# Azure Data Lake Storage Gen1 Sink Connector [Deprecated] for Confluent Platform #### IMPORTANT This connector is deprecated and reaches end of life (EOL) on the Confluent Platform 8.2 end of support (EOS) date. Confluent recommends migrating to [Azure Data Lake Storage Gen2 Sink Connector](https://docs.confluent.io/kafka-connectors/azure-data-lake-gen2-sink/current/overview.html) before the EOS date. For more information, see [Connector support lifecycle policy](https://docs.confluent.io/platform/current/connect/supported.html#support-policy-for-self-managed-connectors). You can use the Kafka Connect Azure Data Lake Storage Gen1 Sink connector to export data from Apache Kafka® topics to Azure Data Lake Storage Gen1 files in either Avro or JSON formats. Depending on your environment, the Azure Data Lake Storage Gen1 Sink connector can export data by guaranteeing exactly-once delivery semantics to consumers of the Azure Data Lake Storage Gen1 files it produces. The Azure Data Lake Storage Gen1 Sink connector periodically polls data from Kafka and, in turn, uploads it to Azure Data Lake Storage Gen1. A partitioner is used to split the data of every Kafka partition into chunks. Each chunk of data is represented as an Azure Data Lake Storage Gen1 file. The key name encodes the topic, the Kafka partition, and the start offset of this data chunk. If no partitioner is specified in the configuration, the default partitioner which preserves Kafka partitioning is used. The size of each data chunk is determined by the number of records written to Azure Data Lake Storage Gen1 and by schema compatibility. ## Features The Microsoft Azure Data Lake Storage Gen1 Sink connector includes variety of features: - [Exactly once delivery](#azure-data-lake-storage-gen1-exactly-once-delivery) - [Dead Letter Queue](#azure-data-lake-storage-gen1-dead-letter-queue) - [Multiple tasks](#azure-data-lake-storage-gen1-multiple-tasks) - [Pluggable data format with or without schema](#azure-data-lake-storage-gen1-pluggable-data-format) - [Schema evolution](#azure-data-lake-storage-gen1-schema-evolution) - [Pluggable partitioner](#azure-data-lake-storage-gen1-pluggable-partitioner) ### Exactly once delivery Records that are exported using a deterministic partitioner are delivered with exactly-once semantics. ### Dead Letter Queue This connector supports the Dead Letter Queue (DLQ) functionality. For information about accessing and using the DLQ, see [Confluent Platform Dead Letter Queue](/platform/current/connect/concepts.html#dead-letter-queue). ### Multiple tasks The Azure Data Lake Storage Gen1 Sink connector supports running one or more tasks. You can specify the number of tasks in the `tasks.max` configuration parameter. Multiple tasks may improve performance when moving a large amount of data. ### Pluggable data format with or without schema Out of the box, the connector supports writing data to Azure Data Lake Storage Gen1 in Avro and JSON format. Besides records with schema, the connector supports exporting plain JSON and as byte array records without schema in text files, one record per-line. In general, the connector may accept any format that provides an implementation of the `Format` interface. #### IMPORTANT You must use the [AvroConverter](/kafka-connectors/self-managed/userguide.html#configuring-key-and-value-converters), `ProtobufConverter`, or `JsonSchemaConverter` with `ParquetFormat` for this connector. Attempting to use the `JsonConverter` (with or without schemas) results in a NullPointerException and a StackOverflowException. ### Pluggable Data Format with or without Schema Out of the box, the connector supports writing data to Azure Data Lake Storage Gen1 in Avro and JSON format. Besides records with schema, the connector supports exporting plain JSON and as byte array records without schema in text files, one record per-line. In general, the connector may accept any format that provides an implementation of the `Format` interface. #### IMPORTANT You must use the [AvroConverter](/kafka-connectors/self-managed/userguide.html#configuring-key-and-value-converters), `ProtobufConverter`, or `JsonSchemaConverter` with `ParquetFormat` for this connector. Attempting to use the `JsonConverter` (with or without schemas) results in a NullPointerException and a StackOverflowException. ### Schema evolution When schemas are used, the connector supports schema evolution based on schema compatibility modes. The available modes are: `NONE`, `BACKWARD`, `FORWARD` and `FULL` and a selection can be made by setting the property `schema.compatibility` in the connector’s configuration. When the connector observes a schema change, it decides whether to roll the file or project the record to the proper schema according to the `schema.compatibility` configuration in use. ### Pluggable partitioner The connector comes out of the box with partitioners that support default partitioning based on Kafka partitions, field partitioning, and time-based partitioning in days or hours. You may implement your own partitioners by extending the `Partitioner` class. Additionally, you can customize time-based partitioning by extending the `TimeBasedPartitioner` class. ## Install the Azure Data Lake Storage Gen1 Sink 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. - 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-data-lake-gen1-storage: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-data-lake-gen1-storage:1.1.1 ``` - You must authorize the connector user (or principal) to have write permissions on the **uncommitted** folder; else, the connector will throw an exception. ### Install the connector manually [Download and extract the ZIP file](https://www.confluent.io/hub/confluentinc/kafka-connect-azure-data-lake-gen1-storage) for your connector and then follow the manual connector installation [instructions](/kafka-connectors/self-managed/userguide.html#connect-installing-plugins). ## 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. For license properties, see [Confluent Platform license](configuration_options.md#azure-data-lake-connector-license-config). For information about the license topic, see [License topic configuration](configuration_options.md#connect-azure-data-lake-gen1-storage-license-topic-configuration). ## Configuration Properties For a complete list of configuration properties for this connector, see [Configuration Reference for Azure Data Lake Storage Gen1 Sink Connector for Confluent Platform](configuration_options.md#connect-azure-data-lake-gen1-storage-configuration-options). ## Mapping records to Azure Data Lake Storage Gen1 Objects The Azure Data Lake Storage Gen1 Sink connector consumes records from the specified topics, organizes them into different partitions, writes batches of records in each partition to an file, and then uploads those files to the Azure Data Lake Storage Gen1 bucket. It uses Azure Data Lake Storage Gen1 object paths that include the Kafka topic and partition, the computed partition, and the filename. The Azure Data Lake Storage Gen1 Sink connector offers several ways to customize this behavior, including: * [Controlling the names of the Azure Data Lake Storage Gen1 objects](#azure-data-lake-storage-gen1-object-names) * [Determining how records are partitioned into Azure Data Lake Storage Gen1 objects](#azure-data-lake-storage-gen1-partitioners) * [The format used to serialize sets of records into Azure Data Lake Storage Gen1 objects](#azure-data-lake-storage-gen1-formats) * [When to upload Azure Data Lake Storage Gen1 objects](#azure-data-lake-storage-gen1-rotations) ### Azure Data Lake Storage Gen1 object names The Azure Data Lake Storage Gen1 data model is a flat structure: each bucket stores objects, and the name of each Azure Data Lake Storage Gen1 object serves as the unique key. However, a logical hierarchy can be inferred when the Azure Data Lake Storage Gen1 object names uses directory delimiters, such as `/`. The Azure Data Lake Storage Gen1 Sink connector allows you to customize the names of the Azure Data Lake Storage Gen1 objects it uploads to the Azure Data Lake Storage Gen1 bucket. In general, the names of the Azure Data Lake Storage Gen1 object uploaded by the Azure Data Lake Storage Gen1 Sink connector follow this format: ```bash ///++. ``` where: * `` is specified with the connector’s `topics.dir` configuration property, which defaults to the literal value `topics` and helps create uniquely named Azure Data Lake Storage Gen1 objects that don’t clash with existing Azure Data Lake Storage Gen1 objects in the same bucket. * `` corresponds to the name of the Kafka topic from which the records in this Azure Data Lake Storage Gen1 object were read. * `` is generated by the Azure Data Lake Storage Gen1 Sink Connector’s partitioner (see [Partitioning records into Azure Data Lake Storage Gen1 objects](#azure-data-lake-storage-gen1-partitioners)). * `` is the Kafka partition number from which the records in this Azure Data Lake Storage Gen1 object were read. * `` is the Kafka offset of the first record written to this Azure Data Lake Storage Gen1 object. \* `` is the extension identifying the format in which the records are serialized in this Azure Data Lake Storage Gen1 object. If desired, the `/` and `+` characters can be changed using the connector’s `directory.delim` and `file.delim` configuration properties. ### Partitioning records into Azure Data Lake Storage Gen1 objects The Azure Data Lake Storage Gen1 Sink connector’s partitioner determines how records read from a Kafka topic are partitioned into Azure Data Lake Storage Gen1 objects. The partitioner determines the `` portion of the Azure Data Lake Storage Gen1 object names (see [Azure Data Lake Storage Gen1 object names](#azure-data-lake-storage-gen1-object-names)). The partitioner is specified in the connector configuration with the `partitioner.class` configuration property. The Azure Data Lake Storage Gen1 Sink connector comes with the following partitioners: * **Default Kafka Partitioner**: The `io.confluent.connect.storage.partitioner.DefaultPartitioner` preserves the same topic partitions as in Kafka, and records from each topic partition ultimately end up in Azure Data Lake Storage Gen1 objects with names that include the Kafka topic and Kafka partitions. The `` is always `/partition=`, resulting in Azure Data Lake Storage Gen1 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 Data Lake Storage Gen1 Sink connector 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 `` portion of the Azure Data Lake Storage Gen1 object name. For example, when `path.format='year'=YYYY/'month'=MM/'day'=dd/'hour'=HH`, Azure Data Lake Storage Gen1 object names will have the form `//year=YYYY/month=MM/day=dd/hour=HH/++.`. * The `partition.duration.ms` configuration property defines the maximum granularity of the Azure Data Lake Storage Gen1 objects within a single encoded partition directory. For example, setting `partition.duration.ms=600000` (10 minutes) will result in each Azure Data Lake Storage Gen1 object in that directory having no more than 10 minutes of records. * The `locale` configuration property specifies the JDK’s locale used for formatting dates and times. For example, use `en-US` for US English, `en-GB` for UK English, and `fr-FR` for French (in France). These may vary by Java version; see the [available locales](https://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](https://www.localeplanet.com/java), such as [those within the “en_US” locale](https://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 Data Lake Storage Gen1 object in each daily directory). This partitioner always results in Azure Data Lake Storage Gen1 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](https://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](https://www.localeplanet.com/java), such as [those within the “en_US” locale](https://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 Data Lake Storage Gen1 object in each hourly directory). This partitioner always results in Azure Data Lake Storage Gen1 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](https://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](https://www.localeplanet.com/java), such as [those within the “en_US” locale](https://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. As noted below, the choice of `timestamp.extractor` affects whether the Azure Data Lake Storage Gen1 Sink connector can support exactly once delivery. You can also choose to use a custom partitioner by implementing the `io.confluent.connect.storage.partitioner.Partitioner` interface, packaging your implementation into a JAR file, and then: 1. Place the JAR file into the `share/java/kafka-connect-azure-data-lake-gen1-storage` directory of your Confluent Platform installation **on each worker node**. 2. Restart all of the Connect worker nodes. 3. Configure Azure Data Lake Storage Gen1 Sink connectors to use your fully-qualified partitioner class name. ### Azure Data Lake Storage Gen1 object formats The Azure Data Lake Storage Gen1 Sink connector can serialize multiple records into each Azure Data Lake Storage Gen1 object using a number of formats. The connector’s `format.class` configuration property identifies the name of the Java class that implements the `io.confluent.connect.storage.format.Format` interface, and the Azure Data Lake Storage Gen1 Sink connector comes with several implementations: * **Avro**: Use `format.class=io.confluent.connect.azure.storage.format.avro.AvroFormat` to write the Azure Data Lake Storage Gen1 object as an Avro container file and will include the Avro schema in the container file followed by one or more records. The connector’s `avro.codec` configuration property specifies the Avro compression code, and values can be `null` (the default) for no Avro compression, `deflate` to use the deflate algorithm as specified in [RFC 1951](https://www.rfc-editor.org/info/rfc1951), `snappy` to use Google’s [Snappy](https://google.github.io/snappy/) compression library, and `bzip2` for BZip2 compression. Optionally set `enhanced.avro.schema.support=true` to enable enum symbol preservation and package name awareness. * **JSON**: Use `format.class=io.confluent.connect.azure.storage.format.json.JsonFormat` to write the Azure Data Lake Storage Gen1 object as a file containing one JSON serialized record per line. The connector’s `az.compression.type` configuration property can be set to `none` (the default) for no compression or `gzip` for GZip compression. * **Parquet**: Use `format.class=io.confluent.connect.azure.storage.format.parquet.ParquetFormat` to write the Azure Data Lake Storage Gen1 object as a Parquet file columnar storage format. The connector’s `parquet.codec` configuration property specifies the Parquet compression code, and values can be `snappy` (the default) to use Google’s [Snappy](https://code.google.com/p/snappy/) compression library, `none` for no compression, `gzip` to use GNU’s [GZip](https://www.gnu.org/software/gzip/) compression library, `lzo` to use [LZO (Lempel–Ziv–Oberhumer)](https://www.oberhumer.com/opensource/lzo/) compression library, `brotli` to use Google’s [Brotli](https://github.com/google/brotli/) compression library, `lz4` to use BSD licensed [LZ4](https://github.com/lz4/lz4/) compression library and `zstd` to use Facebook’s [ZStandard](https://github.com/facebook/zstd/) compression library. * **Raw Bytes**: Use `format.class=io.confluent.connect.azure.storage.format.bytearray.ByteArrayFormat` to write the raw serialized record values delimited with the JDK’s line separator to the Azure Data Lake Storage Gen1 object. This requires using the `value.converter=org.apache.kafka.connect.converters.ByteArrayConverter` with the connector. Use a different delimiter by specifying the connector `format.bytearray.separator` configuration property. You can also choose to use a custom partitioner by implementing the `io.confluent.connect.storage.format.Format` interface, packaging your implementation into a JAR file, and then: 1. Place the JAR file into the `share/java/kafka-connect-azure-data-lake-gen1-storage` directory of your Confluent Platform installation **on each worker node**. 2. Restart all of the Connect worker nodes. 3. Configure Azure Data Lake Storage Gen1 Sink connectors with `format.class` set to the fully-qualified class name of your format implementation. ### Azure Data Lake Storage Gen1 object uploads As the Azure Data Lake Storage Gen1 Sink connector processes each record, it uses the partitioner to determine which encoded partition to write the record. This continues for each partition until the connector determines that a partition has enough records and should be flushed and uploaded to the Azure Data Lake Storage Gen1 bucket using the Azure Data Lake Storage Gen1 object name for that partition. This technique of knowing when to flush a partition file and upload it to Azure Data Lake Storage is called the *rotation strategy*, and there are a number of ways to control this behavior. * **Maximum number of records**: The connector’s `flush.size` configuration property specifies the maximum number of records that should be written to a single Azure Data Lake Storage Gen1 object. There is no default for this setting. #### IMPORTANT **Rotation strategy logic:** In the following rotation strategies, the logic to flush files to storage is triggered when a new record arrives, *after* the defined interval or scheduled interval time. Flushing files is also triggered periodically by the `offset.flush.interval.ms` setting defined in the Connect worker configuration. The `offset.flush.interval.ms` setting defaults to 60000 ms (60 seconds). If you enable the properties `rotate.interval.ms` or `rotate.schedule.interval.ms` and ingestion rate is low, you should set `offset.flush.interval.ms` to a smaller value so that records flush at the rotation interval (or close to the interval) . Leaving the `offset.flush.interval.ms` set to the default 60 seconds may cause records to stay in an open file for longer than expected, if no new records get processed that trigger rotation. * **Maximum span of record time**: In this rotation strategy, the connector’s `rotate.interval.ms` property specifies the maximum timespan in milliseconds a file can remain open and ready for additional records. The timestamp for each file starts with the **record timestamp** of the first record written to the file, as determined by the partitioner’s `timestamp.extractor`. As long as the next record’s timestamp fits within the timespan specified by the `rotate.interval.ms` property, the record is written to the file. If a record’s timestamp does not fit within the timespan of `rotate.interval.ms`, the connector flushes the file, uploads it to Azure Data Lake Storage Gen1, and commits the offsets of the records in that file. After this, the connector creates a new file with a timespan that starts with the first record, and writes the first record to the file. * **Scheduled rotation**: In this rotation strategy, the connector’s `rotate.schedule.interval.ms` specifies the maximum timespan in milliseconds a file can remain open and ready for additional records. Unlike `rotate.interval.ms`, with scheduled rotation the timestamp for each file starts with the **system time** that the first record is written to the file. You **must** have the partitioner parameter `timezone` configured (defaults to an empty string) when using this configuration property, otherwise the connector fails with an exception. As long as a record is processed within the timespan specified by `rotate.schedule.interval.ms`, the record will be written to the file. As soon as a new record is processed *after* the timespan for the current file, the file is flushed, uploaded to Azure Data Lake Storage Gen1, and the offset of the records in the file are committed. A new file is created with a timespan that starts with the current system time, and the new record is written to the file. This configuration is useful when you have to commit your data based on current server time, for example at the beginning of every hour. The default value `-1` means that this feature is disabled. Scheduled rotation uses `rotate.schedule.interval.ms` to close the file and upload to Azure Data Lake Storage Gen1 on a regular basis using the current time, rather than the record time. Even if the connector has no more records to process, Connect will still call the connector at least every `offset.flush.interval.ms`, as defined in the Connect worker’s configuration file. And every time this occurs, the connector uses the current time to determine if the currently opened file should be closed and uploaded to Azure Data Lake Storage Gen1. These strategies can be combined as needed. However, when using either of the two rotation strategies described above, the connector only closes and uploads a file to Azure Data Lake Storage Gen1 when the next file does not belong based upon the timestamp. In other words, if the connector has no more records to process, the connector may keep the file open for a significant period of time, until the connector can process another record. #### NOTE Not all rotation strategies are compatible with the Azure Data Lake Storage Gen1 Sink connector’s ability to deliver Azure Data Lake Storage Gen1 objects exactly once with eventual consistency. See the [Exactly-once delivery](#azure-data-lake-gen1-sink-exactly-once) section for details. ## Quick Start In this quick start, the Azure Data Lake Storage Gen1 Sink connector is used to export data produced by the Avro console producer to Azure Data Lake Storage Gen1. #### IMPORTANT **Before you begin:** Create an Azure Data Lake Storage Gen1 account and grant **write access** to the user completing these procedures. See [Get started with Azure Data Lake Storage Gen1 using the Azure portal](https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-get-started-portal) for additional information. Also see [Service-to-service authentication with Azure Data Lake Storage Gen1 using Azure Active Directory](https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory) for information on setting up the account needed for the Azure Data Lake Storage Gen1 Sink connector. For an example of how to get Kafka Connect connected to [Confluent Cloud](/cloud/current/index.html), see [Connect Self-Managed Kafka Connect to Confluent Cloud](/cloud/current/cp-component/connect-cloud-config.html#distributed-cluster). Install the connector through the [Confluent Hub Client](/kafka-connectors/self-managed/confluent-hub/client.html). ```bash # run from your Confluent Platform installation directory confluent connect plugin install confluentinc/kafka-connect-azure-data-lake-gen1-storage:latest ``` Start the services using the Confluent CLI. ```bash confluent local start ``` Every service start in order, printing a message with its status. ```bash Starting Zookeeper Zookeeper is [UP] Starting Kafka Kafka is [UP] Starting Schema Registry Schema Registry is [UP] Starting Kafka REST Kafka REST is [UP] Starting Connect Connect is [UP] Starting KSQL Server KSQL Server is [UP] Starting Control Center Control Center is [UP] ``` #### NOTE Make sure the Azure Data Lake Storage Gen1 Sink connector has write access to the Azure Data Lake Storage Gen1 account shown in `azure.datalake.account.name` and can deploy credentials successfully. To import a few records with a simple schema in Kafka, start the Avro console producer as follows: ```bash ./bin/kafka-avro-console-producer --broker-list localhost:9092 --topic datalake_topic \ --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}' ``` Then, in the console producer, enter the following: ```bash {"f1": "value1"} {"f1": "value2"} {"f1": "value3"} {"f1": "value4"} {"f1": "value5"} {"f1": "value6"} {"f1": "value7"} {"f1": "value8"} {"f1": "value9"} ``` The nine records entered are published to the Kafka topic `datalake_topic` in Avro format. Create a `datalake.properties` file with the following contents: ```properties name=datalake-sink connector.class=io.confluent.connect.azure.datalake.gen1.AzureDataLakeGen1StorageSinkConnector tasks.max=1 topics=datalake_topic flush.size=3 azure.datalake.client.id= azure.datalake.client.key= azure.datalake.account.name= azure.datalake.token.endpoint= format.class=io.confluent.connect.azure.storage.format.avro.AvroFormat confluent.topic.bootstrap.servers=localhost:9092 confluent.topic.replication.factor=1 ``` Before starting the connector, make sure that the configurations in `datalake.properties` are properly set to your configurations of Azure Data Lake Storage Gen1. For this example, make sure that `azure.datalake.account.name` points to your Data Lake store, `azure.datalake.client.id` is set to your user id, and `azure.datalake.client.key` is set to your user’s secret key. The user ID or client ID should have permission to write to the Azure Data Lake Storage Gen1 Account. Finally, set `azure.datalake.token.endpoint` to the Oauth 2 endpoint as described [here](https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory#step-4-get-the-oauth-20-token-endpoint-only-for-java-based-applications), and use the v1 token endpoint. Then start the Azure Data Lake Storage Gen1 Sink connector by loading its configuration with the following command. ```bash confluent local load datalake-sink --config datalake.properties { "name": "datalake-sink", "config": { "name":"datalake-sink", "connector.class":"io.confluent.connect.azure.datalake.gen1.AzureDataLakeGen1StorageSinkConnector", "tasks.max":"1", "topics":"datalake_topic", "flush.size":"3", "azure.datalake.client.id":"", "azure.datalake.client.key":"", "azure.datalake.account.name":"", "azure.datalake.token.endpoint":"", "format.class":"io.confluent.connect.azure.storage.format.avro.AvroFormat", "confluent.topic.bootstrap.servers":"localhost:9092", "confluent.topic.replication.factor":"1" }, "tasks": [] } ``` Check that the connector started successfully. Review the Connect worker’s log by entering the following: ```bash confluent local services connect log ``` Towards the end of the log you should see that the connector starts, logs a few messages, and then uploads data from Kafka to Azure Data Lake Storage Gen1. Once the connector has ingested some records, check that the data is available in Azure Data Lake Storage Gen1. Use the following Azure CLI command: ```bash az dls fs list --account --path /topics ``` Once you navigate into the subfolders, you should see three objects with keys. ```bash topics/datalake_topic/partition=0/datalake_topic+0+0000000000.avro topics/datalake_topic/partition=0/datalake_topic+0+0000000003.avro topics/datalake_topic/partition=0/datalake_topic+0+0000000006.avro ``` Each file is encoded as `++.`. To verify the contents, copy each file from Azure Data Lake Storage Gen1 to your local filesystem. Use the following Azure CLI command changing the destination to what makes sense for you: ```bash az dls fs download --account --source-path /topics/datalake_topic/partition=0/datalake_topic+0+0000000000.avro --destination-path "C:\connect\datalake_topic+0+0000000000.avro" ``` Use `avro-tools-1.9.0.jar` (available in [Apache mirrors](https://mirror.metrocast.net/apache/avro/avro-1.9.0/java/avro-tools-1.9.0.jar)) to print the records. ```bash java -jar avro-tools-1.8.2.jar tojson datalake_topic+0+0000000000.avro ``` For the file above, you should see the following output: ```bash {"f1":"value1"} {"f1":"value2"} {"f1":"value3"} ``` The rest of the records are contained in the other two files. Finally, stop the Connect worker and all other Confluent services by running: ```bash confluent local services stop ``` Your output should resemble: ```none Stopping Control Center Control Center is [DOWN] Stopping KSQL Server KSQL Server is [DOWN] Stopping Connect Connect is [DOWN] Stopping Kafka REST Kafka REST is [DOWN] Stopping Schema Registry Schema Registry is [DOWN] Stopping Kafka Kafka is [DOWN] Stopping Zookeeper Zookeeper is [DOWN] ``` You can stop all services and remove any data generated during this quick start by entering the following command: ```bash confluent local destroy ``` Your output should resemble: ```bash Stopping Control Center Control Center is [DOWN] Stopping KSQL Server KSQL Server is [DOWN] Stopping Connect Connect is [DOWN] Stopping Kafka REST Kafka REST is [DOWN] Stopping Schema Registry Schema Registry is [DOWN] Stopping Kafka Kafka is [DOWN] Stopping Zookeeper Zookeeper is [DOWN] Deleting: /var/folders/ty/rqbqmjv54rg_v10ykmrgd1_80000gp/T/confluent.PkQpsKfE ``` ## Exactly-once delivery The Azure Data Lake Storage Gen1 Sink connector is able to provide exactly-once semantics to consumers of the objects it exports to Azure Data Lake Storage Gen1, **if** the connector is supplied with a deterministic partitioner. Currently, out of the available partitioners, the default and field partitioners are always deterministic. `TimeBasedPartitioner` can be deterministic with some configurations as discussed below. This implies that when any of these partitioners is used, file splitting always happens at the same offsets for a given set of Kafka records. These partitioners take into account `flush.size` and `schema.compatibility` to decide when to roll and save a new file to Azure Data Lake Storage Gen1. The connector always delivers files in Azure Data Lake Storage Gen1 that contain the same records, even under the presence of failures. If a connector task fails before an upload completes, the file will be still in the `temp/` folder . If, on the other hand, a failure occurs after the upload has completed, but before the corresponding offset is committed to Kafka by the connector, then a re-upload will take place. However, this type of re-upload is transparent to the user of the Azure Data Lake Storage Gen1 folder, who at any time will have access to the same records made eventually available by successful uploads to Azure Data Lake Storage Gen1. To guarantee exactly-once semantics with the `TimeBasedPartitioner`, the connector must be configured to use a deterministic implementation of `TimestampExtractor` and a deterministic rotation strategy. The deterministic timestamp extractors are Kafka records (`timestamp.extractor=Record`) or record fields (`timestamp.extractor=RecordField`). The deterministic rotation strategy configuration is `rotate.interval.ms` (setting `rotate.schedule.interval.ms` is nondeterministic and will invalidate exactly-once guarantees). ![image](.hidden/docs-common/kafka-connectors/self-managed/images/connect-s3-eos.png) ## Schema Evolution The Azure Data Lake Storage Gen1 Sink connector supports schema evolution and reacts to schema changes of data according to the `schema.compatibility` configuration. This section explains how the connector reacts to schema evolution under different values of `schema.compatibility`. The `schema.compatibility` can be set to `NONE`, `BACKWARD`, `FORWARD` and `FULL`, which means NO compatibility, BACKWARD compatibility, FORWARD compatibility and FULL compatibility respectively. * **NO Compatibility**: By default, the `schema.compatibility` is set to `NONE`. In this case, the connector ensures that each file written to Azure Data Lake Storage Gen1 has the proper schema. When the connector observes a schema change in data, it commits the current set of files for the affected topic partitions and writes the data with new schema in new files. * **BACKWARD Compatibility**: If a schema is evolved in a backward compatible way, the connector can always use the latest schema to query all the data uniformly. For example, removing fields is a backward compatible change to a schema, since when the connector encounters records written with the old schema that contain these fields, the connector can just ignore them. Adding a field with a default value is also backward compatible. If `BACKWARD` is specified in the `schema.compatibility`, the connector keeps track of the latest schema used in writing data to Azure Data Lake Storage Gen1. If a data record with a schema version larger than current latest schema arrives, the connector commits the current set of files and writes the data record with new schema to new files. For data records arriving at a later time that use an earlier schema, the connector projects the data record to the latest schema before writing to the same set of files in Azure Data Lake Storage Gen1. * **FORWARD Compatibility**: If a schema is evolved in a forward compatible way, the connector can always use the oldest schema to query all the data uniformly. Removing a field that had a default value is forward compatible, since the old schema will use the default value when the field is missing. If `FORWARD` is specified in the `schema.compatibility`, the connector projects the data to the oldest schema before writing to the same set of files in Azure Data Lake Storage Gen1. * **Full Compatibility**: Full compatibility means that old data can be read with the new schema and new data can also be read with the old schema. If `FULL` is specified in the `schema.compatibility`, the connector performs the same action as `BACKWARD`. Schema evolution in the Azure Data Lake Storage Gen1 Sink connector works the same way as [Schema Evolution](https://docs.confluent.io/kafka-connect-s3-sink/current/overview.html#schema-evolution). ### Write JSON message values into Azure Data Lake Storage Gen1 The example settings file is shown below: ```bash name=datalake-sink connector.class=io.confluent.connect.azure.datalake.gen1.AzureDataLakeGen1StorageSinkConnector tasks.max=1 topics=datalake_topic flush.size=100 # Required configuration azure.datalake.client.id= azure.datalake.client.key= # The following define the information used to validate the license stored in Kafka confluent.license= confluent.topic.bootstrap.servers=localhost:9092 ``` The first few settings are common to most connectors. `topics` specifies the topics to export data from, in this case `datalake_topic`. The property `flush.size` specifies the number of records per partition the connector needs to write to before completing a multiblock upload to Azure Data Lake Storage Gen1. The `azure.datalake.client.id` and `azure.datalake.client.key` are your required Azure credentials. This is a licensed Confluent connector. Enter the following for testing purposes. For more information, see [Azure Data Lake Storage Gen1 Licensing](#connect-azure-data-lake-gen1-storage-license). ```bash azure.datalake.account.name= azure.datalake.token.endpoint= ``` The next settings are specific to Azure Data Lake Storage Gen1. A mandatory setting is the name of your Azure Data Lake Gen1 store/account `azure.datalake.account.name` to host the exported Kafka records. Another mandatory configuration setting is `azure.datalake.token.endpoint`. The connector authenticates access to your data lake using this URL. ```bash format.class=io.confluent.connect.azure.storage.format.json.JsonFormat partitioner.class=io.confluent.connect.storage.partitioner.DefaultPartitioner ``` These class settings are required to specify the output file format, which is currently `io.confluent.connect.azure.storage.format.avro.AvroFormat`, `io.confluent.connect.azure.storage.format.json.JsonFormat` or `io.confluent.connect.azure.storage.format.bytearray.ByteArrayFormat`, and the partitioner class ```bash schema.compatibility=NONE ``` Finally, schema evolution is disabled in this example by setting `schema.compatibility` to `NONE`, as explained above. For detailed descriptions for all the available configuration options of the Azure Data Lake Storage Gen1 Sink connector go to [Configuration Reference for Azure Data Lake Storage Gen1 Sink Connector for Confluent Platform](configuration_options.md#connect-azure-data-lake-gen1-storage-configuration-options). ### Write raw message values into Azure Data Lake Storage Gen1 It is possible to use the Azure Data Lake Storage Gen1 Sink connector to write out the unmodified original message values into newline-separated files in Azure Data Lake Storage Gen1. To accomplish this configure Kafka Connect so it does not deserialize any of the messages, and configure the Azure Data Lake Storage Gen1 Sink connector to store the message values in a binary format in Azure Data Lake Storage Gen1. The first part of the Azure Data Lake Storage Gen1 Sink connector configuration is similar to other examples. ```bash name=datalake-raw-sink connector.class=io.confluent.connect.azure.datalake.gen1.AzureDataLakeGen1StorageSinkConnector tasks.max=1 topics=datalake_topic flush.size=3 ``` The `topics` setting specifies the topics you want to export data from, which is `datalake_topic` in the example. The property `flush.size` specifies the number of records per partition the connector needs to write before completing an upload to Azure Data Lake Storage Gen1. Next, configure container name, block size, and compression type. ```bash azure.datalake.account.name=myconfluentdatalake azure.datalake.token.endpoint=https://login.microsoftonline.com/a7d99622-a589-4520-8ce3-c280ed1cb00c/oauth2/token azure.datalake.client.id=21aaeb79-1956-486a-bc36-baa1f710d567 azure.datalake.client.key=HGw@4@DSkjBRslXA4vuR:-lxQ4H3+PTs az.compression.type=gzip ``` The next settings are specific to Azure Data Lake Storage Gen1. A mandatory setting is the account name of your Gen1 Azure Data Lake, `azure.datalake.account.name` which will host the exported Kafka records. Another mandatory configuration setting is `azure.datalake.token.endpoint`. The connector authenticates access to your data lake using this URL. The `azure.datalake.client.id` and `azure.datalake.client.key` are your required Azure client credentials. The `az.compression.type` specifies that the Azure Data Lake Storage Gen1 Sink connector should compress all Azure Data Lake Storage Gen1 files using GZIP compression, adding the `.gz` extension to any files (see below). This example configuration is typical of most Azure Data Lake Storage Gen1 Sink connectors. Now, configure the connector to read the raw message values and write them in binary format: ```bash value.converter=org.apache.kafka.connect.converters.ByteArrayConverter format.class=io.confluent.connect.azure.storage.format.bytearray.ByteArrayFormat schema.compatibility=NONE ``` The `value.converter` setting overrides the connector default in the Connect worker configuration. `ByteArrayConverter` is used to instruct Connect to skip deserializing the message values and provide the message values in their raw binary form. The `format.class` setting is used to instruct the Azure Data Lake Storage Gen1 Sink connector to write these binary message values as-is into Azure Data Lake Storage Gen1 files. By default the messages written to the same Azure Data Lake Storage Gen1 file are separated by a newline character sequence, but you can control this with the `format.bytearray.separator` setting. You may want to consider setting this if your messages might contain newlines. Also, by default the files written to Azure Data Lake Storage Gen1 have an extension of `.bin` (before compression, if enabled), or you can use the `format.bytearray.extension` setting to change the pre-compression filename extension. Next, you need to decide how you want to partition the consumed messages in Azure Data Lake Storage Gen1 files. You have a few options, including the default partitioner that preserves the same partitions as in Kafka: ```bash partitioner.class=io.confluent.connect.storage.partitioner.DefaultPartitioner ``` Or, you could partition using the timestamp of the Kafka messages. ```bash partitioner.class=io.confluent.connect.storage.partitioner.TimeBasedPartitioner timestamp.extractor=Record ``` Or, you can use the timestamp that the Azure Data Lake Storage Gen1 Sink connector processes each message. ```bash partitioner.class=io.confluent.connect.storage.partitioner.TimeBasedPartitioner timestamp.extractor=Wallclock ``` Custom partitioners are always an option, too. Just be aware that since the record value is an opaque binary value, Connect cannot extract timestamps from fields using the `RecordField` option. The Azure Data Lake Storage Gen1 Sink connector configuration outlined above results in newline-delimited gzipped objects in Azure Data Lake Storage Gen1 with `.bin.gz`.