Elastic 5 Sink

Download connector Elastic5 Connector for Kafka 1.0 Elastic5 Connector for Kafka 0.11

This Elastic Search Sink allows you to write events from Kafka to Elastic Search. The connector converts the value from the Kafka Connect SinkRecords to Json and uses Elastic4s’s JSON insert functionality to index a document. The Sink creates an Index and Type corresponding to the topic name and uses the JSON insert functionality from elastic4s client.


This connector is suited for Elastic Search 5.x - For Elastic Search 2.x look here


  • Apache Kafka 0.11.x of above
  • Kafka Connect 0.11.x or above
  • Elastic Search 5.4
  • Java 1.8


  1. The KCQL routing querying - Topic to index mapping and Field selection, auto creation, suffixes and tagging
  2. XPack security integration
  3. Error polices.

KCQL Support

{ INSERT | UPSERT } INTO elastic_index SELECT { FIELD, ... } FROM kafka_topic
[PK FIELD,...]


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

The Elastic5 sink supports KCQL, Kafka Connect Query Language. The following support KCQL is available:

  1. Field selection
  2. Selection of target index
  3. Auto creation of indexes
  4. Tagging indexes with a document type
  5. Adding index name suffixes with a data pattern
  6. Upsert and insert records.

Primary Keys

The PK keyword can be used to specifiy the fields which will be used for the key value. The field values will be concatenated and separated by a -. If no fields are set the topic name, partition and message offset are used.

Document Type

WITHDOCTYPE allows you to associate a document type to the document inserted.

Index Suffix

WITHINDEXSUFFIX allows you to specify a suffix to your index and we support date format. All you have to say is ‘_suffix_{YYYY-MM-dd}’


-- Insert mode, select all fields from topicA and write to indexA

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

UPSERT INTO indexC SELECT id, string_field FROM topicC PK id

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

Auto Index Creation

The Sink will automatically create missing indexes at startup.

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.


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


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 for example between integrity constraint violations and or other exceptions thrown by any drivers or target systems.


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 –> Elastic and paste your configuration


Elastic Setup

Download and start ElasticSearch.

curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-5.4.0.tar.gz
tar -xvf elasticsearch-5.4.0.tar.gz
cd elasticsearch-5.4.0/bin
./elasticsearch --cluster.name elasticsearch

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 (Distributed)

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 Stream Reactor.

Once the Connect has started we can now use the kafka-connect-tools cli to post in our distributed properties file for Elastic. 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 elastic-sink < conf/elastic-sink.properties

connect.elastic.kcql=INSERT INTO index_1 SELECT * FROM orders-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.

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 Andrew Stevenson

Test Records


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 orders-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 an id field of type int and a random_field of type string.

bin/kafka-avro-console-producer \
 --broker-list localhost:9092 --topic orders-topic \
 --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"id","type":"int"},
{"name":"random_field", "type": "string"}]}'

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

{"id": 999, "random_field": "foo"}
{"id": 888, "random_field": "bar"}


Config Description Type Value
name Name of the connector string This must be unique across the Connect cluster
The topics to sink.
The connector will check this matchs the KCQL statement
tasks.max The number of tasks to scale output int 1
connector.class Name of the connector class string com.datamountaineer.streamreactor.connect.elastic5.ElasticSinkConnector

Connector Configurations

Config Description Type
connect.elastic.url Url of the Elastic cluster string
connect.elastic.kcql Kafka connect query language expression string

Optional Configurations

Config Description Type Default
Enables secure connection.
By providing a value for the entry the sink will end up
creating a secure connection.
The entry is a ; separated list of key=value sequence
Provides the list of plugins to enable.
The entry is a ; separated list of full
classpath (The classes need to derive
from org.elasticsearch.plugins.Plugin)
Specifies the wait time for
pushing the records to ES
long 300000
connect.elastic.url.prefix URL connection string prefix string elasticsearch
Name of the elastic search cluster,
used in local mode for setting the connection
string elasticsearch
TCP or HTTP. Elastic4s client type
to use, HTTP or TCP, the default is TCP
string TCP
If HTTP mode is set, user for HTTP auth
Leave empty if no auth
If HTTP mode is set, password for HTTP auth
Leave empty if no auth
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.elastic.max.retires option
string THROW
The maximum number of times a message
is retried. Only valid when the
connect.elastic.error.policy is set to THROW
string 10
The interval, in milliseconds between retries,
if the sink is using
connect.elastic.error.policy set to RETRY
string 60000
Enables the output for how many
records have been processed
boolean false


connect.elastic.kcql=INSERT INTO INDEX_1 SELECT field1, field2 FROM TOPIC1

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.

Elastic Search is very flexible about what is inserted. All documents in Elasticsearch are stored in an index. We do not need to tell Elasticsearch in advance what an index will look like (eg what fields it will contain) as Elasticsearch will adapt the index dynamically as more documents are added, but we must at least create the index first. The Sink connector automatically creates the index at startup if it doesn’t exist.

The Elastic Search Sink will automatically index 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 on the compatibility settings of the Schema registry.


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/


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