HDFS 2 Sink Connector for Confluent Platform¶

The Kafka Connect HDFS 2 Sink connector allows you to export data from Kafka topics to HDFS 2.x files in a variety of formats and integrates with Hive to make data immediately available for querying with HiveQL.

Note that his connector is released separately from the HDFS 3.x connector. If you are targeting an HDFS 3.x distribution, see the HDFS 3 Sink connector documentation for more details.

The connector periodically polls data from Kafka and writes them to HDFS. The data from each Kafka topic is partitioned by the provided partitioner and divided into chunks. Each chunk of data is represented as an HDFS file with topic, kafka partition, start and end offsets of this data chunk in the filename. If no partitioner is specified in the configuration, the default partitioner which preserves the Kafka partitioning is used. The size of each data chunk is determined by the number of records written to HDFS, the time written to HDFS and schema compatibility.

The HDFS 2 Sink connector integrates with Hive, and when Hive is enabled, the connector creates an external Hive partitioned table for each Kafka topic and updates the table according to the available data in HDFS.

Features¶

The HDFS 2 Sink connector includes the following features:

Exactly once delivery
Dead Letter Queue
Multiple tasks
Extensible data format

Exactly once delivery¶

The connector uses a write-ahead log to ensure each record exports to HDFS exactly once. Also, the connector manages offsets committed by encoding the Kafka offset information into HDFS files. Storing the offset information in HDFS files allows the connector to start from the last committed offsets in case of failures and task restarts.

Note that in addition to committing offset information to HDFS, offset information is also sent to Kafka Connect for connector progress monitoring. Upon startup, the HDFS Connector attempts to restore offsets from HDFS files. In the absence of files in HDFS, the connector attempts to find offsets for its consumer group in the __consumer_offsets topic. If offsets are not found, the consumer relies on the offset management policy specified in the consumer auto.offset.reset configuration property to start exporting data to HDFS. The default is auto.offset.reset = earliest.

Dead Letter Queue¶

This connector supports the Dead Letter Queue (DLQ) functionality. For information about accessing and using the DLQ, see Confluent Platform Dead Letter Queue.

Multiple tasks¶

The HDFS 2 Sink connector supports running one or more tasks. You can specify the number of tasks in the tasks.max configuration parameter. This can lead to performance gains when multiple files need to be parsed.

Extensible data format¶

Out of the box, the connector supports writing data to HDFS in Avro and Parquet format. Also, you can write other formats to HDFS by extending the Format class.

Hive Integration: The connector supports Hive integration out of the box, and when it is enabled, the connector automatically creates a Hive external partitioned table for each topic exported to HDFS.
Secure HDFS and Hive Metastore Support: The connector supports Kerberos authentication and works with secure HDFS and Hive metastore.
Pluggable Partitioner: The connector supports default partitioner, field partitioner, and time-based partitioner which includes daily and hourly partitioner. You can implement your own partitioner by extending the Partitioner class. You can also customize time-based partitioner by extending the TimeBasedPartitioner class.

Schema Evolution: Schema evolution only works if the records are generated with the default naming strategy, which is TopicNameStrategy. An error may occur if other naming strategies are used. This is because records are not compatible with each other. schema.compatibility should be set to NONE if other naming strategies are used. This may result in small object files because the sink connector creates a new file every time the schema ID changes between records. See Subject Name Strategy for more information about naming strategies.

Limitations¶

The HDFS 2 Sink connector does not permit you to run multiple HDFS clients with different Kerberos configurations in the same worker. This prevents several connector instances from running on the same worker to:

Connect to multiple Kerberos environments
Use different user keytabs

Install the HDFS Connector¶

You can install this connector by using the confluent connect plugin install command, or by manually downloading the ZIP file.

Prerequisites¶

You must install the connector on every machine where Connect will run.
Confluent CLI (requires a separate installation)
An installation of the latest (latest) connector version.

Install the connector using the Confluent CLI¶

To install the latest connector version, navigate to your Confluent Platform installation directory and run the following command:

confluent connect plugin install confluentinc/kafka-connect-hdfs:latest

You can install a specific version by replacing latest with a version number as shown in the following example:

cconfluent connect plugin install confluentinc/kafka-connect-hdfs:10.2.0

Caution

You can’t mix schema and schemaless records in storage using kafka-connect-storage-common. Attempting this causes a runtime exception. If you are using the self-managed version of this connector, this issue will be evident when you review the log files (only available for the self-managed connector).

Install the connector manually¶

Download and extract the ZIP file for your connector and then follow the manual connector installation instructions.

License¶

This connector is available under the Confluent Community License.

Configuration Properties¶

For a complete list of configuration properties for this connector, see Configuration Reference for HDFS 2 Sink Connector for Confluent Platform.

For an example of how to connect Kafka Connect to Confluent Cloud, see Distributed Cluster.

Quick start¶

This quick start uses the HDFS connector to export data produced by the Avro console producer to HDFS and assumes the following:

You have started the required services with the default configurations and you should make necessary changes according to the actual configurations used.
Security is not configured for HDFS and Hive metastore. To make the necessary security configurations, see Secure HDFS and Hive metastore.

Before you start Confluent Platform, make sure Hadoop is running locally or remotely and that you know the HDFS URL. For Hive integration, you need to have Hive installed and to know the metastore thrift URI. You also need to ensure the connector user has write access to the directories specified in topics.dir and logs.dir. The default value of topics.dir is /topics and the default value of logs.dir is /logs, if you don’t specify the two configurations, make sure that the connector user has write access to /topics and /logs. You may need to create /topics and /logs before running the connector as the connector usually don’t have write access to /.

Complete the following steps:

Start all the necessary services using the Confluent CLI.

If not already in your PATH, add Confluent’s bin directory by running: export PATH=<path-to-confluent>/bin:$PATH

confluent local services start

Every service will start in order, printing a message with its status:

Starting Zookeeper
Zookeeper is [UP]
Starting Kafka
Kafka is [UP]
Starting Schema Registry
Schema Registry is [UP]
Starting Kafka REST
Kafka REST is [UP]
Starting Connect
Connect is [UP]
Starting KSQL Server
KSQL Server is [UP]
Starting Control Center
Control Center is [UP]

Start the Avro console producer to import a few records to Kafka:

  ./bin/kafka-avro-console-producer --broker-list localhost:9092 --topic test_hdfs \
--property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'

In the console producer, enter the following:
```
{"f1": "value1"}
{"f1": "value2"}
{"f1": "value3"}
```
The three records entered are published to the Kafka topic test_hdfs in Avro format.

Before starting the connector, ensure the configurations in etc/kafka-connect-hdfs/quickstart-hdfs.properties are properly set to your configurations of Hadoop (for example, hdfs.url points to the proper HDFS and using FQDN in the host). Then start connector by loading its configuration with the following command.

Note that you must include a double dash (--) between the topic name and your flag. For more information, see this post.

confluent local services connect connector load hdfs-sink --config etc/kafka-connect-hdfs/quickstart-hdfs.properties

{
  "name": "hdfs-sink",
  "config": {
    "connector.class": "io.confluent.connect.hdfs.HdfsSinkConnector",
    "tasks.max": "1",
    "topics": "test_hdfs",
    "hdfs.url": "hdfs://localhost:9000",
    "flush.size": "3",
    "name": "hdfs-sink"
  },
  "tasks": []
}

Verify the connector started successfully by viewing the Connect worker’s log:

confluent local services connect log

Towards the end of the log you should see that the connector starts, logs a few messages, and then exports data from Kafka to HDFS. Once the connector finishes ingesting data to HDFS, check that the data is available in HDFS:

hadoop fs -ls /topics/test_hdfs/partition=0

You should see a file with name /topics/test_hdfs/partition=0/test_hdfs+0+0000000000+0000000002.avro The file name is encoded as topic+kafkaPartition+startOffset+endOffset.format.

You can use avro-tools-1.8.2.jar (available in Apache mirrors) to extract the content of the file. Run avro-tools directly on Hadoop as:

    hadoop jar avro-tools-1.8.2.jar tojson \
  hdfs://<namenode>/topics/test_hdfs/partition=0/test_hdfs+0+0000000000+0000000002.avro

where "<namenode>" is the HDFS name node hostname.

Or, if you experience issues, first copy the avro file from HDFS to the local
filesystem and try again with Java:

.. codewithvars:: bash

    hadoop fs -copyToLocal /topics/test_hdfs/partition=0/test_hdfs+0+0000000000+0000000002.avro \
    /tmp/test_hdfs+0+0000000000+0000000002.avro

    java -jar avro-tools-1.8.2.jar tojson /tmp/test_hdfs+0+0000000000+0000000002.avro

You should see the following output:

.. codewithvars:: bash

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

Stop the Kafka Connect worker as well as all the rest of Confluent Platform by running:

confluent local stop

Your output should resemble:

Stopping Control Center
Control Center is [DOWN]
Stopping KSQL Server
KSQL Server is [DOWN]
Stopping Connect
Connect is [DOWN]
Stopping Kafka REST
Kafka REST is [DOWN]
Stopping Schema Registry
Schema Registry is [DOWN]
Stopping Kafka
Kafka is [DOWN]
Stopping Zookeeper
Zookeeper is [DOWN]

You may also stop all the services and wipe out any data generated during this quick start by running the following command:

confluent local destroy

Your output should resemble:

Stopping Control Center
Control Center is [DOWN]
Stopping KSQL Server
KSQL Server is [DOWN]
Stopping Connect
Connect is [DOWN]
Stopping Kafka REST
Kafka REST is [DOWN]
Stopping Schema Registry
Schema Registry is [DOWN]
Stopping Kafka
Kafka is [DOWN]
Stopping Zookeeper
Zookeeper is [DOWN]
Deleting: /var/folders/ty/rqbqmjv54rg_v10ykmrgd1_80000gp/T/confluent.PkQpsKfE

Note that if you want to run the quick start with Hive integration, before starting the connector, you need to add the following configurations to etc/kafka-connect-hdfs/quickstart-hdfs.properties:

hive.integration=true
hive.metastore.uris=thrift uri to your Hive metastore
schema.compatibility=BACKWARD

After the connector finishes ingesting data to HDFS, you can use Hive to check the data:

$hive>SELECT * FROM test_hdfs;

If you leave the hive.metastore.uris empty, an embedded Hive metastore will be created in the directory the connector is started. You need to start Hive in that specific directory to query the data.

Configuration¶

This section gives example configurations that cover common scenarios. For a complete description of the available configuration options, see Configuration Reference for HDFS 2 Sink Connector for Confluent Platform.

Example¶

Here is the content of etc/kafka-connect-hdfs/quickstart-hdfs.properties:

name=hdfs-sink
connector.class=io.confluent.connect.hdfs.HdfsSinkConnector
tasks.max=1
topics=test_hdfs
hdfs.url=hdfs://localhost:9000
flush.size=3

The first few settings are common settings you’ll specify for all connectors. The topics specifies the topics to export data from, in this case test_hdfs. The hdfs.url specifies the HDFS to write data to. You should set this according to your configuration. The flush.size specifies the number of records the connector need to write before invoking file commits.

For high-availability HDFS deployments, set hadoop.conf.dir to the directory containing hdfs-site.xml. Then, set hdfs.url to the Namenode nameservice ID (for example, nameservice1).

Hive integration¶

At minimum, you need to specify hive.integration, hive.metastore.uris and schema.compatibility when integrating Hive. Here is an example configuration:

hive.integration=true
hive.metastore.uris=thrift://localhost:9083 # FQDN for the host part
schema.compatibility=BACKWARD

You should adjust the hive.metastore.uris according to your Hive configurations. Also, you should note the following

If you don’t specify the hive.metastore.uris, the connector will use a local metastore with Derby in the directory running the connector. You need to run Hive in this directory in order to see the Hive metadata change.
As connector tasks are long running, the connections to Hive metastore are kept open until tasks are stopped. In the default Hive configuration, reconnecting to Hive metastore creates a new connection. When the number of tasks is large, it is possible that the retries can cause the number of open connections to exceed the max allowed connections in the operating system. Thus it is recommended to set hcatalog.hive.client.cache.disabled to true in hive.xml.

Also, to support schema evolution, the schema.compatibility to be BACKWARD, FORWARD and FULL. This ensures that Hive can query the data written to HDFS with different schemas using the latest Hive table schema. For more information on schema compatibility, see Schema Evolution.

Secure HDFS and Hive metastore¶

To work with secure HDFS and Hive metastore, you need to specify hdfs.authentication.kerberos, connect.hdfs.principal, connect.keytab, hdfs.namenode.principal:

hdfs.authentication.kerberos=true
connect.hdfs.principal=connect-hdfs/_HOST@YOUR-REALM.COM
connect.hdfs.keytab=path to the connector keytab
hdfs.namenode.principal=namenode principal

You need to create the Kafka connect principals and keytab files using Kerberos and distribute the keytab file to all hosts running the connector and ensure that only the connector user has read access to the keytab file. Also, you should note the following:

When security is enabled, you need to use FQDN for the host part of hdfs.url and hive.metastore.uris.
Currently, the connector requires that the principal and the keytab path to be the same on all the hosts running the connector. The hostname in the hdfs.namenode.principal configuration should be the fully qualified domain name (FQDN) of the Namenode host rather than the _HOST placeholder.

Partitioners and Storage¶

The storage connector’s partitioner determines how records read from a Apache Kafka® topic are partitioned into storage objects. The partitioner determines the <encodedPartition> portion of the storage object name. The partitioner is specified in the connector configuration with the partitioner.class configuration property.

Partitioners¶

The connector comes with the following partitioners:

Default Kafka Partitioner: The io.confluent.connect.storage.partitioner.DefaultPartitioner preserves the same topic partitions as the partitions in the Kafka records. Each topic partition ultimately ends up as a storage object with a name that includes both the Kafka topic and Kafka partition. The <encodedPartition> is always <topicName>/partition=<kafkaPartition>, resulting in storage object names like <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 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 seconds. This partitioner requires the following connector configuration properties:
- The path.format configuration property specifies the pattern used for the <encodedPartition> portion of the storage object name. For example, when path.format='year'=YYYY/'month'=MM/'day'=dd/'hour'=HH, 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 storage objects within a single encoded partition directory. For example, setting partition.duration.ms=600000 (10 minutes) results in each storage object in that directory having no more than 10 minutes of records. * The locale configuration property specifies the 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.
- 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.
- 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, and 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 storage object in each daily directory). This partitioner always results in 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 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.
- The timezone configuration property specifies the current timezone in which the dates and times are set. 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.
- 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, and 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 storage object in each hourly directory). This partitioner always results in 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 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. * 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.
- 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, and RecordField to extract the timestamp from one of the fields in the record’s value as specified by the timestamp.field configuration property. As described in the following section, the choice of timestamp.extractor affects whether the storage connector can support exactly once delivery.

You can also choose to use a custom partitioner by implementing the io.confluent.connect.storage.partitioner.Partitioner interface by packaging your implementation into a JAR file and then completing the following steps:

Place the JAR file into the Confluent Platform directory path share/java/kafka-connect-<storage-connector-name> on each worker node.
Restart all of the Connect worker nodes.
Configure storage connectors to use your fully-qualified partitioner class name.

Storage object formats¶

The storage connector can serialize multiple records into each storage object using a number of formats. The connector’s format.class configuration property identifies the name of the Java class that implements the io.confluent.connect.storage.format.Format interface. Several of these implementations are described below:

Avro: Use format.class=io.confluent.connect.hdfs.avro.AvroFormat to write the storage object as an Avro container file and 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.

Values are listed below:
- null (the default) for no Avro compression.
- deflate to use the deflate algorithm as specified in RFC 1951,
- snappy to use the Google Snappy compression library.
- 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.hdfs.json.JsonFormat to write the storage 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.hdfs.parquet.ParquetFormat to write the storage object as a Parquet file columnar storage format. The connector’s parquet.codec configuration property specifies the Parquet compression code. Values are listed below:
- snappy (the default) to use the Google Snappy compression library.
- none for no compression.
- gzip to use the GNU GZip compression library.
- lzo to use LZO (Lempel–Ziv–Oberhumer) compression library.
- brotli to use the Google Brotli compression library.
- lz4 to use BSD licensed LZ4 compression library.
- zstd to use Facebook’s ZStandard compression library.

You can also choose to use a custom format by implementing the io.confluent.connect.storage.format.Format interface by packaging your implementation into a JAR file and then completing the following steps:

Place the JAR file into the Confluent Platform directory path share/java/kafka-connect-<storage-connector-name> on each worker node.
Restart all of the Connect worker nodes.
Configure storage connectors to use your fully-qualified format class name.

Note

For this connector, you must use the AvroConverter, ProtobufConverter, or JsonSchemaConverter with ParquetFormat for this connector. Attempting to use the JsonConverter (with or without schemas) results in a NullPointerException and a StackOverflowException.

Storage object uploads¶

As the storage connector processes each record, it uses the partitioner to determine which encoded partition to write the record. This continues for each partition until the connector determines that a partition has enough records and should be flushed and uploaded to storage using the storage object name for that partition. This technique of knowing when to flush a partition file and upload it to 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 storage object. There is no default for this setting.

Important

Rotation strategy logic: In the following rotation strategies, the logic to flush files to storage is triggered when a new record arrives, after the defined interval or scheduled interval time. Flushing files is also triggered periodically by the offset.flush.interval.ms setting defined in the Connect worker configuration. The offset.flush.interval.ms setting defaults to 60000 ms (60 seconds). If you enable the properties rotate.interval.ms or rotate.schedule.interval.ms and ingestion rate is low, you should set offset.flush.interval.ms to a smaller value so that records flush at the rotation interval (or close to the interval) . Leaving the offset.flush.interval.ms set to the default 60 seconds may cause records to stay in an open file for longer than expected, if no new records get processed that trigger rotation.
Maximum span of record time: In this rotation strategy, the connector’s rotate.interval.ms property specifies the maximum timespan in milliseconds a file can remain open and ready for additional records. The timestamp for each file starts with the record timestamp of the first record written to the file, as determined by the partitioner’s timestamp.extractor. As long as the next record’s timestamp fits within the timespan specified by the rotate.interval.ms property, the record is written to the file. If a record’s timestamp does not fit within the timespan of rotate.interval.ms, the connector flushes the file, uploads it to storage, and commits the offsets of the records in that file. After this, the connector creates a new file with a timespan that starts with the first record, and writes the first record to the file.

Scheduled rotation: In this rotation strategy, the connector’s rotate.schedule.interval.ms specifies the maximum timespan in milliseconds a file can remain open and ready for additional records. Unlike rotate.interval.ms, with scheduled rotation the timestamp for each file starts with the system time that the first record is written to the file. You must have the partitioner parameter timezone configured (defaults to an empty string) when using this configuration property, otherwise the connector fails with an exception.

As long as a record is processed within the timespan specified by rotate.schedule.interval.ms, the record will be written to the file. As soon as a new record is processed after the timespan for the current file, the file is flushed, uploaded to storage, and the offset of the records in the file are committed. A new file is created with a timespan that starts with the current system time, and the new record is written to the file. This configuration is useful when you have to commit your data based on current server time, for example at the beginning of every hour. The default value -1 means that this feature is disabled.

Scheduled rotation uses rotate.schedule.interval.ms to close the file and upload to storage on a regular basis using the current time, rather than the record time. Even if the connector has no more records to process, Connect will still call the connector at least every offset.flush.interval.ms, as defined in the Connect worker’s configuration file. And every time this occurs, the connector uses the current time to determine if the currently opened file should be closed and uploaded to storage.

These strategies can be combined as needed. However, when using either of the two rotation strategies described above, the connector only closes and uploads a file to storage when the next file does not belong based upon the timestamp. In other words, if the connector has no more records to process, the connector may keep the file open for a significant period of time, until the connector can process another record.

Schema Evolution¶

The HDFS 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.

Important

Schema evolution only works if the records are generated with the default naming strategy, which is TopicNameStrategy. An error may occur if other naming strategies are used. This is because records are not compatible with each other. schema.compatibility should be set to NONE if other naming strategies are used. This may result in small object files because the sink connector creates a new file every time the schema ID changes between records. See Subject Name Strategy for more information about naming strategies.

NO Compatibility: By default, the schema.compatibility is set to NONE. In this case, the connector ensures that each file written to HDFS has the proper schema. When the connector observes a schema change in data, it commits the current set of files for the affected topic partitions and writes the data with new schema in new files.
BACKWARD Compatibility: If a schema is evolved in a backward compatible way, you can always use the latest schema to query all the data uniformly. For example, removing fields is a backward compatible change to a schema, since when you encounter records written with the old schema that contain these fields, you can just ignore them. Adding a field with a default value is also backward compatible.

If BACKWARD is specified in the schema.compatibility, the connector keeps track of the latest schema used in writing data to HDFS, and if a data record with a schema version larger than current latest schema arrives, the connector commits the current set of files and writes the data record with new schema to new files. For data records arriving at a later time with schema of an earlier version, the connector projects the data record to the latest schema before writing to the same set of files in HDFS.
FORWARD Compatibility: If a schema is evolved in a forward compatible way, you can always use the oldest schema to query all the data uniformly. Removing a field that had a default value is forward compatible, since the old schema will use the default value when the field is missing.

If FORWARD is specified in the schema.compatibility, the connector projects the data to the oldest schema before writing to the same set of files in HDFS.
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.

If Hive integration is enabled, you need to specify the schema.compatibility to be BACKWARD, FORWARD or FULL. This ensures that the Hive table schema is able to query all the data under a topic written with different schemas. If the schema.compatibility is set to BACKWARD or FULL, the Hive table schema for a topic will be equivalent to the latest schema in the HDFS files under that topic that can query the whole data of that topic. If the schema.compatibility is set to FORWARD, the Hive table schema of a topic is equivalent to the oldest schema of the HFDS files under that topic that can query the whole data of that topic.