> For the complete documentation index, see [llms.txt](https://docs.dckapintegrator.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dckapintegrator.com/tutorials/workflow-builder-tutorial/3.-hello-system/3.5-lets-create-data-in-shopify.md). # 3.5 🌳 Let's Create Data in Shopify ### Why Pre-processor? #### 🔁 1. Dynamic Value Generation 👉 “System-generated fields” Example:\ For every sync, you want to send a tag like: * `ITEM-1, ITEM-2, ITEM-3` Why Pre-processor?\ Because this requires a counter (loop) — mapping cannot generate sequence-based values. #### ❓ 2. Conditional Field Inclusion 👉 “Send only if meaningful” Example:\ Send product\_type only when category exists * If category = "Clothing" → send * If category = null → don’t send Why Pre-processor?\ Because this needs an if condition — mapping cannot decide whether to include/exclude fields. #### 🔄 3. Data Restructuring 👉 “Change format of data” Example:\ Input: `["v1", "v2"]` Output: `[{"note": "v1"}, {"note": "v2"}]` **Why Pre-processor?**\ Because this requires changing structure (loop + transformation) — not just direct mapping. “Whenever data needs to be generated, filtered, or reshaped, we use a Pre-processor.” \ Scenario: Assume you receive 3 products from an external system (PIM). You need to: * Add tags → ITEM-1, ITEM-2, ITEM-3 * Send product\_type only if category exists * Convert notes into Shopify metafields structure
#### Step 1: Create the Workflow Create a new Batch-type workflow #### Step 2: Mock Source Data Using Code Runner To keep things simple, simulate the PIM response. Add a Code Runner step. Paste the following code: \# Assign final output to variable "response" at the end. ``` response = [ { "name": "Shirt", "category": "Clothing", "notes": ["v1", "v2"] }, { "name": "Mug", "category": None, "notes": ["x1"] }, { "name": "Notebook", "category": "Stationery", "notes": [] } ] ``` #### Step 3: Add Loop Add a Loop. Keep the cursor inside the iterable box and choose Code Runner Step from Data Hub -> {{1}} 👉 This loops through each product in the array #### Step 4: Add Mapping (Shopify Create Product) * Search for Shopify (Beta) * Select your credentials * Select Create a New Product API and click Next * Payload Setup: Use loop item → {{2.items}} Keep the cursor inside the payload box, click on plus icon before the loop and choose item from Data Hub. {{2.items}} is automatically populated. This means we are using every product (JSON object) as payload in API Call. * In the Data Hub, Add Mock Data for Code Runner ``` [ { "name": "", "category": "", "notes": [] }, { "name": "", "category": "", "notes": [] } ] ``` * Click on Add Mapping. name → title * Click on Save and Exit to return to the workflows page.
#### Step 5: Understand the Requirement Data we have: ``` [ { "name": "Shirt", "category": "Clothing", "notes": ["v1", "v2"] }, { "name": "Mug", "category": null, "notes": ["x1"] }, { "name": "Notebook", "category": "Stationery", "notes": [] } ] ``` Data we want to send: For first product creation, ``` { "product": { "title": "Shirt", "tags": "ITEM-1", "product_type": "Clothing", "metafields": [ { "namespace": "custom", "key": "note", "value": "v1", "type": "single_line_text_field" }, { "namespace": "custom", "key": "note", "value": "v2", "type": "single_line_text_field" } ] } } ``` For second product creation, ``` { "product": { "title": "Mug", "tags": "ITEM-2", "metafields": [ { "namespace": "custom", "key": "note", "value": "x1", "type": "single_line_text_field" } ] } } ``` 👉 Notice: * ❌ product\_type is NOT sent (because category = null) For third product creation, ``` { "product": { "title": "Notebook", "tags": "ITEM-3", "product_type": "Stationery", } } ``` 👉 Notice: * ❌ No metafields (because notes = empty) Observing carefully what we have and what we expect: #### Requirements: * Requirement 1: Generate “ITEM-\” and send it in Tags * Requirement 2: If category is null, don’t send product\_type * Requirement 3: If notes are not empty, structure metafields as shown above; if empty, don’t send #### Step 6: Add Pre-Processor Now, let us implement these requirements one by one via the Pre-processor. **Requirement 1: Generate “ITEM-\” and send in Tags** Click on Add Pre-processor.
Let’s add the number based on the looping index. It starts from 0. Hence, we need to add 1 to it. To use a looping index, we need to add it to inputs. * Give a variable name * For value, choose the index under plus icon of Loop Step in Data Hub. In the screenshot, product\_number is used as a variable.
The value needs to be incremented and sent in tags with prefix “ITEM-”, followed by the number. To use the value of a variable, use inputs\[“variable\_name”].
You can copy paste the below code to your Preprocessor.
`payload["tags"] = f"ITEM-{inputs['product_number']+1}"` **Requirement 2: If category is null, don’t send product\_type.**
Click on Add under inputs. We need the product data (i.e., JSON object). * Keep the variable name as product\_data * For value, choose the item under plus icon of Loop Step in Data Hub. Now, in this product data: * If category is None, we should not send product\_type * Otherwise, we should send it To do that, add the below code to the Pre-processor ``` if inputs["product_data"].get("category"): payload["product_type"]=inputs["product_data"]["category"] ```
**Requirement 3: Structure metafields** Since there are many usages of inputs\["product\_data"] To keep it simple, let us re-write as ``` payload["tags"] = f"ITEM-{inputs['product_number']+1}" item = inputs["product_data"] if item.get("category"): payload["product_type"]=item["category"] ``` Now, let us add the logic for metafields restructuring if notes exist: ``` if item.get("notes"): payload["metafields"] = [ { "namespace": "custom", "key": "note", "value": note, "type": "single_line_text_field" } for note in item["notes"] ] ``` Cool, we have discussed some of the use cases of the Pre-processor. Now, let us visualize the output. Ensure that: * You have turned on console logs for the API Call step * You have checked Mapping & Modifiers Response Save the workflow. Run the sync. Wait for a while, and then check the logs.
First API Request Information: ``` { "product": { "title": "Shirt" }, "tags": "ITEM-1", "product_type": "Clothing", "metafields": [ { "namespace": "custom", "key": "note", "value": "v1", "type": "single_line_text_field" }, { "namespace": "custom", "key": "note", "value": "v2", "type": "single_line_text_field" } ] } ``` Second API Request Information: ``` { "product": { "title": "Mug" }, "tags": "ITEM-2", "metafields": [ { "namespace": "custom", "key": "note", "value": "x1", "type": "single_line_text_field" } ] } ``` Third API Request Information: ``` { "product": { "title": "Notebook" }, "tags": "ITEM-3", "product_type": "Stationery" } ``` #### 🎉 Success! Products have been created successfully in Shopify with dynamic tags, conditional fields, and structured metafields.
--- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.dckapintegrator.com/tutorials/workflow-builder-tutorial/3.-hello-system/3.5-lets-create-data-in-shopify.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.