Package 'zstdlite'

Initialise a ZSTD compression context

Description

Compression contexts can be re-used, meaning that they don't have to be created each time a compression function is called. This can make things faster when performing multiple compression operations.

Usage

zstd_cctx(level = 3L, num_threads = 1L, include_checksum = FALSE, dict = NULL)

Arguments

level	Compression level. Default: 3. Valid range is [-5, 22] with -5 representing the mode with least compression and 22 representing the mode with most compression. Note level = 0 corresponds to the default level and is equivalent to level = 3
num_threads	Number of compression threads. Default 1. Using more threads can result in faster compression, but the magnitude of this speed-up depends on lots of factors e.g. cpu, drive speed, type of data compression level etc.
include_checksum	Include a checksum with the compressed data? Default: FALSE. If TRUE then a 32-bit hash of the original uncompressed data will be appended to the compressed data and checked for validity during decompression. See matching option for decompression in zstd_dctx() argument validate_checksum.
dict	Dictionary. Default: NULL. Can either be a raw vector or a filename. This dictionary can be created with zstd_train_dict_compress() , zstd_train_dict_seriazlie() or any other tool supporting zstd dictionary creation. Note: compressed data created with a dictionary must be decompressed with the same dictionary.

Value

External pointer to a ZSTD Compression Context which can be passed to zstd_serialize() and zstd_compress()

Examples

cctx <- zstd_cctx(level = 4)

---

Get the configuration settings of a compression context

Description

Get the configuration settings of a compression context

Usage

zstd_cctx_settings(cctx)

Arguments

cctx	ZSTD compression context, as created by zstd_cctx()

Value

named list of configuration options

Examples

cctx <- zstd_cctx()
zstd_cctx_settings(cctx)

---

Compress/Decompress raw vectors and character strings.

Description

This function is appropriate when handling data from other systems e.g. data compressed with the zstd command-line, or other compression programs.

Usage

zstd_compress(src, ..., file = NULL, cctx = NULL, use_file_streaming = FALSE)

zstd_decompress(
  src,
  type = "raw",
  ...,
  dctx = NULL,
  use_file_streaming = FALSE
)

Arguments

src	Source data to be compressed. This may be a raw vector, or a character string
...	extra arguments passed to zstd_cctx() or zstd_dctx() context initializers. Note: These argument are only used when cctx or dctx is NULL
file	filename in which to serialize data. If NULL (the default), then serialize the results to a raw vector
cctx	ZSTD Compression Context created by zstd_cctx() or NULL. Default: NULL will create a default compression context on-the-fly
use_file_streaming	Use the streaming interface when reading or writing to a file? This may reduce memory allocations and make better use of mutlithreading. Default: FALSE
type	Should data be returned as a 'raw' vector or as a 'string'? Default: 'raw'
dctx	ZSTD Decompression Context created by zstd_dctx() or NULL. Default: NULL will create a default decompression context on-the-fly.

Value

Raw vector of compressed data, or NULL if file created with compressed data

Examples

dat <- sample(as.raw(1:10), 1000, replace = TRUE)
vec <- zstd_compress(dat)
zstd_decompress(vec)

tmp <- tempfile()
zstd_compress(dat, file = tmp)
zstd_decompress(tmp)

---

Initialise a ZSTD decompression context

Description

Decompression contexts can be re-used, meaning that they don't have to be created each time a decompression function is called. This can make things faster when performing multiple decompression operations.

Usage

zstd_dctx(validate_checksum = TRUE, dict = NULL)

Arguments

validate_checksum	If a checksum is present on the comrpessed data, should the checksum be validated? Default: TRUE. Set to FALSE to ignore the checksum, which may lead to a minor speed improvement. If no checksum is present in the compressed data, then this option has no effect.
dict	Dictionary. Default: NULL. Can either be a raw vector or a filename. This dictionary can be created with zstd_train_dict_compress() , zstd_train_dict_seriazlie() or any other tool supporting zstd dictionary creation. Note: compressed data created with a dictionary must be decompressed with the same dictionary.

Value

External pointer to a ZSTD Decompression Context which can be passed to zstd_unserialize() and zstd_decompress()

Examples

dctx <- zstd_dctx(validate_checksum = FALSE)

---

Get the configuration settings of a decompression context

Description

Get the configuration settings of a decompression context

Usage

zstd_dctx_settings(dctx)

Arguments

dctx	ZSTD decompression context, as created by zstd_dctx()

Value

named list of configuration options

Examples

dctx <- zstd_dctx()
zstd_dctx_settings(dctx)

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Get the Dictionary ID of a dictionary or a vector compressed data.

Description

Dictionary IDs are generated automatically when a dictionary is created. When using a dictionary for compression, the same dictionary must be used during decompression. ZSTD internally does this check for matching IDs when attempting to decompress. This function exposes the dictionary ID to aid in handling and tracking dictionaries in R.

Usage

zstd_dict_id(dict)

Arguments

dict	raw vector or filename. This object could contain either a zstd dictionary, or a compressed object. If it is a compressed object, then it will return the dictionary id which was used to compress it.

Value

Signed integer value representing the Dictionary ID. If data does not represent a dictionary, or data which was compressed with a dictionary, then a value of 0 is returned.

Examples

dict_file <- system.file("sample_dict.raw", package = "zstdlite", mustWork = TRUE)
dict <- readBin(dict_file, raw(), file.size(dict_file))
zstd_dict_id(dict)
compressed_mtcars <- zstd_serialize(mtcars, dict = dict)
zstd_dict_id(compressed_mtcars)

---

Serialize/Unserialize arbitrary R objects to a compressed stream of bytes using Zstandard

Description

Serialize/Unserialize arbitrary R objects to a compressed stream of bytes using Zstandard

Usage

zstd_serialize(robj, ..., file = NULL, cctx = NULL, use_file_streaming = FALSE)

zstd_unserialize(src, ..., dctx = NULL, use_file_streaming = FALSE)

Arguments

robj	Any R object understood by base::serialize()
...	extra arguments passed to zstd_cctx() or zstd_dctx() context initializers. Note: These argument are only used when cctx or dctx is NULL
file	filename in which to serialize data. If NULL (the default), then serialize the results to a raw vector
cctx	ZSTD Compression Context created by zstd_cctx() or NULL. Default: NULL will create a default compression context on-the-fly
use_file_streaming	Use the streaming interface when reading or writing to a file? This may reduce memory allocations and make better use of mutlithreading. Default: FALSE
src	Raw vector or filename containing a ZSTD compressed serialized representation of an R object
dctx	ZSTD Decompression Context created by zstd_dctx() or NULL. Default: NULL will create a default decompression context on-the-fly.

Value

Raw vector of compressed serialized data, or NULL if file created with compressed data

Examples

vec <- zstd_serialize(mtcars)
zstd_unserialize(vec)

tmp <- tempfile()
zstd_serialize(mtcars, file = tmp)
zstd_unserialize(tmp)

---

Train a dictionary for use with zstd_compress() and zstd_decompress()

Description

This function requires multiple samples representative of the expected data to train a dictionary for use during compression.

Usage

zstd_train_dict_compress(
  samples,
  size = 1e+05,
  optim = FALSE,
  optim_shrink_allow = 0
)

Arguments

samples	list of raw vectors, or length-1 character vectors. Each raw vector or string, should be a complete example of something to be compressed with zstd_compress()
size	Maximum size of dictionary in bytes. Default: 112640 (110 kB) matches the default size set by the command line version of zstd. Actual dictionary created may be smaller than this if (1) there was not enough training data to make use of this size (2) optim_shrink_allow was set and a smaller dictionary was found to be almost as useful.
optim	optimize the dictionary. Default FALSE. If TRUE, then ZSTD will spend time optimizing the dictionary. This can be a very length operation.
optim_shrink_allow	integer value representing a percentage. If non-zero, then a search will be carried out for dictionaries of a smaller size which are up to optim_shrink_allow percent worse than the maximum sized dictionary. Default: 0 means that no shrinking will be done.

Value

raw vector containing a ZSTD dictionary

Examples

# This example shows the mechanics of creating and training a dictionary but
# may not be a great example of when a dictionary might be useful
cars <- rownames(mtcars)
samples <- lapply(seq_len(1000), \(x) serialize(sample(cars), NULL))
zstd_train_dict_compress(samples, size = 5000)

---

Train a dictionary for use with zstd_serialize() and zstd_unserialize()

Description

Train a dictionary for use with zstd_serialize() and zstd_unserialize()

Usage

zstd_train_dict_serialize(
  samples,
  size = 1e+05,
  optim = FALSE,
  optim_shrink_allow = 0
)

Arguments

samples	list of example R objects to train a dictionary to be used with zstd_serialize()
size	Maximum size of dictionary in bytes. Default: 112640 (110 kB) matches the default size set by the command line version of zstd. Actual dictionary created may be smaller than this if (1) there was not enough training data to make use of this size (2) optim_shrink_allow was set and a smaller dictionary was found to be almost as useful.
optim	optimize the dictionary. Default FALSE. If TRUE, then ZSTD will spend time optimizing the dictionary. This can be a very length operation.
optim_shrink_allow	integer value representing a percentage. If non-zero, then a search will be carried out for dictionaries of a smaller size which are up to optim_shrink_allow percent worse than the maximum sized dictionary. Default: 0 means that no shrinking will be done.

Value

raw vector containing a ZSTD dictionary

Examples

# This example shows the mechanics of creating and training a dictionary but
# may not be a great example of when a dictionary might be useful
cars <- rownames(mtcars)
samples <- lapply(seq_len(1000), \(x) sample(cars))
zstd_train_dict_serialize(samples, size = 5000)

---

Get version string of zstd C library

Description

Get version string of zstd C library

Usage

zstd_version()

Value

String containing version number of zstd C library

Examples

zstd_version()

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