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 AVROTime-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
androtate.interval.ms
can be used withflush.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
androtate.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 minuterotate.schedule.interval.ms
condition tripped before theflush.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 and DNS.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 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 topics you want to connect from the Topics list.
To create a new topic, click +Add new topic.
- Select the way you want to provide Kafka Cluster credentials. You can
choose one of the following options:
- Global Access: Allows your connector to access everything you have access to. With global access, connector access will be linked to your account. This option is not recommended for production.
- Granular access: Limits the access for your connector. You will be able to manage connector access through a service account. This option is recommended for production.
- Use an existing API key: Allows you to enter an API key and secret part you have stored. You can enter an API key and secret (or generate these in the Cloud Console).
- Click Continue.
- Enter the destination details:
- 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.
- Click Continue.
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. Settingrotate.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’srotate.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.
Based on the number of topic partitions you select, you will be provided with a recommended number of tasks.
- To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
- 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
orKAFKA_API_KEY
(the default). To use an API key and secret, specify the configuration propertieskafka.api.key
andkafka.api.secret
, as shown in the example configuration (above). To use a service account, specify the Resource ID in the propertykafka.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 Google Cloud 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 (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."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
androtate.interval.ms
can be used withflush.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
androtate.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 minuterotate.schedule.interval.ms
condition tripped before theflush.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 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.
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 Google Cloud 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 and DNS.
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.
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 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 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 Google Cloud 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.