Build and operate the data processing systems behind analytics and ML on Google Cloud, including BigQuery, Dataflow, and Pub/Sub, and track your readiness for the Professional Data Engineer exam.
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Google Cloud feature
Spot VMs
Topic in 4 exam domains
- Spot VMs · Feature
- Spot VMs · Concept · Compute Engine
Also called: preemptible instance, Preemptible VM Instances, spot, spot instance, spot instances, Spot VM
Discounted Compute Engine virtual machine instances suitable for fault-tolerant workloads that can withstand disruptions.
Where this appears on the exam
3 objectives in 1 of 5 domains
Related topics
FoundationsBuilding blocksBuilding blocksThis topicWorks alongsideBuilds on theseAppliedApplication Default CredentialsFeatureCompute EngineServiceCloud MonitoringServiceGKEServicemanaged instance groupsFeatureNode Auto-ProvisioningFeature
Spot VMsFeature
autoscalerFeatureCloud Load BalancingServicemanaged instance groupFeatureinstance templateConceptManaged Service for Apache SparkServicePoints to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
- Spot VMs is part of Compute Engine From the official documentation: “Spot VMs are virtual machine (VM) instances that are excess Compute Engine capacity.”
- managed instance group works with Spot VMs From the official documentation: “This document explains how to create a managed instance group (MIG) that uses Spot VMs.”
- managed instance groups works with Spot VMs From the official documentation: “You can create Spot VMs in a [MIG](/compute/docs/instance-groups/manager) by specifying the spot provisioning model in an [instance template](/compute/docs/instance-templates) before you create or update the group.”
- Node Auto-Provisioning works with Spot VMs From the official documentation: “Both the cluster autoscaler and node auto-provisioning support using Spot VMs.”
- Spot VMs is part of GKE From the official documentation: “The following table describes the modifiable options for the termination and graceful shutdown period for Spot VMs in GKE.”
- Application Default Credentials works with Compute Engine From the official documentation: “To authenticate to Compute Engine, set up Application Default Credentials.”
- Application Default Credentials secures Compute Engine From the official documentation: “To authenticate to Compute Engine, set up Application Default Credentials.”
- Compute Engine is an alternative to GKE From the official documentation: “To use TPU7x, you can use Google Kubernetes Engine (GKE) or Compute Engine.”
- managed instance groups is part of Compute Engine From the official documentation: “Compute Engine provides a straightforward way to implement autoscaling through managed instance groups.”
- Cloud Monitoring depends on Application Default Credentials From the official documentation: “To authenticate to Monitoring, set up Application Default Credentials.”
- Cloud Monitoring works with GKE From the official documentation: “Enable Cloud Monitoring on GKE Clusters”
- managed instance groups depends on instance template From the official documentation: “Managed instance groups require that you create an instance template.”
- instance template works with managed instance group From the official documentation: “Create a global instance template that you can use to create a managed instance group (MIG) of GPU VMs.”
- managed instance group depends on instance template From the official documentation: “All MIGs require an instance template.”
- Application Default Credentials works with Managed Service for Apache Spark From the official documentation: “To authenticate to Managed Service for Apache Spark, set up Application Default Credentials.”
- Application Default Credentials secures Managed Service for Apache Spark From the official documentation: “To authenticate to Managed Service for Apache Spark, set up Application Default Credentials.”
- autoscaler is part of managed instance groups From the official documentation: “Although autoscaling is a feature of MIGs, it is a separate REST resource.”
- GKE depends on Compute Engine From the official documentation: “For example, GKE deploys clusters on Compute Engine VMs.”
- instance template is part of Compute Engine From the official documentation: “Creating a Compute Engine instance template and managed instance group (MIG).”
- instance template configures managed instance group From the official documentation: “Create the MIG using the template.”
- Application Default Credentials secures Cloud Monitoring From the official documentation: “To authenticate to Monitoring, set up Application Default Credentials.”
- Application Default Credentials works with GKE From the official documentation: “To authenticate to GKE, set up Application Default Credentials.”
- autoscaler is part of Compute Engine From the official documentation: “This example assumes you are familiar with Docker containers, as well as Compute Engine resources, particularly HTTP load balancing, autoscaling, managed instance groups, and instance templates.”
- autoscaler configures managed instance group From the official documentation: “You can set a MIG's target size automatically by using an autoscaler or set the size manually.”
- Cloud Load Balancing works with Cloud Monitoring From the official documentation: “Key performance and health metrics reported by Cloud Monitoring for Cloud Load Balancing resources.”
- Cloud Load Balancing works with managed instance group From the official documentation: “It configures the load balancer to connect to the instance group.”
- Cloud Monitoring works with Compute Engine From the official documentation: “Many Google services like Compute Engine send telemetry to Cloud Monitoring.”
- Compute Engine works with Managed Service for Apache Spark From the official documentation: “Yes, Managed Service for Apache Spark has native and automatic integrations with Compute Engine, Cloud Storage, Bigtable, BigQuery, Logging, and Cloud Monitoring.”
- managed instance group is part of Compute Engine From the official documentation: “Checks if a Compute Engine Managed Instance Group (MIG) is configured as Zonal.”
- Managed Service for Apache Spark depends on Application Default Credentials From the official documentation: “To authenticate to Managed Service for Apache Spark, set up Application Default Credentials.”
- Application Default Credentials works with Cloud Monitoring From the official documentation: “To authenticate to Monitoring, set up Application Default Credentials.”
- Application Default Credentials configures Compute Engine From the official documentation: “To authenticate to Compute Engine, set up Application Default Credentials.”
- autoscaler is part of managed instance group From the official documentation: “Although autoscaling is a feature of managed instance groups, it is a separate API resource.”
- autoscaler works with managed instance group From the official documentation: “If you delete a MIG using the gcloud CLI, any autoscalers attached to the MIG are also deleted.”
- Cloud Load Balancing works with GKE From the official documentation: “GKE and GKE Enterprise network controllers also support the deployment of Cloud Load Balancing for built-in integration with container networking.”
- Cloud Load Balancing works with managed instance groups From the official documentation: “Use [managed instance groups (MIGs)](/compute/docs/instance-groups#managed_instance_groups) to create groups of VMs so that load balancers can direct traffic to more than one VM in case a single VM becomes unhealthy.”
- Cloud Monitoring works with managed instance groups From the official documentation: “To be alerted of unexpected results, use [Cloud Monitoring](/monitoring) to monitor the managed instance groups and network traffic.”
- Cloud Monitoring works with Managed Service for Apache Spark From the official documentation: “Monitoring uses workload metadata and metrics to provide insights into the health and performance of Managed Service for Apache Spark workloads.”
- Compute Engine works with GKE From the official documentation: “For example, Google Kubernetes Engine (GKE) can cause usage in Compute Engine.”
- GKE works with Managed Service for Apache Spark From the official documentation: “You can create Managed Service for Apache Spark clusters on: - [Managed Service for Apache Spark clusters on Compute Engine](/dataproc/docs/guides/create-cluster) - [Managed Service for Apache Spark clusters on GKE](/dataproc/docs/guides/dpgke/dataproc-gke-overview)”
- instance template is part of managed instance group From the official documentation: “Each VM in a MIG is based on an instance template.”
- instance template works with managed instance groups From the official documentation: “Define an instance template once and use the MIG to create multiple identical TPU VMs.”
- instance template configures managed instance groups From the official documentation: “Using the instance template, configure a [managed instance group](/compute/docs/instance-groups/creating-groups-of-managed-instances) with a target size of 1.”
- managed instance group is part of managed instance groups From the official documentation: “You can build highly available deployments of stateful workloads on VMs using stateful managed instance groups (stateful MIGs).”
- Managed Service for Apache Spark depends on Compute Engine From the official documentation: “Managed Service for Apache Spark clusters are built on [Compute Engine](/compute) instances.”
- Managed Service for Apache Spark depends on managed instance group From the official documentation: “If you're using Managed Service for Apache Spark, the process of [launching preemptible worker nodes](/dataproc/docs/concepts/compute/secondary-vms#preemptible_workers) is managed by a managed instance group.”
- Node Auto-Provisioning is part of GKE From the official documentation: “With node auto-provisioning, GKE automatically provisions node pools based on cluster resource demand.”
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