patch 4.0

2022-10-24 10:34:53 +08:00
parent 4ad6e00ec3
commit 93a1074b0c
10533 changed files with 2588271 additions and 2299373 deletions
--- a/unittest/sql/optimizer/storage_perf/fit_get.py
+++ b/unittest/sql/optimizer/storage_perf/fit_get.py
@ -0,0 +1,137 @@
+#!/bin/env python
+__author__ = 'dongyun.zdy'
+
+
+import math
+import numpy as np
+from scipy.optimize import leastsq
+from scipy.optimize import curve_fit
+import sys
+from lmfit import Model
+import getopt
+
+STARTUP = 0.0
+
+def get_model_form(args,
+                   # Tstartup,
+                   Tper_row,
+                   Tper_col,
+                   Tcorrection1,
+                   Tcorrection2,
+                   # Tlog3
+                   ):
+    (
+        Nrow,
+        Ncol,
+    ) = args
+
+    global STARTUP
+    total_cost = STARTUP#Tstartup
+    total_cost += Nrow * (Tper_row + Ncol * Tper_col)
+    total_cost += Tcorrection1 * math.log(Tcorrection2 * Nrow , 2)
+    return total_cost
+
+def get_model_arr(arg_sets,
+                  # Tstartup,
+                  Tper_row,
+                  Tper_col,
+                  Tcorrection1,
+                  Tcorrection2,
+                  # Tlog3
+                  ):
+    res = []
+    for single_arg_set in arg_sets:
+        res.append(get_model_form(single_arg_set,
+                                  # Tstartup,
+                                  Tper_row,
+                                  Tper_col,
+                                  Tcorrection1,
+                                  Tcorrection2,
+                                  # Tlog3
+                                  ))
+    return np.array(res)
+
+get_model = Model(get_model_arr)
+# get_model.set_param_hint("Tstartup", min=0.0)
+get_model.set_param_hint("Tper_row", min=0.0)
+get_model.set_param_hint("Tper_col", min=0.0)
+get_model.set_param_hint("Tcorrection1", min=0.0)
+get_model.set_param_hint("Tcorrection2", min=0.0)
+# get_model.set_param_hint("Tlog3", min=0.0)
+
+
+def extract_info_from_line(line):
+    splited = line.split(",")
+    line_info = []
+    for item in splited:
+        line_info.append(float(item))
+    return line_info
+
+
+if __name__ == '__main__':
+    file_name = "scan_model.res.formal.prep"
+    out_file_name = "scan_model.fit"
+
+    # sys.argv.extend("-i get.IO.prep -o get.IO.model".split(" "))
+
+    output_fit_res = False
+    wrong_arg = False
+    opts,args = getopt.getopt(sys.argv[1:],"i:o:")
+    for op, value in opts:
+        if "-i" == op:
+            file_name = value
+        elif "-o" == op:
+            output_fit_res = True
+            out_file_name = value
+        else:
+            wrong_arg = True
+
+    if wrong_arg:
+        print "wrong arg"
+        sys.exit(1)
+
+    if file_name.find('rc') != -1:
+        STARTUP = 170.0
+    elif file_name.find('bc') != -1:
+        STARTUP = 210.0
+    else:
+        STARTUP = 520.0
+
+    file = open(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[0], case_param[1]))
+        times.append(case_param[4])
+    file.close()
+    arg_sets_np = np.array(arg_sets)
+    times_np = np.array(times)
+    #10, 0.20406430879623488, 0.016618100054245379, 14.0, 4.5, 37.0, -0.005, 0.5, -7.0
+    result = get_model.fit(times_np, arg_sets=arg_sets_np,
+                           # Tstartup=10.0,
+                           Tper_row=10.0,
+                           Tper_col=1.0,
+                           Tcorrection1=1.0,
+                           Tcorrection2=1.0,
+                           # Tlog3=1.0,
+                           )
+
+
+    # res_line = str(result.best_values["Tstartup"]) + ","
+    res_line = str(result.best_values["Tper_row"]) + ","
+    res_line += str(result.best_values["Tper_col"]) + ","
+    res_line += str(result.best_values["Tcorrection1"]) + ","
+    res_line += str(result.best_values["Tcorrection2"]) #+ ","
+    # res_line += str(result.best_values["Tlog3"])
+
+    print result.fit_report()
+
+    if output_fit_res:
+        out_file = open(out_file_name, "w")
+        out_file.write(res_line)
+        out_file.close()