Why JSONPath Improves Data Access in Our Engine
In digital systems, data drives decision logic. Rule engines process that data. As data volume and structure change, access needs to stay flexible. JSONPath solves that problem.
JSONPath is our preferred way to access data inside a rule engine. It gives direct access to nested values without hard-coding every attribute. That makes rule maintenance simpler.
Traditional rule engines often depend on a fixed set of attributes, but JSONPath works with dynamic structures. New fields can be added without manual updates to attribute definitions. It also handles arrays cleanly, which matters when decision logic reads lists of objects.
There is a small learning curve. The syntax is different from basic field lookups. The trade-off is worth it when the ruleset needs to stay flexible and readable.
Understanding JSONPath with a Practical Example
To see how JSONPath works, consider this JSON object:
{
"employees": [
{
"id": 1,
"name": "John",
"department": "Sales",
"email": "john@example.com"
},
{
"id": 2,
"name": "Jane",
"department": "Marketing",
"email": "jane@example.com"
}
]
}
This object contains an array of employees. Each employee has several attributes.
Here is how JSONPath can access specific parts of the data:
1. Accessing a specific attribute of an object in an array
If we want the "name" attribute of the first employee, we use:
$['employees'][0]['name']
The result is "John".
2. Using a wildcard to access data
The wildcard (*) operator returns the same attribute from every object in the array. To get the "email" attribute of all employees, use:
$['employees'][*]['email']
The result is a list of emails: ["john@example.com", "jane@example.com"].
3. Using an array slice to access data
JSONPath also supports array slices. To get the names of the first two employees, use:
$['employees'][0:2]['name']
This returns: ["John", "Jane"].
4. Access all attributes of a particular object
JSONPath can also return the full object. To get all attributes of the first employee, use:
$['employees'][0]
The result is the full employee object: {"id": 1, "name": "John", "department": "Sales", "email": "john@example.com"}.
These examples show why JSONPath is useful. It makes JSON data easier to navigate inside a rule engine. That keeps decision logic clear and easier to maintain.
Conclusion
JSONPath lets us process data dynamically. It handles changing structures. Constant rewrites are not required. The result is a rule engine that stays readable and capable. That is why JSONPath is our choice for data access.
Frequently asked questions
When should a lender use JSONPath rather than map each input attribute explicitly?
Use JSONPath when the source has genuinely variable nested structures, such as a list of accounts that can grow or shrink. For stable inputs used in core credit policy, explicit mappings are usually easier to review and test. The trade-off is flexibility against the extra care needed to define what happens when a value is absent or appears in an unexpected place.
How should we test a decision rule that reads values from an array?
First define what the rule means when the array is empty, contains one item, or contains several. Test each of those cases, plus missing values, duplicate items, and different item orders. Decide explicitly whether the policy uses a total, a maximum, or a condition that any item meets. Otherwise, a change in source order can change the decision unexpectedly.
What should a decision rule do when a nested value is missing or has the wrong type?
Treat missing, invalid, and genuine zero values as separate conditions. Set a policy-approved outcome for each, such as requesting corrected data or sending the application for review. Do not quietly substitute zero or another default, because that can make incomplete data look like a valid assessment. Record which input condition affected the decision.
How should we release a decision rule after a data source changes its structure?
Keep a set of representative test applications and run the revised rule set against them before release. Include ordinary cases and boundary cases, then compare the results with the current release and investigate every difference. Have the policy owner approve the changed outcomes. The main trade-off is the time spent checking compatibility against the cost of an unexpected change to live decisions.
