Create a cluster
These instructions are for Unity Catalog enabled workspaces. For documentation on the non-Unity Catalog legacy UI, see Configure clusters.
The cluster creation UI let’s you select the cluster configuration specifics, including:
- The policy
- The access mode, which controls the security features used when interacting with data
- The runtime version
- The cluster worker and driver node types
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 by admins 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.
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.
Personal compute policy
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 as an option when you create a cluster, then you haven’t been given access to the policy. Contact your administrator to request access to the Personal Compute policy or an appropriate equivalent policy.
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. See single user limitations.|
|Shared||Always (Premium plan required)||Yes||Python (on Databricks Runtime 11.1 and above), SQL, Scala (on Unity Catalog-enabled clusters using Databricks Runtime 13.3 and above)||Can be used by multiple users with data isolation among users. See shared limitations.|
|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. There are additional access mode limitations for Structured Streaming on Unity Catalog, see Structured Streaming support.
To read from a view, you must have
SELECTon all referenced tables and views.
Dynamic views are not supported.
When used with credential passthrough, Unity Catalog features are disabled.
You cannot use a single user cluster to query tables created by a Unity Catalog-enabled Delta Live Tables pipeline, including streaming tables and materialized views created in Databricks SQL. To query tables created by a Delta Live Tables pipeline, you must use a shared cluster using Databricks Runtime 13.1 and above.
When used with credential passthrough, Unity Catalog features are disabled.
Custom containers are not supported.
Spark-submit jobs are not supported.
Databricks Runtime ML is not supported.
Cannot use R, RDD APIs, or clients that directly read the data from cloud storage, such as DBUtils.
Can use Scala only on Databricks Runtime 13.3 and above.
The following limitations exist for user-defined functions (UDFs):
- Cannot use Hive or Scala UDFs
- In Databricks Runtime 13.1 and below, you cannot use Python UDFs, including UDAFs, UDTFs, and Pandas on Spark (
mapInPandas). In Databricks Runtime 13.2 and above, Python UDFs are supported.
- See User-defined functions (UDFs) in Unity Catalog.
Must run commands on cluster nodes as a low-privilege user forbidden from accessing sensitive parts of the filesystem. In Databricks Runtime 11.3 and below, you can only create network connections to ports 80 and 443.
Cannot connect to the instance metadata service or Azure WireServer.
Attempts to get around these restrictions will fail. These restrictions are in place so that users can’t access unprivileged data through the cluster.
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 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 runtimes.
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 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 advanced machine learning use cases, consider the specialized Databricks Runtime version.
All Databricks Runtime versions include Apache Spark. New versions add components and updates that improve usability, performance, and security.
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:
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.
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.
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 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.
To save cost, you can choose to use spot instances, also known as Azure Spot VMs by checking the Spot instances checkbox.
The first instance will always be on-demand (the driver node is always on-demand) and subsequent instances will be spot instances. If spot instances are evicted due to unavailability, on-demand instances are deployed to replace evicted instances.
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 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
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 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.
spark.databricks.aggressiveWindowDownSSpark 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 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.
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|
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.
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.
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
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.
For detailed information about how pool and cluster tag types work together, see Monitor usage using cluster, pool, and workspace tags
To configure cluster tags:
- In the Tags section, add a key-value pair for each custom tag.
- Click Add.
To fine tune Spark jobs, you can provide custom Spark configuration properties in a cluster configuration.
On the cluster configuration page, click the Advanced Options toggle.
Click the Spark tab.
In Spark config, enter the configuration properties as one key-value pair per line.
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:
For example, to set a Spark configuration property called
password to the value of the secret stored in
For more information, see Syntax for referencing secrets in a Spark configuration property or environment variable.
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.
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.
On the cluster configuration page, click the Advanced Options toggle.
Click the Spark tab.
Set the environment variables in the Environment Variables field.
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
To configure the log delivery location:
- On the cluster configuration page, click the Advanced Options toggle.
- Click the Logging tab.
- Select a destination type.
- Enter the cluster log path.
This feature is also available in the REST API. See the Clusters API.