Text Processing for Programmers

I was reading a blog about coding interviews, and one comment made near the bottom struck me, “…“Um… grep?” then they’re probably OK…”  As I read that comment, I realized I’d never answer that way, and I agreed with the author that was a problem. That began my dabble in grep, awk and sed, and these tools will change your workflow and even how you think about profiling code.  Grep has even become a verb in my daily life, “Is this greppable?” is my mantra.  Flash forward a few months and once again I had a task for these powerful text processing tools, convert a mysql database to sqlite. Sounds easy, but with file sizes of >700MB, you have to be efficient.

As part of a machine learning project for a graduate class I’m using the enron email public dataset. This dataset has been further processed and cleaned at Carnagie Mellon. This dataset is so valuable because it is real world email from a functioning orginazation. This dataset is used in human factors research, machine learning, and as in my usecase, data security. I downloaded the mysql version and since I intended to use Python to do my processing I wanted to convert it to sqlite. [suffusion-adsense client=‘ca-pub-6284398857369558’ slot=‘8519108503’ width=‘300’ height=‘250’]

My basic process is:

  1. Import the dataset into a mysql database

  2. Use this gist to dump the database into sqlite.

Cool. So Step 1.

jwright@ubuntu:~$ mysql -u root -p -h localhost enron < enron-mysqldump.sql 
Enter password: 
ERROR 1064 (42000) at line 10: You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'TYPE=MyISAM' at line 8
jwright@ubuntu:~$ grep enron-mysqldump.sql 'TYPE-MyISAM'
grep: TYPE-MyISAM: No such file or directory

Damn. Well, lets see what the problem is… Remember this file is >700 MB so I don’t want to just open it in notepad.

jwright@ubuntu:~$ grep 'TYPE=MyISAM' enron-mysqldump.sql

Oh. That’s greppable, awesome.

sed 's/TYPE=MyISAM/engine=myisam/g' enron-mysqldump.sql > enron-mysqldump_filtered.sql

Now we have a clean file for import.

mysql -u root -p -h localhost enron < enron-mysqldump_filtered.sql
./mysql2sqlite.sh -u root -p enron | sqlite3 enron.db

732MB database converted in just a few minutes. Mostly just I/O time. I believe all good programmers show know these tools. I know personally, when I have to export data for profiling or metrics, I do it in a way that I can easily filter with awk, or sed to a format octave can process. Automating measurements will dreastically decrease your cycle time and reduce mistakes.

So “Um… grep?”

Hell yes grep!