Required Pods per Cluster / Pods per Node = Total Number of Nodes Needed
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Planning your environment according to object maximums
- OpenShift Container Platform Tested cluster maximums for major releases
- OpenShift Container Platform tested cluster maximums
- OpenShift Container Platform environment and configuration on which the cluster maximums are tested
- How to plan your environment according to tested cluster maximums
- How to plan your environment according to application requirements
Consider the following tested object maximums when you plan your OpenShift Container Platform cluster.
These guidelines are based on the largest possible cluster. For smaller clusters, the maximums are lower. There are many factors that influence the stated thresholds, including the etcd version or storage data format.
In most cases, exceeding these numbers results in lower overall performance. It does not necessarily mean that the cluster will fail.
OpenShift Container Platform Tested cluster maximums for major releases
Tested Cloud Platforms for OpenShift Container Platform 3.x: Red Hat OpenStack Platform (RHOSP), Amazon Web Services and Microsoft Azure. Tested Cloud Platforms for OpenShift Container Platform 4.x: Amazon Web Services, Microsoft Azure and Google Cloud Platform.
| Maximum type | 3.x tested maximum | 4.x tested maximum |
|---|---|---|
Number of Nodes |
2,000 |
2,000 |
Number of Pods [1] |
150,000 |
150,000 |
Number of Pods per node |
250 |
500 [2] |
Number of Pods per core |
There is no default value. |
There is no default value. |
Number of Namespaces [3] |
10,000 |
10,000 |
Number of Builds |
10,000 (Default pod RAM 512 Mi) - Pipeline Strategy |
10,000 (Default pod RAM 512 Mi) - Source-to-Image (S2I) build strategy |
Number of Pods per namespace [4] |
25,000 |
25,000 |
Number of Services [5] |
10,000 |
10,000 |
Number of Services per Namespace |
5,000 |
5,000 |
Number of Back-ends per Service |
5,000 |
5,000 |
Number of Deployments per Namespace [4] |
2,000 |
2,000 |
OpenShift Container Platform tested cluster maximums
| Limit type | 3.10 tested maximum | 3.11 tested maximum | 4.1 tested maximum | 4.2 tested maximum |
|---|---|---|---|---|
Number of Nodes |
2,000 |
2,000 |
2,000 |
2,000 |
Number of Pods [6] |
150,000 |
150,000 |
150,000 |
150,000 |
Number of Pods per node |
250 |
250 |
250 |
250 |
Number of Pods per core |
There is no default value. |
There is no default value. |
There is no default value. |
There is no default value. |
Number of Namespaces [7] |
10,000 |
10,000 |
10,000 |
10,000 |
Number of builds |
10,000 (Default pod RAM 512 Mi) |
10,000 (Default pod RAM 512 Mi) |
10,000 (Default pod RAM 512 Mi) |
10,000 (Default pod RAM 512 Mi) |
Number of Pods per Namespace [8] |
3,000 |
25,000 |
25,000 |
25,000 |
Number of Services [9] |
10,000 |
10,000 |
10,000 |
10,000 |
Number of Services per Namespace |
5,000 |
5,000 |
5,000 |
5,000 |
Number of Back-ends per Service |
5,000 |
5,000 |
5,000 |
5,000 |
Number of Deployments per Namespace [8] |
2,000 |
2,000 |
2,000 |
2,000 |
In OpenShift Container Platform 4.2, half of a CPU core (500 millicore) is reserved by the system compared to OpenShift Container Platform 3.11 and previous versions.
OpenShift Container Platform environment and configuration on which the cluster maximums are tested
Google Cloud Platform:
| Node | Flavor | vCPU | RAM(GiB) | Disk type | Disk size(GiB)/IOPS | Count | Region |
|---|---|---|---|---|---|---|---|
Master/Etcd |
n1-highmem-16 |
16 |
104 |
Regional/Zonal SSD |
220 |
3 |
us-east4 |
Infra [10] |
n1-standard-64 |
64 |
240 |
Regional/Zonal SSD |
100 |
3 |
us-east4 |
Workload [11] |
n1-standard-16 |
16 |
60 |
Regional/Zonal SSD |
500 [12] |
1 |
us-east4 |
Worker |
n1-standard-8 |
8 |
30 |
Regional/Zonal SSD |
100 |
3/25/250 [13] |
us-east4 |
AWS cloud platform:
| Node | Flavor | vCPU | RAM(GiB) | Disk type | Disk size(GiB)/IOPS | Count | Region |
|---|---|---|---|---|---|---|---|
Master/Etcd [14] |
r5.4xlarge |
16 |
128 |
io1 |
220 / 3000 |
3 |
us-west-2 |
Infra [15] |
m5.12xlarge |
48 |
192 |
gp2 |
100 |
3 |
us-west-2 |
Workload [16] |
m5.4xlarge |
16 |
64 |
gp2 |
500 [17] |
1 |
us-west-2 |
Worker |
m5.large |
2 |
8 |
gp2 |
100 |
2000 |
us-west-2 |
How to plan your environment according to tested cluster maximums
|
Oversubscribing the physical resources on a node affects resource guarantees the Kubernetes scheduler makes during pod placement. Learn what measures you can take to avoid memory swapping. Some of the tested maximums are stretched only in a single dimension. They will vary when many objects are running on the cluster. The numbers noted in this documentation are based on Red Hat’s test methodology, setup, configuration, and tunings. These numbers can vary based on your own individual setup and environments. |
While planning your environment, determine how many pods are expected to fit per node:
The current maximum number of pods per node is 250. However, the number of pods that fit on a node is dependent on the application itself. Consider the application’s memory, CPU, and storage requirements, as described in How to plan your environment according to application requirements.
Example scenario
If you want to scope your cluster at 2200 pods, assuming the 250 maximum pods per node, you would need at least nine nodes:
2200 / 250 = 8.8
If you increase the number of nodes to 20, then the pod distribution changes to 110 pods per node:
2200 / 20 = 110
Where:
Required Pods per Cluster / Total Number of Nodes = Expected Pods per Node
How to plan your environment according to application requirements
Consider an example application environment:
| Pod type | Pod quantity | Max memory | CPU cores | Persistent storage |
|---|---|---|---|---|
apache |
100 |
500 MB |
0.5 |
1 GB |
node.js |
200 |
1 GB |
1 |
1 GB |
postgresql |
100 |
1 GB |
2 |
10 GB |
JBoss EAP |
100 |
1 GB |
1 |
1 GB |
Extrapolated requirements: 550 CPU cores, 450GB RAM, and 1.4TB storage.
Instance size for nodes can be modulated up or down, depending on your preference. Nodes are often resource overcommitted. In this deployment scenario, you can choose to run additional smaller nodes or fewer larger nodes to provide the same amount of resources. Factors such as operational agility and cost-per-instance should be considered.
| Node type | Quantity | CPUs | RAM (GB) |
|---|---|---|---|
Nodes (option 1) |
100 |
4 |
16 |
Nodes (option 2) |
50 |
8 |
32 |
Nodes (option 3) |
25 |
16 |
64 |
Some applications lend themselves well to overcommitted environments, and some do not. Most Java applications and applications that use huge pages are examples of applications that would not allow for overcommitment. That memory can not be used for other applications. In the example above, the environment would be roughly 30 percent overcommitted, a common ratio.
1. The Pod count displayed here is the number of test Pods. The actual number of Pods depends on the application’s memory, CPU, and storage requirements.
2. This was tested on a cluster with 100 worker nodes with 500 Pods per worker node. The default
maxPods is still 250. To get to 500 maxPods, the cluster must be created with a hostPrefix of 22 in the install-config.yaml file and maxPods set to 500 using a custom KubeletConfig. The maximum number of Pods with attached Persistant Volume Claims (PVC) depends on storage backend from where PVC are allocated. In our tests, only OpenShift Container Storage v4 (OCS v4) was able to satisfy the number of Pods per node discussed in this document.
3. When there are a large number of active projects, etcd might suffer from poor performance if the keyspace grows excessively large and exceeds the space quota. Periodic maintenance of etcd, including defragmentaion, is highly recommended to free etcd storage.
4. There are a number of control loops in the system that must iterate over all objects in a given namespace as a reaction to some changes in state. Having a large number of objects of a given type in a single namespace can make those loops expensive and slow down processing given state changes. The limit assumes that the system has enough CPU, memory, and disk to satisfy the application requirements.
5. Each Service port and each Service back-end has a corresponding entry in iptables. The number of back-ends of a given Service impact the size of the endpoints objects, which impacts the size of data that is being sent all over the system.
6. The Pod count displayed here is the number of test Pods. The actual number of Pods depends on the application’s memory, CPU, and storage requirements.
7. When there are a large number of active projects, etcd might suffer from poor performance if the keyspace grows excessively large and exceeds the space quota. Periodic maintenance of etcd, including defragmentaion, is highly recommended to free etcd storage.
8. There are a number of control loops in the system that must iterate over all objects in a given namespace as a reaction to some changes in state. Having a large number of objects of a given type in a single namespace can make those loops expensive and slow down processing given state changes. The limit assumes that the system has enough CPU, memory, and disk to satisfy the application requirements.
9. Each service port and each service back-end has a corresponding entry in iptables. The number of back-ends of a given service impact the size of the endpoints objects, which impacts the size of data that is being sent all over the system.
10. Infra nodes are used to host Monitoring, ingress and Registry components to make sure they have enough resources to run at large scale.
11. Workload node is dedicated to run performance and scalability workload generators.
12. Larger disk size is used to have enough space to store large amounts of data collected during the performance and scalability test run.
13. Cluster is scaled in iterations and performance and scalability tests are executed at the specified node counts.
14. io1 disk with 3000 IOPS is used for master/etcd nodes as etcd is I/O intensive and latency sensitive.
15. Infra nodes are used to host Monitoring, ingress and Registry components to make sure they have enough resources to run at large scale.
16. Workload node is dedicated to run performance and scalability workload generators.
17. Larger disk size is used to have enough space to store large amounts of data collected during the performance and scalability test run.