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Google Cloud Storage Source Connector for Confluent Cloud¶

注釈

If you are installing the connector locally for Confluent Platform, see Google Cloud Storage (GCS) Source Connector for Confluent Platform.

The managed Google Cloud Storage (GCS) Source connector can read data from any type of file naming convention listed under a GCS bucket (that is, the filenames in the bucket don’t have to be in a specific format). The connector can read file data in any of the supported formats (for example, JSON, Avro, and Byte Array).

Features¶

The GCS Source connector provides the following features:

At least once delivery: The connector guarantees that records are delivered at least once.
Supports multiple tasks: The connector supports running one or more tasks.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Refer to Confluent Cloud connector limitations for additional information.

IAM Policy for GCS¶

The following permissions are required for the GCS Source connector:

storage.buckets.get
storage.objects.get
storage.objects.list

For more information, see IAM permissions for Cloud Storage.

You may also grant a Service Account the following roles on the bucket:

Storage Object Viewer
Storage Legacy Bucket Reader

Quick Start¶

Use this quick start to get up and running with the Confluent Cloud GCS Source connector. The quick start provides the basics of selecting the connector and configuring it to get files from a GCS bucket.

Prerequisites

Authorized access to a Confluent Cloud cluster on アマゾンウェブサービス (AWS), Microsoft Azure (Azure), or Google Cloud Platform (GCP).
The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
Schema Registry must be enabled to use a スキーマレジストリ-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See スキーマレジストリ Enabled Environments for additional information.
For networking considerations, see Networking and DNS Considerations. To use static egress IPs, see 静的なエグレス IP アドレス.
An IAM policy allowing bucket access. See IAM Policy for GCS.
A GCP 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.
Confluent Cloud Schema Registry must be enabled for your cluster, if you are using a messaging schema (like Apache Avro). See スキーマの操作.

Using the Confluent Cloud Console¶

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 Data integration, and then click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector.¶

Click the Google Cloud Storage Source connector card.

Step 4: Enter the connector details.¶

注釈

Be sure you have all your prerequisites completed.
An asterisk ( * ) designates a required entry.

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.

注釈

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

Select an Input message format: Supports AVRO, JSON (schemaless), STRING, or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format, like Avro. Refer to Confluent Cloud connector limitations for additional information.
Select an Output Kafka record value format: Defaults to the file format selected for the input message format. AVRO, BYTES, JSON, JSON_SR, PROTOBUF, and STRING. A valid schema must be available in Schema Registry if using a schema-based format.
Enter the Topic Name Regex Patterns. A list of topics along with a regex expression of the files which are to be sent to that topic. For example, "my-topic:.*" sends all files to "my-topic". The expression "special-topic:.*\.json+*"" sends only files ending with ".json" to "special-topic". The connector ignores (doesn't source) other files not matching any patterns. The connector sends files that match multiple mappings to the first topic in the list that maps the file.
Enter a Topics directory. This is a top-level directory name where data is stored in the bucket. Defaults to topics.
Show advanced configurations
- GCS Part Upload Retries. This is the number of times the connector retries uploading a GCS part. Defaults to 3 retries. When set to 0, the connector does not retry an upload that fails.
- Enter the Retry Backoff time in milliseconds (ms). This sets how many ms to wait before attempting the first retry of a failed request. Upon a failure, this connector may wait up to twice as long as the previous wait, up to the maximum number of retries. This avoids retrying in a tight loop under failure scenarios.
- Enter a Directory Delimiter Character. The pattern to use as the delimiter character for directories. Defaults to /.
- Select the Behavior on error. Defaults to FAIL.
- Select a Byte Array Line Separator. String inserted between records when using ByteArrayFormat as input.data.format. Defaults to \\n and may contain escape sequences like \\n. An input record that contains the line separator looks like multiple records in the storage object input.
- Enter a Task Batch Size: The number of files assigned to each task at a time. Defaults to 10. The maximum value supported is 2000 and the minimum value is 1.
- Enter a File Discovery Starting Timestamp. A UNIX timestamp (that is, seconds since Jan 1, 1970 UTC) that denotes where to start processing files. The connector ignores any file encountered having an earlier creation time. Defaults to 0, which is Jan 1, 1970 (i.e., the beginning of data in the bucket).
- Enter an GCS poll interval in milliseconds (ms). Defaults to 60000 ms (one minute). The minimum interval allowed is 1000 ms (one second).
- Set the Max records per poll. The maximum amount of records to return each time the connector polls storage. Defaults to 200. The maximum value supported is 10000 and the minimum value is 1.
- For information about transforms and predicates, see the Single Message Transforms (SMT) documentation for details. See サポートされない変換 for a list of SMTs that are not supported with this connector.
See Configuration Properties for all property values and definitions.
Click Continue.

Step 5: Check the Kafka topic.¶

After the connector is running, verify that messages are populating your Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

参考

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.

Using the Confluent CLI¶

Complete the following steps to set up and run the connector using the Confluent CLI.

注釈

Make sure you have all your prerequisites completed.
The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.

Step 1: List the available connectors.¶

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.¶

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

confluent connect plugin describe GcsSource

Example output:

The following are required configs:
connector.class : GcsSource
name
topic.regex.list
kafka.api.key : ["kafka.api.key" is required when "kafka.auth.mode==KAFKA_API_KEY"]
kafka.api.secret : ["kafka.api.secret" is required when "kafka.auth.mode==KAFKA_API_KEY"]
gcs.credentials.json
gcs.bucket.name
input.data.format
output.data.format
tasks.max

Step 3: Create the connector configuration file.¶

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

{
  "connector.class": "GcsSource",
  "name": "GcsSourceConnector_0",
  "topic.regex.list": "kafka-topic-for-json:*",
  "kafka.auth.mode": "SERVICE_ACCOUNT",
  "kafka.service.account.id": "<service-account-resource-ID>",
  "input.data.format": "JSON",
  "output.data.format": "JSON",
  "gcs.credentials.json": "",
  "gcs.bucket.name": "<bucket-name>",
  "tasks.max": "1",
}

Note the following required property definitions:

"connector.class": Identifies the connector plugin name.
"name": Sets a name for your new connector.
"topic.regex.list": A list of topics along with a regex expression of the files which are to be sent to that topic. In the example above, "kafka-topic-for-json:.*" sends all files to "kafka-topic-for-json". The expression "special-topic:.*\.json+*"" sends only files ending with ".json" to "special-topic". The connector ignores (doesn't source) other files not matching any patterns. The connector sends files that match multiple mappings to the first topic in the list that maps the file.

"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
```
"input.data.format": Supports Avro, JSON (schemaless), String, or Bytes. A valid schema must be available in Schema Registry to use a schema-based message format, like Avro. Refer to Confluent Cloud connector limitations for additional information.
"output.data.format": Defaults to the file format selected for the input message format. AVRO, BYTES, JSON, JSON_SR, PROTOBUF, and STRING. A valid schema must be available in Schema Registry if using a schema-based format.
"gcs.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.
"tasks.max": The total number of tasks to run in parallel. More tasks may improve performance.
Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.

For configuration property values and descriptions, see Configuration Properties.

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. You can use an online converter tool to do this. For example: JSON to String Online Converter.

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 and other properties have been omitted.

ちなみに

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": "GcsSource",
      "name": "GcsSourceConnector_0",
      "kafka.api.key": "<my-kafka-api-key>",
      "kafka.api.secret": "<my-kafka-api-secret>",
      ... omitted ...
      "gcs.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\"}",
      "tasks.max": "1"
  }

Add all the converted string content to the "gcs.credentials.json" section of your configuration file as shown in the example above.

Step 4: Load the properties file and create the connector.¶

Enter the following command to load the configuration and start the connector:

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

For example:

confluent connect create --config gcs-source-config.json

Example output:

Created connector GcsSourceConnector_0 lcc-ix4dl

Step 5: Check the connector status.¶

Enter the following command to check the connector status:

confluent connect list

Example output:

ID          |       Name             | Status  |  Type
+-----------+------------------------+---------+--------+
lcc-ix4dl   | GcsSourceConnector_0   | RUNNING | source

Step 6. Check the Kafka topic.¶

After the connector is running, verify records are populating the Kafka topic.

注釈

The GCS Source connector loads and filters all object names in the bucket before it starts sourcing records. When starting up, the connector may display RUNNING but not show any throughput. This is because bucket loading is not finished. For buckets with a large amount of objects, bucket loading can take several minutes to complete.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Configuration Properties¶

Use the following configuration properties with this connector.

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

Which topic(s) do you want to send data to?¶

topic.regex.list

A list of topics along with a regex expression of the files which are to be sent to that topic. For example: "my-topic:.*" will send all files to "my-topic", while a list containing only the expression "special-topic:.*.json" will send only files starting with ".json" to "special-topic", and all other files not matching any patterns will be ignored and not sourced. Files that match multiple mappings will be sent to the first topic in the list that maps the file.

Type: list
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

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

Type: password
Importance: high

GCP credentials¶

gcs.credentials.json

GCP service account JSON file with read permissions for Google Cloud Storage.

Type: password
Importance: high

Google Cloud Storage details¶

gcs.bucket.name

The name of the GCS bucket.

Type: string
Importance: high

gcs.part.retries

Number of upload retries of a single GCS part. Zero means no retries

Type: int
Default: 3
Importance: medium

gcs.retry.backoff.ms

How long to wait in milliseconds before attempting the first retry of a failed GCS request. Upon a failure, this connector may wait up to twice as long as the previous wait, up to the maximum number of retries. This avoids retrying in a tight loop under failure scenarios.

Type: int
Default: 200
Importance: medium

Input and output messages¶

input.data.format

Sets the input message format. Valid entries are AVRO, JSON, or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO.

Type: string
Importance: high

output.data.format

Set the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO. Note that the output message format defaults to the value in the Input Message Format field. If no value for this property is provided, the value specified for the 'input.data.format' property is used.

Type: string
Importance: high

Storage¶

topics.dir

Top-level directory (in the GCS bucket) where data to be ingested is stored.

Type: string
Default: topics
Importance: high

directory.delim

Directory delimiter pattern.

Type: string
Default: /
Importance: medium

behavior.on.error

Should the task halt when it encounters an error or continue to the next file.

Type: string
Default: FAIL
Importance: high

format.bytearray.separator

String inserted between records for ByteArrayFormat. Defaults to n and may contain escape sequences like n. An input record that contains the line separator looks like multiple records in the storage object input.

Type: string
Default: ""
Importance: medium

task.batch.size

The number of files assigned to each task at a time

Type: int
Default: 10
Valid Values: [1,...,2000]
Importance: high

file.discovery.starting.timestamp

A unix timestamp (seconds since Jan 1, 1970 UTC) that denotes where to start processing files. Any file encountered with a creation time earlier than this will be ignored.

Type: long
Default: 0
Importance: high

Data polling policy¶

gcs.poll.interval.ms

Frequency in milliseconds to poll for new or removed folders. This may result in updated task configurations starting to poll for data in added folders or stopping polling for data in removed folders

Type: long
Default: 60000 (1 minute)
Valid Values: [1000,...]
Importance: medium

record.batch.max.size

The maximum amount of records to return each time storage is polled.

Type: int
Default: 200
Valid Values: [1,...,10000]
Importance: medium

Number of tasks for this connector¶

tasks.max

The total number of tasks to run in parallel.

Type: int
Valid Values: [1,...,1000]
Importance: high

Next Steps¶

参考

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.