Chapter 1. Planning OpenShift Container Storage deployment on Microsoft Azure

Use this section to understand the requirements to install OpenShift Container Storage on Microsoft Azure.

1.1. Requirements for installing OpenShift Container Storage on Microsoft Azure

Instance type

Standard_D16s_v3

Node

  • CPU: 16 vCPUs
  • Memory: 64 GiB memory
  • Disk: Each disk of size 0.5 TiB or 2 TiB or 4 TiB storage
  • OSD: 3 OSDs in three different availability zones of Azure

Mon

10 GiB storage per Mon on each node

Platform

OpenShift Container Platform 4.5 and later

Default storage class

managed-premium

1.2. Sizing and scaling

The initial cluster of 3 nodes can later be expanded to a maximum of 9 nodes that can support up to 27 disks (3 disks on each node). In case of more than 3 worker nodes, the distribution of the disks depends on OpenShift scheduling and available resources.

Expand the cluster in sets of three nodes to ensure that your storage is replicated, and to ensure you can use at least three availability zones.

Note

You can expand the storage capacity only in the increment of the capacity selected at the time of installation.

The following tables shows the supported configurations for Red Hat OpenShift Container Storage.

Table 1.1. Initial configuration across 3 nodes

DisksDisks per nodeTotal capacityUsable storage capacity

0.5 TiB

1

1.5 TiB

0.5 TiB

2 TiB

1

6 TiB

2 TiB

4 TiB

1

12 TiB

4 TiB

Table 1.2. Expanded configuration of up to 9 nodes

Disk size (N)Maximum disks per nodeMaximum total capacity (= 27 disks x N)Maximum usable storage capacity

0.5 TiB

3

13.5 TiB

4.5 TiB

2 TiB

3

54 TiB

18 TiB

4 TiB

3

108 TiB

36 TiB

1.3. Supported workload types

Red Hat OpenShift Container Storage provides storage appropriate for a number of workload types.

Block storage is suitable for databases and other low-latency transactional workloads. Some examples of supported workloads are Red Hat OpenShift Container Platform logging and monitoring, and PostgreSQL.

Object storage is for video and audio files, compressed data archives, and the data used to train artificial intelligence or machine learning programs. In addition, object storage can be used for any application developed with a cloud-first approach.

File storage is for continuous integration and delivery, web application file storage, and artificial intelligence or machine learning data aggregation. Supported workloads include Red Hat OpenShift Container Platform registry and messaging using JBoss AMQ.