more updates 3

Author: kquinn1204

content/patterns/retail/_index.md

@@ -1,6 +1,6 @@
 ---
-title: Demonstrating Retail example applications
-weight: 10
+title: Demonstrating retail example applications
+weight: 40
 aliases: /retail/application-demos/
 ---
 
@@ -9,3 +9,4 @@ aliases: /retail/application-demos/
 :_content-type: ASSEMBLY
 include::modules/comm-attributes.adoc[]
 
+include::modules/retail-example-applications.adoc[]

content/patterns/retail/application.adoc

@@ -1,199 +1,14 @@
-== OpenShift Cluster Sizing for the Retail Pattern
+---
+title: Cluster Sizing
+weight: 60
+aliases: /retail/retail-cluster-sizing/
+---
 
-=== Tested Platforms
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
 
-The *retail* pattern has been tested in the following Certified Cloud
-Providers.
+include::modules/comm-attributes.adoc[]
+include::modules/retail/metadata-retail.adoc[]
 
-[cols="<,<",options="header",]
-|===
-|*Certified Cloud Providers* |4.10
-|Amazon Web Services |:heavy_check_mark:
-|Microsoft Azure |
-|Google Cloud Platform |
-|===
-
-=== General OpenShift Minimum Requirements
-
-OpenShift 4 has the following minimum requirements for sizing of nodes:
-
-* *Minimum 4 vCPU* (additional are strongly recommended).
-* *Minimum 16 GB RAM* (additional memory is strongly recommended,
-especially if etcd is colocated on masters).
-* *Minimum 40 GB* hard disk space for the file system containing /var/.
-* *Minimum 1 GB* hard disk space for the file system containing
-/usr/local/bin/.
-
-There are several applications that comprise the *retail* pattern. In
-addition, the *retail* pattern also includes a number of supporting
-operators that are installed by *OpenShift GitOps* using ArgoCD.
-
-==== Retail Pattern OpenShift Datacenter HUB Cluster Size
-
-The retail pattern has been tested with a defined set of specifically
-tested configurations that represent the most common combinations that
-Red Hat OpenShift Container Platform (OCP) customers are using or
-deploying for the x86_64 architecture.
-
-The Datacenter HUB OpenShift Cluster is made up of the the following on
-the AWS deployment tested:
-
-[cols="<,^,<,<",options="header",]
-|===
-|Node Type |Number of nodes |Cloud Provider |Instance Type
-|Master |3 |Amazon Web Services |m5.xlarge
-|Worker |3 |Amazon Web Services |m5.xlarge
-|===
-
-The Datacenter HUB OpenShift cluster needs to be a bit bigger than the
-Factory/Edge clusters because this is where the developers will be
-running pipelines to build and deploy the *Industrial Edge* pattern on
-the cluster. The above cluster sizing is close to a *minimum* size for a
-Datacenter HUB cluster. In the next few sections we take some snapshots
-of the cluster utilization while the *Industrial Edge* pattern is
-running. Keep in mind that resources will have to be added as more
-developers are working building their applications.
-
-==== Retail Pattern OpenShift Store Edge Cluster Size
-
-The OpenShift cluster is made of 3 Nodes combining Master/Workers for
-the Edge/Factory cluster.
-
-[cols="^,^,^,^",options="header",]
-|===
-|Node Type |Number of nodes |Cloud Provider |Instance Type
-|Master/Worker |3 |Google Cloud |n1-standard-8
-|Master/Worker |3 |Amazon Cloud Services |m5.2xlarge
-|Master/Worker |3 |Microsoft Azure |Standard_D8s_v3
-|===
-
-==== AWS Instance Types
-
-The *retail* pattern was tested with the highlighted AWS instances in
-*bold*. The OpenShift installer will let you know if the instance type
-meets the minimum requirements for a cluster.
-
-The message that the openshift installer will give you will be similar
-to this message
-
-[source,text]
-----
-INFO Credentials loaded from default AWS environment variables
-FATAL failed to fetch Metadata: failed to load asset "Install Config": [controlPlane.platform.aws.type: Invalid value: "m4.large": instance type does not meet minimum resource requirements of 4 vCPUs, controlPlane.platform.aws.type: Invalid value: "m4.large": instance type does not meet minimum resource requirements of 16384 MiB Memory]
-----
-
-Below you can find a list of the AWS instance types that can be used to
-deploy the *retail* pattern.
-
-[width="100%",cols="^26%,^20%,^20%,^17%,^17%",options="header",]
-|===
-|Instance type |Default vCPUs |Memory (GiB) |Datacenter |Factory/Edge
-| | | |3x3 OCP Cluster |3 Node OCP Cluster
-|m4.xlarge |4 |16 |N |N
-|m4.2xlarge |8 |32 |Y |Y
-|m4.4xlarge |16 |64 |Y |Y
-|m4.10xlarge |40 |160 |Y |Y
-|m4.16xlarge |64 |256 |Y |Y
-|*m5.xlarge* |4 |16 |Y |N
-|m5.2xlarge |8 |32 |Y |Y
-|m5.4xlarge |16 |64 |Y |Y
-|m5.8xlarge |32 |128 |Y |Y
-|m5.12xlarge |48 |192 |Y |Y
-|m5.16xlarge |64 |256 |Y |Y
-|m5.24xlarge |96 |384 |Y |Y
-|===
-
-The OpenShift cluster is made of 3 Masters and 3 Workers for the
-Datacenter and the Edge/Factory cluster are made of 3 Master/Worker
-nodes. For the node sizes we used the *m5.xlarge* on AWS and this
-instance type met the minimum requirements to deploy the *retail*
-pattern successfully on the Datacenter hub. On the Factory/Edge cluster
-we used the *m5.2xlarge* since the minimum cluster was comprised of 3
-nodes. .
-
-To understand better what types of nodes you can use on other Cloud
-Providers we provide some of the details below.
-
-==== Azure Instance Types
-
-The *retail* pattern was also deployed on Azure using the
-*Standard_D8s_v3* VM size. Below is a table of different VM sizes
-available for Azure. Keep in mind that due to limited access to Azure we
-only used the *Standard_D8s_v3* VM size.
-
-The OpenShift cluster is made of 3 Master and 3 Workers for the
-Datacenter cluster.
-
-The OpenShift cluster is made of 3 Nodes combining Master/Workers for
-the Edge/Factory cluster.
-
-[width="100%",cols="<34%,<33%,<33%",options="header",]
-|===
-|Type |Sizes |Description
-|https://docs.microsoft.com/en-us/azure/virtual-machines/sizes-general[General
-purpose] |B, Dsv3, Dv3, Dasv4, Dav4, DSv2, Dv2, Av2, DC, DCv2, Dv4,
-Dsv4, Ddv4, Ddsv4 |Balanced CPU-to-memory ratio. Ideal for testing and
-development, small to medium databases, and low to medium traffic web
-servers.
-
-|https://docs.microsoft.com/en-us/azure/virtual-machines/sizes-compute[Compute
-optimized] |F, Fs, Fsv2, FX |High CPU-to-memory ratio. Good for medium
-traffic web servers, network appliances, batch processes, and
-application servers.
-
-|https://docs.microsoft.com/en-us/azure/virtual-machines/sizes-memory[Memory
-optimized] |Esv3, Ev3, Easv4, Eav4, Ev4, Esv4, Edv4, Edsv4, Mv2, M,
-DSv2, Dv2 |High memory-to-CPU ratio. Great for relational database
-servers, medium to large caches, and in-memory analytics.
-
-|https://docs.microsoft.com/en-us/azure/virtual-machines/sizes-storage[Storage
-optimized] |Lsv2 |High disk throughput and IO ideal for Big Data, SQL,
-NoSQL databases, data warehousing and large transactional databases.
-
-|https://docs.microsoft.com/en-us/azure/virtual-machines/sizes-gpu[GPU]
-|NC, NCv2, NCv3, NCasT4_v3, ND, NDv2, NV, NVv3, NVv4 |Specialized
-virtual machines targeted for heavy graphic rendering and video editing,
-as well as model training and inferencing (ND) with deep learning.
-Available with single or multiple GPUs.
-
-|https://docs.microsoft.com/en-us/azure/virtual-machines/sizes-hpc[High
-performance compute] |HB, HBv2, HBv3, HC, H |Our fastest and most
-powerful CPU virtual machines with optional high-throughput network
-interfaces (RDMA).
-|===
-
-For more information please refer to the
-https://docs.microsoft.com/en-us/azure/virtual-machines/sizes[Azure VM
-Size Page].
-
-==== Google Cloud (GCP) Instance Types
-
-The *retail* pattern was also deployed on GCP using the *n1-standard-8*
-VM size. Below is a table of different VM sizes available for GCP. Keep
-in mind that due to limited access to GCP we only used the
-*n1-standard-8* VM size.
-
-The OpenShift cluster is made of 3 Master and 3 Workers for the
-Datacenter cluster.
-
-The OpenShift cluster is made of 3 Nodes combining Master/Workers for
-the Edge/Factory cluster.
-
-The following table provides VM recommendations for different workloads.
-
-[verse]
---
-*General purpose* | *Workload optimized*
-Cost-optimized | Balanced | Scale-out optimized | Memory-optimized |Compute-optimized | Accelerator-optimized
-:—- | :—- | :—- | :—- | :—- | :—-
-E2 | N2, N2D, N1 | T2D | M2, M1 | C2 | A2
---
-
-Day-to-day computing at a lower cost | Balanced price/performance across
-a wide range of VM shapes | Best performance/cost for scale-out
-workloads | Ultra high-memory workloads | Ultra high performance for
-compute-intensive workloads | Optimized for high performance computing
-workloads
-
-For more information please refer to the
-https://cloud.google.com/compute/docs/machine-types[GCP VM Size Page].
+include::modules/cluster-sizing-template.adoc[]