Christina Daskalaki
1 Dec 2016
Read in about 6 min
Time-series data stores are of particular interest these days and influxDB is a popular open source distributed time-series database. In this tutorial we will integrate Kafka with InfluxDB using Kafka Connect and implement a Scala Avro message producer to test the setup.
The steps we are going to follow are:
Finally, we will verify the data in influxDB and visualize them in Chronograph.
Data in influxDb is organized in time series where each time series has points, one for each discrete sample of the metric. Points consists of:
time: the timestampmeasurement : which conceptually matches the idea of a SQL tabletags : key-value pairs in order to store index values, usually metadata.fields : key-value pairs, containing the value itself, non indexed.null values aren’t stored.
The structure is of the data is:
measurement,tagKey1=tagVal1,tagKey2=tagVal2 fieldK1=fieldV1,fieldK1=fieldV1
Example:
INSERT temperature,machineId=1,type=boiler actualTemperature=30,targetTemperature=32
For both the InfluxDB and Kafka ecosystem setup we are going to use Docker. If you don’t have docker installed on your machine, follow the instructions on their website.
Run the docker image:
$ docker run -p 8086:8086 -v influxdb:/var/lib/influxdb influxdbEnter the InfluxDB Shell and create a database and a user:
$ influx --host localhost --port 8086
> CREATE DATABASE devices
> CREATE USER root WITH PASSWORD 'root'
> GRANT ALL ON devices to rootYou may find more info on this docker image here.
In order to set up Kafka we will need a set of systems to be installed. We are going to use fast-data-dev docker image, which includes everything we need for this tutorial:
Run the docker image:
docker run --rm -it -p 2181:2181 -p 3030:3030 -p 8081:8081 -p 8082:8082 -p 8083:8083 -p 9092:9092 -e ADV_HOST=localhost landoop/fast-data-devYou may find more on fast-data-dev documentation here.
While this is running for the first time it will run some integration tests and create some topics and connectors for you. You are able to access the landing page for the container at http://localhost:3030
Once it’s up and running you may access:
docker exec <ID> tail -f /var/log/broker.logdocker exec <ID> tail -f /var/log/connect-distributed.logdocker run --rm -it --net=host landoop/fast-data-dev bashData usually originate from somewhere. For example, an embedded sensor can produce data at frequent intervals. For the shake of the example we are going to create a simple Kafka Producer in Scala to transmit some metrics
kafka-avro-console-producercan also be used instead of the Scala app
The data structure looks like this:
case class DeviceMeasurements(deviceId: Int,
temperature: Int,
moreData: String,
timestamp: Long)We are going to send these messages in a Kafka topic named as device-measurements-topic
Our fast-data-dev docker image provides Kafka Connect and has already added the influxDB connector in the classpath
of the available plugins.
Let’s find the IP address of the docker used by the influxDB container (usually 172.17.0.x)
docker inspect <ID or NAME> | grep IPA
"SecondaryIPAddresses": null,
"IPAddress": "172.17.0.2",
"IPAMConfig": nullWe will now use the kafka-connect-ui to configure the connector:
or from command line:
cat << EOF > influxdb.json
{
"name": "influx-sink-connector",
"config": {
"connector.class": "com.datamountaineer.streamreactor.connect.influx.InfluxSinkConnector",
"tasks.max": "1",
"topics": "device-measurements-topic",
"connect.influx.username": "root",
"connect.influx.password": "root",
"connect.influx.db": "devices",
"connect.influx.url": "http://172.17.0.2:8086",
"connect.influx.kcql": "INSERT INTO deviceMeasurement SELECT deviceId, temperature, timestamp FROM device-measurements-topic WITHTIMESTAMP timestamp"
}
}
EOF
curl -X POST -H "Content-Type: application/json" -H "Accept: application/json" -d @influxdb.json http://localhost:8083/connectorsInfluxDB Sink connector supports KCQL (Kafka Connect Query Language) and this allows us to filter fields from a Kafka topic without needing to do any extra processing. Let’s say in our case we are only interested in storing the temperature for a particular device in a time series within InfluxDB.
The query looks like this:
INSERT INTO deviceMeasurement SELECT deviceId, temperature, timestamp
FROM topicDeviceMeasurement WITHTIMESTAMP timestamp
deviceMeasurementis the time series namespace (measurement) for influxDB
WITHTIMESTAMPwill add thetimein the point. If we omit it from our query then influx will add a system timestamp.
Now lets create a simple producer that sends these types of event messages into Kafka. We are going to use Avro format so that we can maintain the schema of the data in the Schema Registry. In this example both the key and the value of the Kafka message will be in Avro format. Of course you may use any other Serializer.
import java.util.Properties
import java.util.concurrent.Future
import com.sksamuel.avro4s.{FromRecord, RecordFormat, ToRecord}
import org.apache.avro.generic.GenericRecord
import org.apache.kafka.clients.producer._
class KProducer[K <: Product, V <: Product] {
val kafkaProps = new Properties()
kafkaProps.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "127.0.0.1:9092")
kafkaProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
classOf[KafkaAvroSerializer].getCanonicalName)
kafkaProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
classOf[KafkaAvroSerializer].getCanonicalName)
kafkaProps.put("schema.registry.url", "http://127.0.0.1:8081")
private lazy val producer = new KafkaProducer[GenericRecord, GenericRecord](kafkaProps)
def produce(topic: String, key: K, value: V, partition: Int = 0)
(implicit to: ToRecord[K], from: FromRecord[K], to: ToRecord[V], from: FromRecord[V])
: Future[RecordMetadata] = {
val keyRec = RecordFormat[K].to(key)
val valueRec = RecordFormat[V].to(value)
val data: ProducerRecord[GenericRecord, GenericRecord] = new ProducerRecord(topic, partition, keyRec, valueRec)
producer.send(data)
}
}We create the case classes that describe the schemas for the key and the value and then use call the producer with some random values.
import com.github.nscala_time.time.Imports._
import scala.util.{Failure, Random, Success, Try}
object Example extends App {
case class Key(deviceId:Int)
case class DeviceMeasurements(deviceId:Int, temperature:Int, moreData:String, timestamp:Long)
val producer = new KProducer[Key, DeviceMeasurements]()
for (a <- 1 to 1000) { //you may want to adjust the number of messages here
val deviceMeasurement = getMeasurement(-10, 50)
val deviceID = getMeasurement(0, 10)
val timestamp = DateTime.now().getMillis
Try(producer.produce("device-measurements-topic",
Key(deviceID),
DeviceMeasurements(deviceID,deviceMeasurement,"",timestamp))) match {
case Success(m) =>
val metadata = m.get()
println("Success writing to Kafka topic:" + metadata.topic(),
metadata.offset(),
metadata.partition(),
new DateTime(metadata.timestamp()))
case Failure(f) => println("Failed writing to Kafka",f)
}
}
def getMeasurement(min:Int, max:Int) = Random.nextInt(max - min) + min
}In your schema registry you will find 2 automatically registered schemas: one for the key called device-measurement-topic-key and one for the value device-measurement-topic-value.
We can see the schemas created in http://localhost:3030/schema-registry-ui
We can see the Kafka messages generated in http://localhost:3030/kafka-topics-ui
and also InfluxDB should also be getting data:
> USE devices
> SELECT * FROM deviceMeasurement
InfluxDB provides a visualization tool called Chronograf.
Here is how our time series chart may look like:
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