File Structure of JSON Format
Decision Tables exported or imported in JSON format require certain mandatory attributes to maintain a consistent structure. Below are the required and optional components of a decision table JSON file.
Mandatory Elements
name: name of the decision table;type: always"decision-table"for decision tables;status: the current state of the decision table ("published" or "pending");inputSchema: describes the input variables of the decision table. Each variable should be defined as an object, which can hold different attributes or substructures;outputSchema: describes the expected output variables from the decision tabledecisionTable: contains the rule itself:columns: defines the different types of columns in the table, such as input (conditions), calculation, and result columns;condition: condition columns specify the criteria that inputs need to meet;calculation: columns that calculate values based on the input or other conditions;columnOutput: defines the result of the decision table based on the calculations or conditions;
rows: rows of the decision table;cells: each row contains cells, which correspond to the column definitions, and they hold specific values or functions applied to the data;active:a boolean attribute that defines if a row is active (true) or not;
Optional Elements
description: a textual description of the decision table;ruleAlias: an alias for the rule. If this is left as an empty string, the system will generate one automatically upon import;createdIn: the date and time of the rule's creation, following the ISO 8601 format. If left empty, this will be generated automatically upon import;lastUpdate: the date and time of the last update, also in ISO 8601 format. If empty, it will be automatically generated upon import;tags: an array containing tags for categorizing the rule. It may be an empty array;visualData: contains information about the visual presentation of the columns, including:columns: an array where each object contains:columnId: the unique identifier for the column (string);width: the width of the column in the UI (number);
auditLog: specifies the audit logging configuration;active: defines whether audit logging is active;debug: contains a nested attributeactive, which controls whether debug logging is enabled;ttl: time to live (in days) for the audit logs (optional, with a default of 14 days).
Examples
Minimal File Structure Example:
{
"name": "",
"description": "",
"type": "decision-table",
"status": "",
"inputSchema": {},
"outputSchema": {},
"decisionTable": {
"columns": [],
"rows": []
}
}File Structure Example:
{
"name": "Example table",
"description": "",
"inputSchema": {
"input": {}
},
"outputSchema": {
"output": {}
},
"decisionTable": {
"columns": [
{
"condition": {
"type": "simple",
"name": "Condition",
"definition": {
"content": "input",
"type": "INPUT_VARIABLE",
"valid": false
}
},
"columnId": "ec57bb7c-8e90-4aee-da49-17b607a6b09a",
"type": "input",
"alias": "col-green-elephant"
},
{
"calculation": {
"variable": "calcVar",
"name": "Calculation"
},
"columnId": "a93899f3-d75b-8f64-3b05-781bc42e836a",
"type": "calc",
"alias": "col-turquoise-eagle"
},
{
"columnOutput": {
"type": "simple",
"outputVariable": "output",
"name": "Result"
},
"columnId": "2e46eb73-de05-51bc-5913-4b261bbe2069",
"type": "output",
"alias": "col-violet-lion"
}
],
"rows": [
{
"active": true,
"cells": [
{
"column": "ec57bb7c-8e90-4aee-da49-17b607a6b09a",
"scalarCondition": {
"operator": "function",
"value": {
"functionName": "EQUAL",
"parameters": [
1,
"1"
]
},
"type": "function",
"stringValue": "EQ(1, \"1\")"
},
"type": "input"
},
{
"column": "a93899f3-d75b-8f64-3b05-781bc42e836a",
"outputScalarValue": {
"value": {
"functionName": "ABS",
"parameters": [
{
"functionName": "MINUS_CHAR",
"parameters": [
2
]
}
]
},
"type": "function",
"stringValue": "ABS(-2)"
},
"type": "calc"
},
{
"column": "2e46eb73-de05-51bc-5913-4b261bbe2069",
"outputScalarValue": {
"type": "function",
"value": "\"1 equals '1', and absolute value of -2 is {calcVar}\"",
"stringValue": "\"1 equals '1', and absolute value of -2 is {calcVar}\""
},
"type": "output"
}
]
},
{
"active": true,
"cells": [
{
"column": "ec57bb7c-8e90-4aee-da49-17b607a6b09a",
"scalarCondition": {
"operator": "function",
"value": {
"functionName": "LESS",
"parameters": [
2,
6
]
},
"type": "function",
"stringValue": "LT(2, 6)"
},
"type": "input"
},
{
"column": "a93899f3-d75b-8f64-3b05-781bc42e836a",
"outputScalarValue": {
"value": {
"functionName": "AVG",
"parameters": [
10,
16,
80,
5
]
},
"type": "function",
"stringValue": "AVG(10, 16, 80, 5)"
},
"type": "calc"
},
{
"column": "2e46eb73-de05-51bc-5913-4b261bbe2069",
"outputScalarValue": {
"value": "\"2 is less than 6, and average of 10, 16, 80 and 5 is {calcVar}\"",
"type": "function",
"stringValue": "\"2 is less than 6, and average of 10, 16, 80 and 5 is {calcVar}\""
},
"type": "output"
}
]
}
]
},
"type": "decision-table",
"status": "published",
"auditLog": {
"active": false,
"debug": {
"active": false
},
"ttl": 14
},
"visualData": {
"columns": [
{
"columnId": "2e46eb73-de05-51bc-5913-4b261bbe2069",
"width": 708
},
{
"columnId": "ec57bb7c-8e90-4aee-da49-17b607a6b09a",
"width": 297
},
{
"columnId": "a93899f3-d75b-8f64-3b05-781bc42e836a",
"width": 466
}
]
},
"ruleAlias": "comprehensive-vole",
"createdIn": "2024-09-25T10:01:54.140Z",
"lastUpdate": "2024-09-25T11:10:12.365Z",
"tags": []
}Conclusion
When working with Decision Tables in JSON format, it's crucial to adhere to the structure, ensuring that mandatory elements like name, type, status, and schemas are defined. Optional elements such as descriptions, audit logs, and visual data offer flexibility and can improve clarity and management. By following these guidelines, users can maintain a consistent and reliable Decision Table structure that ensures smooth data handling and integration.
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