apoc.export.csv.data

Procedure APOC Core

apoc.export.csv.data(nodes,rels,file,config) - exports given nodes and relationships as csv to the provided file

Signature

apoc.export.csv.data(nodes :: LIST? OF NODE?, rels :: LIST? OF RELATIONSHIP?, file :: STRING?, config :: MAP?) :: (file :: STRING?, source :: STRING?, format :: STRING?, nodes :: INTEGER?, relationships :: INTEGER?, properties :: INTEGER?, time :: INTEGER?, rows :: INTEGER?, batchSize :: INTEGER?, batches :: INTEGER?, done :: BOOLEAN?, data :: STRING?)

Input parameters

Name	Type	Default
nodes	LIST? OF NODE?	null
rels	LIST? OF RELATIONSHIP?	null
file	STRING?	null
config	MAP?	null

Name

Type

Default

nodes

LIST? OF NODE?

null

rels

LIST? OF RELATIONSHIP?

null

file

STRING?

null

config

MAP?

null

Output parameters

Name	Type
file	STRING?
source	STRING?
format	STRING?
nodes	INTEGER?
relationships	INTEGER?
properties	INTEGER?
time	INTEGER?
rows	INTEGER?
batchSize	INTEGER?
batches	INTEGER?
done	BOOLEAN?
data	STRING?

Name

Type

file

STRING?

source

STRING?

format

STRING?

nodes

INTEGER?

relationships

INTEGER?

properties

INTEGER?

time

INTEGER?

rows

INTEGER?

batchSize

INTEGER?

batches

INTEGER?

done

BOOLEAN?

data

STRING?

Usage Examples

The examples in this section are based on the following sample graph:

CREATE (TheMatrix:Movie {title:'The Matrix', released:1999, tagline:'Welcome to the Real World'})
CREATE (Keanu:Person {name:'Keanu Reeves', born:1964})
CREATE (Carrie:Person {name:'Carrie-Anne Moss', born:1967})
CREATE (Laurence:Person {name:'Laurence Fishburne', born:1961})
CREATE (Hugo:Person {name:'Hugo Weaving', born:1960})
CREATE (LillyW:Person {name:'Lilly Wachowski', born:1967})
CREATE (LanaW:Person {name:'Lana Wachowski', born:1965})
CREATE (JoelS:Person {name:'Joel Silver', born:1952})
CREATE
(Keanu)-[:ACTED_IN {roles:['Neo']}]->(TheMatrix),
(Carrie)-[:ACTED_IN {roles:['Trinity']}]->(TheMatrix),
(Laurence)-[:ACTED_IN {roles:['Morpheus']}]->(TheMatrix),
(Hugo)-[:ACTED_IN {roles:['Agent Smith']}]->(TheMatrix),
(LillyW)-[:DIRECTED]->(TheMatrix),
(LanaW)-[:DIRECTED]->(TheMatrix),
(JoelS)-[:PRODUCED]->(TheMatrix);

The Neo4j Browser visualization below shows the imported graph:

The apoc.export.csv.data procedure exports the specified nodes and relationships to a CSV file or as a stream.

The following query exports all nodes with the :Person label with a name property that starts with L to the file movies-l.csv:

MATCH (person:Person)
WHERE person.name STARTS WITH "L"
WITH collect(person) AS people
CALL apoc.export.csv.data(people, [], "movies-l.csv", {})
YIELD file, source, format, nodes, relationships, properties, time, rows, batchSize, batches, done, data
RETURN file, source, format, nodes, relationships, properties, time, rows, batchSize, batches, done, data

Table 1. Results
file	source	format	nodes	relationships	properties	time	rows	batchSize	batches	done	data
"movies-l.csv"	"data: nodes(3), rels(0)"	"csv"	3	0	6	2	3	20000	1	TRUE	NULL

The contents of movies-l.csv are shown below:

"_id","_labels","born","name","_start","_end","_type"
"191",":Person","1961","Laurence Fishburne",,,
"193",":Person","1967","Lilly Wachowski",,,
"194",":Person","1965","Lana Wachowski",,,

The following query exports all ACTED_IN relationships and the nodes with Person and Movie labels on either side of that relationship to the file movies-actedIn.csv:

MATCH (person:Person)-[actedIn:ACTED_IN]->(movie:Movie)
WITH collect(DISTINCT person) AS people, collect(DISTINCT movie) AS movies, collect(actedIn) AS actedInRels
CALL apoc.export.csv.data(people + movies, actedInRels, "movies-actedIn.csv", {})
YIELD file, source, format, nodes, relationships, properties, time, rows, batchSize, batches, done, data
RETURN file, source, format, nodes, relationships, properties, time, rows, batchSize, batches, done, data

Table 2. Results
file	source	format	nodes	relationships	properties	time	rows	batchSize	batches	done	data
"movies-actedIn.csv"	"data: nodes(5), rels(4)"	"csv"	5	4	15	2	9	20000	1	TRUE	NULL

The contents of movies-actedIn.csv are shown below:

"_id","_labels","born","name","released","tagline","title","_start","_end","_type","roles"
"189",":Person","1964","Keanu Reeves","","","",,,,
"190",":Person","1967","Carrie-Anne Moss","","","",,,,
"191",":Person","1961","Laurence Fishburne","","","",,,,
"192",":Person","1960","Hugo Weaving","","","",,,,
"188",":Movie","","","1999","Welcome to the Real World","The Matrix",,,,
,,,,,,,"189","188","ACTED_IN","[""Neo""]"
,,,,,,,"190","188","ACTED_IN","[""Trinity""]"
,,,,,,,"191","188","ACTED_IN","[""Morpheus""]"
,,,,,,,"192","188","ACTED_IN","[""Agent Smith""]"

The following query returns a stream of all ACTED_IN relationships and the nodes with Person and Movie labels on either side of that relationship in the data column:

MATCH (person:Person)-[actedIn:ACTED_IN]->(movie:Movie)
WITH collect(DISTINCT person) AS people, collect(DISTINCT movie) AS movies, collect(actedIn) AS actedInRels
CALL apoc.export.csv.data(people + movies, actedInRels, null, {stream: true})
YIELD file, nodes, relationships, properties, data
RETURN file, nodes, relationships, properties, data

Table 3. Results
file	nodes	relationships	properties	data
NULL	5	4	15	"\"_id\",\"_labels\",\"born\",\"name\",\"released\",\"tagline\",\"title\",\"_start\",\"_end\",\"_type\",\"roles\" \"190\",\":Person\",\"1967\",\"Carrie-Anne Moss\",\"\",\"\",\"\",,,, \"189\",\":Person\",\"1964\",\"Keanu Reeves\",\"\",\"\",\"\",,,, \"191\",\":Person\",\"1961\",\"Laurence Fishburne\",\"\",\"\",\"\",,,, \"192\",\":Person\",\"1960\",\"Hugo Weaving\",\"\",\"\",\"\",,,, \"188\",\":Movie\",\"\",\"\",\"1999\",\"Welcome to the Real World\",\"The Matrix\",,,, ,,,,,,,\"189\",\"188\",\"ACTED_IN\",\"[\"\"Neo\"\"]\" ,,,,,,,\"190\",\"188\",\"ACTED_IN\",\"[\"\"Trinity\"\"]\" ,,,,,,,\"191\",\"188\",\"ACTED_IN\",\"[\"\"Morpheus\"\"]\" ,,,,,,,\"192\",\"188\",\"ACTED_IN\",\"[\"\"Agent Smith\"\"]\" "

More documentation of apoc.export.csv.data