Professional Cloud Developer
Professional Cloud Developer
Intermediate
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Intermediate
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Deploying containers to GKE
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Professional Cloud Developer
Gauge your current knowledge
Gauge your current knowledge
Artifact Registry is the central storage location for container images and build dependencies in Google Cloud. When developers integrate this registry into their deployment pipeline, they can automate storing and retrieving images, which creates a consistent and reliable environment for containerized applications. The registry acts as a single source of truth for all container images used across development, staging, and production environments.
To keep the pipeline secure, developers should use vulnerability scanning to find security risks in container images before they reach production. Binary Authorization adds another layer of protection by ensuring that only trusted images are deployed to GKE clusters. This service requires attestations that prove an image has passed security scans, completed testing, or received approval from a product owner before it can run.
Workload Identity provides the recommended way to give GKE applications access to other Google Cloud resources without managing long-lived keys. It connects Kubernetes service accounts to Google Cloud service accounts, following the principle of least privilege where each application receives only the permissions it needs. This approach avoids storing sensitive credentials inside clusters.
Integrating these security tools into a Cloud Build and Cloud Deploy pipeline automates the entire container lifecycle. During continuous integration, Cloud Build builds the container image, runs automated tests, and stores the final image in Artifact Registry. Using unique service accounts for each deployment stage provides better isolation between environments and makes the infrastructure easier to audit.
Deploying applications to Google Kubernetes Engine (GKE) involves managing containers within a cluster of virtual machines called nodes. Developers use Kubernetes objects like Deployments to manage sets of identical Pods, which are the smallest deployable units that can be created and managed.
GKE supports several deployment strategies to update applications without causing downtime. A Rolling Update is the default method that progressively replaces old Pods with new ones while maintaining service availability. The Recreate strategy scales down the old version completely before starting the new one. A Blue-Green deployment runs two identical environments simultaneously for instant traffic switching. For more control, a Canary Deployment releases a new version to a small group of users first, allowing teams to identify bugs in production before affecting everyone.
Health checks maintain high availability by monitoring application status. Liveness probes determine if a container is running correctly and should be restarted if it fails. Readiness probes check if a container is prepared to accept traffic, preventing requests from being sent to Pods that are still initializing or unhealthy. Configuring these probes correctly ensures the system only sends traffic to healthy instances.
A strong CI/CD pipeline using GitOps methodology treats infrastructure as code, storing all configuration in version-controlled repositories. Promoting the same container image through development, staging, and production environments reduces errors caused by rebuilding code. Services like Artifact Registry, Binary Authorization, and Config Sync help maintain consistency and security across environments.
Cloud Build automatically triggers a new build whenever source code changes, handling the entire process of creating container images from code. The automation process includes building the container image, running tests to check for errors, and storing successful images in a central registry. This eliminates manual work and ensures every change is tested before deployment.
Once built, images are stored in Artifact Registry using semantic versioning or Git commit hashes as labels. Unique tags for every build help teams identify exactly which version of code runs in each environment and make it easier to roll back to previous versions if problems occur.
Cloud Deploy automates the progression of container images through different stages like staging and production. This service manages deployment logic so developers can focus on writing code rather than handling manual infrastructure steps. Promoting the same image through every environment ensures the exact code tested in staging reaches users.
Many teams adopt GitOps, storing infrastructure configurations in a Git repository to create a single source of truth for GKE cluster state. Using declarative files makes it easier to review changes before applying them. Separating clusters into different environments like development and production protects live applications from untested changes.
Security integrates early in the CI/CD workflow through vulnerability scanning, which automatically checks container images for known threats. Creating immutable artifacts by keeping configuration and secrets separate from container images prevents sensitive data from being embedded in deployed code. Binary Authorization ensures only trusted, scanned images can run on the cluster.
GKE is a managed platform for running containerized applications at scale. Developers use Kubernetes manifests, which are YAML files describing the desired cluster state, to deploy applications. These files define essential components like Pods, which are the smallest deployable units, and Deployments, which manage groups of identical Pods. Using manifests ensures deployment is repeatable, auditable, and consistent across environments.
Effective resource management requires specifying resource requests and limits in manifests. A request defines the minimum CPU or memory a container needs to run, while a limit sets the maximum it can consume. Setting these values correctly optimizes cluster utilization and prevents one application from exhausting all available resources. In Autopilot mode, Google Cloud manages the nodes and users only pay for resources their Pods actually request.
ConfigMaps store non-sensitive configuration data like environment variables, while Secrets provide secure storage for sensitive information like passwords and API keys. This externalized configuration allows the same container image to be used across development, staging, and production without modification.
GKE supports deployment strategies like rolling updates that replace old Pods gradually to avoid downtime. Automating these processes through CI/CD using Cloud Build, Artifact Registry, and Cloud Deploy represents best practice for modern cloud development. Security measures like Role-Based Access Control (RBAC) define who can perform actions within the cluster, while Binary Authorization ensures only trusted images are deployed.
Exposing applications on GKE requires creating reliable access points so users and services can connect to workloads. Services provide stable networking for Pods, with types including NodePort, LoadBalancer, and ClusterIP that determine who can access the application and how traffic routes. The right Service type affects availability, traffic flow, and visibility outside the cluster.
Ingress provides Layer 7 HTTP(S) routing to expose applications beyond the cluster. When creating an Ingress resource, GKE automatically provisions an external Application Load Balancer using features like URL maps, host rules, and health checks. Ingress works well for multiple Services under one IP, path-based routing, SSL termination, and global HTTP(S) load balancing.
The Gateway API offers a newer, more flexible alternative to Ingress. A Gateway provides more expressive traffic policy management and multi-tenant configurations. When deployed, GKE configures a Cloud Load Balancer similarly to Ingress but with improved support for advanced routing, multiple protocols, and cleaner role separation. For modern workloads, Gateway API is the recommended choice.
Traffic distribution uses network endpoint groups (NEGs) to register Pods as load balancer backends, enabling container-native load balancing. This approach provides more accurate routing, lower latency, and better observability. GKE automatically creates health checks based on readiness probes or BackendConfig settings, ensuring traffic only reaches Pods ready to serve requests.
Understanding routing and connectivity options matters for both internal and external access. Internal load balancers keep traffic within the VPC, while external load balancers publish applications to the internet. Features like static IPs, Private Service Connect, and local traffic policies provide fine-grained control over routing and security. Combined with scaling best practices, these tools help deploy applications that remain stable as demand changes.
Kubernetes health checks are essential for maintaining application resilience and self-healing within Google Kubernetes Engine (GKE). These checks ensure that GKE only routes user traffic to healthy instances of an application. To achieve system stability, developers must tune timing and threshold configurations to balance fast failure detection with the prevention of unnecessary restarts.
Developers configure specific parameters to control the timing and behavior of these checks. These parameters determine how and when GKE evaluates a container's health:
Slow-booting applications require a startup probe to prevent the system from killing containers before they are fully initialized. When a startup probe is active, GKE disables other health checks until the application finishes its startup process. Setting a higher failure threshold on this probe provides the container enough time to load data or warm up caches safely.
Troubleshooting probe failures requires checking for mismatches between the probe configurations and the application settings. For example, the probe port must match the actual listening port of the application inside the container, and network traffic must be allowed through firewall rules. Developers can use Cloud Monitoring to track metrics like restart counts and CPU usage, allowing them to proactively optimize these settings.
GKE manages container lifecycles and controls traffic flow using three distinct types of health checks. Each type of probe serves a unique purpose and triggers different orchestration actions when a failure occurs. Understanding when to use each probe ensures that the application remains highly available and does not drop user requests.
A liveness probe determines whether a container is still running correctly or has entered a broken state like a deadlock. If a liveness probe fails, GKE transitions the container into a terminating state and automatically restarts it. Developers should keep the logic of this probe simple and avoid dependencies on external services to prevent unnecessary restart loops.
A readiness probe checks when an application is prepared to accept network traffic. The GKE load balancer depends on this probe to decide when to route traffic to a Pod, keeping it out of the active pool until it is ready. For example, the probe might report success only after a container has successfully loaded a large database cache into memory.
A startup probe protects slow-starting applications during their initial boot sequence. It suspends all liveness and readiness checks until the application signals that it has fully started up. This prevents GKE from prematurely killing a container that is still initializing but otherwise healthy.
Configuring these probes requires aligning the container port settings with the actual application port in the Pod configuration file. If a developer changes a probe configuration on a running Pod, they must redeploy both the Pod and the Ingress resource to synchronize the load balancer. Common troubleshooting steps include verifying firewall rules for health check IP ranges and examining Pod logs to diagnose connectivity issues.
GKE supports diverse probe handlers to accommodate different architectural patterns and communication protocols. Choosing the correct handler depends on how the application exposes its health status to the network. These handlers allow the orchestrator to interact with different types of workloads, from simple web servers to complex microservices.
Developers can select from several standard protocols to implement health checks:
To monitor these health checks over time, developers can integrate tools like Cloud Logging and Google Cloud Managed Service for Prometheus. These services can collect metrics from complex workloads using tools like JMX exporters to capture system health details. During maintenance events or updates, the load balancer uses these metrics and health checks to redirect traffic away from unhealthy nodes to keep the application available.
The Horizontal Pod Autoscaler (HPA) in Google Kubernetes Engine (GKE) typically scales Pods based on basic resource use like CPU and memory. However, it can also scale using more specific signals that better reflect your application's workload. These are divided into two types: Custom Metrics, which come from inside your Kubernetes cluster (like requests per second), and External Metrics, which come from outside services (like the number of messages in a Google Cloud Pub/Sub queue). Using these allows the HPA to react to real performance issues, not just high CPU.
To use custom or external metrics, you need the GKE Custom Metrics Adapter. This adapter acts as a bridge, allowing the HPA to read metrics stored in Cloud Monitoring. It often works with the Managed Service for Prometheus, which collects detailed application data. This setup is crucial for tracking complex signals like request latency or queue depth, which standard monitoring might miss.
You define which metrics to use in the HPA's configuration file (manifest). You can mix different metric types, like Pod-based and External metrics, in the spec.metrics field to create a strong scaling strategy. To test if it works, you can run a load test and use the command kubectl get hpa --watch to see the HPA change the replica count in real time. This confirms your application can handle traffic spikes correctly.
For the HPA to work at all, you must define resource requests and limits in your Pod's specification. These values tell Kubernetes how much CPU and memory each Pod is expected to use and the maximum it can use. Without these definitions, the HPA cannot calculate the average utilization percentage it needs to decide when to scale.
You configure target utilization thresholds for metrics like CPU and memory. This tells the HPA, "scale up when average usage goes above this percentage." It's also a best practice to use scaling policies and stabilization windows (explained in the next section) to control how fast the HPA adds or removes Pods. This prevents the system from overreacting to small, temporary changes in traffic.
You should regularly test your autoscaling configuration. Performing a load test by sending a burst of traffic verifies that the HPA increases Pods when needed. GKE also offers features like utilization-based load balancing through the Gateway API. By setting a maximum utilization threshold in a GCPBackendPolicy, traffic can be rebalanced before Pods become overloaded, improving overall performance and efficiency.
The HPA uses a continuous control loop to check metrics and decide when to scale. You can configure scaling behaviors to control the speed, or velocity, of these changes. You set limits on how many Pods can be added or removed at once, either as a fixed number or a percentage of the current count. This prevents the cluster from making drastic, unstable changes.
A key setting is the stabilization window, especially for scaling down. This is a waiting period the HPA observes after a spike in demand ends. It ensures that a drop in traffic is sustained before removing Pods. This prevents thrashing, where Pods are rapidly created and deleted due to minor, fleeting fluctuations, which wastes resources and can hurt performance.
The HPA can evaluate multiple metrics simultaneously, like CPU usage combined with a custom metric for request queue length. When it does this, it calculates a scaling recommendation from each metric and chooses the highest value. This ensures the application has enough Pods to handle the worst bottleneck. You must monitor the HPA (using kubectl get hpa) to ensure all metrics are reporting correctly; an unknown status indicates a problem with the metrics pipeline.