left. The remaining columns detail
performance of various compression APIs in the Python bindings.
+-------+-----------------+-----------------+-----------------+---------------+ | Level | Native | Simple | Stream In | Stream Out | | | Comp / Decomp | Comp / Decomp | Comp / Decomp | Comp | +=======+=================+=================+=================+===============+ | 1 | 490 / 1338 MB/s | 458 / 1266 MB/s | 407 / 1156 MB/s | 405 MB/s | +-------+-----------------+-----------------+-----------------+---------------+ | 2 | 412 / 1288 MB/s | 381 / 1203 MB/s | 345 / 1128 MB/s | 349 MB/s | +-------+-----------------+-----------------+-----------------+---------------+ | 3 | 342 / 1312 MB/s | 319 / 1182 MB/s | 285 / 1165 MB/s | 287 MB/s | +-------+-----------------+-----------------+-----------------+---------------+ | 11 | 64 / 1506 MB/s | 66 / 1436 MB/s | 56 / 1342 MB/s | 57 MB/s | +-------+-----------------+-----------------+-----------------+---------------+
Again, these are very unscientific. But it shows that Python is capable of compressing at several hundred MB/s and decompressing at over 1 GB/s.
https://pypi.python.org/pypi/zstd is an alternative Python binding to Zstandard. At the time this was written, the latest release of that package (1.0.0.2) had the following significant differences from this package:
The source repository for this project contains a vendored copy of the Zstandard source code. This is done for a few reasons.
First, Zstandard is relatively new and not yet widely available as a system package. Providing a copy of the source code enables the Python C extension to be compiled without requiring the user to obtain the Zstandard source code separately.
Second, Zstandard has both a stable public API and an experimental API. The experimental API is actually quite useful (contains functionality for training dictionaries for example), so it is something we wish to expose to Python. However, the experimental API is only available via static linking. Furthermore, the experimental API can change at any time. So, control over the exact version of the Zstandard library linked against is important to ensure known behavior.
Once you have the source code, the extension can be built via setup.py::
$ python setup.py build_ext
We recommend testing with nose::
$ nosetests
A Tox configuration is present to test against multiple Python versions::
$ tox
Tests use the hypothesis Python package to perform fuzzing. If you
don't have it, those tests won't run.
There is also an experimental CFFI module. You need the cffi Python
package installed to build and test that.
To create a virtualenv with all development dependencies, do something like the following::
# Python 2 $ virtualenv venv
# Python 3 $ python3 -m venv venv
$ source venv/bin/activate $ pip install cffi hypothesis nose tox
The compiled C extension provides a zstd Python module. This module
exposes the following interfaces.
The ZstdCompressor class provides an interface for performing
compression operations.
Each instance is associated with parameters that control compression behavior. These come from the following named arguments (all optional):
level Integer compression level. Valid values are between 1 and 22. dict_data Compression dictionary to use.
Note: When using dictionary data and compress() is called multiple
times, the CompressionParameters derived from an integer compression
level and the first compressed data's size will be reused for all
subsequent operations. This may not be desirable if source data size
varies significantly.
compressionparams
A CompressionParameters instance (overrides the level value).
writechecksum
Whether a 4 byte checksum should be written with the compressed data.
Defaults to False. If True, the decompressor can verify that decompressed
data matches the original input data.
writecontentsize
Whether the size of the uncompressed data will be written into the
header of compressed data. Defaults to False. The data will only be
written if the compressor knows the size of the input data. This is
likely not true for streaming compression.
writedictid
Whether to write the dictionary ID into the compressed data.
Defaults to True. The dictionary ID is only written if a dictionary
is being used.
Simple API ^^^^^^^^^^
compress(data) compresses and returns data as a one-shot operation.::
cctx = zstd.ZsdCompressor() compressed = cctx.compress(b'data to compress')
Streaming Input API ^^^^^^^^^^^^^^^^^^^
write_to(fh) (which behaves as a context manager) allows you to stream
data into a compressor.::
cctx = zstd.ZstdCompressor(level=10) with cctx.write_to(fh) as compressor: compressor.write(b'chunk 0') compressor.write(b'chunk 1') ...
The argument to write_to() must have a write(data) method. As
compressed data is available, write() will be called with the comrpessed
data as its argument. Many common Python types implement write(), including
open file handles and io.BytesIO.
write_to() returns an object representing a streaming compressor instance.
It must be used as a context manager. That object's write(data) method
is used to feed data into the compressor.
If the size of the data being fed to this streaming compressor is known, you can declare it before compression begins::
cctx = zstd.ZstdCompressor() with cctx.writeto(fh, size=datalen) as compressor: compressor.write(chunk0) compressor.write(chunk1) ...
Declaring the size of the source data allows compression parameters to
be tuned. And if write_content_size is used, it also results in the
content size being written into the frame header of the output data.
The size of chunks being write() to the destination can be specified::
cctx = zstd.ZstdCompressor()
with cctx.write_to(fh, write_size=32768) as compressor:
...
To see how much memory is being used by the streaming compressor::
cctx = zstd.ZstdCompressor()
with cctx.write_to(fh) as compressor:
...
byte_size = compressor.memory_size()
Streaming Output API ^^^^^^^^^^^^^^^^^^^^
read_from(reader) provides a mechanism to stream data out of a compressor
as an iterator of data chunks.::
cctx = zstd.ZstdCompressor() for chunk in cctx.read_from(fh): # Do something with emitted data.
read_from() accepts an object that has a read(size) method or conforms
to the buffer protocol. (bytes and memoryview are 2 common types that
provide the buffer protocol.)
Uncompressed data is fetched from the source either by calling read(size)
or by fetching a slice of data from the object directly (in the case where
the buffer protocol is being used). The returned iterator consists of chunks
of compressed data.
Like write_to(), read_from() also accepts a size argument
declaring the size of the input stream::
cctx = zstd.ZstdCompressor()
for chunk in cctx.read_from(fh, size=some_int):
pass
You can also control the size that data is read() from the source and
the ideal size of output chunks::
cctx = zstd.ZstdCompressor()
for chunk in cctx.read_from(fh, read_size=16384, write_size=8192):
pass
Stream Copying API ^^^^^^^^^^^^^^^^^^
copy_stream(ifh, ofh) can be used to copy data between 2 streams while
compressing it.::
cctx = zstd.ZstdCompressor() cctx.copy_stream(ifh, ofh)
For example, say you wish to compress a file::
cctx = zstd.ZstdCompressor() with open(inputpath, 'rb') as ifh, open(outputpath, 'wb') as ofh: cctx.copy_stream(ifh, ofh)
It is also possible to declare the size of the source stream::
cctx = zstd.ZstdCompressor() cctx.copystream(ifh, ofh, size=lenof_input)
You can also specify how large the chunks that are read() and write()
from and to the streams::
cctx = zstd.ZstdCompressor() cctx.copystream(ifh, ofh, readsize=32768, write_size=16384)
The stream copier returns a 2-tuple of bytes read and written::
cctx = zstd.ZstdCompressor() readcount, writecount = cctx.copy_stream(ifh, ofh)
Compressor API ^^^^^^^^^^^^^^
compressobj() returns an object that exposes compress(data) and
flush() methods. Each returns compressed data or an empty bytes.
The purpose of compressobj() is to provide an API-compatible interface
with zlib.compressobj and bz2.BZ2Compressor. This allows callers to
swap in different compressor objects while using the same API.
Once flush() is called, the compressor will no longer accept new data
to compress(). flush() must be called to end the compression
context. If not called, the returned data may be incomplete.
Here is how this API should be used::
cctx = zstd.ZstdCompressor() cobj = cctx.compressobj() data = cobj.compress(b'raw input 0') data = cobj.compress(b'raw input 1') data = cobj.flush()
For best performance results, keep input chunks under 256KB. This avoids extra allocations for a large output object.
It is possible to declare the input size of the data that will be fed into the compressor::
cctx = zstd.ZstdCompressor() cobj = cctx.compressobj(size=6) data = cobj.compress(b'foobar') data = cobj.flush()
The ZstdDecompressor class provides an interface for performing
decompression.
Each instance is associated with parameters that control decompression. These come from the following named arguments (all optional):
dict_data Compression dictionary to use.
The interface of this class is very similar to ZstdCompressor (by design).
Simple API ^^^^^^^^^^
decompress(data) can be used to decompress an entire compressed zstd
frame in a single operation.::
dctx = zstd.ZstdDecompressor()
decompressed = dctx.decompress(data)
By default, decompress(data) will only work on data written with the content
size encoded in its header. This can be achieved by creating a
ZstdCompressor with write_content_size=True. If compressed data without
an embedded content size is seen, zstd.ZstdError will be raised.
If the compressed data doesn't have its content size embedded within it,
decompression can be attempted by specifying the max_output_size
argument.::
dctx = zstd.ZstdDecompressor()
uncompressed = dctx.decompress(data, max_output_size=1048576)
Ideally, max_output_size will be identical to the decompressed output
size.
If max_output_size is too small to hold the decompressed data,
zstd.ZstdError will be raised.
If max_output_size is larger than the decompressed data, the allocated
output buffer will be resized to only use the space required.
Please note that an allocation of the requested max_output_size will be
performed every time the method is called. Setting to a very large value could
result in a lot of work for the memory allocator and may result in
MemoryError being raised if the allocation fails.
If the exact size of decompressed data is unknown, it is strongly recommended to use a streaming API.
Streaming Input API ^^^^^^^^^^^^^^^^^^^
write_to(fh) can be used to incrementally send compressed data to a
decompressor.::
dctx = zstd.ZstdDecompressor()
with dctx.write_to(fh) as decompressor:
decompressor.write(compressed_data)
This behaves similarly to zstd.ZstdCompressor: compressed data is written to
the decompressor by calling write(data) and decompressed output is written
to the output object by calling its write(data) method.
The size of chunks being write() to the destination can be specified::
dctx = zstd.ZstdDecompressor()
with dctx.write_to(fh, write_size=16384) as decompressor:
pass
You can see how much memory is being used by the decompressor::
dctx = zstd.ZstdDecompressor()
with dctx.write_to(fh) as decompressor:
byte_size = decompressor.memory_size()
Streaming Output API ^^^^^^^^^^^^^^^^^^^^
read_from(fh) provides a mechanism to stream decompressed data out of a
compressed source as an iterator of data chunks.::
dctx = zstd.ZstdDecompressor()
for chunk in dctx.read_from(fh):
# Do something with original data.
read_from() accepts a) an object with a read(size) method that will
return compressed bytes b) an object conforming to the buffer protocol that
can expose its data as a contiguous range of bytes. The bytes and
memoryview types expose this buffer protocol.
read_from() returns an iterator whose elements are chunks of the
decompressed data.
The size of requested read() from the source can be specified::
dctx = zstd.ZstdDecompressor()
for chunk in dctx.read_from(fh, read_size=16384):
pass
It is also possible to skip leading bytes in the input data::
dctx = zstd.ZstdDecompressor()
for chunk in dctx.read_from(fh, skip_bytes=1):
pass
Skipping leading bytes is useful if the source data contains extra
header data but you want to avoid the overhead of making a buffer copy
or allocating a new memoryview object in order to decompress the data.
Similarly to ZstdCompressor.read_from(), the consumer of the iterator
controls when data is decompressed. If the iterator isn't consumed,
decompression is put on hold.
When read_from() is passed an object conforming to the buffer protocol,
the behavior may seem similar to what occurs when the simple decompression
API is used. However, this API works when the decompressed size is unknown.
Furthermore, if feeding large inputs, the decompressor will work in chunks
instead of performing a single operation.
Stream Copying API ^^^^^^^^^^^^^^^^^^
copy_stream(ifh, ofh) can be used to copy data across 2 streams while
performing decompression.::
dctx = zstd.ZstdDecompressor()
dctx.copy_stream(ifh, ofh)
e.g. to decompress a file to another file::
dctx = zstd.ZstdDecompressor()
with open(input_path, 'rb') as ifh, open(output_path, 'wb') as ofh:
dctx.copy_stream(ifh, ofh)
The size of chunks being read() and write() from and to the streams
can be specified::
dctx = zstd.ZstdDecompressor()
dctx.copy_stream(ifh, ofh, read_size=8192, write_size=16384)
Decompressor API ^^^^^^^^^^^^^^^^
decompressobj() returns an object that exposes a decompress(data)
methods. Compressed data chunks are fed into decompress(data) and
uncompressed output (or an empty bytes) is returned. Output from subsequent
calls needs to be concatenated to reassemble the full decompressed byte
sequence.
The purpose of decompressobj() is to provide an API-compatible interface
with zlib.decompressobj and bz2.BZ2Decompressor. This allows callers
to swap in different decompressor objects while using the same API.
Each object is single use: once an input frame is decoded, decompress()
can no longer be called.
Here is how this API should be used::
dctx = zstd.ZstdDeompressor() dobj = cctx.decompressobj() data = dobj.decompress(compressedchunk0) data = dobj.decompress(compressedchunk1)
Various forms of compression and decompression APIs are provided because each are suitable for different use cases.
The simple/one-shot APIs are useful for small data, when the decompressed
data size is known (either recorded in the zstd frame header via
write_content_size or known via an out-of-band mechanism, such as a file
size).
A limitation of the simple APIs is that input or output data must fit in memory. And unless using advanced tricks with Python buffer objects, both input and output must fit in memory simultaneously.
Another limitation is that compression or decompression is performed as a single operation. So if you feed large input, it could take a long time for the function to return.
The streaming APIs do not have the limitations of the simple API. The cost to this is they are more complex to use than a single function call.
The streaming APIs put the caller in control of compression and decompression behavior by allowing them to directly control either the input or output side of the operation.
With the streaming input APIs, the caller feeds data into the compressor or decompressor as they see fit. Output data will only be written after the caller has explicitly written data.
With the streaming output APIs, the caller consumes output from the compressor or decompressor as they see fit. The compressor or decompressor will only consume data from the source when the caller is ready to receive it.
One end of the streaming APIs involves a file-like object that must
write() output data or read() input data. Depending on what the
backing storage for these objects is, those operations may not complete quickly.
For example, when streaming compressed data to a file, the write() into
a streaming compressor could result in a write() to the filesystem, which
may take a long time to finish due to slow I/O on the filesystem. So, there
may be overhead in streaming APIs beyond the compression and decompression
operations.
Zstandard allows dictionaries to be used when compressing and decompressing data. The idea is that if you are compressing a lot of similar data, you can precompute common properties of that data (such as recurring byte sequences) to achieve better compression ratios.
In Python, compression dictionaries are represented as the
ZstdCompressionDict type.
Instances can be constructed from bytes::
dict_data = zstd.ZstdCompressionDict(data)
More interestingly, instances can be created by training on sample data::
dictdata = zstd.traindictionary(size, samples)
This takes a list of bytes instances and creates and returns a
ZstdCompressionDict.
You can see how many bytes are in the dictionary by calling len()::
dictdata = zstd.traindictionary(size, samples)
dictsize = len(dictdata) # will not be larger than size
Once you have a dictionary, you can pass it to the objects performing compression and decompression::
dictdata = zstd.traindictionary(16384, samples)
cctx = zstd.ZstdCompressor(dictdata=dictdata) for sourcedata in inputdata: compressed = cctx.compress(source_data) # Do something with compressed data.
dctx = zstd.ZstdDecompressor(dictdata=dictdata)
for compresseddata in inputdata:
buffer = io.BytesIO()
with dctx.writeto(buffer) as decompressor:
decompressor.write(compresseddata)
# Do something with raw data in buffer.
Dictionaries have unique integer IDs. You can retrieve this ID via::
dictid = zstd.dictionaryid(dict_data)
You can obtain the raw data in the dict (useful for persisting and constructing
a ZstdCompressionDict later) via as_bytes()::
dictdata = zstd.traindictionary(size, samples) rawdata = dictdata.as_bytes()
Zstandard's integer compression levels along with the input size and dictionary size are converted into a data structure defining multiple parameters to tune behavior of the compression algorithm. It is possible to use define this data structure explicitly to have lower-level control over compression behavior.
The zstd.CompressionParameters type represents this data structure.
You can see how Zstandard converts compression levels to this data structure
by calling zstd.get_compression_parameters(). e.g.::
params = zstd.get_compression_parameters(5)
This function also accepts the uncompressed data size and dictionary size to adjust parameters::
params = zstd.get_compression_parameters(3, source_size=len(data), dict_size=len(dict_data))
You can also construct compression parameters from their low-level components::
params = zstd.CompressionParameters(20, 6, 12, 5, 4, 10, zstd.STRATEGY_FAST)
You can then configure a compressor to use the custom parameters::
cctx = zstd.ZstdCompressor(compression_params=params)
The members of the CompressionParameters tuple are as follows::
zstd.STRATEGY_ constants)You'll need to read the Zstandard documentation for what these parameters do.
estimatecompressioncontext_size(CompressionParameters) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Given a CompressionParameters struct, estimate the memory size required
to perform compression.
estimatedecompressioncontext_size() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Estimate the memory size requirements for a decompressor instance.
The following module constants/attributes are exposed:
ZSTDVERSION
This module attribute exposes a 3-tuple of the Zstandard version. e.g.
(1, 0, 0)
MAXCOMPRESSIONLEVEL
Integer max compression level accepted by compression functions
COMPRESSIONRECOMMENDEDINPUTSIZE
Recommended chunk size to feed to compressor functions
COMPRESSIONRECOMMENDEDOUTPUTSIZE
Recommended chunk size for compression output
DECOMPRESSIONRECOMMENDEDINPUTSIZE
Recommended chunk size to feed into decompresor functions
DECOMPRESSIONRECOMMENDEDOUTPUT_SIZE
Recommended chunk size for decompression output
FRAMEHEADER bytes containing header of the Zstandard frame MAGICNUMBER Frame header as an integer
WINDOWLOGMIN Minimum value for compression parameter WINDOWLOGMAX Maximum value for compression parameter CHAINLOGMIN Minimum value for compression parameter CHAINLOGMAX Maximum value for compression parameter HASHLOGMIN Minimum value for compression parameter HASHLOGMAX Maximum value for compression parameter SEARCHLOGMIN Minimum value for compression parameter SEARCHLOGMAX Maximum value for compression parameter SEARCHLENGTHMIN Minimum value for compression parameter SEARCHLENGTHMAX Maximum value for compression parameter TARGETLENGTHMIN Minimum value for compression parameter TARGETLENGTHMAX Maximum value for compression parameter STRATEGYFAST Compression strategory STRATEGYDFAST Compression strategory STRATEGYGREEDY Compression strategory STRATEGYLAZY Compression strategory STRATEGYLAZY2 Compression strategory STRATEGYBTLAZY2 Compression strategory STRATEGY_BTOPT Compression strategory
Many of the Zstandard APIs used by this module are marked as experimental within the Zstandard project. This includes a large number of useful features, such as compression and frame parameters and parts of dictionary compression.
It is unclear how Zstandard's C API will evolve over time, especially with regards to this experimental functionality. We will try to maintain backwards compatibility at the Python API level. However, we cannot guarantee this for things not under our control.
Since a copy of the Zstandard source code is distributed with this module and since we compile against it, the behavior of a specific version of this module should be constant for all of time. So if you pin the version of this module used in your projects (which is a Python best practice), you should be buffered from unwanted future changes.
A lot of time has been invested into this project by the author.
If you find this project useful and would like to thank the author for their work, consider donating some money. Any amount is appreciated.
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