The following step by step instructions will guide you to process the tracking log files and get into MOOCdb format, we provide the following detailed instructions. First step is to install a machine with required packages and disk space.
Step 1: Installing the required packages or downloading a VM¶
Option 1: To run the software you may want to download a VM from Amazon cloud.
This virtual machine image comes with all packages installed which are required to run the MOOCdb pipeline. To get a link to the image and request the ‘.pem’ file, please email email@example.com.
- When instantiating this virtual machine on Amazon or locally, please provision the disk space (hard disk) such that there is atlease three times the size of the decrypted- uncompressed file size of the tracking logs.
Option 2: Install all the packages on your local machine
The following packages are required on a MOOCdb machine
- Install Unidecode package available here
- Install ijson package which can be found here
- Install python-setuptools
- Install pip using sudo easy_install pip
- Install pandas
- Download the code from MOOCdb github:
Make sure your Pandas version is higher than 0.14.0. If it is below that you would have to update Pandas by running:
pip install pandas –upgrade
You may have to upgrade numpy and numexpr before upgrading pandas, if upgrading pandas gives you an error. The command to upgrade numpy and numexpr is the same:
pip install numpy –upgrade
pip install numexpr –upgrade
Step 2: Processing the tracking logs¶
If your course is through edX you would get the files shown below. The most important and perhaps most tedious is processing the tracking log files. Some of the files listed below in the table could be representative of what MIT delivers to us. But tracking_log.json is the largest file and contains the detailed clickstream events. These are the events which are recorded along with event type.
Unzip tracking log file:
All raw data files in ‘data/raw/<course_name>’ have the same prefix in the format of ‘<course_name>__<creation date>‘, we will call the prefix ‘COURSE_PREFIX‘.
From within the tracking log file folder, run the command:
gzip -d COURSE_PREFIX__tracking_log.json.gz
This will extract the tracking log file into .json format, ready to be piped.
If there are multiple log files, merge all the log files for a single course into one log file.
Run JSON to relation code (a.k.a apipe):
This tutorial covers the transfer of JSON tracking log file to CSV files. The code is written by Andreas Paepcke from Stanford. JSON tracking log file is stored with other raw data files. We will call the raw data files as ‘raw data‘ and the output CSV as ‘intermediary CSV‘.
Let us suppose that we want to pipe the course named ‘<course_name>‘. We assume that the raw data is stored in the folder:
Create a folder called intermeidary_csv under the folder named ‘<course_name>‘
Create another folder called moocdb_csv under the folder named ‘<course_name>‘
Launch the piping:
From within the import.openedx.json_to_relation folder, run command:
bash scripts/transformGivenLogfiles.sh /.../<course_name>/intermediary_csv/
As show in the command above, transfromGivenLogFiles.sh takes two arguments. First argument is the path to the destination folder, and second argument is the tracking log json file to pipe. ‘/.../‘ represents the path to the directory where the <course_name> folder is located on your machine. The command may run for a few hours and depends on the size of the raw json tracking log file.The output csv files will be in ‘/.../<course_name>/intermediary_csv‘. The following gives an example of the output csv files produced for link5_10x course:link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_ABExperimentTable.csv link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_AccountTable.csv link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_AnswerTable.csv link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_CorrectMapTable.csv link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_EdxTrackEventTable.csv link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_EventIpTable.csv link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_InputStateTable.csv link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_LoadInfoTable.csv link5_10x_trace_merged.2014-11-02T23_46_45.622627_28028.sql_StateTable.csv
Run relation to MOOCdb (a.k.a qpipe):
This tutorial covers the transfer of CSV files as output by Andreas Paepcke’s json_to_relation to MOOCdb CSV files. We will call the source CSV as ‘intermediary CSV‘ and the output CSV as ‘MOOCdb CSV‘.
Let us suppose that we want to pipe to MOOCdb the course named ‘<course_name>’. We assume that the course’s log file has been processed by json_to_relation, and that the output files are stored in the folder :
We want the MOOCdb CSV to be written to folder
QUOTECHAR: The quote character used in the intermediary CSV files. Most commonly a single quote (‘)
TIMESTAMP_FORMAT: describes the timestamp pattern used in ‘*_EdxTrackEventTable.csv‘ intermediary CSV file. See python doc to understand syntax.
COURSE_NAME: The name of the folder containing the intermediary CSV files. Here it is ‘<course_name>’.
CSV_PREFIX: All the intermediary CSV file names in ‘/.../<course_name>/intermediary_csv/‘ folder share a common prefix that was generated when running JSON to relation. This prefix is also the name of the only ‘.sql’ file in the folder. For example, in the above case this prefix would be :
The variables not mentioned in the tutorial must simply be left untouched.
Launch the piping:
When the variables mentioned above have been correctly edited in config.py, the script is ready to launch. From within the import.openedx.qpipe folder, run the command:
time python main.py
Delete log file:
When the piping is done, if everything went well, go to the output directory ‘/.../<course_name>/moocdb_csv/‘ and delete the ‘log.org‘ file that takes a lot of space.
Load course into MySQL:
Copy the file ‘/.../<course_name>/moocdb_csv/6002x_2013_spring/moocdb.sql‘ to ‘/.../<course_name>/moocdb_csv/‘ folder. Change directory to ‘/.../<course_name>/moocdb_csv/‘. Replace ‘6002x_spring_2013’ by ‘<course_name>’ in moocdb.sql file.
Run the command:
mysql -u root -p --local-infile=1 < moocdb.sql
This creates a database named ‘<course_name>‘ in MySQL, and loads the CSV data into it.