Here are some results from a little testing I recent did on extracting data from an Oracle database and what effect the arraysize makes and which method might be the quickest.
The arraysize determines how many records will be retrieved in each each batch. When a query is issued to the database, the results are returned to the calling programme in batches of a certain size. Depending on the nature of the application and the number of records being retrieved, will determine the arraysize value. The value of this can have a dramatic effect on your query and application response times. Sometimes a small value works very well but sometimes you might need a larger value.
My test involved using an Oracle Database Cloud instance, using Python and the following values for the arraysize.
arraysize = (5, 50, 500, 1000, 2000, 3000, 4000, 5000)
The first test was to see what effect these arraysizes have on retrieving all the data from a table. The in question has 73,668 records. So not a large table. The test loops through this list of values and fetches all the data, using the fetchall function (part of cx_Oracle), and then displays the time taken to retrieve the results.
# import the Oracle Python library
import cx_Oracle
import datetime
import pandas as pd
import numpy as np
# setting display width for outputs in PyCharm
desired_width = 280
pd.set_option('display.width', desired_width)
np.set_printoptions(linewidth=desired_width)
pd.set_option('display.max_columns',30)
# define the login details
p_username = "************"
p_password = "************"
p_host = "************"
p_service = "************"
p_port = "1521"
print('--------------------------------------------------------------------------')
print(' Testing the time to extract data from an Oracle Database.')
print(' using different approaches.')
print('---')
# create the connection
con = cx_Oracle.connect(user=p_username, password=p_password, dsn=p_host+"/"+p_service+":"+p_port)
print('')
print(' Test 1: Extracting data using Cursor for different Array sizes')
print(' Array Size = 5, 50, 500, 1000, 2000, 3000, 4000, 5000')
print('')
print(' Starting test at : ', datetime.datetime.now())
beginTime = datetime.datetime.now()
cur_array_size = (5, 50, 500, 1000, 2000, 3000, 4000, 5000)
sql = 'select * from banking_marketing_data_balance_v'
for size in cur_array_size:
startTime = datetime.datetime.now()
cur = con.cursor()
cur.arraysize = size
results = cur.execute(sql).fetchall()
print(' Time taken : array size = ', size, ' = ', datetime.datetime.now()-startTime, ' seconds, num of records = ', len(results))
cur.close()
print('')
print(' Test 1: Time take = ', datetime.datetime.now()-beginTime)
print('')
And here are the results from this first test.
Starting test at : 2018-11-14 15:51:15.530002
Time taken : array size = 5 = 0:36:31.855690 seconds, num of records = 73668
Time taken : array size = 50 = 0:05:32.444967 seconds, num of records = 73668
Time taken : array size = 500 = 0:00:40.757931 seconds, num of records = 73668
Time taken : array size = 1000 = 0:00:14.306910 seconds, num of records = 73668
Time taken : array size = 2000 = 0:00:10.182356 seconds, num of records = 73668
Time taken : array size = 3000 = 0:00:20.894687 seconds, num of records = 73668
Time taken : array size = 4000 = 0:00:07.843796 seconds, num of records = 73668
Time taken : array size = 5000 = 0:00:06.242697 seconds, num of records = 73668
As you can see the variation in the results.
You may get different performance results based on your location, network connectivity and proximity of the database. I was at home (Ireland) using wifi and my database was located somewhere in USA. I ran the rest a number of times and the timings varied by +/- 15%, which is a lot!
When the data is retrieved in this manner you can process the data set in the returned results set. Or what is more traditional you will want to work with the data set as a panda. The next two test look at a couple of methods of querying the data and storing the result sets in a panda.
For these two test, I'll set the arraysize = 3000. Let's see what happens.
For the second test I'll again use the fetchall() function to retrieve the data set. From that I extract the names of the columns and then create a panda combining the results data set and the column names.
startTime = datetime.datetime.now()
print(' Starting test at : ', startTime)
cur = con.cursor()
cur.arraysize = cur_array_size
results = cur.execute(sql).fetchall()
print(' Fetched ', len(results), ' in ', datetime.datetime.now()-startTime, ' seconds at ', datetime.datetime.now())
startTime2 = datetime.datetime.now()
col_names = []
for i in range(0, len(cur.description)):
col_names.append(cur.description[i][0])
print(' Fetched data & Created the list of Column names in ', datetime.datetime.now()-startTime, ' seconds at ', datetime.datetime.now())
The results from this are.
Fetched 73668 in 0:00:07.778850 seconds at 2018-11-14 16:35:07.840910
Fetched data & Created the list of Column names in 0:00:07.779043 seconds at 2018-11-14 16:35:07.841093
Finished creating Dataframe in 0:00:07.975074 seconds at 2018-11-14 16:35:08.037134
Test 2: Total Time take = 0:00:07.975614
Now that was quick. Fetching the data set in just over 7.7788 seconds. Creating the column names as fractions of a millisecond, and then the final creation of the panda took approx 0.13 seconds.
For the third these I used the pandas library function called read_sql(). This function takes two inputs. The first is the query to be processed and the second the name of the database connection.
print(' Test 3: Test timing for read_sql into a dataframe')
cur_array_size = 3000
print(' will use arraysize = ', cur_array_size)
print('')
startTime = datetime.datetime.now()
print(' Starting test at : ', startTime)
df2 = pd.read_sql(sql, con)
print(' Finished creating Dataframe in ', datetime.datetime.now()-startTime, ' seconds at ', datetime.datetime.now())
# close the connection at end of experiments
con.close()
and the results from this are.
Test 3: Test timing for read_sql into a dataframe will use arraysize = 3000
Starting test at : 2018-11-14 16:35:08.095189
Finished creating Dataframe in 0:02:03.200411 seconds at 2018-11-14 16:37:11.295611
You can see that it took just over 2 minutes to create the panda data frame using the read_sql() function, compared to just under 8 seconds using the previous method.
It is important to test the various options for processing your data and find the one that works best in your environment. As with most languages there can be many ways to do the same thing. The challenge is to work out which one you should use.
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