Querying DuckDB Data as a Graph

A step-by-step tutorial to using PuppyGraph to query data in DuckDB

Summary

In this tutorial, you will:

Create a DuckDB database and load it with example data;
Start a PuppyGraph Docker container and query the DuckDB data as a graph.

Prerequisites

Please ensure that docker compose is available. The installation can be verified by running:

docker compose version

See https://docs.docker.com/compose/install/ for Docker Compose installation instructions and https://www.docker.com/get-started/ for more details on Docker.

Accessing the PuppyGraph Web UI requires a browser. However, the tutorial offers alternative instructions for those who wish to exclusively use the CLI.

Deployment

Create a file docker-compose.yaml with the following content:

docker-compose.yaml

version: "3"

services:
  puppygraph:
    image: puppygraph/puppygraph:stable
    pull_policy: always
    container_name: puppygraph
    environment:
      - PUPPYGRAPH_USERNAME=puppygraph
      - PUPPYGRAPH_PASSWORD=puppygraph123
    networks:
      duckdb_net:
    ports:
      - "8081:8081"
      - "8182:8182"
      - "7687:7687"
    volumes:
      - share_content:/home/share
  duckdb:
    image: puppygraph/duckdb-ubuntu:latest
    container_name: duckdb
    networks:
      duckdb_net:
    volumes:
      - share_content:/home/share
networks:
  duckdb_net:
    name: puppy-duckdb
volumes:
  share_content:
    name: puppy-duckdb

Then run the following command to start DuckDB and PuppyGraph:

docker compose up -d

[+] Running 1/1
 ✔ puppygraph Pulled                                                                                                                                                                                                                                                              2.0s
[+] Running 4/4
 ✔ Network puppy-duckdb   Created                                                                                                                                                                                                                                                 0.1s
 ✔ Volume "puppy-duckdb"  Created                                                                                                                                                                                                                                                 0.0s
 ✔ Container puppygraph   Started                                                                                                                                                                                                                                                 0.1s
 ✔ Container duckdb       Started

Data Preparation

This tutorial is designed to be comprehensive and standalone, so it includes steps to populate data in DuckDB. In practical scenarios, PuppyGraph can query data directly from your existing DuckDB databases.

Run the following command to start a DuckDB shell:

docker exec -it duckdb duckdb /home/share/demo.db

This command also creates a database file /home/share/demo.db.

v0.9.2 3c695d7ba9
Enter ".help" for usage hints.
D

Then execute the following SQL statements in the shell to create tables and insert data.

create schema modern;
create table modern.person (id text, name text, age integer);
insert into modern.person values
                              ('v1', 'marko', 29),
                              ('v2', 'vadas', 27),
                              ('v4', 'josh', 32),
                              ('v6', 'peter', 35);

create table modern.software (id text, name text, lang text);
insert into modern.software values
                                ('v3', 'lop', 'java'),
                                ('v5', 'ripple', 'java');

create table modern.created (id text, from_id text, to_id text, weight double precision);
insert into modern.created values
                               ('e9', 'v1', 'v3', 0.4),
                               ('e10', 'v4', 'v5', 1.0),
                               ('e11', 'v4', 'v3', 0.4),
                               ('e12', 'v6', 'v3', 0.2);

create table modern.knows (id text, from_id text, to_id text, weight double precision);
insert into modern.knows values
                             ('e7', 'v1', 'v2', 0.5),
                             ('e8', 'v1', 'v4', 1.0);

The above SQL creates the following tables:

name

age

Type .exit to quit the DuckDB shell.

Modeling a Graph

We then define a graph on top of the data tables we just created. Actually, this is the "Modern" graph defined by Apache Tinkerpop.

A schema instructs PuppyGraph on mapping data from the DuckDB into a graph. PuppyGraph offers various methods for schema creation. For this tutorial, we've already prepared a schema to help save time.

Create a PuppyGraph schema file schema.json with the following content:

schema.json

{
  "catalogs": [
    {
      "name": "duckdb_data",
      "type": "duckdb",
      "jdbc": {
        "jdbcUri": "jdbc:duckdb:/home/share/demo.db",
        "driverClass": "org.duckdb.DuckDBDriver"
      }
    }
  ],
  "vertices": [
    {
      "label": "person",
      "mappedTableSource": {
        "catalog": "duckdb_data",
        "schema": "modern",
        "table": "person",
        "metaFields": {
          "id": "id"
        }
      },
      "attributes": [
        {
          "type": "Int",
          "name": "age"
        },
        {
          "type": "String",
          "name": "name"
        }
      ]
    },
    {
      "label": "software",
      "mappedTableSource": {
        "catalog": "duckdb_data",
        "schema": "modern",
        "table": "software",
        "metaFields": {
          "id": "id"
        }
      },
      "attributes": [
        {
          "type": "String",
          "name": "lang"
        },
        {
          "type": "String",
          "name": "name"
        }
      ]
    }
  ],
  "edges": [
    {
      "label": "knows",
      "mappedTableSource": {
        "catalog": "duckdb_data",
        "schema": "modern",
        "table": "knows",
        "metaFields": {
          "id": "id",
          "from": "from_id",
          "to": "to_id"
        }
      },
      "from": "person",
      "to": "person",
      "attributes": [
        {
          "type": "Double",
          "name": "weight"
        }
      ]
    },
    {
      "label": "created",
      "mappedTableSource": {
        "catalog": "duckdb_data",
        "schema": "modern",
        "table": "created",
        "metaFields": {
          "id": "id",
          "from": "from_id",
          "to": "to_id"
        }
      },
      "from": "person",
      "to": "software",
      "attributes": [
        {
          "type": "Double",
          "name": "weight"
        }
      ]
    }
  ]
}

Log into PuppyGraph Web UI at http://localhost:8081 with username puppygraph and password puppygraph123.

Upload the schema by selecting the file schema.json in the Upload Graph Schema JSON block and clicking on Upload.

Once the schema is uploaded, the schema page shows the visualized graph schema as follows.

Alternative: Schema Uploading via CLI

Alternatively, run the following command to upload the schema file:

curl -XPOST -H "content-type: application/json" --data-binary @./schema.json --user "puppygraph:puppygraph123" localhost:8081/schema

The response shows that graph schema has been uploaded successfully:

{"Status":"OK","Message":"Schema uploaded and gremlin server restarted"}

Querying the Graph

In this tutorial we will use the Gremlin query language to query the Graph. Gremlin is a graph query language developed by Apache TinkerPop. Prior knowledge of Gremlin is not necessary to follow the tutorial. To learn more about it, visit https://tinkerpop.apache.org/gremlin.html.

Click on the Query panel the left side. The Gremlin Query tab offers an interactive environment for querying the graph using Gremlin.

Queries are entered on the left side, and the right side displays the graph visualization.

The first query retrieves the property of the person named "marko".

Copy the following query, paste it in the query input, and click on the run button.

g.V().has("name", "marko").valueMap()

The output is plain text like the following:

Rows: 1
age              29
name             marko

Now let's also leverage the visualization. The next query gets all the software created by people known to "marko".

Copy the following query, paste it in the query input, and click on the run button.

g.V().has("name", "marko")
  .out("knows").out("created").path()

The output is as follows. There are two paths in the result as "marko" knows "josh" who created "lop" and "ripple".

Alternative: Querying the graph via CLI

Alternatively, we can query the graph via CLI.

Execute the following command to access the PuppyGraph Gremlin CLI

docker exec -it puppygraph ./bin/console

The welcome screen appears as follows:

  ____                                     ____                          _
 |  _ \   _   _   _ __    _ __    _   _   / ___|  _ __    __ _   _ __   | |__
 | |_) | | | | | | '_ \  | '_ \  | | | | | |  _  | '__|  / _` | | '_ \  | '_ \
 |  __/  | |_| | | |_) | | |_) | | |_| | | |_| | | |    | (_| | | |_) | | | | |
 |_|      \__,_| | .__/  | .__/   \__, |  \____| |_|     \__,_| | .__/  |_| |_|
                 |_|     |_|      |___/                         |_|
Welcome to PuppyGraph!
version: 0.10

puppy-gremlin>

Run the following queries in the console to query the Graph.

g.V().has("name", "marko").valueMap()

Properties of the person named "marko":

puppy-gremlin> g.V().has("name", "marko").valueMap()
Done! Elapsed time: 0.059s, rows: 1
==>map[age:29 name:marko]

g.V().has("name", "marko").out("knows").out("created").valueMap()

All the software created by the people known to "marko":

puppy-gremlin> g.V().has("name", "marko").out("knows").out("created").valueMap()
Done! Elapsed time: 0.042s, rows: 2
==>map[lang:java name:lop]
==>map[lang:java name:ripple]

To exit PuppyGraph Gremlin Console, enter the command:

:exit

Cleaning up

Run the following command to shut down and remove the containers:

docker compose down

Remove the data volume if needed:

docker volume rm puppy-duckdb

PreviousQuerying Databricks Data as a Graph NextQuerying Iceberg Data as a Graph

Last updated 5 months ago