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Neo4j and Cypher Query Language Cheat Sheet

Neo4j and Cypher Query Language Cheat Sheet

Back to Databases
Updated 2026-05-15
Next Topic: Neon Serverless Postgres Cheat Sheet

Neo4j is a native graph database built on a property graph model where nodes, relationships, properties, and labels form the foundation of data representation. Unlike relational databases that rely on joins, Neo4j uses index-free adjacency where each node directly references its connected relationships, enabling constant-time traversals regardless of database size. Cypher®, Neo4j's declarative query language, combines SQL-like familiarity with ASCII-art pattern syntax to express graph patterns intuitively—making complex multi-hop queries readable and performant through pattern matching. Understanding Cypher's clause composition model (where clauses chain together like Unix pipes) is essential: each clause transforms the working set of data and passes it forward, allowing you to build sophisticated queries from simple building blocks.

What This Cheat Sheet Covers

This topic spans 16 focused tables and 134 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Graph Data Model FundamentalsTable 2: MATCH Clause Pattern SyntaxTable 3: Write OperationsTable 4: WHERE Clause Predicates and FilteringTable 5: Aggregation FunctionsTable 6: Path Functions and Shortest Path QueriesTable 7: Query Composition and Control FlowTable 8: Result Modifiers and OrderingTable 9: Type and Metadata FunctionsTable 10: List Functions and ComprehensionsTable 11: Temporal Functions and Date OperationsTable 12: Indexes and Constraints for PerformanceTable 13: Data Import MethodsTable 14: Query Performance AnalysisTable 15: APOC Core Procedures (Extended Functionality)Table 16: Graph Data Science Library Algorithms

Table 1: Graph Data Model Fundamentals

Everything in Neo4j reduces to a handful of building blocks—nodes for the entities, relationships to connect them, labels to categorize, and properties to hold the data. Once these terms and the ASCII-art way they're written (parentheses for nodes, square brackets for relationships) click, the rest of Cypher reads almost like a diagram of your data.

ConceptExampleDescription
Node
(n:Person {name: "Alice"})
• Represents an entity in the graph with optional labels and properties
• nodes are enclosed in parentheses
Relationship
-[:KNOWS {since: 2020}]->
• Connects two nodes with a single type and optional properties
• always has a direction but can be traversed bidirectionally
Label
(n:Person:Employee)
• Categorizes nodes for grouping and indexing
• nodes can have multiple labels separated by colons
Property
{name: "Alice", age: 30}
• Key-value pair stored on nodes or relationships
• supports primitive types, lists, and temporal/spatial values

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