redisgraph-bulk-loader

A Python utility for building RedisGraph databases from CSV inputs

Requirements

The bulk loader utility requires a Python 3 interpreter.

A Redis server with the RedisGraph module must be running. Installation instructions may be found at: https://oss.redis.com/redisgraph/

Installation

The bulk loader can be installed using pip:

pip install redisgraph-bulk-loader

Or

pip install git+https://github.com/RedisGraph/redisgraph-bulk-loader.git@master

Usage

Pip installation exposes redisgraph-bulk-insert as a command to invoke this tool:

redisgraph-bulk-insert GRAPHNAME [OPTIONS]

Installation by cloning the repository allows the script to be invoked via Python like so:

python3 redisgraph_bulk_loader/bulk_insert.py GRAPHNAME [OPTIONS]

The only required arguments are the name to give the newly-created graph (which can appear anywhere) and at least one node CSV file. The nodes and relationship flags should be specified once per input file.

redisgraph-bulk-insert GRAPH_DEMO -n example/Person.csv -n example/Country.csv -r example/KNOWS.csv -r example/VISITED.csv

The label (for nodes) or relationship type (for relationships) is derived from the base name of the input CSV file. In this example, we'll construct two sets of nodes, labeled Person and Country, and two types of relationships - KNOWS and VISITED.

RedisGraph does not impose a schema on properties, so the same property key can have values of differing types, such as strings and integers. As such, the bulk loader's default behaviour is to infer the type for each field independently for each value. This can cause unexpected behaviors when, for example, a property expected to always have string values has a field that can be cast to an integer or double. To avoid this, use the --enforce-schema flag and update your CSV headers as described in Input Schemas.

Extended parameter descriptions

The flags for max-token-count, max-buffer-size, and max-token-size are typically not required. They should only be specified if the memory overhead of graph creation is too high, or raised if the volume of Redis calls is too high. The bulk loader builds large graphs by sending binary tokens (each of which holds multiple nodes or relations) to Redis in batches.

--quote is maintained for backwards compatibility, and allows some control over Python's type inference in the default mode. --enforce-schema-type is preferred.

--enforce-schema-type indicates that input CSV headers will follow the form described in Input Schemas.

--nodes-with-label and --relations-with-type allows the node label or relationship type to be explicitly written instead of inferring them from the filename. For example, --relations-with-type HAS_TAG post_hasTag_tag.csv will add all relationships described in the specified CSV with the type HAS_TAG. To specify miltiple label separate them with ':'. For example, --nodes-with-label Actor:Director actors.csv will add all nodes described in the specified CSV with the labels Actor and Director.

Input constraints

Node identifiers

• If both nodes and relations are being created, each node must be associated with a unique identifier.
• If not using --enforce-schema, the identifier is the first column of each label CSV file. If this column's name starts with an underscore (_), the identifier is internal to the bulk loader operation and does not appear in the resulting graph. Otherwise, it is treated as a node property.
• Each identifier must be entirely unique across all label files. ID namespaces can be used to write more granular identifiers.
• Source and destination nodes in relation CSV files should be referred to by their identifiers.
• The uniqueness restriction is lifted if only nodes are being created.

Entity properties

• Property types do not need to be explicitly provided.
• Properties are not required to be exclusively composed of any type.
• The types currently supported by the bulk loader are:
  • bool: either true or false (case-insensitive, not quote-interpolated).
  • integer: an unquoted value that can be read as an integer type.
  • double: an unquoted value that can be read as a floating-point type.
  • string: any field that is either quote-interpolated or cannot be casted to a numeric or boolean type.
  • array: A bracket-interpolated array of elements of any types. Strings within the array must be explicitly quote-interpolated. Array properties require use of a non-comma delimiter for the CSV (-o).
• Cypher does not allow NULL values to be assigned to properties.
• The default behaviour is to infer the property type, attempting to cast it to integer, float, boolean, or string in that order.
• The --enforce-schema flag and an Input Schema should be used if type inference is not desired.

Label file format:

• Each row must have the same number of fields.
• Leading and trailing whitespace is ignored.
• If not using an Input Schema, the first field of a label file will be the node identifier, as described in Node Identifiers.
• All fields are property keys that will be associated with each node.

Relationship files

• Each row must have the same number of fields.
• Leading and trailing whitespace is ignored.
• If not using an Input Schema, the first two fields of each row are the source and destination node identifiers. The names of these fields in the header do not matter.
• If the file has more than 2 fields, all subsequent fields are relationship properties that adhere to the same rules as node properties.
• Described relationships are always considered to be directed (source->destination).

Input CSV example

Store.csv

storeNum | Location | daysOpen |
118 | 123 Main St | ['Mon', 'Wed', 'Fri']
136 | 55 Elm St | ['Sat', 'Sun']

This CSV would be inserted with the command: redisgraph-bulk-insert StoreGraph --separator \| --nodes Store.csv

(Since the pipe character has meaning in the terminal, it must be backslash-escaped.)

All storeNum properties will be inserted as integers, Location will be inserted as strings, and daysOpen will be inserted as arrays of strings.

Input Schemas

If the --enforce-schema flag is specified, all input CSVs will be expected to specify each column's data type in the header.

This format lifts some constraints of the default CSV format, such as ID fields being the first column.

Most header fields should be a colon-separated pair of the property name and its data type, such as Name:STRING. Certain data types do not require a name string, as indicated below.

If an ID column has a name string, the value will be added to each node as a property. This property will be a string by default, though it may be switched to integer using the --id-type argument. If the name string is not provided, the ID is internal to the bulk loader operation and will not appear in the graph. START_ID and END_ID columns will never be added as properties.

ID Namespaces

Typically, node identifiers need to be unique across all input CSVs. When using an input schema, it is (optionally) possible to create ID namespaces, and the identifier only needs to be unique across its namespace. This is particularly useful when each input CSV has primary keys which overlap with others.

To introduce a namespace, follow the :ID type string with a parentheses-interpolated namespace string, such as :ID(User). The same namespace should be specified in the :START_ID or :END_ID field of relation files, as in :START_ID(User).

Input Schema CSV examples

User.csv

:ID(User), name:STRING, rank:INT
0, "Jeffrey", 5
1, "Filipe", 8

FOLLOWS.csv

:START_ID(User), :END_ID(User), reaction_count:INT
0, 1, 25
1, 0, 10

Inserting these CSVs with the command: redisgraph-bulk-insert SocialGraph --enforce-schema --nodes User.csv --relations FOLLOWS.csv

Will produce a graph named SocialGraph with 2 users, Jeffrey and Filipe. Jeffrey follows Filipe, and that relation has a reaction_count of 25. Filipe also follows Jeffrey, with a reaction_count of 10.

Performing bulk updates

Pip installation also exposes the command redisgraph-bulk-update:

redisgraph-bulk-update GRAPHNAME [OPTIONS]

Installation by cloning the repository allows the bulk updater to be invoked via Python like so:

python3 redisgraph_bulk_loader/bulk_update.py GRAPHNAME [OPTIONS]

The bulk updater allows a CSV file to be read in batches and committed to RedisGraph according to the provided query.

For example, given the CSV files described in Input Schema CSV examples, the bulk loader could create the same nodes and relationships with the commands:

redisgraph-bulk-update SocialGraph --csv User.csv --query "MERGE (:User {id: row[0], name: row[1], rank: row[2]})"
redisgraph-bulk-update SocialGraph --csv FOLLOWS.csv --query "MATCH (start {id: row[0]}), (end {id: row[1]}) MERGE (start)-[f:FOLLOWS]->(end) SET f.reaction_count = row[2]"

When using the bulk updater, it is essential to sanitize CSV inputs beforehand, as RedisGraph will commit changes to the graph incrementally. As such, malformed inputs may leave the graph in a partially-updated state.