## Problem My script failed when `try` lacked an explicit import from `dw::Runtime`. The runtime reported `Unable to resolve reference of: try` during execution. The documentation describes `try` as a function that returns a Result object for handling errors docs. ## Input { "order": { "id": "ORD-2001", "total": "12.5O" } } ## Working configuration %dw 2.0 // try() lives in dw::Runtime — not imported by default import try from dw::Runtime // RFC 7807 media type is accepted as-is by the JSON writer output application/problem+json var attempt = try(() -> payload.order.total as Number) --- if (attempt.success) { total: attempt.result } else { "type": "https://example.com/problems/invalid-total", "title": "Invalid order total", "status": 400, "detail": attempt.error.message, "instance": "/orders/" ++ payload.order.id, "errorKind": attempt.error.kind } ## Output { "type": "https://example.com/problems/invalid-total", "title": "Invalid order total", "status": 400, "detail": "Cannot coerce String (12.5O) to Number", "instance": "/orders/ORD-2001", "errorKind": "InvalidNumberException" } The `detail` field contains the string `Cannot coerce String (12.5O) to Number`. The `errorKind` field holds the value `InvalidNumberException`. ## The trap The missing import causes a resolution failure when the script executes. Running `fail_noimport.dwl` produces an exit code of 255 and reports `Unable to resolve reference of: try`. ## What I do now I always add `import try from dw::Runtime` at the top of every script that uses the function. I verify the import exists before running the transformation to prevent resolution errors. Runs shown: DataWeave CLI 2.12.2 104 DataWeave patterns + 8 Exchange modules with 208 MUnit tests: github.com/shakarbisetty/mulesoft-cookbook | 60-second walkthroughs: youtube.com/@SanThaParv

## Problem My DataWeave script threw an unresolved reference error when calling `partition` without an explicit import. The Arrays module must be imported to your DataWeave code by adding the line `import * from dw::core::Arrays` to the header of your DataWeave script docs. ## Input { "records": [ { "id": "ORD-1001", "email": "ana@example.com" }, { "id": "ORD-1002", "email": "bad-address" }, { "id": "ORD-1003", "email": "raj@example.org" }, { "id": "ORD-1004" }, { "id": "ORD-1005", "email": "mei@example.net" } ] } ## Working configuration %dw 2.0 // partition() lives in dw::core::Arrays — not imported by default import * from dw::core::Arrays output application/json // keys are ALWAYS success/failure — re-map them to domain names var split = payload.records partition (r) -> (r.email default "") matches /.+@.+\..+/ --- { accepted: split.success map (r) -> r.id, rejected: split.failure map (r) -> { id: r.id, reason: "missing or invalid email" }, retryCount: sizeOf(split.failure) } ## Output { "accepted": [ "ORD-1001", "ORD-1003", "ORD-1005" ], "rejected": [ { "id": "ORD-1002", "reason": "missing or invalid email" }, { "id": "ORD-1004", "reason": "missing or invalid email" } ], "retryCount": 2 } The `accepted` array contains IDs from records with valid emails, while the `rejected` array holds objects for records missing or invalidating email addresses. The `retryCount` field reports the size of the failure partition as two. ## The trap The trap is an unresolved reference error when calling `partition` without importing the Arrays module. ## What I do now I add `import * from dw::core::Arrays` to the DataWeave header before using `partition`. I map the `success` and `failure` keys to domain names like `accepted` and `rejected`. I calculate retry counts using `sizeOf` on the failure partition. Runs shown: DataWeave CLI 2.12.2 104 DataWeave patterns + 8 Exchange modules with 208 MUnit tests: github.com/shakarbisetty/mulesoft-cookbook | 60-second walkthroughs: youtube.com/@SanThaParv

How Karthik Bettadapura co-founded DataWeave and built an AI-powered competitive intelligence platform that reads the retail web for global brands.

Monitor competitive price and product availability changes from top grocery stores and supermarket chains across the United States. Over 26,000 products analyzed across 11 categories.