Resume Processor for the MetPlus project
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Resume Processor for the MetPlus project

Requirements on development environment

  • Java SDK version 8
  • MongoDB


  1. Clone this repository by using the command

git clone resumeCruncher

  1. Enter the source folder (All commands in this document assume you are currently inside the source folder directory)

cd resumeCruncher


In order to install and run the application you need to have access to one mongo instance to save the cruncher information. You can use a Local Mongo instance, a Docker container, a Virtual Machine or a database as a Service like mlab

Examples of installation of mongo

In the following points you there is more information on how to create a mongo database

(Example) Local mongo database

Follow the instruction to on to install in your operating system the database

When this is done jump to the mongo database configuration section

(Example) Mongo in docker container

Requirement for this options is to have Docker installed in your system.

1 - Create docker image:

docker run -it -p 27017:27017 --name pets-mongo -d mongo --noauth

This command creates a new image called pets-mongo and publishes the mongo port so we can access it from the host machine

2 - Access the mongo instance to create the user and the database

docker exec -it pets-mongo mongo resumeCruncher

Mongo configuration

Before we can start the Cruncher we need to create a database to connect to, and a user for authentication. To accomplish that connect to the Mongo instance using mongo CLI or a a visual app like robomongo and execute the following:

 use resumeCruncher
 db.createUser({user: 'testing_user', pwd: 'testing_user', roles: [{role: 'readWrite', db: 'resumeCruncher'}]});

Configuration files

  • app/src/main/resources/application.yml

    In this file the application configuration is stored

    1. Tomcat configuration
    2. Log level
    3. Database connection information
    4. Default cruncher configuration

Structure of the configuration

The configuration file is structures as per Spring using a multi profile following the example

We are using a default configuration plus specific configuration for production, development and unit-database profiles.

To launch with a specific profile the application should be launch with the following command:

$ SPRING_PROFILES_ACTIVE="production" ./gradlew startCruncher

The default profile is development

Tomcat configuration

The only change to tomcat configuration is the server port and it looks like this:

 port: 8443

Other options are available, please refer to the documentation of spring boot

Log level

The application is using the default logger of spring boot. To change the log level of the framework and libraries:

logging.level: INFO ERROR ERROR WARN

To change the Cruncher specific logs: DEBUG

Database connection information

The database configuration can be found in blocks like the following and there is one for each profile.

    username: username      # Username to connect to the mongo database
    password: password      # Password of the User in mongo database
    name: resumeCruncher    # Name of the database in mongo
    host: localhost         # Hostname where the mongo database is running
    port: 27017             # Port the mongo databse is listening

Make sure to update the correct section of this file depending on the profile that is going to be used.

Optional configuration

All the previous configuration can also be archived by using the URI to the database. In order to use this URI replace the previous section with the next one

    uri: mongodb://username:password@hostname:port/database_name

Default cruncher configuration

Web application configuration

In this section it is possible to change the username and password used to connect to the cruncher server

    username: backend_admin     # Username that should be provided in the HTTP parameter X-Auth-Username
    password: backendpassword   # Password that should be provided in the HTTP parameter X-Auth-Password 

Cunchers default configuration

This section of configuration is the section that uses more space, because it contains the initial configuration for all the crunchers.

At this point there are 2 crunchers and they have the following configuration:

Expression based cruncher

This cruncher is a very basic implementation of a word/expression counting algorithm, that adds up all the occurencies of a word/expression and based on the words/expression more common matches documents.

The options for this cruncher looks like this

    case-sensitive: false                      # Should the matching be case sensitive or not
    ignoreListWordSearch: true                 # Ignore the words in the ignore list
    merge-list:                                # List of tokens to be merged, this is a hash that contains
      "software development":                  # the final name and a list of expressions that will be replace by that final name
        - "software development"
        - "software development lifecycle"
        - "cook"
        - "line cook"
    ignore-list:                               # List of words or expressions that will be ignored when matching
      - "a"                                    # this is used to archive more accuracy on the matching

Naive Bayes based cruncher

This cruncher uses a different approach, based in the Naive Bayes algorithm to match categories. This algorithm need a set of trainning documments for each possible category. The bigger the set the more accurate is the algorithm.

  learn-database:              # Trainning set, this is a hash that contains the category name
    "category one":            # and a list with different documents that belong to each category
      - "blah blah blah"
    "category two":
      - "bamm bamm bamm"
  clean-expressions:           # To ensure that common words do not polute the results, we can
    - "a"                      # can provide a list of words or expressions that will be removed
    - "or"                     # from the document before the algorithm run

Using the application

Enter the sources folder before doing the next step

To start the application:

./gradlew startCruncher

To send requests to the application a browser can be user pointing to the address http://localhost:8443/....

The API documentation can be found in:

To start you need to authenticate doing

Before we can start the Cruncher we need to create a user and a database to connect to.POST /api/v1/authenticate HTTP/1.1
Accept: application/json
X-Auth-Username: backend_admin
X-Auth-Password: backendpassword
Host: localhost

The answer will get you a token in the following request you need to include the token like:

GET /api/v1/curriculum/asdasdasd HTTP/1.1
X-Auth-Token: 377430e2-db04-4d10-b119-6a7448cbdc19
Host: localhost

Unit Testing

Before running the tests make sure to have mongodb launched in your machine or if you want to use one external server edit the file app/src/main/resources/application.yml with the new definition

After that just run the following command

> SPRING_ACTIVE_PROFILE="unit-test" ./gradlew check


  1. Fork it ( )
  2. Talk with us in Slack channel #metplus
  3. Find a story to work in Waffle
  4. Create your feature branch (git checkout -b my-new-feature)
  5. Test changes don't break anything (./gradlew check)
  6. Add test for your new feature
  7. Commit your changes (git commit -am 'Add some feature')
  8. Push to the branch (git push origin my-new-feature)
  9. Create a new Pull Request


Naive Bayes Classifier is based on the source developed by Philipp Nolte.

The Initial source can be found in: