Query the database

Once you have connected to the database, you can run queries using Cypher and the method Driver.execute_query().

Write to the database

To create two nodes representing persons named Alice and David, and a relationship KNOWS between them, use the Cypher clause CREATE:

Create two nodes and a relationship

summary = driver.execute_query(""" (1)
    CREATE (a:Person {name: $name})
    CREATE (b:Person {name: $friendName})
    CREATE (a)-[:KNOWS]->(b)
    """,
    name="Alice", friendName="David",  (2)
    database_="<database-name>",  (3)
).summary
print("Created {nodes_created} nodes in {time} ms.".format(
    nodes_created=summary.counters.nodes_created,
    time=summary.result_available_after
))

1	The Cypher query
2	The query parameters, as keyword arguments
3	The database to run the query on

Read from the database

To retrieve information from the database, use the Cypher clause MATCH:

Retrieve all Person nodes who like other Person s

records, summary, keys = driver.execute_query("""
    MATCH (p:Person)-[:KNOWS]->(:Person)
    RETURN p.name AS name
    """,
    database_="<database-name>",
)

# Loop through results and do something with them
for record in records:  (1)
    print(record.data())  # get record as dict

# Summary information  (2)
print("The query `{query}` returned {records_count} records in {time} ms.".format(
    query=summary.query, records_count=len(records),
    time=summary.result_available_after
))

1	`records` contains the result as an array of `Record` objects
2	`summary` contains the summary of execution returned by the server

Update the database

To update an entity’s information in the database, use the Cypher clauses MATCH and SET:

Update node Alice to add an age property

records, summary, keys = driver.execute_query("""
    MATCH (p:Person {name: $name})
    SET p.age = $age
    """, name="Alice", age=42,
    database_="<database-name>",
)
print(f"Query counters: {summary.counters}.")

To create a new relationship, linking it to two already existing node, use a combination of the Cypher clauses MATCH and CREATE:

Create a relationship :KNOWS between Alice and Bob

records, summary, keys = driver.execute_query("""
    MATCH (alice:Person {name: $name})  (1)
    MATCH (bob:Person {name: $friend})  (2)
    CREATE (alice)-[:KNOWS]->(bob)  (3)
    """, name="Alice", friend="Bob",
    database_="<database-name>",
)
print(f"Query counters: {summary.counters}.")

1	Retrieve the person node named `Alice` and bind it to a variable `alice`
2	Retrieve the person node named `Bob` and bind it to a variable `bob`
3	Create a new `:KNOWS` relationship outgoing from the node bound to `alice` and attach to it the `Person` node named `Bob`

Delete from the database

To remove a node and any relationship attached to it, use the Cypher clause DETACH DELETE:

Remove the Alice node

# This does not delete _only_ p, but also all its relationships!
records, summary, keys = driver.execute_query("""
    MATCH (p:Person {name: $name})
    DETACH DELETE p
    """, name="Alice",
    database_="<database-name>",
)
print(f"Query counters: {summary.counters}.")

Query parameters

Do not hardcode or concatenate parameters directly into queries. Instead, always use placeholders and provide dynamic data as Cypher parameters. This is for:

performance benefits: Neo4j compiles and caches queries, but can only do so if the query structure is unchanged;
security reasons: see protecting against Cypher injection.

Query parameters can be passed either as several keyword arguments, or grouped together in a dictionary passed as value to the parameters_ keyword argument. In case of mix, keyword-argument parameters take precedence over dictionary ones.

Pass query parameters as keyword arguments

driver.execute_query(
    "MERGE (:Person {name: $name})",
    name="Alice", age=42,
    database_="<database-name>",
)

Pass query parameters in a dictionary

parameters = {
    "name": "Alice",
    "age": 42
}
driver.execute_query(
    "MERGE (:Person {name: $name})",
    parameters_=parameters,
    database_="<database-name>",
)

None of your keyword query parameters may end with a single underscore. This is to avoid collisions with the keyword configuration parameters. If you need to use such parameter names, pass them in the parameters_ dictionary.

There can be circumstances where your query structure prevents the usage of parameters in all its parts. For those rare use cases, see Dynamic values in property keys, relationship types, and labels.

Error handling

A query run may fail for a number of reasons, with different exceptions being raised. When using driver.execute_query(), the driver automatically retries to run a failed query if the failure is deemed to be transient (for example due to temporary server unavailability).

An exception will be raised if the operation keeps failing after the configured maximum retry time.

All exceptions coming from the server are subclasses of Neo4jError. You can use an exception’s code to stably identify a specific error; error messages are instead not stable markers, and should not be relied upon.

Basic error handling

# from neo4j.exceptions import Neo4jError

try:
    driver.execute_query('MATCH (p:Person) RETURN', database_='<database-name>')
except Neo4jError as e:
    print('Neo4j error code:', e.code)
    print('Exception message:', e.message)
'''
Neo4j error code: Neo.ClientError.Statement.SyntaxError
Exception message: Invalid input '': expected an expression, '*', 'ALL' or 'DISTINCT' (line 1, column 24 (offset: 23))
"MATCH (p:Person) RETURN"
                        ^
'''

Exception objects also expose errors as GQL-status objects. The main difference between Neo4j error codes and GQL error codes is that the GQL ones are more granular: a single Neo4j error code might be broken in several, more specific GQL error codes.

The actual cause that triggered an exception is sometimes found in the optional GQL-status object __cause__, which is itself a Neo4jError. You might need to recursively traverse the cause chain before reaching the root cause of the exception you caught. In the example below, the exception’s GQL status code is 42001, but the actual source of the error has status code 42I06.

Usage of Neo4jError with GQL-related methods

# from neo4j.exceptions import Neo4jError

try:
    driver.execute_query('MATCH (p:Person) RETURN', database_='<database-name>')
except Neo4jError as e:
    print('Exception GQL status:', e.gql_status)
    print('Exception GQL status description:', e.gql_status_description)
    print('Exception GQL classification:', e.gql_classification)
    print('Exception GQL cause:', e.__cause__)
    print('Exception GQL diagnostic record:', e.diagnostic_record)
'''
Exception GQL status: 42001
Exception GQL status description: error: syntax error or access rule violation - invalid syntax
Exception GQL classification: GqlErrorClassification.CLIENT_ERROR
Exception GQL cause: {gql_status: 42I06} {gql_status_description: error: syntax error or access rule violation - invalid input. Invalid input '', expected: an expression, '*', 'ALL' or 'DISTINCT'.} {message: 42I06: Invalid input '', expected: an expression, '*', 'ALL' or 'DISTINCT'.} {diagnostic_record: {'_classification': 'CLIENT_ERROR', '_position': {'line': 1, 'column': 24, 'offset': 23}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}} {raw_classification: CLIENT_ERROR}
Exception GQL diagnostic record: {'_classification': 'CLIENT_ERROR', '_position': {'line': 1, 'column': 24, 'offset': 23}, 'OPERATION': '', 'OPERATION_CODE': '0', 'CURRENT_SCHEMA': '/'}
'''

GQL status codes are particularly helpful when you want your application to behave differently depending on the exact error that was raised by the server.

Distinguishing between different error codes

# from neo4j.exceptions import Neo4jError

try:
    driver.execute_query('MATCH (p:Person) RETURN', database_='<database-name>')
except Neo4jError as e:
    if e.find_by_gql_status('42001'):
        # Neo.ClientError.Statement.SyntaxError
        # special handling of syntax error in query
        print(e.message)
    elif e.find_by_gql_status('42NFF'):
        # Neo.ClientError.Security.Forbidden
        # special handling of user not having CREATE permissions
        print(e.message)
    else:
        # handling of all other exceptions
        print(e.message)

The GQL status code 50N42 is returned when an exception does not have a GQL-status object. This can happen if the driver is connected to an older Neo4j server. Don’t rely on this status code, as future Neo4j server versions might change it with a more appropriate one.

Transient server errors can be retried without need to alter the original request. You can discover whether an error is transient via the method Neo4jError.is_retryable(), which gives insights into whether a further attempt might be successful. This is particular useful when running queries in explicit transactions, to know if a failed query is worth re-running.

Query configuration

You can supply further keyword arguments to alter the default behavior of .execute_query(). Configuration parameters are suffixed with _.

Database selection

Always specify the database explicitly with the database_ parameter, even on single-database instances. This allows the driver to work more efficiently, as it saves a network round-trip to the server to resolve the home database. If no database is given, the user’s home database is used.

driver.execute_query(
    "MATCH (p:Person) RETURN p.name",
    database_="<database-name>",
)

Specifying the database through the configuration method is preferred over the USE Cypher clause. If the server runs on a cluster, queries with USE require server-side routing to be enabled. Queries can also take longer to execute as they may not reach the right cluster member at the first attempt, and need to be routed to one containing the requested database.

Request routing

In a cluster environment, all queries are directed to the leader node by default. To improve performance on read queries, you can use the argument routing_="r" to route a query to the read nodes.

driver.execute_query(
    "MATCH (p:Person) RETURN p.name",
    routing_="r",  # short for neo4j.RoutingControl.READ
    database_="<database-name>",
)

Although executing a write query in read mode results in a runtime error, you should not rely on this for access control. The difference between the two modes is that read transactions will be routed to any node of a cluster, whereas write ones are directed to primaries. There is no security guarantee that a write query submitted in read mode will be rejected.

Run queries as a different user

You can execute a query through a different user with the parameter auth_. Switching user at the query level is cheaper than creating a new Driver object. The query is then run within the security context of the given user (i.e., home database, permissions, etc.).

driver.execute_query(
    "MATCH (p:Person) RETURN p.name",
    auth_=("<username>", "<password>"),
    database_="<database-name>",
)

The parameter impersonated_user_ provides a similar functionality. The difference is that you don’t need to know a user’s password to impersonate them, but the user under which the Driver was created needs to have the appropriate permissions.

driver.execute_query(
    "MATCH (p:Person) RETURN p.name",
    impersonated_user_="<username>",
    database_="<database-name>",
)

Transform query result

You can transform a query’s result into a different data structure using the result_transformer_ argument. The driver provides built-in methods to transform the result into a pandas dataframe or into a graph, but you can also craft your own transformer.

For more information, see Manipulate query results.

A full example

from neo4j import GraphDatabase
from neo4j.exceptions import Neo4jError


URI = "<database-uri>"
AUTH = ("<username>", "<password>")

people = [{"name": "Alice", "age": 42, "friends": ["Bob", "Peter", "Anna"]},
          {"name": "Bob", "age": 19},
          {"name": "Peter", "age": 50},
          {"name": "Anna", "age": 30}]

with GraphDatabase.driver(URI, auth=AUTH) as driver:
    try:
        # Create some nodes
        for person in people:
            records, summary, keys = driver.execute_query(
                "MERGE (p:Person {name: $person.name, age: $person.age})",
                person=person,
                database_="<database-name>",
            )

        # Create some relationships
        for person in people:
            if person.get("friends"):
                records, summary, keys = driver.execute_query("""
                    MATCH (p:Person {name: $person.name})
                    UNWIND $person.friends AS friend_name
                    MATCH (friend:Person {name: friend_name})
                    MERGE (p)-[:KNOWS]->(friend)
                    """, person=person,
                    database_="<database-name>",
                )

        # Retrieve Alice's friends who are under 40
        records, summary, keys = driver.execute_query("""
            MATCH (p:Person {name: $name})-[:KNOWS]-(friend:Person)
            WHERE friend.age < $age
            RETURN friend
            """, name="Alice", age=40,
            routing_="r",
            database_="<database-name>",
        )
        # Loop through results and do something with them
        for record in records:
            print(record)
        # Summary information
        print("The query `{query}` returned {records_count} records in {time} ms.".format(
            query=summary.query, records_count=len(records),
            time=summary.result_available_after
        ))

    except Neo4jError as e:
        print(e)
        # further logging/processing

For more information see API documentation → Driver.execute_query().

Glossary

LTS: A Long Term Support release is one guaranteed to be supported for a number of years. Neo4j 4.4 and 5.26 are LTS versions.
Aura: Aura is Neo4j’s fully managed cloud service. It comes with both free and paid plans.
Cypher: Cypher is Neo4j’s graph query language that lets you retrieve data from the database. It is like SQL, but for graphs.
APOC: Awesome Procedures On Cypher (APOC) is a library of (many) functions that can not be easily expressed in Cypher itself.
Bolt: Bolt is the protocol used for interaction between Neo4j instances and drivers. It listens on port 7687 by default.
ACID: Atomicity, Consistency, Isolation, Durability (ACID) are properties guaranteeing that database transactions are processed reliably. An ACID-compliant DBMS ensures that the data in the database remains accurate and consistent despite failures.
eventual consistency: A database is eventually consistent if it provides the guarantee that all cluster members will, at some point in time, store the latest version of the data.
causal consistency: A database is causally consistent if read and write queries are seen by every member of the cluster in the same order. This is stronger than eventual consistency.
NULL: The null marker is not a type but a placeholder for absence of value. For more information, see Cypher → Working with null.
transaction: A transaction is a unit of work that is either committed in its entirety or rolled back on failure. An example is a bank transfer: it involves multiple steps, but they must all succeed or be reverted, to avoid money being subtracted from one account but not added to the other.
backpressure: Backpressure is a force opposing the flow of data. It ensures that the client is not being overwhelmed by data faster than it can handle.
bookmark: A bookmark is a token representing some state of the database. By passing one or multiple bookmarks along with a query, the server will make sure that the query does not get executed before the represented state(s) have been established.
transaction function: A transaction function is a callback executed by an execute_read or execute_write call. The driver automatically re-executes the callback in case of server failure.
Driver: A Driver object holds the details required to establish connections with a Neo4j database.