InfluxDB 2 Source Connector for Confluent Cloud¶

The fully-managed InfluxDB 2 Source connector for Confluent Cloud allows you to import data from an InfluxDB host into an Apache Kafka® topic.

The connector loads data by periodically executing an InfluxDB query and creating an output record for each row in the result set. By default, all measurements in a database are copied, each to its own output topic. The connector monitors the database for new measurements and adapts automatically. When copying data from a measurement, the connector loads only new records.

Note

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

Features¶

The InfluxDB 2 Source connector supports the following features:

At least once delivery: This connector guarantees that records from the Kafka topic are delivered at least once.
Supports one task: The connector supports running a single task, which is initiated when in QUERY mode. Otherwise, the connector initiates tasks based on the minimum number of measurements or max-tasks configured.

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 Source 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 InfluxDB 2 Source connector. The quick start provides the basics of selecting the connector and configuring it to stream events to Apache Kafka®.

Prerequisites

Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud (Google Cloud).
The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
Authorized access to query the InfluxDB bucket. For more information, see Query data.

Note

The connector requires --read-bucket permission for the bucket where it sends data. For more information, see Query data.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

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 Source connector card.

Step 4: Enter the connector details¶

Note

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

At the Add InfluxDB 2 Source Connector screen, complete the following:

In the Kafka Topic Name Prefix field, define a topic prefix your connector will use to publish to Kafka topics. The connector publishes Kafka topics using the following naming convention: <topic.prefix><tableName>.

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 Bucket: The name of the bucket from which the connector queries data.
- InfluxDB Token: Token to authenticate with the InfluxDB host.
- InfluxDB Organization ID: The InfluxDB organization ID.
Click Continue.

Add the following configuration details:
- Select the output record value format (data going to the Kafka topic): AVRO, JSON, JSON_SR (JSON Schema), or PROTOBUF. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). See Schema Registry Enabled Environments for additional information.
- Mode: The mode that the connector uses to poll measurements in InfluxDB. bulk performs a bulk load of the entire measurement to the Kafka topic each time the connector polls InfluxDB. timestamp uses the timestamp to detect newly created rows and writes these to the Kafka topic.
- Topic Mapper: Determines how to map topics. bucket maps to a topic name using the topic prefix + bucket name. All records go into the same topic. measurement maps to a topic name using the topic prefix + measurement name. All the records from the same measurement go into the same topic.
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 to wait before retrying. Defaults to 1000 milliseconds.
- Max Retries: The maximum number of times to retry on errors before the connector fails the task.
- Flux Query: If specified, this query will be executed and the resultant records will be pushed to desired Apache Kafka topic. Use this setting if there’s a need to select subset of fields or tags, perform aggregations or filter data.
- Max Points Per Batch: The maximum number of points to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.
- Delay Interval (ms): How long to wait after a record having a timestamp appears before the connector includes it in the result. You may choose to add a delay to allow transactions with earlier timestamps to complete. The first execution will fetch all available records (i.e., starting at Unix Epoch) until the current time, minus this delay. Every following execution gets data from the time of the last record fetched in the previous batch until the current time, minus this delay.
- Measurements Included: A comma-separated list of measurements to include. If specified, the Measurements Excluded (influxdb.measurement.blacklist) cannot be set. If left empty, all measurements are included.
- Measurements Excluded: A comma-separated list of measurements to exclude. If specified, the Measurements Included (influxdb.measurement.whitelist) cannot be set.
- Transforms and Predicates: For more information, see the Single Message Transforms (SMT) documentation.
For all property and value 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.

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": "InfluxDB2Source",
  "name": "InfluxDB2Source_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>",
  "topic.prefix": "<topic-prefix>",
  "output.data.format": "JSON",
  "tasks.max": "1",
}

Note the following property definitions:

"connector.class": Identifies the connector plugin name.
"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 permission for the bucket where it sends data. For more information, see Query data.

For more information, see writing data to InfluxDB.
"influxdb.bucket": The bucket where the connector sends data.
"output.data.format": Supported formats are 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).
"tasks.max": Enter the number of tasks to use with the connector. The connector supports running a single task, which is initiated when in QUERY mode. Otherwise, the connector initiates tasks based on the minimum number of measurements or max-tasks configured.

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-source-config.json

Example output:

Created connector InfluxDB2Source_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   | InfluxDB2Source_0       | RUNNING | Source |       |

Step 5: Check for files¶

Verify that data is being produced at the Kafka topic.

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

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

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

influxdb.bucket

Bucket from which this connector will read the data from.

Type: string
Importance: medium

Read Configuration¶

query

If specified, this query will be executed and the resultant records will be pushed to desired Apache Kafka topic. Use this setting if there’s a need to select subset of fields or tags, perform aggregations or filter data. The query should follow the template - import "influxdata/influxdb/schema" from(bucket:$influxdb.bucket) |> range(start: $startTimestamp, stop: $endTimestamp) |> <Your custom query criteria here> |> schema.fieldsAsCols() |> limit(n: $batch.size) In case of mode=bulk, the connector will run the query as-is each time it polls. The range criteria should be filled in by the user. Flux does not allow unbounded queries as they are resource intensive. If you use mode=timestamp, the values for $startTimestamp and $endTimestamp will be filled by the connector with appropriate source offsets.Users should replace other criteria mentioned at - <Your custom query criteria here>. The connector will replace $influxdb.bucket and $batch.size with the values from the corresponding configurations.

Type: string
Importance: medium

mode

The mode in which measurements in InfluxDB has to be polled. Supported modes are : bulk performs a bulk load of the entire measurement to desired Apache Kafka topic, each time it is polled. timestamp uses the timestamp to detect newly created rows and writes them to desired Apache Kafka topic.

Type: string
Default: timestamp
Importance: medium

topic.mapper

Configuration to decide how to map topics Supported options are : bucket - Topic name is Topic Prefix + Bucket name. All the records go into same topic. Or measurement - Topic name is Topic Prefix + Measurement name. All the records from same measurement go into same topic.

Type: string
Default: bucket
Importance: medium

topic.prefix

Prefix that should be prepended to measurement names to determine the name of the Apache Kafka topic to publish data to, in case of custom query, it should be the full name of the Apache Kafka topic.

Type: string
Importance: medium

batch.size

Maximum number of points to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.

Type: int
Default: 5000
Importance: medium

timestamp.delay.interval.ms

How long to wait after a record with certain timestamp appears before we include it in the result. You may choose to add some delay to allow transactions with earlier timestamp to complete. The first execution will fetch all available records (i.e. starting at Unix Epoch) until current time minus the delay. Every following execution will get data from the time of the last record fetched in the previous batch until current time minus the delay.

Type: int
Default: 0
Importance: medium

influxdb.measurement.whitelist

Comma separated list of measurements to include in copying. If specified, Measurements Excluded cannot be set. If left empty, all measurements will be included.

Type: string
Importance: medium

influxdb.measurement.blacklist

Comma separated list of measurements to exclude from copying. If specified, Measurements Included cannot be set.

Type: string
Importance: medium

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

Output messages¶

output.data.format

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

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.