6.3.7. HBase Sink

A Connector and Sink to write events from Kafka to HBase. The connector takes the value from the Kafka Connect SinkRecords and inserts a new entry to HBase.

The Sink supports:

  1. The KCQL routing querying - Kafka topic payload field selection is supported, allowing you to select fields written to HBase.
  2. Topic to table routing via KCQL.
  3. RowKey selection - Selection of fields to use as the row key, if none specified the topic name, partition and offset are used via KCQL.
  4. Error policies. Prerequisites

  • Confluent 3.3
  • HBase 1.2.0
  • Java 1.8
  • Scala 2.11 Setup HBase Setup

Download and extract HBase:

wget https://www.apache.org/dist/hbase/1.2.1/hbase-1.2.1-bin.tar.gz
tar -xvf hbase-1.2.1-bin.tar.gz -C hbase

Edit conf/hbase-site.xml and add the following content:

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

The hbase.cluster.distributed is required since when you start hbase it will try and start it’s own Zookeeper, but in this case we want to use Confluents.

Now start HBase and check the logs to ensure it’s up:

bin/start-hbase.sh Sink Connector QuickStart

We you start the Confluent Platform, Kafka Connect is started in distributed mode (confluent start). In this mode a Rest Endpoint on port 8083 is exposed to accept connector configurations. We developed Command Line Interface to make interacting with the Connect Rest API easier. The CLI can be found in the Stream Reactor download under the bin folder. Alternatively the Jar can be pulled from our GitHub releases page. HBase Table

The Sink expects a precreated table in HBase. In the HBase shell create the test table, go to your HBase install location.

bin/hbase shell
hbase(main):001:0> create 'person',{NAME=>'d', VERSIONS=>1}

hbase(main):001:0> list
1 row(s) in 0.9530 seconds

hbase(main):002:0> describe 'person'
 'person', {NAME => 'd', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'false', DATA_BLOCK_ENCOD true
 _SCOPE => '0'}
1 row(s) in 0.0810 seconds Starting the Connector (Distributed)

Download, unpack and install the Stream Reactor and Confluent. Follow the instructions here if you haven’t already done so. All paths in the quickstart are based in the location you installed the Stream Reactor.

Once the Connect has started we can now use the kafka-connect-tools cli to post in our distributed properties file for HBase. If you are using the dockers you will have to set the following environment variable to for the CLI to connect to the Rest API of Kafka Connect of your container.

export KAFKA_CONNECT_REST="http://myserver:myport"
➜  bin/connect-cli create hbase-sink < conf/hbase-sink.properties

#Connector name=`hbase-sink`
connect.hbase.kcql=INSERT INTO person SELECT * FROM hbase-topic PK firstName, lastName
#task ids: 0

This hbase-sink.properties configuration defines:

  1. The name of the sink.
  2. The Sink class.
  3. The max number of tasks the connector is allowed to created. Should not be greater than the number of partitions in the Source topics otherwise tasks will be idle.
  4. The Source kafka topics to take events from.
  5. The HBase column family to write to.
  6. The KCQL routing querying.

Use the Confluent CLI to view Connects logs.

# Get the logs from Connect
confluent log connect

# Follow logs from Connect
confluent log connect -f

We can use the CLI to check if the connector is up but you should be able to see this in logs as-well.

#check for running connectors with the CLI
➜ bin/connect-cli ps
    ____        __        __  ___                  __        _
   / __ \____ _/ /_____ _/  |/  /___  __  ______  / /_____ _(_)___  ___  ___  _____
  / / / / __ `/ __/ __ `/ /|_/ / __ \/ / / / __ \/ __/ __ `/ / __ \/ _ \/ _ \/ ___/
 / /_/ / /_/ / /_/ /_/ / /  / / /_/ / /_/ / / / / /_/ /_/ / / / / /  __/  __/ /
/_____/\\_,\\\\\\\__,_/_/  /_/\___\\\\\,\/_/ /_/\\_/\__,_/_/_/ /_/\___/\___/_/
      / / / / __ )____ _________ / ___/(_)___  / /__
     / /_/ / __  / __ `/ ___/ _ \\__ \/ / __ \/ //_/
    / __  / /_/ / /_/ (__  )  __/__/ / / / / / ,<
   /_/ /_/_____/\__,_/____/\___/____/_/_/ /_/_/|_|
By Stefan Bocutiu (com.datamountaineer.streamreactor.connect.hbase.HbaseSinkTask:44) Test Records

Now we need to put some records it to the test_table topics. We can use the kafka-avro-console-producer to do this.

Start the producer and pass in a schema to register in the Schema Registry. The schema has a firstname field of type string, a lastname field of type string, an age field of type int and a salary field of type double.

${CONFLUENT_HOME}/bin/kafka-avro-console-producer \
  --broker-list localhost:9092 --topic hbase-topic \
  --property value.schema='{"type":"record","name":"User",

Now the producer is waiting for input. Paste in the following:

{"firstName": "John", "lastName": "Smith", "age":30, "salary": 4830}
{"firstName": "Anna", "lastName": "Jones", "age":28, "salary": 5430} Check for records in HBase

Now check the logs of the connector you should see this

INFO Sink task org.apache.kafka.connect.runtime.WorkerSinkTask@48ffb4dc finished initialization and start (org.apache.kafka.connect.runtime.WorkerSinkTask:155)
INFO Writing 2 rows to Hbase... (com.datamountaineer.streamreactor.connect.hbase.writers.HbaseWriter:83)

In HBase:

hbase(main):004:0* scan 'person'
ROW                                                  COLUMN+CELL
 Anna\x0AJones                                       column=d:age, timestamp=1463056888641, value=\x00\x00\x00\x1C
 Anna\x0AJones                                       column=d:firstName, timestamp=1463056888641, value=Anna
 Anna\x0AJones                                       column=d:income, timestamp=1463056888641, value=@\xB56\x00\x00\x00\x00\x00
 Anna\x0AJones                                       column=d:lastName, timestamp=1463056888641, value=Jones
 John\x0ASmith                                       column=d:age, timestamp=1463056693877, value=\x00\x00\x00\x1E
 John\x0ASmith                                       column=d:firstName, timestamp=1463056693877, value=John
 John\x0ASmith                                       column=d:income, timestamp=1463056693877, value=@\xB2\xDE\x00\x00\x00\x00\x00
 John\x0ASmith                                       column=d:lastName, timestamp=1463056693877, value=Smith
2 row(s) in 0.0260 seconds

Now stop the connector. Features

The HBase Sink writes records from Kafka to HBase.

The Sink supports:

  1. Field selection - Kafka topic payload field selection is supported, allowing you to select fields written to HBase.
  2. Topic to table routing.
  3. RowKey selection - Selection of fields to use as the row key, if none specified the topic name, partition and offset are used.
  4. Error policies. Kafka Connect Query Language

K afka C onnect Q uery L anguage found here GitHub repo allows for routing and mapping using a SQL like syntax, consolidating typically features in to one configuration option.

The HBase Sink supports the following:

INSERT INTO <table> SELECT <fields> FROM <source topic> <PK> primary_key_cols


#Insert mode, select all fields from topicA and write to tableA and use the default rowkey (topic name, partition, offset)

#Insert mode, select 3 fields and rename from topicB and write to tableB, use field y from the topic as the row key
INSERT INTO tableB SELECT x AS a, y AS b and z AS c FROM topicB PK y

This is set in the connect.hbase.kcql option. Error Polices

The Sink has three error policies that determine how failed writes to the target database are handled. The error policies affect the behaviour of the schema evolution characteristics of the sink. See the schema evolution section for more information.


Any error on write to the target database will be propagated up and processing is stopped. This is the default behaviour.


Any error on write to the target database is ignored and processing continues.


This can lead to missed errors if you don’t have adequate monitoring. Data is not lost as it’s still in Kafka subject to Kafka’s retention policy. The Sink currently does not distinguish between integrity constraint violations and or other expections thrown by drivers.


Any error on write to the target database causes the RetryIterable exception to be thrown. This causes the Kafka connect framework to pause and replay the message. Offsets are not committed. For example, if the table is offline it will cause a write failure, the message can be replayed. With the Retry policy the issue can be fixed without stopping the sink.

The length of time the Sink will retry can be controlled by using the connect.hbase.max.retries and the connect.hbase.retry.interval. Configurations


The hbase column family.

  • Type: string
  • Importance: high
  • Optional: no


Kafka connect query language expression. Allows for expressive topic to table routing, field selection and renaming. Fields to be used as the row key can be set by specifing the PK. The below example uses field1 and field2 are the row key.



If no primary keys are specified the topic name, partition and offset converted to bytes are used as the HBase rowkey.

  • Type: string
  • Importance: high
  • Optional: no


Specifies the action to be taken if an error occurs while inserting the data.

There are three available options, noop, the error is swallowed, throw, the error is allowed to propagate and retry. For retry the Kafka message is redelivered up to a maximum number of times specified by the connect.hbase.max.retries option. The connect.hbase.retry.interval option specifies the interval between retries.

The errors will be logged automatically.

  • Type: string
  • Importance: medium
  • Optional: yes
  • Default: RETRY


The maximum number of times a message is retried. Only valid when the connect.hbase.error.policy is set to retry.

  • Type: string
  • Importance: medium
  • Optional: yes
  • Default: 10


The interval, in milliseconds between retries if the Sink is using connect.hbase.error.policy set to RETRY.

  • Type: int
  • Importance: medium
  • Optional: yes
  • Default : 60000 (1 minute)


Enables the output for how many records have been processed.

  • Type: boolean
  • Importance: medium
  • Optional: yes
  • Default : false Example

connect.hbase.kcql=INSERT INTO person SELECT * FROM TOPIC1
name=hbase-test Schema Evolution

Upstream changes to schemas are handled by Schema registry which will validate the addition and removal or fields, data type changes and if defaults are set. The Schema Registry enforces Avro schema evolution rules. More information can be found here.

The HBase Sink will automatically write and update the HBase table if new fields are added to the Source topic, if fields are removed the Kafka Connect framework will return the default value for this field, dependent of the compatibility settings of the Schema registry. This value will be put into the HBase column family cell based on the connect.hbase.kcql value. Deployment Guidelines

Ensure the hbase-site.xml is on the the classpath of the connector. Distributed Mode

Connect, in production should be run in distributed mode.

  1. Install the Confluent Platform on each server that will form your Connect Cluster.
  2. Create a folder on the server called plugins/streamreactor/libs.
  3. Copy into the folder created in step 2 the required connector jars from the stream reactor download.
  4. Edit connect-avro-distributed.properties in the etc/schema-registry folder where you installed Confluent and uncomment the plugin.path option. Set it to the path you deployed the stream reactor connector jars in step 2.
  5. Start Connect, bin/connect-distributed etc/schema-registry/connect-avro-distributed.properties

Connect Workers are long running processes so set an init.d or systemctl service accordingly.

Connector configurations can then be push to any of the workers in the Cluster via the CLI or curl, if using the CLI remember to set the location of the Connect worker you are pushing to as it defaults to localhost.

export KAFKA_CONNECT_REST="http://myserver:myport" Kubernetes

Helm Charts are provided at our repo, add the repo to your Helm instance and install. We recommend using the Landscaper to manage Helm Values since typically each Connector instance has it’s own deployment.

Add the Helm charts to your Helm instance:

helm repo add datamountaineer https://datamountaineer.github.io/helm-charts/ TroubleShooting

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