Amazon DynamoDB Sink Connector for Confluent Cloud¶

The fully-managed Amazon DynamoDB Sink connector for Confluent Cloud is used to export messages from Apache Kafka® to Amazon DynamoDB, allowing you to export your Kafka data into your DynamoDB key-value and document database.

The connector periodically polls data from Kafka and writes it to Amazon DynamoDB. The data from each Kafka topic is batched and sent to DynamoDB. Because of constraints from DynamoDB, each batch can only contain one change per key, and each failure in a batch must be handled before the next batch is processed. These constraints ensure exactly once delivery. When a table doesn’t exist, the connector creates the table dynamically (depending on the connector configuration and permissions).

Note

This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see Amazon DynamoDB Sink Connector for Confluent Platform.
If you require private networking for fully-managed connectors, make sure to set up the proper networking beforehand. For more information, see Manage Networking for Confluent Cloud Connectors.

Features¶

Auto-created tables: Tables can be auto-created based on topic names and auto-evolved based on the record schema.
Select configuration properties:
- aws.dynamodb.pk.hash: Defines how the DynamoDB table hash key is extracted from the records. By default, the Kafka partition number where the record is generated is used as the hash key. Other record references can be used to create the hash key. See DynamoDB hash keys and sort keys for examples.
- aws.dynamodb.pk.sort: Defines how the DynamoDB table sort key is extracted from the records. By default, the record offset is used as the sort key. The sort key can be created from other references. See DynamoDB hash keys and sort keys for examples.

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 DynamoDB 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.

DynamoDB IAM policy¶

Create an IAM user for the connector. Assign an IAM policy to the user you create. The policy must have the following minimum permissions.

CreateTable
BatchWriteItem
Scan
DescribeTable

You can copy the following JSON policy. For more information, see Creating policies on the JSON tab.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "<optional-identifier>",
            "Effect": "Allow",
            "Action": [
                "dynamodb:CreateTable",
                "dynamodb:BatchWriteItem",
                "dynamodb:Scan",
                "dynamodb:DescribeTable"
            ],
            "Resource": "*"
        }
    ]
}

DynamoDB hash keys and sort keys¶

The following examples show how the aws.dynamodb.pk.hash and aws.dynamodb.pk.sort are used. The following Avro record is used for the examples:

{
     "ordertime": 1511538140542,
     "orderid": 3243,
     "itemid": "Item_117",
     "orderunits": 1.135368875862714,
     "address": {
       "city": "City_43",
       "state": "State_53",
     }
}

Example 1

The table hash key is set to the "partition" number where the record was generated. The table sort key is the record "offset". The following example uses these default configuration properties:

"aws.dynamodb.pk.hash":"partition"
"aws.dynamodb.pk.sort":"offset"

Using these properties, the table in DynamoDB would be similar to the following example:

offset	address	itemid	orderid	ordertime	orderunits
6075	{“city”:{“S”:City_66}, “state”:{“S”:”State_42},…}	Item_246	6075	1503153618445	3.0818679447783652
6076	{“city”:{“S”:City_38}, “state”:{“S”:”State_49},…}	Item_536	6076	1515872966736	1.6264301342871472
6077	{“city”:{“S”:City_32}, “state”:{“S”:”State_62},…}	Item_997	6077	1515872966736	4.189731783402986

Example 2

The table hash key is set to "value.orderid". The table sort key is "". Note that in this example, no sort key is required so you can use an empty string: "aws.dynamodb.pk.sort":"".

"aws.dynamodb.pk.hash":"value.orderid"
"aws.dynamodb.pk.sort":""

Using these properties, the table in DynamoDB would be similar to the following example:

orderid	address	itemid	ordertime	orderunits
2007	{“city”:{“S”:City_69}, “state”:{“S”:”State_19},…}	Item_809	1502071602628	8.9866703527786968
2011	{“city”:{“S”:City_32}, “state”:{“S”:”State_11},…}	Item_524	1494848995282	2.581428966318308
2012	{“city”:{“S”:City_88}, “state”:{“S”:”State_94},…}	Item_169	1491811930181	1.5716303109073455

Example 3

The table hash key is set to "value.orderid". The table sort key is set to "value.ordertime". Note that in this example, one of the record fields ("ordertime") is used as the sort key.

"aws.dynamodb.pk.hash":"value.orderid"
"aws.dynamodb.pk.sort":"value.ordertime"

Using these properties, the table in DynamoDB would be similar to the following example:

orderid	ordertime	address	itemid	orderunits
4520	1519049522647	{“city”:{“S”:City_99}, “state”:{“S”:”State_38},…}	Item_650	7.658775648983428
4522	1519049522647	{“city”:{“S”:City_72}, “state”:{“S”:”State_89},…}	Item_503	2.1383312466612261
4523	1507101063792	{“city”:{“S”:City_74}, “state”:{“S”:”State_99},…}	Item_369	2.1383312466612261

Managing Throughput¶

When the connector creates a table automatically, 10 write capacity units are provisioned. If the connector needs to send records faster than the provisioned capacity, you may see the following error message:

Hit provisioning capacity, will retry indefinitely.. Increase your throughput capacity

You can increase the write capacity or use Amazon DynamoDB Auto Scaling.

Quick Start¶

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

Prerequisites

Authorized access to a Confluent Cloud cluster on Amazon Web Services.
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.
Authorized access to AWS and the Amazon DynamoDB database. For more information, see DynamoDB IAM policy.
The database must be in the same region as your Confluent Cloud cluster.
For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.

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.

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 DynamoDB 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 DynamoDB 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.

Note

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

Select the Input Kafka record value (data coming from the Kafka topic): AVRO, JSON_SR, PROTOBUF, or JSON. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON Schema, or Protobuf). See Schema Registry Enabled Environments for additional information.
In the DynamoDB hash key and DynamoDB sort key fields, enter the hash key and sort key, respectively. By default, the Kafka partition number is used for the hash key and the record offset is used as the sort key. For a few examples of how these keys work with other record references, see DynamoDB hash keys and sort keys. Note that the maximum size of a partition using the default configuration is limited to 10 GB (defined by Amazon DynamoDB).
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?.
- Table name format: A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name. For example, to create a table named kafka-orders based on a Kafka topic named orders, you would enter kafka-${topic} in this field.
- Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.
See Configuration Properties for all property values and definitions.
Click Continue.

Step 5: Check the results in DynamoDB¶

Check to verify that the database is being populated.

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 required and optional connector properties.

{
  "name": "DynamoDbSinkConnector_0",
  "config": {
    "topics": "pageviews",
    "input.data.format": "AVRO",
    "connector.class": "DynamoDbSink",
    "name": "DynamoDbSinkConnector_0",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "aws.access.key.id": "********************",
    "aws.secret.access.key": "****************************************",
    "aws.dynamodb.pk.hash": "value.userid",
    "aws.dynamodb.pk.sort": "value.pageid",
    "table.name.format": "kafka-${topic}",
    "tasks.max": "1"
  }
}

Note the following property definitions:

"name": Sets a name for your new connector.
"connector.class": Identifies the connector plugin name.
"topics": Identifies the topic name or a comma-separated list of topic names.

"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, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
"aws.dynamodb.pk.hash": Defines how the DynamoDB table hash key is extracted from the records. By default, the Kafka partition number where the record is generated is used as the hash key. The hash key can be created from other record references. See DynamoDB hash keys and sort keys for examples. Note that the maximum size of a partition using the default configuration is limited to 10 GB (defined by Amazon DynamoDB).
"aws.dynamodb.pk.sort": Defines how the DynamoDB table sort key is extracted from the records. By default, the record offset is used as the sort key. If no sort key is required, use an empty string for this property "". The sort key can be created from other record references. See DynamoDB hash keys and sort keys for examples.
"table.name.format": The property is optional and defaults to the name of the Kafka topic. To create a table name format use the syntax ${topic}. For example, kafka_${topic} for the topic orders maps to the table name kafka_orders.
"tasks.max": Maximum number of tasks the connector can run. See Confluent Cloud connector limitations for additional task information.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this connector.

See Configuration Properties for all property values and definitions.

Step 4: Load the configuration 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 dynamodb-sink-config.json

Example output:

Created connector DynamoDbSinkConnector_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   | DynamoDbSinkConnector_0 | RUNNING | sink

Step 6: Check the results in Redshift.¶

Check to verify that the database is being populated.

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 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
Default: JSON
Importance: high

input.key.format

Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, or STRING. 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: BYTES
Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, 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

AWS credentials¶

aws.access.key.id

Type: password
Importance: high

aws.secret.access.key

Type: password
Importance: high

DynamoDB Parameters¶

aws.dynamodb.pk.hash

Type: string
Default: partition
Importance: high

aws.dynamodb.pk.sort

Type: string
Default: offset
Importance: high

table.name.format

A format string for the destination table 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 table name ‘kafka_orders’.

Type: string
Default: ${topic}
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.
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