Import data from CSV files

If your data is in CSV format, you can import it into a running Memgraph database from a designated CSV files using the LOAD CSV Cypher clause. The clause reads row by row from a CSV file, binds the contents of the parsed row to the variable you specified and populates the database if it is empty, or appends new data to an existing dataset. Memgraph supports the Excel CSV dialect, as it's the most common one.

LOAD CSV clause cannot be used with a Memgraph Cloud instance because at the moment it is impossible to make files accessible by Memgraph.

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If the data is importing slower than expected, you can speed up the import by creating indexes on nodes to improve the import speed of relationships, or switching the storage mode to analytical.

If the import speed is still unsatisfactory, don't hesitate to reach out on Discord (opens in a new tab).

LOAD CSV clause syntax

The syntax of the LOAD CSV clause is:

LOAD CSV FROM <csv-location> ( WITH | NO ) HEADER [IGNORE BAD] [DELIMITER <delimiter-string>] [QUOTE <quote-string>] [NULLIF <nullif-string>] AS <variable-name>
  • <csv-location> is a string of the location to the CSV file.
    Without a URL protocol it refers to a file path. There are no restrictions on where in your file system the file can be located, as long as the path is valid (i.e., the file exists). If you are using Docker to run Memgraph, you will need to copy the files from your local directory into Docker container where Memgraph can access them.
    If using http://, https://, or ftp:// the CSV file will be fetched over the network.

  • ( WITH | NO ) HEADER flag specifies whether the CSV file has a header, in which case it will be parsed as a map, or it doesn't have a header, in which case it will be parsed as a list.

    If the WITH HEADER option is set, the very first line in the file will be parsed as the header, and any remaining rows will be parsed as regular rows. The value bound to the row variable will be a map of the form:

    { ( "header_field" : "row_value" )? ( , "header_field" : "row_value" )* }

    If the NO HEADER option is set, then each row is parsed as a list of values. The contents of the row can be accessed using the list index syntax. Note that in this mode, there are no restrictions on the number of values a row contains. This isn't recommended, as the user must manually handle the varying number of values in a row.

  • IGNORE BAD flag specifies whether rows containing errors should be ignored or not. If it's set, the parser attempts to return the first valid row from the CSV file. If it isn't set, an exception will be thrown on the first invalid row encountered.

  • DELIMITER <delimiter-string> option enables the user to specify the CSV delimiter character. If it isn't set, the default delimiter character , is assumed.

  • QUOTE <quote-string> option enables the user to specify the CSV quote character. If it isn't set, the default quote character " is assumed.

  • NULLIF <nullif-string> option enables you to specify a sequence of characters that will be parsed as null. By default, all empty columns in Memgraph are treated as empty strings, so if this option is not used, no values will be treated as null.

  • <variable-name> is a symbolic name representing the variable to which the contents of the parsed row will be bound to, enabling access to the row contents later in the query. The variable doesn't have to be used in any subsequent clause.

LOAD CSV clause specificities

When using the LOAD CSV clause please keep in mind:

  • The parser parses the values as strings so it's up to the user to convert the parsed row values to the appropriate type. This can be done using the built-in conversion functions such as ToInteger, ToFloat, ToBoolean etc. Consult the documentation on the available conversion functions.

    If all values are indeed strings and the file has a header, you can import data using the following query:

    LOAD CSV FROM "/people.csv" WITH HEADER AS row
    CREATE (p:People) SET p += row;
  • The LOAD CSV clause is not a standalone clause, which means that a valid query must contain at least one more clause, for example:

    LOAD CSV FROM "/people.csv" WITH HEADER AS row
    CREATE (p:People) SET p += row;

    In this regard, the following query will throw an exception:

    LOAD CSV FROM "/file.csv" WITH HEADER AS row;
  • Adding a MATCH or MERGE clause before the LOAD CSV allows you to match certain entities in the graph before running LOAD CSV, which is an optimization as matched entities do not need to be searched for every row in the CSV file.

    But, the MATCH or MERGE clause can be used prior the LOAD CSV clause only if the clause returns only one row. Returning multiple rows before calling the LOAD CSV clause will cause a Memgraph runtime error.

  • The LOAD CSV clause can be used at most once per query, so the queries like the one below wll throw an exception:

    LOAD CSV FROM "/x.csv" WITH HEADER as x
    LOAD CSV FROM "/y.csv" WITH HEADER as y
    CREATE (n:A {p1 : x, p2 : y});

Increase import speed

The LOAD CSV clause will create relationships much faster, and consequently speed up data import, if you create indexes on nodes or node properties once you import them:

  CREATE INDEX ON :Node(id);

If the LOAD CSV clause is merging data instead of creating it, create indexes before running the LOAD CSV clause.

You can also speed up import if you switch Memgraph to analytical storage mode. In the analytical storage mode there are no ACID guarantees besides manually created snapshots but it does increase the import speed up to 6 times with 6 times less memory consumption. After import you can switch the storage mode back to transactional and enable ACID guarantees.

You can switch between modes within the session using the following query:

STORAGE MODE IN_MEMORY_{TRANSACTIONAL|ANALYTICAL};

When in the analytical storage mode, don't import data using multiple threads.

The LOAD CSV clause can handle CSV's which are compressed with gzip or bzip2. This can speed up time it takes to fetch and/or load the file.

If you are using on-disk storage mode consider using Edge import mode to get the best import performance.

Import files WITH and NO HEADER

The goal of this example is to import two CSV files.

One file contains data we will use to create nodes labeled :People, and the other file will be used to connect those nodes with the :IS_FRIENDS_WITH.

There are also two variations of the files, files with a header, and files without a header.

Download the files

Check the location of the CSV files

If you are working with Docker, copy the files from your local directory into the Docker container so Memgraph can access them.

Import nodes

These CSV files have a header, which means the HEADER option of the LOAD CSV clause needs to be set to WITH. Each row will be parsed as a map, and the fields can be accessed using the property lookup syntax (e.g. id:row.id).

The following query will load row by row from the people_nodes_wh CSV file, and create a new node for each row with properties based on the parsed row values:

LOAD CSV FROM "/path-to/people_nodes_wh.csv" WITH HEADER AS row
CREATE (p:Person {id: row.id, name: row.name});

If successful, you should receive an Empty set (0.014 sec) message.

Create indexes

Creating an index on a property used to connect nodes with relationships, in this case, the id property of the :Person nodes, will speed up the import of relationships, especially with large datasets:

CREATE INDEX ON :Person(id);

Import relationships

Import the data from the people_relationships_wh.csv file:

LOAD CSV FROM "/path-to/people_relationships.csv" WITH HEADER AS row
MATCH (p1:Person {id: row.id_from}), (p2:Person {id: row.id_to})
CREATE (p1)-[:IS_FRIENDS_WITH]->(p2);

If successful, you should receive an Empty set (0.014 sec) message.

Final result

Run the following query to see how the imported data looks as a graph:

MATCH p=()-[]-() RETURN p;


Import multiple CSV files with distinct graph objects

In this example, the data is split across four files, each file contains nodes of a single label or relationships of a single type. All files have a header.

Download the files

  • people_nodes.csv (opens in a new tab) is used to create nodes labeled :Person.
    The file contains the following data:

    id,name,age,city
    100,Daniel,30,London
    101,Alex,15,Paris
    102,Sarah,17,London
    103,Mia,25,Zagreb
    104,Lucy,21,Paris
  • restaurants_nodes.csv (opens in a new tab) is used to create nodes labeled :Restaurants.
    The file contains the following data:

    id,name,menu
    200,Mc Donalds,Fries;BigMac;McChicken;Apple Pie
    201,KFC,Fried Chicken;Fries;Chicken Bucket
    202,Subway,Ham Sandwich;Turkey Sandwich;Foot-long
    203,Dominos,Pepperoni Pizza;Double Dish Pizza;Cheese filled Crust
  • people_relationships.csv (opens in a new tab) is used to connect people with the :IS_FRIENDS_WITH relationship.
    The file contains the following data:

    first_person,second_person,met_in
    100,102,2014
    103,101,2021
    102,103,2005
    101,104,2005
    104,100,2018
    101,102,2017
    100,103,2001
  • restaurants_relationships.csv (opens in a new tab) is used to connect people with restaurants using the :ATE_AT relationship.
    The file contains the following data:

    PERSON_ID,REST_ID,liked
    100,200,true
    103,201,false
    104,200,true
    101,202,false
    101,203,false
    101,200,true
    102,201,true

Check the location of the CSV files

If you are working with Docker, copy the files from your local directory into the Docker container so Memgraph can access them.

Import nodes

The CSV files have a header, which means the HEADER option of the LOAD CSV clause needs to be set to WITH. Each row will be parsed as a map, and the fields can be accessed using the property lookup syntax (e.g. id: row.id).

The following query will load row by row from the file, and create a new node for each row with properties based on the parsed row values:

LOAD CSV FROM "/path-to/people_nodes.csv" WITH HEADER AS row
CREATE (n:Person {id: row.id, name: row.name, age: ToInteger(row.age), city: row.city});

In the same manner, the following query will create new nodes for each restaurant:

LOAD CSV FROM "/path-to/restaurants_nodes.csv" WITH HEADER AS row
CREATE (n:Restaurant {id: row.id, name: row.name, menu: row.menu});

Create indexes

Creating an index on a property used to connect nodes with relationships, in this case, the id property of the :Person nodes, will speed up the import of relationships, especially with large datasets:

CREATE INDEX ON :Person(id);

Import relationships

The following query will create relationships between the people nodes:

LOAD CSV FROM "/path-to/people_relationships.csv" WITH HEADER AS row
MATCH (p1:Person {id: row.first_person})
MATCH (p2:Person {id: row.second_person})
CREATE (p1)-[f:IS_FRIENDS_WITH]->(p2)
SET f.met_in = row.met_in;

The following query will create relationships between people and restaurants where they ate:

LOAD CSV FROM "/path-to/restaurants_relationships.csv" WITH HEADER AS row
MATCH (p1:Person {id: row.PERSON_ID})
MATCH (re:Restaurant {id: row.REST_ID})
CREATE (p1)-[ate:ATE_AT]->(re)
SET ate.liked = ToBoolean(row.liked);

Final result

Run the following query to see how the imported data looks as a graph:

MATCH p=()-[]-() RETURN p;