Querying PostgreSQL Data as a Graph

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

Summary

In this tutorial, you will:

  • Create a PostgreSQL database and load it with example data;

  • Start a PuppyGraph Docker container and query the PostgreSQL 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:
      postgres_net:
    ports:
      - "8081:8081"
      - "8182:8182"
      - "7687:7687"
  postgres:
    image: postgres:14.1-alpine
    container_name: postgres
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres123
    networks:
      postgres_net:
    ports:
      - "5432:5432"
    volumes:
      - ./postgres-data:/var/lib/postgresql/data
networks:
  postgres_net:
    name: puppy-postgres

▶️ Then run the following command to start Postgres and PuppyGraph:

docker compose up -d
[+] Running 1/1
 ✔ puppygraph Pulled
[+] Running 3/3
 ✔ Network puppy-postgres  Created
 ✔ Container postgres      Started
 ✔ Container puppygraph    Started

Data Preparation

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

▶️ Run the following command to start a PostgreSQL shell to access the database:

docker exec -it postgres psql -h postgres -U postgres

It will show a password prompt:

Password for user postgres:

▶️ Input the password postgres123 to access the PostgreSQL client shell. 

psql (14.1)
Type "help" for help.

postgres=#

▶️ 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:

idnameage

v1

marko

29

v2

vadas

27

v4

josh

32

v6

peter

35

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.

Modern Graph

A schema instructs PuppyGraph on mapping data from the PostgreSQL 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": "postgres_data",
      "type": "postgresql",
      "jdbc": {
        "username": "postgres",
        "password": "postgres123",
        "jdbcUri": "jdbc:postgresql://postgres:5432/postgres",
        "driverClass": "org.postgresql.Driver"
      }
    }
  ],
  "graph": {
    "vertices": [
      {
        "label": "person",
        "oneToOne": {
          "tableSource": {
            "catalog": "postgres_data",
            "schema": "modern",
            "table": "person"
          },
          "id": {
            "fields": [
              {
                "type": "String",
                "field": "id",
                "alias": "id"
              }
            ]
          },
          "attributes": [
            {
              "type": "Int",
              "field": "age",
              "alias": "age"
            },
            {
              "type": "String",
              "field": "name",
              "alias": "name"
            }
          ]
        }
      },
      {
        "label": "software",
        "oneToOne": {
          "tableSource": {
            "catalog": "postgres_data",
            "schema": "modern",
            "table": "software"
          },
          "id": {
            "fields": [
              {
                "type": "String",
                "field": "id",
                "alias": "id"
              }
            ]
          },
          "attributes": [
            {
              "type": "String",
              "field": "lang",
              "alias": "lang"
            },
            {
              "type": "String",
              "field": "name",
              "alias": "name"
            }
          ]
        }
      }
    ],
    "edges": [
      {
        "label": "knows",
        "fromVertex": "person",
        "toVertex": "person",
        "tableSource": {
          "catalog": "postgres_data",
          "schema": "modern",
          "table": "knows"
        },
        "id": {
          "fields": [
            {
              "type": "String",
              "field": "id",
              "alias": "id"
            }
          ]
        },
        "fromId": {
          "fields": [
            {
              "type": "String",
              "field": "from_id",
              "alias": "from_id"
            }
          ]
        },
        "toId": {
          "fields": [
            {
              "type": "String",
              "field": "to_id",
              "alias": "to_id"
            }
          ]
        },
        "attributes": [
          {
            "type": "Double",
            "field": "weight",
            "alias": "weight"
          }
        ]
      },
      {
        "label": "created",
        "fromVertex": "person",
        "toVertex": "software",
        "tableSource": {
          "catalog": "postgres_data",
          "schema": "modern",
          "table": "created"
        },
        "id": {
          "fields": [
            {
              "type": "String",
              "field": "id",
              "alias": "id"
            }
          ]
        },
        "fromId": {
          "fields": [
            {
              "type": "String",
              "field": "from_id",
              "alias": "from_id"
            }
          ]
        },
        "toId": {
          "fields": [
            {
              "type": "String",
              "field": "to_id",
              "alias": "to_id"
            }
          ]
        },
        "attributes": [
          {
            "type": "Double",
            "field": "weight",
            "alias": "weight"
          }
        ]
      }
    ]
  }
}

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

PuppyGraph Login

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

Upload Schema Page

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

Visualized Schema

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.

Interactive Gremlin Query Page

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".

Interactive Query with Results

Alternative: Querying the graph via CLI

Alternatively, we can query the graph via CLI.

▶️ Execute the following command to access the PuppyGraph Gremlin Console

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]

▶️ To exit PuppyGraph Gremlin Console, enter the command:

:exit

Cleaning up

▶️ Run the following command to shut down and remove the services:

docker compose down

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