The tutorial guides you through the steps to load sample nested clickstream data from the [Koalas to the Max](https://www.koalastothemax.com/) game into a Kafka topic, then ingest the data into Druid.
Before you follow the steps in this tutorial, download Druid as described in the [quickstart](index.md) using the [automatic single-machine configuration](../operations/single-server.md) and have it running on your local machine. You don't need to have loaded any data.
> Druid and Kafka both rely on [Apache ZooKeeper](https://zookeeper.apache.org/) to coordinate and manage services. Because Druid is already running, Kafka attaches to the Druid ZooKeeper instance when it starts up.<br />
>
> In a production environment where you're running Druid and Kafka on different machines, [start the Kafka ZooKeeper](https://kafka.apache.org/quickstart) before you start the Kafka broker.
The Druid console data loader presents you with several screens to configure each section of the supervisor spec, then creates an ingestion task to ingest the Kafka data.
The data loader automatically tries to determine the correct parser for the data. For the sample data, it selects input format `json`. You can play around with the different options to get a preview of how Druid parses your data.
6. Click **Next: ...** three times to go past the **Transform** and **Filter** steps to **Configure schema**. You don't need to enter anything in these two steps because applying transforms and filters is out of scope for this tutorial.
7. In the **Configure schema** step, you can select data types for the columns and configure [dimensions](../ingestion/schema-model.md#dimensions) and [metrics](../ingestion/schema-model.md#metrics) to ingest into Druid. The console does most of this for you, but you need to create JSON-type dimensions for the three nested columns in the data.
9. Select `day` as the **Segment granularity**. Since this is a small dataset, you don't need to make any further adjustments. Click **Next: Tune** to fine tune how Druid ingests data.
10. In **Input tuning**, set **Use earliest offset** to `True`—this is very important because you want to consume the data from the start of the stream. There are no other changes to make here, so click **Next: Publish**.
The console presents the spec you've constructed. You can click the buttons above the spec to make changes in previous steps and see how the changes update the spec. You can also edit the spec directly and see it reflected in the previous steps.
The task view auto-refreshes, so wait until the supervisor launches a task. The status changes from **Pending** to **Running** as Druid starts to ingest data.
> If the datasource doesn't appear after a minute you might not have set the supervisor to read data from the start of the stream—the `Use earliest offset` setting in the **Tune** step. Go to the **Ingestion** page and terminate the supervisor using the **Actions(...)** menu. [Load the sample data](#load-data-with-the-console-data-loader) again and apply the correct setting when you get to the **Tune** step.
### Submit a supervisor spec
As an alternative to using the data loader, you can submit a supervisor spec to Druid. You can do this in the console or using the Druid API.
1. Click **Ingestion** in the console, then click the ellipses next to the refresh button and select **Submit JSON supervisor**.
2. Paste this spec into the JSON window and click **Submit**.
```json
{
"type": "kafka",
"spec": {
"ioConfig": {
"type": "kafka",
"consumerProperties": {
"bootstrap.servers": "localhost:9092"
},
"topic": "kttm",
"inputFormat": {
"type": "json"
},
"useEarliestOffset": true
},
"tuningConfig": {
"type": "kafka"
},
"dataSchema": {
"dataSource": "kttm-kafka-supervisor-console",
"timestampSpec": {
"column": "timestamp",
"format": "iso"
},
"dimensionsSpec": {
"dimensions": [
"session",
"number",
"client_ip",
"language",
"adblock_list",
"app_version",
"path",
"loaded_image",
"referrer",
"referrer_host",
"server_ip",
"screen",
"window",
{
"type": "long",
"name": "session_length"
},
"timezone",
"timezone_offset",
{
"type": "json",
"name": "event"
},
{
"type": "json",
"name": "agent"
},
{
"type": "json",
"name": "geo_ip"
}
]
},
"granularitySpec": {
"queryGranularity": "none",
"rollup": false,
"segmentGranularity": "day"
}
}
}
}
```
This starts the supervisor—the supervisor spawns tasks that start listening for incoming data.
3. Click **Tasks** on the console home page to monitor the status of the job. This spec writes the data in the `kttm` topic to a datasource named `kttm-kafka-supervisor-console`.
#### Use the API
You can also use the Druid API to submit a supervisor spec.
1. Run the following command to download the sample spec:
After Druid successfully creates the supervisor, you get a response containing the supervisor ID: `{"id":"kttm-kafka-supervisor-api"}`.
3. Click **Tasks** on the console home page to monitor the status of the job. This spec writes the data in the `kttm` topic to a datasource named `kttm-kafka-supervisor-api`.
## Query your data
After Druid sends data to the Kafka stream, it is immediately available for querying. Click **Query** in the Druid console to run SQL queries against the datasource.
Since this tutorial ingests a small dataset, you can run the query `SELECT * FROM "kttm-kafka"` to return all of the data in the dataset you created.
- [Apache Kafka ingestion](../development/extensions-core/kafka-ingestion.md) for more information on loading data from Kafka streams.
- [Apache Kafka supervisor reference](../development/extensions-core/kafka-supervisor-reference.md) for Kafka supervisor configuration information.
- [Apache Kafka supervisor operations reference](../development/extensions-core/kafka-supervisor-operations.md) for information on running and maintaining Kafka supervisors for Druid.
