100 lines
2.5 KiB
C++
100 lines
2.5 KiB
C++
/* -*- c++ -*- */
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#ifndef ADAPTIVESAMPLER_H
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#define ADAPTIVESAMPLER_H
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// Simple exponential-backoff adaptive time series sampler. Will
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// record at most max_samples samples out of however many samples are
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// thrown at it. Makes a vague effort to do this evenly over the
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// samples given to it. The sampling is time invariant (i.e. if you
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// start inserting samples at a slower rate, they will be
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// under-represented).
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#include <assert.h>
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#include <errno.h>
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#include <inttypes.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <algorithm>
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#include <vector>
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#include "log.h"
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template <class T> class AdaptiveSampler {
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public:
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std::vector<T> samples;
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unsigned int sample_rate;
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unsigned int max_samples;
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unsigned int total_samples;
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AdaptiveSampler() = delete;
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AdaptiveSampler(int max) :
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sample_rate(1), max_samples(max), total_samples(0) {
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}
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void sample(T s) {
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total_samples++;
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if (drand48() < (1/(double) sample_rate))
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samples.push_back(s);
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// Throw out half of the samples, double sample_rate.
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if (samples.size() >= max_samples) {
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sample_rate *= 2;
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std::vector<T> half_samples;
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for (unsigned int i = 0; i < samples.size(); i++) {
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if (drand48() > .5) half_samples.push_back(samples[i]);
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}
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samples = half_samples;
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}
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}
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void save_samples(const char* type, const char* filename) {
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FILE *file;
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if ((file = fopen(filename, "a")) == NULL) {
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W("fopen() failed: %s", strerror(errno));
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return;
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}
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for (size_t i = 0; i < samples.size(); i++) {
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fprintf(file, "%s %" PRIu64 " %f\n", type, i, samples[i]);
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}
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}
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double average() {
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double result = 0.0;
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size_t length = samples.size();
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for (size_t i = 0; i < length; i++) result += samples[i];
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return result/length;
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}
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void print_header() {
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printf("#%-6s %6s %8s %8s %8s %8s %8s %8s\n", "type", "size",
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"min", "max", "avg", "90th", "95th", "99th");
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}
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void print_stats(const char *type, const char *size) {
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std::vector<double> samples_copy = samples;
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size_t l = samples_copy.size();
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if (l == 0) {
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printf("%-7s %6s %8.1f %8.1f %8.1f %8.1f %8.1f %8.1f\n", type, size,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0);
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return;
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}
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sort(samples_copy.begin(), samples_copy.end());
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printf("%-7s %6s %8.1f %8.1f %8.1f %8.1f %8.1f %8.1f\n", type, size,
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samples_copy[0], samples_copy[l-1], average(),
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samples_copy[(l*90)/100], samples_copy[(l*95)/100],
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samples_copy[(l*99)/100]);
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}
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};
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#endif // ADAPTIVESAMPLER_H
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