ClickHouse/dbms/scripts/gen-bias-data.py
2016-06-07 11:23:15 +03:00

263 lines
7.4 KiB
Python
Executable File

#!/usr/bin/python3.4
# -*- coding: utf-8 -*-
import sys
import argparse
import tempfile
import random
import subprocess
import bisect
from copy import deepcopy
# Псевдослучайный генератор уникальных чисел.
# http://preshing.com/20121224/how-to-generate-a-sequence-of-unique-random-integers/
class UniqueRandomGenerator:
prime = 4294967291
def __init__(self, seed_base, seed_offset):
self.index = self.permutePQR(self.permutePQR(seed_base) + 0x682f0161)
self.intermediate_offset = self.permutePQR(self.permutePQR(seed_offset) + 0x46790905)
def next(self):
val = self.permutePQR((self.permutePQR(self.index) + self.intermediate_offset) ^ 0x5bf03635)
self.index = self.index + 1
return val
def permutePQR(self, x):
if x >=self.prime:
return x
else:
residue = (x * x) % self.prime
if x <= self.prime/2:
return residue
else:
return self.prime - residue
# Создать таблицу содержащую уникальные значения.
def generate_data_source(host, port, http_port, min_cardinality, max_cardinality, count):
chunk_size = round((max_cardinality - min_cardinality) / float(count))
used_values = 0
cur_count = 0
next_size = 0
sup = 32768
n1 = random.randrange(0, sup)
n2 = random.randrange(0, sup)
urng = UniqueRandomGenerator(n1, n2)
is_first = True
with tempfile.TemporaryDirectory() as tmp_dir:
filename = tmp_dir + '/table.txt'
with open(filename, 'w+b') as file_handle:
while cur_count < count:
if is_first == True:
is_first = False
if min_cardinality != 0:
next_size = min_cardinality + 1
else:
next_size = chunk_size
else:
next_size += chunk_size
while used_values < next_size:
h = urng.next()
used_values = used_values + 1
out = str(h) + "\t" + str(cur_count) + "\n";
file_handle.write(bytes(out, 'UTF-8'));
cur_count = cur_count + 1
query = "DROP TABLE IF EXISTS data_source"
subprocess.check_output(["clickhouse-client", "--host", host, "--port", str(port), "--query", query])
query = "CREATE TABLE data_source(UserID UInt64, KeyID UInt64) ENGINE=TinyLog"
subprocess.check_output(["clickhouse-client", "--host", host, "--port", str(port), "--query", query])
cat = subprocess.Popen(("cat", filename), stdout=subprocess.PIPE)
subprocess.check_output(("POST", "http://{0}:{1}/?query=INSERT INTO data_source FORMAT TabSeparated".format(host, http_port)), stdin=cat.stdout)
cat.wait()
def perform_query(host, port):
query = "SELECT runningAccumulate(uniqExactState(UserID)) AS exact, "
query += "runningAccumulate(uniqCombinedRawState(UserID)) AS approx "
query += "FROM data_source GROUP BY KeyID"
return subprocess.check_output(["clickhouse-client", "--host", host, "--port", port, "--query", query])
def parse_clickhouse_response(response):
parsed = []
lines = response.decode().split("\n")
for cur_line in lines:
rows = cur_line.split("\t")
if len(rows) == 2:
parsed.append([float(rows[0]), float(rows[1])])
return parsed
def accumulate_data(accumulated_data, data):
if not accumulated_data:
accumulated_data = deepcopy(data)
else:
for row1, row2 in zip(accumulated_data, data):
row1[1] += row2[1];
return accumulated_data
def generate_raw_result(accumulated_data, count):
expected_tab = []
bias_tab = []
for row in accumulated_data:
exact = row[0]
expected = row[1] / count
bias = expected - exact
expected_tab.append(expected)
bias_tab.append(bias)
return [ expected_tab, bias_tab ]
def generate_sample(raw_estimates, biases, n_samples):
result = []
min_card = raw_estimates[0]
max_card = raw_estimates[len(raw_estimates) - 1]
step = (max_card - min_card) / (n_samples - 1)
for i in range(0, n_samples + 1):
x = min_card + i * step
j = bisect.bisect_left(raw_estimates, x)
if j == len(raw_estimates):
result.append((raw_estimates[j - 1], biases[j - 1]))
elif raw_estimates[j] == x:
result.append((raw_estimates[j], biases[j]))
else:
# Найти 6 ближайших соседей. Вычислить среднее арифметическое.
# 6 точек слева x [j-6 j-5 j-4 j-3 j-2 j-1]
begin = max(j - 6, 0) - 1
end = j - 1
T = []
for k in range(end, begin, -1):
T.append(x - raw_estimates[k])
# 6 точек справа x [j j+1 j+2 j+3 j+4 j+5]
begin = j
end = min(j + 5, len(raw_estimates) - 1) + 1
U = []
for k in range(begin, end):
U.append(raw_estimates[k] - x)
# Сливаем расстояния.
V = []
lim = min(len(T), len(U))
k1 = 0
k2 = 0
while k1 < lim and k2 < lim:
if T[k1] == U[k2]:
V.append(j - k1 - 1)
V.append(j + k2)
k1 = k1 + 1
k2 = k2 + 1
elif T[k1] < U[k2]:
V.append(j - k1 - 1)
k1 = k1 + 1
else:
V.append(j + k2)
k2 = k2 + 1
if k1 < len(T):
while k1 < len(T):
V.append(j - k1 - 1)
k1 = k1 + 1
elif k2 < len(U):
while k2 < len(U):
V.append(j + k2)
k2 = k2 + 1
# Выбираем 6 ближайших точек.
# Вычисляем средние.
begin = 0
end = min(len(V), 6)
sum = 0
bias = 0
for k in range(begin, end):
sum += raw_estimates[V[k]]
bias += biases[V[k]]
sum /= float(end)
bias /= float(end)
result.append((sum, bias))
# Пропустить последовательные результаты, чьи оценки одинаковые.
final_result = []
last = -1
for entry in result:
if entry[0] != last:
final_result.append((entry[0], entry[1]))
last = entry[0]
return final_result
def dump_arrays(data):
print("Size of each array: {0}\n".format(len(data)))
is_first = True
sep = ''
print("raw_estimates = ")
print("{")
for row in data:
print("\t{0}{1}".format(sep, row[0]))
if is_first == True:
is_first = False
sep = ","
print("};")
is_first = True
sep = ""
print("\nbiases = ")
print("{")
for row in data:
print("\t{0}{1}".format(sep, row[1]))
if is_first == True:
is_first = False
sep = ","
print("};")
def start():
parser = argparse.ArgumentParser(description = "Generate bias correction tables for HyperLogLog-based functions.")
parser.add_argument("-x", "--host", default="127.0.0.1", help="ClickHouse server host name");
parser.add_argument("-p", "--port", type=int, default=9000, help="ClickHouse server TCP port");
parser.add_argument("-t", "--http_port", type=int, default=8123, help="ClickHouse server HTTP port");
parser.add_argument("-i", "--iterations", type=int, default=5000, help="number of iterations");
parser.add_argument("-m", "--min_cardinality", type=int, default=16384, help="minimal cardinality");
parser.add_argument("-M", "--max_cardinality", type=int, default=655360, help="maximal cardinality");
parser.add_argument("-s", "--samples", type=int, default=200, help="number of sampled values");
args = parser.parse_args()
accumulated_data = []
for i in range(0, args.iterations):
print(i + 1)
sys.stdout.flush()
generate_data_source(args.host, str(args.port), str(args.http_port), args.min_cardinality, args.max_cardinality, 1000)
response = perform_query(args.host, str(args.port))
data = parse_clickhouse_response(response)
accumulated_data = accumulate_data(accumulated_data, data)
result = generate_raw_result(accumulated_data, args.iterations)
sampled_data = generate_sample(result[0], result[1], args.samples)
dump_arrays(sampled_data)
if __name__ == "__main__": start()