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Scala Spark Integration With Apache NiFi

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Scala Spark Integration With Apache NiFi

You don't want all of your Scala code in a continuous block like Apache Zeppelin, so see how to execute Scala Apache Spark code in JARs from Apache NiFi.

· Big Data Zone ·
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Learn how to execute Scala Apache Spark code in JARs from Apache NiFi — because you don't want all of your Scala code in a continuous block like Apache Zeppelin. We're going to use Apache NiFi, Apache Livy, Apache Spark, and Scala.


Here's the inline Scala code:

Apache Zeppelin running the Same Scala Job (have to add the JAR to the interpreter for Spark and restart):

Grafana Charts of Apache NiFi Run:

Log search helps you find errors:

Run code for your Spark class:

Setting up your ExecuteSparkInteractive processor:

Setting up your Spark service for Scala:

Tracking the job in Livy UI:

Tracking the job in Spark UI:

I was looking at pulling code from Git and putting it into a NiFi attribute and running directly. For bigger projects, you'll have many classes and dependencies that may require a full IDE and SBT build cycle. Once I build a Scala JAR, I want to run against that.

Example code:

package com.dataflowdeveloper.example
import org.apache.spark.sql.SparkSession
class Example () {
  def run( spark: SparkSession) {
      try {
        val shdf = spark.read.json("hdfs://princeton0.field.hortonworks.com:8020/smartPlugRaw")
      val stuffdf = spark.sql("SELECT * FROM smartplug")
      } catch {
        case e: Exception =>

=--- Run that with

import com.dataflowdeveloper.example.Example
println("Before run")
val job = new Example()
println("After run")

=== after run
{"text\/plain":"After run"}

Here's the GitHub link.

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big data ,tutorial ,scala ,apache spark ,apache nifi

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