Work with your Spark cluster

Create an on-demand Spark cluster with the desired cluster settings attached to a Workspace or Job.

Create an on-demand Spark cluster attached to a Domino Workspace

Click New Workspace.
From Launch New Workspace, select Compute Cluster.
Specify the cluster settings and launch you workspace. After the workspace is running, it has access to the Spark cluster you configured.

The hardware tier for your workspace determines the compute resources available to your Spark driver process.

Create an on-demand Spark cluster in a Workspace

Create a cluster with Jobs

From the Jobs menu, click Run.
From Start a Job select Compute Cluster.
Specify the cluster settings and launch your job. The job has access to the Spark cluster you configured.

Create a cluster with Jobs

You can use any Python script that contains a PySpark job.

You can also use spark-submit to submit jobs. However, this is not recognized automatically as a Domino supported job type, so you must wrap it with a shell script unless you included a copy of spark-submit.sh when you prepared your compute environment.

The following is an example of a simple wrapper my-spark-submit.sh.

#!/usr/bin/env bash

spark-submit $@

Understand your cluster settings

Domino makes it simple to specify key settings when creating a Spark cluster.

Cluster settings

Min Executors

Number of Executors that will be available to your Spark application when the cluster starts. If Auto-scale workers is not enabled, this will always be the size of the cluster. The combined capacity of the executors will be available for your workloads.

When you instantiate Spark context with the default settings, the spark.executor.instances Spark setting is set to the number specified in the previous window.
Max Executors

The maximum number of Spark executors that the cluster can reach when Auto-scale workers is enabled. See Cluster autoscaling for more details.
Cluster size: Limit

The maximum number of executors that you can make available to your cluster is limited by the number of per-user executions that your Domino administrator has configured for your deployment.

In addition to the number of Spark application executors, you will need 1 slot for your cluster master and 1 slot for your workspace or job.
Executor Hardware Tier

The amount of compute resources (CPU and memory) that will be made available to Spark executors.

When you instantiate Spark context with the default settings, the spark.executor.cores and spark.executor.memory Spark settings will be set to the values corresponding to the Hardware Tier.

For cores, the number of cores will be rounded up to the nearest integer. For example, a Hardware Tier with 1.5 cores will result in spark.executor.cores set to 2 when creating the default context.
Master Hardware Tier

Same mechanics as the Executor Hardware Tier, but applied to the resources that will be available for your Spark cluster master.

The master in the Domino configuration has a pretty limited role in Spark application scheduling, and it does not need a significant amount of resources.

By default, any Hardware Tier is available when selecting resources for your Executor and Master. Domino administrators can optionally configure compute cluster dedicated hardware tiers.
Cluster compute environment

Designates your environment. Projects already using this environment will be allowed to continue to use it until a new project environment is set.
Dedicated local storage per executor

The amount of dedicated storage in Gigabytes (2^30 bytes) that will be available to each executor.

The storage will be automatically mounted to /tmp which is the default location for spark.local.dir that serves as the location for RDDs, when they need to be on disk, and for shuffle data.

The storage will be automatically provisioned when the cluster is created and de-provisioned when it is shut down.

Warning

The local storage per executor should not be used for storing any data which needs to be available after the cluster is shut down.

Advanced cluster configuration topics

Spark executors versus workers

Domino gives you the ability to have flexible resource allocation through Hardware Tiers which makes it convenient to plan your cluster resources needed for your application in terms of executors as opposed to workers. When creating on-demand Spark clusters, Domino will automatically manage the relationship between Spark executors and workers (see Spark definitions). By default, there will be one worker for each executor.

Resource overhead

When setting up worker and executor memory limits, Domino ensures that the worker containers have additional memory on top of the Hardware Tier memory definition to account for the recommended overhead of 10% of executor memory with a minimum of 384MB. The usable executor memory will always be based on the value from the actual Hardware Tier.

You might have to take this into account when designing Hardware Tiers to achieve optimal packing of Spark worker containers on the physical instances of the Domino Kubernetes cluster.

Because of the worker memory overhead and the approach of using 1 worker per executor, it is possible that using lots of executors with smaller Hardware Tiers may result in excessive memory overhead.

If this is undesirable, an experienced data scientist can choose a large Hardware Tier when creating the cluster and then overwrite the executor core and memory settings to achieve the desired effect. See Adding and overwriting parameters.

Cluster auto-scaling

Cluster autoscaling

Cluster auto-scaling allows you to start with a small cluster, which then automatically scales up and down in response to the resource consumption of your workload. This approach utilizes resources more efficiently for bursty workloads.

By default, the cluster size will increase when the average CPU utilization of your workload reaches 80%. A Domino administrator can further refine the auto-scaling settings by including memory utilization or changing the desired scaling thresholds.

Scale down happens if resource utilization is low for a period of at least 5 minutes. Note that depending on the workload that you are executing, scale down may terminate cluster nodes that contain intermediate results which would need to be recomputed.

Connect to your cluster

When provisioning your on-demand Spark cluster, Domino sets up key default cluster configuration parameters (for example, spark.master, spark.driver.host) to the appropriate cluster URLs so that creating SparkSession or SparkContext with default context will properly connect the cluster to your workspace or job.

Create SparkSession

You can create SparkSession using the following:

from pyspark.sql import SparkSession

spark = SparkSession
        .builder
        .appName("MyAppName")
        .getOrCreate()

 # You can examine the full config
spark.sparkContext.getConf().getAll()

Create SparkContext

Alternatively, you can create SparkContext using the following:

from pyspark import SparkContext

conf = SparkConf().setAppName(appName).setMaster(master)
sc = SparkContext(conf=conf)

Add and overwrite parameters

It is possible to add additional config parameters or overwrite any of the defaults by manipulating your context configuration.

from pyspark.sql import SparkSession

spark = SparkSession
        .builder
        .appName("MyAppName")
        .config("spark.some.config.option", "some-value")
        .getOrCreate()

You can also supply additional configuration settings that will apply for any clusters associated with a Domino project from Settings > Integrations > Domino managed on-demand cluster

Configure Project settings

Warning

You should not overwrite the default configuration values for spark.master, spark.driver.host since you may lose proper connectivity between your cluster and workspace or job.

Access the Spark UI

Spark provides a suite of web user interfaces that you can use to monitor the status and resource consumption of your Spark cluster.

Domino makes the Spark web UI available for active on-demand clusters attached to both workspaces and jobs.

Note	In a hybrid Domino deployment, if the data plane is not configured for workspaces, then the user interface link is disabled. An administrator can Enable a data plane for workspaces.

Spark UI from Workspaces

The Spark UI is available from a dedicated tab in your workspace.

Access the Spark UI from Workspaces

Spark UI from Jobs

The Spark UI is also available for running jobs from Details tab.

Access the Spark UI from Jobs

Spark UI compatibility mode

With Domino 4.5 and higher, if an on-Demand Spark cluster uses Spark 3.0.1 or earlier, some links in the Spark UI will not function properly. This will not affect actual Spark processing.

For cases where you want to run an older version, you can configure Domino in Spark web UI compatibility mode using one of the following options:

Set for the entire Domino instance

A Domino administrator can set the Domino central configuration setting com.cerebro.domino.computegrid.computeCluster.spark.proxyCompatibility to legacy.
Set on a Domino Project level

Alternatively, this can be set at a project level from Settings > Integrations > Domino managed on-demand cluster

Warning

When Compatibility mode is set, the UI for Spark 3.1.1 and higher will not function properly.

Cluster lifecycle

On workspace or job startup, a Domino on-demand Spark cluster with the desired cluster settings is automatically provisioned and attached to the workspace or job as soon as the cluster becomes available.

On workspace or job termination, the on-demand Spark cluster and all associated resources are automatically terminated and de-provisioned. This includes any compute resources and storage allocated for the cluster.

Cluster network security

The on-demand Spark clusters created by Domino are not meant for sharing between multiple users. Each cluster is associated with a given workspace or a job instance. Access to the cluster and the Spark web UI is restricted only to users who can access the workspace or the job attached to it. This restriction is enforced at the networking level and the cluster is only reachable from the execution that provisioned it.