In this blog, we are going to use kinesis as a source and kafka as a consumer.

Let’s get started.

Step 1:

Apache Flink provides the kinesis and kafka connector dependencies. Let’s add them in our build.sbt:

name := "flink-demo"

version := "0.1"

scalaVersion := "2.12.8"

libraryDependencies ++= Seq(
  "org.apache.flink" %% "flink-scala" % "1.10.0",
  "org.apache.flink" %% "flink-connector-kinesis" % "1.10.0",
  "org.apache.flink" %% "flink-connector-kafka" % "1.10.0",
  "org.apache.flink" %% "flink-streaming-scala" % "1.10.0"
)

Step 2:

The next step is to create a pointer to the environment on which this program runs.

val env = StreamExecutionEnvironment.getExecutionEnvironment

Step 3:

Setting parallelism of x here will cause all operators (such as join, map, reduce) to run with x parallel instance.

I am using 1 as it is a demo application.

env.setParallelism(1)

Step 4:

Disabling the aws cbor, as we are testing locally.

System.setProperty("com.amazonaws.sdk.disableCbor", "true")
System.setProperty("org.apache.flink.kinesis.shaded.com.amazonaws.sdk.disableCbor", "true")

Step 5:

Defining Kinesis consumer properties.

• Region
• Stream Position – TRIM_HORIZON to read all the records available in the stream
• Aws keys
• Do not worry about the endpoint, it is set to http://localhost:4568 as we will test the kinesis using localstack.

Do not worry about the endpoint, it is set to http://localhost:4568 as we will test the kinesis using localstack.

#apache flink #flink #scala ##apache-flink ##kinesis #apache #flink streaming #kafka #scala