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
- Authorized access to a Confluent Cloud cluster on Amazon Web Services (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 Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
- An Amazon S3 bucket in the same region as your Confluent Cloud cluster.
- 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.
- 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.
- 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 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:
- 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 following AWS credential details:
- AWS access key ID: The AWS Access Key used to connect to Amazon S3.
- AWS secret access key: The AWS Secret Key used to connect to Amazon S3.
- Enter the following S3 Bucket details:
- S3 bucket name: S3 bucket name.
- AWS Region: Set to the AWS region where your S3 bucket resides.
- S3 Path-style Access: Whether to use S3 path-style access. For more information, see the AWS Path-style access documentation.
- Click Continue.
Configure the following:
Select an Input Message Format. Supports AVRO, BYTES, or JSON. A valid schema must be available in Schema Registry to use a schema-based message format, like Avro.
Select an Output Message Format. Defaults to the file format selected for the input message format. Supports 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 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 propertytopic1:.*\.json
sources all files ending in.json
to a Kafka topic namedtopic1
. 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 directory. Top-level directory name where data to be ingested is stored. Defaults to
topics
.Note
If you enter a blank space instead of accepting the default option
topics
, the connector reads all the data specified under the Amazon S3 bucket.
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?.
S3 poll interval in milliseconds (ms). Defaults to
60000
ms (one minute).Max records per poll. The maximum amount of records to return each time the connector polls storage. Defaults to
200
. The maximum value supported is10000
and the minimum value is1
.Number of Retries on S3 Errors: 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).
Retry Backoff on S3 Errors (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.
S3 Accelerated Endpoint: Use an S3 accelerated endpoint.
Send S3 Expect Continue Request: Enable/disable use of the HTTP/1.1 handshake using
EXPECT: 100-CONTINUE
during a multi- part upload. Iftrue
, the client waits for a 100 (CONTINUE) response before sending the request body. Iffalse
, the client uploads the entire request body without checking if the server is willing to accept the request.S3 Server Side Encryption Algorithm: The S3 server-side encryption algorithm.
S3 Server Side Encryption Customer-Provided Key (SSE-C): The S3 Server-Side Encryption customer-provided key (SSE-C).
Task Batch Size: The number of files assigned to each task at a time. Defaults to
10
. The maximum value supported is2000
and the minimum value is1
.File Discovery Starting Timestamp: A UNIX timestamp (i.e., seconds since Jan 1, 1970 UTC) that denotes where to start processing files. The connector ignores any file encountered having an earlier creation time.
Directory Delimiter Character: The pattern to use as the delimiter character for directories. Defaults to
/
.Behavior on Errors: Error handling behavior setting for storage connectors. Must be configured to one of the following:
IGNORE
orFAIL
.Byte Array Line Separator: String inserted between records when using 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.Transforms and Predicates: For details, see the Single Message Transforms (SMT) documentation.
For all property values and definitions, see Configuration Properties.
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 tasks, use the Range Slider to select the desired number of tasks.
- Click Continue.
Verify the connection details by previewing the running configuration.
Once you’ve validated that the properties are configured to your satisfaction, click Launch.
The status for the connector should go from Provisioning to Running.
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 propertytopic1:.*\.json
sources all files ending in.json
to a Kafka topic namedtopic1
. 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 istopics
. 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
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
"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.