Redis Sink

Download connector Redis Connector for Kafka 1.1 Redis Connector for Kafka 1.0

This Redis sink connector allows you to write messages from Kafka to Redis. The connector takes the value from the Kafka Connect SinkRecords and inserts a new entry to Redis.

Prerequisites

  • Apache Kafka 0.11.x of above
  • Kafka Connect 0.11.x or above
  • Jedis 2.8.1 or above
  • Java 1.8

Features

  1. The KCQL routing querying - Kafka topic payload field selection is supported, allowing you to select fields written to Redis
  2. Error policies for handling failures
  3. Storing as one or more Stored Sets.

KCQL Support

[INSERT INTO cache|sortedSet] SELECT { FIELD, ... } FROM kafka_topic_name [PK FIELD] [STOREAS SortedSet(key=FIELD)]

Tip

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

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

  1. Field selection
  2. Target sortedSet, cache or multiple sorted set selection
  3. Key selection - which fields to use as the key.

Cache Mode

The purpose of this mode is to cache in Redis [Key-Value] pairs. Imagine a Kafka topic with currency foreign exchange rate messages:

{ "symbol": "USDGBP" , "price": 0.7943 }
{ "symbol": "EURGBP" , "price": 0.8597 }

You may want to store in Redis: the symbol as the Key and the price as the Value. This will effectively make Redis a caching system, which multiple other application can access to get the (latest) value. To achieve that using this particular Kafka Redis Sink Connector, you need to specify the KCQL as:

INSERT INTO cache SELECT price from yahoo-fx PK symbol

This will update the keys USDGBP , EURGBP with the relevant price using the (default) JSON format:

Key=EURGBP  Value={ "price": 0.7943 }

Sorted Sets

To insert messages from a Kafka topic into 1 Sorted Set use the following KCQL syntax:

INSERT INTO cpu_stats SELECT * from cpuTopic STOREAS SortedSet(score=timestamp)

This will create and add entries to the (sorted set) named cpu_stats. The entries will be ordered in the Redis set based on the score that we define it to be the value of the timestamp field of the AVRO message from Kafka. In the above example, we are selecting and storing all the fields of the Kafka message.

Multiple Sorted Sets

You can create multiple sorted sets by promoting each value of one field from the Kafka message into one Sorted Set and selecting which values to store into the sorted-sets. You can achieve that by using the KCQL syntax and defining with the filed using PK (primary key)

SELECT temperature, humidity FROM sensorsTopic PK sensorID STOREAS SortedSet(score=timestamp)

Note

Notice we have dropped the INSERT clause.

We can prefix the name of the Key using the INSERT statement for Multiple SortedSets:

INSERT INTO FX- SELECT price from yahoo-fx PK symbol STOREAS SortedSet(score=timestamp)

This will create key with names FX-USDGBP , FX-EURGBP etc.

Warning

This plugin requires JSON to be parsed using the AVRO Converter and therefore does not support schemaless JSON.

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 system.

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-redis-sink-connector.png

Redis Setup

Download and install Redis.

➜  wget http://download.redis.io/redis-stable.tar.gz
➜  tar xvzf redis-stable.tar.gz
➜  cd redis-stable
➜  sudo make install

Start Redis

➜  bin/redis-server

Check Redis is running:

➜  redis-cli ping
    PONG
➜  sudo service redis-server status

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 (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 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 Redis. 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 redis-sink < conf/redis-sink.properties

name=redis-sink
connector.class=com.datamountaineer.streamreactor.connect.redis.sink.RedisSinkConnector
tasks.max=1
topics=redis-topic
connect.redis.host=localhost
connect.redis.port=6379
connect.redis.kcql=INSERT INTO TABLE1 SELECT * FROM redis-topic

Warning

If your Redis server is expecting the connection to be authenticated you will need to provide an extra setting:

connect.redis.connection.password=$REDIS_PASSWORD

Don’t set the value to empty if no password is required.

If you switch back to the terminal you started Kafka Connect in, you should see the Redis 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
redis-sink
  INFO
    __                    __
   / /   ____ _____  ____/ /___  ____  ____
  / /   / __ `/ __ \/ __  / __ \/ __ \/ __ \
 / /___/ /_/ / / / / /_/ / /_/ / /_/ / /_/ /
/_____/\__,_/_/ /_/\__,_/\____/\____/ .___/
                                   /_/
      ____           ___      _____ _       __
     / __ \___  ____/ (_)____/ ___/(_)___  / /__
    / /_/ / _ \/ __  / / ___/\__ \/ / __ \/ //_/
   / _, _/  __/ /_/ / (__  )___/ / / / / / ,<
  /_/ |_|\___/\__,_/_/____//____/_/_/ /_/_/|_|

   (com.datamountaineer.streamreactor.connect.redis.sink.config.RedisSinkConfig:165)
  INFO Settings:
  RedisSinkSettings(RedisConnectionInfo(localhost,6379,None),RedisKey(FIELDS,WrappedArray(firstName, lastName)),PayloadFields(false,Map(firstName -> firstName, lastName -> lastName, age -> age, salary -> income)))
         (com.datamountaineer.streamreactor.connect.redis.sink.RedisSinkTask:65)
  INFO Sink task org.apache.kafka.connect.runtime.WorkerSinkTask@44b24eaa finished initialization and start (org.apache.kafka.connect.runtime.WorkerSinkTask:155)

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 redis-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 redis-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 Redis

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

INFO Received record from topic:redis-topic partition:0 and offset:0 (com.datamountaineer.streamreactor.connect.redis.sink.writer.RedisDbWriter:48)
INFO Empty list of records received. (com.datamountaineer.streamreactor.connect.redis.sink.RedisSinkTask:75)

Let’s check Redis.

redis-cli

127.0.0.1:6379> keys *
1) "John.Smith"
2) "11"
3) "10"
127.0.0.1:6379>
127.0.0.1:6379> get "John.Smith"
"{\"firstName\":\"John\",\"lastName\":\"Smith\",\"age\":30,\"income\":4830.0}"

Now stop the connector.

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.redis.sink.RedisSinkConnector

Connector Configurations

Config Description Type
connect.redis.kcql Kafka connect query language expression string
connect.redis.host Specifies the Redis server string
connect.redis.port Specifies the Redis server port number int

Optional Configurations

Config Description Type Default
connect.redis.password
Specifies the authorization password.
If you don’t have a password set up on
the Redis server don’t provide the value or you will
see this error: ERR Client sent AUTH, but no password is set
string  
connect.redis.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.redis.max.retires option
string THROW
connect.redis.max.retires
The maximum number of times a message
is retried. Only valid when the
connect.redis.error.policy is set to THROW
string 10
connect.redis.retry.interval
The interval, in milliseconds between retries,
if the sink is using
connect.redis.error.policy set to RETRY
string 60000
connect.progress.enabled
Enables the output for how many
records have been processed
boolean false

Example

name=redis-sink
connector.class=com.datamountaineer.streamreactor.connect.redis.sink.RedisSinkConnector
tasks.max=1
topics=redis-topic
connect.redis.host=localhost
connect.redis.port=6379
connect.redis.kcql=INSERT INTO TABLE1 SELECT * FROM redis-topic

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 Redis Sink will automatically write and update the Redis 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 Redis column family cell based on the connect.redis.kcql mappings.

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