Compute configuration reference

Note

The organization of this article assumes you are using the simple form compute UI. For an overview of the simple form updates, see Use the simple form to manage compute.

This article explains the configuration settings available when creating a new all-purpose or job compute resource. Most users create compute resources using their assigned policies, which limits the configurable settings. If you don’t see a particular setting in your UI, it’s because the policy you’ve selected does not allow you to configure that setting.

Simple compute form

The configurations and management tools described in this article apply to both all-purpose and job compute. For more considerations on configuring job compute, see Configure compute for jobs.

Create a new all-purpose compute resource

To create a new all-purpose compute resource:

  1. In the workspace sidebar, click Compute.

  2. Click the Create compute button.

  3. Configure the compute resource.

  4. Click Create.

You new compute resource will automatically start spinning up and be ready to use shortly.

Compute policy

Policies are a set of rules used to limit the configuration options available to users when they create compute resources. If a user doesn’t have the Unrestricted cluster creation entitlement, then they can only create compute resources using their granted policies.

To create compute resources according to a policy, select a policy from the Policy drop-down menu.

By default, all users have access to the Personal Compute policy, allowing them to create single-machine compute resources. If you need access to Personal Compute or any additional policies, reach out to your workspace admin.

Performance settings

The following settings appear under the Performance section of the simple form compute UI:

Databricks Runtime versions

Databricks Runtime is the set of core components that run on your compute. Select the runtime using the Databricks Runtime Version drop-down menu. For details on specific Databricks Runtime versions, see Databricks Runtime release notes versions and compatibility. All versions include Apache Spark. Databricks recommends the following:

  • For all-purpose compute, use the most current version to ensure you have the latest optimizations and the most up-to-date compatibility between your code and preloaded packages.

  • For job compute 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.

Use Photon acceleration

Photon is available on compute running Databricks Runtime 9.1 LTS and above.

To enable or disable Photon acceleration, select the Use Photon Acceleration checkbox. To learn more about Photon, see What is Photon?.

Worker node type

A compute resource 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. The driver node setting is underneath the Advanced performance section.

Different families of instance types fit different use cases, such as memory-intensive or compute-intensive workloads. You can also select a pool to use as the worker or driver node.

Important

Do not use a pool with preemptible VM instances as your driver type. Select an on-demand driver type to prevent your driver from being reclaimed. See Connect to pools.

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

Tip

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

Worker node IP addresses

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

Default provisioned worker node storage

Databricks provisions the following storage for each worker node:

  • One boot disk per instance that is used by the host operating system and Databricks internal services. The boot disk is 100 GB for non-GPU instances and 200 GB for GPU instances.

  • Local SSD used by the Spark worker. This hosts Spark services and logs. Each local SSD is 375GB. To configure the number of attached local SSDs, see Instance types with local SSDs.

  • Remote SSDs when storage autoscaling is enabled. These start at 150 GB at creation and autoscale as needed.

GPU instance types

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

Instance types with local SSDs

For the latest list of instance types, the prices of each, and the size of the local SSDs, see the GCP pricing estimator.

Instance types that have local SSDs are encrypted with default Google Cloud server-side encryption and automatically use disk caching for improved performance. Cache sizes on all instance types are set automatically, so you do not need to set the disk usage explicitly.

Configure local SSDs for your compute

You can configure the number of local SSDs to attach to your compute resource when you use the Clusters API to create your compute resource.

To configure the number of local SSDs, set a value for local_ssd_count in the gcp_attributes object. Each instance type can only support a certain number of attached local SSDs. The value specified in local_ssd_count must be valid for both the driver and worker instance type. For more information, see the GCP doc for Local SSDs and machine types.

Single-node compute

The Single node checkbox allows you to create a single node compute resource.

Single node compute is intended for jobs that use small amounts of data or non-distributed workloads such as single-node machine learning libraries. Multi-node compute should be used for larger jobs with distributed workloads.

Single node properties

A single node compute resource has the following properties:

  • Runs Spark locally.

  • Driver acts as both master and worker, with no worker nodes.

  • Spawns one executor thread per logical core in the compute resource, minus 1 core for the driver.

  • Saves all stderr, stdout, and log4j log outputs in the driver log.

  • Can’t be converted to a multi-node compute resource.

Selecting single or multi node

Consider your use case when deciding between single or multi-node compute:

  • Large-scale data processing will exhaust the resources on a single node compute resource. For these workloads, Databricks recommends using multi-node compute.

  • Single-node compute is not designed to be shared. To avoid resource conflicts, Databricks recommends using a multi-node compute resource when the compute must be shared.

  • A multi-node compute resource can’t be scaled to 0 workers. Use single node compute instead.

  • Single-node compute is not compatible with process isolation.

  • GPU scheduling is not enabled on single node compute.

  • On single-node compute, Spark cannot read Parquet files with a UDT column. The following error message results:

    The Spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached.

    To work around this problem, disable the native Parquet reader:

    spark.conf.set("spark.databricks.io.parquet.nativeReader.enabled", False)

Enable autoscaling

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

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

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

Note

When the compute resource is running, the compute details 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, 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 utilization because you don’t need to provision the compute 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 compute resource.

  • Autoscaling can reduce overall costs compared to a statically-sized compute resource.

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

Note

Autoscaling is not available for spark-submit jobs.

Note

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 Optimize the cluster utilization of Delta Live Tables pipelines with enhanced autoscaling.

How autoscaling behaves

Autoscaling has the following characteristics:

  • Starts by 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 compute has been idle for at least 30 seconds.

  • Scales down exponentially, starting with 1 node.

Autoscaling with pools

If you are attaching your compute resource to a pool, consider the following:

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

Autoscaling example

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

Initial size

Size after reconfiguration

6

6

12

10

3

5

Advanced performance settings

The following setting appear under the Advanced performance section in the simple form compute UI.

..azure:

#### <a id="spot-instances"></a> Spot instances

To save cost, you can choose to use [spot instances, also known as Azure Spot VMs](https://learn.microsoft.com/azure/virtual-machines/spot-vms) by checking the **Spot instances** checkbox.

![Configure spot](/_static/images/clusters/spot-azure.png)

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, <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. This on-demand failback is only supported for spot instances that have been fully acquired and are running. Spot instances that fail during setup are not automatically replaced.

Additionally, when new nodes are added to existing compute resources, <Databricks> attempts to acquire spot instances for those nodes.

Preemptible instances

A preemptible VM instance is an instance that you can create and run at a much lower price than normal instances. However, Google Cloud might stop (preempt) these instances if it requires access to those resources for other tasks. Preemptible instances use excess Google Compute Engine capacity, so their availability varies with usage.

When you create a new compute resource, you can enable preemptible VM instances in two different ways:

  1. When you create compute using the UI, click the Use preemtible instances checkbox under Advanced performance.

  2. When you create an instance pool using the UI, set On-Demand/Preemptible to All preemptible, Preemptible with fallback GCP, or On demand GCP. If preemptible VM instances are not available, by default the compute will fall back to using on-demand VM instances. To configure the fall-back behavior, set gcp_attributes.gcp_availability to PREEMPTIBLE_GCP or PREEMPTIBLE_WITH_FALLBACK_GCP. The default is ON_DEMAND_GCP.

{
  "instance_pool_name": "Preemptible w/o fallback API test",
  "node_type_id": "n1-highmem-4",
  "gcp_attributes": {
      "availability": "PREEMPTIBLE_GCP"
  }
}

Next, create a new compute resource and set Pool to a preemptible instance pool.

Automatic termination

You can set auto termination for compute under the Advanced performance section. During compute creation, specify an inactivity period in minutes after which you want the compute resource to terminate.

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

Driver type

You can select the driver type under the Advanced performance section. The driver node maintains state information of all notebooks attached to the compute resource. The driver node also maintains the SparkContext, interprets all the commands you run from a notebook or a library on the compute resource, 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.

Tip

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

Tags

Tags allow you to easily monitor the cost of cloud resources used by various groups in your organization. Specify tags as key-value pairs when you create compute, and Databricks applies these tags to Databricks Runtime pods and persistent volumes on the GKE cluster and to DBU usage reports.

The Databricks billable usage graphs in the account console can aggregate usage by individual tags. The billable usage CSV reports downloaded from the same page also include default and custom tags. Tags also propagate to GKE and GCE labels.

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

To add tags to your compute resource:

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

  2. Click Add.

Advanced settings

The following settings appear under the Advanced section of the simple form compute UI:

Access modes

Access mode is a security feature that determines who can use the compute resource and the data they can access using the compute resource. Every compute resource in Databricks has an access mode. Access mode settings are found under the Advanced section of the simple form compute UI.

Access mode selection is Auto by default, meaning the access mode is automatically chosen for you based on your selected Databricks Runtime. Machine learning runtimes and Databricks Runtimes lower than 14.3 default to Dedicated, otherwise Standard is used.

Databricks recommends that you use standard access mode for all workloads. Use dedicated access mode only if your required functionality is not supported by standard access mode.

Access Mode

Visible to user

UC Support

Supported Languages

Notes

Dedicated (formerly single user)

Always

Yes

Python, SQL, Scala, R

Can be assigned to and used by a single user or group.

Standard (formerly shared)

Always

Yes

Python, SQL, Scala (on Unity Catalog-enabled compute using Databricks Runtime 13.3 LTS and above)

Can be used by multiple users with data isolation among users.

For detailed information about the functionality support for each of these access modes, see Compute access mode limitations for Unity Catalog.

Note

In Databricks Runtime 13.3 LTS and above, init scripts and libraries are supported by all access modes. Requirements and levels of support vary. See Where can init scripts be installed? and Compute-scoped libraries.

Google service account

To associate this compute resource with a Google service account using Google Identity, click Advanced then Google service account and add your Google service account email address in the Google service account field. This value is used to authenticate with the GCS and BigQuery data sources.

Important

The service account that you use to access GCS and BigQuery data sources must be in the same project as the service account that was specified when setting up your Databricks account.

Availability zones

In the compute configuration page, under Advanced > Instances, select the compute resource’s availability zone. This setting lets you specify which availability zone you want the compute resource to use. By default, the availability zone setting is set to Auto. With a setting of Auto, a single availability zone is automatically picked for you.

You can also choose a specific zone. Choosing a specific zone is useful primarily if your organization has purchased reserved instances in specific availability zones.

High availability zone

You can also select HA as the availability zone. High availability (HA) is a system feature designed to provide a consistent level of uptime for prolonged periods. Using an HA zone configuration can reduce the probability of single-zone availability issues, such as zonal unavailability or being unable to source instance capacity in a zone.

When HA is selected as the availability zone, Databricks balances instance placement across the zones in a region. This could lead to an increase in price through inter-zone egress charges.

Enable autoscaling local storage

The configure Enable autoscaling local storage, open the Advanced section and click the Instances tab.

Google Cloud compute instances can be supplemented with additional storage at the worker level using zonal solid state persistent disks.

With autoscaling local storage, Databricks monitors the amount of free disk space available on your compute’s Spark workers. If a worker begins to run too low on disk, Databricks automatically resizes the zonal SSD PD before it runs out of disk space. Zonal-SSD PD volumes are attached up to a limit of 5 TB of total disk space per instance (including the instance’s local storage).

Local disk encryption

Instance types that have local SSDs are encrypted with default Google Cloud server-side encryption. See Instance types with local SSDs.

Spark configuration

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

  1. On the compute configuration page, click the Advanced 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 compute using the Clusters API, set Spark properties in the spark_conf field in the create cluster API or Update cluster API.

To enforce Spark configurations on compute, workspace admins can use compute 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 Manage secrets.

Environment variables

Configure custom environment variables that you can access from init scripts running on the compute resource. Databricks also provides predefined environment variables that you can use in init scripts. You cannot override these predefined environment variables.

  1. On the compute configuration page, click the Advanced toggle.

  2. Click the Spark tab.

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

    Environment Variables field

    Note

    ENV is a reserved word and cannot be used as the name of an environment variable.

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

Compute log delivery

When you create compute, 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 compute resource is terminated, Databricks guarantees to deliver all logs generated up until the compute resource was terminated.

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

To configure the log delivery location:

  1. On the compute page, click the Advanced toggle.

  2. Click the Logging tab.

  3. Select a destination type.

  4. Enter the compute log path.

    The log path must be a DBFS path that begins with dbfs:/.

Note

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