Using Custom Graph¶

This guide walks you through the process of using custom graph data in GraphScope Interactive. The process comprises three main steps:

Creating a new graph,
Importing graph data, and
Starting the service with the new graph.

We use a simple graph, which contains only one kind vertices with label person and one kind edges with label knows, to demonstrate creating a new graph in Interactive.

Step 1: Create a New Graph¶

Before starting, please make sure you are in the GLOBAL context.

gsctl use GLOBAL

First you need to define the vertex types and edge types of your graph, i.e. here is a sample definition of a test_graph. Save the file to disk with name test_graph.yaml.

name: test_graph
description: "This is a test graph"
schema:
  vertex_types:
    - type_name: person
      properties:
        - property_name: id
          property_type:
            primitive_type: DT_SIGNED_INT64
        - property_name: name
          property_type:
            string:
              long_text: ""
        - property_name: age
          property_type:
            primitive_type: DT_SIGNED_INT32
      primary_keys:
        - id
  edge_types:
    - type_name: knows
      vertex_type_pair_relations:
        - source_vertex: person
          destination_vertex: person
          relation: MANY_TO_MANY
      properties:
        - property_name: weight
          property_type:
            primitive_type: DT_DOUBLE

In this file:

For each vertex type, specify its name, allowed properties, primary keys (if any), and other relevant details.
For each edge type, define the source/destination vertex types and their associated properties.

To create a new graph test_graph, execute the following command:

gsctl create graph -f ./test_graph.yaml

For a comprehensive list of supported types, please refer to the data model page.

Step 2: Import Graph Data¶

To import your data, you need to first bind the data source and then submit a bulk loading job.

Bind Data Source¶

To create a new graph, you will need the original data of the graph. We currently support files in CSV format. Fortunately, we have prepared it for you, and you can find it here modern-graph. The import.yaml file maps raw data fields to the schema of the “modern” graph created in Step 1. Here’s an illustrative example import.yaml, note that each vertex/edge type need at least one input for bulk loading. In the following example, we will import data to the new graph from local file person.csv and person_knows_person.csv. You can download the files from GitHub, with following commands.

wget https://raw.githubusercontent.com/alibaba/GraphScope/main/flex/interactive/examples/modern_graph/person.csv
wget https://raw.githubusercontent.com/alibaba/GraphScope/main/flex/interactive/examples/modern_graph/person_knows_person.csv

After successfully downloading them, remember to replace @/path/to/person.csv and @/path/to/person_knows_person.csv with the actual path to files.

Note

@ means the file is a local file and need to be uploaded.

vertex_mappings:
  - type_name: person
    inputs:
      - "@/path/to/person.csv"
    column_mappings:
      - column:
          index: 0
          name: id
        property: id
      - column:
          index: 1
          name: name
        property: name
      - column:
          index: 2
          name: age
        property: age
edge_mappings:
  - type_triplet:
      edge: knows
      source_vertex: person
      destination_vertex: person
    inputs:
      - "@/path/to/person_knows_person.csv"
    source_vertex_mappings:
      - column:
          index: 0
          name: person.id
        property: id
    destination_vertex_mappings:
      - column:
          index: 1
          name: person.id
        property: id
    column_mappings:
      - column:
          index: 2
          name: weight
        property: weight

Note: The provided yaml file above offers a basic configuration for data importing. For a comprehensive understanding of data import configurations, please consult the data import page.

Now bind the datasource to test_graph with following command

gsctl create datasource -f ./import.yaml -g test_graph

Create Data Loading Job¶

So far, we have only created the dataource, a job config job_config.yaml is also needed to data import. A job_config.yaml is also needed to specify the configuration for bulk loading.

loading_config:
  import_option: overwrite
  format:
    type: csv
    metadata:
      delimiter: "|"
      header_row: "true"

vertices:
  - type_name: person

edges:
  - type_name: knows
    source_vertex: person
    destination_vertex: person

Now create a bulk loading job with following command

gsctl create loaderjob -f ./job_config.yaml -g test_graph

a message like Create job xxx successfully will be printed.

Wait the job to finish by checking the job status with following command

gsctl desc job <job_id>

Step 3: Start the Service with the New Graph¶

After you have obtained a successful status with gsctl desc job <job_id>, you can now switch to the context of the test_graph graph.

gsctl use GRAPH test_graph

Step 4: A More Complicated Movies Graph(optional)¶

The above graph is very simple, which only contains one kind vertices and one kind edges. For a more complicated example, We’ll use the movies graph as an example, you can download the files from our Github Repo.

wget https://interactive-release.oss-cn-hangzhou.aliyuncs.com/dataset/movies/movies.zip

Try to use the following configuration files to create movie_graph!

movie_graph.yaml¶

name: movies
schema:
  vertex_types:
    - type_name: Movie
      properties: 
        - property_name: id
          property_type:
            primitive_type: DT_SIGNED_INT64
        - property_name: released
          property_type:
            primitive_type: DT_SIGNED_INT32
        - property_name: tagline
          property_type:
            string:
              long_text: ""
        - property_name: title
          property_type:
            string:
              long_text: ""
      primary_keys:
        - id
    - type_name: Person
      properties:
        - property_name: id
          property_type:
            primitive_type: DT_SIGNED_INT64
        - property_name: born
          property_type:
            primitive_type: DT_SIGNED_INT32
        - property_name: name
          property_type:
            string:
              long_text: ""
      primary_keys:
        - id
    - type_name: User
      properties:
        - property_name: id
          property_type:
            primitive_type: DT_SIGNED_INT64
        - property_name: born
          property_type:
            primitive_type: DT_SIGNED_INT32
        - property_name: name
          property_type:
            string:
              long_text: ""
      primary_keys:
        - id
  edge_types:
    - type_name: ACTED_IN
      vertex_type_pair_relations:
        - source_vertex: Person
          destination_vertex: Movie
          relation: MANY_TO_MANY
    - type_name: DIRECTED
      vertex_type_pair_relations:
        - source_vertex: Person
          destination_vertex: Movie
          relation: MANY_TO_MANY
    - type_name: REVIEW
      vertex_type_pair_relations:
        - source_vertex: User
          destination_vertex: Movie
          relation: MANY_TO_MANY
      properties:
        - property_name: rating
          property_type:
            primitive_type: DT_SIGNED_INT32
    - type_name: FOLLOWS
      vertex_type_pair_relations:
        - source_vertex: User
          destination_vertex: Person
          relation: MANY_TO_MANY
    - type_name: WROTE
      vertex_type_pair_relations:
        - source_vertex: Person
          destination_vertex: Movie
          relation: MANY_TO_MANY
    - type_name: PRODUCED
      vertex_type_pair_relations:
        - source_vertex: Person
          destination_vertex: Movie
          relation: MANY_TO_MANY

import.yaml¶

vertex_mappings:
  - type_name: Person  # must align with the schema
    inputs:
      - "@/path/to/Person.csv"
  - type_name: Movie
    inputs:
      - "@/path/to/Movie.csv"
edge_mappings:
  - type_triplet:
      edge: ACTED_IN
      source_vertex:  Person
      destination_vertex:  Movie
    inputs:
      - "@/path/to/ACTED_IN.csv"
  - type_triplet:
      edge: DIRECTED
      source_vertex:  Person
      destination_vertex:  Movie
    inputs:
      - "@/path/to/DIRECTED.csv"
  - type_triplet:
      edge: FOLLOWS
      source_vertex:  Person
      destination_vertex:  Person
    inputs:
      - "@/path/to/FOLLOWS.csv"
  - type_triplet:
      edge: PRODUCED
      source_vertex:  Person
      destination_vertex:  Movie
    inputs:
      - "@/path/to/PRODUCED.csv"
  - type_triplet:
      edge: REVIEW
      source_vertex:  Person
      destination_vertex:  Movie
    column_mappings:
      - column:
          index: 3
          name: rating
        property: rating
    inputs:
      - "@/path/to/REVIEWED.csv"
  - type_triplet:
      edge: WROTE
      source_vertex:  Person
      destination_vertex:  Movie
    inputs:
      - "@/path/to/WROTE.csv"

job_config.yaml¶

loading_config:
  import_option: overwrite
  format:
    type: csv
    metadata:
      delimiter: "|"
      header_row: "true"

vertices:
  - type_name: Person
  - type_name: Movie

edges:
  - type_name: ACTED_IN
    source_vertex:  Person
    destination_vertex:  Movie
  - type_name: DIRECTED
    source_vertex:  Person
    destination_vertex:  Movie
  - type_name: FOLLOWS
    source_vertex:  Person
    destination_vertex:  Person
  - type_name: PRODUCED
    source_vertex:  Person
    destination_vertex:  Movie
  - type_name: REVIEW
    source_vertex:  Person
    destination_vertex:  Movie
  - type_name: WROTE
    source_vertex:  Person
    destination_vertex:  Movie

Try other graphs¶

In addition to movies graph, we have also prepared the graph_algo graph. You can find the raw CSV files, graph.yaml, and import.yaml in the ./examples/graph_algo/ directory. You can import the graph_algo graph just like importing the movies graph. There are also some sample cypher queries, you can find them at GraphScope/flex/interactive/examples/graph_algo.