Create a cluster

This article explains the configuration options available for cluster creation in the Azure Databricks UI. For other methods, run the command databricks clusters create -h in the Databricks CLI, call the POST /api/2.0/clusters/create operation in the Databricks REST API, or use the databricks_cluster resource in the Databricks Terraform provider.

This article focuses on all-purpose more than job clusters, although many of the configurations and management tools described apply equally to both cluster types. To learn more about creating job clusters, see Use Azure Databricks compute with your jobs.


These instructions are for Unity Catalog enabled workspaces. For documentation on the non-Unity Catalog legacy UI, see Configure clusters.

The cluster creation UI lets you select the cluster configuration specifics, including:

Create a new cluster

To create a new cluster, click New > Cluster in your workspace sidebar. This takes you to the New compute page, where you will select your cluster’s specifications.


The configuration options you see on this page will vary depending on the policies you have access to. If you don’t see a setting in your UI, it’s because your policy does not allow you to configure that setting.


Policies are a set of rules used to limit the configuration options available to users when they create a cluster. To configure a cluster according to a policy, select a policy from the Policy dropdown.

Each workspace includes default policies you can use for specific use cases. By default, all users have access to the Personal Compute policy, allowing them to create single-machine compute resources (if you don’t see the Personal Compute policy then your admin hasn’t given you access). For details on all the default policies, see Default policies and policy families.

Policies have access control lists that regulate which users and groups have access to the policies.

If a user doesn’t have the unrestricted cluster creation entitlement, then they can only create clusters using their granted policies.

Access modes

Cluster access mode is a security feature that determines who can use a cluster and what data they can access via the cluster. When you create any cluster in Azure Databricks, you must select an access mode.


Databricks recommends that you use shared access mode for all workloads. Only use the assigned access mode if your required functionality is not supported by shared access mode.

Access Mode Visible to user UC Support Supported Languages Notes
Single user Always Yes Python, SQL, Scala, R Can be assigned to and used by a single user.
Shared Always (Premium plan required) Yes Python (on Databricks Runtime 11.3 LTS and above), SQL, Scala (on Unity Catalog-enabled clusters using Databricks Runtime 13.3 LTS and above) Can be used by multiple users with data isolation among users.
No Isolation Shared Admins can hide this cluster type by enforcing user isolation in the admin settings page. No Python, SQL, Scala, R There is a related account-level setting for No Isolation Shared clusters.
Custom Hidden (For all new clusters) No Python, SQL, Scala, R This option is shown only if you have existing clusters without a specified access mode.

You can upgrade an existing cluster to meet the requirements of Unity Catalog by setting its cluster access mode to Single User or Shared.

All cluster access modes have some limitations. Clusters configured with Unity Catalog have additional limitations and differences in behavior. Structured Streaming has additional limitations on some cluster access modes. See Compute access mode limitations for Unity Catalog.

Do init scripts and libraries work with Unity Catalog access modes?

In Databricks Runtime 13.3 LTS and above, init scripts and libraries are supported on all access modes. Requirements and support vary. See Compute compatibility with libraries and init scripts.

Databricks Runtime versions

Databricks Runtime is the set of core components that run on your clusters. Select the runtime using the Databricks Runtime Version dropdown when you create or edit a cluster. For details on specific Databricks Runtime versions, see Databricks Runtime release notes versions and compatibility.

Which Databricks Runtime version should you use?

  • For all-purpose compute, Databricks recommends using the latest Databricks Runtime version. Using the most current version will ensure you have the latest optimizations and the most up-to-date compatibility between your code and preloaded packages.
  • For job clusters running operational workloads, consider using the Long Term Support (LTS) Databricks Runtime version. Using the LTS version will ensure you don’t run into compatibility issues and can thoroughly test your workload before upgrading.
  • For data science and machine learning use cases, consider Databricks Runtime ML version.

All Databricks Runtime versions include Apache Spark. New versions add components and updates that improve usability, performance, and security.

Enable Photon acceleration

Photon is enabled by default on clusters running Databricks Runtime 9.1 LTS and above.

To enable or disable Photon acceleration, select the Use Photon Acceleration checkbox.

If desired, you can specify the instance type in the Worker Type and Driver Type dropdowns. Databricks recommends the following instance types for optimal price and performance:

  • Standard_E4ds_v4
  • Standard_E8ds_v4
  • Standard_E16ds_v4

Worker and driver node types

A cluster consists of one driver node and zero or more worker nodes. You can pick separate cloud provider instance types for the driver and worker nodes, although by default the driver node uses the same instance type as the worker node. Different families of instance types fit different use cases, such as memory-intensive or compute-intensive workloads.

Worker type

Azure Databricks worker nodes run the Spark executors and other services required for proper functioning clusters. When you distribute your workload with Spark, all the distributed processing happens on worker nodes. Azure Databricks runs one executor per worker node. Therefore, the terms executor and worker are used interchangeably in the context of the Databricks architecture.


To run a Spark job, you need at least one worker node. If a cluster has zero workers, you can run non-Spark commands on the driver node, but Spark commands will fail.

Worker node IP addresses

Azure Databricks launches worker nodes with two private IP addresses each. The node’s primary private IP address hosts Azure Databricks internal traffic. The secondary private IP address is used by the Spark container for intra-cluster communication. This model allows Azure Databricks to provide isolation between multiple clusters in the same workspace.

Driver type

The driver node maintains state information of all notebooks attached to the cluster. The driver node also maintains the SparkContext, interprets all the commands you run from a notebook or a library on the cluster, and runs the Apache Spark master that coordinates with the Spark executors.

The default value of the driver node type is the same as the worker node type. You can choose a larger driver node type with more memory if you are planning to collect() a lot of data from Spark workers and analyze them in the notebook.


Since the driver node maintains all of the state information of the notebooks attached, make sure to detach unused notebooks from the driver node.

GPU instance types

For computationally challenging tasks that demand high performance, like those associated with deep learning, Azure Databricks supports clusters accelerated with graphics processing units (GPUs). For more information, see GPU-enabled clusters.

Azure confidential computing VMs

Azure confidential computing VM types prevent unauthorized access to data while it’s in use, including from the cloud operator. This VM type is beneficial to highly regulated industries and regions, as well as businesses with sensitive data in the cloud. For more information on Azure’s confidential computing, see Azure confidential computing.

To run your workloads using Azure confidential computing VMs, select from the DC or EC series VM types in the worker and driver node dropdowns. See Azure Confidential VM options.

Spot instances

To save cost, you can choose to use spot instances, also known as Azure Spot VMs by checking the Spot instances checkbox.

Configure spot

The first instance will always be on-demand (the driver node is always on-demand) and subsequent instances will be spot instances.

If instances are evicted due to unavailability, Azure Databricks will attempt to acquire new spot instances to replace the evicted instances. If spot instances can’t be acquired, on-demand instances are deployed to replace the evicted instances. Additionally, when new nodes are added to existing compute, Azure Databricks will attempt to acquire spot instances for those nodes.

Enable autoscaling

When Enable autoscaling is checked, you can provide a minimum and maximum number of workers for the cluster. Databricks then chooses the appropriate number of workers required to run your job.

To set the minimum and the maximum number of workers your cluster will autoscale between, use the Min workers and Max workers fields next to the Worker type dropdown.

If you don’t enable autoscaling, you will enter a fixed number of workers in the Workers field next to the Worker type dropdown.


When the cluster is running, the cluster detail page displays the number of allocated workers. You can compare number of allocated workers with the worker configuration and make adjustments as needed.

Benefits of autoscaling

With autoscaling, Azure Databricks dynamically reallocates workers to account for the characteristics of your job. Certain parts of your pipeline may be more computationally demanding than others, and Databricks automatically adds additional workers during these phases of your job (and removes them when they’re no longer needed).

Autoscaling makes it easier to achieve high cluster utilization because you don’t need to provision the cluster to match a workload. This applies especially to workloads whose requirements change over time (like exploring a dataset during the course of a day), but it can also apply to a one-time shorter workload whose provisioning requirements are unknown. Autoscaling thus offers two advantages:

  • Workloads can run faster compared to a constant-sized under-provisioned cluster.
  • Autoscaling clusters can reduce overall costs compared to a statically-sized cluster.

Depending on the constant size of the cluster and the workload, autoscaling gives you one or both of these benefits at the same time. The cluster size can go below the minimum number of workers selected when the cloud provider terminates instances. In this case, Azure Databricks continuously retries to re-provision instances in order to maintain the minimum number of workers.


Autoscaling is not available for spark-submit jobs.


Compute auto-scaling has limitations scaling down cluster size for Structured Streaming workloads. Databricks recommends using Delta Live Tables with Enhanced Autoscaling for streaming workloads. See What is Enhanced Autoscaling?.

How autoscaling behaves

Workspace in the Premium and Enterprise pricing plans use optimized autoscaling. Workspaces on the standard pricing plan use standard autoscaling.

Optimized autoscaling has the following characteristics:

  • Scales up from min to max in 2 steps.
  • Can scale down, even if the cluster is not idle, by looking at the shuffle file state.
  • Scales down based on a percentage of current nodes.
  • On job clusters, scales down if the cluster is underutilized over the last 40 seconds.
  • On all-purpose clusters, scales down if the cluster is underutilized over the last 150 seconds.
  • The spark.databricks.aggressiveWindowDownS Spark configuration property specifies in seconds how often a cluster makes down-scaling decisions. Increasing the value causes a cluster to scale down more slowly. The maximum value is 600.

Standard autoscaling is used in standard plan workspaces. Standard autoscaling has the following characteristics:

  • Starts with adding 8 nodes. Then scales up exponentially, taking as many steps as required to reach the max.
  • Scales down when 90% of the nodes are not busy for 10 minutes and the cluster has been idle for at least 30 seconds.
  • Scales down exponentially, starting with 1 node.

Autoscaling with pools

If you are using an instance pool:

  • Make sure the cluster size requested is less than or equal to the minimum number of idle instances in the pool. If it is larger, cluster startup time will be equivalent to a cluster that doesn’t use a pool.
  • Make sure the maximum cluster size is less than or equal to the maximum capacity of the pool. If it is larger, the cluster creation will fail.

Autoscaling example

If you reconfigure a static cluster to be an autoscaling cluster, Azure Databricks immediately resizes the cluster within the minimum and maximum bounds and then starts autoscaling. As an example, the following table demonstrates what happens to clusters with a certain initial size if you reconfigure a cluster to autoscale between 5 and 10 nodes.

Initial size Size after reconfiguration
6 6
12 10
3 5

Autoscaling local storage

It can often be difficult to estimate how much disk space a particular job will take. To save you from having to estimate how many gigabytes of managed disk to attach to your cluster at creation time, Azure Databricks automatically enables autoscaling local storage on all Azure Databricks clusters.

With autoscaling local storage, Azure Databricks monitors the amount of free disk space available on your cluster’s Spark workers. If a worker begins to run too low on disk, Databricks automatically attaches a new managed disk to the worker before it runs out of disk space. Disks are attached up to a limit of 5 TB of total disk space per virtual machine (including the virtual machine’s initial local storage).

The managed disks attached to a virtual machine are detached only when the virtual machine is returned to Azure. That is, managed disks are never detached from a virtual machine as long as they are part of a running cluster. To scale down managed disk usage, Azure Databricks recommends using this feature in a cluster configured with autoscaling compute or automatic termination.

Automatic termination

You can also set auto termination for a cluster. During cluster creation, you can specify an inactivity period in minutes after which you want the cluster to terminate.

If the difference between the current time and the last command run on the cluster is more than the inactivity period specified, Azure Databricks automatically terminates that cluster. For more information on cluster termination, see Terminate a cluster.

For detailed information about how pool and cluster tag types work together, see Monitor usage using tags

To configure cluster tags:

  1. In the Tags section, add a key-value pair for each custom tag.
  2. Click Add.

Local disk encryption


This feature is in Public Preview.

Some instance types you use to run clusters may have locally attached disks. Azure Databricks may store shuffle data or ephemeral data on these locally attached disks. To ensure that all data at rest is encrypted for all storage types, including shuffle data that is stored temporarily on your cluster’s local disks, you can enable local disk encryption.


Your workloads may run more slowly because of the performance impact of reading and writing encrypted data to and from local volumes.

When local disk encryption is enabled, Azure Databricks generates an encryption key locally that is unique to each cluster node and is used to encrypt all data stored on local disks. The scope of the key is local to each cluster node and is destroyed along with the cluster node itself. During its lifetime, the key resides in memory for encryption and decryption and is stored encrypted on the disk.

To enable local disk encryption, you must use the Clusters API. During cluster creation or edit, set enable_local_disk_encryption to true.

Cluster tags

Cluster tags allow you to easily monitor the cost of cloud resources used by various groups in your organization. You can specify tags as key-value pairs when you create a cluster, and Azure Databricks applies these tags to cloud resources like VMs and disk volumes, as well as DBU usage reports.

For clusters launched from pools, the custom cluster tags are only applied to DBU usage reports and do not propagate to cloud resources.

Spark configuration

To fine-tune Spark jobs, you can provide custom Spark configuration properties in a cluster configuration.

  1. On the cluster configuration page, click the Advanced Options toggle.

  2. Click the Spark tab.

    Spark configuration

    In Spark config, enter the configuration properties as one key-value pair per line.

When you configure a cluster using the Clusters API, set Spark properties in the spark_conf field in the Create new cluster API or Update cluster configuration API.

To enforce Spark configurations on clusters, workspace admins can use cluster policies.

Retrieve a Spark configuration property from a secret

Databricks recommends storing sensitive information, such as passwords, in a secret instead of plaintext. To reference a secret in the Spark configuration, use the following syntax:

spark.<property-name> {{secrets/<scope-name>/<secret-name>}}

For example, to set a Spark configuration property called password to the value of the secret stored in secrets/acme_app/password:

spark.password {{secrets/acme-app/password}}

For more information, see Syntax for referencing secrets in a Spark configuration property or environment variable.

SSH access to clusters

For security reasons, in Azure Databricks the SSH port is closed by default. If you want to enable SSH access to your Spark clusters, see SSH to the cluster driver node.


SSH can be enabled only if your workspace is deployed in your own Azure virtual network.

Environment variables

You can configure custom environment variables that you can access from init scripts running on a cluster. Databricks also provides predefined environment variables that you can use in init scripts. You cannot override these predefined environment variables.

  1. On the cluster configuration page, click the Advanced Options toggle.

  2. Click the Spark tab.

  3. Set the environment variables in the Environment Variables field.

    Environment Variables field

You can also set environment variables using the spark_env_vars field in the Create new cluster API or Update cluster configuration API.

Cluster log delivery

When you create a cluster, you can specify a location to deliver the logs for the Spark driver node, worker nodes, and events. Logs are delivered every five minutes and archived hourly in your chosen destination. When a cluster is terminated, Azure Databricks guarantees to deliver all logs generated up until the cluster was terminated.

The destination of the logs depends on the cluster ID. If the specified destination is dbfs:/cluster-log-delivery, cluster logs for 0630-191345-leap375 are delivered to dbfs:/cluster-log-delivery/0630-191345-leap375.

To configure the log delivery location:

  1. On the cluster configuration page, click the Advanced Options toggle.
  2. Click the Logging tab.
  3. Select a destination type.
  4. Enter the cluster log path.


This feature is also available in the REST API. See the Clusters API.