SFTP Sink Connector for Confluent Cloud¶

You can use the fully-managed SFTP Sink connector for Confluent Cloud to export data from Apache Kafka® topics to files in an SFTP directory.

The fully-managed SFTP Sink connector periodically polls data from Kafka and writes this data to SFTP files. A time-based partitioner is used to split the data of every Kafka partition into chunks. Each chunk of data is represented as a file. The file 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 and by schema compatibility.

Note

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

Features¶

The SFTP Sink connector supports the following features:

Exactly once delivery: Records that are exported using a deterministic partitioner are delivered with exactly-once semantics.
Partitioner: The connector supports the TimeBasedPartitioner class based on the Kafka class TimeStamp. Time-based partitioning options are daily or hourly.
Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
Supported data formats: The connector supports Avro, JSON Schema (JSON-SR), Protobuf, JSON (schemaless), and Bytes input data formats. The connector supports Avro and JSON output formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf).

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 SFTP Sink Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

Quick Start¶

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

Prerequisites

Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
Access to an SFTP host.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink connector.

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 SFTP 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 SFTP Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Enter the SFTP Details:
- SFTP Host: Enter the host address for the SFTP server. For example 192.168.1.231.
- SFTP Port: Enter the SFTP host port to use. If no port number is entered, this defaults to 22.
- Username: Enter the user name that the connector will use to connect to the host.
- Password: Enter the password for the user name entered. A password is not required if a PEM file is used for key based authentication to the host.
- PEM File: If using TLS, upload the PEM file that contains the private key for the SFTP user.
- TLS passphrase: If the private key is encrypted, enter the passphrase to decrypt the private key.
Click Continue.

Note

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

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, JSON (schemaless), or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format (for example, AVRO, JSON_SR (JSON Schema), or PROTOBUF).
Select the Output Kafka record value format (data coming from the connector): AVRO or JSON.
Select the Time interval that sets how you want your messages grouped in the file system. For example, if you select Hourly, messages are grouped into folders for each hour data is streamed to the file system.
Enter the Flush size. This value defaults to 1000. The default value can be raised (and lowered, if running a dedicated cluster). Advanced users may define how the connector flushes records to S3 by clicking the following:
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?.
- Topic directory (Optional): This is a top-level directory name to use for stored data. Defaults to topics if not used.
- SFTP Working Directory (Optional): Path of the top level directory where the connector should write to. Defaults to /home/${sftp.username}.
- Path format (Optional): This configures the time-based partitioning path created in the file system. 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.
Note

The Path format, Topic directory, and Time interval properties can be used to build a directory structure for stored data. For example: You set Time interval to HOURLY, Topics directory to json_logs/hourly, and Path format to 'dt'=YYYY-MM-dd/'hr'=HH. The result is the directory structure: filesystem://store-name/json_logs/hourly/<Topic-Name>/dt=2020-02-06/hr=09/<files>.
- Maximum span of record time (in ms) before scheduled rotation (Optional): 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. See Configuration Properties for details about the two optional rotation interval properties.
- Compression Type (Optional): Select a compression type for files. Use deflate, snappy, or bzip2 for AVRO output. Use gzip for JSON output.
- Maximum span of record time (in ms) before rotation (Optional): 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 files starts with the timestamp of the first record inserted in the file.
- Timestamp field name: 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: Use a valid timezone. Defaults to UTC if not used.
- Locale: Select a locale. Defaults to en.
Click Continue.

Step 5: Check for files.¶

Verify that records are being produced on the SFTP host.

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¶

To set up and run the connector using the Confluent CLI, complete the following steps.

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": "SftpSink",
  "topics": "orders",
  "input.data.format": "JSON",
  "name": "SftpSinkConnector_0",
  "kafka.api.key": "****************",
  "kafka.api.secret": "*********************************",
  "sftp.host": "192.168.1.231",
  "sftp.username": "connect-user",
  "sftp.password:": "****************",
  "output.data.format": "JSON",
  "time.interval": "HOURLY",
  "rotate.schedule.interval.ms": "",
  "rotate.interval.ms": "",
  "tasks.max": "1",
}

Note the following property definitions:

"connector.class": Identifies the connector plugin name.
"topics": Enter the topic name or a comma-separated list of topic names.
"input.data.format": Sets (data coming from the Kafka topic): AVRO, PROTOBUF, JSON_SR (JSON Schema), JSON (schemaless), or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
"name": Sets a name for your new connector.
"kafka.api.key" and "kafka.api.secret": These credentials are either the cluster API key and secret or the service account API key and secret.
"sftp.host": Enter the host address for the SFTP server. For example 192.168.1.231. Note that the port defaults to 22. To change this, add the property "sftp.port".
"sftp.username": Enter the user name that the connector will use to connect to the host.
"output.data.format": Enter AVRO or JSON (schemaless). A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
"time.interval": Sets how you want your messages grouped in the file system. Options are HOURLY or DAILY.
"rotate.schedule.interval.ms" and "rotate.interval.ms": See the Configuration Properties for the property descriptions.
"tasks.max": Enter the maximum number of tasks for the connector to use. More tasks may improve performance.

Note

The properties path.format, topics.dir, and time.interval properties can be used to build a directory structure for stored data. For example, you set time.interval to HOURLY, topics.dir to json_logs/hourly, and path.format` to ``'dt'=YYYY-MM-dd/'hr'=HH. The result is the directory structure: filesystem://store-name/json_logs/hourly/<Topic-Name>/dt=2020-02-06/hr=09/<files>. See the Configuration Properties for property values and definitions.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and descriptions.

Step 3: 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 sftp-sink-config.json

Example output:

Created connector SftpSinkConnector_0 lcc-do6vzd

Step 4: Check the connector status.¶

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID           |             Name              | Status  | Type | Trace
+------------+-------------------------------+---------+------+-------+
lcc-do6vzd   | SftpSinkConnector_0           | RUNNING | sink |       |

Step 5: Check for files.¶

Verify that records are being produced on the SFTP host.

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

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

Configuration Properties¶

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

Which topics do you want to get data from?¶

topics

Identifies the topic name or a comma-separated list of topic names.

Type: list
Importance: high

Schema Config¶

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

Type: string
Default: default
Importance: medium

Input messages¶

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, 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
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

SFTP Details¶

sftp.host

Host address of the SFTP server.

Type: string
Importance: high

sftp.port

Port number of the SFTP server.

Type: int
Default: 22
Importance: medium

sftp.username

Username for the SFTP connection.

Type: string
Importance: high

sftp.password

Password for the SFTP connection (not required if using TLS).

Type: password
Importance: high

tls.pemfile

PEM file to be used for authentication via TLS.

Type: password
Importance: high

tls.passphrase

Passphrase that will be used to decrypt the private key if the given private key is encrypted.

Type: password
Importance: high

sftp.working.dir

Path of the top level directory where the connector should write to (defaults to /home/${sftp.username}).

Type: string
Default: /home/${sftp.username}
Importance: medium

Output messages¶

output.data.format

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

Organize my data by…¶

topics.dir

Top-level directory where ingested data is stored.

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, filesystem://store-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,…] for non-dedicated clusters and [1,…] for dedicated clusters
Importance: high

timestamp.field

Sets the field that contains the timestamp used for the TimeBasedPartitioner

Type: string
Default: “”
Importance: high

timezone

Sets the timezone used by the TimeBasedPartitioner.

Type: string
Default: UTC
Importance: high

locale

Sets the locale to use with TimeBasedPartitioner.

Type: string
Default: en
Importance: high

compression.codec

Compression type for files. ‘deflate’, ‘snappy’ and ‘bzip2’ can be used when the output format is AVRO; ‘gzip’ can be used when the output format is JSON.

Type: string
Importance: high

value.converter.connect.meta.data

Toggle for enabling/disabling connect converter to add its meta data to the output schema or not.

Type: boolean
Default: true
Importance: medium

Consumer configuration¶

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

Type: long
Default: 300000 (5 minutes)
Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
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] for non-dedicated clusters and [1,…] for dedicated clusters
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.