Azure Blob Storage Source Connector for Confluent Platform

The Kafka Connect Azure Blob Storage Source Connector provides the capability to read data exported to Azure Blob Storage by the Kafka Connect Azure Blob Storage Sink connector and publish it back to an Kafka topic. Depending on the format and partitioner used to write the data to Azure Blob Storage, this connector can write to the destination topic using the same partitions as the original messages exported to Azure Blob Storage and maintain the same message order. The connector selects folders based on the partitioner configuration and reads each folders Azure Blob objects in alphabetical order. Each record is read based on the format selected. Configuration is designed to mirror the Kafka Connect Azure Blob Storage Sink connector and should be possible to create source connector configs with only minor changes to the original sink configuration.

Important

The recommended practice is to create topics manually in the destination Kafka cluster with the correct number of partitions before running the source connector. If the topics don’t exist, Connect relies on auto-topic creation and the number of partitions are based upon the Kafka broker defaults. If there are more partitions in the destination cluster, the extra partitions aren’t used. If there are fewer partitions in the destination cluster, the connector task throws an exception and stops when it tries to write to a Kafka partition that doesn’t exist.

Be aware of the following connector actions:

  • The connector ignores any Azure Blob object with a name that doesn’t start with the configured topics directory. This name is “/topics/” by default.
  • The connector ignores any Azure Blob object that is below the topics directory but has an extension that doesn’t match the configured format. For example, a JSON file is ignored when format.class is set for Avro files.
  • The connector stops and fails if the Azure Blob object’s name doesn’t match the expected format or is in an unexpected location.

Avoid the following configuration issues:

  • A file with the correct extension and a valid name format like <topic>+<partition>+<offset>.<extension>, placed in a folder of a different topic is read normally and written to the topic defined by its filename.
  • If a field partitioner is incorrectly configured to match the expected folder, it can break the ordering guarantees of Azure Blob Storage sink that used a deterministic sink partitioner.

Features

The Azure Blob Storage Source Connector offers a variety of features:

  • Pluggable Data Format with or without Schema: Out of the box, the connector supports reading data from Azure Blob Storage in Avro and JSON format. Besides records with schema, the connector supports importing plain JSON 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.
  • At Least Once Delivery: In the event of a task failure the connector guarantees no messages are lost, although the last few messages may be processed again.
  • Matching Source Partitioning: Messages are put back on the same Kafka partition for that topic when it was written.
  • Source Partition Ordering: If the DefaultPartitioner or a TimeBasedPartitioner is used, the connector reads records back in time order in each topic-source partition. If a FieldPartitioner is used, it isn’t possible to guarantee the order of these messages.
  • 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.

Important

All partitioners will notice new topic folders with the inbuilt task reconfiguration thread. The DefaultPartitioner detects new partition folders. The FieldPartitioner notices new folders for the fields specified. However, the TimeBasedPartititoner doesn’t detect new files for a new time period.

Important

Be careful when both the Connect Azure Blob Storage Sink connector and the Azure Blob Storage Source Connector use the same Kafka cluster, since this results in the source connector writing to the same topic being consumed by the sink connector. This causes a continuous feedback loop that creates an ever-increasing number of duplicate Kafka records and Azure Blob Storage objects. You can avoid this feedback loop by writing to a different topic than the one being consumed by the sink connector. Use the RegexRouter with the source connector to change the names of the topics where the records are written. Or, use the Extract Topic SMT with the source connector to change the topic name based upon a field in each message.

Prerequisites

The following are required to run the Kafka Connect Azure Blob Storage Source Connector:

  • Kafka Broker: Confluent Platform 3.3.0 or above, or Kafka 0.11.0 or above
  • Connect: Confluent Platform 4.0.0 or above, or Kafka 1.0.0 or above
  • Java 1.8

Install the Azure Blob Storage Source Connector

You can install this connector by using the Confluent Hub client (recommended) or you can manually download the ZIP file.

Install the connector using Confluent Hub

Prerequisite
Confluent Hub Client must be installed. This is installed by default with Confluent Enterprise.

Navigate to your Confluent Platform installation directory and run the following command to install the latest (latest) connector version. The connector must be installed on every machine where Connect will run.

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

You can install a specific version by replacing latest with a version number. For example:

confluent-hub install confluentinc/kafka-connect-azure-blob-storage-source:1.0.0-preview

Install 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, this connector is available under a Confluent enterprise license. Confluent issues enterprise license keys to subscribers, along with providing enterprise-level support for Confluent Platform and your connectors. If you are a subscriber, please contact Confluent Support at support@confluent.io for more information.

See Confluent Platform license for license properties and License topic configuration for information about the license topic.

Quick Start

The following quickstart uses the AzureBlobStorageSinkConnector to write an Avro file from the Kafka topic named blob_topic to Azure Blob Storage. Also, the AzureBlobStorageSinkConnector should be completely stopped before starting the AzureBlobStorageSourceConnector to avoid creating source/sink cycle. Then, the AzureBlobStorageSourceConnector loads that Avro file from Azure Blob Storage to the Kafka topic named copy_of_blob_topic.

  1. Follow the instructions from Connect Azure Blob Storage Sink connector to set up the data to use below.

  2. Install the connector through the Confluent Hub Client.

    # run from your Confluent Platform installation directory
    confluent-hub install confluentinc/kafka-connect-azure-blob-storage-source:latest
    

    Tip

    By default, the plugin is installed into share/confluent-hub-components and the directory is added to the plugin path. If this is the first connector you have installed, you may need to restart the connect server for the plugin path change to take effect.

Property-based example

  1. Create a quickstart-azureblobstoragesource.properties file with the following contents. This file should be placed under Confluent Platform installation directory. This configuration is used typically along with standalone workers.

    name=azure-blob-storage-source
    tasks.max=1
    connector.class=io.confluent.connect.azure.blob.storage.AzureBlobStorageSourceConnector
    
    # enter your Azure blob account, key and container name here
    azblob.account.name=<your-account>
    azblob.account.key=<your-key>
    azblob.container.name=<container-name>
    
    format.class=io.confluent.connect.azure.blob.storage.format.avro.AvroFormat
    confluent.topic.bootstrap.servers=localhost:9092
    confluent.topic.replication.factor=1
    

    Tip

    The following define the Confluent license stored in Kafka, so we need the Kafka bootstrap addresses. replication.factor may not be larger than the number of Kafka brokers in the destination cluster, so here we set this to ‘1’ for demonstration purposes. Always use at least ‘3’ in production configurations.

  2. Edit the quickstart-azureblobstoragesource.properties to add the following properties:

    transforms=AddPrefix
    transforms.AddPrefix.type=org.apache.kafka.connect.transforms.RegexRouter
    transforms.AddPrefix.regex=.*
    transforms.AddPrefix.replacement=copy_of_$0
    

    Important

    Adding this renames the output of topic of the messages to copy_of_blob_topic. This prevents a continuous feedback loop of messages.

  3. Load the Azure Blob Storage Source Connector.

    Caution

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

    confluent local load azblobstorage-source -- -d quickstart-azureblobstoragesource.properties
    

    Important

    Don’t use the Confluent CLI in production environments.

  4. Confirm that the connector is in a RUNNING state.

    confluent local status azureblobstorage-source
    
  5. Confirm that the messages are being sent to Kafka.

    kafka-avro-console-consumer \
        --bootstrap-server localhost:9092 \
        --property schema.registry.url=http://localhost:8081 \
        --topic copy_of_blob_topic \
        --from-beginning | jq '.'
    
  6. The response should be 9 records as follows.

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

REST-based example

  1. Use this setting with distributed workers. Write the following JSON to config.json, configure all of the required values, and use the following command to post the configuration to one of the distributed connect workers. Check here for more information about the Kafka Connect REST API

    {
      "name" : "AzureBlobStorageSourceConnector",
      "config" : {
        "connector.class" : "io.confluent.connect.azure.blob.storage.AzureBlobStorageSourceConnector",
        "tasks.max" : "1",
        "azblob.account.name" : "your-account",
        "azblob.account.key" : "your-key",
        "azblob.container.name" : "confluent-kafka-connect-azBlobStorage-testing",
        "format.class" : "io.confluent.connect.azure.blob.storage.format.avro.AvroFormat",
        "confluent.topic.bootstrap.servers" : "localhost:9092",
        "confluent.topic.replication.factor" : "1",
        "transforms" : "AddPrefix",
        "transforms.AddPrefix.type" : "org.apache.kafka.connect.transforms.RegexRouter",
        "transforms.AddPrefix.regex" : ".*",
        "transforms.AddPrefix.replacement" : "copy_of_$0"
      }
    }
    

    Note

    Change the confluent.topic.bootstrap.servers property to include your broker address(es), and change the confluent.topic.replication.factor to 3 for staging or production use.

  2. Use curl to post a configuration to one of the Kafka Connect Workers. Change http://localhost:8083/ to the endpoint of one of your Kafka Connect worker(s).

    curl -s -X POST -H 'Content-Type: application/json' --data @config.json http://localhost:8083/connectors
    
  3. Use the following command to update the configuration of existing connector.

    curl -s -X PUT -H 'Content-Type: application/json' --data @config.json http://localhost:8083/connectors/AzureBlobStorageSourceConnector/config
    
  4. To consume records written by connector to the configured Kafka topic, run the following command:

    kafka-avro-console-consumer --bootstrap-server localhost:9092 --property schema.registry.url=http://localhost:8081  --topic copy_of_blob_topic --from-beginning
    

Azure Blob Storage Source Connector Partitions

The Azure Blob Storage Source connector’s partitioner determines how records read from Azure Blob Storage objects are pushed into a Kafka topic.

The partitioner is specified in the connector configuration with the partitioner.class configuration property. The Azure Blob Storage Source connector comes with the following partitioner:

  • Default Partitioner: The io.confluent.connect.storage.partitioner.DefaultPartitioner reads records from each Azure Blob Storage objects with names that include the Kafka topic and push it to the same topic partitions as in Kafka. 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 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> path of the Azure Blob Storage object. 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 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 Blob Storage object in each daily directory). This partitioner always reads from 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 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 Blob Storage object in each hourly directory). This partitioner always reads from 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 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.

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 Source connectors to use your fully-qualified partitioner class name.

Azure Blob Storage Source Connector Data Formats

Azure Blob Storage source connector supports several data formats:

  • Avro Format: For supporting Avro Format. You must configure the format.class = io.confluent.connect.azure.blob.storage.format.avro.AvroFormat.
  • Parquet Format: For supporting Parquet Format. You must configure the format.class = io.confluent.connect.azure.blob.storage.format.parquet.ParquetFormat.
  • JSON Format: For supporting JSON Format. You must configure the format.class = io.confluent.connect.azure.blob.storage.format.json.JsonFormat.
  • Raw Bytes Format: For supporting Raw Bytes Format. You must configure the format.class = io.confluent.connect.azure.blob.storage.format.bytearray.ByteArrayFormat.

Troubleshooting Connector and Task Failures

Stack Trace

You can use the Connect REST API to check the status of the connectors and tasks. If a task or connector has failed, the trace field will include a reason and a stack trace.

Fewer Partitions in Destination Cluster

If there are fewer partitions in the destination cluster than in the source topic, the connector task throws an exception and immediately stops when it tries to write to a Kafka partition that does not exist. You will see the following error messages in the Connect worker log. The recommended practice is to create topics manually in the destination Kafka cluster with the correct number of partitions before running the source connector.

INFO WorkerSourceTask{id=azblob-source-0} Committing offsets
(org.apache.kafka.connect.runtime.WorkerSourceTask:409)
INFO WorkerSourceTask{id=azblob-source-0} flushing 1 outstanding messages for offset commit
(org.apache.kafka.connect.runtime.WorkerSourceTask:426)
ERROR WorkerSourceTask{id=azblob-source-0} Failed to flush, timed out while waiting
for producer to flush outstanding 1 messages (org.apache.kafka.connect.runtime.WorkerSourceTask:431)
ERROR WorkerSourceTask{id=azblob-source-0} Failed to commit offsets
(org.apache.kafka.connect.runtime.SourceTaskOffsetCommitter:114)

Additional Documentation