Amazon S3 Sink Connector for Confluent Cloud

You can use the fully-managed Amazon S3 Sink connector for Confluent Cloud to export Avro, JSON Schema, Protobuf, JSON (schemaless), or Bytes data from Apache Kafka® topics to S3 objects in Avro, Parquet, JSON, or Bytes format. Depending on your environment, the S3 connector can export data by guaranteeing exactly-once delivery semantics to consumers of the S3 objects it produces.

The fully-managed Amazon S3 Sink connector periodically polls data from Kafka and in turn uploads it to S3. A time-based partitioner is used to split the data of every Kafka partition into chunks. Each chunk of data is represented as an S3 object. The key name encodes the topic, the Kafka partition, and the start offset of this data chunk. The size of each data chunk is determined by the number of records written to S3 and by schema compatibility.

Note

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

Features

The Amazon S3 Sink connector provides the following features:

• Exactly Once Delivery: Records that are exported using a deterministic partitioner are delivered with exactly-once semantics regardless of the eventual consistency of Amazon S3.

  Note that if versioning is enabled for the S3 bucket, you might see multiple versions of the same file in S3; but, if you view the most recent version among those files, you will see that the persistence of data exactly once remains valid.

• Data Format with or without a Schema: The connector supports input data from Kafka topics in Avro, JSON Schema, Protobuf, JSON (schemaless), or Bytes format and exports data to Amazon S3 in Avro, Parquet, JSON, or Bytes format. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro). See Schema Registry Enabled Environments for additional information.

• Partitioner: The connector supports the TimeBasedPartitioner class based on the Kafka class TimeStamp. Time-based partitioning options are daily or hourly.

• Scheduled Rotation and Rotation Interval: The connector supports a regularly scheduled interval for closing and uploading files to storage. See Scheduled Rotation for details.

• 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.

• Writing Record Keys and Headers: In addition to writing the value files to storage, you can enable the connector to write the associated Kafka record keys and headers to storage as files. To enable writing keys, set the configuration property store.kafka.keys to true. To enable writing headers, set store.kafka.headers to true. After enabling these configuration properties, the connector writes keys and headers as additional files. These files use the same name as the associated file that stores the record values, with an extension identifying the part of the record (for example, <filename>.keys.avro and <filename>.headers.avro). Key and header files have a one-to-one mapping to the associated value files.

  Consider the following when enabling this feature:

  • If you configure the connector to store keys or headers as files and the Kafka record has no key or headers present, the connector writes the record to the DLQ. The record will not be in the stored output in Amazon S3.
  • If both store.kafka.keys and store.kafka.headers are set to true, schema evolution will only work for record values, and not keys and headers. If the record headers and keys have schemas, and records are sent with a different schema from the initial one, the connector stops and the task fails.

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.

Limitations

Be sure to review the following information.

• For connector limitations, see Amazon S3 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.

User Account IAM Policy

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

• ListAllMyBuckets
• ListBucket
• GetBucketLocation
• ListBucketMultipartUploads
• PutObject
• GetObject
• AbortMultipartUpload
• ListMultipartUploadParts

Copy the following JSON to create the IAM policy for the user account. Change <bucket-name> to a real bucket name. For more information, see Create and attach a policy to an IAM user. This is the IAM policy for the user account and not a bucket policy.

Note

• If you use object tagging in the S3 bucket, set the connector configuration property s3.object.tagging to true. When you enable object tagging, you must also include s3:PutObjectTagging in the IAM policy for the user account. This optional entry is highlighted in the following JSON example.
• If you use AWS Key Management Service (KMS), you must modify the key policy to grant IAM user account permission for the kms:GenerateDataKey and kms:Decrypt actions. This will allow the connector to access the S3 bucket. For more information, see this AWS knowledge center article.

{
   "Version":"2012-10-17",
   "Statement":[
      {
         "Effect":"Allow",
         "Action":[
            "s3:ListAllMyBuckets"
         ],
         "Resource":"arn:aws:s3:::*"
      },
      {
         "Effect":"Allow",
         "Action":[
            "s3:ListBucket",
            "s3:GetBucketLocation",
            "s3:ListBucketMultipartUploads"
         ],
         "Resource":"arn:aws:s3:::<bucket-name>"
      },
      {
         "Effect":"Allow",
         "Action":[
            "s3:PutObject",
            "s3:PutObjectTagging",
            "s3:GetObject",
            "s3:AbortMultipartUpload",
            "s3:ListMultipartUploadParts"
         ],
         "Resource":"arn:aws:s3:::<bucket-name>/*"
      }
   ]
}

Quick Start

Use this quick start to get up and running with the Confluent Cloud S3 Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to an S3 bucket.

Prerequisites

Ensure you meet all the following prerequisites:

• Authorized access to a Confluent Cloud cluster on AWS.
• 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_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
• The data system the sink connector is connecting to should be in the same region as your Confluent Cloud cluster. If you use a different region or cloud platform, be aware that you may incur additional data transfer charges. Contact your Confluent account team or Confluent Support if you need to use Confluent Cloud and connect to a data system that is in a different region or on a different cloud platform.
• For networking considerations, see Networking, DNS, and service endpoints. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors. If connecting from a privately networked cluster, see Private Network Connectivity.
• An AWS User Account IAM Policy configured for bucket access.
• An AWS account configured with Access Keys. You use these access keys when setting up the connector.

• 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.
• (Optional) Confluent Cloud Schema Registry enabled for your cluster, if you are using a messaging schema (like Apache Avro). See Work with schemas.

Caution

You can’t mix schema and schemaless records in storage using kafka-connect-storage-common. Attempting this causes a runtime exception.

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 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 Amazon S3 Sink connector screen, complete the following:

Select topicKafka credentialsAuthenticationConfigurationSizingReview and Launch

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.

Step 5. Check the S3 bucket.

1. Check the S3 Bucket by going to the AWS Management Console and select Storage > S3.

2. Open your S3 bucket.

3. Open your topic folder and each subsequent folder until you see your messages displayed.

   

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 the required connector properties.

{
   "name" : "confluent-s3-sink",
   "connector.class": "S3_SINK",
   "kafka.auth.mode": "KAFKA_API_KEY",
   "kafka.api.key": "<my-kafka-api-key>",
   "kafka.api.secret": "<my-kafka-api-secret>",
   "aws.access.key.id" : "<my-aws-access-key>",
   "aws.secret.access.key": "<my-aws-access-key-secret>",
   "input.data.format": "JSON",
   "output.data.format": "JSON",
   "compression.codec": "JSON - gzip",
   "s3.compression.level": "6",
   "s3.bucket.name": "<my-bucket-name>",
   "time.interval" : "HOURLY",
   "flush.size": "1000",
   "tasks.max" : "1",
   "topics": "<topic-1>, <topic-2>"
}

Note the following required property definitions:

• "name": Sets a name for your new connector.
• "connector.class": Identifies the connector plugin name.

• "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": Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, or BYTES. 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

  The following input-to-output formats are not supported for this connector:

  • Input format JSON to output format AVRO
  • Input format JSON to output format PARQUET

• "output.data.format": Sets the output Kafka record value format (data coming from the connector). Valid entries are AVRO, PARQUET, JSON, or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro).

• "compression.codec": Sets the compression type. Valid entries are AVRO - bzip2, AVRO - deflate, AVRO - snappy, BYTES - gzip, or JSON - gzip. For PARQUET only compression types "PARQUET - none", "PARQUET - gzip", and "PARQUET - snappy" are currently supported.

• "s3.compression.level": Sets a gzip level. Valid entries are from 1 to 9. Selecting 1 results in high-speed compression and a low compression ratio. Selecting 9 provides the highest compression ratio and a much slower compression speed. The default gzip compression level is 6.

• "time.interval": Sets how your messages are grouped in the S3 bucket. Valid entries are DAILY or HOURLY.

• (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. Note that performing a compatible schema change may cause the connector to flush data prior to whatever is configured for flush.size.

  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.

• "tasks.max": Enter the maximum number of tasks for the connector to use.

• "topics": Enter the topic name or a comma-separated list of topic names.

  Tip

  The time.interval property above and the following optional properties topics.dir and path.format can be used to build a directory structure for data stored in S3. For example: You set "time.interval" : "HOURLY", "topics.dir" : "json_logs/hourly", and "path.format" : "'dt'=YYYY-MM-dd/'hr'=HH". The result in S3 is the directory structure: s3://<s3-bucket-name>/json_logs/hourly/<Topic-Name>/dt=2020-02-06/hr=09/<files>.

The following are optional properties that can be used to organize your data in storage:

• "topics.dir": A top-level directory path to use for data stored in S3. Defaults to topics if not used.
• ""path.format": Configures the time-based partitioning path created in S3. The property converts the UNIX timestamp to a date format string. If not used, this property defaults to 'year'=YYYY/'month'=MM/'day'=dd/'hour'=HH if an Hourly time.interval was selected or 'year'=YYYY/'month'=MM/'day'=dd if a Daily Time interval was selected.
• rotate.schedule.interval.ms and rotate.interval.ms: See Scheduled Rotation for details about using these properties.
• timestamp.field: The record field used for the timestamp, which is used with the time-base partitioner. If not used, this defaults to the timestamp when the Kafka record was produced or stored by the Kafka broker.
• "timezone": A valid timezone. For example, you can use EST, PST, WET, or UTC. Defaults to UTC if not used.
• "locale". The locale to use with the time-based partitioner. Used to format dates and times. For example, you can use en-US for English (USA), en-GB for English (UK), en-IN for English (India), or fr-FR for French (France). Defaults to en. For a list of locale IDs, see Java locales.

See Configuration Properties for property values and definitions.

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-sink-config.json

Example output:

Created connector confluent-s3-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   | confluent-s3-sink | RUNNING | sink

Step 6: Check the S3 bucket.

1. Go to the AWS Management Console and select Storage > S3.

2. Open your S3 bucket.

3. Open your topic folder and each subsequent folder until you see your messages displayed.

   

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.

Scheduled Rotation

Two optional properties are available that allow you to set up a rotation schedule. These properties are provided in the Cloud Console (shown below) and in the Confluent CLI.

• rotate.schedule.interval.ms (Scheduled rotation): This property allows you to configure a regular schedule for when files are closed and uploaded to storage. The default value is -1 (disabled). For example, when this is set for 600000 ms, you will see files available in the storage bucket at least every 10 minutes. rotate.schedule.interval.ms does not require a continuous stream of data.

  Note

  Using the rotate.schedule.interval.ms property results in a non-deterministic environment and invalidates exactly-once guarantees.

• rotate.interval.ms (Rotation interval): This property allows you to specify the maximum time span (in milliseconds) that a file can remain open for additional records. When using this property, the time span interval for the file starts with the timestamp of the first record added to the file. The connector closes and uploads the file to storage when the timestamp of a subsequent record falls outside the time span set by the first file’s timestamp. The minimum value is 600000 ms (10 minutes). This property defaults to the interval set by the time.interval property. rotate.interval.ms requires a continuous stream of data.

  Important

  The start and end of the time span interval is determined using file timestamps. For this reason, a file could potentially remain open for a long time if a record does not arrive with a timestamp falling outside the time span set by the first file’s timestamp.

Schema Evolution

The Amazon S3 connector supports schema evolution and reacts to schema changes of record data according to the schema.compatibility configuration. You can set schema.compatibility to NONE, BACKWARD, FORWARD and FULL. Review the following information for details.

Note

• If you see a large number of small files in the S3 bucket, it may be that consecutive records in a partition have incompatible schemas, leading to the connector closing and creating many files for each record.
• The following schema compatibility information is specific to S3 file schemas and not associated with Schema Registry operation.

• NONE (NO compatibility): NONE should only be used when records use the same schema. By default, the schema.compatibility is set to NONE.

  The connector ensures that each file written to S3 has the proper schema. When the connector observes a schema change in data, it commits the current set of files for the affected topic partitions and writes the data with new schema in a new file.

  For example:

  When two consecutive records arrive (R1 and R2), the connector checks both record schemas (R1/S1 and R2/S2) for compatibility. If the schemas are not identical, the connector commits R1/S1 and creates a new file for R2/S2.

• FORWARD Compatibility: If you set schema.compatibility to FORWARD, the connector compares schemas and uses the earliest version to query all the data uniformly. Removing a field that had a default value is forward compatible, since the earlier schema will use the default value when the field is missing.

  For example:

  When two consecutive records arrive (R1 and R2), the connector checks both record schemas (R1/S1 and R2/S2) for compatibility. If the schema types are not identical, the schema names are compared. The schema names are not identical, the schema parameters are compared. If these are not identical, the S2 version must be later than S1 or the schemas are not compatible and the connector commits R1/S1 and creates a new file for R2/S2.

• BACKWARD Compatibility: If you set schema.compatibility to BACKWARD, the connector keeps track of the latest schema used in writing data to S3, and if a record with a later schema version than current schema arrives, the connector commits the current set of files and writes the record using the new schema to a new file. For records that arrive later which are using an earlier schema, the connector projects the record to the latest schema before writing to the same set of files in S3. This supports rolling back a schema to an earlier version.

  For example:

  When two consecutive records arrive (R1 and R2), the connector checks both record schemas (R1/S1 and R2/S2) for compatibility. If the schema types are not identical, the schema names are compared. The schema names are not identical, the schema parameters are compared. If these are not identical, the S2 version must be earlier than S1 or the schemas are not compatible and the connector commits R1/S1 and creates a new file for R2/S2

• FULL Compatibility: Full compatibility means that old data can be read with the new schema and new data can also be read with the old schema. FULL performs the same action as BACKWARD.

Private Network Connectivity

The Amazon Web Services S3 Sink connector can attach to an Amazon S3 bucket from your privately networked Confluent Cloud cluster (AWS PrivateLink or peered VPCs). You can do this because of an existing AWS gateway endpoint. To set this up, you use an S3 bucket policy with an allow statement and a deny statement.

The following shows an example of how to create this bucket policy.

• The first statement (PolicyForAllowUploadWithACL) in the JSON example allows the specified principal (arn:aws:iam::<Confluent-Cloud-AWS-Account-ID>:root) to perform s3:PutObject actions on objects in a specific S3 bucket (<S3-bucket-name>/*), but only if the ACL is set to "bucket-owner-full-control".
• The second statement (Access-to-specific-VPC-only) in the JSON example denies all S3 actions (s3:*) to all principals (*) for the resources specified (arn:aws:s3:::<S3-bucket-name> and its objects) unless the request originates from your private source VPC (vpc-1234abcde56).

Be sure to replace all placeholder values with the actual account ID, bucket name, and VPC name.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "PolicyForAllowUploadWithACL",
            "Effect": "Allow",
            "Principal": {
              "AWS": "arn:aws:iam::<Confluent-Cloud-AWS-Account-ID>:root"
            },
            "Action": "s3:PutObject",
            "Resource": "arn:aws:s3:::<S3-bucket-name>/*",
            "Condition": {
              "StringEquals": {
                  "s3:x-amz-acl": "bucket-owner-full-control"
              }
            }
        },
        {
            "Sid": "Access-to-specific-VPC-only",
            "Principal": "*",
            "Action": "s3:*",
            "Effect": "Deny",
            "Resource": ["arn:aws:s3:::<S3-bucket-name>",
                          "arn:aws:s3:::<S3-bucket-name>/*"],
            "Condition": {
              "StringNotEquals": {
                "aws:SourceVpc": "vpc-1234abcde56"
              }
            }
        }
    ]
}

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, JSON 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.

• Type: string
• Default: JSON
• Importance: high

value.converter.reference.subject.name.strategy

Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.

• Type: string
• Default: DefaultReferenceSubjectNameStrategy
• Importance: 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

Amazon S3 details

aws.access.key.id

• Type: password
• Importance: high

aws.secret.access.key

• Type: password
• Importance: high

s3.region

The AWS region where the S3 bucket is defined. If no value for this property is provided, the value specified for the ‘kafka.region’ property is used.

• Type: string
• Importance: low

s3.bucket.name

An Amazon S3 bucket must be in the same region as your Confluent Cloud cluster.

• Type: string
• Importance: high

s3.ssea.name

The S3 Server Side Encryption Algorithm.

• Type: string
• Default: “”
• Importance: low

s3.sse.customer.key

The S3 Server Side Encryption Customer-Provided Key (SSE-C)

• Type: password
• Importance: low

store.url

The object storage connection URL, if applicable. For example: ‘https://bucket.s3-aws-region.amazonaws.com’

• Type: string
• Default: “”
• Importance: medium

s3.sse.kms.key.id

The name of the AWS Key Management Service (AWS-KMS) key to be used for server side encryption of the S3 objects. No encryption is used when no key is provided, but it is enabled when KMS is specified as encryption algorithm with a valid key name.

• Type: string
• Importance: low

s3.part.size

The Part Size(bytes) in S3 Multi-part Uploads.

• Type: int
• Default: 5242880
• Valid Values: [5242880,…,2147483647]
• Importance: high

s3.wan.mode

Use S3 accelerated endpoint.

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

Output messages

output.data.format

Set the output message format for values. Valid entries are AVRO, JSON, PARQUET 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 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

output.keys.format

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

• Type: string
• Default: AVRO
• Valid Values: AVRO, BYTES, JSON, PARQUET
• Importance: high

output.headers.format

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

• Type: string
• Default: AVRO
• Valid Values: AVRO, BYTES, JSON, PARQUET
• Importance: high

json.decimal.format

Controls which format json converter will serialize decimals in. This value can be either ‘BASE64’ (default) or ‘NUMERIC’ and is applicable only when the output format is JSON.

• Type: string
• Default: BASE64
• Importance: low

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, s3://<s3-bucket-name>/json_logs/daily/<Topic-Name>/dt=2020-02-06/hr=09/<files>, please put topics.dir=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, s3://<s3-bucket-name>/json_logs/daily/<Topic-Name>/dt=2020-02-06/hr=09/<files>, use the properties: topics.dir=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

compression.codec

Compression type for files written to S3.

• Type: string
• Valid Values: AVRO - bzip2, AVRO - deflate, AVRO - snappy, BYTES - gzip, JSON - gzip, PARQUET - gzip, PARQUET - none, PARQUET - snappy
• Importance: high

timezone

Sets the timezone used by the TimeBasedPartitioner.

• Type: string
• Default: UTC
• Importance: high

behavior.on.null.values

How to handle records with null values, e.g Kafka tombstone records. Valid options are ‘ignore’, ‘fail’ and ‘write’. Default is ‘ignore’

• Type: string
• Default: ignore
• Importance: low

subject.name.strategy

Strategy used for deriving subject name from topic and record schema name.

• Type: string
• Default: TopicNameStrategy
• Valid Values: TopicNameStrategy, TopicRecordNameStrategy
• Importance: low

tombstone.encoded.partition

Output s3 folder to write the tombstone records to. The configured partitioner would map tombstone records to this output folder.

• Type: string
• Default: tombstone
• Importance: low

s3.compression.level

Gzip compression level for files written to S3. Applied when using JSON or BYTES input.

• Type: int
• Valid Values: [-1,…,9]
• Importance: high

locale

Sets the locale to use with TimeBasedPartitioner.

• Type: string
• Default: en
• Importance: high

enhanced.avro.schema.support

When set to true, this property preserves Avro schema package information and Enums when going from Avro schema to Connect schema. This information is added back in when going from Connect schema to Avro schema.

• Type: boolean
• Default: true
• Importance: low

s3.schema.partition.affix.type

Append the record schema name to prefix or suffix in the s3 path after the topic name. None will not append the schema name in the s3 path.

• Type: string
• Default: NONE
• Valid Values: NONE, PREFIX, SUFFIX
• Importance: low

s3.acl.canned

An S3 canned ACL header value to apply when writing objects.

• Type: string
• Valid Values: authenticated-read, aws-exec-read, bucket-owner-full-control, bucket-owner-read, log-delivery-write, private, public-read, public-read-write
• Importance: low

schema.compatibility

The schema compatibility rule to use when the connector is observing schema changes.

• Type: string
• Default: NONE
• 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

store.kafka.keys

Enable or disable writing record keys to storage

• Type: boolean
• Default: false
• Importance: low

store.kafka.headers

Enable or disable writing record headers to storage.

• Type: boolean
• Default: false
• Importance: low

s3.object.tagging

Tag S3 objects with start and end offsets, as well as record count.

• Type: boolean
• Default: false
• Importance: low

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.