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
Horizontal Pod Autoscaler
Topic in 1 exam domain
- Horizontal Pod Autoscaler · Feature
- Horizontal Pod Autoscaler · Concept
Also called: GKE Horizontal Pod Autoscaler
A Kubernetes autoscaler configured to automatically scale pods, such as Envoy gateways, based on workload demand.
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1 subdomain in 1 of 5 domains
Related topics
FoundationsBuilding blocksBuilding blocksBuilding blocksBuilding blocksBuilding blocksThis topicWorks alongsideAppliedCloud LoggingServiceBigQueryServiceCustomer-Managed Encryption KeysFeatureCloud StorageServiceCloud SQLServiceArtifact RegistryServiceGKEService
Horizontal Pod AutoscalerFeature
Cluster AutoscalerFeatureCloud MonitoringServiceManaged Service for Apache AirflowServicePoints to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
- Cloud Logging works with Horizontal Pod Autoscaler From the official documentation: “The horizontal Pod autoscaler emits decision events, which are stored as log entries in Cloud Logging.”
- Cluster Autoscaler works with Horizontal Pod Autoscaler From the official documentation: “It's a best practice to enable CA whenever you are using either HPA or VPA.”
- Horizontal Pod Autoscaler is part of GKE From the official documentation: “Configure the Horizontal Pod Autoscaler, a GKE feature that can automatically increase or decrease the number of Pods for a workload depending on specified metrics.”
- Managed Service for Apache Airflow depends on Horizontal Pod Autoscaler From the official documentation: “Managed Airflow autoscaling uses three different autoscalers provided by GKE: - [Horizontal Pod Autoscaler (HPA)](/kubernetes-engine/docs/concepts/horizontalpodautoscaler)”
- BigQuery is an alternative to Cloud Storage From the official documentation: “The `import_result_sink` must be a Cloud Storage path or a BigQuery table.”
- BigQuery works with Cloud Storage From the official documentation: “Sample to load Avro data from Cloud Storage into a new BigQuery table”
- BigQuery works with Cloud SQL From the official documentation: “Add credentials to connect BigQuery to Cloud SQL.”
- BigQuery works with Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
- Cloud SQL works with Cloud Storage From the official documentation: “Exports data from a Cloud SQL instance to a Cloud Storage bucket as a SQL dump or CSV file.”
- Customer-Managed Encryption Keys secures Cloud Storage From the official documentation: “CMEKs encrypt data at rest in Cloud SQL, Cloud Storage, and within GKE clusters.”
- BigQuery works with Cloud Monitoring From the official documentation: “You can use Monitoring to export your metrics to BigQuery.”
- Cloud Logging works with GKE From the official documentation: “Enable Cloud Logging on GKE Clusters”
- Cloud Monitoring works with GKE From the official documentation: “Enable Cloud Monitoring on GKE Clusters”
- Customer-Managed Encryption Keys secures Cloud SQL From the official documentation: “Enable CMEK for Cloud SQL Databases”
- Cloud SQL works with GKE From the official documentation: “The app uses Cloud SQL, Vertex AI, and either Google Kubernetes Engine (GKE) or Cloud Run.”
- Cloud Storage works with Customer-Managed Encryption Keys From the official documentation: “Cloud Storage supports CMEK for routing logs.”
- Cloud Logging works with Cloud Monitoring From the official documentation: “You can store and view query-response activity logs in Cloud Logging and set up logs-based monitoring by using Cloud Monitoring.”
- Cloud Logging works with Cloud SQL From the official documentation: “Database Migration Service and Cloud SQL use Cloud Logging.”
- Cloud Logging works with Cloud Storage From the official documentation: “Cloud Storage writes various log entries to Cloud Logging in the Google Cloud console.”
- Customer-Managed Encryption Keys secures BigQuery From the official documentation: “You can either create CMEKs manually to protect your BigQuery resources or use Cloud KMS Autokey.”
- BigQuery works with Managed Service for Apache Airflow From the official documentation: “You can use Managed Airflow to orchestrate services in your data pipelines, such as triggering a job in BigQuery or starting a Dataflow pipeline.”
- Customer-Managed Encryption Keys secures Managed Service for Apache Airflow From the official documentation: “This page describes how to use [Customer Managed Encryption Keys (CMEK)](/kms/docs/cmek) to protect Managed Airflow environments.”
- Artifact Registry works with GKE From the official documentation: “In the next tutorial, you deploy those images from the Artifact Registry repository to a GKE cluster:”
- Artifact Registry works with Managed Service for Apache Airflow From the official documentation: “Don't modify or delete the images built by Managed Airflow in Artifact Registry or the repository itself.”
- BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
- Cloud Logging is an alternative to Cloud Storage From the official documentation: “Instead, they are routed to either Cloud Storage (recommended) or Cloud Logging, depending on your organization's configuration.”
- Cloud Logging works with Customer-Managed Encryption Keys From the official documentation: “To use Cloud Logging with Customer-managed encryption keys (CMEK), you must complete the steps in the [Enable CMEK for an organization](/logging/docs/routing/managed-encryption#enable) page in the Cloud Logging documentation.”
- Cloud Monitoring works with Cloud SQL From the official documentation: “You can also get more details by viewing the Cloud Monitoring logs for your Cloud SQL destination instance.”
- Cloud Monitoring works with Cluster Autoscaler From the official documentation: “GKE cluster autoscaler exposes metrics to Cloud Monitoring that provide insights into its decisions, especially in relation to ComputeClasses.”
- Cloud Monitoring works with Managed Service for Apache Airflow From the official documentation: “To learn about Cloud Logging and Cloud Monitoring for your Managed Airflow environment, see [Monitor environments](/composer/docs/composer-2/monitor-environments).”
- Cloud Storage works with Managed Service for Apache Airflow From the official documentation: “When you upload your DAG file to the `dags/` folder in Cloud Storage, Managed Airflow parses the file.”
- Cluster Autoscaler is part of GKE From the official documentation: “GKE's *cluster autoscaler* automatically resizes the number of nodes in a given node pool, based on the demands of your workloads.”
- Customer-Managed Encryption Keys secures Artifact Registry From the official documentation: “Artifact Registry also supports [organization policy constraints](/artifact-registry/docs/cmek#org-policy) that can require CMEK to protect resources.”
- Customer-Managed Encryption Keys depends on Cloud Storage From the official documentation: “If used for storing Customer-managed Encryption Keys (CMEK): Cloud Storage (See [Cloud Storage pricing](/storage/pricing).)”
- Customer-Managed Encryption Keys secures GKE From the official documentation: “CMEKs encrypt data at rest in Cloud SQL, Cloud Storage, and within GKE clusters.”
- GKE works with Managed Service for Apache Airflow From the official documentation: “Managed Airflow runs your workloads on GKE.”
- Managed Service for Apache Airflow depends on Cloud SQL From the official documentation: “Managed Airflow uses Cloud SQL to run Airflow database.”
- Artifact Registry works with Cloud Logging From the official documentation: “Artifact Registry sends the following types of audit log data to Cloud Logging:”
- Artifact Registry works with Cloud Monitoring From the official documentation: “By default, Artifact Registry is configured to do the following: - Send system logs and audit logs to Cloud Logging - Send system metrics and resource metrics to Cloud Monitoring”
- Artifact Registry depends on Cloud Storage From the official documentation: “The registry service hosts your custom images in Cloud Storage under your Google Cloud project.”
- Artifact Registry works with Cloud Storage From the official documentation: “| Artifact Registry | Stored in Cloud Storage, for each data chunk | |”
- BigQuery depends on Cloud Storage From the official documentation: “BigQuery relies on Cloud Storage for staging file data and supports reading from and writing to several other sources and Google Cloud services.”
- BigQuery works with GKE From the official documentation: “Subsequently, by integrating BigQuery with GKE, you can use BigQuery to efficiently handle massive datasets (like Google Analytics) and the model's natural language understanding capabilities to generate meaningful insights.”
- Cloud Logging is an alternative to Cloud Monitoring From the official documentation: “To see the status of your invalidation request or find a definitive list of recently submitted requests, you can use Cloud Logging or Cloud Monitoring.”
- Cloud Logging works with Cluster Autoscaler From the official documentation: “The cluster autoscaler emits events detailing scale-up, scale-down, and other significant decisions. These events are available as log entries in Cloud Logging.”
- Cloud Logging depends on Customer-Managed Encryption Keys From the official documentation: “To use Cloud Logging with Customer-managed encryption keys (CMEK), you must complete the steps in the [Enable CMEK for an organization](/logging/docs/routing/managed-encryption#enable) page in the Cloud Logging documentation.”
- Cloud Logging works with Managed Service for Apache Airflow From the official documentation: “To learn about Cloud Logging and Cloud Monitoring for your Managed Airflow environment, see [Monitor environments](/composer/docs/composer-2/monitor-environments).”
- Cloud SQL depends on Cloud Storage From the official documentation: “When ingesting data from Cloud SQL, data is first staged to a Cloud Storage bucket.”
- Cloud SQL works with Managed Service for Apache Airflow From the official documentation: “For Private IP environments, Managed Airflow assigns only private IP ([RFC 1918](https://www.ietf.org/rfc/rfc1918.txt)) addresses to the managed Google Kubernetes Engine and Cloud SQL VMs in your environment.”
- Cloud Storage works with GKE From the official documentation: “This example configures GKE to store Pod snapshots in the `FOLDER_PATH/` path inside the Cloud Storage bucket `BUCKET_NAME`:”
- Customer-Managed Encryption Keys is part of BigQuery From the official documentation: “If you require more control over encryption keys, BigQuery supports [customer-managed encryption keys](/bigquery/docs/customer-managed-encryption) in the [Cloud Key Management Service](/kms/docs).”
- Customer-Managed Encryption Keys configures Cloud Logging From the official documentation: “To use Cloud Logging with Customer-managed encryption keys (CMEK), you must complete the steps in the [Enable CMEK for an organization](/logging/docs/routing/managed-encryption#enable) page in the Cloud Logging documentation.”
- Customer-Managed Encryption Keys secures Cloud Logging From the official documentation: “To use Cloud Logging with Customer-managed encryption keys (CMEK), you must complete the steps in the [Enable CMEK for an organization](/logging/docs/routing/managed-encryption#enable) page in the Cloud Logging documentation.”
- Customer-Managed Encryption Keys is part of Cloud SQL From the official documentation: “Cloud SQL also lets you add another layer of encryption to data using [customer-managed encryption keys (CMEK)](/sql/docs/postgres/cmek).”
- Customer-Managed Encryption Keys is part of Cloud Storage From the official documentation: “[Cloud Storage](/storage/docs/introduction) provides features such as customer-managed encryption, lifecycle management, and aging and retention policies.”
- GKE depends on Artifact Registry From the official documentation: “When you create or update a cluster, GKE pulls container images for the Kubernetes system software running on the control plane and nodes from Artifact Registry repositories in the `pkg.dev` or the `gcr.io` domain.”
- Managed Service for Apache Airflow depends on BigQuery From the official documentation: “This happens because Google Kubernetes Engine depends on BigQuery and Managed Service for Apache Airflow inherits this dependency from the Kubernetes Engine API.”
- Managed Service for Apache Airflow depends on Cluster Autoscaler From the official documentation: “Managed Airflow autoscaling uses three different autoscalers provided by GKE: - [Horizontal Pod Autoscaler (HPA)](/kubernetes-engine/docs/concepts/horizontalpodautoscaler) - [Cluster Autoscaler (CA)](/kubernetes-engine/docs/concepts/cluster-autoscaler)”
- Managed Service for Apache Airflow depends on GKE From the official documentation: “Managed Airflow runs your workloads on GKE.”
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