Version: v3.x (DDN)

Get Started with Hasura DDN and Elasticsearch

Overview

This tutorial takes about fifteen minutes to complete. You'll learn how to:

Set up a new Hasura DDN project
Connect it to an Elasticsearch instance
Generate Hasura metadata
Create a build
Run your first query

Additionally, we'll familiarize you with the steps and workflows necessary to iterate on your API.

This tutorial assumes you're starting from scratch. We'll use a locally running Elasticsearch instance via Docker and connect it to Hasura, but you can easily follow the steps if you already have data seeded or are using Elasticsearch's hosted service; Hasura will never modify your source schema.

Prerequisites

Install the DDN CLI

macOS and Linux
Windows

Simply run the installer script in your terminal:

curl -L https://graphql-engine-cdn.hasura.io/ddn/cli/v4/get.sh | bash

Download the latest DDN CLI installer for Windows.
Run the DDN_CLI_Setup.exe installer file and follow the instructions. This will only take a minute.
By default, the DDN CLI is installed under C:\Users\{Username}\AppData\Local\Programs\DDN_CLI
The DDN CLI is added to your %PATH% environment variable so that you can use the ddn command from your terminal.

Install Docker

The Docker based workflow helps you iterate and develop locally without deploying any changes to Hasura DDN, making the development experience faster and your feedback loops shorter. You'll need Docker Compose v2.27.1 or later.

Validate the installation

You can verify that the DDN CLI is installed correctly by running:

ddn doctor

Tutorial

Step 1. Authenticate your CLI

Before you can create a new Hasura DDN project, you need to authenticate your CLI:

ddn auth login

This will launch a browser window prompting you to log in or sign up for Hasura DDN. After you log in, the CLI will acknowledge your login, giving you access to Hasura Cloud resources.

Step 2. Scaffold out a new local project

Next, create a new local project:
ddn supergraph init my-project && cd my-project

Once you move into this directory, you'll see your project scaffolded out for you. You can view the structure by either running ls in your terminal, or by opening the directory in your preferred editor.

Step 3. Initialize your Elasticsearch connector

In your project directory, run:

ddn connector init my_es -i

From the dropdown, start typing elasticsearch and hit enter to accept the default port. Then, provide the following values:

ELASTICSEARCH_URL

http://local.hasura.dev:9200/

ELASTICSEARCH_USERNAME

elastic

ELASTICSEARCH_PASSWORD

elastic

For now, you can leave the rest of the values as blanks (or their defaults, if present).

Step 4. Start the local Elasticsearch container

Begin by starting a local Elasticsearch container on port 9200:
docker run --rm -p 127.0.0.1:9200:9200 -d --name elasticsearch \
  -e ELASTIC_PASSWORD="elastic" \
  -e "discovery.type=single-node" \
  -e "xpack.security.http.ssl.enabled=false" \
  -e "xpack.license.self_generated.type=trial" \
  docker.elastic.co/elasticsearch/elasticsearch:8.17.1

Then, add a mapping to the database:
curl -X PUT "http://localhost:9200/customers/" -u elastic:elastic -H 'Content-Type: application/json' -d'
{
  "mappings": {
    "properties": {
      "customer_id": {
        "type": "keyword"
      },
      "name": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword"
          }
        }
      },
      "email": {
        "type": "keyword",
        "index": true
      },
      "location": {
        "type": "geo_point"
      }
    }
  }
}
'

Finally, add some data to the database:
curl -X POST "http://localhost:9200/_bulk" -u elastic:elastic -H 'Content-Type: application/json' -d'
{ "index": { "_index": "customers", "_id": "1" } }
{ "customer_id": "CUST001", "name": "John Doe", "email": "[email protected]", "location": { "lat": 40.7128, "lon": -74.0060 } }
{ "index": { "_index": "customers", "_id": "2" } }
{ "customer_id": "CUST002", "name": "Jane Smith", "email": "[email protected]", "location": { "lat": 34.0522, "lon": -118.2437 } }
{ "index": { "_index": "customers", "_id": "3" } }
{ "customer_id": "CUST003", "name": "Alice Johnson", "email": "[email protected]", "location": { "lat": 51.5074, "lon": -0.1278 } }
{ "index": { "_index": "customers", "_id": "4" } }
{ "customer_id": "CUST004", "name": "Bob Brown", "email": "[email protected]", "location": { "lat": 48.8566, "lon": 2.3522 } }
{ "index": { "_index": "customers", "_id": "5" } }
{ "customer_id": "CUST005", "name": "Charlie Davis", "email": "[email protected]", "location": { "lat": 35.6895, "lon": 139.6917 } }
'

You can verify the data by running:
curl --location 'http://localhost:9200/customers/_search' \
-H 'Content-Type: application/json' \
-u elastic:elastic \
--data '{
  "_source": [
    "_id",
    "name"
  ]
}'

Step 5. Introspect your Elasticsearch database

Next, use the CLI to introspect your Elasticsearch database:

ddn connector introspect my_es

After running this, you should see a representation of your database's schema in the app/connector/my_es/configuration.json file; you can view this using cat or open the file in your editor.

Additionally, you can check which resources are available — and their status — at any point using the CLI:

ddn connector show-resources my_es

Step 6. Add your model

Now, track the index from your Elasticsearch database as a model in your DDN metadata:
ddn models add my_es customers

Open the app/metadata directory and you'll find a newly-generated file: Customers.hml. The DDN CLI will use this Hasura Metadata Language file to represent the customers index from Elasticsearch in your API as a model.

tip

You can import all the indexes from your Elasticsearch database as models by running ddn model add my_es "*".

Step 7. Create a new build

To create a local build, run:

ddn supergraph build local

The build is stored as a set of JSON files in engine/build.

Step 8. Start your local services

Start your local Hasura DDN Engine and Elasticsearch connector:

ddn run docker-start

Your terminal will be taken over by logs for the different services.

Step 9. Run your first query

In a new terminal tab, open your local console:

ddn console --local

In the GraphiQL explorer of the console, write this query:
query MyQuery {
  customers {
    email
    name
  }
}

You'll get the following response:
{
  "data": {
    "customers": [
      {
        "email": "[email protected]",
        "name": "John Doe"
      },
      {
        "email": "[email protected]",
        "name": "Jane Smith"
      },
      {
        "email": "[email protected]",
        "name": "Alice Johnson"
      },
      {
        "email": "[email protected]",
        "name": "Bob Brown"
      },
      {
        "email": "[email protected]",
        "name": "Charlie Davis"
      }
    ]
  }
}

Step 10. Iterate on your Elasticsearch schema

Let's add a new index for transactions:
curl -X PUT "http://localhost:9200/transactions/" -u elastic:elastic -H 'Content-Type: application/json' -d'
{
  "mappings": {
    "properties": {
      "transaction_id": {
        "type": "keyword"
      },
      "timestamp": {
        "type": "date",
        "format": "strict_date_optional_time||epoch_millis"
      },
      "customer_id": {
        "type": "keyword"
      },
      "transaction_details": {
        "properties": {
          "item_id": {
            "type": "keyword"
          },
          "item_name": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "price": {
            "type": "float"
          },
          "quantity": {
            "type": "integer"
          },
          "currency": {
            "type": "keyword"
          }
        }
      }
    }
  }
}
'

Add some data to the new index:
curl -X POST "http://localhost:9200/_bulk" -u elastic:elastic -H 'Content-Type: application/json' -d'
{ "index": { "_index": "transactions", "_id": "1" } }
{ "transaction_id": "TXN001", "timestamp": "2024-09-01T12:00:00", "customer_id": "CUST001", "transaction_details": { "item_id": "ITEM001", "item_name": "Laptop", "price": 999.99, "quantity": 1, "currency": "USD" } }
{ "index": { "_index": "transactions", "_id": "2" } }
{ "transaction_id": "TXN002", "timestamp": "2024-09-02T14:30:00", "customer_id": "CUST002", "transaction_details": { "item_id": "ITEM002", "item_name": "Smartphone", "price": 599.99, "quantity": 2, "currency": "USD" } }
{ "index": { "_index": "transactions", "_id": "3" } }
{ "transaction_id": "TXN003", "timestamp": "2024-09-03T09:45:00", "customer_id": "CUST003", "transaction_details": { "item_id": "ITEM003", "item_name": "Tablet", "price": 299.99, "quantity": 1, "currency": "USD" } }
{ "index": { "_index": "transactions", "_id": "4" } }
{ "transaction_id": "TXN004", "timestamp": "2024-09-04T16:15:00", "customer_id": "CUST004", "transaction_details": { "item_id": "ITEM004", "item_name": "Headphones", "price": 199.99, "quantity": 1, "currency": "USD" } }
{ "index": { "_index": "transactions", "_id": "5" } }
{ "transaction_id": "TXN005", "timestamp": "2024-09-05T11:30:00", "customer_id": "CUST005", "transaction_details": { "item_id": "ITEM005", "item_name": "Monitor", "price": 149.99, "quantity": 2, "currency": "USD" } }
'

Step 11. Refresh your metadata and rebuild your project

tip

The following steps are necessary each time you make changes to your source schema. This includes, adding, modifying, or dropping tables.

Step 11.1. Kill your services

Bring down the services by pressing CTRL+C in the terminal tab logging their activity.

Step 11.2. Re-introspect your data source

Run the introspection command again:

ddn connector introspect my_es

In app/connector/my_es/configuration.json, you'll see schema updated to include the transactions index. In app/metadata/my_es.hml, you'll see transactions present in the metadata as well.

Step 11.3. Update your metadata

Add the transactions index as a model:
ddn model add my_es transactions

Open the app/metadata directory and you'll find a newly-generated file: Transactions.hml. The DDN CLI will use this Hasura Metadata Language file to represent the transactions index from Elasticsearch in your API as a model.

tip

You can import all the indexes from your Elasticsearch database as models by running ddn model add my_es "*".

Step 11.4. Create a new build

Next, create a new build:

ddn supergraph build local

Step 11.5 Restart your services

Bring everything back up:

ddn run docker-start

Step 12. Query your new build

Head back to your console and query the transactions model:
query MyQuery {
  transactions {
    customerId
    transactionId
    transactionDetails {
      currency
      price
    }
  }
}

You'll get a response like this:
{
  "data": {
    "transactions": [
      {
        "customerId": "CUST001",
        "transactionId": "TXN001",
        "transactionDetails": [
          {
            "currency": "USD",
            "price": 999.99
          }
        ]
      },
      {
        "customerId": "CUST002",
        "transactionId": "TXN002",
        "transactionDetails": [
          {
            "currency": "USD",
            "price": 599.99
          }
        ]
      },
      {
        "customerId": "CUST003",
        "transactionId": "TXN003",
        "transactionDetails": [
          {
            "currency": "USD",
            "price": 299.99
          }
        ]
      },
      {
        "customerId": "CUST004",
        "transactionId": "TXN004",
        "transactionDetails": [
          {
            "currency": "USD",
            "price": 199.99
          }
        ]
      },
      {
        "customerId": "CUST005",
        "transactionId": "TXN005",
        "transactionDetails": [
          {
            "currency": "USD",
            "price": 149.99
          }
        ]
      }
    ]
  }
}

Next steps

Congratulations on completing your first Hasura DDN project with Elasticsearch! 🎉

Here's what you just accomplished:

You started with a fresh project and connected it to a local Elasticsearch database.
You set up metadata to represent your tables and relationships, which acts as the blueprint for your API.
Then, you created a build — essentially compiling everything into a ready-to-use API — and successfully ran your first GraphQL queries to fetch data.
Along the way, you learned how to iterate on your schema and refresh your metadata to reflect changes.

Now, you're equipped to connect and expose your data, empowering you to iterate and scale with confidence. Great work!

Take a look at our Elasticsearch docs to learn more about how to use Hasura DDN with Elasticsearch. Or, if you're ready, get started with adding permissions to control access to your API.

Get Started with Hasura DDN and Elasticsearch

Overview

Prerequisites

Tutorial

Step 1. Authenticate your CLI

Step 2. Scaffold out a new local project

Step 3. Initialize your Elasticsearch connector

Step 4. Start the local Elasticsearch container

Step 5. Introspect your Elasticsearch database

Step 6. Add your model

Step 7. Create a new build

Step 8. Start your local services

Step 9. Run your first query

Step 10. Iterate on your Elasticsearch schema

Step 11. Refresh your metadata and rebuild your project

Step 11.1. Kill your services

Step 11.2. Re-introspect your data source

Step 11.3. Update your metadata

Step 11.4. Create a new build

Step 11.5 Restart your services

Step 12. Query your new build

Next steps

Build an API in Less Than a Minute with Hasura DDN

Was this helpful?

Get Started with Hasura DDN and Elasticsearch

Overview​

Prerequisites​

Tutorial​

Step 1. Authenticate your CLI​

Step 2. Scaffold out a new local project​

Step 3. Initialize your Elasticsearch connector​

Step 4. Start the local Elasticsearch container​

Step 5. Introspect your Elasticsearch database​

Step 6. Add your model​

Step 7. Create a new build​

Step 8. Start your local services​

Step 9. Run your first query​

Step 10. Iterate on your Elasticsearch schema​

Step 11. Refresh your metadata and rebuild your project​

Step 11.1. Kill your services​

Step 11.2. Re-introspect your data source​

Step 11.3. Update your metadata​

Step 11.4. Create a new build​

Step 11.5 Restart your services​

Step 12. Query your new build​

Next steps​

Build an API in Less Than a Minute with Hasura DDN

Was this helpful?

Overview

Prerequisites

Tutorial

Step 1. Authenticate your CLI

Step 2. Scaffold out a new local project

Step 3. Initialize your Elasticsearch connector

Step 4. Start the local Elasticsearch container

Step 5. Introspect your Elasticsearch database

Step 6. Add your model

Step 7. Create a new build

Step 8. Start your local services

Step 9. Run your first query

Step 10. Iterate on your Elasticsearch schema

Step 11. Refresh your metadata and rebuild your project

Step 11.1. Kill your services

Step 11.2. Re-introspect your data source

Step 11.3. Update your metadata

Step 11.4. Create a new build

Step 11.5 Restart your services

Step 12. Query your new build

Next steps