Connecting to Redshift

It is also possible to query existing data on Redshift directly without loading it to PuppyGraph. Here is an demo of the feature.

In the demo, the Redshift data source stores people and referral information. To query the data as a graph, we model people as vertices and the referral relationship between people as edges.

Prerequisites

The demo assumes that PuppyGraph has been deployed at localhost according to the instruction in Launching PuppyGraph from AWS Marketplace or Launching PuppyGraph in Docker. And the firewall rules of the machine and Redshift is set up properly to allow PuppyGraph access Redshift.

In this demo, we use the username puppygraph and password puppygraph123.

Data Preparation (Optional)

Person Table

ID	Age	Name
v1	29	marko
v2	27	vadas

Referral Table

RefID	Source	Referred	Weight
e1	v1	v2	0.5

The demo uses people and referral information as shown above.

The following steps will create tables and insert data to Redshift in the Amazon Redshift Query editor v2. We assume that the redshift environment has been set up and use user name and password to connect.

Firstly, create database with query editor.

Then, create tables in the database.

Finally, execute follow SQL in query editor to insert data into tables.

insert into demo.public.person values ('v1', 29, 'marko'), ('v2', 27, 'vadas');
insert into demo.public.referral values ('e1', 'v1', 'v2', 0.5);

Upload the schema

Now the data are ready in Redshift. We need a PuppyGraph schema before querying it. Let's create a schema file redshift.json:

redshift.json

{
  "catalogs": [
    {
      "name": "jdbc_redshift",
      "type": "redshift",
      "jdbc": {
        "username": "puppy",
        "password": "puppy",
        "jdbcUri": "jdbc:redshift://[group_name].[account_id].[region].redshift-serverless.amazonaws.com:5439/demo",
        "driverClass": "com.amazon.redshift.Driver"
      }
    }
  ],
  "vertices": [
    {
      "label": "person",
      "mappedTableSource": {
        "catalog": "jdbc_redshift",
        "schema": "public",
        "table": "person",
        "metaFields": {
          "id": "id"
        }
      },
      "attributes": [
        {
          "type": "Int",
          "name": "age"
        },
        {
          "type": "String",
          "name": "name"
        }
      ]
    }
  ],
  "edges": [
    {
      "label": "knows",
      "mappedTableSource": {
        "catalog": "jdbc_redshift",
        "schema": "public",
        "table": "referral",
        "metaFields": {
          "id": "refid",
          "from": "source",
          "to": "referred"
        }
      },
      "from": "person",
      "to": "person",
      "attributes": [
        {
          "type": "Double",
          "name": "weight"
        }
      ]
    }
  ]
}

Here are some notes on this schema:

1. A catalog jdbc_redshift is added to specify the remote data source in Redshift.

   • Set type to redshift.

   • Set driverClass to com.amazon.redshift.Driver.

2. Replace username and password with your actual usernmae and password.

3. Replace jdbcUri with your actual JDBC URL.

4. The label of the vertices and edges do not have to be the same as the names of corresponding tables in Redshift. There is a mappedTableSource field in each of the vertex and edge types specifying the actual schema (public) and table (referral).

5. Additionally, the mappedTableSource marks meta columns in the tables. For example, the fieldsfrom and to describe which columns in the table form the endpoints of edges.

Now we can upload the schema file redshift.json to PuppyGraph with the following shell command, assuming that the PuppyGraph is running on localhost:

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

Query the data

Connecting to PuppyGraph at http://localhost:8081 and start gremlin console from the "Query" section:

[PuppyGraph]> console
         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
plugin activated: tinkerpop.server
plugin activated: tinkerpop.utilities
plugin activated: tinkerpop.tinkergraph

Now we have connected to the Gremlin Console. We can query the graph:

gremlin> g.V().hasLabel("person").out("knows").values("name")
==>vadas

\