Amazon S3 Source Connector for Confluent Cloud

The fully-managed Amazon S3 Source connector for Confluent Cloud reads data from files in an S3 bucket. The file names don’t have to be in a specific format. The file format has to be supported (for example, JSON, Avro and Byte Array) for the connector to read from.

Note

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Generalized Amazon S3 Source Connector for Confluent Platform.

Features

The Amazon S3 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 Managed and Custom Connectors section.

Refer to Confluent Cloud connector limitations for additional information.

IAM Policy for S3

The AWS user account accessing the S3 bucket must have the following permissions:

  • ListBucket
  • GetObject

Note

This is the IAM policy for the user account and not a bucket policy.

For more information, see Create and attach a policy to an IAM user.

Quick Start

Use this quick start to get up and running with the Confluent Cloud Amazon S3 Source connector. The quick start provides the basics of selecting the connector and configuring it to get files from an Amazon S3 bucket.

Prerequisites
  • 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 Work with schemas.

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 Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector.

Click the Amazon S3 Source connector card.

Amazon S3 Source connector card

Step 4: Enter the connector details.

Note

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

At the Add Amazon S3 Source Connector screen, complete the following:

  1. 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).
  2. Click Continue.

Step 5. Check the Kafka topic.

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

Note

The S3 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 Managed and Custom Connectors section.

Using the Confluent CLI

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

Note

Make sure you have all your prerequisites 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 the required connector properties.

{
  "connector.class": "S3Source",
  "name": "S3SourceConnector_0",
  "topic.regex.list": "topic1:.*\.json",
  "topics.dir": " ",
  "kafka.auth.mode": "SERVICE_ACCOUNT",
  "kafka.service.account.id": "<service-account-resource-ID>",
  "input.data.format": "JSON",
  "output.data.format": "BYTES",
  "aws.access.key.id": "<access-key>",
  "aws.secret.access.id": "<secret-access-id>",
  "s3.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 comma-separated list of pairs in the format <kafka topic>:<regex>. The connector uses this list to map file paths to Kafka topics. For example, the property topic1:.*\.json sources all files ending in .json to a Kafka topic named topic1. You can specify multiple of these <kafka topic>:<regex> mappings to send different sets of files to different topics. Any files that aren’t mapped by a regex are ignored. The connector sends files that match multiple mappings to the first topic in the list that maps the file.
  • "topics.dir": (Optional) If this property is not used, the default folder where the connector reads data from is topics. If you set this property to a blank space (as shown in the example configuration), the connector reads all data in the S3 bucket.

  • "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), or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format, like Avro.

  • "output.data.format": Defaults to the file format selected for the input data format. AVRO, BYTES, JSON, JSON_SR, PROTOBUF, and STRING. A valid schema must be available in Schema Registry if using a schema-based format.

  • "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.

Step 4: Load the properties 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 s3-source-config.json

Example output:

Created connector S3SourceConnector_0 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   | S3SourceConnector_0   | RUNNING | source

Step 6. Check the Kafka topic.

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

Note

The S3 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 Managed and Custom Connectors section.

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

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

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

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 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
  • Valid Values: AVRO, BYTES, JSON
  • Importance: high
output.data.format

Set the output message format for values. Valid entries are AVRO, JSON, JSON_SR, PROTOBUF, STRING, or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR and PROTOBUF. If no value for this property is provided, the value specified for the ‘input.data.format’ property is used.

  • Type: string
  • Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING
  • Importance: high

AWS credentials

aws.access.key.id

The AWS Access Key used to connect to Amazon S3.

  • Type: password
  • Importance: high
aws.secret.access.key

The AWS Secret Key used to connect to Amazon S3.

  • Type: password
  • Importance: high

How should we connect to your S3 bucket?

s3.bucket.name
  • Type: string
  • Importance: high
s3.region

Set to the AWS region where your S3 bucket resides.

  • Type: string
  • Importance: high
s3.part.retries

The number of times a single S3 API call should be retried in the case that it fails with a “retriable” error (such as a throttling exception). Once this limit is exceeded, the Kafka Connect poll itself may retry (based upon the Kafka Connect-based retry configuration).

  • Type: int
  • Default: 3
  • Importance: medium
s3.retry.backoff.ms

How long to wait in milliseconds before attempting the first retry of a failed S3 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
ui.s3.wan.mode

Use an S3 accelerated endpoint.

  • Type: string
  • Default: NO
  • Valid Values: NO, YES
  • Importance: medium
ui.s3.path.style.access

Whether to use s3 path-style access.

  • Type: string
  • Default: NO
  • Valid Values: NO, YES
  • Importance: medium
s3.http.send.expect.continue

Enable/disable use of the HTTP/1.1 handshake using EXPECT: 100-CONTINUE during multi-part upload. If true, the client waits for a 100 (CONTINUE) response before sending the request body. If false, the client uploads the entire request body without checking if the server is willing to accept the request.

  • Type: string
  • Default: YES
  • Valid Values: NO, YES
  • Importance: medium
ui.s3.ssea.name

The S3 server-side encryption algorithm.

  • Type: string
  • Default: NONE
  • Valid Values: AES256, AWS:KMS, NONE
  • Importance: medium
s3.sse.customer.key

The S3 Server-Side Encryption customer-provided key (SSE-C).

  • Type: password
  • Importance: medium

Storage

topics.dir

Top-level directory (in the S3 bucket) where data to be ingested is stored.

  • Type: string
  • Default: topics
  • Importance: high
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
directory.delim

Directory delimiter pattern.

  • Type: string
  • Default: /
  • Importance: medium
ui.behavior.on.error

Error handling behavior setting for storage connectors. Must be configured to one of the following: IGNORE, FAIL

  • Type: string
  • Default: FAIL
  • Valid Values: FAIL, IGNORE
  • Importance: medium
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
  • Importance: medium
format.json.schema.enable

Enable reading of JSON messages with schema embedded

  • Type: boolean
  • Default: false
  • Importance: medium

Data polling policy

s3.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

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.

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