386320d266
This is extracted from another git repo. This is the first release, and the prior commit history is not terribly interesting, so I'm not going to bother using filter-branch to try to cleanly isolate the history for this tool. Cheers.
99 lines
2.5 KiB
C++
99 lines
2.5 KiB
C++
/* -*- c++ -*- */
|
|
|
|
#ifndef ADAPTIVESAMPLER_H
|
|
#define ADAPTIVESAMPLER_H
|
|
|
|
// Simple exponential-backoff adaptive time series sampler. Will
|
|
// record at most max_samples samples out of however many samples are
|
|
// thrown at it. Makes a vague effort to do this evenly over the
|
|
// samples given to it. The sampling is time invariant (i.e. if you
|
|
// start inserting samples at a slower rate, they will be
|
|
// under-represented).
|
|
|
|
#include <assert.h>
|
|
#include <inttypes.h>
|
|
#include <stdio.h>
|
|
#include <stdlib.h>
|
|
#include <string.h>
|
|
|
|
#include <algorithm>
|
|
#include <vector>
|
|
|
|
#include "log.h"
|
|
|
|
template <class T> class AdaptiveSampler {
|
|
public:
|
|
std::vector<T> samples;
|
|
unsigned int sample_rate;
|
|
unsigned int max_samples;
|
|
unsigned int total_samples;
|
|
|
|
AdaptiveSampler() = delete;
|
|
AdaptiveSampler(int max) :
|
|
sample_rate(1), max_samples(max), total_samples(0) {
|
|
}
|
|
|
|
void sample(T s) {
|
|
total_samples++;
|
|
|
|
if (drand48() < (1/(double) sample_rate))
|
|
samples.push_back(s);
|
|
|
|
// Throw out half of the samples, double sample_rate.
|
|
if (samples.size() >= max_samples) {
|
|
sample_rate *= 2;
|
|
|
|
std::vector<T> half_samples;
|
|
for (unsigned int i = 0; i < samples.size(); i++) {
|
|
if (drand48() > .5) half_samples.push_back(samples[i]);
|
|
}
|
|
samples = half_samples;
|
|
}
|
|
}
|
|
|
|
void save_samples(const char* type, const char* filename) {
|
|
FILE *file;
|
|
|
|
if ((file = fopen(filename, "a")) == NULL) {
|
|
W("fopen() failed: %s", strerror(errno));
|
|
return;
|
|
}
|
|
|
|
for (size_t i = 0; i < samples.size(); i++) {
|
|
fprintf(file, "%s %" PRIu64 " %f\n", type, i, samples[i]);
|
|
}
|
|
}
|
|
|
|
double average() {
|
|
double result = 0.0;
|
|
size_t length = samples.size();
|
|
for (size_t i = 0; i < length; i++) result += samples[i];
|
|
return result/length;
|
|
}
|
|
|
|
void print_header() {
|
|
printf("#%-6s %6s %8s %8s %8s %8s %8s %8s\n", "type", "size",
|
|
"min", "max", "avg", "90th", "95th", "99th");
|
|
}
|
|
|
|
void print_stats(const char *type, const char *size) {
|
|
std::vector<double> samples_copy = samples;
|
|
size_t l = samples_copy.size();
|
|
|
|
if (l == 0) {
|
|
printf("%-7s %6s %8.1f %8.1f %8.1f %8.1f %8.1f %8.1f\n", type, size,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0);
|
|
return;
|
|
}
|
|
|
|
sort(samples_copy.begin(), samples_copy.end());
|
|
|
|
printf("%-7s %6s %8.1f %8.1f %8.1f %8.1f %8.1f %8.1f\n", type, size,
|
|
samples_copy[0], samples_copy[l-1], average(),
|
|
samples_copy[(l*90)/100], samples_copy[(l*95)/100],
|
|
samples_copy[(l*99)/100]);
|
|
}
|
|
};
|
|
|
|
#endif // ADAPTIVESAMPLER_H
|