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This tutorial demonstrates how to remove duplicate records from a dataset using JavaScript logic inside n8n's Code nodes. It simulates real-world data cleaning by generating sample user data with intentional duplicates (based on email addresses) and walks you through the process of deduplication step-by-step.
The process includes:
Creating Sample Data with duplicates.
Filtering Out Duplicates using filter() and findIndex() based on email.
Displaying Cleaned Results with simple statistics for before-and-after comparison.
This is ideal for scenarios like CRM imports, ETL processes, and general data hygiene.
🔹 Step 1: Manual Trigger
Node: When clicking 'Test workflow'
Purpose: Initiates the workflow manually for testing.
🔹 Step 2: Generate Sample Data
Node: Create Sample Data (Code node)
What it does:
Creates 6 users, including 2 intentional duplicates (by email).
Outputs data as usersJson with metadata (totalCount, message).
Mimics real-world messy datasets.
🔹 Step 3: Deduplicate the Data
Node: Deduplicate Users (Code node)
What it does:
Parses usersJson.
Uses .filter() + .findIndex() to keep only the first instance of each email.
Logs total, unique, and removed counts.
Outputs clean user list as separate items.
🔹 Step 4: Display Results
Node: Display Results (Code node)
What it does:
Outputs structured summary:
Unique users
Status
Timestamp
Prepares results for review or downstream use.
📈 Sample Output
Original count: 6 users
Deduplicated count: 4 users
Duplicates removed: 2 users
🎯 Learning Objectives
You'll learn how to:
Use .filter() and .findIndex() in n8n Code nodes
Clean JSON data within workflows
Create simple, effective deduplication pipelines
Output structured summaries for reporting or integration
🧠Best Practices
Validate input format (e.g., JSON schema)
Handle null or missing fields gracefully
Use logging for visibility
Add error handling for production use
Use pagination/chunking for large datasets
