# Backup and Restore Azure Blob Storage Source Connector for Confluent Platform The Kafka Connect Backup and Restore 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](https://docs.confluent.io/kafka-connect-azure-blob-storage-sink/current/) and publish it back to a 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](https://docs.confluent.io/kafka-connect-azure-blob-storage-sink/current/) 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](/kafka-connectors/self-managed/userguide.html#connect-source-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 continuously checks for the presence of the required partition and does not produce records but remains in the running state. 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 `++.`, 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 Backup and Restore Azure Blob Storage Source connector includes the following features: * **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 built-in 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. - Be careful when both the [Azure Blob Storage Sink connector](https://docs.confluent.io/kafka-connect-azure-blob-storage-sink/current/) 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](/platform/current/connect/transforms/regexrouter.html) with the source connector to change the names of the topics where the records are written. Or, use the [ExtractTopic SMT](/platform/current/connect/transforms/extracttopic.html) with the source connector to change the topic name based upon a field in each message. ## Limitations For the `TimeBasedPartitioner`, the capacity to scale the connector across various time ranges is limited in Backup and Restore mode. Currently, the connector does not support processing data that spans several years. ## Prerequisites ### 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. ### Multiple tasks The Backup and Restore Azure Blob Storage Source 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. ### Matching source partitioning Messages are put back on the same Kafka partition for the 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. 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. Be careful when both the [Connect Azure Blob Storage Sink connector](https://docs.confluent.io/kafka-connect-azure-blob-storage-sink/current/) and the Backup and Restore 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. You can use either the [RegexRouter](/platform/current/connect/transforms/regexrouter.html) with the source connector to change the names of the topics where the records are written or the [ExtractTopic SMT](/platform/current/connect/transforms/extracttopic.html) with the source connector to change the topic name based upon a field in each message. ## 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](/platform/current/installation/license.html#enterprise-subscription-license) keys to subscribers, along with [enterprise-level support](https://www.confluent.io/subscription/) for Confluent Platform and your connectors. If you are a subscriber, you can contact [Confluent Support](https://support.confluent.io/) for more information. See [Confluent Platform license](../configuration.md#azure-blob-storage-source-connector-license-config) for license properties and [Confluent license properties](https://docs.confluent.io/kafka-connect-azure-blob-storage-sink/current/configuration_options.html#confluent-license-properties) for information about the license topic. ## Configuration Properties For a complete list of configuration properties for this connector, see [Configuration Reference for Azure Blob Storage Source Connector for Confluent Platform](../configuration.md#azure-blob-storage-source-configuration-options). ## Install the Backup and Restore Azure Blob Storage Source Connector You can install this connector by using the [confluent connect plugin install](https://docs.confluent.io/confluent-cli/current/command-reference/connect/plugin/confluent_connect_plugin_install.html) command, or by manually downloading the ZIP file. ### Prerequisites - You must install the connector on every machine where Connect will run. - Kafka Broker: Confluent Platform 3.3.0 or later, or Kafka 0.11.0 or later. - Connect: Confluent Platform 4.0.0 or later, or Kafka 1.0.0 or later. - Java 1.8. - 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: ```bash confluent connect plugin install confluentinc/kafka-connect-azure-blob-storage-source:latest ``` You can install a specific version by replacing `latest` with a version number as shown in the following example: ```bash confluent connect plugin install confluentinc/kafka-connect-azure-blob-storage-source:3.1.0 ``` ### Install the connector manually [Download and extract the ZIP file](https://www.confluent.io/hub/confluentinc/kafka-connect-azure-blob-storage) for your connector and then follow the manual connector installation [instructions](/kafka-connectors/self-managed/userguide.html#connect-installing-plugins). ## Quick Start The following quick start 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`. For an example of how to get Kafka Connect connected to [Confluent Cloud](/cloud/current/index.html), see [Connect Self-Managed Kafka Connect to Confluent Cloud](/cloud/current/cp-component/connect-cloud-config.html#distributed-cluster). 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](https://docs.confluent.io/current/connect/managing/confluent-hub/client.html). ```bash # run from your Confluent Platform installation directory confluent connect plugin install confluentinc/kafka-connect-azure-blob-storage-source:latest ``` ### 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](/platform/current/connect/concepts.html#standalone-workers). ```properties 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= azblob.account.key= azblob.container.name= format.class=io.confluent.connect.azure.blob.storage.format.avro.AvroFormat confluent.topic.bootstrap.servers=localhost:9092 confluent.topic.replication.factor=1 ``` 2. Edit the `quickstart-azureblobstoragesource.properties` to add the following properties: ```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 Backup and Restore Azure Blob Storage Source connector. ```bash confluent local load azblobstorage-source --config quickstart-azureblobstoragesource.properties ``` #### IMPORTANT Don’t use the [Confluent CLI](https://docs.confluent.io/confluent-cli/current/index.html) in production environments. 4. Confirm that the connector is in a `RUNNING` state. ```bash confluent local status azureblobstorage-source ``` 5. Confirm that the messages are being sent to Kafka. ```bash 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. ```bash {"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](/platform/current/connect/concepts.html#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 [Kafka Connect REST Interface](/platform/current/connect/references/restapi.html) ```json { "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). ```bash 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. ```bash 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: ```bash kafka-avro-console-consumer --bootstrap-server localhost:9092 --property schema.registry.url=http://localhost:8081 --topic copy_of_blob_topic --from-beginning ``` ## Backup and Restore Azure Blob Storage Source Connector Partitions The Backup and Restore 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 Backup and Restore 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 `` is always `/partition=`, resulting in Azure Blob Storage object names such as `//partition=/++.`. * **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 `` is always `/=`, resulting in Azure Blob Storage object names of the form `//=/++.`. * **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 `` 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 `//year=YYYY/month=MM/day=dd/hour=HH/++.`. * 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](http://www.localeplanet.com/java/). * 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](http://www.localeplanet.com/java), such as [those within the “en_US” locale](http://www.localeplanet.com/java/en-US/index.html). * 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 `//year=YYYY/month=MM/day=dd/++.`. 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](http://www.localeplanet.com/java/). * 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](http://www.localeplanet.com/java), such as [those within the “en_US” locale](http://www.localeplanet.com/java/en-US/index.html). * 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 `//year=YYYY/month=MM/day=dd/hour=HH/++.`. 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](http://www.localeplanet.com/java/). * 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](http://www.localeplanet.com/java), such as [those within the “en_US” locale](http://www.localeplanet.com/java/en-US/index.html). * 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 Backup and Restore Azure Blob Storage Source connectors to use your fully-qualified partitioner class name. ## Backup and Restore Azure Blob Storage Source Connector Data Formats Backup and Restore 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`. * **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 [Kafka Connect REST Interface](/platform/current/connect/references/restapi.html) 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. #### Error Handling The following `behavior.on.error` configuration properties set how the connector handles errors. * `fail` : The connector stops processing when an error occurs. The full batch of records will not be sent to Kafka if any record in the batch is corrupted. * `ignore` : The corrupted record is ignored. The connector continues processing the next record. For Avro, the connector ignores the file containing a corrupted record and continues processing records for the next file. * `log` : Logs an error message and continues processing the next record. For Avro, the connector ignores the file containing a corrupted record and continues processing records for the next file. #### NOTE The connector always ignores a file which is not in `++.` format.