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package promsketch
import (
"bufio"
"fmt"
"math"
"os"
"strconv"
"testing"
"time"
)
// Test cost (compute + memory) and accuracy under sliding window
func TestSamplingGSumCAIDA(t *testing.T) {
query_window_size_input := []int64{1000000, 7000000, 100000, 10000}
// query_window_size_input := []int64{1000000, 7000000}
total_length := int64(7000000)
readCAIDA()
for _, query_window_size := range query_window_size_input {
cost_query_interval_gsum := int64(query_window_size / 10)
// Create a scenario
t1 := make([]int64, 0)
t2 := make([]int64, 0)
t1 = append(t1, int64(0))
t2 = append(t2, query_window_size-1)
t1 = append(t1, int64(query_window_size/3))
t2 = append(t2, int64(query_window_size/3)*2)
// suffix length
for i := int64(query_window_size / 10); i < int64(query_window_size); i += query_window_size / 100 {
t1 = append(t1, query_window_size-i)
t2 = append(t2, query_window_size-1)
}
fmt.Println("t1:", t1)
fmt.Println("t2:", t2)
fmt.Println("Finished reading input timeseries.")
for test_case := 0; test_case < 5; test_case += 1 {
filename := "new_cost_analysis/caida_gsum_sampling_optimized_cost_" + strconv.Itoa(int(query_window_size)) + "_" + strconv.Itoa(test_case) + ".txt"
fmt.Println(filename)
f, err := os.OpenFile(filename, os.O_WRONLY|os.O_CREATE|os.O_TRUNC, 0755)
if err != nil {
panic(err)
}
defer f.Close()
w := bufio.NewWriter(f)
fmt.Fprintln(w, "t1:", t1)
fmt.Fprintln(w, "t2:", t2)
// Sampling
rate_input := []float64{0.1, 0.2, 0.3, 0.4, 0.5}
for _, rate := range rate_input {
fmt.Fprintln(w, "Sampling", rate)
w.Flush()
sampler := NewUniformSampling(query_window_size, rate, int(float64(query_window_size)*rate))
total_gt_query_compute := 0.0
total_total_query := 0.0
total_compute_sampling := 0.0
insert_compute_sampling := 0.0
total_query := make([]int, len(t1))
gt_query_time := make([]float64, len(t1))
sampling_query_time := make([]float64, len(t1))
total_sampling_distinct_err := make([]float64, len(t1))
total_sampling_l1_err := make([]float64, len(t1))
total_sampling_l2_err := make([]float64, len(t1))
total_sampling_entropy_err := make([]float64, len(t1))
total_sampling_distinct_err2 := make([]float64, len(t1))
total_sampling_l1_err2 := make([]float64, len(t1))
total_sampling_l2_err2 := make([]float64, len(t1))
total_sampling_entropy_err2 := make([]float64, len(t1))
for j := 0; j < len(t1); j++ {
total_query[j] = 0
gt_query_time[j] = 0
sampling_query_time[j] = 0
total_sampling_distinct_err[j] = 0
total_sampling_l1_err[j] = 0
total_sampling_l2_err[j] = 0
total_sampling_entropy_err[j] = 0
total_sampling_distinct_err2[j] = 0
total_sampling_l1_err2[j] = 0
total_sampling_l2_err2[j] = 0
total_sampling_entropy_err2[j] = 0
}
for t := int64(0); t < total_length; t++ {
start := time.Now()
sampler.Insert(t, cases[0].vec[t].F)
elapsed := time.Since(start)
insert_compute_sampling += float64(elapsed.Microseconds())
if t == total_length-1 || (t >= query_window_size-1 && (t+1)%cost_query_interval_gsum == 0) {
for j := range len(t1) {
total_query[j] += 1
total_total_query += 1
start_t := t1[j] + t - query_window_size + 1
end_t := t2[j] + t - query_window_size + 1
start := time.Now()
sampling_l1 := sampler.QueryL1(start_t, end_t)
sampling_l2 := sampler.QueryL2(start_t, end_t)
sampling_entropy := sampler.QueryEntropy(start_t, end_t)
sampling_distinct := sampler.QueryDistinct(start_t, end_t)
elapsed := time.Since(start)
sampling_query_time[j] += float64(elapsed.Microseconds())
total_compute_sampling += float64(elapsed.Microseconds())
start = time.Now()
values := make([]float64, 0)
for tt := start_t; tt <= end_t; tt++ {
values = append(values, float64(cases[0].vec[tt].F))
}
gt_distinct, gt_l1, gt_entropy, gt_l2 := gsum(values)
elapsed = time.Since(start)
gt_query_time[j] += float64(elapsed.Microseconds()) * 4
total_gt_query_compute += float64(elapsed.Microseconds()) * 4
w.Flush()
distinct_err := AbsFloat64(gt_distinct-sampling_distinct) / gt_distinct * 100
l1_err := AbsFloat64(gt_l1-sampling_l1) / gt_l1 * 100
l2_err := AbsFloat64(gt_l2-sampling_l2) / gt_l2 * 100
entropy_err := AbsFloat64(gt_entropy-sampling_entropy) / gt_entropy * 100
total_sampling_distinct_err[j] += distinct_err
total_sampling_l1_err[j] += l1_err
total_sampling_l2_err[j] += l2_err
total_sampling_entropy_err[j] += entropy_err
total_sampling_distinct_err2[j] += distinct_err * distinct_err
total_sampling_l1_err2[j] += l1_err * l1_err
total_sampling_l2_err2[j] += l2_err * l2_err
total_sampling_entropy_err2[j] += entropy_err * entropy_err
}
}
}
fmt.Println("sampling insert compute/item:", insert_compute_sampling/float64(total_length), "us")
fmt.Println("sampling query compute/query:", total_compute_sampling/total_total_query, "us")
fmt.Println("exact baseline query compute/query:", total_gt_query_compute/total_total_query, "us")
fmt.Println("sampling total compute:", total_compute_sampling+insert_compute_sampling, "us")
fmt.Println("sampling memory:", sampler.GetMemory(), "KB")
fmt.Println("exact baseline memory:", query_window_size*8/1024, "KB")
for j := 0; j < len(t1); j++ {
// fmt.Println("sampling window size=", t2[j]-t1[j]+1, "avg err:", total_sampling_distinct_err[j]/float64(total_query[j]), total_sampling_l1_err[j]/float64(total_query[j]), total_sampling_entropy_err[j]/float64(total_query[j]), total_sampling_l2_err[j]/float64(total_query[j]))
fmt.Fprintln(w, "sampling window size err=", t2[j]-t1[j]+1, "avg err:", total_sampling_distinct_err[j]/float64(total_query[j]), total_sampling_l1_err[j]/float64(total_query[j]), total_sampling_entropy_err[j]/float64(total_query[j]), total_sampling_l2_err[j]/float64(total_query[j]))
stdvar_distinct := total_sampling_distinct_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_distinct_err[j]/float64(total_query[j]), 2)
stdvar_l1 := total_sampling_l1_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_l1_err[j]/float64(total_query[j]), 2)
stdvar_entropy := total_sampling_entropy_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_entropy_err[j]/float64(total_query[j]), 2)
stdvar_l2 := total_sampling_l2_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_l2_err[j]/float64(total_query[j]), 2)
stdvar_distinct = math.Sqrt(stdvar_distinct)
stdvar_l1 = math.Sqrt(stdvar_l1)
stdvar_entropy = math.Sqrt(stdvar_entropy)
stdvar_l2 = math.Sqrt(stdvar_l2)
fmt.Fprintln(w, "sampling window size stdvar=", t2[j]-t1[j]+1, "stdvar:", stdvar_distinct, stdvar_l1, stdvar_entropy, stdvar_l2)
}
for j := 0; j < len(t1); j++ {
fmt.Fprintln(w, "sampling estimate query time=", sampling_query_time[j]/float64(total_query[j]), "us", "gt query time=", gt_query_time[j]/float64(total_query[j]), "window size=", t2[j]-t1[j]+1)
}
w.Flush()
fmt.Fprintln(w, "sampling insert compute/item:", insert_compute_sampling/float64(total_length), "us")
fmt.Fprintln(w, "sampling query compute/query:", total_compute_sampling/total_total_query, "us")
fmt.Fprintln(w, "exact baseline query compute/query:", total_gt_query_compute/total_total_query, "us")
fmt.Fprintln(w, "sampling total compute:", total_compute_sampling+insert_compute_sampling, "us")
fmt.Fprintln(w, "sampling memory:", sampler.GetMemory(), "KB")
fmt.Fprintln(w, "exact baseline memory:", query_window_size*8/1024, "KB")
w.Flush()
}
}
}
}
// Test cost (compute + memory) and accuracy under sliding window
func TestSamplingGSumZipf(t *testing.T) {
query_window_size_input := []int64{1000000, 7000000, 100000, 10000}
// query_window_size_input := []int64{1000000, 7000000}
total_length := int64(7000000)
readZipf()
for _, query_window_size := range query_window_size_input {
cost_query_interval_gsum := int64(query_window_size / 10)
// Create a scenario
t1 := make([]int64, 0)
t2 := make([]int64, 0)
t1 = append(t1, int64(0))
t2 = append(t2, query_window_size-1)
t1 = append(t1, int64(query_window_size/3))
t2 = append(t2, int64(query_window_size/3)*2)
// suffix length
for i := int64(query_window_size / 10); i < int64(query_window_size); i += query_window_size / 100 {
t1 = append(t1, query_window_size-i)
t2 = append(t2, query_window_size-1)
}
fmt.Println("t1:", t1)
fmt.Println("t2:", t2)
fmt.Println("Finished reading input timeseries.")
for test_case := 0; test_case < 5; test_case += 1 {
filename := "new_cost_analysis/zipf_gsum_sampling_optimized_cost_" + strconv.Itoa(int(query_window_size)) + "_" + strconv.Itoa(test_case) + ".txt"
fmt.Println(filename)
f, err := os.OpenFile(filename, os.O_WRONLY|os.O_CREATE|os.O_TRUNC, 0755)
if err != nil {
panic(err)
}
defer f.Close()
w := bufio.NewWriter(f)
fmt.Fprintln(w, "t1:", t1)
fmt.Fprintln(w, "t2:", t2)
// Sampling
rate_input := []float64{0.1, 0.2, 0.3, 0.4, 0.5}
for _, rate := range rate_input {
fmt.Fprintln(w, "Sampling", rate)
w.Flush()
sampler := NewUniformSampling(query_window_size, rate, int(float64(query_window_size)*rate))
total_gt_query_compute := 0.0
total_total_query := 0.0
total_compute_sampling := 0.0
insert_compute_sampling := 0.0
total_query := make([]int, len(t1))
gt_query_time := make([]float64, len(t1))
sampling_query_time := make([]float64, len(t1))
total_sampling_distinct_err := make([]float64, len(t1))
total_sampling_l1_err := make([]float64, len(t1))
total_sampling_l2_err := make([]float64, len(t1))
total_sampling_entropy_err := make([]float64, len(t1))
total_sampling_distinct_err2 := make([]float64, len(t1))
total_sampling_l1_err2 := make([]float64, len(t1))
total_sampling_l2_err2 := make([]float64, len(t1))
total_sampling_entropy_err2 := make([]float64, len(t1))
for j := 0; j < len(t1); j++ {
total_query[j] = 0
gt_query_time[j] = 0
sampling_query_time[j] = 0
total_sampling_distinct_err[j] = 0
total_sampling_l1_err[j] = 0
total_sampling_l2_err[j] = 0
total_sampling_entropy_err[j] = 0
total_sampling_distinct_err2[j] = 0
total_sampling_l1_err2[j] = 0
total_sampling_l2_err2[j] = 0
total_sampling_entropy_err2[j] = 0
}
for t := int64(0); t < total_length; t++ {
start := time.Now()
sampler.Insert(t, cases[0].vec[t].F)
elapsed := time.Since(start)
insert_compute_sampling += float64(elapsed.Microseconds())
if t == total_length-1 || (t >= query_window_size-1 && (t+1)%cost_query_interval_gsum == 0) {
for j := range len(t1) {
total_query[j] += 1
total_total_query += 1
start_t := t1[j] + t - query_window_size + 1
end_t := t2[j] + t - query_window_size + 1
start := time.Now()
sampling_l1 := sampler.QueryL1(start_t, end_t)
sampling_l2 := sampler.QueryL2(start_t, end_t)
sampling_entropy := sampler.QueryEntropy(start_t, end_t)
sampling_distinct := sampler.QueryDistinct(start_t, end_t)
elapsed := time.Since(start)
sampling_query_time[j] += float64(elapsed.Microseconds())
total_compute_sampling += float64(elapsed.Microseconds())
start = time.Now()
values := make([]float64, 0)
for tt := start_t; tt <= end_t; tt++ {
values = append(values, float64(cases[0].vec[tt].F))
}
gt_distinct, gt_l1, gt_entropy, gt_l2 := gsum(values)
elapsed = time.Since(start)
gt_query_time[j] += float64(elapsed.Microseconds()) * 4
total_gt_query_compute += float64(elapsed.Microseconds()) * 4
w.Flush()
distinct_err := AbsFloat64(gt_distinct-sampling_distinct) / gt_distinct * 100
l1_err := AbsFloat64(gt_l1-sampling_l1) / gt_l1 * 100
l2_err := AbsFloat64(gt_l2-sampling_l2) / gt_l2 * 100
entropy_err := AbsFloat64(gt_entropy-sampling_entropy) / gt_entropy * 100
total_sampling_distinct_err[j] += distinct_err
total_sampling_l1_err[j] += l1_err
total_sampling_l2_err[j] += l2_err
total_sampling_entropy_err[j] += entropy_err
total_sampling_distinct_err2[j] += distinct_err * distinct_err
total_sampling_l1_err2[j] += l1_err * l1_err
total_sampling_l2_err2[j] += l2_err * l2_err
total_sampling_entropy_err2[j] += entropy_err * entropy_err
}
}
}
fmt.Println("sampling insert compute/item:", insert_compute_sampling/float64(total_length), "us")
fmt.Println("sampling query compute/query:", total_compute_sampling/total_total_query, "us")
fmt.Println("exact baseline query compute/query:", total_gt_query_compute/total_total_query, "us")
fmt.Println("sampling total compute:", total_compute_sampling+insert_compute_sampling, "us")
fmt.Println("sampling memory:", sampler.GetMemory(), "KB")
fmt.Println("exact baseline memory:", query_window_size*8/1024, "KB")
for j := 0; j < len(t1); j++ {
// fmt.Println("sampling window size=", t2[j]-t1[j]+1, "avg err:", total_sampling_distinct_err[j]/float64(total_query[j]), total_sampling_l1_err[j]/float64(total_query[j]), total_sampling_entropy_err[j]/float64(total_query[j]), total_sampling_l2_err[j]/float64(total_query[j]))
fmt.Fprintln(w, "sampling window size err=", t2[j]-t1[j]+1, "avg err:", total_sampling_distinct_err[j]/float64(total_query[j]), total_sampling_l1_err[j]/float64(total_query[j]), total_sampling_entropy_err[j]/float64(total_query[j]), total_sampling_l2_err[j]/float64(total_query[j]))
stdvar_distinct := total_sampling_distinct_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_distinct_err[j]/float64(total_query[j]), 2)
stdvar_l1 := total_sampling_l1_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_l1_err[j]/float64(total_query[j]), 2)
stdvar_entropy := total_sampling_entropy_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_entropy_err[j]/float64(total_query[j]), 2)
stdvar_l2 := total_sampling_l2_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_l2_err[j]/float64(total_query[j]), 2)
stdvar_distinct = math.Sqrt(stdvar_distinct)
stdvar_l1 = math.Sqrt(stdvar_l1)
stdvar_entropy = math.Sqrt(stdvar_entropy)
stdvar_l2 = math.Sqrt(stdvar_l2)
fmt.Fprintln(w, "sampling window size stdvar=", t2[j]-t1[j]+1, "stdvar:", stdvar_distinct, stdvar_l1, stdvar_entropy, stdvar_l2)
}
for j := 0; j < len(t1); j++ {
fmt.Fprintln(w, "sampling estimate query time=", sampling_query_time[j]/float64(total_query[j]), "us", "gt query time=", gt_query_time[j]/float64(total_query[j]), "window size=", t2[j]-t1[j]+1)
}
w.Flush()
fmt.Fprintln(w, "sampling insert compute/item:", insert_compute_sampling/float64(total_length), "us")
fmt.Fprintln(w, "sampling query compute/query:", total_compute_sampling/total_total_query, "us")
fmt.Fprintln(w, "exact baseline query compute/query:", total_gt_query_compute/total_total_query, "us")
fmt.Fprintln(w, "sampling total compute:", total_compute_sampling+insert_compute_sampling, "us")
fmt.Fprintln(w, "sampling memory:", sampler.GetMemory(), "KB")
fmt.Fprintln(w, "exact baseline memory:", query_window_size*8/1024, "KB")
w.Flush()
}
}
}
}
// Test cost (compute + memory) and accuracy under sliding window
func TestSamplingGSumDynamic(t *testing.T) {
query_window_size_input := []int64{1000000, 7000000, 100000, 10000}
// query_window_size_input := []int64{1000000, 7000000}
total_length := int64(7000000)
readDynamic()
for _, query_window_size := range query_window_size_input {
cost_query_interval_gsum := int64(query_window_size / 10)
// Create a scenario
t1 := make([]int64, 0)
t2 := make([]int64, 0)
t1 = append(t1, int64(0))
t2 = append(t2, query_window_size-1)
t1 = append(t1, int64(query_window_size/3))
t2 = append(t2, int64(query_window_size/3)*2)
// suffix length
for i := int64(query_window_size / 10); i < int64(query_window_size); i += query_window_size / 100 {
t1 = append(t1, query_window_size-i)
t2 = append(t2, query_window_size-1)
}
fmt.Println("t1:", t1)
fmt.Println("t2:", t2)
fmt.Println("Finished reading input timeseries.")
for test_case := 0; test_case < 5; test_case += 1 {
filename := "new_cost_analysis/dynamic_gsum_sampling_optimized_cost_" + strconv.Itoa(int(query_window_size)) + "_" + strconv.Itoa(test_case) + ".txt"
fmt.Println(filename)
f, err := os.OpenFile(filename, os.O_WRONLY|os.O_CREATE|os.O_TRUNC, 0755)
if err != nil {
panic(err)
}
defer f.Close()
w := bufio.NewWriter(f)
fmt.Fprintln(w, "t1:", t1)
fmt.Fprintln(w, "t2:", t2)
// Sampling
rate_input := []float64{0.1, 0.2, 0.3, 0.4, 0.5}
for _, rate := range rate_input {
fmt.Fprintln(w, "Sampling", rate)
w.Flush()
sampler := NewUniformSampling(query_window_size, rate, int(float64(query_window_size)*rate))
total_gt_query_compute := 0.0
total_total_query := 0.0
total_compute_sampling := 0.0
insert_compute_sampling := 0.0
total_query := make([]int, len(t1))
gt_query_time := make([]float64, len(t1))
sampling_query_time := make([]float64, len(t1))
total_sampling_distinct_err := make([]float64, len(t1))
total_sampling_l1_err := make([]float64, len(t1))
total_sampling_l2_err := make([]float64, len(t1))
total_sampling_entropy_err := make([]float64, len(t1))
total_sampling_distinct_err2 := make([]float64, len(t1))
total_sampling_l1_err2 := make([]float64, len(t1))
total_sampling_l2_err2 := make([]float64, len(t1))
total_sampling_entropy_err2 := make([]float64, len(t1))
for j := 0; j < len(t1); j++ {
total_query[j] = 0
gt_query_time[j] = 0
sampling_query_time[j] = 0
total_sampling_distinct_err[j] = 0
total_sampling_l1_err[j] = 0
total_sampling_l2_err[j] = 0
total_sampling_entropy_err[j] = 0
total_sampling_distinct_err2[j] = 0
total_sampling_l1_err2[j] = 0
total_sampling_l2_err2[j] = 0
total_sampling_entropy_err2[j] = 0
}
for t := int64(0); t < total_length; t++ {
start := time.Now()
sampler.Insert(t, cases[0].vec[t].F)
elapsed := time.Since(start)
insert_compute_sampling += float64(elapsed.Microseconds())
if t == total_length-1 || (t >= query_window_size-1 && (t+1)%cost_query_interval_gsum == 0) {
for j := range len(t1) {
total_query[j] += 1
total_total_query += 1
start_t := t1[j] + t - query_window_size + 1
end_t := t2[j] + t - query_window_size + 1
start := time.Now()
sampling_l1 := sampler.QueryL1(start_t, end_t)
sampling_l2 := sampler.QueryL2(start_t, end_t)
sampling_entropy := sampler.QueryEntropy(start_t, end_t)
sampling_distinct := sampler.QueryDistinct(start_t, end_t)
elapsed := time.Since(start)
sampling_query_time[j] += float64(elapsed.Microseconds())
total_compute_sampling += float64(elapsed.Microseconds())
start = time.Now()
values := make([]float64, 0)
for tt := start_t; tt <= end_t; tt++ {
values = append(values, float64(cases[0].vec[tt].F))
}
gt_distinct, gt_l1, gt_entropy, gt_l2 := gsum(values)
elapsed = time.Since(start)
gt_query_time[j] += float64(elapsed.Microseconds()) * 4
total_gt_query_compute += float64(elapsed.Microseconds()) * 4
w.Flush()
distinct_err := AbsFloat64(gt_distinct-sampling_distinct) / gt_distinct * 100
l1_err := AbsFloat64(gt_l1-sampling_l1) / gt_l1 * 100
l2_err := AbsFloat64(gt_l2-sampling_l2) / gt_l2 * 100
entropy_err := AbsFloat64(gt_entropy-sampling_entropy) / gt_entropy * 100
total_sampling_distinct_err[j] += distinct_err
total_sampling_l1_err[j] += l1_err
total_sampling_l2_err[j] += l2_err
total_sampling_entropy_err[j] += entropy_err
total_sampling_distinct_err2[j] += distinct_err * distinct_err
total_sampling_l1_err2[j] += l1_err * l1_err
total_sampling_l2_err2[j] += l2_err * l2_err
total_sampling_entropy_err2[j] += entropy_err * entropy_err
}
}
}
fmt.Println("sampling insert compute/item:", insert_compute_sampling/float64(total_length), "us")
fmt.Println("sampling query compute/query:", total_compute_sampling/total_total_query, "us")
fmt.Println("exact baseline query compute/query:", total_gt_query_compute/total_total_query, "us")
fmt.Println("sampling total compute:", total_compute_sampling+insert_compute_sampling, "us")
fmt.Println("sampling memory:", sampler.GetMemory(), "KB")
fmt.Println("exact baseline memory:", query_window_size*8/1024, "KB")
for j := 0; j < len(t1); j++ {
// fmt.Println("sampling window size=", t2[j]-t1[j]+1, "avg err:", total_sampling_distinct_err[j]/float64(total_query[j]), total_sampling_l1_err[j]/float64(total_query[j]), total_sampling_entropy_err[j]/float64(total_query[j]), total_sampling_l2_err[j]/float64(total_query[j]))
fmt.Fprintln(w, "sampling window size err=", t2[j]-t1[j]+1, "avg err:", total_sampling_distinct_err[j]/float64(total_query[j]), total_sampling_l1_err[j]/float64(total_query[j]), total_sampling_entropy_err[j]/float64(total_query[j]), total_sampling_l2_err[j]/float64(total_query[j]))
stdvar_distinct := total_sampling_distinct_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_distinct_err[j]/float64(total_query[j]), 2)
stdvar_l1 := total_sampling_l1_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_l1_err[j]/float64(total_query[j]), 2)
stdvar_entropy := total_sampling_entropy_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_entropy_err[j]/float64(total_query[j]), 2)
stdvar_l2 := total_sampling_l2_err2[j]/float64(total_query[j]) - math.Pow(total_sampling_l2_err[j]/float64(total_query[j]), 2)
stdvar_distinct = math.Sqrt(stdvar_distinct)
stdvar_l1 = math.Sqrt(stdvar_l1)
stdvar_entropy = math.Sqrt(stdvar_entropy)
stdvar_l2 = math.Sqrt(stdvar_l2)
fmt.Fprintln(w, "sampling window size stdvar=", t2[j]-t1[j]+1, "stdvar:", stdvar_distinct, stdvar_l1, stdvar_entropy, stdvar_l2)
}
for j := 0; j < len(t1); j++ {
fmt.Fprintln(w, "sampling estimate query time=", sampling_query_time[j]/float64(total_query[j]), "us", "gt query time=", gt_query_time[j]/float64(total_query[j]), "window size=", t2[j]-t1[j]+1)
}
w.Flush()
fmt.Fprintln(w, "sampling insert compute/item:", insert_compute_sampling/float64(total_length), "us")
fmt.Fprintln(w, "sampling query compute/query:", total_compute_sampling/total_total_query, "us")
fmt.Fprintln(w, "exact baseline query compute/query:", total_gt_query_compute/total_total_query, "us")
fmt.Fprintln(w, "sampling total compute:", total_compute_sampling+insert_compute_sampling, "us")
fmt.Fprintln(w, "sampling memory:", sampler.GetMemory(), "KB")
fmt.Fprintln(w, "exact baseline memory:", query_window_size*8/1024, "KB")
w.Flush()
}
}
}
}