Set up clusters in HDInsight with Apache Hadoop, Apache Spark, Apache Kafka, and more

Learn how to set up and configure Apache Hadoop, Apache Spark, Apache Kafka, Interactive Query, or Apache HBase or in HDInsight. Also, learn how to customize clusters and add security by joining them to a domain.

A Hadoop cluster consists of several virtual machines (nodes) that are used for distributed processing of tasks. Azure HDInsight handles implementation details of installation and configuration of individual nodes, so you only have to provide general configuration information.

If you're using multiple clusters together, you'll want to create a virtual network, and if you're using a Spark cluster you'll also want to use the Hive Warehouse Connector. For more information, see Plan a virtual network for Azure HDInsight and Integrate Apache Spark and Apache Hive with the Hive Warehouse Connector.

Cluster setup methods

The following table shows the different methods you can use to set up an HDInsight cluster.

This article walks you through setup in the Azure portal, where you can create an HDInsight cluster.

Basics

Project details

Azure Resource Manager helps you work with the resources in your application as a group, referred to as an Azure resource group. You can deploy, update, monitor, or delete all the resources for your application in a single coordinated operation.

Cluster details

Cluster name

HDInsight cluster names have the following restrictions:

• Allowed characters: a-z, 0-9, A-Z
• Max length: 59
• Reserved names: apps
• The cluster naming scope is for all Azure, across all subscriptions. So the cluster name must be unique worldwide.
• First six characters must be unique within a virtual network

Region

You don't need to specify the cluster location explicitly: The cluster is in the same location as the default storage. For a list of supported regions, select the Region drop-down list on HDInsight pricing.

Cluster type

Azure HDInsight currently provides the following cluster types, each with a set of components to provide certain functionalities.

Version

Choose the version of HDInsight for this cluster. For more information, see Supported HDInsight versions.

Cluster credentials

With HDInsight clusters, you can configure two user accounts during cluster creation:

• Cluster login username: The default username is admin. It uses the basic configuration on the Azure portal. Sometimes it's called "Cluster user," or "HTTP user."
• Secure Shell (SSH) username: Used to connect to the cluster through SSH. For more information, see Use SSH with HDInsight.

The HTTP username has the following restrictions:

• Allowed special characters: _ and @
• Characters not allowed: #;."',/:!*?$(){}[]<>|&--=+%~^space`
• Max length: 20

The SSH username has the following restrictions:

• Allowed special characters:_ and @
• Characters not allowed: #;."',/:!*?$(){}[]<>|&--=+%~^space`
• Max length: 64
• Reserved names: hadoop, users, oozie, hive, mapred, ambari-qa, zookeeper, tez, hdfs, sqoop, yarn, hcat, ams, hbase, administrator, admin, user, user1, test, user2, test1, user3, admin1, 1, 123, a, actuser, adm, admin2, aspnet, backup, console, David, guest, John, owner, root, server, sql, support, support_388945a0, sys, test2, test3, user4, user5, spark

Storage

Although an on-premises installation of Hadoop uses the Hadoop Distributed File System (HDFS) for storage on the cluster, in the cloud you use storage endpoints connected to cluster. Using cloud storage means you can safely delete the HDInsight clusters used for computation while still retaining your data.

HDInsight clusters can use the following storage options:

• Azure Data Lake Storage Gen2
• Azure Data Lake Storage Gen1
• Azure Storage General Purpose v2
• Azure Storage General Purpose v1
• Azure Storage Block blob (only supported as secondary storage)

For more information on storage options with HDInsight, see Compare storage options for use with Azure HDInsight clusters.

During configuration, for the default storage endpoint you specify a blob container of an Azure Storage account or Data Lake Storage. The default storage contains application and system logs. Optionally, you can specify additional linked Azure Storage accounts and Data Lake Storage accounts that the cluster can access. The HDInsight cluster and the dependent storage accounts must be in the same Azure location.

Metastore settings

You can create optional Hive or Apache Oozie metastores. However, not all cluster types support metastores, and Azure Synapse Analytics isn't compatible with metastores.

For more information, see Use external metadata stores in Azure HDInsight.

SQL database for Hive

If you want to retain your Hive tables after you delete an HDInsight cluster, use a custom metastore. You can then attach the metastore to another HDInsight cluster.

An HDInsight metastore that is created for one HDInsight cluster version can't be shared across different HDInsight cluster versions. For a list of HDInsight versions, see Supported HDInsight versions.

SQL database for Oozie

To increase performance when using Oozie, use a custom metastore. A metastore can also provide access to Oozie job data after you delete your cluster.

SQL database for Ambari

Ambari is used to monitor HDInsight clusters, make configuration changes, and store cluster management information as well as job history. The custom Ambari DB feature allows you to deploy a new cluster and setup Ambari in an external database that you manage. For more information, see Custom Ambari DB.

Security + networking

Enterprise security package

For Hadoop, Spark, HBase, Kafka, and Interactive Query cluster types, you can choose to enable the Enterprise Security Package. This package provides option to have a more secure cluster setup by using Apache Ranger and integrating with Microsoft Entra ID. For more information, see Overview of enterprise security in Azure HDInsight.

The Enterprise security package allows you to integrate HDInsight with Active Directory and Apache Ranger. Multiple users can be created using the Enterprise security package.

For more information on creating domain-joined HDInsight cluster, see Create domain-joined HDInsight sandbox environment.

TLS

For more information, see Transport Layer Security

Virtual network

If your solution requires technologies that are spread across multiple HDInsight cluster types, an Azure virtual network can connect the required cluster types. This configuration allows the clusters, and any code you deploy to them, to directly communicate with each other.

For more information on using an Azure virtual network with HDInsight, see Plan a virtual network for HDInsight.

For an example of using two cluster types within an Azure virtual network, see Use Apache Spark Structured Streaming with Apache Kafka. For more information about using HDInsight with a virtual network, including specific configuration requirements for the virtual network, see Plan a virtual network for HDInsight.

Disk encryption setting

For more information, see Customer-managed key disk encryption.

Kafka REST proxy

This setting is only available for cluster type Kafka. For more information, see Using a REST proxy.

Identity

For more information, see Managed identities in Azure HDInsight.

Configuration + pricing

You're billed for node usage for as long as the cluster exists. Billing starts when a cluster is created and stops when the cluster is deleted. Clusters can't be de-allocated or put on hold.

Node configuration

Each cluster type has its own number of nodes, terminology for nodes, and default VM size. In the following table, the number of nodes for each node type is in parentheses.

Type	Nodes	Diagram
Hadoop	Head node (2), Worker node (1+)
HBase	Head server (2), region server (1+), master/ZooKeeper node (3)
Spark	Head node (2), Worker node (1+), ZooKeeper node (3) (free for A1 ZooKeeper VM size)

For more information, see Default node configuration and virtual machine sizes for clusters in "What are the Hadoop components and versions in HDInsight?"

The cost of HDInsight clusters is determined by the number of nodes and the virtual machines sizes for the nodes.

Different cluster types have different node types, numbers of nodes, and node sizes:

• Hadoop cluster type default:

  • Two head nodes

  • Four Worker nodes

If you're just trying out HDInsight, we recommend you use one Worker node. For more information about HDInsight pricing, see HDInsight pricing.

When you use the Azure portal to configure the cluster, the node size is available through the Configuration + pricing tab. In the portal, you can also see the cost associated with the different node sizes.

Virtual machine sizes

When you deploy clusters, choose compute resources based on the solution you plan to deploy. The following VMs are used for HDInsight clusters:

• A and D1-4 series VMs: General-purpose Linux VM sizes
• D11-14 series VM: Memory-optimized Linux VM sizes

To find out what value you should use to specify a VM size while creating a cluster using the different SDKs or while using Azure PowerShell, see VM sizes to use for HDInsight clusters. From this linked article, use the value in the Size column of the tables.

For more information, see Sizes for virtual machines. For information about pricing of the various sizes, see HDInsight pricing.

Disk attachment

HDInsight cluster comes with pre-defined disk space based on SKU. If you run some large applications, can lead to insufficient disk space, with disk full error - LinkId=221672#ERROR_NOT_ENOUGH_DISK_SPACE and job failures.

More discs can be added to the cluster using the new feature NodeManager’s local directory. At the time of Hive and Spark cluster creation, the number of discs can be selected and added to the worker nodes. The selected disk, which will be of size 1TB each, would be part of NodeManager's local directories.

1. From Configuration + pricing tab
2. Select Enable managed disk option
3. From Standard disks, Enter the Number of disks
4. Choose your Worker node

You can verify the number of disks from Review + create tab, under Cluster configuration

Add application

HDInsight application is an application, that users can install on a Linux-based HDInsight cluster. You can use applications provided by Microsoft, third parties, or developed by you. For more information, see Install third-party Apache Hadoop applications on Azure HDInsight.

Most of the HDInsight applications are installed on an empty edge node. An empty edge node is a Linux virtual machine with the same client tools installed and configured as in the head node. You can use the edge node for accessing the cluster, testing your client applications, and hosting your client applications. For more information, see Use empty edge nodes in HDInsight.

Script actions

You can install additional components or customize cluster configuration by using scripts during creation. Such scripts are invoked via Script Action, which is a configuration option that can be used from the Azure portal, HDInsight Windows PowerShell cmdlets, or the HDInsight .NET SDK. For more information, see Customize HDInsight cluster using Script Action.

Some native Java components, like Apache Mahout and Cascading, can be run on the cluster as Java Archive (JAR) files. These JAR files can be distributed to Azure Storage and submitted to HDInsight clusters with Hadoop job submission mechanisms. For more information, see Submit Apache Hadoop jobs programmatically.

Sometimes, you want to configure the following configuration files during the creation process:

• clusterIdentity.xml
• core-site.xml
• gateway.xml
• hbase-env.xml
• hbase-site.xml
• hdfs-site.xml
• hive-env.xml
• hive-site.xml
• mapred-site
• oozie-site.xml
• oozie-env.xml
• tez-site.xml
• webhcat-site.xml
• yarn-site.xml

For more information, see Customize HDInsight clusters using Bootstrap.

Next steps

• Troubleshoot cluster creation failures with Azure HDInsight
• What are HDInsight, the Apache Hadoop ecosystem, and Hadoop clusters?
• Get started using Apache Hadoop in HDInsight
• Work in Apache Hadoop on HDInsight from a Windows PC