Log Analyzer

Analyzes a server's log files to uncover most popular posts and most popular authors.

Motivation

This is a project from Udacity's Full Stack Nanodegree program. Its intent is to show how SQL can be used to analyze a website log.

Getting Started

Provided you'll find a Vagrant environment with all dependencies.

Prerequisites

To run Vagrant, you will need to install Vagrant and VirtualBox.

Installing

From within the project root:

1. Start Vagrant environment

$ cd vagrant
$ vagrant up

It can take a little while for this to complete since it needs to download and install everything 2. SSH into Vagrant environment:

$ vagrant ssh

3. Change to project directory

$ cd /vagrant

4. Load data into the news database. A test log is included:

$ psql -d news -f newsdata.sql

5. Create database views:

$ python3 create_db_views.py

6. Run the analyzer:

$ python3 log_analyzer.py

If all works, you should get 3 tables like this:

----------------Popular Articles-----------------

 Candidate is jerk, alleges rival | 338647 views
-------------------------------------------------
 Bears love berries, alleges bear | 253801 views
-------------------------------------------------
 Bad things gone, say good people | 170098 views

Design

Data is retrieved from the database using 3 functions:

# Returns 3 most popular articles
get_three_popular_articles()

# Returns a list of all authors, in order of popularity (number of views)
get_popular_authors()

# Returns the percentage of errors (404 errors / total request) grouped by day
# min is the minimum error percentage to filter
get_error_data(min=0)

There is also a helper module called table_maker to handle formatting the data

Built With

• psycopg

Acknowledgments

Original virtual machine from Udacity