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Project Description

This project provides Python bindings for interfacing with the Zstandard compression library. A C extension and CFFI interface are provided.

The primary goal of the project is to provide a rich interface to the underlying C API through a Pythonic interface while not sacrificing performance. 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.

State of Project

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.

This project is vendored and distributed with Mercurial 4.1, where it is used in a production capacity.

There is continuous integration for Python versions 2.6, 2.7, and 3.3+ on Linux x86_x64 and Windows x86 and x86_64. The author is reasonably confident the extension is stable and works as advertised on these platforms.

Expected Changes

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 feature complete and all tests run against both the C extension and CFFI bindings to ensure behavior parity.

Requirements

This extension is designed to run with Python 2.6, 2.7, 3.3, 3.4, 3.5, and 3.6 on common platforms (Linux, Windows, and OS X). Only x86_64 is currently well-tested as an architecture.

Installing

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.

Performance

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 Comp / Decomp Simple Comp / Decomp Stream In Comp / Decomp Stream Out 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.

Comparison to Other Python Bindings

https://pypi.python.org/pypi/zstd is an alternate Python binding to Zstandard. At the time this was written, the latest release of that package (1.1.2) only exposed the simple APIs for compression and decompression. This package exposes much more of the zstd API, including streaming and dictionary compression. This package also has CFFI support.

Bundling of Zstandard Source Code

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.

Instructions for Building and Testing

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

API

The compiled C extension provides a zstd Python module. This module exposes the following interfaces.

ZstdCompressor

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.

compression_params
A CompressionParameters instance (overrides the level value).
write_checksum
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.
write_content_size
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.
write_dict_id
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.

Unless specified otherwise, assume that no two methods of ZstdCompressor instances can be called from multiple Python threads simultaneously. In other words, assume instances are not thread safe unless stated otherwise.

Simple API

compress(data) compresses and returns data as a one-shot operation.:

cctx = zstd.ZstdCompressor()
compressed = cctx.compress(b'data to compress')

Unless compression_params or dict_data are passed to the ZstdCompressor, each invocation of compress() will calculate the optimal compression parameters for the configured compression level and input data size (some parameters are fine-tuned for small input sizes).

If a compression dictionary is being used, the compression parameters determined from the first input’s size will be reused for subsequent operations.

There is currently a deficiency in zstd’s C APIs that makes it difficult to round trip empty inputs when write_content_size=True. Attempting this will raise a ValueError unless allow_empty=True is passed 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 compressed 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.

A flush() method can be called to evict whatever data remains within the compressor’s internal state into the output object. This may result in 0 or more write() calls to the output object.

Both write() and flush() return the number of bytes written to the object’s write(). In many cases, small inputs do not accumulate enough data to cause a write and write() will return 0.

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.write_to(fh, size=data_len) 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.

If reading from the source via read(), read() will be called until it raises or returns an empty bytes (b''). It is perfectly valid for the source to deliver fewer bytes than were what requested by read(size).

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

Unlike write_to(), read_from() does not give direct control over the sizes of chunks fed into the compressor. Instead, chunk sizes will be whatever the object being read from delivers. These will often be of a uniform size.

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(input_path, 'rb') as ifh, open(output_path, 'wb') as ofh:
    cctx.copy_stream(ifh, ofh)

It is also possible to declare the size of the source stream:

cctx = zstd.ZstdCompressor()
cctx.copy_stream(ifh, ofh, size=len_of_input)

You can also specify how large the chunks that are read() and write() from and to the streams:

cctx = zstd.ZstdCompressor()
cctx.copy_stream(ifh, ofh, read_size=32768, write_size=16384)

The stream copier returns a 2-tuple of bytes read and written:

cctx = zstd.ZstdCompressor()
read_count, write_count = 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.

flush() accepts an optional argument indicating how to end the stream. zstd.COMPRESSOBJ_FLUSH_FINISH (the default) ends the compression stream. Once this type of flush is performed, compress() and flush() can no longer be called. This type of flush must be called to end the compression context. If not called, returned data may be incomplete.

A zstd.COMPRESSOBJ_FLUSH_BLOCK argument to flush() will flush a zstd block. Flushes of this type can be performed multiple times. The next call to compress() will begin a new zstd block.

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()

Or to flush blocks:

cctx.zstd.ZstdCompressor()
cobj = cctx.compressobj()
data = cobj.compress(b'chunk in first block')
data = cobj.flush(zstd.COMPRESSOBJ_FLUSH_BLOCK)
data = cobj.compress(b'chunk in second block')
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()

ZstdDecompressor

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).

Unless specified otherwise, assume that no two methods of ZstdDecompressor instances can be called from multiple Python threads simultaneously. In other words, assume instances are not thread safe unless stated otherwise.

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.

Calls to write() will return the number of bytes written to the output object. Not all inputs will result in bytes being written, so return values of 0 are possible.

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(compressed_chunk_0)
data = dobj.decompress(compressed_chunk_1)

Content-Only Dictionary Chain Decompression

decompress_content_dict_chain(frames) performs decompression of a list of zstd frames produced using chained content-only dictionary compression. Such a list of frames is produced by compressing discrete inputs where each non-initial input is compressed with a content-only dictionary consisting of the content of the previous input.

For example, say you have the following inputs:

inputs = [b'input 1', b'input 2', b'input 3']

The zstd frame chain consists of:

  1. b'input 1' compressed in standalone/discrete mode
  2. b'input 2' compressed using b'input 1' as a content-only dictionary
  3. b'input 3' compressed using b'input 2' as a content-only dictionary

Each zstd frame must have the content size written.

The following Python code can be used to produce a content-only dictionary chain:

def make_chain(inputs):
    frames = []

        # First frame is compressed in standalone/discrete mode.
        zctx = zstd.ZstdCompressor(write_content_size=True)
        frames.append(zctx.compress(inputs[0]))

        # Subsequent frames use the previous fulltext as a content-only dictionary
        for i, raw in enumerate(inputs[1:]):
            dict_data = zstd.ZstdCompressionDict(inputs[i])
                zctx = zstd.ZstdCompressor(write_content_size=True, dict_data=dict_data)
                frames.append(zctx.compress(raw))

        return frames

decompress_content_dict_chain() returns the uncompressed data of the last element in the input chain.

It is possible to implement content-only dictionary chain decompression on top of other Python APIs. However, this function will likely be significantly faster, especially for long input chains, as it avoids the overhead of instantiating and passing around intermediate objects between C and Python.

Choosing an API

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.

Dictionary Creation and Management

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)

It is possible to construct a dictionary from any data. Unless the data begins with a magic header, the dictionary will be treated as content-only. Content-only dictionaries allow compression operations that follow to reference raw data within the content. For one use of content-only dictionaries, see ZstdDecompressor.decompress_content_dict_chain().

More interestingly, instances can be created by training on sample data:

dict_data = zstd.train_dictionary(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():

dict_data = zstd.train_dictionary(size, samples)
dict_size = len(dict_data)  # will not be larger than ``size``

Once you have a dictionary, you can pass it to the objects performing compression and decompression:

dict_data = zstd.train_dictionary(16384, samples)

cctx = zstd.ZstdCompressor(dict_data=dict_data)
for source_data in input_data:
    compressed = cctx.compress(source_data)
    # Do something with compressed data.

dctx = zstd.ZstdDecompressor(dict_data=dict_data)
for compressed_data in input_data:
    buffer = io.BytesIO()
    with dctx.write_to(buffer) as decompressor:
        decompressor.write(compressed_data)
    # Do something with raw data in ``buffer``.

Dictionaries have unique integer IDs. You can retrieve this ID via:

dict_id = zstd.dictionary_id(dict_data)

You can obtain the raw data in the dict (useful for persisting and constructing a ZstdCompressionDict later) via as_bytes():

dict_data = zstd.train_dictionary(size, samples)
raw_data = dict_data.as_bytes()

Explicit Compression Parameters

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/attributes of CompressionParameters instances are as follows:

* window_log
* chain_log
* hash_log
* search_log
* search_length
* target_length
* strategy

This is the order the arguments are passed to the constructor if not using named arguments.

You’ll need to read the Zstandard documentation for what these parameters do.

Frame Inspection

Data emitted from zstd compression is encapsulated in a frame. This frame begins with a 4 byte magic number header followed by 2 to 14 bytes describing the frame in more detail. For more info, see https://github.com/facebook/zstd/blob/master/doc/zstd_compression_format.md.

zstd.get_frame_parameters(data) parses a zstd frame header from a bytes instance and return a FrameParameters object describing the frame.

Depending on which fields are present in the frame and their values, the length of the frame parameters varies. If insufficient bytes are passed in to fully parse the frame parameters, ZstdError is raised. To ensure frame parameters can be parsed, pass in at least 18 bytes.

FrameParameters instances have the following attributes:

content_size
Integer size of original, uncompressed content. This will be 0 if the original content size isn’t written to the frame (controlled with the write_content_size argument to ZstdCompressor) or if the input content size was 0.
window_size
Integer size of maximum back-reference distance in compressed data.
dict_id
Integer of dictionary ID used for compression. 0 if no dictionary ID was used or if the dictionary ID was 0.
has_checksum
Bool indicating whether a 4 byte content checksum is stored at the end of the frame.

Misc Functionality

estimate_compression_context_size(CompressionParameters)

Given a CompressionParameters struct, estimate the memory size required to perform compression.

estimate_decompression_context_size()

Estimate the memory size requirements for a decompressor instance.

Constants

The following module constants/attributes are exposed:

ZSTD_VERSION
This module attribute exposes a 3-tuple of the Zstandard version. e.g. (1, 0, 0)
MAX_COMPRESSION_LEVEL
Integer max compression level accepted by compression functions
COMPRESSION_RECOMMENDED_INPUT_SIZE
Recommended chunk size to feed to compressor functions
COMPRESSION_RECOMMENDED_OUTPUT_SIZE
Recommended chunk size for compression output
DECOMPRESSION_RECOMMENDED_INPUT_SIZE
Recommended chunk size to feed into decompresor functions
DECOMPRESSION_RECOMMENDED_OUTPUT_SIZE
Recommended chunk size for decompression output
FRAME_HEADER
bytes containing header of the Zstandard frame
MAGIC_NUMBER
Frame header as an integer
WINDOWLOG_MIN
Minimum value for compression parameter
WINDOWLOG_MAX
Maximum value for compression parameter
CHAINLOG_MIN
Minimum value for compression parameter
CHAINLOG_MAX
Maximum value for compression parameter
HASHLOG_MIN
Minimum value for compression parameter
HASHLOG_MAX
Maximum value for compression parameter
SEARCHLOG_MIN
Minimum value for compression parameter
SEARCHLOG_MAX
Maximum value for compression parameter
SEARCHLENGTH_MIN
Minimum value for compression parameter
SEARCHLENGTH_MAX
Maximum value for compression parameter
TARGETLENGTH_MIN
Minimum value for compression parameter
TARGETLENGTH_MAX
Maximum value for compression parameter
STRATEGY_FAST
Compression strategy
STRATEGY_DFAST
Compression strategy
STRATEGY_GREEDY
Compression strategy
STRATEGY_LAZY
Compression strategy
STRATEGY_LAZY2
Compression strategy
STRATEGY_BTLAZY2
Compression strategy
STRATEGY_BTOPT
Compression strategy

Performance Considerations

The ZstdCompressor and ZstdDecompressor types maintain state to a persistent compression or decompression context. Reusing a ZstdCompressor or ZstdDecompressor instance for multiple operations is faster than instantiating a new ZstdCompressor or ZstdDecompressor for each operation. The differences are magnified as the size of data decreases. For example, the difference between context reuse and non-reuse for 100,000 100 byte inputs will be significant (possiby over 10x faster to reuse contexts) whereas 10 1,000,000 byte inputs will be more similar in speed (because the time spent doing compression dwarfs time spent creating new contexts).

Note on Zstandard’s Experimental API

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.

Release History

Release History

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This version

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TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.6.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.5.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.5.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.5.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.4.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.3.3

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.3.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.3.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.3.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.2.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.2.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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