# Google Cloud Dataproc Sink Connector [End of Life] for Confluent Cloud
#### IMPORTANT
This connector reached its end of life (EOL) on March 31, 2026.
For more information, see [Deprecated and end of life connectors](overview.md#deprecated-connectors).
The fully managed Google Cloud Dataproc Sink connector for Confluent Cloud integrates
Apache Kafka® with managed HDFS instances in [Google Cloud Dataproc](https://cloud.google.com/dataproc/). The connector periodically polls data
from Kafka and writes this data to HDFS. The connector supports Avro, JSON
Schema, Protobuf, or JSON (schemaless) input data formats and Avro, JSON, and
String output formats.
Confluent Cloud is available through [Google Cloud Marketplace](https://console.cloud.google.com/marketplace/product/confluent-prod/apache-kafka-on-confluent-cloud?inv=1&invt=Ab2Ryw)
or [directly from Confluent](https://www.confluent.io/get-started/).
#### NOTE
This is a Quick Start for the fully managed cloud connector. If you are
installing the connector locally for Confluent Platform, see [Google Cloud Dataproc Sink
connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/gcp-dataproc/current/).
The fully managed Google Cloud Dataproc Sink connector integrates with Hive.
When it is enabled, the connector automatically creates an external Hive
partitioned table for each Kafka topic and updates the table according to the
available data in HDFS.
#### IMPORTANT
Preview features are not currently supported and are not recommended for
production use. A preview feature is a Confluent Cloud component that is being
introduced to gain early feedback. Preview connectors and features can be
used for evaluation and non-production testing purposes or to provide feedback
to Confluent. Comments, questions, and suggestions related to preview features
are encouraged and can be submitted to [ccloud-connect-preview@confluent.io](mailto:ccloud-connect-preview@confluent.io).
Note that Preview connectors are billed in the same way as other managed
connectors. For more information, see [Managed connectors and custom connectors](../billing/billing-dimensions.md#connect-billing).
## Features
The Google Cloud Dataproc Sink connector provides the following features:
* **Exactly Once Delivery**: The connector uses a write ahead log to ensure each record exports to HDFS exactly once. Also, the connector manages the offsets commit by encoding the Kafka offset information into the file so that the connector can start from the last committed offsets in case of failures and task restarts.
* **Data formats:** The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) input data formats and Avro, JSON, and String output formats. [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf).
* **Hive Integration**: The connector supports Hive integration. When it is enabled, the connector automatically creates a Hive external partitioned table for each topic exported to HDFS. `input.data.format` should be **AVRO**
* **Time-Based Partitioner**: The connector supports a daily and hourly partitioner.
* **Seamless Dataproc Integration**: The only connection requirements are the Google Cloud credentials and the Dataproc cluster name and project. No need to get the HDFS URL or adjust a Hadoop configuration.
* **High Availability (HA) Cluster Support**: No additional configuration is required to connect to a multi-master HA cluster.
* **Flush size:** Defaults to 1000. The value can be increased if needed. The value can be lowered (1 minimum) if you are running a [Dedicated Confluent Cloud cluster](../clusters/cluster-types.md#dedicated-cluster). The minimum value is 1000 for non-dedicated clusters.
The following scenarios describe a couple of ways records may be flushed to storage:
* You use the default setting of 1000 and your topic has six partitions. Files start to be created in storage after more than 1000 records exist in each partition.
* You use the default setting of 1000 and the partitioner is set to Hourly. 500 records arrive at one partition from 2:00pm to 3:00pm. At 3:00pm, an additional 5 records arrive at the partition. You will see 500 records in storage at 3:00pm.
#### NOTE
The properties `rotate.schedule.interval.ms` and `rotate.interval.ms`
can be used with `flush.size` to determine when files are created in
storage. These parameters kick in and files are stored based on which
condition is met first.
For FieldPartitioner in fully managed connectors, an additional automatic
rotation occurs when the number of open files reaches 50. This uploads the
current batch of files to storage and creates a new batch.
For example: You have one topic partition. You set `flush.size=1000` and
`rotate.schedule.interval.ms=600000` (10 minutes). 500 records arrive at
the topic partition from 12:01 to 12:10. 500 additional records arrive from
12:11 to 12:20. You will see two files in the storage bucket with 500
records in each file. This is because the 10 minute
`rotate.schedule.interval.ms` condition tripped before the
`flush.size=1000` condition was met.
For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.
#### IMPORTANT
Preview features are not currently supported and are not recommended for
production use. A preview feature is a Confluent Cloud component that is being
introduced to gain early feedback. Preview connectors and features can be
used for evaluation and non-production testing purposes or to provide feedback
to Confluent. Comments, questions, and suggestions related to preview features
are encouraged and can be submitted to [ccloud-connect-preview@confluent.io](mailto:ccloud-connect-preview@confluent.io).
Note that Preview connectors are billed in the same way as other managed
connectors. For more information, see [Managed connectors and custom connectors](../billing/billing-dimensions.md#connect-billing).
## Limitations
Be sure to review the following information.
* For connector limitations, see [Google Cloud Dataproc Sink Connector](limits.md#gcp-dataproc-sink-limits) limitations.
* If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations).
## Quick Start
Use this quick start to get up and running with the Confluent Cloud Google Cloud
Dataproc Sink connector. The quick start provides the basics of selecting the
connector and configuring it to stream events to HDFS.
Prerequisites
: - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Google Cloud in a VPC peering configuration with the Google Dataproc cluster.
#### NOTE
For a non-VPC peered environment, public inbound traffic access (`0.0.0.0/0`) must be allowed to the VPC where the Dataproc cluster is located. You must also make configuration changes to allow public access to the Dataproc cluster while retaining the private IP addresses for the Dataproc master and worker nodes (HDFS NameNode and DataNodes). For configuration details, see [Configuring a non-VPC peering environment](#cc-gcp-dataproc-public-ip-private-ip-config). For more information about public Internet access to resources, see [Networking and DNS](overview.md#connect-internet-access-resources).
- The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html).
- [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf).
- The [Google Cloud Resource Manager API](https://cloud.google.com/resource-manager/reference/rest) is enabled for your project.
- An operating Dataproc cluster. For the steps necessary to create a cluster, see [Create a cluster](https://cloud.google.com/dataproc/docs/guides/create-cluster).
- The Dataproc image version must be 1.4 (or later). See [Cloud Dataproc Image version list](https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions).
- A Google Cloud [service account](https://cloud.google.com/iam/docs/creating-managing-service-accounts). You download service account [credentials as a JSON file](https://cloud.google.com/iam/docs/creating-managing-service-account-keys). These credentials are used when setting up the connector configuration.
- Kafka cluster credentials. The following lists the different ways you can provide credentials.
- Enter an existing [service account](service-account.md#s3-cloud-service-account) resource ID.
- Create a Confluent Cloud [service account](service-account.md#s3-cloud-service-account) for the connector. Make sure to review the ACL entries required in the [service account documentation](service-account.md#s3-cloud-service-account). Some connectors have specific ACL requirements.
- Create a Confluent Cloud API key and secret. To create a key and secret, you can use [confluent api-key create](https://docs.confluent.io/confluent-cli/current/command-reference/api-key/confluent_api-key_create.html) *or* you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.
### Using the Confluent Cloud Console
Complete the following steps to set up and run the connector.
#### Step 1: Launch your Confluent Cloud cluster
To create and launch a Kafka cluster in Confluent Cloud, see [Create a kafka cluster in Confluent Cloud](../get-started/index.md#cloud-create-kafka-cluster).
#### Step 2: Add a connector
In the left navigation menu, click **Connectors**. If you already have connectors in your cluster, click **+ Add
connector**.
#### Step 3: Select your connector
Click the **Google Cloud Dataproc Sink** connector card.
#### Step 4: Enter the connector details
#### NOTE
* Ensure you have all your [prerequisites](#cc-gcp-dataproc-sink-prereqs) completed.
* An asterisk ( \* ) designates a required entry.
At the **Add Google Cloud Dataproc Sink Connector** screen, complete the following:
### Topic selection
If you’ve already populated your Kafka topics, select the topics you want
to connect from the **Topics** list.
To create a new topic, click **+Add new topic**.
### Kafka access
1. Select the way you want to provide **Kafka Cluster credentials**. You can
choose one of the following options:
- **My account**: This setting allows your connector to globally access everything
that you have access to. With a user account, the connector uses an API key and
secret to access the Kafka cluster. This option is not recommended for production.
- **Service account**: This setting limits the access for your connector by using a
[service account](service-account.md#s3-cloud-service-account). This option is recommended for
production.
- **Use an existing API key**: This setting allows you to specify an API key and a
secret pair. You can use an existing pair or create a new one. This method is not
recommended for production environments.
#### NOTE
Freight clusters support only service accounts for Kafka authentication.
2. Click **Continue**.
### Authentication
1. Configure the authentication properties:
- **GCP credentials file**: Upload your Google Cloud credentials JSON file.
- **Dataproc project ID**: ID for the Google Cloud project where the Dataproc cluster is located.
- **Dataproc cluster name**: Google Cloud Dataproc cluster name.
- **Dataproc namenode address**: Comma-separated list of namenodes to
use. If present overrides the namenodes that were detected through
the Dataproc cluster.
- **Use datanode hostname**: Configuration indicating whether to use datanode hostnames when connecting to datanodes.
2. Click **Continue**.
### Configuration
#### NOTE
Configuration properties that are not shown in the
Cloud Console use the default values. See
[Configuration Properties](#cc-gcp-dataproc-sink-config-properties) for all property values
and definitions.
- **Input Kafka record value format**: Select the input Kafka record value format (data coming from the
Kafka topic). Valid entires AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON
(schemaless). A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format (for example,
AVRO, JSON_SR, or PROTOBUF.
#### NOTE
Input format JSON to output format AVRO does not work for the
preview connector.
- **Output message format**: Sets the output message format. Valid entries are AVRO, JSON, or STRING.
Note that the output message format defaults to the value in the input message format field.
If either PROTOBUF or JSON_SR is selected as the input message format, you should select one explicitly.
A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format
(for example, AVRO).
- **Hive integration**: For **Hive integration**, select whether you want to use Hive integration.
- **Time interval**: For **Time interval**, select the time-based partitioning interval you want to use.
- **Flush size**: In the **Flush size** field, enter the number of records that will be
written to storage before invoking file commits. The flush size
defaults to 1000. This value can be raised and lowered (if running a
dedicated cluster).
### **Show advanced configurations**
- **Schema context**: Select a schema context to use for this connector, if using
a schema-based data format. This property defaults to the **Default** context,
which configures the connector to use the default schema set up for Schema Registry in your
Confluent Cloud environment. A schema context allows you to use separate schemas (like
schema sub-registries) tied to topics in different Kafka clusters that share the
same Schema Registry environment. For example, if you select a non-default context, a
**Source** connector uses only that schema context to register a schema and a
**Sink** connector uses only that schema context to read from. For more
information about setting up a schema context, see [What are schema contexts and when should you use them?](../sr/faqs-cc.md#faq-schema-contexts).
- **HDFS logs directory**: Top-level directory where write-ahead logs are stored.
- **Maximum span of record time (in ms) before scheduled rotation**: Scheduled rotation uses `rotate.schedule.interval.ms` to close the
file and upload to storage on a regular basis using the current time,
rather than the record time. Setting `rotate.schedule.interval.ms`
is nondeterministic and will invalidate exactly-once guarantees.
Minimum value is 600000 ms (10 minutes).
- **Hive metastore URIs**: The Hive metastore URIs. Can be an IP
address or fully-qualified domain name and port of the metastore
host.
- **Maximum span of record time (in ms) before rotation**: The
connector’s rotation interval specifies the maximum timespan (in
milliseconds) a file can remain open and ready for additional
records. In other words, when using `rotate.interval.ms`, the
timestamp for each file starts with the timestamp of the first record
inserted in the file. The connector closes and uploads a file to the
blob store when the next record’s timestamp does not fit into the
file’s `rotate.interval` time span from the first records’s
timestamp. If the connector has no more records to process, the
connector may keep the file open until the connector can process
another record (which can be a long time). Minimum value is 600000 ms
(10 minutes).
- **Hive configuration directory**: Hive configuration directory.
- **Timestamp field name**: Sets the field that contains the timestamp used for the `TimeBasedPartitioner`.
- **Hive home directory**: Hive home directory.
- **Timezone**: Sets the timezone used by the `TimeBasedPartitioner`.
- **Hive database**: The database to use when the connector creates tables in Hive.
- **Locale**: Sets the locale to use with the `TimeBasedPartitioner`.
- **Topics directory**: Top-level directory where ingested data is stored.
- **Path format**: Used to set the data directories when partitioning
with `TimeBasedPartitioner`. The format set in this configuration
converts the Unix timestamp to a valid directory string.
**Additional Configs**
- **value.converter.replace.null.with.default**: Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.
- **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
- **Value Converter Reference Subject Name Strategy**: Sets the subject reference name strategy for values. Valid entries are `DefaultReferenceSubjectNameStrategy` or `QualifiedReferenceSubjectNameStrategy`. You can use this strategy only with `PROTOBUF` format; the default strategy is `DefaultReferenceSubjectNameStrategy`.
- **Schema ID For Value Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **value.converter.schemas.enable**: Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.
- **errors.tolerance**: Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.
- **value.converter.ignore.default.for.nullables**: When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.
- **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers.
- **Value Converter Decimal Format**: Specifies the `JSON` or `JSON_SR` serialization format for Connect `DECIMAL` logical type values with two allowed literals:
`BASE64` to serialize `DECIMAL` logical types as base64 encoded binary data, and
`NUMERIC` to serialize `DECIMAL` logical type values in `JSON` or `JSON_SR` as a number representing the decimal value.
- **Schema GUID For Key Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Schema GUID For Value Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Connect Meta Data**: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
- **Value Converter Value Subject Name Strategy**: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
- **Key Converter Key Subject Name Strategy**: Determines how to construct the subject name for key schema registration.
- **Schema ID For Key Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
**Auto-restart policy**
- **Enable Connector Auto-restart**: Enables the auto-restart behavior of the connector and its
task in the event of user-actionable errors. Defaults to `true`, enabling the connector to
automatically restart in case of user-actionable errors. Set this property to `false` to
disable auto-restart for failed connectors. If disabled, you must manually restart the connector.
**Consumer configuration**
- **Max poll interval(ms)**: Sets the maximum delay between subsequent consume requests to Kafka. Use this property to
improve connector performance in cases when the connector cannot send records to the sink system.
The default is 300,000 milliseconds (5 minutes).
- **Max poll records**: Sets the maximum number of records to consume from Kafka in a single request. Use this property to
improve connector performance in cases when the connector cannot send records to the sink system.
The default is 500 records.
**Transforms**
- **Single Message Transformations**: To add a new SMT, see [Add transforms](single-message-transforms.md#cc-single-message-transforms-ui).
For more information about unsupported SMTs, see
[Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms).
**Processing position**
- **Set offsets**: Click **Set offsets** to define a specific offset for
this connector to begin procession data from. For more information
on managing offsets, see [Manage offsets](offsets.md#connect-custom-offsets).
- Click **Continue**.
### Sizing
Based on the number of topic partitions you select, you will be provided
with a recommended number of tasks.
1. To change the number of recommended tasks, enter the number of
[tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in
the **Tasks** field.
2. Click **Continue**.
### Review and Launch
1. Verify the connection details.
2. Click **Launch**.
The status for the connector should go from **Provisioning** to
**Running**.
#### Step 5: Check the Dataproc cluster
Go to your Dataproc cluster and make sure the topic is being populated with records.
For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.
### Using the Confluent CLI
Complete the following steps to set up and run the connector using the Confluent CLI.
#### NOTE
Make sure you have all your [prerequisites](#cc-gcp-dataproc-sink-prereqs) completed.
#### Step 1: List the available connectors
Enter the following command to list available connectors:
```none
confluent connect plugin list
```
#### Step 2: List the connector configuration properties
Enter the following command to show the connector configuration properties:
```none
confluent connect plugin describe
```
The command output shows the required and optional configuration properties.
#### Step 3: Create the connector configuration file
Create a JSON file that contains the connector configuration properties. The following example shows required and optional connector properties.
```none
{
"connector.class": "DataprocSink",
"name": "dataproc-test",
"kafka.auth.mode": "KAFKA_API_KEY",
"kafka.api.key": "",
"kafka.api.secret": "",
"topics": "",
"input.data.format": "AVRO",
"gcp.dataproc.credentials.json": "",
"gcp.dataproc.projectId": "",
"gcp.dataproc.namenode": "",
"logs.dir": "",
"output.data.format": "AVRO",
"flush.size": "1000",
"time.interval": "HOURLY",
"tasks.max": "1"
}
```
Note the following property definitions:
* `"connector.class"`: Identifies the connector plugin name.
* `"name"`: Sets a name for your new connector.
* `"kafka.auth.mode"`: Identifies the connector authentication mode you want to use. There are two options: `SERVICE_ACCOUNT` or `KAFKA_API_KEY` (the default). To use an API key and secret, specify the configuration properties `kafka.api.key` and `kafka.api.secret`, as shown in the example configuration (above). To use a [service account](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=`. To list the available service account resource IDs, use the following command:
```bash
confluent iam service-account list
```
For example:
```bash
confluent iam service-account list
Id | Resource ID | Name | Description
+---------+-------------+-------------------+-------------------
123456 | sa-l1r23m | sa-1 | Service account 1
789101 | sa-l4d56p | sa-2 | Service account 2
```
* `"topics"`: Identifies the topic name or a comma-separated list of topic names.
* `"input.data.format"`: Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are **AVRO**, **JSON_SR**, **PROTOBUF**, or **JSON**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
#### NOTE
Input format JSON to output format AVRO does not work for the preview connector.
* `"gcp.dataproc.credentials.json"`: This contains the contents of the downloaded JSON file. See [Formatting Google Cloud credentials](#cc-gcp-dataproc-json-config-format) for details about how to format and use the contents of the downloaded credentials file.
* `"gcp.dataproc.namenode"`: For VPC-peered environments, this is the internal IP address of the HDFS NameNode (Google Cloud Dataproc master node). For non-VPC-peered environments, this is the FQDN that resolves to the public IP address or the public IP address of the NameNode (for example: `cluster1-m.confluentinc.com`). For non-VPC-peered environment configuration details, see [Configuring a non-VPC peering environment](#cc-gcp-dataproc-public-ip-private-ip-config).
* `"logs.dir"`: This is the top-level HDFS directory where write-ahead logs are stored.
* `"output.data.format"`: Sets the output Kafka record value format. Valid entries are **AVRO**, **JSON**, or **STRING**. You must have Confluent Cloud Schema Registry configured if using a schema-based output format (for example, Avro).
* (Optional) `flush.size`: Defaults to 1000. The value can be increased if needed. The value can be lowered (1 minimum) if you are running a [Dedicated Confluent Cloud cluster](../clusters/cluster-types.md#dedicated-cluster). The minimum value is 1000 for non-dedicated clusters.
The following scenarios describe a couple of ways records may be flushed to storage:
* You use the default setting of 1000 and your topic has six partitions. Files start to be created in storage after more than 1000 records exist in each partition.
* You use the default setting of 1000 and the partitioner is set to Hourly. 500 records arrive at one partition from 2:00pm to 3:00pm. At 3:00pm, an additional 5 records arrive at the partition. You will see 500 records in storage at 3:00pm.
#### NOTE
The properties `rotate.schedule.interval.ms` and `rotate.interval.ms`
can be used with `flush.size` to determine when files are created in
storage. These parameters kick in and files are stored based on which
condition is met first.
For FieldPartitioner in fully managed connectors, an additional automatic
rotation occurs when the number of open files reaches 50. This uploads the
current batch of files to storage and creates a new batch.
For example: You have one topic partition. You set `flush.size=1000` and
`rotate.schedule.interval.ms=600000` (10 minutes). 500 records arrive at
the topic partition from 12:01 to 12:10. 500 additional records arrive from
12:11 to 12:20. You will see two files in the storage bucket with 500
records in each file. This is because the 10 minute
`rotate.schedule.interval.ms` condition tripped before the
`flush.size=1000` condition was met.
* `"time.interval"`: Sets how your messages are grouped. Valid entries are **DAILY** or **HOURLY**.
**SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation.
See [Configuration Properties](#cc-gcp-dataproc-sink-config-properties) for all property values and
definitions.
##### Formatting Google Cloud credentials
The contents of the downloaded credentials file must be converted to string format before it can be used in the connector configuration.
1. Convert the JSON file contents into string format.
2. Add the escape character `\` before all `\n` entries in the Private Key section so that each section begins with `\\n` (see the highlighted lines below). The example below has been formatted so that the `\\n` entries are easier to see. Most of the credentials key has been omitted.
```json
{
"connector.class": "DataprocSink",
"name": "dataproc-sink",
"kafka.api.key": "",
"kafka.api.secret": "",
"topics": "",
"data.format": "AVRO",
"gcp.dataproc.credentials.json" : "{\"type\":\"service_account\",\"project_id\":\"connect-
1234567\",\"private_key_id\":\"omitted\",
\"private_key\":\"-----BEGIN PRIVATE KEY-----
\\nMIIEvAIBADANBgkqhkiG9w0BA
\\n6MhBA9TIXB4dPiYYNOYwbfy0Lki8zGn7T6wovGS5pzsIh
\\nOAQ8oRolFp\rdwc2cC5wyZ2+E+bhwn
\\nPdCTW+oZoodY\\nOGB18cCKn5mJRzpiYsb5eGv2fN\/J
\\n...rest of key omitted...
\\n-----END PRIVATE KEY-----\\n\",
\"client_email\":\"pub-sub@connect-123456789.iam.gserviceaccount.com\",
\"client_id\":\"123456789\",\"auth_uri\":\"https:\/\/accounts.google.com\/o\/oauth2\/
auth\",\"token_uri\":\"https:\/\/oauth2.googleapis.com\/
token\",\"auth_provider_x509_cert_url\":\"https:\/\/
www.googleapis.com\/oauth2\/v1\/
certs\",\"client_x509_cert_url\":\"https:\/\/www.googleapis.com\/
robot\/v1\/metadata\/x509\/pub-sub%40connect-
123456789.iam.gserviceaccount.com\"}",
"gcp.dataproc.projectId": "",
"gcp.dataproc.cluster": "",
"logs.dir": "",
"flush.size": "1000",
"time.interval": "HOURLY",
"tasks.max": "1"
}
```
3. Add all the converted string content to the `"gcp.dataproc.credentials.json"` section of your configuration file as shown in the example above.
#### Step 4: Load the configuration file and create the connector
Enter the following command to load the configuration and start the connector:
```none
confluent connect cluster create --config-file .json
```
For example:
```none
confluent connect cluster create --config-file dataproc-sink-config.json
```
Example output:
```none
Created connector dataproc-sink lcc-ix4dl
```
#### Step 5: Check the connector status
Enter the following command to check the connector status:
```none
confluent connect cluster list
```
Example output:
```none
ID | Name | Status | Type
+-----------+-----------------+---------+------+
lcc-ix4dl | dataproc-sink | RUNNING | sink
```
#### Step 6: Check the Dataproc cluster.
Go to your Dataproc cluster and make sure the topic is being populated with records.
For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.
## Configuration Properties
Use the following configuration properties with the fully managed connector. For
self-managed connector property definitions and other details, see the connector
docs in [Self-managed connectors for Confluent Platform](/platform/current/connect/kafka_connectors.html).
### Which topics do you want to get data from?
`topics`
: Identifies the topic name or a comma-separated list of topic names.
* Type: list
* Importance: high
### Schema Config
`schema.context.name`
: Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.
* Type: string
* Default: default
* Importance: medium
### Input messages
`input.data.format`
: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.
* Type: string
* Default: JSON
* Importance: high
### How should we connect to your data?
`name`
: Sets a name for your connector.
* Type: string
* Valid Values: A string at most 64 characters long
* Importance: high
### Kafka Cluster credentials
`kafka.auth.mode`
: Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode, whenever possible.
* Type: string
* Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY
* Importance: high
`kafka.api.key`
: Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.
* Type: password
* Importance: high
`kafka.service.account.id`
: The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.
* Type: string
* Importance: high
`kafka.api.secret`
: Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
* Type: password
* Importance: high
### GCP credentials
`gcp.dataproc.credentials.json`
: GCP service account JSON file with write permssions for Dataproc.
* Type: password
* Importance: high
### How should we connect to your Dataproc?
`gcp.dataproc.projectId`
: ID for the GCP project where the Dataproc cluster is located.
* Type: string
* Importance: high
`gcp.dataproc.cluster`
: Name of the GCP Dataproc cluster.
* Type: string
* Importance: high
`gcp.dataproc.namenode`
: Comma separated list of namenodes to use. If present, overrides the namenodes that were detected through Dataproc cluster.
* Type: list
* Importance: medium
`gcp.dataproc.use.datanode.hostname`
: Configuration indicating whether to use datanode hostnames when connecting to datanodes.
* Type: boolean
* Default: false
* Importance: low
### Output messages
`output.data.format`
: Sets the output message format. Valid entries are AVRO, JSON, or STRING.Note that the output message format defaults to the value in the Input Message Format field. If either PROTOBUF or JSON_SR is selected as the input message format, you should select one explicitly. If no value for this property is provided, the value specified for the ‘input.data.format’ property is used.
* Type: string
* Importance: high
### HDFS details
`logs.dir`
: Top-level directory where write-ahead logs are stored.
* Type: string
* Default: logs
* Importance: high
### Hive
`hive.integration`
: Whether or not to use Hive integration.
* Type: boolean
* Default: false
* Importance: high
`hive.metastore.uris`
: The Hive metastore URIs. Can be an IP address or fully-qualified domain name and port of the metastore host.
* Type: string
* Importance: high
`hive.conf.dir`
: Hive configuration directory.
* Type: string
* Default: “”
* Importance: high
`hive.home`
: Hive home directory.
* Type: string
* Default: “”
* Importance: high
`hive.database`
: The database to use when the connector creates tables in Hive.
* Type: string
* Default: false
* Importance: high
### Organize my data by…
`topics.dir`
: Configures the directory to store the data ingested from Kafka. If you want to organize files like the following example, hdfs:///json_logs/daily//dt=2020-02-06/hr=09/, please put topic.directory=json_logs/daily, path.format=’dt’=YYYY-MM-dd/’hr’=HH, and time.interval=HOURLY.
* Type: string
* Default: topics
* Importance: high
`path.format`
: This configuration is used to set the format of the data directories when partitioning with TimeBasedPartitioner. The format set in this configuration converts the Unix timestamp to a valid directory string. To organize files like this example, path.format= hdfs:///json_logs/daily//dt=2020-02-06/hr=09/, use the properties: topic.directory=json_logs/daily, path.format=’dt’=YYYY-MM-dd/’hr’=HH, and time.interval=HOURLY.
* Type: string
* Default: ‘year’=YYYY/’month’=MM/’day’=dd/’hour’=HH
* Importance: high
`time.interval`
: Partitioning interval of data, according to the time ingested to storage.
* Type: string
* Importance: high
`rotate.schedule.interval.ms`
: Scheduled rotation uses rotate.schedule.interval.ms to close the file and upload to storage on a regular basis using the current time, rather than the record time. Setting rotate.schedule.interval.ms is nondeterministic and will invalidate exactly-once guarantees.
* Type: int
* Default: -1
* Importance: medium
`rotate.interval.ms`
: The connector’s rotation interval specifies the maximum timespan (in milliseconds) a file can remain open and ready for additional records. In other words, when using rotate.interval.ms, the timestamp for each file starts with the timestamp of the first record inserted in the file. The connector closes and uploads a file to the blob store when the next record’s timestamp does not fit into the file’s rotate.interval time span from the first record’s timestamp. If the connector has no more records to process, the connector may keep the file open until the connector can process another record (which can be a long time). If no value for this property is provided, the value specified for the ‘time.interval’ property is used.
* Type: int
* Importance: high
`flush.size`
: Number of records written to storage before invoking file commits.
* Type: int
* Default: 1000
* Valid Values: [1000,…] for non-dedicated clusters and [1,…] for dedicated clusters
* Importance: high
`timestamp.field`
: Sets the field that contains the timestamp used for the TimeBasedPartitioner
* Type: string
* Default: “”
* Importance: high
`timezone`
: Sets the timezone used by the TimeBasedPartitioner.
* Type: string
* Default: UTC
* Importance: high
`locale`
: Sets the locale to use with TimeBasedPartitioner.
* Type: string
* Default: en
* Importance: high
### Consumer configuration
`max.poll.interval.ms`
: The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).
* Type: long
* Default: 300000 (5 minutes)
* Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
* Importance: low
`max.poll.records`
: The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.
* Type: long
* Default: 500
* Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
* Importance: low
### Number of tasks for this connector
`tasks.max`
: Maximum number of tasks for the connector.
* Type: int
* Valid Values: [1,…]
* Importance: high
### Additional Configs
`key.converter.use.schema.guid`
: The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: string
* Importance: low
`key.converter.use.schema.id`
: The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: int
* Importance: low
`value.converter.connect.meta.data`
: Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.
* Type: boolean
* Importance: low
`value.converter.use.schema.guid`
: The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: string
* Importance: low
`value.converter.use.schema.id`
: The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: int
* Importance: low
`errors.tolerance`
: Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.
* Type: string
* Default: all
* Importance: low
`key.converter.key.schema.id.deserializer`
: The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.
* Type: string
* Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
* Importance: low
`key.converter.key.subject.name.strategy`
: How to construct the subject name for key schema registration.
* Type: string
* Default: TopicNameStrategy
* Importance: low
`value.converter.decimal.format`
: Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:
BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
* Type: string
* Default: BASE64
* Importance: low
`value.converter.ignore.default.for.nullables`
: When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.
* Type: boolean
* Default: false
* Importance: low
`value.converter.reference.subject.name.strategy`
: Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.
* Type: string
* Default: DefaultReferenceSubjectNameStrategy
* Importance: low
`value.converter.replace.null.with.default`
: Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.
* Type: boolean
* Default: true
* Importance: low
`value.converter.schemas.enable`
: Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.
* Type: boolean
* Default: false
* Importance: low
`value.converter.value.schema.id.deserializer`
: The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.
* Type: string
* Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
* Importance: low
`value.converter.value.subject.name.strategy`
: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
* Type: string
* Default: TopicNameStrategy
* Importance: low
### Auto-restart policy
`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
* Type: boolean
* Default: true
* Importance: medium
## Next Steps
For an example that shows fully managed Confluent Cloud connectors in action with
Confluent Cloud for Apache Flink, see the [Cloud ETL Demo](/platform/current/tutorials/examples/cloud-etl/docs/index.html).
This example also shows how to use Confluent CLI to manage your resources in
Confluent Cloud.
[](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)
## Configuring a non-VPC peering environment
When Confluent Cloud is set up with public endpoints in a non-VPC peering environment,
connector requests originate from a public IP endpoint at the Confluent Cloud VPC where
the Dataproc connector is running. However, the Dataproc cluster VPC does not
provide a public IP address endpoint. Even if each Dataproc node has a Public IP
address configured, the VPC does not, and the Hadoop daemon returns private IP
addresses and private hostnames to the Confluent Cloud connector.
After you complete the following procedure:
* The Dataproc connector can successfully establish connectivity to the Google Cloud Dataproc cluster master node (HDFS NameNode).
* The Google Cloud Dataproc cluster can respond over public IP to the Confluent Cloud VPC and Dataproc connector.
* All Dataproc nodes (HDFS NameNode and DataNodes) in the cluster retain the use of their private IP addresses.
The procedure assumes you are starting a new Dataproc and Confluent Cloud cluster.
Prerequisites
: * Authorization to update Google Cloud instances (Dataproc nodes) and configure DNS record sets for your Google Cloud project account.
* The [gcloud CLI](https://cloud.google.com/sdk/gcloud) must be installed and configured to manage your Google Cloud Dataproc cluster.
* Access to a running Dataproc cluster in Google Cloud.
* The Dataproc cluster must have the Cloud Resource Manager API enabled.
* The Dataproc cluster VPC must have the following ports open (IP ranges: 0.0.0.0/0) for Confluent Cloud connector ingress:
- `tcp:8020`
- `tcp:9000`
- `tcp:9083`
- `tcp:9864-9867`
### Step 1: Add or create record sets in Cloud DNS
To create a configuration in a non-VPC peered environment, you first need to add
or create record sets in the Google Cloud Cloud DNS service. Create the following zones:
* **public zone:** Contains record sets corresponding to the external IP addresses of each Dataproc cluster node.
* **private zone #1:** Contains record sets corresponding to the internal IP addresses of each Dataproc cluster node.
* **private zone #2:** This is a *managed reverse lookup zone*. It contains the reverse internal IP addresses (in 10.in-addr.arpa. format) for each Dataproc cluster node.
You can create DNS zones and record sets using the [gcloud CLI](https://cloud.google.com/sdk/gcloud/reference/dns) or by using the Google Cloud [Cloud DNS console](https://console.cloud.google.com/net-services).
1. Get the instance names, external IP addresses, and internal IP addresses for each of your Dataproc nodes.
```none
gcloud compute instances list --project= --zone --filter ""
```
For example:
```none
gcloud compute instances list --project=ccloud-lab-47372 --zones us-west1-c --filter "cluster-fa79"
NAME ZONE MACHINE_TYPE PREEMPTIBLE INTERNAL_IP EXTERNAL_IP STATUS
cluster-fa79-m us-central1-c n1-standard-4 10.128.0.6 34.67.10.174 RUNNING
cluster-fa79-w-0 us-central1-c n1-standard-4 10.128.0.2 34.72.119.108 RUNNING
cluster-fa79-w-1 us-central1-c n1-standard-4 10.128.0.3 104.154.209.27 RUNNING
```
2. Create or add each instance name and external IP address to a public cloud DNS zone using the [gcloud CLI](https://cloud.google.com/sdk/gcloud) or the [Cloud DNS console](https://cloud.google.com/dns/docs/quickstart?hl=en_US). Once you have created the DNS zone and record sets, view the records in the UI or list them using the following gcloud command.
```none
gcloud dns record-sets list --zone= --project=
```
For example:
```none
gcloud dns record-sets list --zone=ccloud-dataproc-public --project=ccloud-lab-47372
NAME TYPE TTL DATA
ccloud.dataproc.lab.net. NS 21600 ns-cloud-b1.googledomains.com.,ns-cloud-b2.googledomains.com.,ns-cloud-b3.googledomains.com.,ns-cloud-b4.googledomains.com.
ccloud.dataproc.lab.net. SOA 21600 ns-cloud-b1.googledomains.com. cloud-dns-hostmaster.google.com. 1 21600 3600 259200 300
cluster-fa79-m.ccloud.dataproc.lab.net. A 300 34.67.10.174
cluster-fa79-w-0.ccloud.dataproc.lab.net. A 300 34.72.119.108
cluster-fa79-w-1.ccloud.dataproc.lab.net. A 300 104.154.209.27
```
3. Create or add each instance name and internal IP address to a private cloud DNS zone using the [gcloud CLI](https://cloud.google.com/sdk/gcloud) or the [Cloud DNS console](https://cloud.google.com/dns/docs/quickstart?hl=en_US). Once you have created the DNS zone and record sets, view the records in the UI or list them using the following gcloud command.
```none
gcloud dns record-sets list --zone= --project=
```
For example:
```none
gcloud dns record-sets list --zone=ccloud-dataproc-private --project=ccloud-lab-47372
NAME TYPE TTL DATA
ccloud.dataproc.lab.net. NS 21600 ns-gcp-private.googledomains.com.
ccloud.dataproc.lab.net. SOA 21600 ns-gcp-private.googledomains.com. cloud-dns-hostmaster.google.com. 1 21600 3600 259200 300
cluster-fa79-m.ccloud.dataproc.lab.net. A 300 10.128.0.6
cluster-fa79-w-0.ccloud.dataproc.lab.net. A 300 10.128.0.2
cluster-fa79-w-1.ccloud.dataproc.lab.net. A 300 10.128.0.3
```
4. Create or add each instance name and reverse lookup address (`10.in-addr.arpa.`) to a private cloud DNS zone using the [gcloud CLI](https://cloud.google.com/sdk/gcloud) or the [Cloud DNS console](https://cloud.google.com/dns/docs/quickstart?hl=en_US). Once you have created the DNS zone and record sets, view the records in the UI or list them using the following gcloud command.
```none
gcloud dns record-sets list --zone= --project=
```
For example:
```none
gcloud dns record-sets list --zone=ccloud-dataproc-private-reverse --project=ccloud-lab-47372
NAME TYPE TTL DATA
10.in-addr.arpa. NS 21600 ns-gcp-private.googledomains.com.
10.in-addr.arpa. SOA 21600 ns-gcp-private.googledomains.com. cloud-dns-hostmaster.google.com. 1 21600 3600 259200 300
6.0.128.10.in-addr.arpa. PTR 300 cluster-fa79-m.ccloud.dataproc.lab.net.
2.0.128.10.in-addr.arpa. PTR 300 cluster-fa79-w-0.ccloud.dataproc.lab.net.
3.0.128.10.in-addr.arpa. PTR 300 cluster-fa79-w-1.ccloud.dataproc.lab.net.
```
### Step 2: (Optional) Create permanent custom hostnames
#### NOTE
Google Cloud creates a default hostname for each Dataproc instance in the cluster.
You can use the default Google Cloud hostnames instead of creating custom hostnames.
However, you may want to create custom hostnames that correspond to your
network plan or specific cloud application.
Complete the following steps to set custom hostnames for each Dataproc cluster
node. You store the hostname on the nodes using the [gcloud CLI](https://cloud.google.com/sdk/gcloud) and the Google Cloud metadata service (see [Storing and retrieving instance metadata](https://cloud.google.com/compute/docs/storing-retrieving-metadata)).
1. Add a hostname to the Dataproc master node.
```none
gcloud compute instances add-metadata \
--metadata --zone
```
For example:
```none
gcloud compute instances add-metadata cluster-fa79-m \
--metadata hostname=master.cluster1.ccloud.net --zone us-west1-c
```
2. Verify that the master node hostname is configured.
```none
gcloud compute instances describe --format='value[](metadata.items.hostname)' \
--project= --zone
```
For example:
```none
gcloud compute instances describe cluster-fa79-m --format='value[](metadata.items.hostname)' \
--project=cloud-lab-47372 --zone us-west1-c
master.cluster1.ccloud.net
```
3. Add a hostname for each Dataproc worker node. *Complete this step for all worker nodes*.
```none
gcloud compute instances add-metadata --metadata --zone
```
For example:
```none
gcloud compute instances add-metadata cluster-fa79-w-0 \
--metadata hostname=worker0.cluster1.ccloud.net --zone us-west1-c
```
4. Verify that the worker hostname is configured.
```none
gcloud compute instances describe --format='value[](metadata.items.hostname)' \
--project= --zone
```
For example:
```none
gcloud compute instances describe cluster-fa79-w-0 --format='value[](metadata.items.hostname)' \
--project=ccloud-lab-47372 --zone us-west1-c
worker0.cluster1.ccloud.net
```
5. At this point if the nodes restarted, the hostnames would be lost. Make the master hostname persist on restart.
```none
gcloud compute instances add-metadata \
--metadata startup-script="sudo -s hostnamectl set-hostname " \
--zone
```
For example:
```none
gcloud compute instances add-metadata cluster-fa79-m \
--metadata startup-script="sudo -s hostnamectl set-hostname master.cluster1.ccloud.net" \
--zone us-west1-c
Updated [https://www.googleapis.com/compute/v1/projects/ccloud-lab-47372/zones/us-central1-c/instances/cluster-fa79-m].
```
6. Verify that the master node startup script is configured.
```none
gcloud compute instances describe --format='value[](metadata.items.startup-script)' \
--project= --zone
```
For example:
```none
gcloud compute instances describe cluster-fa79-m --format='value[](metadata.items.startup-script)' \
--project=ccloud-lab-47372 --zone us-west1-c
sudo -s hostnamectl set-hostname master.cluster1.ccloud.net
```
7. Make the worker hostnames persist on restart. *Complete this step for all worker nodes*.
```none
gcloud compute instances add-metadata \
--metadata startup-script="sudo -s hostnamectl set-hostname " \
--zone
```
For example:
```none
gcloud compute instances add-metadata cluster-fa79-w-0 \
--metadata startup-script="sudo -s hostnamectl set-hostname worker0.cluster1.ccloud.net" \
--zone us-west1-c
Updated [https://www.googleapis.com/compute/v1/projects/ccloud-lab-47372/zones/us-central1-c/instances/cluster-fa79-w-0].
```
8. Verify that the worker node startup script is configured. *Complete this step for all worker nodes*.
```none
gcloud compute instances describe --format='value[](metadata.items.startup-script)' \
--project= --zone
```
For example:
```none
gcloud compute instances describe cluster-fa79-w-0 --format='value[](metadata.items.startup-script)' \
--project=ccloud-lab-47372 --zone us-west1-c
sudo -s hostnamectl set-hostname worker0.cluster1.ccloud.net
```
### Step 3: Verify external and internal IP mapping
Complete the following steps to verify that the external and internal IP mappings are configured properly.
1. Open a new terminal session and use `nslookup` to get the external address mappings. Use the hostname for each node. *Complete this step for all worker nodes*.
```none
nslookup
```
For example:
```none
nslookup master.cluster1.ccloud.net
Server: 192.168.86.1
Address: 192.168.86.1#53
Non-authoritative answer:
Name: master.cluster1.ccloud.net
Address: 208.91.197.26
```
2. (Optional) Use `ping` to verify reachability to each node. Use the ``.
For example:
```none
ping master.cluster1.ccloud.net
PING master.cluster1.ccloud.net (208.91.197.26): 56 data bytes
64 bytes from 208.91.197.26: icmp_seq=0 ttl=240 time=58.091 ms
64 bytes from 208.91.197.26: icmp_seq=1 ttl=240 time=57.666 ms
64 bytes from 208.91.197.26: icmp_seq=2 ttl=240 time=59.568 ms
```
3. Launch an SSH terminal session on one of the worker nodes. The example below shows the [gcloud CLI](https://cloud.google.com/sdk/gcloud) command you can use.
```none
gcloud beta compute ssh --zone "" "" --project ""
```
For example:
```none
gcloud beta compute ssh --zone "us-west1-c" "worker0.cluster1.ccloud.net" -project "ccloud-lab-47372"
Updating project ssh metadata...
Updated [https://www.googleapis.com/compute/beta/projects/ccloud-lab-47372].
Updating project ssh metadata...done.
Waiting for SSH key to propagate.
Warning: Permanently added [] to the list of known hosts.
... omitted
```
4. On the Dataproc worker node, use `nslookup` to get the internal address mappings for the master node. Use the hostname for each node. *Complete this step for all worker nodes*.
```none
nslookup master.cluster1.ccloud.net
Server: 192.168.86.1
Address: 192.168.86.1#53
Non-authoritative answer:
Name: master.cluster1.ccloud.net
Address: 10.128.0.6
```
### Step 4: Make core-site.xml and hdfs-site.xml modifications
#### NOTE
If you are using the default Google Cloud hostnames, you do not have to complete all of the steps in this procedure. However, make sure to verify everything is set up properly at each step and make sure to **add the public DNS name on each worker node** in the step where this is requested.
Complete the following steps to modify `core-site.xml` and `hfds-site.xml` configuration files to use the new hostnames.
1. Edit the `/etc/hadoop/conf/core-site.xml` on the *master node and all worker nodes*. Update the configuration to refer to the master hostname. The following uses the example master hostname created earlier.
```xml
... omitted
fs.default.namehdfs://master.cluster1.ccloud.netThe old FileSystem used by FsShell.fs.defaultFShdfs://master.cluster1.ccloud.net
The name of the default file system. A URI whose scheme and authority
determine the FileSystem implementation. The uri's scheme determines
the config property (fs.SCHEME.impl) naming the FileSystem
implementation class. The uri's authority is used to determine the
host, port, etc. for a filesystem.
... omitted
```
2. Edit the `/etc/hadoop/conf/hdfs-site.xml` on the *master node and all worker nodes*. Update the configuration to refer to the master hostname. The following uses the example master hostname created earlier.
```xml
... omitted
dfs.namenode.rpc-addressmaster.cluster1.ccloud.net:8020
RPC address that handles all clients requests. If empty then we'll get
the value from ``fs.default.name``. The value of this property will take the
form of hdfs://nn-host1:rpc-port.
... omitted
dfs.namenode.servicerpc-addressmaster.cluster1.ccloud.net:8051falseDataproc Cluster Properties
... omitted
dfs.namenode.lifeline.rpc-addressmaster.cluster1.ccloud.net:8050falseDataproc Cluster Properties
... omitted
```
3. At the end of the `hdfs-site.xml` file on each worker node, add the public DNS name for the node. *Create this section for each worker node*. **This is a required step even if using the default Google Cloud hostnames.**
```xml
... end of file
dfs.datanode.hostnamecluster-fa79-w-0.ccloud.dataproc.lab.net
obscure property
```
### Step 5: Make additional configuration modifications
#### NOTE
If you are using the default Google Cloud hostnames, you do not have to complete all of the steps in this procedure. However, make sure to verify everything is set up properly at each step.
Complete the following steps to make additional configuration changes to the `nodes_include` configuration file and to `etc/hosts` on each node. You do not have to add these lines if you are using the default Google Cloud hostnames.
1. Edit the `/etc/hadoop/conf/nodes_include` on the master node. Add all worker node hostnames. The example below shows the worker hostnames created earlier.
```bash
... omitted
worker0.cluster1.ccloud.net
worker1.cluster1.ccloud.net
```
2. Launch an SSH terminal session on the master node. Add the master hostname and internal IP address to `/etc/hosts`. The additional line is highlighted in the example below.
```bash
127.0.0.1 localhost
::1 localhost ip6-localhost ip6-loopback
ff02::1 ip6-allnodes
ff02::2 ip6-allrouters
10.128.0.6 master.cluster1.ccloud.net # <-- add this line
10.128.0.6 cluster-fa79-m.c.ccloud.dataproc.lab.net.internal cluster-fa79-m # Added by Google
169.254.169.254 metadata.google.internal # Added by Google
```
3. Launch an SSH terminal session on a worker node. Add the worker hostname and internal IP address to `/etc/hosts`. The additional line is highlighted in each example below. *Complete this step for all worker nodes*.
```bash
127.0.0.1 localhost
::1 localhost ip6-localhost ip6-loopback
ff02::1 ip6-allnodes
ff02::2 ip6-allrouters
10.128.0.2 worker0.cluster1.ccloud.net # <-- add this line
10.128.0.2 cluster-fa79-w-0.c.ccloud.dataproc.lab.net.internal cluster-fa79-w-0 # Added by Google
169.254.169.254 metadata.google.internal # Added by Google
```
```bash
127.0.0.1 localhost
::1 localhost ip6-localhost ip6-loopback
ff02::1 ip6-allnodes
ff02::2 ip6-allrouters
10.128.0.3 worker1.cluster1.ccloud.net # <-- add this line
10.128.0.3 cluster-fa79-w-1.c.ccloud.dataproc.lab.net.internal cluster-fa79-w-1 # Added by Google
169.254.169.254 metadata.google.internal # Added by Google
```
### Step 6: Configure the Dataproc connector
Complete [Dataproc connector configuration steps](#cc-gcp-dataproc-sink-cli-steps). Configure the Dataproc connector with the
`gcp.dataproc.use.datanode.hostname` configuration property. The example below
shows this configuration property added to the configuration. This property
defaults to `false` if not used. Note that for HA deployments, the
`gcp.dataproc.namenode` property supports a comma-separated list of namenodes.
```json
{
"connector.class": "DataprocSink",
"name": "dataproc-test",
"kafka.api.key": "",
"kafka.api.secret": "",
"topics": "",
"input.data.format": "AVRO",
"gcp.dataproc.credentials.json": "",
"gcp.dataproc.projectId": "",
"gcp.dataproc.namenode": "",
"gcp.dataproc.use.datanode.hostname": "true"
"logs.dir": "",
"output.data.format": "AVRO",
"flush.size": "1000",
"time.interval": "HOURLY",
"tasks.max": "1"
}
```
After the configuration settings have been completed, the Dataproc cluster VPC nodes respond over a public IP endpoint to the Confluent Cloud cluster and fully managed Dataproc connector as shown below.
