Functions and JSON

DecisionRules supports mixing native functional expressions with JSON. On this page, we will shortly explain how to write these mixed functional/JSON expressions and provide one realistic example of their usage.

Overview

In this section we give a brief overview of all the supported types of mixed functional/JSON expressions together with examples and recommendations for their use.

Placement as key and value

Functional expressions can be passed as a value into an object.

{"animal":{data}}

They can also define the key.

{{data}:["dog","cat","bird"]}

Since JSON only allows strings as keys, make sure that the dynamically generated key evaluates to a string. If not, it will be automatically casted to string, which might not make good sense. Also note that the key must be unique within the object.

Placement inside an array

Functional expressions can also represent an element of an array.

["dog",{data},"bird"]

Variables

In the above examples, we have passed a variable data inside the JSON. This can be any kind of variable supported within functions. Mixed expressions even work with abstract function variables:

ARRAY_MAP({data}, "letter", {"letter": {letter}})

Functions

We can of course use functions.

{"average": AVG(10,30)}

{1+2:"three"}

Combinations

The above listed types of expressions can be arbitrarily combined.

{"animals": {data}, "number": COUNT({data})}

Example

Suppose you have a rule Delivery Price with alias delivery-price which gives you the price of delivery based on package weight. Its input model is

{
  "package": {
    "weight": {}
  }
}

and the output model is

{
  "price": {},
  "currency": {},
  "deliveryInHours": {}
}

Now, imagine we wish to call this rule from a decision table. We can easily do that with the SOLVE function. In the respective output cell where we want to get the result, we can write something like

SOLVE("delivery-price", {"package": {"weight": 12}})

Note that the object in the second argument of the SOLVE function corresponds to the input model of our Delivery Price rule. This is because it supplies the input data for it. The SOLVE function evaluates the rule and returns the output, which may look like this:

[
  {
    "price": 28,
    "currency": "USD",
    "deliveryInHours": "24 Hours"
  }
]

Of course, the exact form and data returned depends on the Delivery Price rule itself. We could now take this output, process it with the help of Data Functions or Array Functions and return it in the outputs or use it in our next steps.

The interesting part comes when we want to pass some dynamic data to our Delivery Price rule. For example, imagine we have an input variable packageWeight and want to solve Delivery Price with the value from this variable. We can do that by simply writing the variable inside to the object:

SOLVE("delivery-price", {"package": {"weight": {packageWeight}}})

The variable packageWeight will be automatically interpreted and passed to the object, which will be then picked up by the SOLVE function and used as input for the Delivery Price rule.

But we can do much more than this. The JSON can be also mixed with arbitrary functions. For example, imagine we want to limit the packageWeight to be always greater than 10 before passing it to the SOLVE function. No problem, just write the appropriate function expression:

SOLVE("delivery-price", {"package": {"weight": MIN(10,{packageWeight})}})

Following this logic, it is possible to mix arbitrary functional expressions and JSON to get the desired behavior.

More examples

The below provided decision table contains examples of functions with JSON. Import the decision table to your space to see them working.

26KB

JSON_Example_v1.json