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HDFS Connector

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

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 chucks. Each chunk of data is represented as an HDFS file with topic, kafka partition, start and end offsets of this data chuck 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 connector integrates with Hive and when it is enabled, the connector automatically creates an external Hive partitioned table for each Kafka topic and updates the table according to the available data in HDFS.


In this Quickstart, we use the HDFS connector to export data produced by the Avro console producer to HDFS.

Start Zookeeper, Kafka and SchemaRegistry if you haven’t done so. The instructions on how to start these services are available at the Confluent Platform QuickStart. You also need to have Hadoop running locally or remotely and make sure that you know the HDFS url. For Hive integration, you need to have Hive installed and to know the metastore thrift uri.

This Quickstart assumes that you started the required services with the default configurations and you should make necessary changes according to the actual configurations used.


You need to make sure the connector user have 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 /.

Also, this Quickstart assumes that security is not configured for HDFS and Hive metastore, please make the necessary configurations change following Secure HDFS and Hive Metastore section.

First, start the Avro console producer:

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

Then in the console producer, type in:

{"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, please make sure that the configurations in etc/kafka-connect-hdfs/ are properly set to your configurations of Hadoop, e.g. hdfs.url points to the proper HDFS and using FQDN in the host. Then run the following command to start Kafka connect with the HDFS connector:

$ ./bin/connect-standalone etc/schema-registry/ \

You should see that the process starts up and logs some 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-1.7.7.jar (available in Apache mirrors) to extract the content of the file. Run avro-tools directly on Hadoop as:

$ hadoop jar avro-tools-1.7.7.jar tojson \

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

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

$ java -jar avro-tools-1.7.7.jar tojson /tmp/test_hdfs+0+0000000000+0000000002.avro

You should see the following output:



If you want to run the Quickstart with Hive integration, before starting the connector, you need to add the following configurations to etc/kafka-connect-hdfs/

hive.metastore.uris=thrift uri to your Hive metastore

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.


The HDFS connector offers a bunch of features:

  • Exactly Once Delivery: The connector uses a write ahead log to ensure each record exports to HDFS exactly once. Also, the connector manages offsets commit by encoding the Kafka offset information into the file so that the we can start from the last committed offsets in case of failures and task restarts.
  • 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.
  • Schema Evolution: The connector supports schema evolution and different schema compatibility levels. When the connector observes a schema change, it projects to the proper schema according to the schema.compatibility configuration. Hive integration is supported if BACKWARD, FORWARD and FULL is specified for schema.compatibility and Hive tables have the table schema that are able to query the whole data under a topic written with different schemas.
  • Secure HDFS and Hive Metastore Support: The connector supports Kerberos authentication and thus works with secure HDFS and Hive metastore.
  • Pluggable Partitioner: The connector supports default partitioner, field partitioner, and time based partitioner including daily and hourly partitioner out of the box. You can implement your own partitioner by extending the Partitioner class. Plus, you can customize time based partitioner by extending the TimeBasedPartitioner class.


This section gives example configurations that cover common scenarios, then provides an exhaustive description of the available configuration options.


Here is the content of etc/kafka-connect-hdfs/


The first few settings are common settings you’ll specify for all connectors. The topics specifies the topics we want to export data from, in this case test_hdfs. The hdfs.url specifies the HDFS we are writing data to and 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 HA HDFS deployments you will need to include hadoop.conf.dir, setting it to a directory which includes hdfs-site.xml. Once hdfs-site.xml is in place and hadoop.conf.dir has been set, hdfs.url may be set to the namenodes nameservice id. i.e. ‘nameservice1’ .

Format and Partitioner

You need to specify the format.class and partitioner.class if you want to write other formats to HDFS or use other partitioners. The following example configurations demonstrates how to write Parquet format and use hourly partitioner:



If you want ot use the field partitioner, you need to specify the configuration as well to specify the field name of the record.

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.metastore.uris=thrift://localhost:9083 # FQDN for the host part

You should adjust the hive.metastore.uris according to your Hive configurations.


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. Please find more information on schema compatibility in the Schema Evolution section.

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:

connect.hdfs.keytab=path to the connector keytab
hdfs.namenode.principal=namenode principal

You need to create the Kafka connect principals and keytab files via Kerboros and distribute the keytab file to all hosts that running the connector and ensures that only the connector user has read access to the keytab file.


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 host part of the hdfs.namenode.prinicipal needs to be the actual FQDN of the Namenode host instead of the _HOST placeholder.

Schema Evolution

The HDFS connector supports schema evolution and reacts to schema changes of data according to the schema.compatibility configuration. In this section, we will explain 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 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, we can always use the latest schema to query all the data uniformly. For example, removing fields is backward compatible change to a schema, since when we encounter records written with the old schema that contain these fields we 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, we 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, we 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.