Cypher is Neo4j’s declarative graph query language. You describe nodes with parentheses, relationships with square brackets, and paths by connecting those patterns. This cheat sheet covers the query forms you use most often to read, create, update, delete, batch-process, and tune graph data.
Cypher syntax at a glance
- Node:
(n), optionally with a variable, label, and properties such as(p:Person {name: $name}). - Relationship:
-[r:ACTED_IN]->, with an optional variable, type, direction, and properties. - Path: a connected pattern such as
(p)-[:ACTED_IN]->(m). - Parameters: values such as
$nameare supplied separately by the driver or client. - Keywords: Cypher keywords are not case-sensitive; variable names are case-sensitive.
Cypher supports clauses for reading, writing, transforming, and removing graph data. A query is usually a pipeline: one clause produces rows that the next clause consumes.
Read a pattern with MATCH
MATCH (p:Person {name: $name})-[:ACTED_IN]->(m:Movie)
RETURN m.title AS title
ORDER BY titleMATCH finds rows that satisfy a graph pattern. The label Person, relationship type ACTED_IN, and label Movie constrain what can match. RETURN chooses the output columns, while ORDER BY sorts them.
Match nodes, relationships, and paths
MATCH (p:Person)-[r:ACTED_IN]->(m:Movie)
RETURN p.name, r, m.titleUse a variable such as p, r, or m when you need to return it, filter on it, or use it in a later clause. Direction matters: -> is outgoing, <- is incoming, and -[]- matches either direction.
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Use OPTIONAL MATCH when a pattern may be missing
MATCH (p:Person {name: $name})
OPTIONAL MATCH (p)-[r:DIRECTED]->(movie)
RETURN p.name, r, movieOPTIONAL MATCH keeps the row from the required part of the query and supplies null for the missing pattern. In this example, the person must exist, but a directed movie does not have to exist.
Keep WHERE beside the pattern it filters
MATCH (p:Person)
WHERE p.country = $country
OPTIONAL MATCH (p)-[:DIRECTED]->(movie)
WHERE movie.released >= $year
RETURN p.name, movie.titleWHERE is a subclause of MATCH, OPTIONAL MATCH, and WITH. Place it immediately after the clause whose pattern or values it filters. Moving a predicate across an optional match can change whether rows are preserved or eliminated.
Transform rows with WITH
MATCH (c:Customer)-[:BUYS]->(p:Product)
WITH c, count(p) AS purchases
WHERE purchases > 2
RETURN c.name, purchases
ORDER BY purchases DESCWITH is a pipeline boundary. It can aggregate, rename, calculate, sort, and filter values before the next stage. Only variables named in WITH continue to the following clause; use WITH * when you intentionally carry every variable forward.
Calculate values between stages
MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WITH p, collect(m.title) AS films
RETURN p.name, filmsAggregating functions such as count() and collect() group rows at the WITH or RETURN boundary. A variable omitted from that boundary is no longer available in the normal outer query scope.
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Create new graph data with CREATE
CREATE (p:Person {name: $name})
RETURN pCREATE always creates the specified pattern each time the clause executes. It does not check whether an equivalent node or relationship already exists.
Create a relationship between existing nodes
MATCH (p:Person {id: $personId}), (m:Movie {id: $movieId})
CREATE (p)-[:ACTED_IN {role: $role}]->(m)
RETURN p, mSeparate the lookup from the write when the endpoints already exist. If the query can run more than once, decide explicitly whether duplicate relationships are acceptable or whether MERGE is more appropriate.
Match or create with MERGE
MERGE (p:Person {email: $email})
ON CREATE SET p.createdAt = datetime()
ON MATCH SET p.lastSeen = datetime()
RETURN pMERGE matches the whole pattern or creates it when that pattern is absent. Its identifying pattern is therefore important: include only the properties that define the entity you intend to find or create. ON CREATE runs for a newly created match; ON MATCH runs when an existing match is found.
MERGE is not a universal uniqueness guarantee under every concurrency and schema configuration. Use an appropriate uniqueness constraint and design the identifying pattern deliberately.
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Update properties with SET and REMOVE
MATCH (p:Person {id: $id})
SET p.city = $city,
p.updatedAt = datetime()
RETURN pUse SET to assign properties or labels. To remove a property, use REMOVE (or assign null where that behavior is appropriate for your application and version).
MATCH (p:Person {id: $id})
REMOVE p.temporaryFlag
RETURN pExpand lists and batches with UNWIND
UNWIND $rows AS row
MERGE (p:Person {id: row.id})
SET p.name = row.name
RETURN count(p) AS processedUNWIND turns each element of a list into a row. It is useful for parameterized batches, imports, and turning collected values back into rows. Validate input shape and choose transaction sizes appropriate to the data volume; very large imports need an operations and batching strategy rather than one unbounded transaction.
Delete nodes and relationships safely
Delete a relationship
MATCH (p:Person)-[r:KNOWS]->(friend:Person)
WHERE p.id = $personId AND friend.id = $friendId
DELETE rDELETE removes the matched entity. A node that still has relationships generally cannot be deleted with plain DELETE.
Delete a node and its connected relationships
MATCH (p:Person {id: $id})
DETACH DELETE pDETACH DELETE removes the node and all relationships connected to it. Treat broad forms such as MATCH (n) DETACH DELETE n as destructive: they remove all graph data. For large deletion jobs, use transactional batching; deleting data does not remove indexes or schema definitions.
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Shape, order, and paginate results
MATCH (m:Movie)
RETURN m.title AS title, m.released AS year
ORDER BY year DESC, title
SKIP $offset
LIMIT $pageSizeRETURN defines the result columns. Use aliases to give client code stable names. Apply ORDER BY before SKIP and LIMIT when you need deterministic pagination, and return only the fields the application needs.
Combine result sets with UNION
MATCH (p:Person)
RETURN p.name AS name
UNION
MATCH (c:Customer)
RETURN c.name AS nameUNION combines results and removes duplicate rows. Use UNION ALL when duplicates are meaningful or deduplication is unnecessary. Each branch must return the same number of columns with compatible names and types.
Inspect and tune a query
Use parameters
Parameterize values such as IDs, names, dates, and lists instead of concatenating them into query text. This improves safety and lets the database reuse plans more effectively.
Compare EXPLAIN and PROFILE
EXPLAIN
MATCH (p:Person {email: $email})
RETURN pEXPLAIN shows the planned operators without executing the query. Replace it with PROFILE when you need runtime operators and measurements from an execution. Use those observations, result size, and your workload to decide whether a change helps; an index or syntax change does not guarantee a particular speedup.
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Indexes and bounded traversals
- Use an appropriate index for frequently searched properties. Neo4j documents range (the default), text, point, and token lookup indexes, as well as full-text and vector indexes.
- Bound variable-length patterns when possible. An unbounded traversal can explore far more of the graph than intended.
- Filter early, carry only required variables through
WITH, and return only required fields. - Check the plan for accidental cartesian products, label scans, or unexpectedly high row counts.
Cypher version notes
Available syntax depends on the Neo4j release and the Cypher version selected by that release. The current documentation describes CYPHER 25 for servers running Neo4j 2025.06 or later, and CYPHER 5 as the Cypher 5 behavior from the Neo4j 2025.06 release. Verify your server version and its matching manual before using version-sensitive syntax.
Newer references include forms such as FILTER, dynamic labels and relationship types, and WHEN. Do not assume those forms work on older deployments; test them against the target version or use the syntax documented for that server.
Quick comparison of commonly confused clauses
| Use case | Choose | Effect |
|---|---|---|
| Required versus optional pattern | MATCH / OPTIONAL MATCH |
MATCH removes rows without a match; OPTIONAL MATCH preserves the row and returns null for the missing part. |
| Always create versus match-or-create | CREATE / MERGE |
CREATE creates the pattern every time; MERGE matches the complete pattern or creates it. |
| Deduplicate versus preserve duplicates | UNION / UNION ALL |
UNION removes duplicate rows; UNION ALL keeps them. |
| Delete one entity versus its connected relationships too | DELETE / DETACH DELETE |
DETACH DELETE removes a node’s connected relationships as well. |
Where to continue learning
Neo4j GraphAcademy’s free Cypher Fundamentals course covers reading and writing graph data. Its intermediate Cypher material includes filtering, variable-length traversal, WITH, subqueries, UNWIND, and parameters. For a book-length treatment, Neo4j’s recommended-books page lists Graph Data Processing with Cypher by Ravindranatha Anthapu, published by Packt; verify the edition and current availability before buying.
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