Amazon SQS Source Connector for Confluent Cloud¶
The fully-managed Amazon Simple Queue Service (SQS) Source connector for Confluent Cloud is used to move messages from an Amazon SQS Queue into Apache Kafka®. It supports both Standard queues and First-In-First-Out (FIFO) queues. The connector polls an Amazon SQS queue, converts SQS messages into Kafka records, and then pushes the records into a Kafka topic.
Note
- This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see Amazon SQS Source 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.
The connector converts an Amazon SQS message into a Kafka record, with the following structure:
- The key encodes the SQS queue name and message ID in a struct. For FIFO queues, it also includes the message group ID.
- The value encodes the body of the SQS message and various message attributes in a struct.
- Each header encodes message attributes that may be present in the SQS message.
For record schema details, see Record Schemas.
For standard queues, the connector supports best-effort ordering guarantees. This means that there is a chance records will end up in a different order in Kafka.
For FIFO queues, the connector guarantees records are inserted into Kafka in the order they were inserted in Amazon SQS, as long as the destination Kafka topic has exactly one partition. If the destination topic has more than one partition, you can use a Single Message Transforms (SMT) to set the partition based on the MessageGroupId field in the key.
Note that the connector provides least once delivery. This means there is a chance that the connector can introduce duplicate records in Kafka for both standard and FIFO queues.
Features¶
The Amazon SQS Source connector provides the following features:
- Topics created automatically: The connector can automatically create Kafka topics.
- At least once delivery: The connector guarantees that records are delivered at least once to the Kafka topic.
- Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
- Automatic retries: The connector will retry all requests (that can be retried) when the Amazon SQS service is unavailable. This value defaults to three retries.
- Supported data formats: The connector supports Avro, JSON Schema (JSON-SR), Protobuf, and JSON (schemaless) output formats. 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.
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 SQS 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 Amazon SQS Source connector. The quick start provides the basics of selecting the connector and configuring it to stream events.
- 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.
- 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.
- An AWS account configured with Access Keys. You use these access keys when setting up the connector.
- Amazon SQS connection details. For more information, see Setting up Amazon SQS.
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 SQS Source Connector screen, complete the following:
- Select the way you want to provide Kafka Cluster credentials. You can
choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
- Click Continue.
- Enter the Amazon SQS connection details:
- AWS Access Key: The Amazon Access Key used to connect to SQS.
- AWS Secret Key: The Amazon Secret Key used to connect to SQS.
- Fully qualified SQS URL: Fully qualified Amazon SQS URL to read
messages from. For example,
https://sqs.us-east-2.amazonaws.com/123456789012/MyQueue
. For details, see Amazon SQS queue and message identifiers. - SQS Region: The AWS region that the SQS queue belongs to. If left empty, the connector attempts to infer the region from the SQS URL.
- Click Continue.
Select the output record value format (Data going to the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON. 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.
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?.
For transforms and predicates, see the Single Message Transforms (SMT) documentation for details. Also, see Configuration Properties for all property values and descriptions.
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 for records¶
Verify that records are 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.
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 entry shows the required configuration properties.
{
"name": "SqsSource_0",
"config": {
"connector.class": "SqsSource",
"name": "SqsSource_0",
"kafka.auth.mode": "KAFKA_API_KEY",
"kafka.api.key": "<my-kafka-api-key>",
"kafka.api.secret": "<my-kafka-api-secret>",
"sqs.url": "https://sqs.us-east-2.amazonaws.com/123456789012/MyQueue",
"kafka.topic": "stocks",
"aws.access.key.id": "<INSERT AWS API KEY>",
"aws.secret.key.id": "<INSERT AWS API SECRET KEY ID>",
"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
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
"sqs.url"
: For example,https://sqs.us-east-2.amazonaws.com/123456789012/MyQueue
. For details, see Amazon SQS queue and message identifiers."sqs.region"
: The AWS region that the SQS queue belongs to. If this property is not used, the connector attempts to infer the region from the SQS URL."aws.access.key.id"
and"aws.secret.key.id"
: Enter the AWS Access Key ID and Secret Key ID. For information about how to set these up, see Access Keys."output.data.format"
: Enter an output data format (data going to the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON (schemaless). 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."tasks.max"
: Enter the number of tasks to use with the connector. 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 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 sqs-source-config.json
Example output:
Created connector SqsSource_0 lcc-do6vzd
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 | Trace
+------------+------------------+---------+--------+-------+
lcc-do6vzd | SqsSource_0 | RUNNING | source | |
Step 6: Check for records.¶
Verify that records are 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
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
Which topic do you want to send data to?¶
kafka.topic
Identifies the topic name to write the data to.
- Type: string
- 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
AWS Credentials¶
aws.access.key.id
The Amazon Access Key used to connect to SQS.
- Type: password
- Importance: high
aws.secret.key.id
The Amazon Secret Key used to connect to SQS.
- Type: password
- Importance: high
How should we connect to Amazon SQS?¶
sqs.url
Fully qualified Amazon SQS URL to read messages from
- Type: string
- Importance: high
sqs.region
The AWS region that the SQS queue belongs to. If left empty, the connector will attempt to infer the region from the SQS URL.
- Type: string
- 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
Record Schemas¶
The Amazon SQS Source connector creates records using following schemas.
Key Schema¶
The Key is a struct
with the following fields:
Field Name | Schema Type | Optional? | Description |
---|---|---|---|
QueueUrl | string | mandatory | The fully qualified SQS queue URL from which the record is generated. |
MessageId | string | mandatory | The unique message ID of the message within Amazon SQS. |
MessageGroupId | string | optional | For FIFO queues, this is the message group ID. |
Value Schema¶
The Value is a struct
with the following fields:
Field Name | Schema Type | Optional? | Description |
---|---|---|---|
Body | string | The body of the SQS message. | |
ApproximateFirstReceiveTimestamp | int64 | Returns the time the message was first received from the queue (epoch time in milliseconds). | |
ApproximateReceiveCount | int32 | Returns the number of times a message has been received across all queues but not deleted. | |
SenderId | string | The IAM user or role that sent this message to SQS. | |
SentTimestamp | int64 | Returns the time the message was sent to the queue (epoch time in milliseconds | |
MessageDeduplicationId | string | Optional | Returns the value provided by the producer that calls the SendMessage action. |
MessageGroupId | string | Optional | Returns the value provided by the producer that calls the SendMessage action. Messages with the same MessageGroupId are returned in sequence. |
SequenceNumber | string | Returns the value provided by Amazon SQS. |
For more information, see Request Parameters.
Header Schema¶
Each message attribute in SQS is converted to a Header in Kafka.
- The header key is the name of the message attribute.
- The header value is the value of the message attribute.
- The header schema depends on the data type of the message attribute.
- String message attributes use a string schema.
- Number message attributes use a string schema.
- Binary message attributes use a bytes schema.
- Custom message attributes use either string or bytes, depending on the type of custom attribute.
For more information, see Message attribute components.
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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