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Submit You do not need to export or convert the database - simply upload the .sqlite file that your program creates. See the example code for the use of the connect() statement. Counting Organizations This application will read the mailbox data (mbox.txt) and count the number of email messages per organization (i.e. domain name of the email address) using a database with th following schema to maintain the counts. CREATE TABLE Counts (org TEXT, count INTEGER) When you have run the program on mbox.txt upload the resulting database file above for grading. If you run the program multiple times in testing or with dfferent files, make sure to empty out the data before each run. You can use this code as a starting point for your application: http://www.py4e.com/code3/emaildb.py Z. The data file for this application is the same as in previous assignments: http://www.py4e.com/code3/mbox.txt . Because the sample code is using an UPDATE statement and committing the results to the database as each record is read in the loop, it might take as long as a few minutes to process all the data. The commit insists on completely writing all the data to disk every time it is called. The program can be speeded up greatly by moving the commit operation outside of the loop. In any database program, there is a balance between the number of operations you execute between commits and the importance of not losing the results of operations that have not yet been committed.

Submit You do not need to export or convert the database - simply upload the .sqlite file that your program creates. See the example code for the use of the connect() statement. Counting Organizations This application will read the mailbox data (mbox.txt) and count the number of email messages per organization (i.e. domain name of the email address) using a database with th following schema to maintain the counts. CREATE TABLE Counts (org TEXT, count INTEGER) When you have run the program on mbox.txt upload the resulting database file above for grading. If you run the program multiple times in testing or with dfferent files, make sure to empty out the data before each run. You can use this code as a starting point for your application: http://www.py4e.com/code3/emaildb.py Z. The data file for this application is the same as in previous assignments: http://www.py4e.com/code3/mbox.txt . Because the sample code is using an UPDATE statement and committing the results to the database as each record is read in the loop, it might take as long as a few minutes to process all the data. The commit insists on completely writing all the data to disk every time it is called. The program can be speeded up greatly by moving the commit operation outside of the loop. In any database program, there is a balance between the number of operations you execute between commits and the importance of not losing the results of operations that have not yet been committed.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Hint: The top organizational count is 536. Submit You do not need to export or convert the database - simply upload the .sqlite file that your program creates. See the example code for the use of the connect() statement. Counting Organizations This application will read the mailbox data (mbox.txt) and count the number of email messages per organization (i.e. domain name of the email address) using a database with the following schema to maintain the counts. CREATE TABLE Counts (org TEXT, count INTEGER) When you have run the program on mbox.txt upload the resulting database file above for grading. If you run the program multiple times in testing or with dfferent files, make sure to empty out the data before each run. You can use this code as a starting point for your application: http://www.py4e.com/code3/emaildb.pyZ. The data file for this application is the same as in previous assignments: http://www.py4e.com/code3/mbox.txt Z. Because the sample code is using an UPDATE statement and committing the results to the database as each record is read in the loop, it might take as long as a few minutes to process all the data. The commit insists on completely writing all the data to disk every time it is called. The program can be speeded up greatly by moving the commit operation outside of the loop. In any database program, there is a balance between the number of operations you execute between commits and the importance of not losing the results of operations that have not yet been committed.
Transcribed Image Text:Hint: The top organizational count is 536. Submit You do not need to export or convert the database - simply upload the .sqlite file that your program creates. See the example code for the use of the connect() statement. Counting Organizations This application will read the mailbox data (mbox.txt) and count the number of email messages per organization (i.e. domain name of the email address) using a database with the following schema to maintain the counts. CREATE TABLE Counts (org TEXT, count INTEGER) When you have run the program on mbox.txt upload the resulting database file above for grading. If you run the program multiple times in testing or with dfferent files, make sure to empty out the data before each run. You can use this code as a starting point for your application: http://www.py4e.com/code3/emaildb.pyZ. The data file for this application is the same as in previous assignments: http://www.py4e.com/code3/mbox.txt Z. Because the sample code is using an UPDATE statement and committing the results to the database as each record is read in the loop, it might take as long as a few minutes to process all the data. The commit insists on completely writing all the data to disk every time it is called. The program can be speeded up greatly by moving the commit operation outside of the loop. In any database program, there is a balance between the number of operations you execute between commits and the importance of not losing the results of operations that have not yet been committed.
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