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featurize2.py
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featurize2.py
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import os
import argparse
import json
import ast
from parser import parser
from collections import OrderedDict
#creates features on selection attributes
def featurize_selections(sql):
statistics_file = open('statistics.json')
statistics = json.load(statistics_file)
node = parser.parse(sql)
featuresDict = {name: [False]*(len(statistics[name]['most_common_values'])+1 if statistics[name]['histogram_bounds'][0] == 'None' else (len(statistics[name]['histogram_bounds'])+len(statistics[name]['most_common_values'])-1)) for name in statistics}
featuresDict = OrderedDict(sorted(featuresDict.items()))
selection_predicates = ast.get_selections(node)
for predicate in selection_predicates:
featuresDict[predicate.left.to_sql()] = hist_featurize(predicate, statistics)+mcv_featurize(predicate, statistics)
#print(predicate.left.to_sql())
#print(featuresDict[predicate.left.to_sql()])
#print(json.dumps(featuresDict, indent=4, sort_keys=True))
features = []
for name in featuresDict:
features = features+featuresDict[name]
print(len(features))
return [int(item) for item in features]
#According to operation featurize the selection predicate - Histogram part
def hist_featurize(node, statistics):
features = []
if(statistics[node.left.to_sql()]['histogram_bounds'] == 'None'):
features = [False]
return features
for bucket in range(len(statistics[node.left.to_sql()]['histogram_bounds'])-1):
low = statistics[node.left.to_sql()]['histogram_bounds'][bucket]
high = statistics[node.left.to_sql()]['histogram_bounds'][bucket+1]
if(node.operation == '<=' or node.operation == '<' or node.operation == '>=' \
or node.operation == '>' or node.operation == '=' or node.operation == 'IS'):
value = node.right.to_sql().replace('\'', '')
if(low.isdigit() and high.isdigit() and value.isdigit()):
low = int(low)
high = int(high)
value = int(value)
elif(node.operation == 'BETWEEN' or node.operation == 'NOT BETWEEN'):
valueR = node.right.right.to_sql().replace('\'', '')
valueL = node.right.left.to_sql().replace('\'', '')
if(low.isdigit() and high.isdigit() and valueR.isdigit() and valueL.isdigit()):
low = int(low)
high = int(high)
valueR = int(valueR)
valueL = int(valueL)
elif(node.operation == 'IN'):
flag = False;
for i in range(len(node.right.items)):
flag = flag or (low <= node.right.items[i].to_sql() and\
high >= node.right.items[i].to_sql())
tempFlag = True;
for i in range(len(node.right.items)):
tempFlag = tempFlag and node.right.items[i].to_sql().isdigit()
if(low.isdigit() and high.isdigit() and tempFlag):
flag = False;
for i in range(len(node.right.items)):
flag = flag or (int(low) <= int(node.right.items[i].to_sql()) and\
int(high) >= int(node.right.items[i].to_sql()))
if(node.operation == '<='):
features.append(low <= value)
elif(node.operation == '<'):
features.append(low < value)
elif(node.operation == '>='):
features.append(high >= value)
elif(node.operation == '>'):
features.append(high > value)
elif(node.operation == '=' or node.operation == 'IS' ):
features.append(low <= value and\
high >= value)
elif(node.operation == '!='):
features.append(low > value or\
high < value)
elif(node.operation == 'BETWEEN'):
features.append(low < valueR and\
high > valueL)
elif(node.operation == 'NOT BETWEEN'):
features.append(low >= valueR or\
high <= valueL)
elif(node.operation == 'IN'):
features.append(flag)
return features
#According to operation featurize the selection predicate - Most Common Value part
def mcv_featurize(node, statistics):
features = []
for mcv in range(len(statistics[node.left.to_sql()]['most_common_values'])):
cv = statistics[node.left.to_sql()]['most_common_values'][mcv]
if(cv == 'None'):
features.append(False)
return
if(node.operation == '<=' or node.operation == '<' or node.operation == '>=' \
or node.operation == '>' or node.operation == '=' or node.operation == 'IS'):
value = node.right.to_sql().replace('\'', '')
if(cv.isdigit() and node.right.to_sql().replace('\'', '').isdigit()):
cv = int(cv)
value = int(value)
elif(node.operation == 'BETWEEN' or node.operation == 'NOT BETWEEN'):
valueR = node.right.right.to_sql().replace('\'', '')
valueL = node.right.left.to_sql().replace('\'', '')
if(cv.isdigit() and valueR.isdigit() and valueL.isdigit()):
cv= int(cv)
valueR = int(valueR)
valueL = int(valueL)
elif(node.operation == 'IN'):
flag = False;
for i in range(len(node.right.items)):
flag = flag or (cv == node.right.items[i].to_sql())
tempFlag = True;
for i in range(len(node.right.items)):
tempFlag = tempFlag and node.right.items[i].to_sql().isdigit()
if(cv.isdigit() and tempFlag):
flag = False;
for i in range(len(node.right.items)):
flag = flag or (int(cv) == int(node.right.items[i].to_sql()))
if(node.operation == '<='):
features.append(cv <= value)
elif(node.operation == '<'):
features.append(cv < value)
elif(node.operation == '>='):
features.append(cv >= value)
elif(node.operation == '>'):
features.append(cv > value)
elif(node.operation == '=' or node.operation == 'IS' ):
features.append(cv == value)
elif(node.operation == '!='):
features.append(cv != value)
elif(node.operation == 'BETWEEN'):
features.append(cv < valueR and\
cv > valueL)
elif(node.operation == 'NOT BETWEEN'):
features.append(cv >= valueR or\
cv <= valueL)
elif(node.operation == 'IN'):
features.append(flag)
return features
f = open('join-order-benchmark/queries/6a.sql', 'r')
featurize_selections(f.read())