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Version: 1.6.1

CSV Import Tool

CSV is a universal and very versatile data format used to store large quantities of data. Each Memgraph database instance has a CSV import tool installed called mg_import_csv. The CSV import tool should be used for initial bulk ingestion of data into the database. Upon ingestion, the CSV importer creates a snapshot that will be used by the database to recover its state on its next startup.

If you are already familiar with the Neo4j bulk import tool, then using the mg_import_csv tool should be easy. The CSV import tool is fully compatible with the Neo4j CSV format. If you already have a pipeline set-up for Neo4j, you should only replace neo4j-admin import with mg_import_csv.

CSV File Format

Each row of a CSV file represents a single entry that should be imported into the database. Both nodes and relationships can be imported into the database using CSV files.

Each set of CSV files must have a header that describes the data that is stored in the CSV files. Each field in the CSV header is in the format <name>[:<type>] which identifies the name that should be used for that column and the type that should be used for that column. The type is optional and defaults to string (see the following chapter).

Each CSV field must be divided using the delimiter and each CSV field can either be quoted or unquoted. When the field is quoted, the first and last character in the field must be the quote character. If the field isn't quoted, and a quote character appears in it, it is treated as a regular character. If a quote character appears inside a quoted string then the quote character must be doubled in order to escape it. Line feeds and carriage returns are ignored in the CSV file, also, the file can't contain a NULL character.

Properties

Both nodes and relationships can have properties added to them. When importing properties, the CSV importer uses the name specified in the header of the corresponding CSV column for the name of the property. A property is designated by specifying one of the following types in the header:

  • integer, int, long, byte, short: creates an integer property
  • float, double: creates a float property
  • boolean, bool: creates a boolean property
  • string, char: creates a string property

When importing a boolean value, the CSV field should contain exactly the text true to import a True boolean value. All other text values are treated as a boolean value False.

If you want to import an array of values, you can do so by appending [] to any of the above types. The values of the array are then determined by splitting the raw CSV value using the array delimiter character.

Assuming that the array delimiter is ;, the following example:

first_name,last_name:string,number:integer,aliases:string[]
John,Doe,1,Johnny;Jo;J-man
Melissa,Doe,2,Mel

Will yield these results:

CREATE ({first_name: "John", last_name: "Doe", number: 1, aliases: ["Johnny", "Jo", "J-man"]});
CREATE ({first_name: "Melissa", last_name: "Doe", number: 2, aliases: ["Mel"]});

Nodes

When importing nodes, several more types can be specified in the header of the CSV file (along with all property types):

  • ID: id of the node that should be used as the node ID when importing relationships
  • LABEL: designates that the field contains additional labels for the node
  • IGNORE: designates that the field should be ignored

The ID field type sets the internal ID that will be used for the node when creating relationships. It is optional and nodes that don't have an ID value specified will be imported, but can't be connected to any relationships. If you want to save the ID value as a property in the database, just specify a name for the ID (user_id:ID). If you just want to use the ID during the import, leave out the name of the field (:ID). The ID field also supports creating separate ID spaces. The ID space is specified with the ID space name appended to the ID type in parentheses (ID(user)). That allows you to have the same IDs (by value) for multiple different node files (for example, numbers from 1 to N). The IDs in each ID space will be treated as an independent set of IDs that don't interfere with IDs in another ID space.

The LABEL field type adds additional labels to the node. The value is treated as an array type so that multiple additional labels can be specified for each node. The value is split using the array delimiter (--array-delimiter flag).

Relationships

In order to be able to import relationships, you must import the nodes in the same invocation of mg_import_csv that is used to import the relationships.

When importing relationships, several more types can be specified in the header of the CSV file (along with all property types):

  • START_ID: id of the start node that should be connected with the relationship
  • END_ID: id of the end node that should be connected with the relationship
  • TYPE: designates the type of the relationship
  • IGNORE: designates that the field should be ignored

The START_ID field type sets the start node that should be connected with the relationship to the end node. The field must be specified and the node ID must be one of the node IDs that were specified in the node CSV files. The name of this field is ignored. If the node ID is in an ID space, you can specify the ID space for the in the same way as for the node ID (START_ID(user)).

The END_ID field type sets the end node that should be connected with the relationship to the start node. The field must be specified and the node ID must be one of the node IDs that were specified in the node CSV files. The name of this field is ignored. If the node ID is in an ID space, you can specify the ID space for the in the same way as for the node ID (END_ID(user)).

The TYPE field type sets the type of the relationship. Each relationship must have a relationship type, but it doesn't necessarily need to be specified in the CSV file, it can also be set externally for the whole CSV file. The name of this field is ignored.

CSV Importer Flags

The importer has many command line options that allow you to customize the way the importer loads your data.

The two main flags that are used to specify the input CSV files are --nodes and --relationships. Basic description of these flags is provided in the table and more detailed explainion can be found further down bellow.

FlagDescription
--nodesUsed to specify CSV files that contain the nodes to the importer.
--relationshipsUsed to specify CSV files that contain the relationships to the importer.
--delimiterSets the delimiter that should be used when splitting the CSV fields (default ,)
--quoteSets the quote character that should be used to quote a CSV field (default ")
--array-delimiterSets the delimiter that should be used when splitting array values (default ;)
--id-typeSpecifies which data type should be used to store the supplied
node IDs when storing them as properties (if the field name is supplied).
The supported values are either STRING or INTEGER. (default STRING)
--ignore-empty-stringsInstructs the importer to treat all empty strings as Null values
instead of an empty string value (default false)
--ignore-extra-columnsInstructs the importer to ignore all columns (instead of raising an error)
that aren't specified after the last specified column in the CSV header. (default false)
--skip-bad-relationshipsInstructs the importer to ignore all relationships (instead of raising an error)
that refer to nodes that don't exist in the node files. (default false)
--skip-duplicate-nodesInstructs the importer to ignore all duplicate nodes (instead of raising an error).
Duplicate nodes are nodes that have an ID that is the same as another node that was already imported. (default false)
--trim-stringsInstructs the importer to trim all of the loaded CSV field values before processing them further.
Trimming the fields removes all leading and trailing whitespace from them. (default false)

The --nodes and --relationships flags are used to specify CSV files that contain the nodes and relationships to the importer. Multiple files can be specified in each supplied --nodes or --relationships flag. Files that are supplied in one --nodes or --relationships flag are treated by the CSV parser as one big CSV file. Only the first line of the first file is parsed for the CSV header, all other files (and rows) are treated as data. This is useful when you have a very large CSV file and don't want to edit its first line just to add a CSV header. Instead, you can specify the header in a separate file (e.g. users_header.csv or friendships_header.csv ) and have the data intact in the large file (e.g. users.csv or friendships.csv). Also, you can supply additional labels for each set of node files.

The format of --nodes flag is: [<label>[:<label>]...=]<file>[,<file>][,<file>].... Take note that only the first <file> part is mandatory, all other parts of the flag value are optional. Multiple --nodes flags can be supplied to describe multiple sets of different node files. For the importer to work, at least one --nodes flag must be supplied.

The format of --relationships flag is: [<type>=]<file>[,<file>][,<file>].... Take note that only the first <file> part is mandatory, all other parts of the flag value are optional. Multiple --relationships flags can be supplied to describe multiple sets of different relationship files. The --relationships flag isn't mandatory.

CSV Parser Logic

The CSV parser uses the same logic as the standard Python CSV parser. The data is parsed in the same way as the following snippet:

import csv
for row in csv.reader(stream, strict=True):
# process 'row'

Python uses 'excel' as the default dialect when parsing CSV files and the default settings for the CSV parser are:

  • delimiter: ','
  • doublequote: True
  • escapechar: None
  • lineterminator: '\r\n'
  • quotechar: '"'
  • skipinitialspace: False

The above snippet can be expanded to:

import csv
for row in csv.reader(stream, delimiter=',', doublequote=True,
escapechar=None, lineterminator='\r\n',
quotechar='"', skipinitialspace=False,
strict=True):
# process 'row'

For more information about the meaning of the above values, see: https://docs.python.org/3/library/csv.html#csv.Dialect