InfluxDB 2 Sink Connector for Confluent Cloud¶

The fully-managed InfluxDB 2 Sink connector for Confluent Cloud writes data from an Apache Kafka® topic to an InfluxDB bucket.

Note

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

Features¶

The InfluxDB 2 Sink connector supports the following features:

At least once delivery: This connector guarantees that records from the Kafka topic are delivered at least once.
Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.

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 InfluxDB 2 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.

Record structure¶

Each record is in JSON format. It can contain a number of InfluxDB fields, a tag section ("tags"), and a measurement section ("measurement"). The following example shows the record structure required for the connector.

{
 "measurement":"measurement-name",
  "tags": {
    "tag1":"value1",
    "tag2":"value2"
  },
 "time-field":<timestamp-in-epochs>,
 "field1":<value>,
 "field2":<value>,
 ...
}

Note the following:

The "tags" section is optional. This section provides the list of tags associated with the set of fields. Each tag must be a key-value pair of type string.
The "measurement" field takes the name of the InfluxDB measurement. This field is optional. However, if you do not provide the measurement name here then you must specify the measurement name in the measurement.name.format configuration property. Also, specifying this field will override whatever is specified in the Kafka record.
You can use multiple fields in a record. Fields can be of type int, float, boolean or string.
You can designate one of the fields to have the record timestamp information using the event.time.fieldname configuration property. If left unspecified, the timestamp used is the Kafka record timestamp.
For AVRO, PROTOBUF, and JSON_SR the structure remains the same. Note that the corresponding schema must be in Schema Registry.

Quick Start¶

Use this quick start to get up and running with the Confluent Cloud InfluxDB 2 Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to an InfluxDB bucket.

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.
Authorized access to write data to InfluxDB. For more information, see writing data to InfluxDB.

Note

The connector requires --read-bucket and --write-bucket permissions for the bucket where it sends data. For more information, see influx auth create.
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 InfluxDB 2 Sink 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 InfluxDB 2 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.

Add the following database connection details:
- InfluxDB API URL: Fully-qualified InfluxDB API URL used for establishing a connection. For example, http://influxdb-test.com:8086.
- InfluxDB Token: Token to authenticate with the InfluxDB host.
- InfluxDB Organization ID: The InfluxDB organization ID.
Click Continue.

Select an Input Kafka record value format (data coming from the Kafka topic): AVRO, PROTOBUF, JSON_SR (JSON Schema), or JSON (schemaless). A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR, or Protobuf).
In the Bucket Name field, enter the bucket where the connector sends data.
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?.
- Backoff Time: Backoff time duration in milliseconds that the connector waits before retrying. Defaults to 1000 ms.
- Max retries: The maximum number of times to retry a task when errors occur and before the task fails. Defaults to 10.
- Enable compression: Determine whether gzip is enabled or not. Defaults to false.
- Event Time field name: The name of field in the Kafka record that contains the event time that the connector uses when it writes to an InfluxDB data point. If nothing is entered, the default value used is the Kafka record timestamp that identifies when the Kafka record was created, which corresponds to the time that the event was processed.
- Measurement Name Format: A format string for the destination measurement name, which may contain ${topic} as a placeholder for the originating topic name.
- Write Precision: The write precision of Influx DB timestamp. Valid values are microseconds, milliseconds, nanoseconds, and seconds. The default value is milliseconds.
- Transforms and Predicates: For details, see the Single Message Transforms (SMT) documentation.
For all property values and definitions, see Configuration Properties.
Click Continue.

Step 5: Check for files¶

Verify that data is being produced at the InfluxDB 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": "InfluxDB2Sink",
  "topics": "orders",
  "input.data.format": "JSON",
  "name": "InfluxDB2Sink_0",
  "kafka.api.key": "****************",
  "kafka.api.secret": "*********************************",
  "influxdb.url": "http://influxdb-test.com:8086",
  "influxdb.token": "***************************",
  "influxdb.org.id": "<organization-id>",
  "influxdb.bucket": "<bucket-name>",
  "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" (data coming from the Kafka topic): Supports AVRO, PROTOBUF, JSON_SR (JSON Schema), 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).
"name": Sets a name for your new connector.

"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
```
"influxdb.url": Fully-qualified InfluxDB API URL used for establishing a connection. For example, http://influxdb-test.com:8086
"influxdb.token": Token to authenticate with the InfluxDB host.
"influxdb.org.id": The InfluxDB organization ID.

Note

The connector requires --read-bucket and write-bucket permissions for the bucket where it sends data. For more information, see influx auth create.

For more information, see writing data to InfluxDB.
"influxdb.bucket": The bucket where the connector sends data.
"tasks.max": Enter the maximum number of tasks for the connector to use. More tasks may improve performance.

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 influxdb2-sink-config.json

Example output:

Created connector InfluxDB2Sink_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   | InfluxDB2Sink_0           | RUNNING | sink |       |

Step 5: Check for files¶

Verify that data is being produced at the InfluxDB 2 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, or plain JSON. 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

InfluxDB¶

influxdb.url

Fully qualified InfluxDB API URL used for establishing connection.

Type: string
Importance: high

influxdb.token

Token to authenticate with influx db.

Type: password
Importance: high

influxdb.org.id

Organization ID.

Type: string
Importance: high

Write Configuration¶

influxdb.bucket

Bucket to which this connector will send the data to

Type: string
Importance: high

write.precision

Write precision of InfluxDB timestamp. Valid values are Seconds, Milliseconds, Microseconds, and Nanoseconds. Note that if the kafka record timestamp is used, instead of specifying a timestamp field, using ‘event.time.fieldname’, then the kafka timestamp(in Milliseconds) will be converted the precision defined here. Otherwise you must provide the correct time unit of the ‘event.time.fieldname’ here.

Type: string
Default: Milliseconds
Importance: medium

event.time.fieldname

The name of field in the Kafka record that contains the event time to be written to an InfluxDB data point. By default (if this config is unspecified), the timestamp written to InfluxDB is the Kafka record timestamp (when the Kafka record was created) which corresponds to the time that the event was processed.

Type: string
Importance: medium

measurement.name.format

A format string for the destination measurement name, which may contain ‘${topic}’ as a placeholder for the originating topic name.

For example, kafka_${topic} for the topic ‘orders’ will map to the measurement name ‘kafka_orders’. If the measurement name format is not provided the connector will use the ‘measurement’ field value present in the kafka message. If such a field is not present in the message the message will be sent to the dlq.

Type: string
Importance: medium

influxdb.gzip.enable

Flag to determine if gzip should be enabled.

Type: boolean
Default: false
Importance: low

Retries¶

retry.backoff.ms

Backoff time duration to wait before retrying

Type: int
Default: 1000 (1 second)
Importance: medium

max.retries

The maximum number of times to retry on errors before failing the task.

Type: int
Default: 10
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.