6.3.15. VoltDB

A Kafka Connector and Sink to write events from Kafka to VoltDB. The connector used the built in stored procedures for inserts and upserts but requires the tables to be pre-created.

The Sink supports:

  1. The KCQL routing querying - Kafka topic payload field selection is supported, allowing you to select fields written to VoltDB.
  2. Topic to table routing via KCQL.
  3. Voltdb write modes, upsert and insert via KCQL.
  4. Error policies for handling failures. Prerequisites

  • Confluent 3.3
  • VoltDB 6.4
  • Java 1.8
  • Scala 2.11 Setup VoltDB Setup

Download VoltDB from here

Unzip the archive

tar -xzf voltdb-ent-*.tar.gz

Start VoltDB:

cd voltdb-ent-*
➜  bin/voltdb create

Build: 6.5 voltdb-6.5-0-gd1fe3fa-local Enterprise Edition
Initializing VoltDB...
 _    __      ____  ____  ____
| |  / /___  / / /_/ __ \/ __ )
| | / / __ \/ / __/ / / / __  |
| |/ / /_/ / / /_/ /_/ / /_/ /


Connecting to VoltDB cluster as the leader...
Host id of this node is: 0
Starting VoltDB with trial license. License expires on Sep 11, 2016.
Initializing the database and command logs. This may take a moment...
WARN: This is not a highly available cluster. K-Safety is set to 0. Confluent Setup

Follow the instructions here. 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. Create Voltdb Table

At present the Sink doesn’t support auto creation of tables so we need to login to VoltDb to create one. In the directory you extracted Voltdb start the sqlcmd shell and enter the following DDL statement. This creates a table called person.

create table person(firstname varchar(128), lastname varchar(128), age int, salary float, primary key (firstname, lastname));
➜  bin ./sqlcmd
SQL Command :: localhost:21212
1> create table person(firstname varchar(128), lastname varchar(128), age int, salary float, primary key (firstname, lastname));
Command succeeded.
2> 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 VoltDB. 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 voltdb-sink < conf/voltdb-sink.properties

#Connector `voltdb-sink`:
connect.volt.kcql=INSERT INTO person SELECT * FROM sink-test
#task ids:

The voltdb-sink.properties file defines:

  1. The name of the sink.
  2. The Sink class.
  3. The max number of tasks the connector is allowed to created.
  4. The topics to read from (Required by framework)
  5. The name of the voltdb host to connect to.
  6. Username to connect as.
  7. The password for the username.
  8. 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
[2016-08-21 20:31:36,398] INFO Finished starting connectors and tasks (org.apache.kafka.connect.runtime.distributed.DistributedHerder:769)
[2016-08-21 20:31:36,406] INFO
 _____                                                    _
(____ \       _                                 _        (_)
 _   \ \ ____| |_  ____ ____   ___  _   _ ____ | |_  ____ _ ____   ____ ____  ____
| |   | / _  |  _)/ _  |    \ / _ \| | | |  _ \|  _)/ _  | |  _ \ / _  ) _  )/ ___)
| |__/ ( ( | | |_( ( | | | | | |_| | |_| | | | | |_( ( | | | | | ( (/ ( (/ /| |
|_____/ \_||_|\___)_||_|_|_|_|\___/ \____|_| |_|\___)_||_|_|_| |_|\____)____)_|
                                    by Stefan Bocutiu
 _    _     _      _____   _           _    _       _
| |  | |   | |_   (____ \ | |         | |  (_)     | |
| |  | |__ | | |_  _   \ \| | _        \ \  _ ____ | |  _
 \ \/ / _ \| |  _)| |   | | || \        \ \| |  _ \| | / )
  \  / |_| | | |__| |__/ /| |_) )   _____) ) | | | | |< (
   \/ \___/|_|\___)_____/ |____/   (______/|_|_| |_|_| \_)
[2016-08-21 20:31:36,407] INFO VoltSinkConfig values:
    connect.volt.error.policy = THROW
    connect.volt.retry.interval = 60000
    connect.volt.kcql = INSERT INTO person SELECT * FROM sink-test
    connect.volt.max.retires = 20
    connect.volt.servers = localhost:21212
    connect.volt.username =
    connect.volt.password =
[2016-08-21 20:31:36,501] INFO Settings:com.datamountaineer.streamreactor.connect.voltdb.config.VoltSettings$@34c34c3e (com.datamountaineer.streamreactor.connect.voltdb.VoltSinkTask:71)
[2016-08-21 20:31:36,565] INFO Connecting to VoltDB... (com.datamountaineer.streamreactor.connect.voltdb.writers.VoltConnectionConnectFn$:28)
[2016-08-21 20:31:36,636] INFO Connected to VoltDB node at: localhost:21212 (com.datamountaineer.streamreactor.connect.voltdb.writers.VoltConnectionConnectFn$:46) 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 sink-test \
  --property value.schema='{"type":"record","name":"User","namespace":"com.datamountaineer.streamreactor.connect.voltdb"

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

{"firstName": "John", "lastName": "Smith", "age":30, "salary": 4830} Check for records in VoltDb

Now check the logs of the connector you should see this:

[2016-08-21 20:41:25,361] INFO Writing complete (com.datamountaineer.streamreactor.connect.voltdb.writers.VoltDbWriter:61)
[2016-08-21 20:41:25,362] INFO Records handled (com.datamountaineer.streamreactor.connect.voltdb.VoltSinkTask:86)

In Voltdb sqlcmd terminal


---------- --------- ---- -------
John       Smith       30  4830.0

(Returned 1 rows in 0.01s)

Now stop the connector. Features

The Sink supports:

  1. Field selection - Kafka topic payload field selection is supported, allowing you to select fields written to VoltDB.
  2. Topic to table routing.
  3. Voltdb write modes, upsert and insert.
  4. Error policies for handling failures. 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 Voltdb Sink supports the following:

INSERT INTO <table> SELECT <fields> FROM <source topic>
UPSERT INTO <table> SELECT <fields> FROM <source topic>


#Insert mode, select all fields from topicA and write to tableA

#Insert mode, select 3 fields and rename from topicB and write to tableB
INSERT INTO tableB SELECT x AS a, y AS b and z AS c FROM topicB

#Upsert mode, select 3 fields and rename from topicB and write to tableB
UPSERT INTO tableB SELECT x AS a, y AS b and z AS c FROM topicB

This is set in the connect.volt.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.volt.max.retries and the connect.volt.retry.interval. Topic Routing

The Sink supports topic routing that allows mapping the messages from topics to a specific table. For example, map a topic called “bloomberg_prices” to a table called “prices”. This mapping is set in the connect.volt.kcql option.


//Select all
INSERT INTO table1 SELECT * FROM topic1; INSERT INTO tableA SELECT * FROM topicC Write Modes

The Sink supports both insert and upsert modes. This mapping is set in the connect.volt.kcql option.


Insert is the default write mode of the sink.

Insert Idempotency

Kafka currently provides at least once delivery semantics. Therefore, this mode may produce errors if unique constraints have been implemented on the target tables. If the error policy has been set to NOOP then the Sink will discard the error and continue to process, however, it currently makes no attempt to distinguish violation of integrity constraints from other exceptions such as casting issues.


The Sink support VoltDB upserts which replaces the existing row if a match is found on the primary keys.

Upsert Idempotency

Kafka currently provides at least once delivery semantics and order is a guaranteed within partitions.

This mode will, if the same record is delivered twice to the sink, result in an idempotent write. The existing record will be updated with the values of the second which are the same.

If records are delivered with the same field or group of fields that are used as the primary key on the target table, but different values, the existing record in the target table will be updated.

Since records are delivered in the order they were written per partition the write is idempotent on failure or restart. Redelivery produces the same result. Configurations


KCQL expression describing field selection and routes.

  • Data type : string
  • Importance : high
  • Optional : no


Comma separated server[:port].

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


The user to connect to the volt database.

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


The password for the voltdb user.

  • 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.volt.max.retries option. The connect.volt.retry.interval option specifies the interval between retries.

The errors will be logged automatically.

  • Type: string
  • Importance: high
  • Default: throw


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

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


Specifies how many records to insert together at one time. If the connect framework provides less records when it is calling the Sink it won’t wait to fulfill this value but rather execute it.

  • Type : int
  • Importance : medium
  • Optional: yes
  • Defaults : 1000


Enables the output for how many records have been processed.

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

No schema evolution is handled by the Sink yet on changes in the upstream topics. Deployment Guidelines 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

Please review the FAQs and join our slack channel.