Chromosoma

Just define your data DNA and generate your dataset.

High Level Overview

Input

Users just need to define the schema model configuration in a json file. For each field of the schema a name, a type and a set of rules need to be defined.

It follows an example of `schema.json definition:

{
   "instances": 10,

   "output": "result",

   "format": "csv",

   "fields":[
      {
         "name":"name",
         "dataType":"string",
         "rules":[
            {
                "type": "set",
                "values": ["dave","simon"],
                "distribution": 1.0
            }
         ]
      },
      {
         "name":"age",
         "dataType":"int",
         "rules":[
            {
               "type":"set",
               "values":[
                  100
               ],
               "distribution":0.1
            },
            {
               "type":"range",
               "min":10,
               "max":99,
               "distribution":0.9
            }
         ]
      },
      {
         "name":"budget",
         "dataType":"decimal",
         "rules":[
            {
               "type":"set",
               "values":[
                  100
               ],
               "distribution":0.5
            },
            {
               "type":"range",
               "min":1,
               "max":10,
               "distribution":0.5
            }
         ]
      },
      {
         "name":"married",
         "dataType":"boolean",
         "rules":[
            {
               "type":"boolean",
               "false":0.0,
               "true":1.0
            }
         ]
      }
   ]
}

Output

result.csv

dave,64,1.3272667719937015,true
dave,66,100.0,true
simon,16,7.887171701724464,true
simon,100,100.0,true
dave,50,4.378826132850798,true
simon,48,100.0,true
simon,24,1.2484780989173947,true
simon,100,100.0,true
dave,37,100.0,true
dave,48,100.0,true
simon,81,9.032302178134143,true

---

Supported Field Types

• string
• int
• decimal
• boolean
• date (TODO)

---

Rules

Every field comes with a set of rules, and every rule comes with a distribution. The distribution you define is used within the generation engine to understand how to model your data. The sum of the rule distributions for a single rule should be equal to 1.

Supported string rules

• String set: the string to be generated is randomly selected from values set

{
  "name": "first name",
  "dataType": "string",
  "rules": [
      {
        "type": "set",
        "values": ["dave","simon"],
        "distribution": 1.0
      }
    ]
}

In the example above, all the first names will be equal to dave or simon.

Supported int rules

• Integer set: the integer to be generated is randomly selected from values set
• Range: the integer to be generated is randomly selected between min and `max

{
     "name":"age",
     "dataType":"int",
     "rules":[
        {
           "type":"set",
           "values":[
              100
           ],
           "distribution":0.1
        },
        {
           "type":"range",
           "min":10,
           "max":99,
           "distribution":0.9
        }
     ]
}

In the example above ~10% of your ages will be equal to 100 and ~90% of your ages will be between 10 and 99.

Supported decimal rules

Same as integer rules (specialisation will be implemented soon).

Supported boolean rules

• Boolean: just define the false and true distribution values

{
     "name":"married",
     "dataType":"boolean",
     "rules":[
        {
           "type":"boolean",
           "false":0.0,
           "true":1.0
        }
     ]
}

In the example above, all the married rows will be equal to false

Supported Output Format

• CSV (with , separator)
• AVRO
• JSON (TODO)
• JDBC (TODO)
• REST (TODO)

---

Generation Engine Modeling

---

How to run

The only supported mode now is standalone.

More interactive running mode will be developed soon.

git clone https://github.com/holydrinker/chromosoma.git
cd chromosoma
sbt assembly
java -jar target/scala-2.12/chromosoma-assembly-0.1.0.jar <path-to-schema>.json