Querying Iceberg Data as a Graph

Summary

In this tutorial, you will:

• Create an Apache Iceberg (Iceberg) data lake and load it with example data;
• Start a PuppyGraph Docker container and query the Iceberg 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

# reference: https://iceberg.apache.org/spark-quickstart/

version: "3"

services:
  spark-iceberg:
    image: tabulario/spark-iceberg
    container_name: spark-iceberg
    build: spark/
    networks:
      iceberg_net:
    depends_on:
      - rest
      - minio
    volumes:
      - ./warehouse:/home/iceberg/warehouse
      - ./notebooks:/home/iceberg/notebooks/notebooks
    environment:
      - AWS_ACCESS_KEY_ID=admin
      - AWS_SECRET_ACCESS_KEY=password
      - AWS_REGION=us-east-1
    ports:
      - 8888:8888
      - 8180:8080
      - 10000:10000
      - 10001:10001
  rest:
    image: tabulario/iceberg-rest
    container_name: iceberg-rest
    networks:
      iceberg_net:
    ports:
      - 8181:8181
    environment:
      - AWS_ACCESS_KEY_ID=admin
      - AWS_SECRET_ACCESS_KEY=password
      - AWS_REGION=us-east-1
      - CATALOG_WAREHOUSE=s3://warehouse/
      - CATALOG_IO__IMPL=org.apache.iceberg.aws.s3.S3FileIO
      - CATALOG_S3_ENDPOINT=http://minio:9000
  minio:
    image: minio/minio
    container_name: minio
    environment:
      - MINIO_ROOT_USER=admin
      - MINIO_ROOT_PASSWORD=password
      - MINIO_DOMAIN=minio
    networks:
      iceberg_net:
        aliases:
          - warehouse.minio
    ports:
      - 9001:9001
      - 9000:9000
    command: ["server", "/data", "--console-address", ":9001"]
  mc:
    depends_on:
      - minio
    image: minio/mc
    container_name: mc
    networks:
      iceberg_net:
    environment:
      - AWS_ACCESS_KEY_ID=admin
      - AWS_SECRET_ACCESS_KEY=password
      - AWS_REGION=us-east-1
    entrypoint: >
      /bin/sh -c "
      until (/usr/bin/mc config host add minio http://minio:9000 admin password) do echo '...waiting...' && sleep 1; done;
      /usr/bin/mc rm -r --force minio/warehouse;
      /usr/bin/mc mb minio/warehouse;
      /usr/bin/mc policy set public minio/warehouse;
      tail -f /dev/null
      "
  puppygraph:
    image: puppygraph/puppygraph:stable
    container_name: puppygraph
    networks:
      iceberg_net:
    environment:
      - PUPPYGRAPH_USERNAME=puppygraph
      - PUPPYGRAPH_PASSWORD=puppygraph123
    ports:
      - "8081:8081"
      - "8182:8182"
      - "7687:7687"
    depends_on:
      - spark-iceberg
networks:
  iceberg_net:
    name: puppy-iceberg

The docker-compose file is based on Iceberg Spark quick start.

Then run the following command to start Iceberg services and PuppyGraph:

docker compose up -d

[+] Running 6/6
 ✔ Network puppy-iceberg         Created
 ✔ Container minio               Started
 ✔ Container mc                  Started
 ✔ Container iceberg-rest        Started
 ✔ Container spark-iceberg       Started
 ✔ Container puppygraph          Started

Data Preparation

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

Run the following command to start a Spark-SQL shell to access Iceberg.

docker exec -it spark-iceberg spark-sql

The shell will be like this:

spark-sql ()>

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

CREATE DATABASE demo.modern;

CREATE EXTERNAL TABLE demo.modern.person (
  id string,
  name string,
  age int
) USING iceberg;

INSERT INTO demo.modern.person VALUES
  ('v1', 'marko', 29),
  ('v2', 'vadas', 27),
  ('v4', 'josh', 32),
  ('v6', 'peter', 35);

CREATE EXTERNAL TABLE demo.modern.software (
  id string,
  name string,
  lang string
) USING iceberg;

INSERT INTO demo.modern.software VALUES
  ('v3', 'lop', 'java'),
  ('v5', 'ripple', 'java');

CREATE EXTERNAL TABLE demo.modern.created (
  id string,
  from_id string,
  to_id string,
  weight double
) USING iceberg;

INSERT INTO demo.modern.created VALUES
  ('e9', 'v1', 'v3', 0.4),
  ('e10', 'v4', 'v5', 1.0),
  ('e11', 'v4', 'v3', 0.4),
  ('e12', 'v6', 'v3', 0.2);

CREATE EXTERNAL TABLE demo.modern.knows (
  id string,
  from_id string,
  to_id string,
  weight double
) USING iceberg;

INSERT INTO demo.modern.knows VALUES
  ('e7', 'v1', 'v2', 0.5),
  ('e8', 'v1', 'v4', 1.0);

The above SQL creates the following tables:

personsoftwareknowscreated

id	name	age
v1	marko	29
v2	vadas	27
v4	josh	32
v6	peter	35

id	name	lang
v3	lop	java
v5	ripple	java

id	from_id	to_id	weight
e7	v1	v2	0.5
e8	v1	v4	1.0

id	from_id	to_id	weight
e9	v1	v3	0.4
e10	v4	v5	1.0
e11	v4	v3	0.4
e12	v6	v3	0.2

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 Iceberg 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": "iceberg_test",
      "type": "iceberg",
      "metastore": {
        "type": "rest",
        "uri": "http://iceberg-rest:8181"
      },
      "storage": {
        "useInstanceProfile": "false",
        "accessKey": "admin",
        "secretKey": "password",
        "enableSsl": "false",
        "endpoint": "http://minio:9000",
        "enablePathStyleAccess": "true"
      }
    }
  ],
  "graph": {
    "vertices": [
      {
        "label": "person",
        "oneToOne": {
          "tableSource": {
            "catalog": "iceberg_test",
            "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": "iceberg_test",
            "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": "iceberg_test",
          "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": "iceberg_test",
          "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.

Query 1Query 2

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 services:

docker compose down

See Also

Please refer to Connecting to Apache Iceberg for connecting to different implementations of Iceberg.