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neo4j-cypher-manual-3.5 Cypher is a declarative graph query language that allows for expressive and efficient querying and updating of the graph. It is designed to be suitable for both developers and operations professionals. Cypher is designed to be simple, yet powerful; highly complicated database queries can be easily expressed, enabling you to focus on your domain, instead of getting lost in database access.
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The Neo4j Cypher Manual v3.5
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê2
2. Syntax. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê7
3. Clauses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê74
4. Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê153
5. Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê283
6. Query tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê313
7. Execution plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê338
8. Deprecations, additions and compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê418
9. Glossary of keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê422
© 2019 Neo4j, Inc.
License: Creative Commons 4.0
This is the Cypher manual for Neo4j version 3.5, authored by the Neo4j Team.
This manual covers the following areas:
• Introduction — Introducing the Cypher query language.
• Syntax — Learn Cypher query syntax.
• Clauses — Reference of Cypher query clauses.
• Functions — Reference of Cypher query functions.
• Schema — Working with indexes and constraints in Cypher.
• Query tuning — Learn to analyze queries and tune them for performance.
• Execution plans — Cypher execution plans and operators.
• Deprecations, additions and compatibility — An overview of language
developments across versions.
• Glossary of keywords — A glossary of Cypher keywords, with links to other
parts of the Cypher manual.
Who should read this?
This manual is written for the developer of a Neo4j client application.
1
Chapter 1. Introduction
This section provides an introduction to the Cypher query language.
1.1. What is Cypher?
Cypher is a declarative graph query language that allows for expressive and efficient querying and
updating of the graph. It is designed to be suitable for both developers and operations professionals.
Cypher is designed to be simple, yet powerful; highly complicated database queries can be easily
expressed, enabling you to focus on your domain, instead of getting lost in database access.
Cypher is inspired by a number of different approaches and builds on established practices for
expressive querying. Many of the keywords, such as WHERE and ORDER BY, are inspired by SQL
(http://en.wikipedia.org/wiki/SQL). Pattern matching borrows expression approaches from SPARQL
(http://en.wikipedia.org/wiki/SPARQL). Some of the list semantics are borrowed from languages such as
Haskell and Python. Cypher’s constructs, based on English prose and neat iconography, make queries
easy both to write, and to read.
Structure
Cypher borrows its structure from SQL — queries are built up using various clauses.
Clauses are chained together, and they feed intermediate result sets between each other. For
example, the matching variables from one MATCH clause will be the context that the next clause exists
in.
The query language is comprised of several distinct clauses. You can read more details about them
later in the manual.
Here are a few clauses used to read from the graph:
•
MATCH: The graph pattern to match. This is the most common way to get data from the graph.
•
WHERE: Not a clause in its own right, but rather part of MATCH, OPTIONAL MATCH and WITH. Adds
constraints to a pattern, or filters the intermediate result passing through WITH.
•
RETURN: What to return.
Let’s see MATCH and RETURN in action.
Imagine an example graph like the following one:
name = 'Joe'
name = 'Steve'
friend
name = 'John'
friend
name = 'Sara'
friend
name = 'Maria'
friend
Figure 1. Example Graph
For example, here is a query which finds a user called 'John' and 'John’s' friends (though not his direct
2
friends) before returning both 'John' and any friends-of-friends that are found.
MATCH (john {name: 'John'})-[:friend]->()-[:friend]->(fof)
RETURN john.name, fof.name
Resulting in:
+----------------------+
| john.name | fof.name |
+----------------------+
| "John" | "Maria" |
| "John" | "Steve" |
+----------------------+
2 rows
Next up we will add filtering to set more parts in motion:
We take a list of user names and find all nodes with names from this list, match their friends and
return only those followed users who have a 'name' property starting with 'S'.
MATCH (user)-[:friend]->(follower)
WHERE user.name IN ['Joe', 'John', 'Sara', 'Maria', 'Steve'] AND follower.name =~ 'S.*'
RETURN user.name, follower.name
Resulting in:
+---------------------------+
| user.name | follower.name |
+---------------------------+
| "Joe" | "Steve" |
| "John" | "Sara" |
+---------------------------+
2 rows
And here are examples of clauses that are used to update the graph:
•
CREATE (and DELETE): Create (and delete) nodes and relationships.
•
SET (and REMOVE): Set values to properties and add labels on nodes using SET and use REMOVE to
remove them.
•
MERGE: Match existing or create new nodes and patterns. This is especially useful together with
unique constraints.
1.2. Querying and updating the graph
Cypher can be used for both querying and updating your graph.
1.2.1. The structure of update queries
A Cypher query part can’t both match and update the graph at the same time.
Every part can either read and match on the graph, or make updates on it.
If you read from the graph and then update the graph, your query implicitly has two parts — the
reading is the first part, and the writing is the second part.
If your query only performs reads, Cypher will be lazy and not actually match the pattern until you ask
for the results. In an updating query, the semantics are that all the reading will be done before any
3
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