Azure Blob Storage Source Connector for Confluent Cloud¶
The fully-managed Azure Storage Source connector for Confluent Cloud reads data from Azure Blob Storage and produces the data as records in Apache Kafka®. The connector can read from any file naming convention listed in Azure Blob Storage. The file names don’t have to be in a specific format, as long as the files are in a supported format. Supported formats include JSON, Avro, String, and Byte Array.
Note
- This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see Azure Blob Storage 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.
Features¶
The Azure Blob Storage Source connector provides the following features:
- At least once delivery: The connector guarantees that records are delivered at least once.
- Supports multiple tasks: The connector supports running one or more tasks.
- Generalized data format support: The connector supports reading data from Azure Blob Storage in Avro, JSON, String, and Bytes format. Besides records with schemas, the connector supports importing schemaless JSON records in text files, one record per line. In general, the connector accepts any format that provides an implementation of the Format interface.
- Offset management capabilities: Supports offset management. For more information, see Manage custom offsets.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.
Limitations¶
Be sure to review the following information.
- For connector limitations, see Azure Blob Storage 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.
Manage custom offsets¶
You can manage the offsets for this connector. Offsets provide information on the point in the system from which the connector is accessing data. For more information, see Manage Offsets for Fully-Managed Connectors in Confluent Cloud.
To manage offsets:
- Manage offsets using Confluent Cloud APIs. For more information, see Cluster API reference.
To get the current offset, make a GET
request that specifies the environment, Kafka cluster, and connector name.
GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets
Host: https://api.confluent.cloud
Response:
Successful calls return HTTP 200
with a JSON payload that describes the offset.
{
"id": "lcc-example123",
"name": "{connector_name}",
"offsets": [
{
"partition": {
"taskId": "lcc-example123-0-in_progress"
},
"offset": {
"earliestIncomplete": "2023-08-03T10:24:25Z",
"completedFiles": "[{\"filePath\":\"topics/abc_0/partition=0/abc_0+0+00000.json\",\"creationTime\":\"2023-08-03T10:24:25Z\"},{\"filePath\":\"topics/abc_1/partition=0/abc_1+0+00000.json\",\"creationTime\":\"2023-08-03T10:34:56Z\"},{\"filePath\":\"topics/abc_3/partition=0/abc_3+0+00000.json\",\"creationTime\":\"2023-08-03T10:48:28Z\"}]",
"recordNum": "98"
}
}
{
"partition": {
"taskId": "lcc-example123-1"
},
"offset": {
"earliestIncomplete": "2023-08-03T10:24:25Z",
"completedFiles": "[{\"filePath\":\"topics/babc_4/partition=0/babc_4+0+00000.json\",\"creationTime\":\"2023-08-03T10:33:04Z\"},{\"filePath\":\"topics/abc_2/partition=0/abc_2+0+00000.json\",\"creationTime\":\"2023-08-03T10:46:06Z\"},{\"filePath\":\"topics/weird/partition=0/weird+0+00000 copy.json\",\"creationTime\":\"2023-08-03T10:51:09Z\"}]",
"recordNum": "99"
}
}
{
"partition": {
"taskId": "lcc-example123-0"
},
"offset": {
"earliestIncomplete": "2023-08-03T10:24:25Z",
"completedFiles": "[{\"filePath\":\"topics/abc_0/partition=0/abc_0+0+00000.json\",\"creationTime\":\"2023-08-03T10:24:25Z\"},{\"filePath\":\"topics/abc_1/partition=0/abc_1+0+00000.json\",\"creationTime\":\"2023-08-03T10:34:56Z\"},{\"filePath\":\"topics/abc_3/partition=0/abc_3+0+00000.json\",\"creationTime\":\"2023-08-03T10:48:28Z\"},{\"filePath\":\"topics/abc_5/partition=0/abc_5+0+00000.json\",\"creationTime\":\"2023-08-03T10:59:06Z\"}]",
"recordNum": "99"
}
}
{
"partition": {
"taskId": "lcc-example123-1-in_progress"
},
"offset": {
"earliestIncomplete": "2023-08-03T10:24:25Z",
"completedFiles": "[{\"filePath\":\"topics/babc_4/partition=0/babc_4+0+00000.json\",\"creationTime\":\"2023-08-03T10:33:04Z\"},{\"filePath\":\"topics/abc_2/partition=0/abc_2+0+00000.json\",\"creationTime\":\"2023-08-03T10:46:06Z\"}]",
"recordNum": "98"
}
}
],
"metadata": {
"observed_at": "2024-03-28T17:57:48.139635200Z"
}
}
Responses include the following information:
- The position of latest offset.
- The observed time of the offset in the metadata portion of the payload. The
observed_at
time indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating its offsets. Useobserved_at
to get a sense for the gap between real time and the time at which the request was made. By default, offsets are observed every minute. CallingGET
repeatedly will fetch more recently observed offsets. - Information about the connector.
You can approach offset updates in two ways:
Modify the
earliestIncomplete
time to reset the offsets so that next scan will source the files withcreationTime
equal to or after the newearliestIncomplete
.If you use this approach, consider this:
- If
earliestIncomplete
is set to a later time, the connector starts sourcing the files withcreationTime
equal to or after theearliestIncomplete
and skips records. - If
earliestIncomplete
is set to an earlier time, the connector might produce duplicate records because it starts sourcing every record from files with acreationTime
equal to or after the earlier time.
- If
If you want to skip processing a file or files, add the files to
completedFiles
.
To update the offset, make a POST
request that specifies the environment, Kafka cluster, and connector
name. Include a JSON payload that specifies new offset and a patch type.
POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request
Host: https://api.confluent.cloud
{
"type": "PATCH",
"offsets": [
{
"partition": {
"taskId": "lcc-devc3m1zkj-0"
},
"offset": {
"completedFiles": "[{\"filePath\":\"source/file_0\",\"creationTime\":\"2024-03-06T17:30:28.391Z\"},{\"filePath\":\"source/file_7\",\"creationTime\":\"2024-03-06T17:30:28.395Z\"},{\"filePath\":\"source/file_9\",\"creationTime\":\"2024-03-06T17:30:28.409Z\"},{\"filePath\":\"source/file_1\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_8\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_6\",\"creationTime\":\"2024-03-06T17:30:28.715Z\"},{\"filePath\":\"source/file_30\",\"creationTime\":\"2024-03-06T17:30:28.969Z\"},{\"filePath\":\"source/file_39\",\"creationTime\":\"2024-03-06T17:30:28.970Z\"},{\"filePath\":\"source/file_37\",\"creationTime\":\"2024-03-06T17:30:28.993Z\"},{\"filePath\":\"source/file_36\",\"creationTime\":\"2024-03-06T17:30:29.265Z\"},{\"filePath\":\"source/file_31\",\"creationTime\":\"2024-03-06T17:30:29.268Z\"},{\"filePath\":\"source/file_38\",\"creationTime\":\"2024-03-06T17:30:29.278Z\"},{\"filePath\":\"source/file_25\",\"creationTime\":\"2024-03-06T17:30:29.549Z\"},{\"filePath\":\"source/file_22\",\"creationTime\":\"2024-03-06T17:30:29.551Z\"},{\"filePath\":\"source/file_13\",\"creationTime\":\"2024-03-06T17:30:29.552Z\"},{\"filePath\":\"source/file_47\",\"creationTime\":\"2024-03-06T17:30:30.015Z\"},{\"filePath\":\"source/file_14\",\"creationTime\":\"2024-03-06T17:30:30.020Z\"},{\"filePath\":\"source/file_40\",\"creationTime\":\"2024-03-06T17:30:30.028Z\"},{\"filePath\":\"source/file_15\",\"creationTime\":\"2024-03-06T17:30:30.305Z\"}]",
"earliestIncomplete": "2024-03-06T17:30:28.391Z",
"recordNum": "0"
}
}
]
}
Considerations:
- You can only make one offset change at a time for a given connector.
- This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see Get the status of an offset request.
- For source connectors, the connector attempts to read from the position defined by the requested offsets.
Response:
Successful calls return HTTP 202 Accepted
with a JSON payload that describes the offset.
{
"id": "lcc-example123",
"name": "{connector_name}",
"offsets": [
{
"partition": {
"taskId": "lcc-example123-0"
},
"offset": {
"completedFiles": "[{\"filePath\":\"source/file_0\",\"creationTime\":\"2024-03-06T17:30:28.391Z\"},{\"filePath\":\"source/file_7\",\"creationTime\":\"2024-03-06T17:30:28.395Z\"},{\"filePath\":\"source/file_9\",\"creationTime\":\"2024-03-06T17:30:28.409Z\"},{\"filePath\":\"source/file_1\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_8\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_6\",\"creationTime\":\"2024-03-06T17:30:28.715Z\"},{\"filePath\":\"source/file_30\",\"creationTime\":\"2024-03-06T17:30:28.969Z\"},{\"filePath\":\"source/file_39\",\"creationTime\":\"2024-03-06T17:30:28.970Z\"},{\"filePath\":\"source/file_37\",\"creationTime\":\"2024-03-06T17:30:28.993Z\"},{\"filePath\":\"source/file_36\",\"creationTime\":\"2024-03-06T17:30:29.265Z\"},{\"filePath\":\"source/file_31\",\"creationTime\":\"2024-03-06T17:30:29.268Z\"},{\"filePath\":\"source/file_38\",\"creationTime\":\"2024-03-06T17:30:29.278Z\"},{\"filePath\":\"source/file_25\",\"creationTime\":\"2024-03-06T17:30:29.549Z\"},{\"filePath\":\"source/file_22\",\"creationTime\":\"2024-03-06T17:30:29.551Z\"},{\"filePath\":\"source/file_13\",\"creationTime\":\"2024-03-06T17:30:29.552Z\"},{\"filePath\":\"source/file_47\",\"creationTime\":\"2024-03-06T17:30:30.015Z\"},{\"filePath\":\"source/file_14\",\"creationTime\":\"2024-03-06T17:30:30.020Z\"},{\"filePath\":\"source/file_40\",\"creationTime\":\"2024-03-06T17:30:30.028Z\"},{\"filePath\":\"source/file_15\",\"creationTime\":\"2024-03-06T17:30:30.305Z\"}]",
"earliestIncomplete": "2024-03-06T17:30:28.391Z",
"recordNum": "0"
}
}
],
"requested_at": "2024-03-28T17:58:45.606796307Z",
"type": "PATCH"
}
Responses include the following information:
- The requested position of the offsets in the source.
- The time of the request to update the offset.
- Information about the connector.
To delete the offset, make a POST
request that specifies the environment, Kafka cluster, and connector
name. Include a JSON payload that specifies the delete type.
POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request
Host: https://api.confluent.cloud
{
"type": "DELETE"
}
Considerations:
- Delete requests delete the offset for the provided partition and reset to the base state. A delete request is as if you created a fresh new connector.
- This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see Get the status of an offset request.
- Do not issue delete and patch requests at the same time.
- For source connectors, the connector attempts to read from the position defined in the base state.
Response:
Successful calls return HTTP 202 Accepted
with a JSON payload that describes the result.
{
"id": "lcc-example123",
"name": "{connector_name}",
"offsets": [],
"requested_at": "2024-03-28T17:59:45.606796307Z",
"type": "DELETE"
}
Responses include the following information:
- Empty offsets.
- The time of the request to delete the offset.
- Information about Kafka cluster and connector.
- The type of request.
To get the status of a previous offset request, make a GET
request that specifies the environment, Kafka cluster, and connector
name.
GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request/status
Host: https://api.confluent.cloud
Considerations:
- The status endpoint always shows the status of the most recent PATCH/DELETE operation.
Response:
Successful calls return HTTP 200
with a JSON payload that describes the result. The following is an example
of an applied patch.
{
"request": {
"id": "lcc-example123",
"name": "{connector_name}",
"offsets": [
{
"partition": {
"taskId": "lcc-example123-0"
},
"offset": {
"completedFiles": "[{\"filePath\":\"source/file_0\",\"creationTime\":\"2024-03-06T17:30:28.391Z\"},{\"filePath\":\"source/file_7\",\"creationTime\":\"2024-03-06T17:30:28.395Z\"},{\"filePath\":\"source/file_9\",\"creationTime\":\"2024-03-06T17:30:28.409Z\"},{\"filePath\":\"source/file_1\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_8\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_6\",\"creationTime\":\"2024-03-06T17:30:28.715Z\"},{\"filePath\":\"source/file_30\",\"creationTime\":\"2024-03-06T17:30:28.969Z\"},{\"filePath\":\"source/file_39\",\"creationTime\":\"2024-03-06T17:30:28.970Z\"},{\"filePath\":\"source/file_37\",\"creationTime\":\"2024-03-06T17:30:28.993Z\"},{\"filePath\":\"source/file_36\",\"creationTime\":\"2024-03-06T17:30:29.265Z\"},{\"filePath\":\"source/file_31\",\"creationTime\":\"2024-03-06T17:30:29.268Z\"},{\"filePath\":\"source/file_38\",\"creationTime\":\"2024-03-06T17:30:29.278Z\"},{\"filePath\":\"source/file_25\",\"creationTime\":\"2024-03-06T17:30:29.549Z\"},{\"filePath\":\"source/file_22\",\"creationTime\":\"2024-03-06T17:30:29.551Z\"},{\"filePath\":\"source/file_13\",\"creationTime\":\"2024-03-06T17:30:29.552Z\"},{\"filePath\":\"source/file_47\",\"creationTime\":\"2024-03-06T17:30:30.015Z\"},{\"filePath\":\"source/file_14\",\"creationTime\":\"2024-03-06T17:30:30.020Z\"},{\"filePath\":\"source/file_40\",\"creationTime\":\"2024-03-06T17:30:30.028Z\"},{\"filePath\":\"source/file_15\",\"creationTime\":\"2024-03-06T17:30:30.305Z\"}]",
"earliestIncomplete": "2024-03-06T17:30:28.391Z",
"recordNum": "0"
}
}
],
"requested_at": "2024-03-28T17:58:45.606796307Z",
"type": "PATCH"
},
"status": {
"phase": "APPLIED",
"message": "The Connect framework-managed offsets for this connector have been altered successfully. However, if this connector manages offsets externally, they will need to be manually altered in the system that the connector uses."
},
"previous_offsets": [
{
"partition": {
"taskId": "lcc-example123-0"
},
"offset": {
"completedFiles": "[{\"filePath\":\"source/file_31\",\"creationTime\":\"2024-03-06T17:30:29.268Z\"},{\"filePath\":\"source/file_38\",\"creationTime\":\"2024-03-06T17:30:29.278Z\"},{\"filePath\":\"source/file_25\",\"creationTime\":\"2024-03-06T17:30:29.549Z\"},{\"filePath\":\"source/file_22\",\"creationTime\":\"2024-03-06T17:30:29.551Z\"},{\"filePath\":\"source/file_13\",\"creationTime\":\"2024-03-06T17:30:29.552Z\"},{\"filePath\":\"source/file_47\",\"creationTime\":\"2024-03-06T17:30:30.015Z\"},{\"filePath\":\"source/file_14\",\"creationTime\":\"2024-03-06T17:30:30.020Z\"},{\"filePath\":\"source/file_40\",\"creationTime\":\"2024-03-06T17:30:30.028Z\"},{\"filePath\":\"source/file_15\",\"creationTime\":\"2024-03-06T17:30:30.305Z\"},{\"filePath\":\"source/file_49\",\"creationTime\":\"2024-03-06T17:30:30.313Z\"},{\"filePath\":\"source/file_12\",\"creationTime\":\"2024-03-06T17:30:30.326Z\"},{\"filePath\":\"source/file_23\",\"creationTime\":\"2024-03-06T17:30:30.600Z\"},{\"filePath\":\"source/file_24\",\"creationTime\":\"2024-03-06T17:30:30.613Z\"},{\"filePath\":\"source/file_48\",\"creationTime\":\"2024-03-06T17:30:30.639Z\"},{\"filePath\":\"source/file_46\",\"creationTime\":\"2024-03-06T17:30:30.899Z\"},{\"filePath\":\"source/file_41\",\"creationTime\":\"2024-03-06T17:30:30.926Z\"},{\"filePath\":\"source/file_3\",\"creationTime\":\"2024-03-06T17:30:30.927Z\"},{\"filePath\":\"source/file_4\",\"creationTime\":\"2024-03-06T17:30:31.198Z\"},{\"filePath\":\"source/file_5\",\"creationTime\":\"2024-03-06T17:30:31.220Z\"},{\"filePath\":\"source/file_2\",\"creationTime\":\"2024-03-06T17:30:31.225Z\"}]",
"earliestIncomplete": "2024-03-06T17:30:29.268Z",
"recordNum": "0"
}
}
],
"applied_at": "2024-03-28T17:58:48.079141883Z"
}
Responses include the following information:
- The original request, including the time it was made.
- The status of the request: applied, pending, or failed.
- The time you issued the status request.
- The previous offsets. These are the offsets that the connector last updated prior to updating the offsets. Use these to try to restore the state of your connector if a patch update causes your connector to fail or to return a connector to its previous state after rolling back.
JSON payload¶
The table below offers a description of the unique fields in the JSON payload for managing offsets of the object store connectors, including the following connectors:
- Amazon S3 Source connector
- Azure Blob Storage Source connector
- Google Cloud Storage (GCS) Source connector
Field | Definition | Required/Optional |
---|---|---|
taskId |
Represents the partition in the following format:
|
Required |
earliestIncomplete |
The position of the latest offset. When a connectors starts or restarts, the connector reads the files
with a creation time equal to or after earliestIncomplete offset. These files are sorted by creation time then filename. |
Required |
completedFiles |
List of sourced files. | Required |
recordNum |
Number of records sourced. | Required |
Quick Start¶
Use this quick start to get up and running with the Confluent Cloud Azure Blob Storage Source connector. The quick start provides the basics of selecting the connector and configuring it to get data from an Azure Blob Storage container.
- 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.
- An Azure block blob storage account and an Azure storage account access key.
- Confluent Cloud Schema Registry must be enabled for your cluster, if you are using a messaging schema (like Avro and JSON_SR). See Work with schemas and Schema Registry Enabled Environments for additional information.
- 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 4: Enter the connector details¶
Note
- Be sure you have all your prerequisites completed.
- An asterisk ( * ) designates a required entry.
- 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.
- Under the Azure Blob Storage details section, enter the following:
- Your storage account name in the Azure Blob Storage Name field.
- The Storage account key in the Azure Blob Storage Account Key field. For information about how to set these up, see Manage storage account access keys.
- The Azure Blob Storage container in the Container name field.
The name must be between three and 63 alphanumeric characters, and
the character
-
can be used.
- Click Continue.
Select an Input message format: Supports AVRO, JSON (schemaless), STRING, or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format, like Avro. Refer to Confluent Cloud connector limitations for additional information.
Select an Output Kafka record value format: Defaults to the file format selected for the input message format. AVRO, BYTES, JSON, JSON_SR, PROTOBUF, and STRING. A valid schema must be available in Schema Registry if using a schema-based format.
Enter the Topic Name Regex Patterns. A list of topics along with a regex expression of the files which are to be sent to that topic. For example,
"my-topic:.*"
sends all files to"my-topic"
. The expression"special-topic:.*\.json+*"
” sends only files ending with".json"
to"special-topic"
. The connector ignores (doesn’t source) other files not matching any patterns. The connector sends files that match multiple mappings to the first topic in the list that maps the file.Enter a Topics directory. This is a top-level directory name where data is stored in the bucket. Defaults to
topics
.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?.
Azure Blob Storage Details
Enter a Azure Blob Storage Max Retries value. Specifies the maximum number of times the connector retries an operation. A value of
1
means1
attempt and1
retry attempt. The actual number of retry attempts is determined by the Azure client and is based on multiple factors, including this property value, the type of exception that occurred, the throttle settings of the underlying Azure client, and other potential Azure client factors. The value defaults to3
.Enter a Retry Backoff (ms) value in milliseconds (ms). This sets how long to wait before attempting the first retry of a failed request. The delay increases (exponentially or linearly) with each retry up to a maximum specified by the value set in Max Retry Backoff (
azblob.retry.max.backoff.ms
). This avoids retrying in a tight loop under failure scenarios. Defaults to4000
ms.Enter a Max Retry Backoff (ms) value. Specifies the maximum delay allowed before retrying an operation. Defaults to
12000
ms.Enter a Request Timeout (ms) value. Specifies the maximum time allowed for a single HTTP request. Defaults to
30
ms.
Storage
Enter a Directory Delimiter Character. The pattern to use as the delimiter character for directories. Defaults to
/
.Select the Behavior on error. Sets whether the connector fails the task on an error or continues to the next file. Defaults to
FAIL
.Select a Byte Array Line Separator. Sets the string inserted between records when using ByteArrayFormat is the input data format. Defaults to
\n
and may contain escape sequences like\n
. An input record that contains the line separator looks like multiple records in the storage object input.Enter a Task Batch Size: The number of files assigned to each task at a time. Defaults to
10
. The maximum value supported is2000
and the minimum value is1
.Enter a File Discovery Starting Timestamp. A UNIX timestamp (in milliseconds) that denotes where to start processing files. The connector ignores any file encountered having an earlier creation time. Defaults to
0
, which is Jan 1, 1970 (that is, the beginning of data in the container).Enter an Azure Blob poll interval in milliseconds (ms). The frequency at which to poll for new or removed folders. This may result in updated task configurations starting to poll for data in added folders or stopping polling for data in removed folders. Defaults to
60000
ms (one minute). The minimum interval allowed is1000
ms (one second).Set the Max records per poll. The maximum amount of records to return each time the connector polls storage. Defaults to
200
. The maximum value supported is10000
and the minimum value is1
.Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.
See Configuration Properties for all property values and definitions.
Click Continue.
Based on the number of topic partitions you select, you will be provided with a recommended minimum number of tasks.
- Enter the maximum number of tasks. The connector supports running one or more tasks. More tasks can improve performance.
- Click Continue.
Verify the connection details by previewing the running configuration.
After you’ve validated that the properties are configured to your satisfaction, click Continue.
Tip
For information about previewing your connector output, see Data Previews for Confluent Cloud Connectors.
Step 5: Check the Kafka topic¶
After the connector is running, verify that messages are populating your Kafka topic.
Note
The Azure Blob Storage Source connector loads and filters all object names in
the container before it starts sourcing records. When starting up, the
connector may display RUNNING
but not show any throughput. This is
because container loading is not finished. For a container with a large
amount of objects, loading can take several minutes to complete.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples 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 example shows the required connector properties.
{
"connector.class": "AzureBlobSource",
"name": "AzureBlobSourceConnector_0",
"topic.regex.list": "kafka-topic-for-json:*",
"kafka.auth.mode": "SERVICE_ACCOUNT",
"kafka.service.account.id": "<service-account-resource-ID>",
"azblob.account.name": "<storage-account-name>",
"azblob.account.key": "<storage-account-key>",
"azblob.container.name": "<container-name>",
"input.data.format": "JSON",
"tasks.max": "1",
}
Note the following required property definitions:
"connector.class"
: Identifies the connector plugin name."name"
: Sets a name for your new connector."topic.regex.list"
: A list of topics along with a regex expression of the files which are to be sent to that topic. In the example above,"kafka- topic-for-json:.*"
sends all files to"kafka-topic-for-json"
. The expression"special-topic:.*\.json+*"
” sends only files ending with".json"
to"special-topic"
. The connector ignores (doesn’t source) other files not matching any patterns. The connector sends files that match multiple mappings to the first topic in the list that maps the file. In case of a single fileabc.json
, path returned by Azure client is the absolute path, so the regex would bespecial-topic:.*abc\.json
.
"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
"azblob.account.name"
: The storage account name to use for the connector."azblob.account.key"
: The storage account key to use. For information about how to set these up, see Manage storage account access keys."azblob.container.name"
: The container name. Must be between three and 63 alphanumeric characters, and the character-
can be used."input.data.format"
: Supports Avro, JSON (schemaless), String, or Bytes. A valid schema must be available in Schema Registry to use a schema-based message format, like Avro. Refer to Confluent Cloud connector limitations for additional information."tasks.max"
: The total number of tasks to run in parallel. More tasks may improve performance.Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.
For configuration property values and descriptions, see Configuration Properties.
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 azure-blob-source-config.json
Example output:
Created connector AzureBlobSourceConnector_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 | AzureBlobSourceConnector_0 | RUNNING | source
Step 6. Check the Kafka topic.¶
After the connector is running, verify records are populating the Kafka topic.
Note
The Azure Blob Storage Source connector loads and filters all object names in
the container before it starts sourcing records. When starting up, the
connector may display RUNNING
but not show any throughput. This is
because container loading is not finished. For a container with a large
amount of objects, loading can take several minutes to complete.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples 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
Which topic(s) do you want to send data to?¶
topic.regex.list
A list of topics along with a regex expression of the files which are to be sent to that topic. For example: “my-topic:.*” will send all files to “my-topic”, while a list containing only the expression “special-topic:.*.json” will send only files starting with “.json” to “special-topic”, and all other files not matching any patterns will be ignored and not sourced. Files that match multiple mappings will be sent to the first topic in the list that maps the file.
- 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
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
Azure Blob Storage details¶
azblob.account.name
Must be between 3-24 alphanumeric characters.
- Type: string
- Valid Values: A string at most 24 characters long
- Importance: high
azblob.account.key
The Azure Storage account key.
- Type: password
- Importance: high
azblob.container.name
Please provide the Azure Blob Storage Container name. Must be between 3-63 alphanumeric and ‘-‘ characters.
- Type: string
- Importance: high
azblob.sas.token
Shared access signature (SAS) is a URI that grants restricted access rights to Azure Storage resources. Please provide a shared access signature to clients who should not be trusted with your storage account key but whom you wish to delegate access to certain storage account resources.
- Type: password
- Importance: medium
azblob.retry.type
The policy specifying the type of retry pattern to use. Should be either Exponential or Fixed.
- Type: string
- Default: EXPONENTIAL
- Valid Values: EXPONENTIAL, FIXED
- Importance: medium
Input and output messages¶
input.data.format
Sets the input message format. Valid entries are AVRO, JSON, BYTES or STRING. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO.
- Type: string
- Valid Values: AVRO, BYTES, JSON, STRING
- Importance: high
output.data.format
Set the output 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. Note that the output message format defaults to the value in the Input Message Format field. If no value for this property is provided, the value specified for the ‘input.data.format’ property is used.
- Type: string
- Importance: high
Storage¶
topics.dir
Top-level directory where data to be ingested is stored.
- Type: string
- Default: topics
- Importance: high
directory.delim
Directory delimiter pattern.
- Type: string
- Default: /
- Importance: medium
behavior.on.error
Should the task halt when it encounters an error or continue to the next file.
- Type: string
- Default: FAIL
- Importance: high
format.bytearray.separator
String inserted between records for ByteArrayFormat. Defaults to n and may contain escape sequences like n. An input record that contains the line separator looks like multiple records in the storage object input.
- Type: string
- Default: “”
- Importance: medium
task.batch.size
The number of files assigned to each task at a time
- Type: int
- Default: 10
- Valid Values: [1,…,2000]
- Importance: high
file.discovery.starting.timestamp
A unix timestamp (seconds since Jan 1, 1970 UTC) that denotes where to start processing files. Any file encountered with a creation time earlier than this will be ignored.
- Type: long
- Default: 0
- Importance: high
Data polling policy¶
azblob.poll.interval.ms
Frequency in milliseconds to poll for new or removed folders. This may result in updated task configurations starting to poll for data in added folders or stopping polling for data in removed folders
- Type: long
- Default: 60000 (1 minute)
- Valid Values: [1000,…]
- Importance: medium
record.batch.max.size
The maximum amount of records to return each time storage is polled.
- Type: int
- Default: 200
- Valid Values: [1,…,10000]
- Importance: medium
Number of tasks for this connector¶
tasks.max
The total number of tasks to run in parallel.
- Type: int
- Valid Values: [1,…,1000]
- 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.