/*
* Copyright (c) Yann Collet, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under both the BSD-style license (found in the
* LICENSE file in the root directory of this source tree) and the GPLv2 (found
* in the COPYING file in the root directory of this source tree).
* You may select, at your option, one of the above-listed licenses.
*/
#ifndef DICTBUILDER_H_001
#define DICTBUILDER_H_001
#if defined (__cplusplus)
extern "C" {
#endif
/*====== Dependencies ======*/
#include <stddef.h> /* size_t */
/* ===== ZDICTLIB_API : control library symbols visibility ===== */
#ifndef ZDICTLIB_VISIBILITY
# if defined(__GNUC__) && (__GNUC__ >= 4)
# define ZDICTLIB_VISIBILITY __attribute__ ((visibility ("default")))
# else
# define ZDICTLIB_VISIBILITY
# endif
#endif
#if defined(ZSTD_DLL_EXPORT) && (ZSTD_DLL_EXPORT==1)
# define ZDICTLIB_API __declspec(dllexport) ZDICTLIB_VISIBILITY
#elif defined(ZSTD_DLL_IMPORT) && (ZSTD_DLL_IMPORT==1)
# define ZDICTLIB_API __declspec(dllimport) ZDICTLIB_VISIBILITY /* It isn't required but allows to generate better code, saving a function pointer load from the IAT and an indirect jump.*/
#else
# define ZDICTLIB_API ZDICTLIB_VISIBILITY
#endif
/*******************************************************************************
* Zstd dictionary builder
*
* FAQ
* ===
* Why should I use a dictionary?
* ------------------------------
*
* Zstd can use dictionaries to improve compression ratio of small data.
* Traditionally small files don't compress well because there is very little
* repetition in a single sample, since it is small. But, if you are compressing
* many similar files, like a bunch of JSON records that share the same
* structure, you can train a dictionary on ahead of time on some samples of
* these files. Then, zstd can use the dictionary to find repetitions that are
* present across samples. This can vastly improve compression ratio.
*
* When is a dictionary useful?
* ----------------------------
*
* Dictionaries are useful when compressing many small files that are similar.
* The larger a file is, the less benefit a dictionary will have. Generally,
* we don't expect dictionary compression to be effective past 100KB. And the
* smaller a file is, the more we would expect the dictionary to help.
*
* How do I use a dictionary?
* --------------------------
*
* Simply pass the dictionary to the zstd compressor with
* `ZSTD_CCtx_loadDictionary()`. The same dictionary must then be passed to
* the decompressor, using `ZSTD_DCtx_loadDictionary()`. There are other
* more advanced functions that allow selecting some options, see zstd.h for
* complete documentation.
*
* What is a zstd dictionary?
* --------------------------
*
* A zstd dictionary has two pieces: Its header, and its content. The header
* contains a magic number, the dictionary ID, and entropy tables. These
* entropy tables allow zstd to save on header costs in the compressed file,
* which really matters for small data. The content is just bytes, which are
* repeated content that is common across many samples.
*
* What is a raw content dictionary?
* ---------------------------------
*
* A raw content dictionary is just bytes. It doesn't have a zstd dictionary
* header, a dictionary ID, or entropy tables. Any buffer is a valid raw
* content dictionary.
*
* How do I train a dictionary?
* ----------------------------
*
* Gather samples from your use case. These samples should be similar to each
* other. If you have several use cases, you could try to train one dictionary
* per use case.
*
* Pass those samples to `ZDICT_trainFromBuffer()` and that will train your
* dictionary. There are a few advanced versions of this function, but this
* is a great starting point. If you want to further tune your dictionary
* you could try `ZDICT_optimizeTrainFromBuffer_cover()`. If that is too slow
* you can try `ZDICT_optimizeTrainFromBuffer_fastCover()`.
*
* If the dictionary training function fails, that is likely because you
* either passed too few samples, or a dictionary would not be effective
* for your data. Look at the messages that the dictionary trainer printed,
* if it doesn't say too few samples, then a dictionary would not be effective.
*
* How large should my dictionary be?
* ----------------------------------
*
* A reasonable dictionary size, the `dictBufferCapacity`, is about 100KB.
* The zstd CLI defaults to a 110KB dictionary. You likely don't need a
* dictionary larger than that. But, most use cases can get away with a
* smaller dictionary. The advanced dictionary builders can automatically
* shrink the dictionary for you, and select a the smallest size that
* doesn't hurt compression ratio too much. See the `shrinkDict` parameter.
* A smaller dictionary can save memory, and potentially speed up
* compression.
*
* How many samples should I provide to the dictionary builder?
* ------------------------------------------------------------
*
* We generally recommend passing ~100x the size of the dictionary
* in samples. A few thousand should suffice. Having too few samples
* can hurt the dictionaries effectiveness. Having more samples will
* only improve the dictionaries effectiveness. But having too many
* samples can slow down the dictionary builder.
*
* How do I determine if a dictionary will be effective?
* -----------------------------------------------------
*
* Simply train a dictionary and try it out. You can use zstd's built in
* benchmarking tool to test the dictionary effectiveness.
*
* # Benchmark levels 1-3 without a dictionary
* zstd -b1e3 -r /path/to/my/files
* # Benchmark levels 1-3 with a dictionary
* zstd -b1e3 -r /path/to/my/files -D /path/to/my/dictionary
*
* When should I retrain a dictionary?
* -----------------------------------
*
* You should retrain a dictionary when its effectiveness drops. Dictionary
* effectiveness drops as the data you are compressing changes. Generally, we do
* expect dictionaries to "decay" over time, as your data changes, but the rate
* at which they decay depends on your use case. Internally, we regularly
* retrain dictionaries, and if the new dictionary performs significantly
* better than the old dictionary, we will ship the new dictionary.
*
* I have a raw content dictionary, how do I turn it into a zstd dictionary?
* -------------------------------------------------------------------------
*
* If you have a raw content dictionary, e.g. by manually constructing it, or
* using a third-party dictionary builder, you can turn it into a zstd
* dictionary by using `ZDICT_finalizeDictionary()`. You'll also have to
* provide some samples of the data. It will add the zstd header to the
* raw content, which contains a dictionary ID and entropy tables, which
* will improve compression ratio, and allow zstd to write the dictionary ID
* into the frame, if you so choose.
*
* Do I have to use zstd's dictionary builder?
* -------------------------------------------
*
* No! You can construct dictionary content however you please, it is just
* bytes. It will always be valid as a raw content dictionary. If you want
* a zstd dictionary, which can improve compression ratio, use
* `ZDICT_finalizeDictionary()`.
*
* What is the attack surface of a zstd dictionary?
* ------------------------------------------------
*
* Zstd is heavily fuzz tested, including loading fuzzed dictionaries, so
* zstd should never crash, or access out-of-bounds memory no matter what
* the dictionary is. However, if an attacker can control the dictionary
* during decompression, they can cause zstd to generate arbitrary bytes,
* just like if they controlled the compressed data.
*
******************************************************************************/
/*! ZDICT_trainFromBuffer():
* Train a dictionary from an array of samples.
* Redirect towards ZDICT_optimizeTrainFromBuffer_fastCover() single-threaded, with d=8, steps=4,
* f=20, and accel=1.
* Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
* supplied with an array of sizes `samplesSizes`, providing the size of each sample, in order.
* The resulting dictionary will be saved into `dictBuffer`.
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
* or an error code, which can be tested with ZDICT_isError().
* Note: Dictionary training will fail if there are not enough samples to construct a
* dictionary, or if most of the samples are too small (< 8 bytes being the lower limit).
* If dictionary training fails, you should use zstd without a dictionary, as the dictionary
* would've been ineffective anyways. If you believe your samples would benefit from a dictionary
* please open an issue with details, and we can look into it.
* Note: ZDICT_trainFromBuffer()'s memory usage is about 6 MB.
* Tips: In general, a reasonable dictionary has a size of ~ 100 KB.
* It's possible to select smaller or larger size, just by specifying `dictBufferCapacity`.
* In general, it's recommended to provide a few thousands samples, though this can vary a lot.
* It's recommended that total size of all samples be about ~x100 times the target size of dictionary.
*/
ZDICTLIB_API size_t ZDICT_trainFromBuffer(void* dictBuffer, size_t dictBufferCapacity,
const void* samplesBuffer,
const size_t* samplesSizes, unsigned nbSamples);
typedef struct {
int compressionLevel; /*< optimize for a specific zstd compression level; 0 means default */
unsigned notificationLevel; /*< Write log to stderr; 0 = none (default); 1 = errors; 2 = progression; 3 = details; 4 = debug; */
unsigned dictID; /*< force dictID value; 0 means auto mode (32-bits random value)
* NOTE: The zstd format reserves some dictionary IDs for future use.
* You may use them in private settings, but be warned that they
* may be used by zstd in a public dictionary registry in the future.
* These dictionary IDs are:
* - low range : <= 32767
* - high range : >= (2^31)
*/
} ZDICT_params_t;
/*! ZDICT_finalizeDictionary():
* Given a custom content as a basis for dictionary, and a set of samples,
* finalize dictionary by adding headers and statistics according to the zstd
* dictionary format.
*
* Samples must be stored concatenated in a flat buffer `samplesBuffer`,
* supplied with an array of sizes `samplesSizes`, providing the size of each
* sample in order. The samples are used to construct the statistics, so they
* should be representative of what you will compress with this dictionary.
*
* The compression level can be set in `parameters`. You should pass the
* compression level you expect to use in production. The statistics for each
* compression level differ, so tuning the dictionary for the compression level
* can help quite a bit.
*
* You can set an explicit dictionary ID in `parameters`, or allow us to pick
* a random dictionary ID for you, but we can't guarantee no collisions.
*
* The dstDictBuffer and the dictContent may overlap, and the content will be
* appended to the end of the header. If the header + the content doesn't fit in
* maxDictSize the beginning of the content is truncated to make room, since it
* is presumed that the most profitable content is at the end of the dictionary,
* since that is the cheapest to reference.
*
* `maxDictSize` must be >= max(dictContentSize, ZSTD_DICTSIZE_MIN).
*
* @return: size of dictionary stored into `dstDictBuffer` (<= `maxDictSize`),
* or an error code, which can be tested by ZDICT_isError().
* Note: ZDICT_finalizeDictionary() will push notifications into stderr if
* instructed to, using notificationLevel>0.
* NOTE: This function currently may fail in several edge cases including:
* * Not enough samples
* * Samples are uncompressible
* * Samples are all exactly the same
*/
ZDICTLIB_API size_t ZDICT_finalizeDictionary(void* dstDictBuffer, size_t maxDictSize,
const void* dictContent, size_t dictContentSize,
const void* samplesBuffer, const size_t* samplesSizes, unsigned nbSamples,
ZDICT_params_t parameters);
/*====== Helper functions ======*/
ZDICTLIB_API unsigned ZDICT_getDictID(const void* dictBuffer, size_t dictSize); /**< extracts dictID; @return zero if error (not a valid dictionary) */
ZDICTLIB_API size_t ZDICT_getDictHeaderSize(const void* dictBuffer, size_t dictSize); /* returns dict header size; returns a ZSTD error code on failure */
ZDICTLIB_API unsigned ZDICT_isError(size_t errorCode);
ZDICTLIB_API const char* ZDICT_getErrorName(size_t errorCode);
#ifdef ZDICT_STATIC_LINKING_ONLY
/* ====================================================================================
* The definitions in this section are considered experimental.
* They should never be used with a dynamic library, as they may change in the future.
* They are provided for advanced usages.
* Use them only in association with static linking.
* ==================================================================================== */
#define ZDICT_DICTSIZE_MIN 256
/* Deprecated: Remove in v1.6.0 */
#define ZDICT_CONTENTSIZE_MIN 128
/*! ZDICT_cover_params_t:
* k and d are the only required parameters.
* For others, value 0 means default.
*/
typedef struct {
unsigned k; /* Segment size : constraint: 0 < k : Reasonable range [16, 2048+] */
unsigned d; /* dmer size : constraint: 0 < d <= k : Reasonable range [6, 16] */
unsigned steps; /* Number of steps : Only used for optimization : 0 means default (40) : Higher means more parameters checked */
unsigned nbThreads; /* Number of threads : constraint: 0 < nbThreads : 1 means single-threaded : Only used for optimization : Ignored if ZSTD_MULTITHREAD is not defined */
double splitPoint; /* Percentage of samples used for training: Only used for optimization : the first nbSamples * splitPoint samples will be used to training, the last nbSamples * (1 - splitPoint) samples will be used for testing, 0 means default (1.0), 1.0 when all samples are used for both training and testing */
unsigned shrinkDict; /* Train dictionaries to shrink in size starting from the minimum size and selects the smallest dictionary that is shrinkDictMaxRegression% worse than the largest dictionary. 0 means no shrinking and 1 means shrinking */
unsigned shrinkDictMaxRegression; /* Sets shrinkDictMaxRegression so that a smaller dictionary can be at worse shrinkDictMaxRegression% worse than the max dict size dictionary. */
ZDICT_params_t zParams;
} ZDICT_cover_params_t;
typedef struct {
unsigned k; /* Segment size : constraint: 0 < k : Reasonable range [16, 2048+] */
unsigned d; /* dmer size : constraint: 0 < d <= k : Reasonable range [6, 16] */
unsigned f; /* log of size of frequency array : constraint: 0 < f <= 31 : 1 means default(20)*/
unsigned steps; /* Number of steps : Only used for optimization : 0 means default (40) : Higher means more parameters checked */
unsigned nbThreads; /* Number of threads : constraint: 0 < nbThreads : 1 means single-threaded : Only used for optimization : Ignored if ZSTD_MULTITHREAD is not defined */
double splitPoint; /* Percentage of samples used for training: Only used for optimization : the first nbSamples * splitPoint samples will be used to training, the last nbSamples * (1 - splitPoint) samples will be used for testing, 0 means default (0.75), 1.0 when all samples are used for both training and testing */
unsigned accel; /* Acceleration level: constraint: 0 < accel <= 10, higher means faster and less accurate, 0 means default(1) */
unsigned shrinkDict; /* Train dictionaries to shrink in size starting from the minimum size and selects the smallest dictionary that is shrinkDictMaxRegression% worse than the largest dictionary. 0 means no shrinking and 1 means shrinking */
unsigned shrinkDictMaxRegression; /* Sets shrinkDictMaxRegression so that a smaller dictionary can be at worse shrinkDictMaxRegression% worse than the max dict size dictionary. */
ZDICT_params_t zParams;
} ZDICT_fastCover_params_t;
/*! ZDICT_trainFromBuffer_cover():
* Train a dictionary from an array of samples using the COVER algorithm.
* Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
* supplied with an array of sizes `samplesSizes`, providing the size of each sample, in order.
* The resulting dictionary will be saved into `dictBuffer`.
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
* or an error code, which can be tested with ZDICT_isError().
* See ZDICT_trainFromBuffer() for details on failure modes.
* Note: ZDICT_trainFromBuffer_cover() requires about 9 bytes of memory for each input byte.
* Tips: In general, a reasonable dictionary has a size of ~ 100 KB.
* It's possible to select smaller or larger size, just by specifying `dictBufferCapacity`.
* In general, it's recommended to provide a few thousands samples, though this can vary a lot.
* It's recommended that total size of all samples be about ~x100 times the target size of dictionary.
*/
ZDICTLIB_API size_t ZDICT_trainFromBuffer_cover(
void *dictBuffer, size_t dictBufferCapacity,
const void *samplesBuffer, const size_t *samplesSizes, unsigned nbSamples,
ZDICT_cover_params_t parameters);
/*! ZDICT_optimizeTrainFromBuffer_cover():
* The same requirements as above hold for all the parameters except `parameters`.
* This function tries many parameter combinations and picks the best parameters.
* `*parameters` is filled with the best parameters found,
* dictionary constructed with those parameters is stored in `dictBuffer`.
*
* All of the parameters d, k, steps are optional.
* If d is non-zero then we don't check multiple values of d, otherwise we check d = {6, 8}.
* if steps is zero it defaults to its default value.
* If k is non-zero then we don't check multiple values of k, otherwise we check steps values in [50, 2000].
*
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
* or an error code, which can be tested with ZDICT_isError().
* On success `*parameters` contains the parameters selected.
* See ZDICT_trainFromBuffer() for details on failure modes.
* Note: ZDICT_optimizeTrainFromBuffer_cover() requires about 8 bytes of memory for each input byte and additionally another 5 bytes of memory for each byte of memory for each thread.
*/
ZDICTLIB_API size_t ZDICT_optimizeTrainFromBuffer_cover(
void* dictBuffer, size_t dictBufferCapacity,
const void* samplesBuffer, const size_t* samplesSizes, unsigned nbSamples,
ZDICT_cover_params_t* parameters);
/*! ZDICT_trainFromBuffer_fastCover():
* Train a dictionary from an array of samples using a modified version of COVER algorithm.
* Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
* supplied with an array of sizes `samplesSizes`, providing the size of each sample, in order.
* d and k are required.
* All other parameters are optional, will use default values if not provided
* The resulting dictionary will be saved into `dictBuffer`.
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
* or an error code, which can be tested with ZDICT_isError().
* See ZDICT_trainFromBuffer() for details on failure modes.
* Note: ZDICT_trainFromBuffer_fastCover() requires 6 * 2^f bytes of memory.
* Tips: In general, a reasonable dictionary has a size of ~ 100 KB.
* It's possible to select smaller or larger size, just by specifying `dictBufferCapacity`.
* In general, it's recommended to provide a few thousands samples, though this can vary a lot.
* It's recommended that total size of all samples be about ~x100 times the target size of dictionary.
*/
ZDICTLIB_API size_t ZDICT_trainFromBuffer_fastCover(void *dictBuffer,
size_t dictBufferCapacity, const void *samplesBuffer,
const size_t *samplesSizes, unsigned nbSamples,
ZDICT_fastCover_params_t parameters);
/*! ZDICT_optimizeTrainFromBuffer_fastCover():
* The same requirements as above hold for all the parameters except `parameters`.
* This function tries many parameter combinations (specifically, k and d combinations)
* and picks the best parameters. `*parameters` is filled with the best parameters found,
* dictionary constructed with those parameters is stored in `dictBuffer`.
* All of the parameters d, k, steps, f, and accel are optional.
* If d is non-zero then we don't check multiple values of d, otherwise we check d = {6, 8}.
* if steps is zero it defaults to its default value.
* If k is non-zero then we don't check multiple values of k, otherwise we check steps values in [50, 2000].
* If f is zero, default value of 20 is used.
* If accel is zero, default value of 1 is used.
*
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
* or an error code, which can be tested with ZDICT_isError().
* On success `*parameters` contains the parameters selected.
* See ZDICT_trainFromBuffer() for details on failure modes.
* Note: ZDICT_optimizeTrainFromBuffer_fastCover() requires about 6 * 2^f bytes of memory for each thread.
*/
ZDICTLIB_API size_t ZDICT_optimizeTrainFromBuffer_fastCover(void* dictBuffer,
size_t dictBufferCapacity, const void* samplesBuffer,
const size_t* samplesSizes, unsigned nbSamples,
ZDICT_fastCover_params_t* parameters);
typedef struct {
unsigned selectivityLevel; /* 0 means default; larger => select more => larger dictionary */
ZDICT_params_t zParams;
} ZDICT_legacy_params_t;
/*! ZDICT_trainFromBuffer_legacy():
* Train a dictionary from an array of samples.
* Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
* supplied with an array of sizes `samplesSizes`, providing the size of each sample, in order.
* The resulting dictionary will be saved into `dictBuffer`.
* `parameters` is optional and can be provided with values set to 0 to mean "default".
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
* or an error code, which can be tested with ZDICT_isError().
* See ZDICT_trainFromBuffer() for details on failure modes.
* Tips: In general, a reasonable dictionary has a size of ~ 100 KB.
* It's possible to select smaller or larger size, just by specifying `dictBufferCapacity`.
* In general, it's recommended to provide a few thousands samples, though this can vary a lot.
* It's recommended that total size of all samples be about ~x100 times the target size of dictionary.
* Note: ZDICT_trainFromBuffer_legacy() will send notifications into stderr if instructed to, using notificationLevel>0.
*/
ZDICTLIB_API size_t ZDICT_trainFromBuffer_legacy(
void* dictBuffer, size_t dictBufferCapacity,
const void* samplesBuffer, const size_t* samplesSizes, unsigned nbSamples,
ZDICT_legacy_params_t parameters);
/* Deprecation warnings */
/* It is generally possible to disable deprecation warnings from compiler,
for example with -Wno-deprecated-declarations for gcc
or _CRT_SECURE_NO_WARNINGS in Visual.
Otherwise, it's also possible to manually define ZDICT_DISABLE_DEPRECATE_WARNINGS */
#ifdef ZDICT_DISABLE_DEPRECATE_WARNINGS
# define ZDICT_DEPRECATED(message) ZDICTLIB_API /* disable deprecation warnings */
#else
# define ZDICT_GCC_VERSION (__GNUC__ * 100 + __GNUC_MINOR__)
# if defined (__cplusplus) && (__cplusplus >= 201402) /* C++14 or greater */
# define ZDICT_DEPRECATED(message) [[deprecated(message)]] ZDICTLIB_API
# elif defined(__clang__) || (ZDICT_GCC_VERSION >= 405)
# define ZDICT_DEPRECATED(message) ZDICTLIB_API __attribute__((deprecated(message)))
# elif (ZDICT_GCC_VERSION >= 301)
# define ZDICT_DEPRECATED(message) ZDICTLIB_API __attribute__((deprecated))
# elif defined(_MSC_VER)
# define ZDICT_DEPRECATED(message) ZDICTLIB_API __declspec(deprecated(message))
# else
# pragma message("WARNING: You need to implement ZDICT_DEPRECATED for this compiler")
# define ZDICT_DEPRECATED(message) ZDICTLIB_API
# endif
#endif /* ZDICT_DISABLE_DEPRECATE_WARNINGS */
ZDICT_DEPRECATED("use ZDICT_finalizeDictionary() instead")
size_t ZDICT_addEntropyTablesFromBuffer(void* dictBuffer, size_t dictContentSize, size_t dictBufferCapacity,
const void* samplesBuffer, const size_t* samplesSizes, unsigned nbSamples);
#endif /* ZDICT_STATIC_LINKING_ONLY */
#if defined (__cplusplus)
}
#endif
#endif /* DICTBUILDER_H_001 */
