Azure Blob Storage Sink Connector for Confluent Platform

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

The Azure Blob Storage Sink connector periodically polls data from Kafka and then uploads the data to Azure Blob Storage. A partitioner is used to split the data of every Kafka partition into chunks. Each chunk of data is represented as an Azure Blob Storage object. 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 Blob Storage and by schema compatibility.

Features

The Azure Blob Storage Sink connector for Confluent Platform includes the following features:

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

Exactly-once delivery

The Azure Blob Storage connector is able to provide exactly-once semantics to consumers of the objects it exports to Azure Blob Storage, under the condition that 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. This implies that when any of these partitioners is used, splitting of files 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 Blob Storage. The connector always delivers files in Azure Blob Storage that contain the same records, even under the presence of failures. If a connector task fails before an upload completes, the file does not become visible to Azure Blob Storage. 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, such a re-upload is transparent to the user of the Azure Blob Storage bucket, who at any time will have access to the same records made eventually available by successful uploads to Azure Blob Storage.

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).

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 Blob Storage Sink connector supports running one or more tasks. You can specify the number of tasks in the tasks.max configuration parameter. This can lead to performance gains when multiple files need to be parsed.

Pluggable data format with or without schema

Out of the box, the connector supports writing data to Azure Blob Storage 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.

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.

Schema evolution

The Azure Blob Storage connector supports schema evolution and reacts to schema changes of data according to the schema.compatibility configuration. This section describes 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 Blob Storage 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, you 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 you encounter records written with the old schema that contain these fields, you 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 Blob Storage, and 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 with schema of an earlier version, the connector projects the data record to the latest schema before writing to the same set of files in Azure Blob Storage.

• FORWARD Compatibility: If a schema is evolved in a forward compatible way, you 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 Blob Storage.

• 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 Blob Storage connector works in the same way as in the HDFS connector and Amazon S3 Sink connector for Confluent Platform.

Limitations

• The connector currently does not write keys or headers to storage.
• To use TLSv1.2 encryption (with the KAFKA_OPTS environment variable) for the Azure Blob Storage Sink connector in Confluent Platform, an environment of JDK-11 or later is recommended. Some earlier patches of JDK-8 (for example, 1.8.0-202) contain bugs and won’t be supported with TLSv1.2 encryption. Confluent tests have verified that TLSv1.2 encryption can be used with later patch versions of JDK-8.

• The connector does not currently support Single Message Transformations (SMTs) that modify the topic name. Additionally, the following transformations are not allowed:
  • io.debezium.transforms.ByLogicalTableRouter
  • io.debezium.transforms.outbox.EventRouter
  • org.apache.kafka.connect.transforms.RegexRouter
  • org.apache.kafka.connect.transforms.TimestampRouter
  • io.confluent.connect.transforms.MessageTimestampRouter
  • io.confluent.connect.transforms.ExtractTopic$Key
  • io.confluent.connect.transforms.ExtractTopic$Value

Install the Azure Blob Storage 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.

Install the connector using the Confluent CLI

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-blob-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-blob-storage:1.6.13

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 and 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 Blob Storage Sink Connector for Confluent Platform.

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

Mapping records to Azure Blob Storage objects

The Azure Blob Storage 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 Blob Storage bucket. It uses Azure Blob Storage object paths that include the Kafka topic and partition, the computed partition, and the filename. The Azure Blob Storage connector offers several ways to customize this behavior, including:

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

Azure Blob Storage object names

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

In general, the names of the Azure Blob Storage object uploaded by the Azure Blob Storage 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 Blob Storage objects that don’t clash with existing Azure Blob Storage objects in the same bucket.
• <topic> corresponds to the name of the Kafka topic from which the records in this Azure Blob Storage object were read.
• <encodedPartition> is generated by the Azure Blob Storage connector’s partitioner (see Partitioning records into Azure Blob Storage objects).
• <kafkaPartition> is the Kafka partition number from which the records in this Azure Blob Storage object were read.
• <startOffset> is the Kafka offset of the first record written to this Azure Blob Storage object.
• <format> is the extension identifying the format in which the records are serialized in this Azure Blob Storage object.

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

Partitioning records into Azure Blob Storage objects

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

The partitioner is specified in the connector configuration with the partitioner.class configuration property. The Azure Blob Storage 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 Blob Storage objects with names that include the Kafka topic and Kafka partitions. The <encodedPartition> is always <topicName>/partition=<kafkaPartition>, resulting in Azure Blob Storage 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 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 Blob Storage 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 Blob Storage object name. For example, when path.format='year'=YYYY/'month'=MM/'day'=dd/'hour'=HH, Azure Blob Storage 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 Blob Storage objects within a single encoded partition directory. For example, setting partition.duration.ms=600000 (10 minutes) will result in each Azure Blob Storage 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 time zones 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 time zones 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 Blob Storage object in each daily directory). This partitioner always results in Azure Blob Storage 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 time zones 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 time zones 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 Blob Storage object in each hourly directory). This partitioner always results in Azure Blob Storage 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 time zones 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 time zones 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 in Azure Blob Storage object uploads, the choice of timestamp.extractor affects whether the Azure Blob Storage 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:

1. Place the JAR file into the share/java/kafka-connect-azure_blob_storage directory of your Confluent Platform installation on each worker node.
2. Restart all of the Connect worker nodes.
3. Configure Azure Blob Storage connectors to use your fully-qualified partitioner class name.

Azure Blob Storage object formats

The Azure Blob Storage connector can serialize multiple records into each Azure Blob Storage 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 Blob Storage connector comes with several implementations:

• Avro: Use format.class=io.confluent.connect.azure.blob.format.avro.AvroFormat to write the Azure Blob Storage 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.blob.format.json.JsonFormat to write the Azure Blob Storage object as a single JSON array containing a JSON object for each record. 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.blob.format.parquet.ParquetFormat to write the Azure Blob Storage 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.

  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.

• Raw Bytes: Use format.class=io.confluent.connect.azure.blob.format.bytearray.ByteArrayFormat to write the raw serialized record values delimited with the JDK’s line separator to the Azure Blob Storage 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. The connector’s az.compression.type configuration property can be set to none (the default) for no compression or gzip for GZip compression.

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:

1. Place the JAR file into the share/java/kafka-connect-azure_blob_storage directory of your Confluent Platform installation on each worker node.
2. Restart all of the Connect worker nodes. #. Configure Azure Blob Storage connectors with format.class set to the fully-qualified class name of your format implementation.

Azure Blob Storage object uploads

As the Azure Blob Storage 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 Blob Storage bucket using the Azure Blob Storage object name for that partition. This technique of knowing when to flush a partition file and upload it to Azure Blob 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 Blob Storage 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 Blob Storage, 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 Blob Storage, 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 Blob Storage 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 Blob Storage.

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 Blob Storage 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 that Not all rotation strategies are compatible with the Azure Blob Storage connector’s ability to deliver Azure Blob Storage objects exactly-once with eventual consistency. For more details, see the Exactly Once section.

The Azure Blob Storage object uploaded by the connector can be quite large. The connector supports using a multi-part upload mechanism. The azure_blob_storage.part.size configuration property defaults to 26214400 bytes (25MB) and specifies the maximum size of each Azure Blob Storage object part used to upload a single Azure Blob Storage object.

Additionally, the schema.compatibility setting (see Schema Evolution) will also affect when one file is closed and uploaded to Azure Blob Storage. If a record cannot be written to one file because its schema has changed relative to the records already in the file, the connector rotates by closing the file, uploading it to Azure Blob Storage, committing offsets for the records in the file, creating a new file, and writing the new record.

Automatic retries

The Azure Blob Storage connector may experience intermittent problems writing to the Azure Blob Storage container. This is because of network partitioning, interruptions, or throttling limits. In many cases, the connector retries the request a number of times before failing. To prevent overloading the network or Azure Blob Storage service, the connector uses an exponential backoff technique to give the network and service time to recover. The retries occur on requests which return HTTP status codes of greater than 500 excluding 501 and 505.

The maximum number of retry attempts is dictated by the azblob.retry.retries Azure Blob Storage connector configuration property. This property defaults to three retry attempts (one initial attempt and three retry attempts). The delay for retries is dependent upon the connector’s azblob.retry.backoff.ms configuration property, which defaults to 4000 milliseconds. The maximum delay between retries is capped by azblob.retry.max.backoff.ms, which defaults to 120000 milliseconds. The actual delay is calculated by taking the lower value between ${azblob.retry.backoff.ms} * (-1 + 2 ^ (attempt-1)) and azblob.retry.max.backoff.ms, where attempt is the number of attempts taken so far in the current iteration. For example, the following table shows the possible wait times before submitting each of the three retries:

Range of backoff times for each retry using the default configuration

Attempt	Calculated Backoff (sec)	Actual Backoff (sec)
1	0.0	0.0
2	4.0	4.0
3	12.0	12.0
4	28.0	28.0

The maximum backoff time eventually reaches saturation and is capped at azblob.retry.max.backoff.ms. In the following example, all attempts starting with attempt six will have the maximum backoff time of 120 seconds:

Range of backoff times when reaching the default cap of two minutes

Attempt	Calculated Backoff (sec)	Actual Backoff (sec)
4	28.0	28.0
5	60.0	60.0
6	124.0	120.0
7	252.0	120.0
8	508.0	120.0
9	1020.0	120.0
10	2044.0	120.0

In the case of a secondary host being provided the behaviour is a little different. After a failed attempt to your normal store.url it will then delay by (.1 second * random(0.8, 1.2)) before calling the azblob.retry.secondary.host.

It’s not advised to set azblob.retry.max.backoff.ms too high since making more attempts after reaching 24 hours isn’t practical. You can adjust both the azblob.retry.retries, azblob.retry.backoff.ms, azblob.retry.max.backoff.ms and azblob.retry.secondary.host connector configuration properties to achieve the desired retry and backoff characteristics.

Also the timeout of a single HTTP request can be configured by editing azblob.connection.timeout.ms.

Note

When transferring large amounts of data, the default TryTimeout is probably not sufficient. You should override this value based on the bandwidth available to the host machine and proximity to the Storage service. A good starting point may be something like (60 seconds per MB of anticipated payload size).

Write JSON message values into Azure Blob Storage

An example settings file is shown in the following example:

name=blob-sink
connector.class=io.confluent.connect.azure.blob.AzureBlobStorageSinkConnector
tasks.max=1
topics=blob_topic
flush.size=100

# Required configuration
azblob.account.name=account
azblob.account.key=accountkey

# 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 blob_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 Blob Storage.

The azblob.account.name and azblob.account.key are your required Azure credentials. To create a storage account, see Create a storage account. For information about license properties, see Confluent Platform license.

azblob.container.name=confluent-kafka-connect-azure-blob-storage-testing
azblob.block.size=5242880

The next settings are specific to Azure Blob Storage. A mandatory setting is the name of your Azure Blob Storage container to host the exported Kafka records. Another useful configuration setting is azblob.block.size. This setting controls the size of each block in the multiblock uploads used to upload a single chunk of Kafka records.

storage.class=io.confluent.connect.azure.blob.storage.AzureBlobStorage
format.class=io.confluent.connect.azure.blob.format.json.JsonFormat
schema.generator.class=io.confluent.connect.storage.hive.schema.DefaultSchemaGenerator
partitioner.class=io.confluent.connect.storage.partitioner.DefaultPartitioner

These class settings are required to specify the Azure Block Storage interface, the output file format, which is currently io.confluent.connect.azure.blob.format.avro.AvroFormat, io.confluent.connect.azure.blob.format.json.JsonFormat or io.confluent.connect.azure.blob.format.bytearray.ByteArrayFormat, and the partitioner class along with its schema generator class. When using a format with no schema definition, it is sufficient to set the schema generator class to its default value.

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 Blob Storage connector go to Configuration Reference for Azure Blob Storage Sink Connector for Confluent Platform.

Maximum file size exception

If a file is created that exceeds the maximum of 50000 blocks, an exception occurs with the following error:

org.apache.kafka.connect.errors.ConnectException: Due to the Azure
constraints, you can only have a block blob of up to a maximum of 50000
blocks and you've exceeded that limit. To resolve the issue please either
decrease your flush.size to make the files smaller or increase your
azblob.block.size.

This error indicates that the file you are trying to create is greater than 50000 multiplied by your azblob.block.size value. To resolve this issue either decrease the flush.size property, or increase the azblob.block.size property. To learn more about Azure Blob Storage’s constraints, see Scalability and performance targets for standard storage accounts.

Write raw message values into Azure Blob Storage

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

The first part of the Azure Blob Storage connector configuration is similar to other examples.

name=blob-raw-sink
connector.class=io.confluent.connect.azure.blob.AzureBlobStorageSinkConnector
tasks.max=1
topics=blob_topic
flush.size=3

The topics setting specifies the topics you want to export data from, which is blob_topic in the example. The property flush.size specifies the number of records per partition the connector needs to write before completing a multipart upload to Azure Blob Storage.

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

azblob.container.name=confluent-kafka-connect-azure-blob-storage-testing
azblob.block.size=5242880
az.compression.type=gzip

The azblob.container.name is mandatory and names your Azure Blob Storage container where the exported Kafka records should be written. And since the Azure Blob Storage connector uses multiblock uploads, you can use the azblob.block.size to control the size of each of these continuous parts used to upload Kafka records into a single Azure Blob Storage object. The part size affects throughput and latency, as an Azure Blob Storage object is visible/available only after all parts are uploaded. The az.compression.type specifies that the Azure Blob Storage connector should compress all Azure Blob Storage objects using GZIP compression, adding the .gz extension to any files (see below).

So far this example configuration is relatively typical of most Azure Blob Storage 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.blob.format.bytearray.ByteArrayFormat
storage.class=io.confluent.connect.azure.blob.storage.AzureBlobStorage
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 instead give the connector the message values in their raw binary form. The format.class setting is used to instruct the Azure Blob Storage connector to write these binary message values as-is into Azure Blob Storage objects. By default the message values written to the same Azure Blob Storage object are separated by a newline character sequence, but you can control this with the format.bytearray.separator setting, and you may want to consider this if your messages might contain newlines. Also, by default the files written to Azure Blob Storage 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 Blob Storage objects. 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 Blob Storage 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 Blob Storage connector configuration outlined above results in newline-delimited gzipped objects in Azure Blob Storage with .bin.gz.

Quick start

In this quick start, the Azure Blob Storage connector is used to export data produced by the Avro console producer to Azure Blob Storage. Before you begin, create an Azure Blob Storage destination container and grant write access to the user or IAM role completing these procedures. For additional information, see Create a block blob storage account. Also, if you’re using Shared Key access instead of account keys, see Configure Azure Storage connection strings for additional information. By default, all resources in Azure Storage are secured and only available to the account owner.

To get started, complete the following steps:

1. Install the connector by running the following command from your Confluent Platform installation directory:

   confluent connect plugin install confluentinc/kafka-connect-azure-blob-storage:latest
   

   Note that by default, the connector will install the plugin into the share/confluent-hub-components directory and add the directory to the plugin path. For details for setting up and using the CLI, see Azure Blob Storage CLI.

2. Start the services using the confluent local.

   confluent local services start
   

   Every service starts 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

   Ensure the Azure Blob Storage connector has write access to the Azure Blob Storage container shown in azblob.container.name and can deploy credentials successfully.

3. 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 blob_topic \
   --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'
   

4. 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 blob_topic in Avro format.

5. Create a blob.properties file with the following contents:

   name=blob-sink
   connector.class=io.confluent.connect.azure.blob.AzureBlobStorageSinkConnector
   tasks.max=1
   topics=blob_topic
   flush.size=3
   azblob.account.name=your-account
   azblob.account.key=your-key
   azblob.container.name=confluent-kafka-connect-azure-blob-storage-testing
   format.class=io.confluent.connect.azure.blob.format.avro.AvroFormat
   confluent.topic.bootstrap.servers=localhost:9092
   confluent.topic.replication.factor=1
   

6. Start the Azure Blob Storage Sink connector by loading its configuration with the following command:

   Important

   • Before starting the connector, ensure the configurations in blob.properties are properly set to your configurations of Azure Blob Storage. For this example, ensure azblob.container.name points to your container, azblob.account.name is set to your account, and azblob.account.key is set to your key.
   • 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 blob-sink --config blob.properties
   

   Configuration

   {
     "name": "blob-sink",
     "config": {
       "name": "blob-sink"
       "connector.class": "io.confluent.connect.azure.blob.AzureBlobStorageSinkConnector",
       "tasks.max": "1",
       "topics": "blob_topic",
       "flush.size": "3",
       "azblob.account.name": "your-account",
       "azblob.account.key": "your-key",
       "azblob.container.name": "confluent-kafka-connect-azure-blob-storage-testing",
       "format.class": "io.confluent.connect.azure.blob.format.avro.AvroFormat",
       "confluent.topic.bootstrap.servers": "localhost:9092",
       "confluent.topic.replication.factor": "1",
     },
     "tasks": []
   }
   

7. 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 Blob Storage.

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

   az storage blob list --container-name confluent-kafka-connect-azure-blob-storage-testing --output table
   

   You should see three objects with keys.

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

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

9. To verify the contents, copy each file from Azure Blob Storage to your local filesystem. Use the following Azure CLI command:

   az storage blob download --container-name confluent-kafka-connect-azure-blob-storage-testing --name topics/blob_topic/partition=0/blob_topic+0+0000000000.avro --file ~/blob_topic+0+0000000000.avro
   

10. To print the records, use avro-tools-1.8.2.jar (available in the Apache Archives):

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

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

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

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

11. Stop the Connect worker and all other Confluent services by running the following command:

    confluent local 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]
    

12. 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