MQTT Sink

Download connector MQTT Connector 1.2 for Kafka MQTT Connector 1.1 for Kafka

This MQTT sink connector allows you to write message from Kafka to MQTT brokers.

Prerequisites

  • Apache Kafka 0.11.x of above
  • Kafka Connect 0.11.x or above
  • Mqtt server
  • Java 1.8

Features

  1. The KCQL routing querying - Topic to measure mapping and Field selection
  2. Error policies for handling failures
  3. Payload support for Schema.Struct and payload Struct, Schema.String and JSON payload and JSON payload with no schema.

KCQL Support

INSERT INTO mqtt_topic SELECT { FIELD, ... } FROM kafka_topic_name

Tip

You can specify multiple KCQL statements separated by ; to have a the connector sink multiple topics.

The MQTT sink supports KCQL, Kafka Connect Query Language. The following support KCQL is available:

  1. Field selection
  2. Target MQTT topic selection.

Examples:

-- Insert into /landoop/demo all fields from kafka_topicA
INSERT INTO /landoop/demo SELECT * FROM kafka_topicA

Payload Support

Schema.Struct and a Struct Payload

If you follow the best practice while producing the events, each message should carry its schema information. The best option is to send AVRO. Your Connector configurations options include:

key.converter=io.confluent.connect.avro.AvroConverter
key.converter.schema.registry.url=http://localhost:8081
value.converter=io.confluent.connect.avro.AvroConverter
value.converter.schema.registry.url=http://localhost:8081

This requires the SchemaRegistry.

Note

This needs to be done in the connect worker properties if using Kafka versions prior to 0.11

Schema.String and a JSON Payload

Sometimes the producer would find it easier to just send a message with Schema.String and a JSON string. In this case your connector configuration should be set to value.converter=org.apache.kafka.connect.json.JsonConverter. This doesn’t require the SchemaRegistry.

key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter

Note

This needs to be done in the connect worker properties if using Kafka versions prior to 0.11

No schema and a JSON Payload

There are many existing systems which are publishing Json over Kafka and bringing them in line with best practices is quite a challenge, hence we added the support. To enable this support you must change the converters in the connector configuration.

key.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable=false
value.converter=org.apache.kafka.connect.json.JsonConverter
value.converter.schemas.enable=false

Note

This needs to be done in the connect worker properties if using Kafka versions prior to 0.11

Error Polices

Landoop sink connectors support error polices. These error polices allow you to control the behaviour of the sink if it encounters an error when writing records to the target system. Since Kafka retains the records, subject to the configured retention policy of the topic, the sink can ignore the error, fail the connector or attempt redelivery.

Throw

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

Noop

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

Warning

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 exceptions thrown by any drivers or target systems.

Retry

Any error on write to the target system 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.

Lenses QuickStart

The easiest way to try out this is using Lenses Box the pre-configured docker, that comes with this connector pre-installed. You would need to Connectors –> New Connector –> Sink –> MQTT and paste your configuration

../../_images/lenses-create-mqtt-sink-connector.png

MQTT Setup

HiveMQ, who provides enterprise MQTT brokers have a web client. We’ll use it for the quickstart. Go to this link, connect and set up a subscription to /landoop/mqtt_sink_topic/+ topic. You should see this.

../../_images/hivemqtt_connection.png

Now we have a web client listening for messages which will come from the MQTT Sink.

Installing the Connector

Connect, in production should be run in distributed mode

  1. Install and configure a Kafka Connect cluster
  2. Create a folder on each server called plugins/lib
  3. Copy into the above folder the required connector jars from the stream reactor download
  4. Edit connect-avro-distributed.properties in the etc/schema-registry folder and uncomment the plugin.path option. Set it to the root directory i.e. plugins 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.

Sink Connector QuickStart

Start Kafka Connect in distributed mode (see install). 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.

Starting the Connector

Download, and install Stream Reactor. Follow the instructions here if you haven’t already done so. All paths in the quickstart are based on 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 MQTT. For the CLI to work including when using the dockers you will have to set the following environment variable to point the Kafka Connect Rest API.

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

➜  bin/connect-cli create mqtt-sink < conf/mqtt-sink.properties

name=mqtt-sink
connector.class=com.datamountaineer.streamreactor.connect.mqtt.sink.MqttSinkConnector
tasks.max=1
topics=kafka-topic
connect.mqtt.hosts=tcp://broker.mqttdashboard.com:1883
connect.mqtt.clean=true
connect.mqtt.timeout=1000
connect.mqtt.keep.alive=1000
connect.mqtt.service.quality=1
connect.mqtt.client.id=dm_sink_id,
connect.mqtt.kcql=INSERT INTO /landoop/mqtt_topic SELECT * FROM kafka-topic

If you switch back to the terminal you started Kafka Connect in, you should see the Elastic Search Sink being accepted and the task starting.

  INFO
    __                    __
   / /   ____ _____  ____/ /___  ____  ____
  / /   / __ `/ __ \/ __  / __ \/ __ \/ __ \
 / /___/ /_/ / / / / /_/ / /_/ / /_/ / /_/ /
/_____/\__,_/_/ /_/\__,_/\____/\____/ .___/
                                   /_/
      __  _______  ____________   _____ _       __
     /  |/  / __ \/_  __/_  __/  / ___/(_)___  / /__
    / /|_/ / / / / / /   / /     \__ \/ / __ \/ //_/  By Andrew Stevenson
   / /  / / /_/ / / /   / /     ___/ / / / / / ,<
  /_/  /_/\___\_\/_/   /_/     /____/_/_/ /_/_/|_|
    (com.datamountaineer.streamreactor.connect.mqtt.sink.MqttSinkTask:41)

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
mqtt-sink

Test Records

Tip

If your input topic doesn’t match the target use Lenses SQL to transform in real-time the input, no Java or Scala required!

Now we need to put some records it to the kafka-topic 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.

bin/kafka-avro-console-producer \
  --broker-list localhost:9092 --topic kafka-topic \
  --property value.schema='{"type":"record","name":"User","namespace":"com.datamountaineer.streamreactor.connect.mqtt"
  ,"fields":[{"name":"firstName","type":"string"},{"name":"lastName","type":"string"},{"name":"age","type":"int"},{"name":"salary","type":"double"}]}'

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

{"firstName": "John", "lastName": "Smith", "age":30, "salary": 4830}

Check for Records in the MQTT Broker

Go back to the browser you started the HiveMQ web client in. You should see the messages arrive in the messages section.

../../_images/hivemqtt_result.png

Configurations

The Kafka Connect framework requires the following in addition to any connectors specific configurations:

Config Description Type Value
name Name of the connector string This must be unique across the Connect cluster
topics
The topics to sink.
The connector will check this matchs the KCQL statement
 string  
tasks.max The number of tasks to scale output int 1
connector.class Name of the connector class string com.datamountaineer.streamreactor.connect.mqtt.sink.MqttSinkConnector

Connector Configurations

Config Description Type
connect.mqtt.kcql
Kafka connect query language expression.
Allows for expressive MQTT topic to Kafka
topic routing. Currently, there is no support
for filtering the fields from the incoming payload
 string
connect.mqtt.hosts Specifies the mqtt connection endpoints string

Optional Configurations

Config Description Type Default
connect.mqtt.service.quality
The Quality of Service (QoS)
level is an agreement between sender
and receiver of a message regarding
the guarantees of delivering a message.
There are 3 QoS levels in MQTT:
At most once (0); At least once (1);
Exactly once (2)
 int 1
connect.mqtt.username Contains the MQTT connection user name string  
connect.mqtt.password Contains the MQTT connection password string  
connect.mqtt.client.id
Provides the client connection
identifier.
If it is not provided the mqtt
will generate one
 string  
connect.mqtt.connection.timeout
Sets the timeout to wait in milliseconds
for the broker connection to be established
 int 300
connect.mqtt.connection.clean
The clean session flag indicates the broker,
whether the client wants to establish a persistent session or not.
A persistent session (the flag is false) means, that the broker
will store all subscriptions for the client and also all missed
messages when subscribing with Quality of Service (QoS) 1 or 2.
If clean session is set to true, the broker won’t store anything
for the client and will also purge all information from a previous
persistent session
 boolean true
connect.mqtt.connection.keep.alive
The keep-alive functionality
assures that the connection is still open and
both broker and client are connected to one another.
Therefore the client specifies a time interval in
seconds and communicates it to the broker during
the establishment of the connection.
The interval is the longest possible period of time in milliseconds,
which broker and client can endure without sending a message
 int 5000
connect.mqtt.connection.ssl.ca.cert
Provides the path to the CA
certificate file to use with the MQTT connection
 string  
connect.mqtt.connection.ssl.cert
Provides the path to the certificate
file to use with the MQTT connection
 string  
connect.mqtt.connection.ssl.key Certificate private key file path string  
connect.mqtt.error.policy
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.mqtt.max.retries option
 string THROW
connect.mqtt.max.retries
The maximum number of times a message
is retried. Only valid when the
connect.mqtt.error.policy is set to RETRY
 string 10
connect.mqtt.retry.interval
The interval, in milliseconds between retries,
if the sink is using
connect.mqtt.error.policy set to RETRY
 string 60000
connect.progress.enabled
Enables the output for how many
records have been processed
 boolean false

Example

name=mqtt-sink
connector.class=com.datamountaineer.streamreactor.connect.mqtt.sink.MqttSinkConnector
tasks.max=1
connect.mqtt.clean=true
connect.mqtt.timeout=1000
connect.mqtt.keep.alive=1000
connect.mqtt.client.id=dm_source_id,
connect.mqtt.hosts=tcp://127.0.0.1:11883
connect.mqtt.service.quality=1
connect.mqtt.kcql=INSERT INTO mqtt_topic SELECT * FROM kafka_topic
connect.progress.enabled=true

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 its own deployment.

Add the Helm charts to your Helm instance:

helm repo add landoop https://landoop.github.io/kafka-helm-charts/

TroubleShooting

Please review the FAQs and join our slack channel