Files
oceanbase/unittest/sql/optimizer/cost_model_utils/pro_hash.py
wangzelin.wzl 93a1074b0c patch 4.0
2022-10-24 17:57:12 +08:00

115 lines
3.4 KiB
Python

__author__ = 'canfang.scf'
from op_generator import op_generator
from cost_test_conf import Config
import subprocess as sp
import os
from lmfit import Model
import numpy as np
hash_cls = op_generator.gen_operator("hash_join")
conf = Config()
conf.u_to_test_op_c = 'hash'
conf.is_not_running_as_unittest_c = True
conf.schema_file_c = 'c10k1x2.schema'
conf.left_row_count_c = 1000
conf.right_row_count_c = 1000
conf.left_min_c = 1
conf.right_min_c = 1
conf.is_random_c = True
hash_op = hash_cls(conf)
# step 3 process data
final_file_name = "hash_join_result_final"
if os.path.exists(final_file_name):
os.remove(final_file_name)
data_cmd = hash_op.get_data_preprocess_cmd()
sp.check_call(data_cmd, shell=True)
# step 4 fit and output
out_model_file_name = "hash_model"
if os.path.exists(out_model_file_name):
os.remove(out_model_file_name)
def hash_model_form(args,
Tstart_up,
Tright_outer_once,
Tleft_outer_once,
#Tjoin_row
):
(
Nres_row,
Nleft_row,
Nright_row,
Nequal_cond,
Nno_matched_right,
Nno_matched_left
) = args
total_cost = Tstart_up # Tstartup
total_cost += Nleft_row * 0.74497774
total_cost += Nright_row * 0.26678144
total_cost += Nequal_cond * 0.86340381
total_cost += Nres_row * 0.28939532
total_cost += Nno_matched_left * Tright_outer_once
total_cost += Nno_matched_right * Tleft_outer_once
return total_cost
def hash_model_arr(arg_sets,
Tstart_up,
Tright_outer_once,
Tleft_outer_once):
res = []
for single_arg_set in arg_sets:
res.append(hash_model_form(single_arg_set,
Tstart_up,
Tright_outer_once,
Tleft_outer_once))
return np.array(res)
def extract_info_from_line(line):
splited = line.split(",")
line_info = []
for item in splited:
line_info.append(float(item))
return line_info
hash_model = Model(hash_model_arr)
hash_model.set_param_hint("Tstart_up", min=0.0)
# hash_model.set_param_hint("Tbuild_htable", min=0.0)
# hash_model.set_param_hint("Tright_row_once", min=0.0)
# hash_model.set_param_hint("Tconvert_tuple", min=0.0)
hash_model.set_param_hint("Tright_outer_once", min=0.0)
hash_model.set_param_hint("Tleft_outer_once", min=0.0)
#hash_model.set_param_hint("Tjoin_row", min=0.0)
file = open(final_file_name, "r")
arg_sets = []
times = []
case_params = []
for line in file:
if line.startswith('#'):
continue
case_param = extract_info_from_line(line)
case_params.append(case_param)
arg_sets.append((case_param[2], case_param[0], case_param[1], case_param[3], case_param[4], case_param[5]))
times.append(case_param[6])
file.close()
arg_sets_np = np.array(arg_sets)
times_np = np.array(times)
result = hash_model.fit(times_np, arg_sets=arg_sets_np,
Tstartup=0.0,
Tright_outer_once=0.0,
Tleft_outer_once=0.0)
res_line = str(result.best_values["Tstart_up"]) + ","
res_line += str(result.best_values["Tright_outer_once"]) + ","
res_line += str(result.best_values["Tleft_outer_once"])
print result.fit_report()
if out_model_file_name:
out_file = open(out_model_file_name, "w")
out_file.write(res_line)
out_file.close()