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A common system for sending user action logging data from various applications into visualization application CODAP after applying transformations


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Data Analytics Log Manager

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Data Analytics Log Manager helps collect log data and provides tools to analyze this data.

It provides a lot of flexibility on the type of log data. A single log entry can contain multiple arbitrary key value pairs (Let's say "application" => "Test App", "event" => "Clicked on Wiki" and so on). The analytics tools are powerful as well as easy to use. We aim to make complex analytics easy for not only programmers but also other users such as researchers, managers, designers etc. who may not have prior exposure to programming.

Data Analytics Log Manager provides the following:

  1. An API for receiving log data from multiple applications and storing them in a database (currently PostgreSQL).

  2. Tools to analyse this data. It is possible to filter the data, create a parent-child relationship (by let's say grouping the data by username and visualizing logs for various usernames as child tables), add synthetic/computed data (let's say the number of times the user performed a particular action) etc.

More information about the project is available on wiki.

You may find it useful if you have an application for which you would like to visualize how your users are using it and find patterns hidden in the data.


Existing Web Analytics tools, like Google Analytics, do not provide logging data at the individual-user data, and so are not usable for certain kinds of analytics. Many of the projects would like to capture detailed logs of actions users take in browser-based activities. The application will act as a shared tool for logging the data, transforming it and using CODAP for visualization.

Project Setup, Docker Compose option

  1. Clone the Log Manager repo. Optionally give the clone directory a more friendly name.

E.g. git clone cc-log-manager

  1. From the root directory of the cloned repo, run:

docker-compose up

  1. By default, the UI should be available to a browser at http://localhost:3000/

Project Setup, Docker Fig option

This project includes a Dockerfile and a Fig (now Docker Compose) configuration that allows you to use containers based on prebuilt Rails and Postgres images, rather than having to install software locally on your machine. (However, production deploys use Heroku rather than Docker images you build.)

  1. Install and run Docker. Mac users will need to install and run [boot2docker] ( as well. Mac users can install these via Homebrew.
  2. Install fig. Note: Docker Compose is an updated version of Fig, so the steps below should work much the same with Compose. However, these instructions were developed using Fig.
  3. Run fig build in the root of this project. This will pull the required images, run bundle install, etc.
  4. To start the server and database containers, run fig up in the root of the project
  5. Initialize the database: fig run web bin/rake db:create db:migrate
  6. If you are using boot2docker, you will need to find the IP address of the server, like this: boot2docker ip
  7. Visit http://localhost:3000/ (or http://<boot2docker ip>:3000/, if appropriate) to run the app

The Fig configuration mounts the project folder as app root in the container. Thus, there is no special step to get your changes into the container (and running the app does not write to the web container's filesystem; logs and caches go into the mounted project folder, and database changes persist in the db container). Of course, you may need to fig restart web after making changes. However, gems are stored in the container, and there is a special step required when updating the Gemfile:

How to bundle install in the Docker container without rebuilding

The default Rails image (rails:onbuild) freezes the bundle for deployment. If you try to change the Gemfile and run

fig run web bundle install

to update Gemfile.lock and install the new gems into the web container, Bundler will tell you to try again with the --no-deployment flag. It's all a lie. Here's how to run bundle install:

  1. fig run web /bin/bash -c "bundle config frozen 0 && bundle install && bundle config frozen 1"
  2. docker ps -a and make a note of the name of the container that just stopped (each fig run web happens in a new container that "forks" the base web container)
  3. docker commit dataanalyticslogmanager_web_run_1 dataanalyticslogmanager_web (where dataanalyticslogmanager_web_run_1 is assumed to be the name of the recently run container, and dataanalyticslogmanager_web the image it uses). This step is necessary so that the next time Fig starts the web container, it uses the saved state of the fig run web step.
  4. You can now docker rm dataanalyticslogmanager_web_run_1

If Fig gets confused

In order to get Fig to work correctly, I had to rename the project directory to data_analytics_log_manager; Fig got confused by the mixed case. This may have been fixed by now.

Project Setup, local install (no Docker)

Install the following:

  1. Rails 4.1+
  2. PostgreSQL 9.2+

Clone the repository:

git clone

Run the following commands:

cd Data-Analytics-Log-Manager/
cp config/database.sample.yml config/database.yml
bundle install
npm install

Setup the database:

Setup the config/database.yml file to allow the Rails application to connect to PostgreSQL.

Either update the username and password with your existing user's value or create a new user in PostgreSQL having username:log_manager and password:log_manager. Now, create database log_manager_development and grant this user all privileges on log_manager_development database or make it a superuser.

Run migrations on database:

rake db:migrate

Run application on localhost:

rails server

To run rails console:

rails console

Configure Mailer:

A user can use Log Manager only after confirming the account. By default, in development emails will be placed in files in tmp/mails.

If you would like to configure a more complex setup, set the following environment variables (see config/initializers/actionmailer.rb):

# Required
ENV["MAILER_DOMAIN_NAME"]     # your server's domain name
ENV["MAILER_SMTP_HOST"]       # the SMTP host to send mail through
ENV["MAILER_SMTP_USER_NAME"]  # the SMTP username you want to use
ENV["MAILER_SMTP_PASSWORD"]   # the SMTP password you want to use

ENV["MAILER_DELIVERY_METHOD"]             # defaults to :file, for production set it to "smtp"
ENV["MAILER_SMTP_PORT"]                   # defaults to 587
ENV["MAILER_SMTP_STARTTLS_AUTO"]          # defaults to true


See the Getting Started Guide for Developers for more information about the technical working of Data Analytics Log Manager.

The style guide for the project is available here.

Feel free to fork the repository and hack on it! Implemented some feature? Well, just send a pull request. Facing problems setting things up or thought of a feature that may be useful for everybody? Contact us or open github issues for questions, bugs, feature requests etc.

Not sure how to start? Email us and let us know you're interested, and what you can do, and we'll figure out something you can help with.


Data Analytics Log Manager is released under the MIT License.


Data Analytics Log Manager started as a Google Summer of Code Project. The project was done by Peeyush Agarwal and The Concord Consortium was the mentoring organization.


A common system for sending user action logging data from various applications into visualization application CODAP after applying transformations







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