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SQL.py
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SQL.py
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import numpy as np
from rewrite import decomposeOr, rewrite, isPushable, getPushupCondition
import pandas as pd
from functools import reduce
import timeit
import sqlparse
import re
from copy import copy
from indexV1 import createIndex, loadTable, chooseBaseTable2, chooseBaseTable3, loadJoinTable
import glob
def mem_usage(pandas_obj):
if isinstance(pandas_obj,pd.DataFrame):
usage_b = pandas_obj.memory_usage(deep=True).sum()
else: # we assume if not a df it's a series
usage_b = pandas_obj.memory_usage(deep=True)
usage_mb = usage_b / 1024 ** 2 # convert bytes to megabytes
return "{:03.2f} MB".format(usage_mb)
def renameCols(table, prefix):
cols = list(table)
cols = [prefix+'.'+attr for attr in cols]
table.columns = [cols]
return table
class csvDB:
def __init__(self):
self.data = []
self.tables = []
self.tempData = []
self.tempTables = []
self.optimize = False
############################################################################
# load multiple csv files
def indexData(self, filenames):
for f in filenames:
if f:
createIndex(f)
############################################################################
def join(self, product, joinTable, joinCondition, rename):
product_ = product
df2 = joinTable
onList = []
attrList = [i.strip() for i in joinCondition.split(" = ")]
if attrList[0] in product_.columns:
df2 = df2.rename(columns={attrList[1]: attrList[0]})
onList.append(attrList[0])
elimed = attrList[1]
else:
df2 = df2.rename(columns={attrList[0]: attrList[1]})
onList.append(attrList[1])
elimed = attrList[0]
product = pd.merge(product, df2, on=onList[0])
return product, (elimed, onList[0])
# return the cartesian product of multiple tables
def cartesian(self, tablesList, rename):
if len(tablesList) == 2:
t1 = tablesList[0].split(' ')
t2 = tablesList[1].split(' ')
df1 = copy(self.data[self.tables.index(t1[0])])
df2 = copy(self.data[self.tables.index(t2[0])])
if rename:
df1 = renameCols(df1, t1[1])
df2 = renameCols(df2, t2[1])
df1['key'] = 1
df2['key'] = 1
product = pd.merge(df1, df2, on='key')
product = product.drop(['key'], axis=1)
return product
if len(tablesList) == 3:
df1 = self.data[self.tables.index(tablesList[0])]
df2 = self.data[self.tables.index(tablesList[1])]
df3 = self.data[self.tables.index(tablesList[2])]
df1['key'] = 1
df2['key'] = 1
df3['key'] = 1
product = pd.merge(df1, df2, df3, on='key')
product = product.drop(['key'], axis=1)
return product
############################################################################
# query optimization methods
def selectionPushUp(self, product, tName, condition):
product_ = product
conditionList = re.split(" and | AND ", condition)
for c in conditionList:
pushable, c_ = isPushable(c)
if pushable and c_.split('.')[0] == tName:
newc= rewrite(c_)
product_ = product_[eval(newc)]
return product_
############################################################################
# execute query here
def executeSearch(self, condition, tables, rename):
distinctFlag = False
tablesList = tables.split(',')
tablesList = [i.strip() for i in tablesList]
joinCondition = ''
if "DISTINCT" in condition:
condition = condition.replace('DISTINCT', '')
distinctFlag = True
if ' ON ' in condition:
joinCondition = condition.split(' ON ')[1].strip()[1:-1]
condition = condition.split(' ON ')[0]
if len(tablesList) == 1:
tableClause = tables.split(' ')
# print(tableClause[1])
# print(condition)
pushupCondition = getPushupCondition(tableClause[1], condition)
# print(pushupCondition)
product = loadTable(tableClause[0], pushupCondition)
product = product.apply(pd.to_numeric, errors='ignore')
# print(product)
if rename:
product = renameCols(product, tableClause[1])
new = rewrite(condition)
# print(new)
rs = product[eval(new)]
elif len(tablesList) == 2:
#choose the base table
tableClause1 = tablesList[0].split(' ')
tableClause2 = tablesList[1].split(' ')
pushupCondition1 = getPushupCondition(tableClause1[1], condition)
pushupCondition2 = getPushupCondition(tableClause2[1], condition)
product, tName = chooseBaseTable2(tableClause1, pushupCondition1, tableClause2, pushupCondition2)
product = product.apply(pd.to_numeric, errors='ignore')
if rename:
product = renameCols(product, tName)
product = self.selectionPushUp(product, tName, condition)
joinCols = joinCondition.split(" = ")
if tName == tableClause1[1]:
joinTableClause = tableClause2
joinTableCondition = pushupCondition2
if joinCols[0].split(".")[0] == tName:
baseColName = joinCols[0]
joinColName = joinCols[1].split(".")[1]
else:
baseColName = joinCols[1]
joinColName = joinCols[0].split(".")[1]
else:
joinTableClause = tableClause1
joinTableCondition = pushupCondition1
if joinCols[0].split(".")[0] == tName:
baseColName = joinCols[0]
joinColName = joinCols[1].split(".")[1]
else:
baseColName = joinCols[1]
joinColName = joinCols[0].split(".")[1]
joinTable, joinTName = loadJoinTable(product, joinTableClause, joinTableCondition, joinColName, baseColName)
joinTable = joinTable.apply(pd.to_numeric, errors='ignore')
if rename:
joinTable = renameCols(joinTable, joinTableClause[1])
joinTable = self.selectionPushUp(joinTable, joinTName, condition)
print(joinTable.shape)
print(product.shape)
product, log = self.join(product, joinTable, joinCondition, rename)
new = rewrite(condition)
rs = product[eval(new)]
elif len(tablesList) == 3:
#choose the base table
tableClause1 = tablesList[0].split(' ')
tableClause2 = tablesList[1].split(' ')
tableClause3 = tablesList[2].split(' ')
tableClauseList = [tableClause1, tableClause2, tableClause3]
pushupCondition1 = getPushupCondition(tableClause1[1], condition)
pushupCondition2 = getPushupCondition(tableClause2[1], condition)
pushupCondition3 = getPushupCondition(tableClause3[1], condition)
pushupConditionList = [pushupCondition1, pushupCondition2, pushupCondition3]
product, tName = chooseBaseTable3(tableClause1, pushupCondition1, tableClause2, pushupCondition2, tableClause3, pushupCondition3)
product = product.apply(pd.to_numeric, errors='ignore')
if rename:
product = renameCols(product, tName)
product = self.selectionPushUp(product, tName, condition)
joinConditions = joinCondition.split(",")
for jc in joinConditions:
if tName+'.' in jc:
firstJoinCondition = jc
if firstJoinCondition == joinConditions[0]:
secondJoinCondition = joinConditions[1]
else:
secondJoinCondition = joinConditions[0]
#process first join condition
joinCols = [i.strip() for i in firstJoinCondition.split(" = ")]
if tName+'.' in joinCols[0]:
joinTableName = joinCols[1].split('.')[0]
for t in tableClauseList:
if joinTableName in t:
joinTableClause = t
joinTableCondition = pushupConditionList[tableClauseList.index(joinTableClause)]
if tName+'.' in joinCols[0]:
baseColName = joinCols[0]
joinColName = joinCols[1].split(".")[1]
else:
baseColName = joinCols[1]
joinColName = joinCols[0].split(".")[1]
else:
joinTableName = joinCols[0].split('.')[0]
for t in tableClauseList:
if joinTableName in t:
joinTableClause = t
joinTableCondition = pushupConditionList[tableClauseList.index(joinTableClause)]
if tName+'.' in joinCols[0]:
baseColName = joinCols[0]
joinColName = joinCols[1].split(".")[1]
else:
baseColName = joinCols[1]
joinColName = joinCols[0].split(".")[1]
joinTable, joinTName = loadJoinTable(product, joinTableClause, joinTableCondition, joinColName, baseColName)
joinTable = joinTable.apply(pd.to_numeric, errors='ignore')
if rename:
joinTable = renameCols(joinTable, joinTableClause[1])
joinTable = self.selectionPushUp(joinTable, joinTName, condition)
product, log = self.join(product, joinTable, firstJoinCondition, rename)
print(product.shape)
#process second join condition
if log[0] in secondJoinCondition:
secondJoinCondition = secondJoinCondition.replace(log[0], log[1])
print(secondJoinCondition)
joinCols = [i.strip() for i in secondJoinCondition.split(" = ")]
if joinCols[0] in product.columns:
joinTableName = joinCols[1].split('.')[0]
else:
joinTableName = joinCols[0].split('.')[0]
for t in tableClauseList:
if joinTableName in t:
joinTableClause = t
joinTableCondition = pushupConditionList[tableClauseList.index(joinTableClause)]
if tName+'.' in joinCols[0]:
baseColName = joinCols[0]
joinColName = joinCols[1].split(".")[1]
else:
baseColName = joinCols[1]
joinColName = joinCols[0].split(".")[1]
print(joinTableClause)
print(joinTableCondition)
print(joinColName)
print(baseColName)
joinTable, joinTName = loadJoinTable(product, joinTableClause, joinTableCondition, joinColName, baseColName)
joinTable = joinTable.apply(pd.to_numeric, errors='ignore')
if rename:
joinTable = renameCols(joinTable, joinTableClause[1])
joinTable = self.selectionPushUp(joinTable, joinTName, condition)
product, onAttr = self.join(product, joinTable, secondJoinCondition, rename)
new = rewrite(condition)
rs = product[eval(new)]
return rs, distinctFlag
# check the existance of all tables
def CheckCSV(self, tables, orFlag):
tablesList = tables.split(',')
renameFlag = False
csvList = glob.glob(('*.csv'))
for t in tablesList:
if t:
t = t.strip()
if len(t.split(' ')) == 2:
renameFlag = True
if t.split(' ')[0] not in csvList:
print("csv file "+ t +" not found.")
return False, renameFlag
return True, renameFlag
# main method to process sql
def executeSQL(self, sql):
if sql == "show tables":
self.showTables()
elif "index " in sql:
source = sql.split(" ")[1:]
self.indexData(source)
else:
#try:
sqlList = decomposeOr(sql)
rsList = []
orFlag = len(sqlList)> 1
start = timeit.default_timer()
for sql_ in sqlList:
# get all the clauses from the sql statement
stmt = sqlparse.parse(sql_)[0]
tables = str(stmt.tokens[6])
selection = str(stmt.tokens[-1])
#check the select statement
if not str(stmt.tokens[2]) == "*":
attributes = str(stmt.tokens[2]).split(',')
attributes = [i.strip() for i in attributes]
else:
attributes = None
checkTables, rename = self.CheckCSV(tables, orFlag)
if checkTables:
# if no where clause, print out the whole table/s
if selection == tables:
tablesList = tables.split(',')
if len(tablesList) == 1:
idx = self.tables.index(tables)
print(self.data[idx])
else:
tablesList = [i.strip() for i in tablesList]
print(self.cartesian(tablesList))
else:
# get rid of "where" clause
condition = selection[6:]
rs, dFlag= self.executeSearch(condition, tables, rename)
# projection
if attributes:
if dFlag:
rsList.append(rs[attributes].drop_duplicates())
else:
rsList.append(rs[attributes])
else:
rsList.append(rs)
finalRs = reduce((lambda df, df2: df.append(df2)), rsList)
print(finalRs)
end = timeit.default_timer()
print("Runtime: " + str(end - start)[:6] + "s")
self.tables = []
self.data = []
#except Exception:
# print("Invalid sql statement")
# show all the tables loaded
def showTables(self):
print(self.tables)
# testing
if __name__ == "__main__" :
db = csvDB()
#
# db.loadData("movies.csv oscars.csv")
# sql1 = "SELECT m.movie_title FROM movies.csv m WHERE m.title_year=1999"
# sql2 = "SELECT movie_title, imdb_score FROM movies.csv WHERE movie_title like '%Harry Potter%'"
# sql3 = "SELECT m1.movie_title, m1.imdb_score FROM movies.csv m1 WHERE m1.movie_title like '%Harry Potter%' AND m1.title_year = 2001"
# sql4 = "SELECT M.title_year, M.movie_title, A.Award, M.imdb_score FROM movies.csv M, oscars.csv A WHERE M.imdb_score < 7 ON (M.movie_title = A.Film)"
# sql5 = "SELECT M.imdb_score FROM movies.csv M, oscars.csv A WHERE M.imdb_score < 7 ON (M.movie_title = A.Film)"
# sql6 = "SELECT M.imdb_score, A.Winner FROM movies.csv M, oscars.csv A WHERE M.imdb_score = (3.1 + A.Winner)*2 AND (M.language like 'S%' OR A.Winner = 1) ON (M.movie_title = A.Film)"
# sql7 = "SELECT M1.imdb_score, M2.imdb_score FROM movies.csv M1, movies.csv M2 WHERE M1.imdb_score > M2.imdb_score and M1.language like 'U%' ON (M1.movie_title = M2.movie_title, M1.title_year = M2.title_year)"
# sql8 = "SELECT M1.director_name, M1.title_year, M1.movie_title, M2.title_year, M2.movie_title, M3.title_year, M3.movie_title FROM movies.csv M1, movies.csv M2, movies.csv M3 WHERE M1.movie_title <> M2.movie_title AND M2.movie_title <> M3.movie_title AND M1.movie_title <> M3.movie_title AND M1.title_year < M2.title_year-10 AND M2.title_year < M3.title_year-10 ON (M1.director_name = M2.director_name, M1.director_name = M3.director_name)"
#
# db.executeSQL(sql7)
# columns:
# review: ['funny' 'user_id' 'review_id' 'text' 'business_id' 'stars' 'date' 'useful' 'cool']
# business: ['address' 'attributes_AcceptsInsurance' 'attributes_AgesAllowed'
# 'attributes_Alcohol' 'attributes_Ambience' 'attributes_BYOB'
# 'attributes_BYOBCorkage' 'attributes_BestNights' 'attributes_BikeParking'
# 'attributes_BusinessAcceptsBitcoin'
# 'attributes_BusinessAcceptsCreditCards' 'attributes_BusinessParking'
# 'attributes_ByAppointmentOnly' 'attributes_Caters' 'attributes_CoatCheck'
# 'attributes_Corkage' 'attributes_DietaryRestrictions'
# 'attributes_DogsAllowed' 'attributes_DriveThru'
# 'attributes_GoodForDancing' 'attributes_GoodForKids'
# 'attributes_GoodForMeal' 'attributes_HairSpecializesIn'
# 'attributes_HappyHour' 'attributes_HasTV' 'attributes_Music'
# 'attributes_NoiseLevel' 'attributes_Open24Hours'
# 'attributes_OutdoorSeating' 'attributes_RestaurantsAttire'
# 'attributes_RestaurantsCounterService' 'attributes_RestaurantsDelivery'
# 'attributes_RestaurantsGoodForGroups' 'attributes_RestaurantsPriceRange2'
# 'attributes_RestaurantsReservations' 'attributes_RestaurantsTableService'
# 'attributes_RestaurantsTakeOut' 'attributes_Smoking'
# 'attributes_WheelchairAccessible' 'attributes_WiFi' 'business_id'
# 'categories' 'city' 'hours_Friday' 'hours_Monday' 'hours_Saturday'
# 'hours_Sunday' 'hours_Thursday' 'hours_Tuesday' 'hours_Wednesday'
# 'is_open' 'latitude' 'longitude' 'name' 'neighborhood' 'postal_code'
# 'review_count' 'stars' 'state']
# photos: ['business_id' 'caption' 'label' 'photo_id']
# test
#db.convertData("review-1m.csv business.csv photos.csv")
sample1 = "SELECT R.review_id, R.stars, R.useful FROM r.csv R WHERE R.stars >= 4 AND R.useful > 20"
sample2 = "SELECT B.name, B.postal_code, R.review_id, R.stars, R.useful FROM business.csv B, r.csv R WHERE B.city = 'Champaign' AND B.state = 'IL' ON (B.business_id = R.business_id)"
sample3 = "SELECT B.name, B.city, B.state, R.stars, P.label FROM business.csv B, r.csv R, photos.csv P WHERE B.city = 'Champaign' AND B.state = 'IL' AND R.stars = 5 AND P.label = 'inside' ON (B.business_id = R.business_id, B.business_id = P.business_id) "
a1 = "SELECT R.review_id, R.funny, R.useful FROM r.csv R WHERE R.funny >= 20 AND R.useful > 30"
a2 = "SELECT B.name, B.city, B.state FROM business.csv B WHERE B.city = 'Champaign' AND B.state = 'IL'"
b1 = "SELECT B.business_id, B.name, B.postal_code, R.stars, R.useful FROM business.csv B, r.csv R WHERE B.name = 'Sushi Ichiban' AND B.postal_code = '61820' ON (B.business_id = R.business_id)"
b2 = "SELECT R1.user_id, R2.user_id, R1.stars, R2.stars FROM r.csv R1, r.csv R2 WHERE R1.stars = 5 AND R2.stars = 1 AND R1.useful > 50 AND R2.useful > 50 ON (R1.business_id = R2.business_id)"
c3 = "SELECT B.name, R1.user_id, R2.user_id FROM business.csv B, r.csv R1, r.csv R2 WHERE R1.stars = 5 AND R2.stars = 1 AND R1.useful > 50 AND R2.useful > 50 ON (B.business_id = R1.business_id, B.business_id = R2.business_id) "
c4 = "SELECT B.name, R1.user_id, R2.user_id FROM business.csv B, r.csv R1, r.csv R2 WHERE R1.stars = 5 AND R2.stars = 1 AND B.city = 'Champaign' ON (B.business_id = R1.business_id, B.business_id = R2.business_id) DISTINCT"
testor1 = "SELECT B.business_id, B.name, B.postal_code, R.stars, R.useful FROM business.csv B, review-1m.csv R WHERE (B.postal_code = '44114' OR B.postal_code = '61820') ON (B.business_id = R.business_id)"
testor2 = "SELECT B.business_id, B.name, B.postal_code, R.stars, R.useful FROM business.csv B, review-1m.csv R WHERE (B.postal_code = '44114' OR R.stars = 5) ON (B.business_id = R.business_id)"
testor3 = "SELECT B.business_id, B.name, B.postal_code, R.stars, R.useful FROM business.csv B, review-1m.csv R WHERE R.useful > 50 AND (B.postal_code = '44114' OR R.stars = 5) ON (B.business_id = R.business_id)"
sql4 = "SELECT B.name, B.postal_code, R.review_id, R.stars, R.useful FROM business.csv B, r.csv R, photos.csv P WHERE P.label = 'outside' AND R.useful > 20 ON (B.business_id = R.business_id, B.business_id = P.business_id)"
tests = [sample2]
# tests = [testor1, testor2, testor3]
for s in tests:
db.executeSQL(s)
#db.executeSQL(sql4)