Important Warning

Update July 2016: This Tech Preview documentation from March 2016 is outdated and deprecated. Please use the latest Confluent Platform documentation instead.

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 will be 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.

Quickstart

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

Start Zookeeper, Kafka and SchemaRegistry is you haven’t done so. The instructions on how to start these services is 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 config and you should make necessary changes according to the actual configurations used.

Note

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 corresponding configuration changes 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 will be 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/quickstart-hdfs.properties is property set to your configuration 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/connect-avro-standalone.properties \
etc/kafka-connect-hdfs/quickstart-hdfs.properties

You should see that the process starts up and logs some messages, and then it will export 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/partitions=0

You should see a file with name /topics/t1/partition=0/test_hdfs+0+0000000000+0000000002.avro The file name is encoded as topic+kafkaPartition+startOffset+endOoffset.format.

You can use avro-tools-1.7.7.jar (http://mirror.metrocast.net/apache/avro/avro-1.7.7/java/avro-tools-1.7.7.jar) to extract the content of the file:

$ hadoop jar avro-tools-1.7.7.jar tojson \
/topics/test_hdfs/partition=0/test_hdfs+0+0000000000+0000000002.avro

You should see the following output:

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

Note

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/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;

Note

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 in order to query the data.

Features

The HDFS connector offers a bunch of features as follows:

  • Exactly Once Delivery: The connector uses a write ahead log to make sure each record exports to HDFS exactly once. Also, the connector manages offset commit by encoding the Kafka offset information into the file so that the we can start from the last committed offset 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 rules. When the connector observes a schema change, it will project 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 the Hive table have the proper table schema to query the whole data under a topic written with different schemas.
  • Secure HDFS and Hive Metastore: 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.

Configuration

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

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 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.

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:

format.class=io.confluent.connect.hdfs.parquet.ParquetFormat
partitioner.class=io.confluent.connect.hdfs.partitioner.HourlyPartitioner

Note

If you want ot use the field partitioner, you need to specify the partition.field.name 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.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.

Note

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.

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:

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 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.

Note

When security is enabled, you need to use FQDN for the host part of hdfs.url and``hive.metastore.uris``.

Note

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.

Configuration Options

flush.size

Number of records written to HDFS before invoking file commits.

  • Type: int
  • Default:
  • Importance: high
hdfs.url

The HDFS connection URL. This configuration has the format of hdfs:://hostname:port and specifies the HDFS to export data to.

  • Type: string
  • Default: “”
  • Importance: high
connect.hdfs.keytab

The path to the keytab file for the HDFS connector principal. This keytab file should only be readable by the connector user.

  • Type: string
  • Default: “”
  • Importance: high
connect.hdfs.principal

The principal to use when HDFS is using Kerberos to for authentication.

  • Type: string
  • Default: “”
  • Importance: high
format.class

The format class to use when writing data to HDFS.

  • Type: string
  • Default: “io.confluent.connect.hdfs.avro.AvroFormat”
  • Importance: high
hadoop.conf.dir

The Hadoop configuration directory.

  • Type: string
  • Default: “”
  • Importance: high
hadoop.home

The Hadoop home directory.

  • Type: string
  • Default: “”
  • Importance: high
hdfs.authentication.kerberos

Configuration indicating whether HDFS is using Kerberos for authentication.

  • Type: boolean
  • Default: false
  • Importance: high
hdfs.namenode.principal

The principal for HDFS Namenode.

  • Type: string
  • Default: “”
  • Importance: high
hive.conf.dir

Hive configuration directory

  • Type: string
  • Default: “”
  • Importance: high
hive.database

The database to use when the connector creates tables in Hive.

  • Type: string
  • Default: “default”
  • Importance: high
hive.home

Hive home directory

  • Type: string
  • Default: “”
  • Importance: high
hive.integration

Configuration indicating whether to integrate with Hive when running the connector.

  • Type: boolean
  • Default: false
  • Importance: high
hive.metastore.uris

The Hive metastore URIs, can be IP address or fully-qualified domain name and port of the metastore host.

  • Type: string
  • Default: “”
  • Importance: high
logs.dir

Top level HDFS directory to store the write ahead logs.

  • Type: string
  • Default: “logs”
  • Importance: high
partitioner.class

The partitioner to use when writing data to HDFS. 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; TimebasedPartitioner, which partitions data according to the time ingested to HDFS.

  • Type: string
  • Default: “io.confluent.connect.hdfs.partitioner.DefaultPartitioner”
  • Importance: high
rotate.interval.ms

The time interval in milliseconds to invoke file commits. This configuration ensures that file commits are invoked every configured interval. This configuration is useful when data ingestion rate is low and the connector didn’t write enough messages to commit files.The default value -1 means that this feature is disabled.

  • Type: long
  • Default: -1
  • Importance: high
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”
  • Importance: high
topics.dir

Top level HDFS directory to store the data ingested from Kafka.

  • Type: string
  • Default: “topics”
  • Importance: high
locale

The locale to use when partitioning with TimeBasedPartitioner.

  • Type: string
  • Default: “”
  • Importance: medium
partition.duration.ms

The duration of a partition milliseconds used by TimeBasedPartitioner. The default value -1 means that we are not using TimebasedPartitioner.

  • Type: long
  • Default: -1
  • Importance: medium
partition.field.name

The name of the partitioning field when FieldPartitioner is used.

  • Type: string
  • Default: “”
  • Importance: medium
path.format

This configuration is used to set 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 will have the format /year=2015/month=12/day=07/hour=15

  • Type: string
  • Default: “”
  • Importance: medium
shutdown.timeout.ms

Clean shutdown timeout. This makes sure that asynchronous Hive metastore updates are completed during connector shutdown.

  • Type: long
  • Default: 3000
  • Importance: medium
timezone

The timezone to use when partitioning with TimeBasedPartitioner.

  • Type: string
  • Default: “”
  • Importance: medium
filename.offset.zero.pad.width

Width to zero pad offsets in HDFS filenames to if the offsets is too short in order to provide fixed width filenames that can be ordered by simple lexicographic sorting.

  • Type: int
  • Default: 10
  • Importance: low
kerberos.ticket.renew.period.ms

The period in milliseconds to renew the Kerberos ticket.

  • Type: long
  • Default: 3600000
  • Importance: low
retry.backoff.ms

The retry backoff in milliseconds. This config is used to notify Kafka connect to retry delivering a message batch or performing recovery in case of transient exceptions.

  • Type: long
  • Default: 5000
  • Importance: low
schema.cache.size

The size of the schema cache used in the Avro converter.

  • Type: int
  • Default: 1000
  • Importance: low
storage.class

The underlying storage layer. The default is HDFS

  • Type: string
  • Default: “io.confluent.connect.hdfs.storage.HdfsStorage”
  • Importance: low

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.