• Graph Schema
  • Structure
  • Properties
• DDL
• Data Ingest
  • With NebulaGraph Importer v4
  • Get Stats
• Credits

supply_chain_demo.mp4

Graph Schema

It come with 4 TAGS and 3 EDGE TYPES, and it looks like this:

Graph: Auto manufacturing supply chain, renderred graph

Let's see its details.

Structure

graph TD
    A[car_model]
    B[feature]
    C[part]
    D[supplier]

    A -->|with_feature| B
    B -->|is_composed_of| C
    C -->|is_supplied_by| D

    style A fill:#f9d,stroke:#333,stroke-width:2px;
    style B fill:#fcc,stroke:#333,stroke-width:2px;
    style C fill:#cfc,stroke:#333,stroke-width:2px;
    style D fill:#ccf,stroke:#333,stroke-width:2px;

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Properties

classDiagram
    class car_model {
        string name
        string number
        int year
        string type
        string engine_type
        string size
        int seats
    }
    class feature {
        string name
        string number
        string type
        string state
    }
    class part {
        string name
        string number
        double price
        string date
    }
    class supplier {
        string name
        string address
        string contact
        string phone_number
    }

    car_model --> feature : with_feature
    feature --> part : is_composed_of
    part --> supplier : is_supplied_by

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DDL

CREATE SPACE IF NOT EXISTS auto_manufacturing_supply_chain (vid_type=FIXED_STRING(64), partition_num=1, replica_factor=1);

CREATE TAG IF NOT EXISTS car_model(name string, number string, year int, type string, engine_type string, size string, seats int);
CREATE TAG IF NOT EXISTS feature(name string, number string, type string, state string);
CREATE TAG IF NOT EXISTS `part`(name string, number string, price double, `date` string);
CREATE TAG IF NOT EXISTS supplier(name string, address string, contact string, phone_number string);

CREATE EDGE IF NOT EXISTS with_feature(version string);
CREATE EDGE IF NOT EXISTS is_composed_of(version string);
CREATE EDGE IF NOT EXISTS is_supplied_by(version string);

Data Ingest

Remove --network=nebula-net and modify the graphd address in importer_v4_config.yaml if you are not running NebulaGraph with docker-compose in same host.

With NebulaGraph Importer v4

docker run --rm -ti \
    --network=nebula-net \
    -v ${PWD}/data_sample:/root \
    -v ${PWD}/importer_v4_config.yaml:/root/importer_v4_config.yaml \
    vesoft/nebula-importer:v4.0.0 \
    --config /root/importer_v4_config.yaml

Result will be like:

{"level":"info","ts":"2023-09-07T08:57:01Z","caller":"manager/manager.go:416","msg":"0s 0s 100.00%(3.1 KiB/3.1 KiB) Records{Finished: 129, Failed: 0, Rate: 3397.47/s}, Requests{Finished: 7, Failed: 0, Latency: 3.069285ms/4.945996ms, Rate: 184.36/s}, Processed{Finished: 129, Failed: 0, Rate: 3397.47/s}"}

Get Stats

SUBMIT JOB STATS;
SHOW STATS;

Result will be like:

Type	Name	Count
Tag	car_model	10
Tag	feature	10
Tag	part	10
Tag	supplier	10
Edge	is_composed_of	14
Edge	is_supplied_by	10
Edge	with_feature	38
Space	vertices	40
Space	edges	62

Credits

The idea of this dataset comes from Julia XIAO, the Customer Success Engineer of NebulaGraph.

Due to I don't have bandwidth to prepare the datagen this time, I instead tried to ask ChatGPT-4 to help generate a small sample data first. See here for the chat log.