Import transforms with coalesce let you update existing records instead of creating duplicates every time you run an import. You'll have a transform that intelligently matches incoming data to existing target records.
Why imports create duplicates without coalesce
By default, ServiceNow import transforms create a new record for every row in your import set, even if that record already exists in the target table. This means running the same import twice creates duplicates, and incremental imports from external systems pile up redundant data. Data administrators and integration developers inherit messy target tables full of duplicate users, duplicate CIs, and duplicate catalog items that should have been updates to existing records.
How coalesce matching works
Coalesce tells the transform which fields to use for matching existing target records. You check the 'Coalesce' box on one or more field mappings, and ServiceNow queries the target table using those field values before deciding whether to insert or update. Single coalesce fields work for simple matching like email address or employee ID. Multiple coalesce fields combine with AND logic — useful when you need compound keys like location plus department, or when no single field is guaranteed unique.
Building robust coalesce logic
Start with the most reliable unique identifier you have — employee ID for users, serial number for CIs, or a business key from your source system. Add secondary coalesce fields only when your primary field isn't sufficient for matching. For production imports, consider data quality issues like leading/trailing spaces, case sensitivity, and null values in your coalesce fields. Well-designed coalesce logic handles both clean initial imports and ongoing incremental updates from external systems.
Before you start
- •Import set and transform map already created
- •Transform map field mappings configured for required fields
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Step by step
Open your transform map
Navigate to System Import Sets > Administration > Transform Maps and open the transform map you want to configure. You'll see the list of field mappings between your import set table and target table.
Select your primary coalesce field
Open the field mapping for the field you want to use for matching — typically an ID field, email address, or other unique identifier. Check the 'Coalesce' checkbox and save the record. This field will be used to query existing records in the target table.
Add secondary coalesce fields if needed
If you need multiple fields for matching, open additional field mappings and check their 'Coalesce' checkboxes. ServiceNow will combine all coalesce fields with AND logic, so a record matches only when all coalesce field values match an existing target record.
Use multiple coalesce fields only when necessary — each additional field makes matching more restrictive.
Test the coalesce behavior
Run your import transform and verify the behavior. When coalesce fields match an existing record, the transform updates that record with new values from the import. When no match is found, it creates a new record. Check both scenarios with test data before running production imports.
Review transform results
After running the transform, check System Import Sets > Import Set Runs to see the results. The 'State' column shows whether each row was inserted (new record) or updated (existing record matched). Use this to verify your coalesce logic is working correctly.
Best practices
Choose coalesce fields that are truly unique and stable — avoid fields that users can modify after record creation.
Clean your import data before transformation — leading spaces, case differences, and special characters will prevent coalesce matches.
Don't use sys_id as a coalesce field unless you're absolutely certain the IDs match between systems — they usually don't.
Test coalesce logic with edge cases like null values, empty strings, and duplicate source data before running production imports.
Monitor transform performance with multiple coalesce fields — complex matching conditions can slow large imports significantly.
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