apoc.export.csv.all

This procedure is not considered safe to run from multiple threads. It is therefore not supported by the parallel runtime (introduced in Neo4j 5.13). For more information, see the Cypher Manual → Parallel runtime.

Details
Syntax	`apoc.export.csv.all(file, config) :: (file, source, format, nodes, relationships, properties, time, rows, batchSize, batches, done, data)`
Description	Exports the full database to the provided CSV file.
Input arguments	Name	Type	Description
	`file`	`STRING`	The name of the file to which the data will be exported.
	`config`	`MAP`	`{ stream = false :: BOOLEAN, batchSize = 20000 :: INTEGER, bulkImport = false :: BOOLEAN, timeoutSeconds = 100 :: INTEGER, compression = 'None' :: STRING, charset = 'UTF_8' :: STRING, quotes = 'always' :: ['always', 'none', 'ifNeeded'], differentiateNulls = false :: BOOLEAN, sampling = false :: BOOLEAN, samplingConfig :: MAP }`
Return arguments	Name	Type	Description
	`file`	`STRING`	The name of the file to which the data was exported.
	`source`	`STRING`	A summary of the exported data.
	`format`	`STRING`	The format the file is exported in.
	`nodes`	`INTEGER`	The number of exported nodes.
	`relationships`	`INTEGER`	The number of exported relationships.
	`properties`	`INTEGER`	The number of exported properties.
	`time`	`INTEGER`	The duration of the export.
	`rows`	`INTEGER`	The number of rows returned.
	`batchSize`	`INTEGER`	The size of the batches the export was run in.
	`batches`	`INTEGER`	The number of batches the export was run in.
	`done`	`BOOLEAN`	Whether the export ran successfully.
	`data`	`ANY`	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.all procedure exports the whole database to a CSV file or as a stream.

The following query exports the whole database to the file movies.csv:

CALL apoc.export.csv.all("movies.csv", {})

Results
file	source	format	nodes	relationships	properties	time	rows	batchSize	batches	done	data
"movies.csv"	"database: nodes(8), rels(7)"	"csv"	8	7	21	39	15	20000	1	TRUE	NULL

The contents of movies.csv are shown below:

movies.csv

"_id","_labels","born","name","released","tagline","title","_start","_end","_type","roles"
"188",":Movie","","","1999","Welcome to the Real World","The Matrix",,,,
"189",":Person","1964","Keanu Reeves","","","",,,,
"190",":Person","1967","Carrie-Anne Moss","","","",,,,
"191",":Person","1961","Laurence Fishburne","","","",,,,
"192",":Person","1960","Hugo Weaving","","","",,,,
"193",":Person","1967","Lilly Wachowski","","","",,,,
"194",":Person","1965","Lana Wachowski","","","",,,,
"195",":Person","1952","Joel Silver","","","",,,,
,,,,,,,"189","188","ACTED_IN","[""Neo""]"
,,,,,,,"190","188","ACTED_IN","[""Trinity""]"
,,,,,,,"191","188","ACTED_IN","[""Morpheus""]"
,,,,,,,"192","188","ACTED_IN","[""Agent Smith""]"
,,,,,,,"193","188","DIRECTED",""
,,,,,,,"194","188","DIRECTED",""
,,,,,,,"195","188","PRODUCED",""

The following query returns a stream of the whole database in the data column:

CALL apoc.export.csv.all(null, {stream:true})
YIELD file, nodes, relationships, properties, data
RETURN file, nodes, relationships, properties, data

Results
file	nodes	relationships	properties	data
NULL	8	7	21	"\"_id\",\"_labels\",\"born\",\"name\",\"released\",\"tagline\",\"title\",\"_start\",\"_end\",\"_type\",\"roles\" \"188\",\":Movie\",\"\",\"\",\"1999\",\"Welcome to the Real World\",\"The Matrix\",,,, \"189\",\":Person\",\"1964\",\"Keanu Reeves\",\"\",\"\",\"\",,,, \"190\",\":Person\",\"1967\",\"Carrie-Anne Moss\",\"\",\"\",\"\",,,, \"191\",\":Person\",\"1961\",\"Laurence Fishburne\",\"\",\"\",\"\",,,, \"192\",\":Person\",\"1960\",\"Hugo Weaving\",\"\",\"\",\"\",,,, \"193\",\":Person\",\"1967\",\"Lilly Wachowski\",\"\",\"\",\"\",,,, \"194\",\":Person\",\"1965\",\"Lana Wachowski\",\"\",\"\",\"\",,,, \"195\",\":Person\",\"1952\",\"Joel Silver\",\"\",\"\",\"\",,,, ,,,,,,,\"189\",\"188\",\"ACTED_IN\",\"[\"\"Neo\"\"]\" ,,,,,,,\"190\",\"188\",\"ACTED_IN\",\"[\"\"Trinity\"\"]\" ,,,,,,,\"191\",\"188\",\"ACTED_IN\",\"[\"\"Morpheus\"\"]\" ,,,,,,,\"192\",\"188\",\"ACTED_IN\",\"[\"\"Agent Smith\"\"]\" ,,,,,,,\"193\",\"188\",\"DIRECTED\",\"\" ,,,,,,,\"194\",\"188\",\"DIRECTED\",\"\" ,,,,,,,\"195\",\"188\",\"PRODUCED\",\"\" "

You can use the configuration sampling (default: false). With this config, the apoc.export.csv.all procedure uses the apoc.meta.nodeTypeProperties and the apoc.meta.relTypeProperties procedures under the hood to get the property types. You can customize the configuration of these 2 apoc.meta.* procedure, using the samplingConfig: MAP configuration, to limit the number of nodes/rels to analyze.

So you can execute with the following data set:

CREATE (:User:Sample {`last:Name`:'Galilei'}), (:User:Sample {address:'Universe'}),
    (:User:Sample {foo:'bar'})-[:KNOWS {one: 'two', three: 'four'}]->(:User:Sample {baz:'baa', foo: true})

Combined with the following query:

CALL apoc.export.csv.all('movies.csv', {sampling: true, samplingConfig: {sample: 1}})

Results
file	source	format	nodes	relationships	properties	time	rows	batchSize	batches	done	data
"movies.csv"	"database: nodes(4), rels(1)"	"csv"	4	1	3	4	5	20000	1	TRUE	NULL

Execution of the above query would output content similar to that below (result could change depending on the sample):

movies.csv

"_id","_labels","baz","foo","_start","_end","_type"
"0",":Sample:User","","",,,
"1",":Sample:User","","",,,
"2",":Sample:User","","bar",,,
"3",":Sample:User","baa","true",,,
,,,,"2","3","KNOWS"

More documentation of apoc.export.csv.all