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Unique constraint

As explained in Graph Algorithms for Data Science

A unique constraint is a database rule requiring specified property values to be unique among the records or graph elements to which it applies.

A unique constraint is a database rule requiring a property value to be unique among nodes with a particular label. In Neo4j, it protects data integrity during imports and automatically creates an index for that property. section 3.2.3

Use unique constraints to:

  • Prevent duplicate identifiers, such as multiple User nodes with the same id. section 3.2.3
  • Stop an import query that would compromise data integrity. section 3.2.3
  • Improve import and analytical query performance through the automatically created index. section 3.2.3
  • Model identifier rules for each label independently, such as User.id, Tweet.id, or Tag.id. section 3.2.3 section 7.1

Constraining imported graph identifiers

Define constraints before importing nodes. These statements allow one User per id and one Tweet per id: CREATE CONSTRAINT IF NOT EXISTS FOR (u:User) REQUIRE u.id IS UNIQUE; CREATE CONSTRAINT IF NOT EXISTS FOR (p:Tweet) REQUIRE p.id IS UNIQUE;. For extracted hashtags, apply the same pattern to Tag.id before MERGE creates or reuses Tag nodes. section 3.2.3 section 7.1

How Graph Algorithms for Data Science covers it

How deep it goes
Explains label-scoped uniqueness, automatic indexing, null allowance, and executable Cypher definitions, but not constraint administration.
Examples
Uses User, Tweet, and Tag identifiers in Twitter data imports as concrete constraint examples.
What it assumes
Assumes basic Cypher and node-label knowledge while treating graph schema constraints as an addition to Neo4j’s schemaless model.
What you can do after
Configure Neo4j imports with label-specific unique property constraints and use them alongside MERGE for deduplicated graph loading.
How it connects
Connects unique constraints with graph schema, data integrity, indexes, primary keys, imports, and MERGE-based deduplication.

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This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.