Kubernetes Resource Limits vs Requests: Ultimate Guide

Kubernetes Resource Limits vs Requests are important settings when working with Kubernetes.

In this article, we will dive deep into what Kubernetes Resource Requests and Limits are, why they are important, and how they can be used to optimize your Kubernetes cluster. We will also provide practical examples and discuss best practices to help you make the most out of these powerful Kubernetes features.

What Are Kubernetes Resource Limits and Requests?

Kubernetes allows you to run multiple containers on a single cluster or even node. However, these containers must share the underlying hardware resources, such as CPU, memory and local storage in the case of pod using ephemeral local storage for scratch space, caching, and for logs. Without proper management, this sharing can lead to resource contention, where one container consumes more than its fair share of resources, leaving others starved and potentially causing application performance issues.

To address this, Kubernetes introduces the concepts of Resource Requests and Limits, which help you define the amount of CPU and memory each container in a pod should have access to.

Kubernetes Resource Requests

A Kubernetes Resource Request specifies the minimum amount of CPU and memory that a container needs to function properly. When you define a resource request, you are telling Kubernetes that this is the minimum resource allocation needed for your container to operate effectively. The Kubernetes scheduler uses this information to determine where to place the pod on a node.

For example, if you request 100m (100 millicores, or 0.1 CPU core) of CPU and 10Mi (10 mebibytes) of memory for a container, Kubernetes will schedule the pod on a node that has at least these resources available. If the node doesn't have enough resources, the pod will not be scheduled until the resources become available.

Kubernetes Resource Limits

A Kubernetes Resource Limit, on the other hand, defines the maximum amount of CPU and memory that a container can consume. This acts as a cap, ensuring that no container can consume more than the allocated resources, thereby protecting other containers from being starved of resources.

For instance, if you set a limit of 0.2 CPU core and 50Mi of memory for a container, Kubernetes will ensure that the container does not exceed this usage. If the container attempts to consume more CPU, Kubernetes will throttle it. If it tries to use more memory than the limit, the container may be terminated and restarted by Kubernetes to free up resources.

›› Check out this guide with Kubernetes CPU limit best practices.

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Kubernetes Resource Limits — Kubernetes Resource Requests

It is worth mentioning that both CPU and memory are classified as compressible and incompressible resources in Kubernetes with different implications.

	Compressible K8s Resources (CPU)	Incompressible K8s Resources (Memory)
Definition	Kubernetes resources that can be throttled or scaled down without immediate critical impact.	Kubernetes resources that cannot be reduced without potentially leading to failure or instability.
Scalability	Overcommitting CPU resources is common as unused CPU can be shared across containers.	Overcommitting memory is risky because exceeding physical memory can lead to OOM kills and instability.
Bursting	CPU usage can burst above requests up to the limit without immediate negative effects, allowing for flexible K8s resource allocation.	Memory usage cannot safely burst above the limit; any attempt to exceed the limit can lead to OOM errors.
Throttling	Throttling CPU reduces performance but doesn't lead to immediate failure; processes just slow down.	Throttling memory is not feasible; if a pod exceeds its memory limit, it can lead to out-of-memory (OOM) errors, causing the pod to crash.
Use Cases	Suitable for applications where variable CPU usage is acceptable and performance degradation is tolerable.	Critical for applications requiring stable memory usage, such as databases and stateful services.

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Why Kubernetes Memory Limits and CPU Limits Have Different Implications:

CPU (Compressible) Limits: The CPU is considered compressible because the system can throttle it. If a process is using too much CPU, Kubernetes can limit its CPU usage without stopping the process, which only slows down the process but keeps it running.
Memory (Incompressible) Limits: Memory, on the other hand, is incompressible. If a process tries to use more memory than what has been allocated, it may be terminated by the system to prevent other processes from being affected. Memory limits are thus more critical as they can lead to application failures if not set correctly.

Why Are Kubernetes Resource Limits and Requests Important?

Kubernetes Resource Limits and Requests are not just technical features—they are critical for the effective and efficient operation of your Kubernetes cluster. Here’s why they matter:

Optimizing Cluster Resource Utilization:
- Without Kubernetes Resource Limits and Requests, K8s might oversubscribe nodes, leading to resource contention where multiple containers are fighting for limited K8s resources. This can result in degraded application performance or even application failure. By defining requests and limits, you ensure that resources are allocated in a way that prevents such contention, leading to more stable and predictable application behavior.
Ensuring Application Performance:
- By setting appropriate K8s Resource Requests, you ensure that each container gets the resources it needs to operate effectively, without being starved by other containers on the same node. This is particularly important for performance-sensitive applications that need guaranteed access to a certain amount of CPU or memory.
Cost Management and Efficiency:
- In cloud environments, where you pay for the resources you use, over-allocating resources can lead to unnecessary costs. On the other hand, under-allocating resources can lead to poor application performance, which can also be costly in terms of customer satisfaction or lost revenue. Properly set Resource Requests and Limits help strike a balance between cost efficiency and performance, ensuring that you’re not paying for more than you need while still meeting your application's requirements.
Preventing Resource Hoarding:
- In multi-tenant clusters, where multiple teams or applications share the same infrastructure, Kubernetes Resource Limits prevent any single team or application from consuming more than its fair share of resources, ensuring that resources are fairly distributed across the cluster.
Supporting Quality of Service (QoS) Classification:
- Kubernetes uses Resource Requests and Limits to classify pods into different Quality of Service (QoS) classes—Guaranteed, Burstable, and Best-Effort. These classifications help Kubernetes decide which pods to prioritize when the cluster is under resource pressure. For example, Guaranteed pods (which have equal requests and limits) are less likely to be evicted than Best-Effort pods (which have no requests or limits).

How to Define Kubernetes Resource Requests vs K8s Resource Limits

Now that we’ve covered the importance of Resource Requests and Limits, let’s look at how to define them in your Kubernetes pod configurations. Here’s a sample YAML file for a pod with both resource requests and limits defined:

apiVersion: v1
kind: Pod
metadata:
  name: resource-demo
spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      requests:
        memory: "64Mi"
        cpu: "250m"
      limits:
        memory: "128Mi"
        cpu: "500m"

In this configuration:

Memory Request: The container requests 64Mi of memory. This means Kubernetes will try to schedule this pod on a node that has at least 64Mi of free memory.
CPU Request: The container requests 250m of CPU (250 millicores). This is the minimum CPU time the container needs.
Memory Limit: The container is allowed to use up to 128Mi of memory. If it exceeds this limit, it will be terminated.
CPU Limit: The container can use up to 500m of CPU. If it tries to use more, Kubernetes will throttle it to keep the usage within the limit.

›› Learn how leading companies achieved significant performance improvements by removing CPU limits.

Best Practices for Setting Kubernetes Resource Requests and K8s Resource Limits

When setting Resource Requests and Limits, it’s important to consider the specific needs of your applications and the characteristics of your workloads. Here are some best practices:

Analyze Your Workload Requirements:

Understand the resource needs of your application by monitoring its behavior over time. Use monitoring tools or Kubernetes metrics-server to collect and analyze CPU and memory usage. This data will help you set realistic and appropriate resource requests and limits.

Avoid Over-Commitment:

While Kubernetes allows over-commitment (allocating more resources than physically available), this should be done with caution. Over-committing resources can lead to contention and degraded performance, especially under high load. Set requests and limits based on actual usage patterns to avoid resource exhaustion.

Use LimitRange:

Ensuring that individual containers do not exceed defined resource constraints. As part of best practices, setting LimitRanges helps maintain cluster stability by preventing any single pod from monopolizing resources, thereby promoting efficient resource allocation across the entire cluster.

Use Resource Quotas:

If you are managing a multi-tenant cluster, consider using Kubernetes Resource Quotas to enforce constraints on the total amount of CPU and memory that can be used within a namespace. This prevents any one team or application from consuming all available resources, ensuring fair distribution.

Regularly Review and Adjust:

Resource usage can change over time as your application evolves. Regularly review and adjust your resource requests and limits to ensure they continue to meet the needs of your applications while optimizing resource utilization.

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›› Take a look at the 8 Kubernetes cluster size best practices to establish and continually maintain the proper Kubernetes requests and limits.

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Practical Example: Monitoring and Adjusting K8s Resource Usage

Let’s say you have a web application running on a Kubernetes cluster. Initially, you set the resource requests and limits as follows:


resources:
  requests:
    memory: "128Mi"
    cpu: "500m"
  limits:
    memory: "256Mi"
    cpu: "1"

After monitoring the application for a week using Grafana, you notice that the application’s memory usage rarely exceeds 100Mi, and CPU usage peaks at around 300m. Based on this data, you decide to adjust the requests and limits to better match the actual usage:


resources:
  requests:
    memory: "100Mi"
    cpu: "300m"
  limits:
    memory: "200Mi"
    cpu: "500m"

By making these adjustments, you ensure that your application still gets the resources it needs while freeing up excess capacity for other workloads. This not only optimizes the resource utilization of your cluster but also helps in reducing costs, especially in a cloud environment where resources are billed on usage.

How Kubernetes Resource Requests vs K8s Resource Limits Impact Cluster Optimization

K8s Resource Requests and Limits are key to optimizing your Kubernetes cluster. When used correctly, they can significantly improve the efficiency and performance of your workloads. Here’s how:

Efficient Resource Utilization:

By setting appropriate resource requests, you ensure that each pod has the necessary resources to function without over-allocating. This leads to more efficient utilization of the cluster’s resources, as each pod gets just what it needs—no more, no less.

Improved Node Packing:

Kubernetes schedules pods on nodes based on available resources. By defining resource requests and limits, you help Kubernetes make better scheduling decisions, leading to improved node packing and reduced resource wastage. This is particularly beneficial in environments with a high density of workloads.

Preventing Resource Starvation:

Resource Limits prevent any single pod from consuming all available resources on a node, which could lead to resource starvation for other pods. By capping resource usage, you ensure a fair distribution of resources across all workloads, preventing performance issues due to resource contention.

Supporting Auto-Scaling:

Kubernetes Horizontal Pod Autoscaler (HPA) relies on CPU and memory metrics to scale pods up or down. By setting appropriate resource requests and limits, you provide the necessary inputs for HPA to make informed scaling decisions, ensuring that your application scales efficiently based on actual demand.

Reducing Operational Costs:

In cloud environments, resources such as CPU and memory are billed based on usage. By optimizing resource requests and limits, you can reduce the overall resource consumption of your cluster, leading to lower operational costs without sacrificing performance.

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Kubernetes Resource Requests and K8s Resource Limits are essential tools for ensuring the efficient and effective operation of your Kubernetes cluster. By carefully setting these parameters, you can optimize resource utilization, improve application performance, prevent resource contention, and manage costs effectively. Incorporating Kubernetes Resource Requests and Limits into your Kubernetes workflows is not just a best practice—it’s a necessity for anyone looking to run a stable, cost-efficient, and high-performing cluster. Whether you’re managing a production environment with hundreds of nodes or a small development cluster, understanding and leveraging these settings will help you get the most out of your Kubernetes deployments.

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By handling Kubernetes cost optimization, PerfectScale frees DevOps, Platform, SRE, and FinOps teams to concentrate on more strategic projects. It assures ongoing optimal scaling of your K8s environment, resulting in lower cloud expenses, fewer SLA/SLO breaches, and reduced outages. Users experience enhanced reliability and stability.

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Happy building!