# Configuration Reference for Azure Data Lake Storage Gen2 Sink Connector for Confluent Platform To use this connector, specify the name of the connector class in the `connector.class` configuration property. ```none connector.class=io.confluent.connect.azure.datalake.gen2.AzureDataLakeGen2SinkConnector ``` Connector-specific configuration properties are described below. #### NOTE These are properties for the self-managed connector. If you are using Confluent Cloud, see [Azure Data Lake Storage Gen2 Sink Connector for Confluent Cloud](/cloud/current/connectors/cc-azure-datalakeGen2-storage-sink.html). ## Connector `format.class` : The format class to use when writing data to the store.
* Type: class * Importance: high `flush.size` : The number of records written to store before invoking file commits. More specifically, the maximum number of records to write in each output object before rolling over and writing a new object. The following section gives a more detailed description of the rotation process:
**Rotation strategy logic**: 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 `rotate.interval.ms` or `rotate.schedule.interval.ms` and the 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 value may cause records to stay in an open file for longer than expected–that is, if no new records get processed that trigger rotation.
* Type: int * Importance: high `rotate.interval.ms` : The time interval in milliseconds to invoke file commits. This setting ensures that file commits are invoked every configured interval. This setting is useful when the data ingestion rate is low and the connector didn’t write enough messages to commit files. The default value of -1 means that this feature is disabled.
* Type: long * Default: -1 * Importance: high `rotate.schedule.interval.ms` : The time interval to invoke file commits periodically, in milliseconds. This setting ensures that file commits are invoked every configured interval. Time of commit is adjusted to 00:00 in the selected timezone. The commit is performed at the scheduled time, regardless of the previous time or number of messages. This configuration is useful when you have to commit your data based on current server time, like at the beginning of every hour. The default value of -1 means that this feature is disabled.
* Type: long * Default: -1 * Importance: medium The following Avro converter properties can be used in the connector configuration: `schema.cache.config` : The size of the schema cache used in the Avro converter.
* Type: int * Default: 1000 * Importance: low `enhanced.avro.schema.support` : Enable enhanced Avro schema support in the Avro Converter. When set to `true`, this property preserves Avro schema package information and Enums when going from Avro schema to Connect schema. This information is added back in when going from Connect schema to Avro schema.
* Type: boolean * Default: false * Importance: low `connect.meta.data` : Allow the Connect converter to add its metadata to the output schema.
* Type: boolean * Default: true * Importance: low
The `connect.meta.data` property preserves the following Connect schema metadata when going from Connect schema to Avro schema. The following metadata is added back in when going from Avro schema to Connect schema.
* doc * version * parameters * default value * name * type For detailed information and configuration examples for Avro converters listed above, see [Using Kafka Connect with Schema Registry](/platform/current/schema-registry/connect.html#avro). `retry.backoff.ms` : The retry backoff in milliseconds. This setting is used to notify Kafka Connect to retry delivering a message batch or to perform a recovery in case of transient exceptions.
* Type: long * Default: 5000 * Importance: low `filename.offset.zero.pad.width` : Width to zero-pad offsets in store’s filenames if offsets are too short, to provide fixed-width filenames that can be ordered by simple lexicographic sorting.
* Type: int * Default: 10 * Valid Values: [0,…] * Importance: low `avro.codec` : The Avro compression codec to be used for output files. Available values are: `null`, `deflate`, `snappy`, and `bzip2`. The CodecSource is `org.apache.avro.file.CodecFactory`.
* Type: string * Default: null * Valid Values: [null, deflate, snappy, bzip2] * Importance: low `parquet.codec` : The Parquet compression codec to be used for output files.
* Type: string * Default: snappy * Valid Values: [none, snappy, gzip, brotli, lz4, lzo, zstd] * Importance: low ## Azure Common `format.bytearray.extension` : Output file extension for `ByteArrayFormat`. Defaults to `.bin`.
* Type: string * Default: .bin * Importance: low `format.bytearray.separator` : String inserted between records in the same file for `ByteArrayFormat`. Defaults to `System.lineSeparator()` and may contain escape sequences, like `\n`. An input record that contains the line separator appears as multiple records in the output Azure object.
* Type: string * Default: null * Importance: low `az.compression.type` : Compression type for file written to Azure. Applied when using `JsonFormat` or `ByteArrayFormat`. Available values are `none`, `gzip`.
* Type: string * Default: none * Valid Values: [none, gzip] * Importance: low ## Schema `schema.compatibility` : The schema compatibility rule to use when the connector is observing schema changes. The supported configurations are `NONE`, `BACKWARD`, `FORWARD`, and `FULL`.
* Type: string * Default: NONE * Valid Values: either one of [forward, backward, none, full], or one of [BACKWARD, FORWARD, NONE, FULL] * Importance: high ## Azure `azure.datalake.gen2.client.id` : The client ID (GUID) of the client obtained from the Azure Active Directory configuration. To establish a connection, you can set all of the following properties:
- `azure.datalake.gen2.client.id` - `azure.datalake.gen2.client.key` - `azure.datalake.gen2.token.endpoint`
#### NOTE SAS authentication is not currently supported. Only Access Key authentication is supported at this time.
* Type: string * Default: null * Importance: high `azure.datalake.gen2.token.endpoint` : The OAuth 2.0 token endpoint associated with the user’s directory (obtain from the Active Directory configuration)
* Type: string * Default: null * Importance: high `azure.datalake.gen2.account.name` : The account name: Must be between 3-23 alphanumeric characters.
* Type: string * Valid Values: Matches regex [a-z0-9]{3,23} * Importance: high `azure.datalake.gen2.client.key` : The secret key of the client.
* Type: password * Default: null * Valid Values: null or password (password must be non-blank) * Importance: high `azure.datalake.gen2.sas.key` : The access key for the Azure storage account.
* Type: password * Default: null * Valid Values: null or password (password must be non-blank) * Importance: high `behavior.on.null.values` : How to handle records with a null value (for example, Kafka tombstone records). Valid options are `ignore` and `fail`.
* Type: string * Default: fail * Valid Values: [ignore, fail] * Importance: low ## Storage `storage.class` : The underlying storage layer. The default value works with Azure Data Lake Storage, but you can use this setting to specify an alternative custom storage implementation.
* Type: class * Default: io.confluent.connect.azure.datalake.gen2.storage.AzureDataLakeGen2Storage * Importance: low `topics.dir` : Top level directory to store the data ingested from Kafka.
* Type: string * Default: topics * Importance: high `store.url` : Storage URL for accessing blob data. Supported formats:
* `abfs://@.dfs.core.windows.net/` * `https://.dfs.core.windows.net//`
If the scheme is omitted, the connector prepends `abfs://` when the value contains `@`. Otherwise, it prepends `https://`.
The connector supports only the `dfs.core.windows.net` endpoint. Other Azure Storage endpoints, for example, `blob.core.windows.net`, are not supported.
Only the `dfs.core.windows.net` endpoint is supported. Other Azure Storage endpoints (for example, `blob.core.windows.net`) are not supported.
* Type: string * Default: null * Importance: low `directory.delim` : Directory delimiter pattern.
* Type: string * Default: / * Importance: medium `file.delim` : File delimiter pattern.
* Type: string * Default: + * Importance: medium ## Partitioner `partitioner.class` : The partitioner to use when writing data to the store. You can use\`\`DefaultPartitioner\`\`, which preserves the Kafka partitions, `FieldPartitioner`, which partitions the data to different directories according to the value of the partitioning field specified in `partition.field.name`, or `TimeBasedPartitioner`, which partitions data according to ingestion time.
* Type: class * Default: io.confluent.connect.storage.partitioner.DefaultPartitioner * Importance: high * Dependents: `partition.field.name`, `partition.duration.ms`, `path.format`, `locale`, `timezone` `partition.field.name` : The name of the partitioning field when `FieldPartitioner` is used.
* Type: list * Default: “” * Importance: medium `partition.duration.ms` : The duration of a partition used by `TimeBasedPartitioner`, in milliseconds. The default value of -1 means that `TimeBasedPartitioner` isn’t used.
* Type: long * Default: -1 * Importance: medium `path.format` : Sets the format of the data directories when partitioning with `TimeBasedPartitioner`. The format set in this configuration converts the Unix timestamp to proper directories strings. For example, if you set `path.format='year'=YYYY/'month'=MM/'day'=dd/'hour'=HH`, the data directories have the format `/year=2015/month=12/day=07/hour=15/`.
* Type: string * Default: “” * Importance: medium `locale` : The locale to use when partitioning with `TimeBasedPartitioner`.
* Type: string * Default: “” * Importance: medium `timezone` : The timezone to use when partitioning with `TimeBasedPartitioner`. Used to format and compute dates and times. All timezone IDs must be specified in the long format, such as `America/Los_Angeles`, `America/New_York`, and `Europe/Paris`, or `UTC`. Alternatively a locale independent, fixed offset, datetime zone can be specified in form `[+-]hh:mm`. Support for these timezones may vary by Java version. See the [available timezones within each locale](https://www.localeplanet.com/java/), such as [those within the US English locale](https://www.localeplanet.com/java/en-US/index.html).
* Type: string * Default: “” * Importance: medium `timestamp.extractor` : The extractor that gets the timestamp for records when partitioning with `TimeBasedPartitioner`. You can set it to `Wallclock`, `Record` or `RecordField` to use one of the built-in timestamp extractors. Also, you can use the fully-qualified class name of a user-defined class that extends the `TimestampExtractor` interface.
* Type: string * Default: Wallclock * Importance: medium `timestamp.field` : The record field to be used as a timestamp by the timestamp extractor.
* Type: string * Default: timestamp * Importance: medium ## CSFLE and CSPE configurations `csfle.enabled` Accepts a boolean value. CSFLE is enabled for the connector if `csfle.enabled` is set to True. * Type: boolean * Default: False `auto.register.schemas` Specifies if the Serializer should attempt to register the Schema with Schema Registry. * Type: boolean * Default: true * Importance: medium `use.latest.version` Only applies when `auto.register.schemas` is set to false. If `auto.register.schemas` is set to false and `use.latest.version` is set to true, then instead of deriving a schema for the object passed to the client for serialization, Schema Registry uses the latest version of the schema in the subject for serialization. * Type: boolean * Default: true * Importance: medium ## Confluent Platform license `confluent.topic.bootstrap.servers` A list of host/port pairs to use for establishing the initial connection to the Kafka cluster used for licensing. All servers in the cluster will be discovered from the initial connection. This list should be in the form host1:port1,host2:port2,…. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down). * Type: list * Importance: high `confluent.topic` Name of the Kafka topic used for Confluent Platform configuration, including licensing information. * Type: string * Default: \_confluent-command * Importance: low `confluent.topic.replication.factor` The replication factor for the Kafka topic used for Confluent Platform configuration, including licensing information. This is used only if the topic does not already exist, and the default of 3 is appropriate for production use. If you are using a development environment with less than 3 brokers, you must set this to the number of brokers (often 1). * Type: int * Default: 3 * Importance: low ### Confluent license properties You can put license-related properties in the connector configuration, or in license-related properties in the [Connect worker configuration](https://docs.confluent.io/kafka-connectors/self-managed/license.html#centralized-license-in-the-kconnect-long-worker) instead of in each connector configuration. This connector is proprietary and requires a license. The license information is stored in the `_confluent-command` topic. If the broker requires SSL for connections, you must include the security-related `confluent.topic.*` properties as described below. `confluent.license` : Confluent issues enterprise license keys to each subscriber. The license key is text that you can copy and paste as the value for `confluent.license`. A trial license allows using the connector for a 30-day trial period. A developer license allows using the connector indefinitely for single-broker development environments.
If you are a subscriber, contact Confluent Support for more information.
* Type: string * Default: “” * Valid Values: Confluent Platform license * Importance: high `confluent.topic.ssl.truststore.location` : The location of the trust store file.
* Type: string * Default: null * Importance: high `confluent.topic.ssl.truststore.password` : The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.
* Type: password * Default: null * Importance: high `confluent.topic.ssl.keystore.location` : The location of the key store file. This is optional for client and can be used for two-way authentication for client.
* Type: string * Default: null * Importance: high `confluent.topic.ssl.keystore.password` : The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.
* Type: password * Default: null * Importance: high `confluent.topic.ssl.key.password` : The password of the private key in the key store file. This is optional for client.
* Type: password * Default: null * Importance: high `confluent.topic.security.protocol` : Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.
* Type: string * Default: “PLAINTEXT” * Importance: medium ### License topic configuration A Confluent enterprise license is stored in the `_confluent-command` topic. This topic is created by default and contains the license that corresponds to the license key supplied through the `confluent.license` property. No public keys are stored in Kafka topics. The following describes how the default `_confluent-command` topic is generated under different scenarios: * A 30-day trial license is automatically generated for the `_confluent-command` topic if you do not add the `confluent.license` property or leave this property empty (for example, `confluent.license=`). * Adding a valid license key (for example, `confluent.license=`) adds a valid license in the `_confluent-command` topic. Here is an example of the minimal properties for development and testing. You can change the name of the `_confluent-command` topic using the `confluent.topic` property (for instance, if your environment has strict naming conventions). The example below shows this change and the configured Kafka bootstrap server. ```none confluent.topic=foo_confluent-command confluent.topic.bootstrap.servers=localhost:9092 ``` The example above shows the minimally required bootstrap server property that you can use for development and testing. For a production environment, you add the normal producer, consumer, and topic configuration properties to the connector properties, prefixed with `confluent.topic.`. ### License topic ACLs The `_confluent-command` topic contains the license that corresponds to the license key supplied through the `confluent.license` property. It is created by default. Connectors that access this topic require the following ACLs configured: * CREATE and DESCRIBE on the resource cluster, if the connector needs to create the topic. * DESCRIBE, READ, and WRITE on the `_confluent-command` topic. #### IMPORTANT You can also use DESCRIBE and READ without WRITE to restrict access to read-only for license topic ACLs. If a topic exists, the LicenseManager will not try to create the topic. You can provide access either individually for each principal that will use the license or use a [wildcard entry](/platform/current/kafka/authorization.html#acl-wildcard-principals) to allow all clients. The following examples show commands that you can use to configure ACLs for the resource cluster and `_confluent-command` topic. 1. Set a CREATE and DESCRIBE ACL on the resource cluster: ```none kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf \ --add --allow-principal User: \ --operation CREATE --operation DESCRIBE --cluster ``` 2. Set a DESCRIBE, READ, and WRITE ACL on the `_confluent-command` topic: ```none kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf \ --add --allow-principal User: \ --operation DESCRIBE --operation READ --operation WRITE --topic _confluent-command ``` ### Override Default Configuration Properties You can override the replication factor using `confluent.topic.replication.factor`. For example, when using a Kafka cluster as a destination with less than three brokers (for development and testing) you should set the `confluent.topic.replication.factor` property to `1`. You can override producer-specific properties by using the `producer.override.*` prefix (for source connectors) and consumer-specific properties by using the `consumer.override.*` prefix (for sink connectors). You can use the defaults or customize the other properties as well. For example, the `confluent.topic.client.id` property defaults to the name of the connector with `-licensing` suffix. You can specify the configuration settings for brokers that require SSL or SASL for client connections using this prefix. You cannot override the cleanup policy of a topic because the topic always has a single partition and is compacted. Also, do not specify serializers and deserializers using this prefix; they are ignored if added.