CoAP Sink

Download connector CoAP Connector for Kafka 1.0 CoAP Connector for Kafka 0.11

This CoAP Sink connector allows you to write messages from Kafka to CoAP.

Prerequisites

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

Features

  1. DTLS secure clients
  2. The KCQL routing querying - Topic to measure mapping and Field selection
  3. Error policies for handling failures.

KCQL Support

INSERT INTO coap_resource SELECT { FIELD, ... } FROM kafka_topic

Tip

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

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

  1. Field selection
  2. Selection of target index.

Kafka messages are stored as Json strings in CoAP.

Example:

-- Insert mode, select all fields from topicA and write to resourceA
INSERT INTO resourceA SELECT * FROM topicA

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

This is set in the connect.coap.kcql option.

DTLS Secure connections

The Connector uses the Californium Java API and for secure connections use the Scandium security module provided by Californium. Scandium (Sc) is an implementation of Datagram Transport Layer Security 1.2, also known as RFC 6347.

Please refer to the Californium certification repo page for more information.

The connector supports:

  1. SSL trust and key stores
  2. Public/Private PEM keys and PSK client/identity
  3. PSK Client Identity

The Sink will attempt secure connections in the following order if the URI schema of connect.coap.uri set to secure, i.e.``coaps``. If connect.coap.username is set PSK client identity authentication is used, if additional connect.coap.private.key.path Public/Private keys authentication will also be attempted. Otherwise SSL trust and key store.

openssl pkcs8 -in privatekey.pem -topk8 -nocrypt -out privatekey-pkcs8.pem

Only cipher suites TLS_PSK_WITH_AES_128_CCM_8 and TLS_PSK_WITH_AES_128_CBC_SHA256 are currently supported.

Warning

The keystore, truststore, public and private files must be available on the local disk of the worker task.

Loading specific certificates can be achieved by providing a comma separated list for the connect.coap.certs configuration option. The certificate chain can be set by the connect.coap.cert.chain.key configuration option.

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 –> CoAP and paste your configuration

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

CoAP Setup

The connector uses Californium Java API under the hood. Copper, a FireFox browser addon is available so you can browse the server and resources.

We will use a simple CoAP test server we have developed for testing. Download the CoAP test server from our github release page and start the server in a new terminal tab.

mkdir coap_server
cd coap_server
wget https://github.com/datamountaineer/coap-test-server/releases/download/v1.0/start-server.sh
chmod +x start-server.sh
./start-server.sh

You will see the server start listening on port 5864 for secure DTLS connections and on port 5633 for insecure connections.

m.DTLSConnector$Worker.java:-1) run() in thread DTLS-Receiver-0.0.0.0/0.0.0.0:5634 at (2017-01-10 15:41:08)
 1 INFO [CoapEndpoint]: Starting endpoint at localhost/127.0.0.1:5633 - (org.eclipse.californium.core.network.CoapEndpoint.java:192) start() in thread main at (2017-01-10 15:41:08)
 1 CONFIG [UDPConnector]: UDPConnector starts up 1 sender threads and 1 receiver threads - (org.eclipse.californium.elements.UDPConnector.java:261) start() in thread main at (2017-01-10 15:41:08)
 1 CONFIG [UDPConnector]: UDPConnector listening on /127.0.0.1:5633, recv buf = 65507, send buf = 65507, recv packet size = 2048 - (org.eclipse.californium.elements.UDPConnector.java:261) start() in thread main at (2017-01-10 15:41:08)
Secure CoAP server powered by Scandium (Sc) is listening on port 5634
UnSecure CoAP server powered by Scandium (Sc) is listening on port 5633

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.

Starting the Connector

Download, and install Stream Reactor to your Kafka Connect cluster. Follow the instructions here if you haven’t already done so. All paths in the quickstart are based on the location you installed Stream Reactor.

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

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

name=coap-sink
connector.class=com.datamountaineer.streamreactor.connect.coap.sink.CoapSinkConnector
tasks.max=1
topics=coap_topic
connect.coap.uri=coap://localhost:5683
connect.coap.kcql=INSERT INTO unsecure SELECT * FROM coap_topic

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

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
coap-sink
INFO
    ____        __        __  ___                  __        _
   / __ \____ _/ /_____ _/  |/  /___  __  ______  / /_____ _(_)___  ___  ___  _____
  / / / / __ `/ __/ __ `/ /|_/ / __ \/ / / / __ \/ __/ __ `/ / __ \/ _ \/ _ \/ ___/
 / /_/ / /_/ / /_/ /_/ / /  / / /_/ / /_/ / / / / /_/ /_/ / / / / /  __/  __/ /
/_____/\__,_/\__/\__,_/_/  /_/\____/\__,_/_/ /_/\__/\__,_/_/_/ /_/\___/\___/_/
         ______                 _____ _       __
        / ____/___  ____ _____ / ___/(_)___  / /__    By Andrew Stevenson
       / /   / __ \/ __ `/ __ \\__ \/ / __ \/ //_/
      / /___/ /_/ / /_/ / /_/ /__/ / / / / / ,<
      \____/\____/\__,_/ .___/____/_/_/ /_/_/|_|
                      /_/ (com.datamountaineer.streamreactor.connect.coap.sink.CoapSinkTask:52)

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 coap_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 coap-topic \
  --property value.schema='{"type":"record","name":"User",
  "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 CoAP server via Copper

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

[2017-01-10 13:47:36,525] INFO Delivered 1 records for coap-topic. (com.datamountaineer.streamreactor.connect.coap.sink.CoapSinkTask:47)

In Firefox go the following URL. If you have not installed Copper do so here .

coap://127.0.0.1:5633/insecure

Hit the get button and the records will be displayed in the bottom panel.

../../_images/coap-copper-sink.png

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.coap.sink.CoapSinkConnector

Connector Configurations

Config Description Type
connect.coap.uri Uri of the CoAP server string
connect.coap.kcql
The KCQL statement to select and
route resources to topics
string

Optional Configurations

Config Description Type Default
connect.coap.port
The port the DTLS connector will bind to
on the Connector host
int 0
connect.coap.host
The hostname the DTLS connector will
bind to on the Connector host
string localhost
connect.coap.username CoAP PSK identity string  
connect.coap.password CoAP PSK secret string  
connect.coap.public.key.file
Path to the public key
file for use in with PSK credentials
string  
connect.coap.private.key.file
Path to the private
key file for use in with PSK credentials in PKCS8 rather than PKCS1
Use open SSL to convert.
openssl pkcs8 -in privatekey.pem -topk8 -nocrypt -out privatekey-pkcs8.pem

Only cipher suites TLS_PSK_WITH_AES_128_CCM_8
and TLS_PSK_WITH_AES_128_CBC_SHA256
are currently supported
string  
connect.coap.keystore.pass The password of the key store string  
connect.coap.keystore.path The path to the keystore string  
connect.coap.truststore.pass The password of the trust store string  
connect.coap.truststore.path The path to the truststore string  
connect.coap.certs The certificates to load from the trust store string  
connect.coap.cert.chain.key The key to use to get the certificate chain string client
connect.coap.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.coap.max.retires option
string THROW
connect.coap.max.retires
The maximum number of times a message
is retried. Only valid when the
connect.coap.error.policy is set to THROW
string 10
connect.coap.retry.interval
The interval, in milliseconds between retries,
if the sink is using
connect.error.policy set to RETRY
string 60000
connect.progress.enabled
Enables the output for how many
records have been processed
boolean false

Example

name=coap-sink
connector.class=com.datamountaineer.streamreactor.connect.coap.sink.CoapSinkConnector
tasks.max=1
topics=coap-topic
connect.coap.uri=coap://localhost:5633
connect.coap.kcql=INSERT INTO insecure SELECT * FROM coap-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