v3.13.0

Azure Toolkit for IntelliJ IDEA 3.13.0 is available in the Marketplace!
This release comes with brand new features to support both App Service Deployment Slot and HDInsight Spark Shell.

Supported Platforms

The Azure Toolkit for IntelliJ IDEA support the following IDEs:

• IntelliJ IDEA Community Edition, version [2018.1, 2018.2]
• IntelliJ IDEA Ultimate Edition, version [2018.1, 2018.2]

Embedded Libraries

In this release, following libraries are embedded.

• Application Insights Java SDK 2.2.0
• Microsoft JDBC Driver 6.1 for SQL Server

Change Notes

Added

• Support to deploy an application to Deployment Slot.
• Support to show and operate Deployment Slots of a Web App in Azure Explorer.
• Support to link an independent Livy server for Spark cluster.
• Add Spark Local interactive console.
• Add Spark HDInsight cluster interactive console (Only for 2018.2, Scala plugin is needed).

Changed

• Change the Spark Job context menu submission dialog, to unify with IntelliJ Run Configuration Setting dialog.
• Move the storage information of HDInsight/Livy cluster to linked into Run Configuration settings.

Fixed

• An issue that the element "filter-mapping" was not removed when disabling telemetry with Application Insights. #2143

v3.12.0

IntelliJ

Added

• Support to deploy applications to Web App (Linux).
• Support to show all the deployment slots for a Web App.
• Support to show the Azure Data Lake Spark resource pool provision log outputs.

Changed

• List Web Apps on both Windows and Linux in Azure Explorer.
• List all app service plans for the selected subscription when creating a new Web App.
• Always upload the web.config file together with the .jar artifact when deploying to Web App (Windows).

Fixed

• Runtime information is not clear enough for Azure Web Apps. #1968
• The issue of Azure Data Lake Spark resource pool Update dialog pops up multi times. #1779 #1920

v3.11.0

IntelliJ

• Added the main class hint when users choose to submit a Spark job using a local artifact file.
• Added Spark cluster GUID for Spark cluster provision failure investigation.
• Added the "AU not enough" warning message in Azure Data Lake Spark resource pool provision.
• Added the job queue query to check AU consumption in Azure Data Lake Spark resource pool provision.
• Fixed cluster total AU by using systemMaxAU instead of maxAU.
• Refresh node automatically when node is clicked in Azure explorer.
• Updated the Azure SDK to 1.14.0.
• Fixed some bugs.

Eclipse

• Updated the Azure SDK to 1.14.0.

v3.10.0

IntelliJ

• Supported to fix Spark job configuration in run configuration before Spark job submission.
• Updated Application Insights library to v2.1.2.
• Fixed some bugs.

Eclipse

• Updated Application Insights library to v2.1.2.

v3.9.0

IntelliJ & Eclipse

• Added Spark 2.3 support.
• Spark in Azure Data Lake private preview refresh and bug fix.
• Fixed some bugs.

v3.8.0

IntelliJ & Eclipse

• Supported to run Spark jobs in Azure Data Lake cluster (in private preview).
• Fixed some bugs.

V3.6.0

IntelliJ & Eclipse

• Updated ApplicationInsights version to v2.0.2.
• Added Spark 2.2 templates for HDInsight.
• Added SSH password expiration check.
• Fixed some bugs.

V3.7.0

IntelliJ

• Users do not need to login again in interactive login mode, if Azure refresh token is still validated.
• Updated ApplicationInsights version to v2.1.0.
• Fixed some bugs.

Eclipse

• Updated ApplicationInsights version to v2.1.0.
• Fixed some bugs.

V3.5.0

IntelliJ & Eclipse

• Added open Azure Storage Explorer for exploring data in HDInsight cluster (blob or ADLS).
• Improved Spark remote debugging.
• Improved Spark job submission correctness check.
• Fixed an login issue.

V3.4.0

IntelliJ & Eclipse

• Users can use Ambari username/password to submit Spark job to HDInsight cluster, in additional to Azure subscription based authentication. This means users without Azure subscription permission can still use Ambari credentials to submit/debug their Spark jobs in HDInsight clusters.
• The dependency on storage permission is removed and users do not need to provide storage credentials for Spark job submission any more (storage credential is still needed if users want to use storage explorer).