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Version: 2.10.1

LOAD CSV Cypher clause

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.

tip

If the data is importing slower than expected, you can speed it up by creating indexes or switching the storage mode to analytical.

If the import speed is still unsatisfactory, don't hesitate to contact us on Discord.

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 filesystem 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 the 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.

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 string: 

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

Examples​

Below, you can find two examples of how to use the LOAD CSV clause depending on the complexity of your data:

One type of nodes and relationships​

Let's import a simple dataset from the people_nodes and people_relationships CSV files.

  1. Download the CSV files:

    • people_nodes.csv file with the following content:

      id,name
      100,Daniel
      101,Alex
      102,Sarah
      103,Mia
      104,Lucy

    • people_relationships.csv file with the following content:

      id_from,id_to
      100,101
      100,102
      100,103
      101,103
      102,104

    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).

  2. Check the location of the CSV file. If you are working with Docker, copy the files from your local directory into the Docker container where Memgraph can access them.

    Transfer CSV files into a Docker container

    1. Start your Memgraph instance using Docker.

    2. Open a new terminal and find the CONTAINER ID of the Memgraph Docker container:

    docker ps

    3. Copy a file from your current directory to the container with the command:

    docker cp ./file_to_copy.csv <CONTAINER ID>:/file_to_copy.csv

    The file is now inside your Docker container, and you can import it using the LOAD CSV clause.

  3. The following query will load row by row from the 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.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.

    If you have a large dataset, it's beneficial to create indexes on a property that will be used to connect nodes and relationships, in this case, the id property.

    CREATE INDEX ON :Person(id);

  4. With the initial nodes in place, you can now create relationships between them by importing the people_relationships.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);
This is how the graph should look like in Memgraph after the import
Run the following query:
MATCH p=()-[]-() RETURN p;

Multiple types of nodes and relationships​

In the case of a more complex graph, we have to deal with multiple node and relationship types.

Let's say we want to create a graph like this:

We will create that graph by using LOAD CSV clause to import four CSV files.

  1. Download the people_nodes.csv file, content of which is:

    id,name,age,city
    100,Daniel,30,London
    101,Alex,15,Paris
    102,Sarah,17,London
    103,Mia,25,Zagreb
    104,Lucy,21,Paris

    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).

  2. Check the location of the CSV file. If you are working with Docker, copy the files from your local directory into the Docker container where Memgraph can access them.

    Transfer CSV files into a Docker container

    1. Start your Memgraph instance using Docker.

    2. Open a new terminal and find the CONTAINER ID of the Memgraph Docker container:

    docker ps

    3. Copy a file from your current directory to the container with the command:

    docker cp ./file_to_copy.csv <CONTAINER ID>:/file_to_copy.csv

    The file is now inside your Docker container, and you can import it using the LOAD CSV clause.

  3. 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});
    This is how the graph should look like in Memgraph after the import:
    Run the following query:
    MATCH (p) RETURN p;

  4. If you have a large dataset, it's beneficial to create indexes on a property that will be used to connect nodes and relationships, in this case, the id property.

    CREATE INDEX ON :Person(id);

Now move on to the people_relationships.csv file.