apoc.export.csv.data
This procedure is not considered safe to run from multiple threads. It is therefore not supported by the parallel runtime. For more information, see the Cypher Manual → Parallel runtime. |
Syntax |
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Description |
Exports the given |
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Input arguments |
Name |
Type |
Description |
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A list of nodes to export. |
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A list of relationships to export. |
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The name of the file to which the data will be exported. |
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Return arguments |
Name |
Type |
Description |
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The name of the file to which the data was exported. |
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A summary of the exported data. |
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The format the file is exported in. |
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The number of exported nodes. |
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The number of exported relationships. |
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The number of exported properties. |
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The duration of the export. |
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The number of rows returned. |
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The size of the batches the export was run in. |
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The number of batches the export was run in. |
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Whether the export ran successfully. |
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The data returned by the export. |
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
file | source | format | nodes | relationships | properties | time | rows | batchSize | batches | done | data |
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"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
file | source | format | nodes | relationships | properties | time | rows | batchSize | batches | done | data |
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"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
file | nodes | relationships | properties | data |
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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\"\"]\" " |