Google Cloud Dataproc Sink Connector for Confluent Cloud¶

The fully-managed Google Cloud Dataproc Sink connector for Confluent Cloud integrates Apache Kafka® with managed HDFS instances in Google Cloud 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.

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.

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.

Note that Preview connectors are billed in the same way as other managed connectors. For more information, see Managed connectors and custom connectors.

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 must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). See Schema Registry Enabled Environments for additional information.
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. 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 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 Managed and Custom Connectors 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.

Note that Preview connectors are billed in the same way as other managed connectors. For more information, see Managed connectors and custom connectors.

Limitations¶

Be sure to review the following information.

For connector limitations, see Google Cloud Dataproc Sink Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

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 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. For more information about public Internet access to resources, see Networking, DNS, and service endpoints.
The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). See Schema Registry Enabled Environments for additional information.
The Google Cloud Resource Manager API is enabled for your project.
An operating Dataproc cluster. For the steps necessary to create a cluster, see Create a cluster.
The Dataproc image version must be 1.4 (or later). See Cloud Dataproc Image version list.
A Google Cloud service account. You download service account credentials as a JSON file. 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 resource ID.
- Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. 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 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¶

See the Quick Start for Confluent Cloud for installation instructions.

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 completed.
An asterisk ( * ) designates a required entry.

At the Add Google Cloud Dataproc Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topic(s) you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Note

Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and definitions.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON (schemaless). A valid schema must be available in Schema Registry to use a schema-based message format (for example, AVRO, JSON_SR, or PROTOBUF. See Schema Registry Enabled Environments for additional information.

Note

Input format JSON to output format AVRO does not work for the preview connector.
Select the Output Kafka record value format (data coming from the connector): AVRO, JSON, or STRING. A valid schema must be available in Schema Registry to use a schema-based message format (for example, AVRO).
For Hive integration, select whether you want to use Hive integration.
For Time interval, select the time-based partitioning interval you want to use.
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?.
- 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.
For information about transforms and predicates, see the Single Message Transforms (SMT) documentation for details. See Unsupported transformations for a list of SMTs that are not supported with this connector.
Click Continue.

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 Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

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 completed.

Step 1: List the available connectors¶

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties¶

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

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.

{
  "connector.class": "DataprocSink",
  "name": "dataproc-test",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "<my-kafka-api-key>",
  "kafka.api.secret": "<my-kafka-api-secret>",
  "topics": "<topic-name>",
  "input.data.format": "AVRO",
  "gcp.dataproc.credentials.json": "<credentials-json-file-contents>",
  "gcp.dataproc.projectId": "<my-dataproc-project-ID",
  "gcp.dataproc.cluster": "<my-dataproc-cluster-name>",
  "gcp.dataproc.namenode": "<IP-address-of-the-namenode>",
  "logs.dir": "<HDFS-logs-directory>",
  "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, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:
```
confluent iam service-account list
```
For example:
```
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 GCP credentials 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 (GCP 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.
"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. 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 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.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and definitions.

Formatting GCP credentials¶

The contents of the downloaded credentials file must be converted to string format before it can be used in the connector configuration.

Convert the JSON file contents into string format.

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.

Tip

A script is available that converts the credentials to a string and also adds additional \ escape characters where needed. See Stringify GCP Credentials.

  {
      "connector.class": "DataprocSink",
      "name": "dataproc-sink",
      "kafka.api.key": "<my-kafka-api-key>",
      "kafka.api.secret": "<my-kafka-api-secret>",
      "topics": "<topic-name>",
      "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": "<my-dataproc-project-ID",
      "gcp.dataproc.region": "<gcp-region>",
      "gcp.dataproc.cluster": "<my-dataproc-cluster-name>",
      "logs.dir": "<HDFS-logs-directory>",
      "flush.size": "1000",
      "time.interval": "HOURLY",
      "tasks.max": "1"
  }

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:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file dataproc-sink-config.json

Example output:

Created connector dataproc-sink lcc-ix4dl

Step 5: Check the connector status¶

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

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 Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

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.

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
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.

Type: string
Default: KAFKA_API_KEY
Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
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://<dataproc-directory>/json_logs/daily/<Topic-Name>/dt=2020-02-06/hr=09/<files>, 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://<dataproc-directory>/json_logs/daily/<Topic-Name>/dt=2020-02-06/hr=09/<files>, 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. Minimum value is 600000ms (10 minutes).

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). Minimum value is 600000ms (10 minutes). 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,…]
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

value.converter.connect.meta.data

Toggle for enabling/disabling connect converter to add its meta data to the output schema or not.

Type: boolean
Default: true
Importance: medium

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]
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]
Importance: low

Number of tasks for this connector¶

tasks.max

Maximum number of tasks for the connector.

Type: int
Valid Values: [1,…]
Importance: high

Next Steps¶

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

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.

Tip

For more information about public Internet access to resources, see Networking, DNS, and service endpoints.

Private IP response to Confluent Cloud¶

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 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.

GCP Cloud DNS console¶

You can create DNS zones and record sets using the gcloud CLI or by using the Google Cloud Cloud DNS console.

Get the instance names, external IP addresses, and internal IP addresses for each of your Dataproc nodes.

gcloud compute instances list --project=<my-gcp-project> --zone <region-zone> --filter "<my-cluster-ID>"

For example:

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

Create or add each instance name and external IP address to a public cloud DNS zone using the gcloud CLI or the Cloud DNS console. Once you have created the DNS zone and record sets, view the records in the UI or list them using the following gcloud command.

gcloud dns record-sets list --zone=<public-dns-zone> --project=<gcp-project-ID>

For example:

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

Create or add each instance name and internal IP address to a private cloud DNS zone using the gcloud CLI or the Cloud DNS console. Once you have created the DNS zone and record sets, view the records in the UI or list them using the following gcloud command.

gcloud dns record-sets list --zone=<private-dns-zone> --project=<gcp-project-ID>

For example:

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

Create or add each instance name and reverse lookup address (10.in-addr.arpa.) to a private cloud DNS zone using the gcloud CLI or the Cloud DNS console. Once you have created the DNS zone and record sets, view the records in the UI or list them using the following gcloud command.

gcloud dns record-sets list --zone=<private-reverse-dns-zone> --project=<gcp-project-ID>

For example:

gcloud dns record-sets list --zone=ccloud-dataproc-private-reverse --project=ccloud-lab-47372
NAME                                       TYPE  TTL    DATA
in-addr.arpa.          NS    21600  ns-gcp-private.googledomains.com.
in-addr.arpa.          SOA   21600  ns-gcp-private.googledomains.com. cloud-dns-hostmaster.google.com. 1 21600 3600 259200 300
0.128.10.in-addr.arpa.  PTR   300    cluster-fa79-m.ccloud.dataproc.lab.net.
0.128.10.in-addr.arpa.  PTR   300    cluster-fa79-w-0.ccloud.dataproc.lab.net.
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 and the Google Cloud metadata service (see Storing and retrieving instance metadata).

Add a hostname to the Dataproc master node.

gcloud compute instances add-metadata <master-instance-name> \
--metadata <master-node-hostname> --zone <region-zone>

For example:

gcloud compute instances add-metadata cluster-fa79-m \
--metadata hostname=master.cluster1.ccloud.net --zone us-west1-c

Verify that the master node hostname is configured.

gcloud compute instances describe <master-instance-name> --format='value[](metadata.items.hostname)' \
--project=<my-gcp-project> --zone <region-zone>

For example:

gcloud compute instances describe cluster-fa79-m --format='value[](metadata.items.hostname)' \
--project=cloud-lab-47372 --zone us-west1-c
master.cluster1.ccloud.net

Add a hostname for each Dataproc worker node. Complete this step for all worker nodes.

gcloud compute instances add-metadata <worker-instance-name> --metadata <worker-node-hostname> --zone <region-zone>

For example:

gcloud compute instances add-metadata cluster-fa79-w-0 \
--metadata hostname=worker0.cluster1.ccloud.net --zone us-west1-c

Verify that the worker hostname is configured.

gcloud compute instances describe <worker-instance-name> --format='value[](metadata.items.hostname)' \
--project=<my-gcp-project> --zone <region-zone>

For example:

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

At this point if the nodes restarted, the hostnames would be lost. Make the master hostname persist on restart.

gcloud compute instances add-metadata <master-instance-name> \
--metadata startup-script="sudo -s hostnamectl set-hostname <master-node-hostname>" \
--zone <region-zone>

For example:

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].

Verify that the master node startup script is configured.

gcloud compute instances describe <master-instance-name> --format='value[](metadata.items.startup-script)' \
--project=<my-gcp-project> --zone <region-zone>

For example:

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

Make the worker hostnames persist on restart. Complete this step for all worker nodes.

gcloud compute instances add-metadata <worker-instance-name> \
--metadata startup-script="sudo -s hostnamectl set-hostname <worker-node-hostname>" \
--zone <region-zone>

For example:

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].

Verify that the worker node startup script is configured. Complete this step for all worker nodes.

gcloud compute instances describe <worker-instance-name> --format='value[](metadata.items.startup-script)' \
--project=<my-gcp-project> --zone <region-zone>

For example:

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.

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.

nslookup <cluster-node-hostname>

For example:

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

(Optional) Use ping to verify reachability to each node. Use the <cluster-node-hostname>.

For example:

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

Launch an SSH terminal session on one of the worker nodes. The example below shows the gcloud CLI command you can use.

gcloud beta compute ssh --zone "<region-zone>" "<cluster-node-hostname>" --project "<my-gcp-project>"

For example:

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

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.

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.

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.

... omitted

<property>
  <name>fs.default.name</name>
  <value>hdfs://master.cluster1.ccloud.net</value>
  <description>The old FileSystem used by FsShell.</description>
</property>
<property>
  <name>fs.defaultFS</name>
  <value>hdfs://master.cluster1.ccloud.net</value>
  <description>
    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.
  </description>
</property>

... omitted

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.

... omitted

<property>
  <name>dfs.namenode.rpc-address</name>
  <value>master.cluster1.ccloud.net:8020</value>
  <description>
    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.
  </description>
</property>

... omitted

<property>
  <name>dfs.namenode.servicerpc-address</name>
  <value>master.cluster1.ccloud.net:8051</value>
  <final>false</final>
  <source>Dataproc Cluster Properties</source>
</property>

... omitted

<property>
  <name>dfs.namenode.lifeline.rpc-address</name>
  <value>master.cluster1.ccloud.net:8050</value>
  <final>false</final>
  <source>Dataproc Cluster Properties</source>
</property>

... omitted

At the end of the hdfs-site.xml file on each worker node, add the public DNS name for the node. Create this <property> section for each worker node. This is a required step even if using the default GCP hostnames.

... end of file

<property>
  <name>dfs.datanode.hostname</name>
  <value>cluster-fa79-w-0.ccloud.dataproc.lab.net</value>
  <description>
     obscure property
  </description>
</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.

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.
```
... omitted

worker0.cluster1.ccloud.net
worker1.cluster1.ccloud.net
```

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.

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

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.

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

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. 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.

{
  "connector.class": "DataprocSink",
  "name": "dataproc-test",
  "kafka.api.key": "<my-kafka-api-key>",
  "kafka.api.secret": "<my-kafka-api-secret>",
  "topics": "<topic-name>",
  "input.data.format": "AVRO",
  "gcp.dataproc.credentials.json": "<credentials-json-file-contents>",
  "gcp.dataproc.projectId": "<my-dataproc-project-ID",
  "gcp.dataproc.cluster": "<my-dataproc-cluster-name>",
  "gcp.dataproc.namenode": "<public-IP-address or FQDN>",
  "gcp.dataproc.use.datanode.hostname": "true"
  "logs.dir": "<HDFS-logs-directory>",
  "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.

Public IP address response to Confluent Cloud¶