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Apache Hadoop releases do not contain binaries like hadoop.dll or winutils.exe, which are required to run hadoop. In order to use it on Windows, the distribution must be compiled from its sources. Jan 10, 2016 I solved this and it worked for me, hope it would help. It's a very small mistake I made - I saved the winutils executable as 'winutils.exe' instead of just winutils. So when the variable gets resolved it's been resolving to winutils.exe.exe which is nowhere in the Hadoop binaries. I removed that '.exe' and triggered the shell, it worked. HADOOP-11003 org.apache.hadoop.util.Shell should not take a dependency on binaries being deployed when used as a library Resolved HADOOP-10775 Shell operations to fail with meaningful errors on windows if winutils.exe not found.

This tutorial shows you how to use the Azure Toolkit for IntelliJ plug-in to develop Apache Spark applications, which are written in Scala, and then submit them to a serverless Apache Spark pool directly from the IntelliJ integrated development environment (IDE). You can use the plug-in in a few ways:

This is a known issue to many Hadoop related projects and has been asked about in many different online communities such as Stack Overflow or Cloudera's issue tracking system. Java.io.IOException: Could not locate executable nullbinwinutils.exe in the Hadoop binaries.
Hadoop requires native libraries on Windows to work properly -that includes accessing the file:// filesystem, where Hadoop uses some Windows APIs to implement posix-like file access permissions. This is implemented in HADOOP.DLL and WINUTILS.EXE. In particular,%HADOOPHOME% BIN WINUTILS.EXE must be locatable.

Develop and submit a Scala Spark application on a Spark pool.
Access your Spark pools resources.
Develop and run a Scala Spark application locally.

In this tutorial, you learn how to:

Use the Azure Toolkit for IntelliJ plug-in
Develop Apache Spark applications
Submit application to Spark pools

Prerequisites

IntelliJ IDEA Community Version.
Azure toolkit plugin 3.27.0-2019.2 – Install from IntelliJ Plugin repository
JDK (Version 1.8).
Scala Plugin – Install from IntelliJ Plugin repository.
The following prerequisite is only for Windows users:
While you're running the local Spark Scala application on a Windows computer, you might get an exception, as explained in SPARK-2356. The exception occurs because WinUtils.exe is missing on Windows.To resolve this error, download the WinUtils executable to a location such as C:WinUtilsbin. Then, add the environment variable HADOOP_HOME, and set the value of the variable to C:WinUtils.

Create a Spark Scala application for a Spark pool

Start IntelliJ IDEA, and select Create New Project to open the New Project window.
Select Apache Spark/HDInsight from the left pane.
Select Spark Project with Samples(Scala) from the main window.
From the Build tool drop-down list, select one of the following types:
- Maven for Scala project-creation wizard support.
- SBT for managing the dependencies and building for the Scala project.
Select Next.

In the New Project window, provide the following information:

Property	Description
Project name	Enter a name. This tutorial uses `myApp`.
Project location	Enter the wanted location to save your project.
Project SDK	It might be blank on your first use of IDEA. Select New.. and navigate to your JDK.
Spark Version	The creation wizard integrates the proper version for Spark SDK and Scala SDK. Synapse only supports Spark 2.4.0.

Select Finish. It may take a few minutes before the project becomes available.
The Spark project automatically creates an artifact for you. To view the artifact, do the following operating:
a. From the menu bar, navigate to File > Project Structure...
b. From the Project Structure window, select Artifacts.
c. Select Cancel after viewing the artifact.
Find LogQuery from myApp > src > main > scala> sample> LogQuery. This tutorial uses LogQuery to run.

Connect to your Spark pools

Sign in to your Azure subscription

From the menu bar, navigate to View > Tool Windows > Azure Explorer.
From Azure Explorer, right-click the Azure node, and then select Sign In.
In the Azure Sign In dialog box, choose Device Login, and then select Sign in.
In the Azure Device Login dialog box, select Copy&Open.
In the browser interface, paste the code, and then select Next.
Enter your Azure credentials, and then close the browser.
After you're signed in, the Select Subscriptions dialog box lists all the Azure subscriptions that are associated with the credentials. Select your subscription and then select Select.
From Azure Explorer, expand Apache Spark on Synapse to view the Workspaces that are in your subscriptions.
To view the Spark pools, you can further expand a workspace.

Remote Run a Spark Scala application on a Spark pool

After creating a Scala application, you can remotely run it.

Open Run/Debug Configurations window by selecting the icon.
In the Run/Debug Configurations dialog window, select +, then select Apache Spark on Synapse.

In the Run/Debug Configurations window, provide the following values, and then select OK:

Property	Value
Spark pools	Select the Spark pools on which you want to run your application.
Select an Artifact to submit	Leave default setting.
Main class name	The default value is the main class from the selected file. You can change the class by selecting the ellipsis(..) and choosing another class.
Job configurations	You can change the default key and values. For more information, see Apache Livy REST API.
Command-line arguments	You can enter arguments separated by space for the main class if needed.
Referenced Jars and Referenced Files	You can enter the paths for the referenced Jars and files if any. You can also browse files in the Azure virtual file system, which currently only supports ADLS Gen2 cluster. For more information: [Apache Spark Configuration]https://spark.apache.org/docs/2.4.5/configuration.html#runtime-environment) and How to upload resources to cluster.
Job Upload Storage	Expand to reveal additional options.
Storage Type	Select Use Azure Blob to upload or Use cluster default storage account to upload from the drop-down list.
Storage Account	Enter your storage account.
Storage Key	Enter your storage key.
Storage Container	Select your storage container from the drop-down list once Storage Account and Storage Key has been entered.

Select SparkJobRun icon to submit your project to the selected Spark pool. The Remote Spark Job in Cluster tab displays the job execution progress at the bottom. You can stop the application by selecting the red button.

Local Run/Debug Apache Spark applications

Winutils Exe Hadoop