Azure Data Lake Storage Gen1 Sink Connector for Confluent Platform¶

You can use the Kafka Connect Azure Data Lake Storage Gen1 Sink connector to export data from Apache Kafka® topics to Azure Data Lake Storage Gen1 files in either Avro or JSON formats. Depending on your environment, the Azure Data Lake Storage Gen1 Sink connector can export data by guaranteeing exactly-once delivery semantics to consumers of the Azure Data Lake Storage Gen1 files it produces.

The Azure Data Lake Storage Gen1 Sink connector periodically polls data from Kafka and, in turn, uploads it to Azure Data Lake Storage Gen1. A partitioner is used to split the data of every Kafka partition into chunks. Each chunk of data is represented as an Azure Data Lake Storage Gen1 file. The key name encodes the topic, the Kafka partition, and the start offset of this data chunk. If no partitioner is specified in the configuration, the default partitioner which preserves Kafka partitioning is used. The size of each data chunk is determined by the number of records written to Azure Data Lake Storage Gen1 and by schema compatibility.

Features¶

The Microsoft Azure Data Lake Storage Gen1 Sink connector includes variety of features:

Exactly once delivery
Dead Letter Queue
Multiple tasks
Pluggable data format with or without schema
Schema evolution
Pluggable partitioner

Exactly once delivery¶

Records that are exported using a deterministic partitioner are delivered with exactly-once semantics.

Dead Letter Queue¶

This connector supports the Dead Letter Queue (DLQ) functionality. For information about accessing and using the DLQ, see Confluent Platform Dead Letter Queue.

Multiple tasks¶

The Azure Data Lake Storage Gen1 Sink connector supports running one or more tasks. You can specify the number of tasks in the tasks.max configuration parameter. Multiple tasks may improve performance when moving a large amount of data.

Pluggable data format with or without schema¶

Out of the box, the connector supports writing data to Azure Data Lake Storage Gen1 in Avro and JSON format. Besides records with schema, the connector supports exporting plain JSON and as byte array records without schema in text files, one record per-line. In general, the connector may accept any format that provides an implementation of the Format interface.

Important

You must use the AvroConverter, ProtobufConverter, or JsonSchemaConverter with ParquetFormat for this connector. Attempting to use the JsonConverter (with or without schemas) results in a NullPointerException and a StackOverflowException.

Pluggable Data Format with or without Schema¶

Out of the box, the connector supports writing data to Azure Data Lake Storage Gen1 in Avro and JSON format. Besides records with schema, the connector supports exporting plain JSON and as byte array records without schema in text files, one record per-line. In general, the connector may accept any format that provides an implementation of the Format interface.

Important

You must use the AvroConverter, ProtobufConverter, or JsonSchemaConverter with ParquetFormat for this connector. Attempting to use the JsonConverter (with or without schemas) results in a NullPointerException and a StackOverflowException.

Schema evolution¶

When schemas are used, the connector supports schema evolution based on schema compatibility modes. The available modes are: NONE, BACKWARD, FORWARD and FULL and a selection can be made by setting the property schema.compatibility in the connector’s configuration. When the connector observes a schema change, it decides whether to roll the file or project the record to the proper schema according to the schema.compatibility configuration in use.

Pluggable partitioner¶

The connector comes out of the box with partitioners that support default partitioning based on Kafka partitions, field partitioning, and time-based partitioning in days or hours. You may implement your own partitioners by extending the Partitioner class. Additionally, you can customize time-based partitioning by extending the TimeBasedPartitioner class.

Tip

By default, connectors inherit the partitioner used for the Kafka topic. You can create a custom partitioner for a connector which you must place in the connector’s /lib folder.

You can also put partitioners in a common location of choice. If you choose this option, you must add a symlink to the location from each connector’s /lib folder. For example, you would place a custom partitioner in the path share/confluent-hub-components/partitioners and then add the symlink share/confluent-hub-components/kafka-connect-s3/lib/partitioners -> ../../partitioners.

Install the Azure Data Lake Storage Gen1 Sink Connector¶

You can install this connector by using the confluent connect plugin install command, or by manually downloading the ZIP file.

Prerequisites¶

You must install the connector on every machine where Connect will run.
An installation of the latest (latest) connector version.

To install the latest connector version, navigate to your Confluent Platform installation directory and run the following command:
```
confluent connect plugin install confluentinc/kafka-connect-azure-data-lake-gen1-storage:latest
```
You can install a specific version by replacing latest with a version number as shown in the following example:
```
confluent connect plugin install confluentinc/kafka-connect-azure-data-lake-gen1-storage:1.1.1
```
You must authorize the connector user (or principal) to have write permissions on the uncommitted folder; else, the connector will throw an exception.

Caution

You can’t mix schema and schemaless records in storage using kafka-connect-storage-common. Attempting this causes a runtime exception. If you are using the self-managed version of this connector, this issue will be evident when you review the log files (only available for the self-managed connector).

Install the connector manually¶

Download and extract the ZIP file for your connector and then follow the manual connector installation instructions.

License¶

You can use this connector for a 30-day trial period without a license key.

After 30 days, you must purchase a connector subscription which includes Confluent enterprise license keys to subscribers, along with enterprise-level support for Confluent Platform and your connectors. If you are a subscriber, you can contact Confluent Support at support@confluent.io for more information.

For license properties, see Confluent Platform license. For information about the license topic, see License topic configuration.

Configuration Properties¶

For a complete list of configuration properties for this connector, see Configuration Reference for Azure Data Lake Storage Gen1 Sink Connector for Confluent Platform.

Mapping records to Azure Data Lake Storage Gen1 Objects¶

The Azure Data Lake Storage Gen1 Sink connector consumes records from the specified topics, organizes them into different partitions, writes batches of records in each partition to an file, and then uploads those files to the Azure Data Lake Storage Gen1 bucket. It uses Azure Data Lake Storage Gen1 object paths that include the Kafka topic and partition, the computed partition, and the filename. The Azure Data Lake Storage Gen1 Sink connector offers several ways to customize this behavior, including:

Controlling the names of the Azure Data Lake Storage Gen1 objects
Determining how records are partitioned into Azure Data Lake Storage Gen1 objects
The format used to serialize sets of records into Azure Data Lake Storage Gen1 objects
When to upload Azure Data Lake Storage Gen1 objects

Azure Data Lake Storage Gen1 object names¶

The Azure Data Lake Storage Gen1 data model is a flat structure: each bucket stores objects, and the name of each Azure Data Lake Storage Gen1 object serves as the unique key. However, a logical hierarchy can be inferred when the Azure Data Lake Storage Gen1 object names uses directory delimiters, such as /. The Azure Data Lake Storage Gen1 Sink connector allows you to customize the names of the Azure Data Lake Storage Gen1 objects it uploads to the Azure Data Lake Storage Gen1 bucket.

In general, the names of the Azure Data Lake Storage Gen1 object uploaded by the Azure Data Lake Storage Gen1 Sink connector follow this format:

<prefix>/<topic>/<encodedPartition>/<topic>+<kafkaPartition>+<startOffset>.<format>

where:

<prefix> is specified with the connector’s topics.dir configuration property, which defaults to the literal value topics and helps create uniquely named Azure Data Lake Storage Gen1 objects that don’t clash with existing Azure Data Lake Storage Gen1 objects in the same bucket.
<topic> corresponds to the name of the Kafka topic from which the records in this Azure Data Lake Storage Gen1 object were read.
<encodedPartition> is generated by the Azure Data Lake Storage Gen1 Sink Connector’s partitioner (see Partitioning records into Azure Data Lake Storage Gen1 objects).
<kafkaPartition> is the Kafka partition number from which the records in this Azure Data Lake Storage Gen1 object were read.
<startOffset> is the Kafka offset of the first record written to this Azure Data Lake Storage Gen1 object. * <format> is the extension identifying the format in which the records are serialized in this Azure Data Lake Storage Gen1 object.

If desired, the / and + characters can be changed using the connector’s directory.delim and file.delim configuration properties.

Partitioning records into Azure Data Lake Storage Gen1 objects¶

The Azure Data Lake Storage Gen1 Sink connector’s partitioner determines how records read from a Kafka topic are partitioned into Azure Data Lake Storage Gen1 objects. The partitioner determines the <encodedPartition> portion of the Azure Data Lake Storage Gen1 object names (see Azure Data Lake Storage Gen1 object names).

The partitioner is specified in the connector configuration with the partitioner.class configuration property. The Azure Data Lake Storage Gen1 Sink connector comes with the following partitioners:

Default Kafka Partitioner: The io.confluent.connect.storage.partitioner.DefaultPartitioner preserves the same topic partitions as in Kafka, and records from each topic partition ultimately end up in Azure Data Lake Storage Gen1 objects with names that include the Kafka topic and Kafka partitions. The <encodedPartition> is always <topicName>/partition=<kafkaPartition>, resulting in Azure Data Lake Storage Gen1 object names such as <prefix>/<topic>/partition=<kafkaPartition>/<topic>+<kafkaPartition>+<startOffset>.<format>.
Field Partitioner: The io.confluent.connect.storage.partitioner.FieldPartitioner determines the partition from the field within each each record identified by the connector’s partition.field.name configuration property, which has no default. This partitioner requires STRUCT record type values. The <encodedPartition> is always <topicName>/<fieldName>=<fieldValue>, resulting in Azure Data Lake Storage Gen1 Sink connector object names of the form <prefix>/<topic>/<fieldName>=<fieldValue>/<topic>+<kafkaPartition>+<startOffset>.<format>.
Time Based Partitioner: The io.confluent.connect.storage.partitioner.TimeBasedPartitioner determines the partition from the year, month, day, hour, minutes, and/or seconds. This partitioner requires the following connector configuration properties:
- The path.format configuration property specifies the pattern used for the <encodedPartition> portion of the Azure Data Lake Storage Gen1 object name. For example, when path.format='year'=YYYY/'month'=MM/'day'=dd/'hour'=HH, Azure Data Lake Storage Gen1 object names will have the form <prefix>/<topic>/year=YYYY/month=MM/day=dd/hour=HH/<topic>+<kafkaPartition>+<startOffset>.<format>.
- The partition.duration.ms configuration property defines the maximum granularity of the Azure Data Lake Storage Gen1 objects within a single encoded partition directory. For example, setting partition.duration.ms=600000 (10 minutes) will result in each Azure Data Lake Storage Gen1 object in that directory having no more than 10 minutes of records.
- The locale configuration property specifies the JDK’s locale used for formatting dates and times. For example, use en-US for US English, en-GB for UK English, and fr-FR for French (in France). These may vary by Java version; see the available locales.
- The timezone configuration property specifies the current timezone in which the dates and times will be treated. Use standard short names for timezones such as UTC or (without daylight savings) PST, EST, and ECT, or longer standard names such as America/Los_Angeles, America/New_York, and Europe/Paris. These may vary by Java version; see the available timezones within each locale, such as those within the “en_US” locale.
- The timestamp.extractor configuration property determines how to obtain a timestamp from each record. Values can include Wallclock (the default) to use the system time when the record is processed, Record to use the timestamp of the Kafka record denoting when it was produced or stored by the broker, RecordField to extract the timestamp from one of the fields in the record’s value as specified by the timestamp.field configuration property.
Daily Partitioner: The io.confluent.connect.storage.partitioner.DailyPartitioner is equivalent to the TimeBasedPartitioner with path.format='year'=YYYY/'month'=MM/'day'=dd and partition.duration.ms=86400000 (one day, for one Azure Data Lake Storage Gen1 object in each daily directory). This partitioner always results in Azure Data Lake Storage Gen1 object names of the form <prefix>/<topic>/year=YYYY/month=MM/day=dd/<topic>+<kafkaPartition>+<startOffset>.<format>. This partitioner requires the following connector configuration properties:
- The locale configuration property specifies the JDK’s locale used for formatting dates and times. For example, use en-US for US English, en-GB for UK English, and fr-FR for French (in France). These may vary by Java version; see the available locales.
- The timezone configuration property specifies the current timezone in which the dates and times will be treated. Use standard short names for timezones such as UTC or (without daylight savings) PST, EST, and ECT, or longer standard names such as America/Los_Angeles, America/New_York, and Europe/Paris. These may vary by Java version; see the available timezones within each locale, such as those within the “en_US” locale.
- The timestamp.extractor configuration property determines how to obtain a timestamp from each record. Values can include Wallclock (the default) to use the system time when the record is processed, Record to use the timestamp of the Kafka record denoting when it was produced or stored by the broker, RecordField to extract the timestamp from one of the fields in the record’s value as specified by the timestamp.field configuration property.
Hourly Partitioner: The io.confluent.connect.storage.partitioner.HourlyPartitioner is equivalent to the TimeBasedPartitioner with path.format='year'=YYYY/'month'=MM/'day'=dd/'hour'=HH and partition.duration.ms=3600000 (one hour, for one Azure Data Lake Storage Gen1 object in each hourly directory). This partitioner always results in Azure Data Lake Storage Gen1 object names of the form <prefix>/<topic>/year=YYYY/month=MM/day=dd/hour=HH/<topic>+<kafkaPartition>+<startOffset>.<format>. This partitioner requires the following connector configuration properties:
- The locale configuration property specifies the JDK’s locale used for formatting dates and times. For example, use en-US for US English, en-GB for UK English, fr-FR for French (in France). These may vary by Java version; see the available locales.
- The timezone configuration property specifies the current timezone in which the dates and times will be treated. Use standard short names for timezones such as UTC or (without daylight savings) PST, EST, and ECT, or longer standard names such as America/Los_Angeles, America/New_York, and Europe/Paris. These may vary by Java version; see the available timezones within each locale, such as those within the “en_US” locale.
- The timestamp.extractor configuration property determines how to obtain a timestamp from each record. Values can include Wallclock (the default) to use the system time when the record is processed, Record to use the timestamp of the Kafka record denoting when it was produced or stored by the broker, RecordField to extract the timestamp from one of the fields in the record’s value as specified by the timestamp.field configuration property.

As noted below, the choice of timestamp.extractor affects whether the Azure Data Lake Storage Gen1 Sink connector can support exactly once delivery.

You can also choose to use a custom partitioner by implementing the io.confluent.connect.storage.partitioner.Partitioner interface, packaging your implementation into a JAR file, and then:

Place the JAR file into the share/java/kafka-connect-azure-data-lake-gen1-storage directory of your Confluent Platform installation on each worker node.
Restart all of the Connect worker nodes.
Configure Azure Data Lake Storage Gen1 Sink connectors to use your fully-qualified partitioner class name.

Azure Data Lake Storage Gen1 object formats¶

The Azure Data Lake Storage Gen1 Sink connector can serialize multiple records into each Azure Data Lake Storage Gen1 object using a number of formats. The connector’s format.class configuration property identifies the name of the Java class that implements the io.confluent.connect.storage.format.Format interface, and the Azure Data Lake Storage Gen1 Sink connector comes with several implementations:

Avro: Use format.class=io.confluent.connect.azure.storage.format.avro.AvroFormat to write the Azure Data Lake Storage Gen1 object as an Avro container file and will include the Avro schema in the container file followed by one or more records. The connector’s avro.codec configuration property specifies the Avro compression code, and values can be null (the default) for no Avro compression, deflate to use the deflate algorithm as specified in RFC 1951, snappy to use Google’s Snappy compression library, and bzip2 for BZip2 compression. Optionally set enhanced.avro.schema.support=true to enable enum symbol preservation and package name awareness.
JSON: Use format.class=io.confluent.connect.azure.storage.format.json.JsonFormat to write the Azure Data Lake Storage Gen1 object as a file containing one JSON serialized record per line. The connector’s az.compression.type configuration property can be set to none (the default) for no compression or gzip for GZip compression.
Parquet: Use format.class=io.confluent.connect.azure.storage.format.parquet.ParquetFormat to write the Azure Data Lake Storage Gen1 object as a Parquet file columnar storage format. The connector’s parquet.codec configuration property specifies the Parquet compression code, and values can be snappy (the default) to use Google’s Snappy compression library, none for no compression, gzip to use GNU’s GZip compression library, lzo to use LZO (Lempel–Ziv–Oberhumer) compression library, brotli to use Google’s Brotli compression library, lz4 to use BSD licensed LZ4 compression library and zstd to use Facebook’s ZStandard compression library.
Raw Bytes: Use format.class=io.confluent.connect.azure.storage.format.bytearray.ByteArrayFormat to write the raw serialized record values delimited with the JDK’s line separator to the Azure Data Lake Storage Gen1 object. This requires using the value.converter=org.apache.kafka.connect.converters.ByteArrayConverter with the connector. Use a different delimiter by specifying the connector format.bytearray.separator configuration property.

You can also choose to use a custom partitioner by implementing the io.confluent.connect.storage.format.Format interface, packaging your implementation into a JAR file, and then:

Place the JAR file into the share/java/kafka-connect-azure-data-lake-gen1-storage directory of your Confluent Platform installation on each worker node.
Restart all of the Connect worker nodes.
Configure Azure Data Lake Storage Gen1 Sink connectors with format.class set to the fully-qualified class name of your format implementation.

Azure Data Lake Storage Gen1 object uploads¶

As the Azure Data Lake Storage Gen1 Sink connector processes each record, it uses the partitioner to determine which encoded partition to write the record. This continues for each partition until the connector determines that a partition has enough records and should be flushed and uploaded to the Azure Data Lake Storage Gen1 bucket using the Azure Data Lake Storage Gen1 object name for that partition. This technique of knowing when to flush a partition file and upload it to Azure Data Lake Storage is called the rotation strategy, and there are a number of ways to control this behavior.

Maximum number of records: The connector’s flush.size configuration property specifies the maximum number of records that should be written to a single Azure Data Lake Storage Gen1 object. There is no default for this setting.

Important

Rotation strategy logic: In the following rotation strategies, the logic to flush files to storage is triggered when a new record arrives, after the defined interval or scheduled interval time. Flushing files is also triggered periodically by the offset.flush.interval.ms setting defined in the Connect worker configuration. The offset.flush.interval.ms setting defaults to 60000 ms (60 seconds). If you enable the properties rotate.interval.ms or rotate.schedule.interval.ms and ingestion rate is low, you should set offset.flush.interval.ms to a smaller value so that records flush at the rotation interval (or close to the interval) . Leaving the offset.flush.interval.ms set to the default 60 seconds may cause records to stay in an open file for longer than expected, if no new records get processed that trigger rotation.
Maximum span of record time: In this rotation strategy, the connector’s rotate.interval.ms property specifies the maximum timespan in milliseconds a file can remain open and ready for additional records. The timestamp for each file starts with the record timestamp of the first record written to the file, as determined by the partitioner’s timestamp.extractor. As long as the next record’s timestamp fits within the timespan specified by the rotate.interval.ms property, the record is written to the file. If a record’s timestamp does not fit within the timespan of rotate.interval.ms, the connector flushes the file, uploads it to Azure Data Lake Storage Gen1, and commits the offsets of the records in that file. After this, the connector creates a new file with a timespan that starts with the first record, and writes the first record to the file.

Scheduled rotation: In this rotation strategy, the connector’s rotate.schedule.interval.ms specifies the maximum timespan in milliseconds a file can remain open and ready for additional records. Unlike rotate.interval.ms, with scheduled rotation the timestamp for each file starts with the system time that the first record is written to the file. You must have the partitioner parameter timezone configured (defaults to an empty string) when using this configuration property, otherwise the connector fails with an exception.

As long as a record is processed within the timespan specified by rotate.schedule.interval.ms, the record will be written to the file. As soon as a new record is processed after the timespan for the current file, the file is flushed, uploaded to Azure Data Lake Storage Gen1, and the offset of the records in the file are committed. A new file is created with a timespan that starts with the current system time, and the new record is written to the file. This configuration is useful when you have to commit your data based on current server time, for example at the beginning of every hour. The default value -1 means that this feature is disabled.

Scheduled rotation uses rotate.schedule.interval.ms to close the file and upload to Azure Data Lake Storage Gen1 on a regular basis using the current time, rather than the record time. Even if the connector has no more records to process, Connect will still call the connector at least every offset.flush.interval.ms, as defined in the Connect worker’s configuration file. And every time this occurs, the connector uses the current time to determine if the currently opened file should be closed and uploaded to Azure Data Lake Storage Gen1.

These strategies can be combined as needed. However, when using either of the two rotation strategies described above, the connector only closes and uploads a file to Azure Data Lake Storage Gen1 when the next file does not belong based upon the timestamp. In other words, if the connector has no more records to process, the connector may keep the file open for a significant period of time, until the connector can process another record.

Note

Not all rotation strategies are compatible with the Azure Data Lake Storage Gen1 Sink connector’s ability to deliver Azure Data Lake Storage Gen1 objects exactly once with eventual consistency. See the Exactly-once delivery section for details.

Quick Start¶

In this quick start, the Azure Data Lake Storage Gen1 Sink connector is used to export data produced by the Avro console producer to Azure Data Lake Storage Gen1.

Important

Before you begin: Create an Azure Data Lake Storage Gen1 account and grant write access to the user completing these procedures. See Get started with Azure Data Lake Storage Gen1 using the Azure portal for additional information. Also see Service-to-service authentication with Azure Data Lake Storage Gen1 using Azure Active Directory for information on setting up the account needed for the Azure Data Lake Storage Gen1 Sink connector.

For an example of how to get Kafka Connect connected to Confluent Cloud, see Connect Self-Managed Kafka Connect to Confluent Cloud.

Install the connector through the Confluent Hub Client.

# run from your Confluent Platform installation directory
confluent connect plugin install confluentinc/kafka-connect-azure-data-lake-gen1-storage:latest

Tip

By default, the connector will install the plugin into the share/confluent-hub-components directory and add the directory to the plugin path. For the plugin path change to take effect, you must restart the Connect worker. For details for setting up and using the Azure CLI, see Azure Data Lake Gen1 CLI.

Start the services using the Confluent CLI.

confluent local services start

Every service start in order, printing a message with its status.

Starting Zookeeper
Zookeeper is [UP]
Starting Kafka
Kafka is [UP]
Starting Schema Registry
Schema Registry is [UP]
Starting Kafka REST
Kafka REST is [UP]
Starting Connect
Connect is [UP]
Starting KSQL Server
KSQL Server is [UP]
Starting Control Center
Control Center is [UP]

Note

Make sure the Azure Data Lake Storage Gen1 Sink connector has write access to the Azure Data Lake Storage Gen1 account shown in azure.datalake.account.name and can deploy credentials successfully.

To import a few records with a simple schema in Kafka, start the Avro console producer as follows:

  ./bin/kafka-avro-console-producer --broker-list localhost:9092 --topic datalake_topic \
--property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'

Then, in the console producer, enter the following:

{"f1": "value1"}
{"f1": "value2"}
{"f1": "value3"}
{"f1": "value4"}
{"f1": "value5"}
{"f1": "value6"}
{"f1": "value7"}
{"f1": "value8"}
{"f1": "value9"}

The nine records entered are published to the Kafka topic datalake_topic in Avro format.

Create a datalake.properties file with the following contents:

name=datalake-sink
connector.class=io.confluent.connect.azure.datalake.gen1.AzureDataLakeGen1StorageSinkConnector
tasks.max=1
topics=datalake_topic
flush.size=3
azure.datalake.client.id=<your client id>
azure.datalake.client.key=<your client key>
azure.datalake.account.name=<your account name>
azure.datalake.token.endpoint=<your azure oauth2 token endpoint>
format.class=io.confluent.connect.azure.storage.format.avro.AvroFormat
confluent.topic.bootstrap.servers=localhost:9092
confluent.topic.replication.factor=1

Before starting the connector, make sure that the configurations in datalake.properties are properly set to your configurations of Azure Data Lake Storage Gen1. For this example, make sure that azure.datalake.account.name points to your Data Lake store, azure.datalake.client.id is set to your user id, and azure.datalake.client.key is set to your user’s secret key. The user ID or client ID should have permission to write to the Azure Data Lake Storage Gen1 Account. Finally, set azure.datalake.token.endpoint to the Oauth 2 endpoint as described here, and use the v1 token endpoint. Then start the Azure Data Lake Storage Gen1 Sink connector by loading its configuration with the following command.

Caution

You must include a double dash (--) between the topic name and your flag. For more information, see this post.

confluent local services connect connector load datalake-sink --config datalake.properties
{
 "name": "datalake-sink",
 "config": {
     "name":"datalake-sink",
     "connector.class":"io.confluent.connect.azure.datalake.gen1.AzureDataLakeGen1StorageSinkConnector",
     "tasks.max":"1",
     "topics":"datalake_topic",
     "flush.size":"3",
     "azure.datalake.client.id":"<your client id>",
     "azure.datalake.client.key":"<your client key>",
     "azure.datalake.account.name":"<your account name>",
     "azure.datalake.token.endpoint":"<your azure oauth2 token endpoint>",
     "format.class":"io.confluent.connect.azure.storage.format.avro.AvroFormat",
     "confluent.topic.bootstrap.servers":"localhost:9092",
     "confluent.topic.replication.factor":"1"
 },
  "tasks": []
}

Check that the connector started successfully. Review the Connect worker’s log by entering the following:

confluent local services connect log

Towards the end of the log you should see that the connector starts, logs a few messages, and then uploads data from Kafka to Azure Data Lake Storage Gen1.

Once the connector has ingested some records, check that the data is available in Azure Data Lake Storage Gen1. Use the following Azure CLI command:

az dls fs list --account <your account name> --path /topics

Once you navigate into the subfolders, you should see three objects with keys.

topics/datalake_topic/partition=0/datalake_topic+0+0000000000.avro
topics/datalake_topic/partition=0/datalake_topic+0+0000000003.avro
topics/datalake_topic/partition=0/datalake_topic+0+0000000006.avro

Each file is encoded as <topic>+<kafkaPartition>+<startOffset>.<format>.

To verify the contents, copy each file from Azure Data Lake Storage Gen1 to your local filesystem. Use the following Azure CLI command changing the destination to what makes sense for you:

az dls fs download --account <your account name> --source-path /topics/datalake_topic/partition=0/datalake_topic+0+0000000000.avro --destination-path "C:\connect\datalake_topic+0+0000000000.avro"

Use avro-tools-1.9.0.jar (available in Apache mirrors) to print the records.

java -jar avro-tools-1.8.2.jar tojson datalake_topic+0+0000000000.avro

For the file above, you should see the following output:

{"f1":"value1"}
{"f1":"value2"}
{"f1":"value3"}

The rest of the records are contained in the other two files.

Finally, stop the Connect worker and all other Confluent services by running:

confluent local services stop

Your output should resemble:

Stopping Control Center
Control Center is [DOWN]
Stopping KSQL Server
KSQL Server is [DOWN]
Stopping Connect
Connect is [DOWN]
Stopping Kafka REST
Kafka REST is [DOWN]
Stopping Schema Registry
Schema Registry is [DOWN]
Stopping Kafka
Kafka is [DOWN]
Stopping Zookeeper
Zookeeper is [DOWN]

You can stop all services and remove any data generated during this quick start by entering the following command:

confluent local destroy

Your output should resemble:

Stopping Control Center
Control Center is [DOWN]
Stopping KSQL Server
KSQL Server is [DOWN]
Stopping Connect
Connect is [DOWN]
Stopping Kafka REST
Kafka REST is [DOWN]
Stopping Schema Registry
Schema Registry is [DOWN]
Stopping Kafka
Kafka is [DOWN]
Stopping Zookeeper
Zookeeper is [DOWN]
Deleting: /var/folders/ty/rqbqmjv54rg_v10ykmrgd1_80000gp/T/confluent.PkQpsKfE

Exactly-once delivery¶

The Azure Data Lake Storage Gen1 Sink connector is able to provide exactly-once semantics to consumers of the objects it exports to Azure Data Lake Storage Gen1, if the connector is supplied with a deterministic partitioner.

Currently, out of the available partitioners, the default and field partitioners are always deterministic. TimeBasedPartitioner can be deterministic with some configurations as discussed below. This implies that when any of these partitioners is used, file splitting always happens at the same offsets for a given set of Kafka records. These partitioners take into account flush.size and schema.compatibility to decide when to roll and save a new file to Azure Data Lake Storage Gen1. The connector always delivers files in Azure Data Lake Storage Gen1 that contain the same records, even under the presence of failures. If a connector task fails before an upload completes, the file will be still in the temp/ folder . If, on the other hand, a failure occurs after the upload has completed, but before the corresponding offset is committed to Kafka by the connector, then a re-upload will take place. However, this type of re-upload is transparent to the user of the Azure Data Lake Storage Gen1 folder, who at any time will have access to the same records made eventually available by successful uploads to Azure Data Lake Storage Gen1.

To guarantee exactly-once semantics with the TimeBasedPartitioner, the connector must be configured to use a deterministic implementation of TimestampExtractor and a deterministic rotation strategy. The deterministic timestamp extractors are Kafka records (timestamp.extractor=Record) or record fields (timestamp.extractor=RecordField). The deterministic rotation strategy configuration is rotate.interval.ms (setting rotate.schedule.interval.ms is nondeterministic and will invalidate exactly-once guarantees).

Schema Evolution¶

The Azure Data Lake Storage Gen1 Sink connector supports schema evolution and reacts to schema changes of data according to the schema.compatibility configuration. This section explains how the connector reacts to schema evolution under different values of schema.compatibility. The schema.compatibility can be set to NONE, BACKWARD, FORWARD and FULL, which means NO compatibility, BACKWARD compatibility, FORWARD compatibility and FULL compatibility respectively.

NO Compatibility: By default, the schema.compatibility is set to NONE. In this case, the connector ensures that each file written to Azure Data Lake Storage Gen1 has the proper schema. When the connector observes a schema change in data, it commits the current set of files for the affected topic partitions and writes the data with new schema in new files.
BACKWARD Compatibility: If a schema is evolved in a backward compatible way, the connector can always use the latest schema to query all the data uniformly. For example, removing fields is a backward compatible change to a schema, since when the connector encounters records written with the old schema that contain these fields, the connector can just ignore them. Adding a field with a default value is also backward compatible.

If BACKWARD is specified in the schema.compatibility, the connector keeps track of the latest schema used in writing data to Azure Data Lake Storage Gen1. If a data record with a schema version larger than current latest schema arrives, the connector commits the current set of files and writes the data record with new schema to new files. For data records arriving at a later time that use an earlier schema, the connector projects the data record to the latest schema before writing to the same set of files in Azure Data Lake Storage Gen1.
FORWARD Compatibility: If a schema is evolved in a forward compatible way, the connector can always use the oldest schema to query all the data uniformly. Removing a field that had a default value is forward compatible, since the old schema will use the default value when the field is missing.

If FORWARD is specified in the schema.compatibility, the connector projects the data to the oldest schema before writing to the same set of files in Azure Data Lake Storage Gen1.
Full Compatibility: Full compatibility means that old data can be read with the new schema and new data can also be read with the old schema.

If FULL is specified in the schema.compatibility, the connector performs the same action as BACKWARD.

Schema evolution in the Azure Data Lake Storage Gen1 Sink connector works the same way as Schema Evolution.

Write JSON message values into Azure Data Lake Storage Gen1¶

The example settings file is shown below:

name=datalake-sink
connector.class=io.confluent.connect.azure.datalake.gen1.AzureDataLakeGen1StorageSinkConnector
tasks.max=1
topics=datalake_topic
flush.size=100

# Required configuration
azure.datalake.client.id=<your client id>
azure.datalake.client.key=<your client key>

# The following define the information used to validate the license stored in Kafka
confluent.license=
confluent.topic.bootstrap.servers=localhost:9092

The first few settings are common to most connectors. topics specifies the topics to export data from, in this case datalake_topic. The property flush.size specifies the number of records per partition the connector needs to write to before completing a multiblock upload to Azure Data Lake Storage Gen1.

The azure.datalake.client.id and azure.datalake.client.key are your required Azure credentials. This is a licensed Confluent connector. Enter the following for testing purposes. For more information, see Azure Data Lake Storage Gen1 Licensing.

azure.datalake.account.name=<your account name>
azure.datalake.token.endpoint=<your azure oauth2 token endpoint>

The next settings are specific to Azure Data Lake Storage Gen1. A mandatory setting is the name of your Azure Data Lake Gen1 store/account azure.datalake.account.name to host the exported Kafka records. Another mandatory configuration setting is azure.datalake.token.endpoint. The connector authenticates access to your data lake using this URL.

format.class=io.confluent.connect.azure.storage.format.json.JsonFormat
partitioner.class=io.confluent.connect.storage.partitioner.DefaultPartitioner

These class settings are required to specify the output file format, which is currently io.confluent.connect.azure.storage.format.avro.AvroFormat, io.confluent.connect.azure.storage.format.json.JsonFormat or io.confluent.connect.azure.storage.format.bytearray.ByteArrayFormat, and the partitioner class

schema.compatibility=NONE

Finally, schema evolution is disabled in this example by setting schema.compatibility to NONE, as explained above.

For detailed descriptions for all the available configuration options of the Azure Data Lake Storage Gen1 Sink connector go to Configuration Reference for Azure Data Lake Storage Gen1 Sink Connector for Confluent Platform.

Write raw message values into Azure Data Lake Storage Gen1¶

It is possible to use the Azure Data Lake Storage Gen1 Sink connector to write out the unmodified original message values into newline-separated files in Azure Data Lake Storage Gen1. To accomplish this configure Kafka Connect so it does not deserialize any of the messages, and configure the Azure Data Lake Storage Gen1 Sink connector to store the message values in a binary format in Azure Data Lake Storage Gen1.

The first part of the Azure Data Lake Storage Gen1 Sink connector configuration is similar to other examples.

name=datalake-raw-sink
connector.class=io.confluent.connect.azure.datalake.gen1.AzureDataLakeGen1StorageSinkConnector
tasks.max=1
topics=datalake_topic
flush.size=3

The topics setting specifies the topics you want to export data from, which is datalake_topic in the example. The property flush.size specifies the number of records per partition the connector needs to write before completing an upload to Azure Data Lake Storage Gen1.

Next, configure container name, block size, and compression type.

azure.datalake.account.name=myconfluentdatalake
azure.datalake.token.endpoint=https://login.microsoftonline.com/a7d99622-a589-4520-8ce3-c280ed1cb00c/oauth2/token
azure.datalake.client.id=21aaeb79-1956-486a-bc36-baa1f710d567
azure.datalake.client.key=HGw@4@DSkjBRslXA4vuR:-lxQ4H3+PTs
az.compression.type=gzip

The next settings are specific to Azure Data Lake Storage Gen1. A mandatory setting is the account name of your Gen1 Azure Data Lake, azure.datalake.account.name which will host the exported Kafka records. Another mandatory configuration setting is azure.datalake.token.endpoint. The connector authenticates access to your data lake using this URL. The azure.datalake.client.id and azure.datalake.client.key are your required Azure client credentials.

The az.compression.type specifies that the Azure Data Lake Storage Gen1 Sink connector should compress all Azure Data Lake Storage Gen1 files using GZIP compression, adding the .gz extension to any files (see below).

This example configuration is typical of most Azure Data Lake Storage Gen1 Sink connectors. Now, configure the connector to read the raw message values and write them in binary format:

value.converter=org.apache.kafka.connect.converters.ByteArrayConverter
format.class=io.confluent.connect.azure.storage.format.bytearray.ByteArrayFormat
schema.compatibility=NONE

The value.converter setting overrides the connector default in the Connect worker configuration. ByteArrayConverter is used to instruct Connect to skip deserializing the message values and provide the message values in their raw binary form. The format.class setting is used to instruct the Azure Data Lake Storage Gen1 Sink connector to write these binary message values as-is into Azure Data Lake Storage Gen1 files. By default the messages written to the same Azure Data Lake Storage Gen1 file are separated by a newline character sequence, but you can control this with the format.bytearray.separator setting. You may want to consider setting this if your messages might contain newlines. Also, by default the files written to Azure Data Lake Storage Gen1 have an extension of .bin (before compression, if enabled), or you can use the format.bytearray.extension setting to change the pre-compression filename extension.

Next, you need to decide how you want to partition the consumed messages in Azure Data Lake Storage Gen1 files. You have a few options, including the default partitioner that preserves the same partitions as in Kafka:

partitioner.class=io.confluent.connect.storage.partitioner.DefaultPartitioner

Or, you could partition using the timestamp of the Kafka messages.

partitioner.class=io.confluent.connect.storage.partitioner.TimeBasedPartitioner
timestamp.extractor=Record

Or, you can use the timestamp that the Azure Data Lake Storage Gen1 Sink connector processes each message.

partitioner.class=io.confluent.connect.storage.partitioner.TimeBasedPartitioner
timestamp.extractor=Wallclock

Custom partitioners are always an option, too. Just be aware that since the record value is an opaque binary value, Connect cannot extract timestamps from fields using the RecordField option.

The Azure Data Lake Storage Gen1 Sink connector configuration outlined above results in newline-delimited gzipped objects in Azure Data Lake Storage Gen1 with .bin.gz.