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query_processing.py
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/
query_processing.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
import pickle
import time
from argparse import ArgumentParser
import psycopg2 as postgres
import numpy as np
import pandas as pd
def generate_queries(cur, n_queries, min_max, encoders, cube=False):
SQL_set = set()
SQL0_set = set()
SQL = []
cardinalities = []
sql_body = """SELECT count(*) FROM {} WHERE {}"""
if cube:
sql_body_cube = """SELECT COALESCE(SUM(count), 0) FROM {}_cube WHERE {}"""
total_columns = len(min_max)
vectors = np.ndarray((n_queries, total_columns*4))
columns = list(sorted(min_max.keys()))
count = 0
while len(SQL) < n_queries:
num_of_predictates = np.random.choice(range(1,total_columns+1))
selected_predicates = np.random.choice(range(total_columns), size=num_of_predictates, replace=False)
selected_predicates = [columns[i] for i in selected_predicates]
selected_values = []
for pred in selected_predicates:
if pred in encoders.keys():
sel = np.random.randint(len(encoders[pred].classes_))
sel = encoders[pred].classes_[sel]
else:
choices = [-1] + list(np.arange(min_max[pred][0], min_max[pred][1]+1, min_max[pred][2]))
sel = np.random.choice(choices)
selected_values.append(sel)
#[[0,0,1], [0,1,0], [1,0,0], [1,0,1], [0,1,1], [1,1,0]]
# <>=
#selected_operators = np.random.choice(["=", ">", "<", "<=", ">=", "!="], size=num_of_predictates)
selected_operators = np.random.choice(["=", ">", "<", "<=", ">=", "!="], size=num_of_predictates)
#selected_operators = ["=" if "id" not in sp else np.random.choice(["=", ">", "<", "<=", ">=", "!="])
# for sp in selected_predicates]
#selected_operators = [np.random.choice(["IS", "IS NOT"]) if selected_values[i] == "-1"
# or selected_values == -1 else x for i,x in enumerate(selected_operators)]
selected_operators = ["IS" if selected_values[i] == "-1" or selected_values[i] == -1 else x
for i,x in enumerate(selected_operators)]
if cube:
sql = sql_body_cube.format(config["view_name"], " AND ".join([" ".join([str(p), str(o), str(v) if not isinstance(v,str) or v == "-1"
else "'{}'".format(v)]) for p,o,v in zip(selected_predicates,
selected_operators,
selected_values)]))
else:
sql = sql_body.format(config["view_name"], " AND ".join([" ".join([str(p), str(o), str(v) if not isinstance(v,str) or v == "-1"
else "'{}'".format(v)]) for p,o,v in zip(selected_predicates,
selected_operators,
selected_values)]))
check_len = len(SQL_set)
sql = sql.replace("-1", "NULL")
SQL_set.add(sql)
if check_len != len(SQL_set) and sql not in SQL0_set:
cur.execute(sql)
card = cur.fetchone()[0]
if card > 0:
SQL.append(sql)
cardinalities.append(card)
vectors[len(SQL)-1] = vectorize_query(sql, min_max, encoders)
else:
SQL0_set.add(sql)
SQL_set.remove(sql)
count += 1
print("Had to generate {} queries.".format(count))
return SQL, vectors, cardinalities
def vectorize_query(query_str, min_max, encoders):
query_str = query_str.replace("NULL", "-1").replace("IS NOT", "!=")
total_columns = len(min_max)
vector = np.zeros(total_columns*4)
predicates = query_str.split("WHERE", maxsplit=1)[1]
operators = {
"=": [0,0,1],
">": [0,1,0],
"<": [1,0,0],
"<=": [1,0,1],
">=": [0,1,1],
"!=": [1,1,0],
"IS": [0,0,1]
}
for exp in predicates.split("AND"):
exp = exp.strip()
pred, op, value = exp.split(" ")
if pred in encoders.keys():
value = encoders[pred].transform([value.replace("'", "")])[0]
else:
value = max(min_max[pred][0], float(value))
idx = list(sorted(min_max.keys())).index(pred)
vector[idx*4:idx*4+3] = operators[op]
vector[idx*4+3] = (value-min_max[pred][0]+min_max[pred][2])/(min_max[pred][1]-min_max[pred][0]+min_max[pred][2])
return vector
if __name__ == '__main__':
parser = ArgumentParser(description='Query processing for local models')
parser.add_argument("-v", "--vectorize", type=str, help="just vectorize queries without sampling them", default=0)
args = parser.parse_args()
with open("config.json", "r") as config_file:
config = json.load(config_file)
with open("min_max_{}.json".format(config["dbname"]), "r") as mm_file, \
open("encoders_{}.pkl".format(config["dbname"]), "rb") as enc_file:
minmax = json.load(mm_file)
encoder = pickle.load(enc_file)
if args.vectorize:
queries = pd.read_csv(config["query_file"])
vectors = np.ndarray((len(queries), len(minmax)*4))
start = time.time()
i = 0
for q in queries["SQL"]:
vectors[i] = vectorize_query(q, minmax, encoder)
i += 1
end = time.time() - start
print("Vectorized {} queries in {:.2f}s.".format(len(queries), end))
vectors = np.column_stack([vectors, queries["cardinality"]])
np.save(config["vector_file"], vectors)
else:
# change login data accordingly
conn = postgres.connect("dbname=imdb user=postgres password=postgres")
conn.set_session(autocommit=True)
cur = conn.cursor()
print("Sampling queries...")
start = time.time()
queries, vectors, card = generate_queries(cur, config["number_of_queries"], minmax, encoder, config["optim"])
end = time.time() - start
print("Sampled {} queries in {:.2f}s.".format(len(queries), end))
vectors = np.column_stack([vectors, card])
np.save(config["vector_file"], vectors)
csv = pd.DataFrame({"SQL": queries, "cardinality": card})
csv["cardinality"] = csv["cardinality"].astype(int)
csv.to_csv(config["query_file"], index=False)
conn.close()