git/bloom.c

207 строки
5.1 KiB
C
Исходник Обычный вид История

#include "git-compat-util.h"
#include "bloom.h"
#include "diff.h"
#include "diffcore.h"
#include "revision.h"
#include "hashmap.h"
define_commit_slab(bloom_filter_slab, struct bloom_filter);
struct bloom_filter_slab bloom_filters;
struct pathmap_hash_entry {
struct hashmap_entry entry;
const char path[FLEX_ARRAY];
};
static uint32_t rotate_left(uint32_t value, int32_t count)
{
uint32_t mask = 8 * sizeof(uint32_t) - 1;
count &= mask;
return ((value << count) | (value >> ((-count) & mask)));
}
bloom.c: introduce core Bloom filter constructs Introduce the constructs for Bloom filters, Bloom filter keys and Bloom filter settings. For details on what Bloom filters are and how they work, refer to Dr. Derrick Stolee's blog post [1]. It provides a concise explanation of the adoption of Bloom filters as described in [2] and [3]. Implementation specifics: 1. We currently use 7 and 10 for the number of hashes and the size of each entry respectively. They served as great starting values, the mathematical details behind this choice are described in [1] and [4]. The implementation, while not completely open to it at the moment, is flexible enough to allow for tweaking these settings in the future. Note: The performance gains we have observed with these values are significant enough that we did not need to tweak these settings. The performance numbers are included in the cover letter of this series and in the commit message of the subsequent commit where we use Bloom filters to speed up `git log -- path`. 2. As described in [1] and [3], we do not need 7 independent hashing functions. We use the Murmur3 hashing scheme, seed it twice and then combine those to procure an arbitrary number of hash values. 3. The filters will be sized according to the number of changes in each commit, in multiples of 8 bit words. [1] Derrick Stolee "Supercharging the Git Commit Graph IV: Bloom Filters" https://devblogs.microsoft.com/devops/super-charging-the-git-commit-graph-iv-Bloom-filters/ [2] Flavio Bonomi, Michael Mitzenmacher, Rina Panigrahy, Sushil Singh, George Varghese "An Improved Construction for Counting Bloom Filters" http://theory.stanford.edu/~rinap/papers/esa2006b.pdf https://doi.org/10.1007/11841036_61 [3] Peter C. Dillinger and Panagiotis Manolios "Bloom Filters in Probabilistic Verification" http://www.ccs.neu.edu/home/pete/pub/Bloom-filters-verification.pdf https://doi.org/10.1007/978-3-540-30494-4_26 [4] Thomas Mueller Graf, Daniel Lemire "Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters" https://arxiv.org/abs/1912.08258 Helped-by: Derrick Stolee <dstolee@microsoft.com> Reviewed-by: Jakub Narębski <jnareb@gmail.com> Signed-off-by: Garima Singh <garima.singh@microsoft.com> Signed-off-by: Junio C Hamano <gitster@pobox.com>
2020-03-30 03:31:25 +03:00
static inline unsigned char get_bitmask(uint32_t pos)
{
return ((unsigned char)1) << (pos & (BITS_PER_WORD - 1));
}
/*
* Calculate the murmur3 32-bit hash value for the given data
* using the given seed.
* Produces a uniformly distributed hash value.
* Not considered to be cryptographically secure.
* Implemented as described in https://en.wikipedia.org/wiki/MurmurHash#Algorithm
*/
uint32_t murmur3_seeded(uint32_t seed, const char *data, size_t len)
{
const uint32_t c1 = 0xcc9e2d51;
const uint32_t c2 = 0x1b873593;
const uint32_t r1 = 15;
const uint32_t r2 = 13;
const uint32_t m = 5;
const uint32_t n = 0xe6546b64;
int i;
uint32_t k1 = 0;
const char *tail;
int len4 = len / sizeof(uint32_t);
uint32_t k;
for (i = 0; i < len4; i++) {
uint32_t byte1 = (uint32_t)data[4*i];
uint32_t byte2 = ((uint32_t)data[4*i + 1]) << 8;
uint32_t byte3 = ((uint32_t)data[4*i + 2]) << 16;
uint32_t byte4 = ((uint32_t)data[4*i + 3]) << 24;
k = byte1 | byte2 | byte3 | byte4;
k *= c1;
k = rotate_left(k, r1);
k *= c2;
seed ^= k;
seed = rotate_left(seed, r2) * m + n;
}
tail = (data + len4 * sizeof(uint32_t));
switch (len & (sizeof(uint32_t) - 1)) {
case 3:
k1 ^= ((uint32_t)tail[2]) << 16;
/*-fallthrough*/
case 2:
k1 ^= ((uint32_t)tail[1]) << 8;
/*-fallthrough*/
case 1:
k1 ^= ((uint32_t)tail[0]) << 0;
k1 *= c1;
k1 = rotate_left(k1, r1);
k1 *= c2;
seed ^= k1;
break;
}
seed ^= (uint32_t)len;
seed ^= (seed >> 16);
seed *= 0x85ebca6b;
seed ^= (seed >> 13);
seed *= 0xc2b2ae35;
seed ^= (seed >> 16);
return seed;
bloom.c: introduce core Bloom filter constructs Introduce the constructs for Bloom filters, Bloom filter keys and Bloom filter settings. For details on what Bloom filters are and how they work, refer to Dr. Derrick Stolee's blog post [1]. It provides a concise explanation of the adoption of Bloom filters as described in [2] and [3]. Implementation specifics: 1. We currently use 7 and 10 for the number of hashes and the size of each entry respectively. They served as great starting values, the mathematical details behind this choice are described in [1] and [4]. The implementation, while not completely open to it at the moment, is flexible enough to allow for tweaking these settings in the future. Note: The performance gains we have observed with these values are significant enough that we did not need to tweak these settings. The performance numbers are included in the cover letter of this series and in the commit message of the subsequent commit where we use Bloom filters to speed up `git log -- path`. 2. As described in [1] and [3], we do not need 7 independent hashing functions. We use the Murmur3 hashing scheme, seed it twice and then combine those to procure an arbitrary number of hash values. 3. The filters will be sized according to the number of changes in each commit, in multiples of 8 bit words. [1] Derrick Stolee "Supercharging the Git Commit Graph IV: Bloom Filters" https://devblogs.microsoft.com/devops/super-charging-the-git-commit-graph-iv-Bloom-filters/ [2] Flavio Bonomi, Michael Mitzenmacher, Rina Panigrahy, Sushil Singh, George Varghese "An Improved Construction for Counting Bloom Filters" http://theory.stanford.edu/~rinap/papers/esa2006b.pdf https://doi.org/10.1007/11841036_61 [3] Peter C. Dillinger and Panagiotis Manolios "Bloom Filters in Probabilistic Verification" http://www.ccs.neu.edu/home/pete/pub/Bloom-filters-verification.pdf https://doi.org/10.1007/978-3-540-30494-4_26 [4] Thomas Mueller Graf, Daniel Lemire "Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters" https://arxiv.org/abs/1912.08258 Helped-by: Derrick Stolee <dstolee@microsoft.com> Reviewed-by: Jakub Narębski <jnareb@gmail.com> Signed-off-by: Garima Singh <garima.singh@microsoft.com> Signed-off-by: Junio C Hamano <gitster@pobox.com>
2020-03-30 03:31:25 +03:00
}
void fill_bloom_key(const char *data,
size_t len,
struct bloom_key *key,
const struct bloom_filter_settings *settings)
{
int i;
const uint32_t seed0 = 0x293ae76f;
const uint32_t seed1 = 0x7e646e2c;
const uint32_t hash0 = murmur3_seeded(seed0, data, len);
const uint32_t hash1 = murmur3_seeded(seed1, data, len);
key->hashes = (uint32_t *)xcalloc(settings->num_hashes, sizeof(uint32_t));
for (i = 0; i < settings->num_hashes; i++)
key->hashes[i] = hash0 + i * hash1;
}
void add_key_to_filter(const struct bloom_key *key,
struct bloom_filter *filter,
const struct bloom_filter_settings *settings)
{
int i;
uint64_t mod = filter->len * BITS_PER_WORD;
for (i = 0; i < settings->num_hashes; i++) {
uint64_t hash_mod = key->hashes[i] % mod;
uint64_t block_pos = hash_mod / BITS_PER_WORD;
filter->data[block_pos] |= get_bitmask(hash_mod);
}
}
void init_bloom_filters(void)
{
init_bloom_filter_slab(&bloom_filters);
}
struct bloom_filter *get_bloom_filter(struct repository *r,
struct commit *c)
{
struct bloom_filter *filter;
struct bloom_filter_settings settings = DEFAULT_BLOOM_FILTER_SETTINGS;
int i;
struct diff_options diffopt;
if (bloom_filters.slab_size == 0)
return NULL;
filter = bloom_filter_slab_at(&bloom_filters, c);
repo_diff_setup(r, &diffopt);
diffopt.flags.recursive = 1;
diff_setup_done(&diffopt);
if (c->parents)
diff_tree_oid(&c->parents->item->object.oid, &c->object.oid, "", &diffopt);
else
diff_tree_oid(NULL, &c->object.oid, "", &diffopt);
diffcore_std(&diffopt);
if (diff_queued_diff.nr <= 512) {
struct hashmap pathmap;
struct pathmap_hash_entry *e;
struct hashmap_iter iter;
hashmap_init(&pathmap, NULL, NULL, 0);
for (i = 0; i < diff_queued_diff.nr; i++) {
const char *path = diff_queued_diff.queue[i]->two->path;
/*
* Add each leading directory of the changed file, i.e. for
* 'dir/subdir/file' add 'dir' and 'dir/subdir' as well, so
* the Bloom filter could be used to speed up commands like
* 'git log dir/subdir', too.
*
* Note that directories are added without the trailing '/'.
*/
do {
char *last_slash = strrchr(path, '/');
FLEX_ALLOC_STR(e, path, path);
hashmap_entry_init(&e->entry, strhash(path));
hashmap_add(&pathmap, &e->entry);
if (!last_slash)
last_slash = (char*)path;
*last_slash = '\0';
} while (*path);
diff_free_filepair(diff_queued_diff.queue[i]);
}
filter->len = (hashmap_get_size(&pathmap) * settings.bits_per_entry + BITS_PER_WORD - 1) / BITS_PER_WORD;
filter->data = xcalloc(filter->len, sizeof(unsigned char));
hashmap_for_each_entry(&pathmap, &iter, e, entry) {
struct bloom_key key;
fill_bloom_key(e->path, strlen(e->path), &key, &settings);
add_key_to_filter(&key, filter, &settings);
}
hashmap_free_entries(&pathmap, struct pathmap_hash_entry, entry);
} else {
for (i = 0; i < diff_queued_diff.nr; i++)
diff_free_filepair(diff_queued_diff.queue[i]);
filter->data = NULL;
filter->len = 0;
}
free(diff_queued_diff.queue);
DIFF_QUEUE_CLEAR(&diff_queued_diff);
return filter;
}