Benchmarks on Count Agregate for big data

# -*- coding: utf- 8 -*-
# Python 2.X


import time
import psycopg2

con = psycopg2.connect(
  database="test",
  user="postgres",
  password="",
  host="127.0.0.1",
  port="5432"
)

cur = con.cursor()

cur.execute('''CREATE TABLE IF NOT EXISTS test
     (id SERIAL PRIMARY KEY NOT NULL,
     name TEXT NOT NULL,
     age INT NOT NULL,
     department CHAR(50));''')


a = time.clock()


# for i in range(1000000): cur.execute("INSERT INTO test (name,age,department) VALUES ('John', 18, 'ICT')")
# con.commit()

##cur.execute("SELECT name, age, department FROM test WHERE id=300000"); rows = cur.fetchall()
##cur.execute("SELECT name, age, department FROM test WHERE id<30"); rows = cur.fetchall()
cur.execute("SELECT COUNT(*) FROM test WHERE id<100000 GROUP BY age"); rows = cur.fetchall()
##cur.execute("show data_directory;"); rows = cur.fetchall()

print time.clock() - a

for u in rows: print u[0]


'''
1 mln notes ~= 100-120 mb/ Count
speed for select for table^1000 notes ~= 3ms
speed for select for table^1000000 notes ~= 100ms
'''


con.close()

print('ok')