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Workload generator (Mixgraph) based on prefix hotness (#5953) 

Summary:
In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters.
For example:
`./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48`
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953

Test Plan: run db_bench with different parameters and checked the results.

Differential Revision: D18053527

Pulled By: zhichao-cao

fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
This commit is contained in:
Zhichao Cao 2019-11-06 12:50:33 -08:00 committed by Facebook Github Bot
parent 50804656d2
commit 8ea087ad16
1 changed files with 194 additions and 9 deletions
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203
tools/db_bench_tool.cc
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@ -1005,6 +1005,22 @@ DEFINE_uint64(
"is the global rate in bytes/second.");
// the parameters of mix_graph
DEFINE_double(keyrange_dist_a, 0.0,
"The parameter 'a' of prefix average access distribution "
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_b, 0.0,
"The parameter 'b' of prefix average access distribution "
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_c, 0.0,
"The parameter 'c' of prefix average access distribution"
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_d, 0.0,
"The parameter 'd' of prefix average access distribution"
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_int64(keyrange_num, 1,
"The number of key ranges that are in the same prefix "
"group, each prefix range will have its key acccess "
"distribution");
DEFINE_double(key_dist_a, 0.0,
"The parameter 'a' of key access distribution model "
"f(x)=a*x^b");
@ -4962,7 +4978,7 @@ class Benchmark {
thread->stats.AddMessage(msg);
}
// THe reverse function of Pareto function
// The inverse function of Pareto distribution
int64_t ParetoCdfInversion(double u, double theta, double k, double sigma) {
double ret;
if (k == 0.0) {
@ -4972,7 +4988,7 @@ class Benchmark {
}
return static_cast<int64_t>(ceil(ret));
}
// inversion of y=ax^b
// The inverse function of power distribution (y=ax^b)
int64_t PowerCdfInversion(double u, double a, double b) {
double ret;
ret = std::pow((u / a), (1 / b));
@ -4993,7 +5009,7 @@ class Benchmark {
}
}
// decide the query type
// Decide the ratio of different query types
// 0 Get, 1 Put, 2 Seek, 3 SeekForPrev, 4 Delete, 5 SingleDelete, 6 merge
class QueryDecider {
public:
@ -5034,7 +5050,157 @@ class Benchmark {
}
};
// The graph wokrload mixed with Get, Put, Iterator
// KeyrangeUnit is the struct of a keyrange. It is used in a keyrange vector
// to transfer a random value to one keyrange based on the hotness.
struct KeyrangeUnit {
int64_t keyrange_start;
int64_t keyrange_access;
int64_t keyrange_keys;
};
// From our observations, the prefix hotness (key-range hotness) follows
// the two-term-exponential distribution: f(x) = a*exp(b*x) + c*exp(d*x).
// However, we cannot directly use the inverse function to decide a
// key-range from a random distribution. To achieve it, we create a list of
// KeyrangeUnit, each KeyrangeUnit occupies a range of integers whose size is
// decided based on the hotness of the key-range. When a random value is
// generated based on uniform distribution, we map it to the KeyrangeUnit Vec
// and one KeyrangeUnit is selected. The probability of a KeyrangeUnit being
// selected is the same as the hotness of this KeyrangeUnit. After that, the
// key can be randomly allocated to the key-range of this KeyrangeUnit, or we
// can based on the power distribution (y=ax^b) to generate the offset of
// the key in the selected key-range. In this way, we generate the keyID
// based on the hotness of the prefix and also the key hotness distribution.
class GenerateTwoTermExpKeys {
public:
int64_t keyrange_rand_max_;
int64_t keyrange_size_;
int64_t keyrange_num_;
bool initiated_;
std::vector<KeyrangeUnit> keyrange_set_;
GenerateTwoTermExpKeys() {
keyrange_rand_max_ = FLAGS_num;
initiated_ = false;
}
~GenerateTwoTermExpKeys() {}
// Initiate the KeyrangeUnit vector and calculate the size of each
// KeyrangeUnit.
Status InitiateExpDistribution(int64_t total_keys, double prefix_a,
double prefix_b, double prefix_c,
double prefix_d) {
int64_t amplify = 0;
int64_t keyrange_start = 0;
initiated_ = true;
if (FLAGS_keyrange_num <= 0) {
keyrange_num_ = 1;
} else {
keyrange_num_ = FLAGS_keyrange_num;
}
keyrange_size_ = total_keys / keyrange_num_;
// Calculate the key-range shares size based on the input parameters
for (int64_t pfx = keyrange_num_; pfx >= 1; pfx--) {
// Step 1. Calculate the probability that this key range will be
// accessed in a query. It is based on the two-term expoential
// distribution
double keyrange_p = prefix_a * std::exp(prefix_b * pfx) +
prefix_c * std::exp(prefix_d * pfx);
if (keyrange_p < std::pow(10.0, -16.0)) {
keyrange_p = 0.0;
}
// Step 2. Calculate the amplify
// In order to allocate a query to a key-range based on the random
// number generated for this query, we need to extend the probability
// of each key range from [0,1] to [0, amplify]. Amplify is calculated
// by 1/(smallest key-range probability). In this way, we ensure that
// all key-ranges are assigned with an Integer that >=0
if (amplify == 0 && keyrange_p > 0) {
amplify = static_cast<int64_t>(std::floor(1 / keyrange_p)) + 1;
}
// Step 3. For each key-range, we calculate its position in the
// [0, amplify] range, including the start, the size (keyrange_access)
KeyrangeUnit p_unit;
p_unit.keyrange_start = keyrange_start;
if (0.0 >= keyrange_p) {
p_unit.keyrange_access = 0;
} else {
p_unit.keyrange_access =
static_cast<int64_t>(std::floor(amplify * keyrange_p));
}
p_unit.keyrange_keys = keyrange_size_;
keyrange_set_.push_back(p_unit);
keyrange_start += p_unit.keyrange_access;
}
keyrange_rand_max_ = keyrange_start;
// Step 4. Shuffle the key-ranges randomly
// Since the access probability is calculated from small to large,
// If we do not re-allocate them, hot key-ranges are always at the end
// and cold key-ranges are at the begin of the key space. Therefore, the
// key-ranges are shuffled and the rand seed is only decide by the
// key-range hotness distribution. With the same distribution parameters
// the shuffle results are the same.
Random64 rand_loca(keyrange_rand_max_);
for (int64_t i = 0; i < FLAGS_keyrange_num; i++) {
int64_t pos = rand_loca.Next() % FLAGS_keyrange_num;
assert(i >= 0 && i < static_cast<int64_t>(keyrange_set_.size()) &&
pos >= 0 && pos < static_cast<int64_t>(keyrange_set_.size()));
std::swap(keyrange_set_[i], keyrange_set_[pos]);
}
// Step 5. Recalculate the prefix start postion after shuffling
int64_t offset = 0;
for (auto& p_unit : keyrange_set_) {
p_unit.keyrange_start = offset;
offset += p_unit.keyrange_access;
}
return Status::OK();
}
// Generate the Key ID according to the input ini_rand and key distribution
int64_t DistGetKeyID(int64_t ini_rand, double key_dist_a,
double key_dist_b) {
int64_t keyrange_rand = ini_rand % keyrange_rand_max_;
// Calculate and select one key-range that contains the new key
int64_t start = 0, end = static_cast<int64_t>(keyrange_set_.size());
while (start + 1 < end) {
int64_t mid = start + (end - start) / 2;
assert(mid >= 0 && mid < static_cast<int64_t>(keyrange_set_.size()));
if (keyrange_rand < keyrange_set_[mid].keyrange_start) {
end = mid;
} else {
start = mid;
}
}
int64_t keyrange_id = start;
// Select one key in the key-range and compose the keyID
int64_t key_offset = 0, key_seed;
if (key_dist_a == 0.0 && key_dist_b == 0.0) {
key_offset = ini_rand % keyrange_size_;
} else {
key_seed = static_cast<int64_t>(
ceil(std::pow((ini_rand / key_dist_a), (1 / key_dist_b))));
Random64 rand_key(key_seed);
key_offset = static_cast<int64_t>(rand_key.Next()) % keyrange_size_;
}
return keyrange_size_ * keyrange_id + key_offset;
}
};
// The social graph wokrload mixed with Get, Put, Iterator queries.
// The value size and iterator length follow Pareto distribution.
// The overall key access follow power distribution. If user models the
// workload based on different key-ranges (or different prefixes), user
// can use two-term-exponential distribution to fit the workload. User
// needs to decides the ratio between Get, Put, Iterator queries before
// starting the benchmark.
void MixGraph(ThreadState* thread) {
int64_t read = 0; // including single gets and Next of iterators
int64_t gets = 0;
@ -5048,6 +5214,8 @@ class Benchmark {
int64_t scan_len_max = FLAGS_mix_max_scan_len;
double write_rate = 1000000.0;
double read_rate = 1000000.0;
bool use_prefix_modeling = false;
GenerateTwoTermExpKeys gen_exp;
std::vector<double> ratio{FLAGS_mix_get_ratio, FLAGS_mix_put_ratio,
FLAGS_mix_seek_ratio};
char value_buffer[default_value_max];
@ -5073,15 +5241,32 @@ class Benchmark {
NewGenericRateLimiter(static_cast<int64_t>(write_rate)));
}
// Decide if user wants to use prefix based key generation
if (FLAGS_keyrange_dist_a != 0.0 || FLAGS_keyrange_dist_b != 0.0 ||
FLAGS_keyrange_dist_c != 0.0 || FLAGS_keyrange_dist_d != 0.0) {
use_prefix_modeling = true;
gen_exp.InitiateExpDistribution(
FLAGS_num, FLAGS_keyrange_dist_a, FLAGS_keyrange_dist_b,
FLAGS_keyrange_dist_c, FLAGS_keyrange_dist_d);
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
int64_t rand_v, key_rand, key_seed;
rand_v = GetRandomKey(&thread->rand) % FLAGS_num;
int64_t ini_rand, rand_v, key_rand, key_seed;
ini_rand = GetRandomKey(&thread->rand);
rand_v = ini_rand % FLAGS_num;
double u = static_cast<double>(rand_v) / FLAGS_num;
key_seed = PowerCdfInversion(u, FLAGS_key_dist_a, FLAGS_key_dist_b);
Random64 rand(key_seed);
key_rand = static_cast<int64_t>(rand.Next()) % FLAGS_num;
// Generate the keyID based on the key hotness and prefix hotness
if (use_prefix_modeling) {
key_rand =
gen_exp.DistGetKeyID(ini_rand, FLAGS_key_dist_a, FLAGS_key_dist_b);
} else {
key_seed = PowerCdfInversion(u, FLAGS_key_dist_a, FLAGS_key_dist_b);
Random64 rand(key_seed);
key_rand = static_cast<int64_t>(rand.Next()) % FLAGS_num;
}
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
int query_type = query.GetType(rand_v);