System requirements
All the Cloud Pak containers are based on Red Hat Universal Base Images (UBI), and are Red Hat and IBM certified. To use the Cloud Pak images, the administrator must make sure that the target cluster on Red Hat OpenShift Container Platform has the capacity for all of the capabilities that you plan to install.
For each stage in your operations (a minimum of three stages is expected "development, preproduction, and production"), you must allocate a cluster of nodes before you install the Cloud Pak. Development, preproduction, and production are stages that are best run on different compute nodes. To achieve resource isolation, each namespace is a virtual cluster within the physical cluster and a Cloud Pak deployment is scoped to a single namespace. High-level resource objects are scoped within namespaces. Low-level resources, such as nodes and persistent volumes, are not in namespaces.
Note: Use the shared_configuration.sc_deployment_license parameter to define
the purpose of the "custom" deployment type
(shared_configuration.sc_deployment_type). Valid values are
production and non-production.
The Detailed system requirements page provides a cluster requirements guideline for IBM Cloud Pak® for Business Automation.
The minimum cluster configuration and physical resources that are
needed to run the Cloud Pak include the following elements:
- Hardware architecture: Intel (amd64 or x86_64 the 64-bit edition for Linux® x86) on all platforms, Linux on IBM Z, or Linux on Power.
- Node counts: Dual compute nodes for nonproduction and production clusters. A minimum of three nodes is needed for medium and large production environments and large test environments. Any cluster configuration needs to adapt to the size of the projects and the workload that is expected.
A cluster where you want to install all of the capabilities needs as a minimum:
- Master (3 nodes): 4 vCPU and 8 Gi memory on each node.
- Worker (8 nodes): 16 vCPU and 32 Gi memory on each node.
Based on your cluster requirement, you can pick a deployment profile
(sc_deployment_profile_size) and enable it during installation. Cloud Pak for Business Automation provides
small, medium, and large deployment profiles. You
can set the profile during installation, in an update, and during an upgrade.
The default profile is small. Before you install the
Cloud Pak, you can change the profile to medium or large. You can
scale up or down a profile anytime after installation. However, if you install with a
medium profile and another Cloud Pak specifies a medium or
large profile then if you scale down to size small, the profile
for the foundational services remains as it is. You can scale down the foundational services to
small only if no other Cloud Pak specifies a medium or
large size.
Note: You can install Cloud Pak foundational services in two different ways:
- A namespace-scoped instance
- The recommended way to install foundational services is in a specified namespace along with your
Cloud Pak deployments. You can install an instance of foundational services per deployment or group
deployments to specific instances. You can configure foundational services for different
environments like development, test, QA, production, or if your company has different organizations
within it and they need to configure a different identity provider for each department.
To install foundational services in a namespace-scoped instance, you must create a common-service-maps configmap in the
kube-publicnamespace. Instructions are provided in multiple places in the installation steps, but you can also find more information in the foundational services documentation, see Creating the common-service-maps configmap. - A cluster-scoped instance
- If installed, every Cloud Pak deployment on the cluster uses the same Identity and Access Management (IAM) user repositories and Platform UI (Zen). A cluster-scoped instance must be used if you upgrade from a version that only supported the cluster-scoped instance. A cluster-scoped instance does use less resources than multiple namespace-scoped instances, but provides less flexibility.
If you install a cluster-scoped instance, you cannot install a namespace-scoped instance on the same cluster. In other words, you cannot have both a cluster-scoped instance and namespace-scoped instances on the same cluster. You cannot switch to use a namespace-scoped instance from a cluster-scoped instance, and you cannot change a deployment to use a different namespace-scoped foundational services instance after it is installed.
The Licensing Service
and the Certificate Manager Service are cluster-scoped regardless of the way that you choose to
install an instance of foundational services. The two services are installed in the namespace that
you specify as the controlNamespace parameter value in the
common-service-maps configmap. The largest configured resource limits (CPU and
memory) for these services across the cluster are applied to the instances in your
controlNamespace.
It is recommended that you set the IBM Cloud Platform UI (Zen) service to the same size as Cloud Pak for Business Automation. The possible values are small, medium, and large.
oc patch AutomationUIConfig iaf-system --type=merge -p '{"spec":{"zenService":{"scaleConfig":"large"}}}'The following table describes each deployment profile.
| Profile | Description | Scaling (per 8-hour day) | Minimum number of worker nodes |
|---|---|---|---|
| Small (no HA) | For environments that are used by 10 developers and 25 users. For environments that are used by a single department with a few users; useful for application development. |
|
8 |
| Medium | For environments that are used by 20 developers and 125 users. For environments that are used by a single department and by limited users. |
|
16 |
| Large | For environments that are used by 50 developers and 625 users. For environments that are shared by multiple departments and users. |
|
32 |
You can use custom resource templates to update the hardware requirements of the services that you want to install.
The following sections provide the default resources for each capability. For more information about the minimum requirements of foundational services, see Hardware requirements and recommendations for foundational services.
- Small profile hardware requirements
- Medium profile hardware requirements
- Large profile hardware requirements
Attention: The values in the hardware requirements tables were derived under specific
operating and environment conditions. The information is accurate under the given conditions, but
results that are obtained in your operating environments might vary significantly. Therefore, IBM
cannot provide any representations, assurances, guarantees, or warranties regarding the performance
of the profiles in your environment.
Small profile hardware requirements
- Table 2 Cloud Pak for Business Automation operator default requirements for a small profile
- Table 3 Automation Decision Services default requirements for a small profile
- Table 4 Automation Document Processing default requirements for a small profile
- Table 5 Automation Workstream Services default requirements for a small profile
- Table 6 Business Automation Application default requirements for a small profile
- Table 7 Business Automation Insights default requirements for a small profile
- Table 8 Business Automation Navigator default requirements for a small profile
- Table 9 Business Automation Studio default requirements for a small profile
- Table 10 Business Automation Workflow default requirements with or without Automation Workstream Services for a small profile
- Table 11 FileNet® Content Manager default requirements for a small profile
- Table 12 Operational Decision Manager default requirements for a small profile
- Table 13 Workflow Process Service Authoring default requirements for a small profile
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| ibm-cp4a-operator | 500 | 1000 | 256 | 1024 | 1 | No |
Note: If you plan to install the operator in all namespaces for more than one instance, add more
resources. You can use the
oc patch csv command to add more
resources:oc patch csv ibm-cp4a-operator.v22.1.0 --type=json -p '[
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/cpu",
"value": "4"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/memory",
"value": "8Gi"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/cpu",
"value": "1500m"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/memory",
"value": "1600Mi"
},
]'| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction | Ephemeral storage Request | Ephemeral storage Limit |
|---|---|---|---|---|---|---|---|---|
| ads-runtime | 500 | 1000 | 2048 | 3072 | 1 | Yes | 300Mi | 500Mi |
| ads-credentials | 250 | 1000 | 800 | 1536 | 1 | No | 300Mi | 600Mi |
| ads-embedded-build | 500 | 2000 | 1024 | 2048 | 1 | No | 1.1Gi | 1.4Gi |
| ads-download | 100 | 300 | 200 | 200 | 1 | No | 300Mi | 500Mi |
| ads-front | 100 | 300 | 256 | 256 | 1 | No | 300Mi | 500Mi |
| ads-gitservice | 500 | 1000 | 800 | 1536 | 1 | No | 400Mi | 600Mi |
| ads-parsing | 250 | 1000 | 800 | 1536 | 1 | No | 300Mi | 500Mi |
| ads-restapi | 500 | 1000 | 800 | 1536 | 1 | No | 300Mi | 1.2Gi |
| ads-run | 500 | 1000 | 800 | 1536 | 1 | No | 300Mi | 700Mi |
Note: Automation Decision Services also creates some jobs that request 200m CPU and 256Mi Memory.
The following jobs are created at the beginning of the installation and do not last long:
- ads-ltpa-creation
- ads-runtime-bai-registration
- ads-ads-runtime-zen-translation-job
- ads-designer-zen-translation-job
The ads-rr-integration and ads-ads-rr-as-runtime-synchro jobs are started every 15 minutes, and are also short-lived.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction | Ephemeral storage Limit |
|---|---|---|---|---|---|---|---|
| OCR Extraction | 200 | 1000 | 1024 | 2560 | 2 | Yes | 3072Mi |
| Classify Process | 200 | 500 | 400 | 2048 | 1 | Yes | 3072Mi |
| Processing Extraction | 500 | 1000 | 1024 | 6656 | 6 | Yes | With Object Detection: 5Gi Without Object Detection: 3Gi |
| Natural Language Extractor | 200 | 500 | 600 | 1440 | 2 | Yes | 3072Mi |
| PostProcessing | 200 | 600 | 400 | 1229 | 1 | No | 3072Mi |
| Setup | 200 | 600 | 600 | 1440 | 2 | No | 3072Mi |
| Deep Learning | 1000 | 2000 | 3072 | 15360 | 2 | No | 7.5Gi |
| Backend | 200 | 1000 | 400 | 2048 | 2 | No | 4608Mi |
| Redis | 100 | 250 | 100 | 640 | 1 | No | 500Mi |
| RabbitMQ | 100 | 1000 | 100 | 1024 | 2 | No | 3072Mi |
| One Conversion | 200 | 1000 | 100 | 4096 | 1 | Yes | 3072Mi |
| Common Git Gateway Service (git-service) | 500 | 1000 | 512 | 1536 | 1 | No | Not applicable |
| Content Designer Repo API (CDRA) | 500 | 1000 | 1024 | 3072 | 1 | No | Not applicable |
| Content Designer UI and REST (CDS) | 500 | 1000 | 512 | 3072 | 1 | No | Not applicable |
| Content Project Deployment Service (CPDS) | 500 | 1000 | 512 | 3072 | 1 | No | Not applicable |
| Mongo database (mongodb) | 500 | 1000 | 512 | 1024 | 1 | No | Not applicable |
| Viewer service (viewone) | 500 | 1000 | 1024 | 3072 | 1 | No | Not applicable |
Important:
- Document Processing - The Deep
Learning optional container has the ability to use NVIDIA GPU if it is available. NVIDIA is the only
supported GPU for Deep Learning in the Document Processing pattern. The GPU worker
nodes must have a unique label, for example
ibm-cloud.kubernetes.io/gpu-enabled:true. You add this label value to the deployment script or to the YAML file of your custom resource when you configure the YAML for deployment. To install the NVIDIA GPU operator, follow these installation instructions. For high-availability, you need a minimum of 2 GPU so that 2 replicas of Deep Learning pods can be started. You can change the replica to 1 if you have 1 GPU on the node. - For Document Processing, the CPU of the worker nodes must meet TensorFlow AVX requirements. For more information, see Hardware requirements for TensorFlow with pip.
- The One Conversion (optional) container uses Scene Text Recognition (STR) to recognize identity cards.
Note:
- Document Processing requires databases for project configuration and processing. These databases must be Db2. The hardware and storage requirements for the databases depend on the system load for each document processing project.
- If you process only fixed-format documents, you might improve performance by disabling deep learning object detection. For more information about the system requirements for Document Processing engine components in this scenario, see IBM Automation Document Processing system requirements when disabling deep learning object detection for fixed-format documents.
- If you deploy with only the
document_processingpattern, you can reduce the sizing for some of the required components. For more information, see IBM Automation Document Processing system requirements for a light production deployment (document_processing pattern only). - It is recommended to use 70% of the available space for projects, for example if you have 5000 Mb, use 3500 Mb for your projects. Because the model size is 153 Mb, it means you should create a maximum of 22 projects. If you want to set up more than 22 projects, you should increase the ephemeral storage for both the Deep Learning and Processing Extraction containers.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Workflow Server | 500 | 2000 | 2048 | 3060 | 1 | Yes |
| Java™ Message Service | 100 | 1000 | 512 | 1024 | 1 | No |
| Process Federation Server operator | 100 | 500 | 20 | 1024 | 1 | No |
| Process Federation Service | 200 | 1000 | 512 | 1024 | 1 | No |
| Process Federation Service-dbareg | 50 | 100 | 512 | 512 | 1 | No |
| Elasticsearch Service | 500 | 800 | 820 | 2048 | 1 | No |
Notes:
Automation Workstream Services also creates some jobs that request 200m CPU and 128Mi Memory:
- basimport-job is created only with Business Automation Studio.
- content-init-job
- db-init-job-pfs
- ltpa-job
- oidc-registry-job
- workplace-init-job
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| App Engine | 300 | 500 | 256 | 1024 | 1 | Yes/No |
| Resource Registry | 100 | 500 | 256 | 512 | 1 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Business Performance Center | 100 | 4000 | 512 | 2000 | 1 | Yes/No |
| Flink task managers | 1000 | 1000 | 1728 | 1728 | Default parallelism 2 |
Yes/No |
| Flink job manager | 1000 | 1000 | 1728 | 1728 | 1 | No |
| Administration REST API | (Optional) 100 | (Optional) 500 | 50 | 120 | 2 | No |
| Management REST API | 100 | 1000 | 50 | 120 | 2 | No |
| Management back end (second container of the same management pod as the previous one) | 100 | 500 | 350 | 512 | 2 | No |
Note: Business Automation Insights relies on Kafka,
Apicurio, and Elasticsearch from IBM Automation foundation. For information about their
system requirements, see the System requirements page of the IBM Automation foundation documentation. Business Automation Insights also creates the
bai-setup and iaf-insights-engine-application-setup Kubernetes
jobs and requests 200m for CPU and 350Mi for memory. The CPU and memory limits are set equal to the
requests. The pods of these Kubernetes jobs run for a short time at the beginning of the
installation, then complete, thus freeing the resources.| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Navigator | 1000 | 1000 | 3072 | 3072 | 1 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| App Engine playback | 300 | 500 | 256 | 1024 | 1 | No |
| BAStudio | 1100 | 2000 | 1752 | 3584 | 1 | No |
| Resource Registry | 100 | 500 | 256 | 512 | 1 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Workflow Server | 500 | 2000 | 2048 | 3060 | 1 | Yes |
| Workflow Authoring | 500 | 2000 | 2048 | 3072 | 1 | No |
| Java Message Service | 100 | 1000 | 512 | 1024 | 1 | No |
| Process Federation Server operator | 100 | 500 | 20 | 1024 | 1 | No |
| Process Federation Service | 200 | 1000 | 512 | 1024 | 1 | No |
| Process Federation Service-dbareg | 50 | 100 | 512 | 512 | 1 | No |
| Elasticsearch Service | 500 | 800 | 820 | 2048 | 1 | No |
| Intelligent Task Prioritization | 500 | 2000 | 1024 | 2560 | 1 | No |
| Workforce Insights | 500 | 2000 | 1024 | 2560 | 1 | No |
Notes:
Intelligent Task Prioritization and Workforce Insights are optional and are not supported on all platforms. For more information, see Detailed system requirements.
Business Automation Workflow also creates
some jobs that request 200m CPU and 128Mi Memory:
- basimport-job is created only with Business Automation Studio.
- case-init-job
- content-init-job
- db-init-job-pfs
- ltpa-job
- oidc-registry-job
- oidc-registry-job-for-webpd is created only with workflow center.
- workplace-init-job
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| CPE | 1000 | 1000 | 3072 | 3072 | 1 | Yes |
| CSS | 1000 | 1000 | 4096 | 4096 | 1 | Yes |
| Enterprise Records (ER) | 500 | 1000 | 1536 | 1536 | 1 | Yes |
| Content Collector for SAP (CC4SAP) | 500 | 1000 | 1536 | 1536 | 1 | Yes |
| CMIS | 500 | 1000 | 1536 | 1536 | 1 | No |
| GraphQL | 500 | 1000 | 1536 | 1536 | 1 | No |
| Task Manager | 500 | 1000 | 1536 | 1536 | 1 | No |
Note: Not all containers are used in every workload. If a feature the Content Services GraphQL API is not used, that container requires less resources or is
optionally not deployed.
In high-volume indexing scenarios, where ingested docs are full-text indexed, the CSS utilization can exceed the CPE utilization. In some cases, this might be 3 - 5 times larger.
For optional processing such as thumbnail generation or text filtering, at least 1 GB of native memory is required by the CPE for each. If both types of processing are expected, add at least 2 GB to the memory requests/limits for the CPE.
With the processing of content, resources required increase with the complexity and size of the content. Increase both memory and CPU for the CPE and CSS services to reflect the type and size of documents in your system. Resource requirements might also increase over time as the amount of data in the system grows.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction | Ephemeral storage Request | Ephemeral storage Limit |
|---|---|---|---|---|---|---|---|---|
| Decision Center | 1000 | 1000 | 4096 | 4096 | 1 | Yes | 1G | 2G |
| Decision Runner | 500 | 500 | 1024 | 2048 | 1 | Yes | 200Mi | 1G |
| Decision Server Runtime | 500 | 1000 | 2048 | 2048 | 1 | Yes | 200Mi | 1G |
| Decision Server Console | 500 | 500 | 512 | 1024 | 1 | No | 200Mi | 1G |
Note: Operational Decision Manager also creates an
odm-oidc-job-registration job that requests 200m CPU and 256Mi Memory. The pod is created at the
beginning of the installation and does not last long.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| IBM Workflow Process Service Authoring | 1100 | 2000 | 1752 | 3072 | 1 | No |
Medium profile hardware requirements
- Table 14 Cloud Pak for Business Automation operator default requirements for a medium profile
- Table 15 Automation Decision Services default requirements for a medium profile
- Table 16 Automation Document Processing default requirements for a medium profile
- Table 17 Automation Workstream Services default requirements for a medium profile
- Table 18 Business Automation Application default requirements for a medium profile
- Table 19 Business Automation Insights default requirements for a medium profile
- Table 20 Business Automation Navigator default requirements for a medium profile
- Table 21 Business Automation Studio default requirements for a medium profile
- Table 22 Business Automation Workflow default requirements with or without Automation Workstream Services for a medium profile
- Table 23 FileNet Content Manager default requirements for a medium profile
- Table 24 Operational Decision Manager default requirements for a medium profile
- Table 25 Workflow Process Service Authoring default requirements for a medium profile
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| ibm-cp4a-operator | 500 | 1000 | 256 | 1024 | 1 | No |
Note: If you plan to install the operator in all namespaces for more than one instance, add more
resources. You can use the
oc patch csv command to add more
resources:oc patch csv ibm-cp4a-operator.v22.1.0 --type=json -p '[
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/cpu",
"value": "4"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/memory",
"value": "8Gi"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/cpu",
"value": "1500m"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/memory",
"value": "1600Mi"
},
]'| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction | Ephemaral storage Request | Ephemaral storage Limit |
|---|---|---|---|---|---|---|---|---|
| ads-runtime | 500 | 1000 | 2048 | 3072 | 2 | Yes | 2.1Gi | 3Gi |
| ads-credentials | 250 | 1000 | 800 | 1536 | 2 | No | 300Mi | 600Mi |
| ads-embedded-build | 500 | 2000 | 1024 | 2048 | 2 | No | 1.1Gi | 1.4Gi |
| ads-download | 100 | 300 | 200 | 200 | 2 | No | 300Mi | 600Mi |
| ads-front | 100 | 300 | 256 | 256 | 2 | No | 300Mi | 600Mi |
| ads-gitservice | 500 | 1000 | 800 | 1536 | 2 | No | 400Mi | 700Mi |
| ads-parsing | 250 | 1000 | 800 | 1536 | 2 | No | 300Mi | 600Mi |
| ads-restapi | 500 | 1000 | 800 | 1536 | 2 | No | 300Mi | 1.2Gi |
| ads-run | 500 | 1000 | 800 | 1536 | 2 | No | 300Mi | 700Mi |
Note: Automation Decision Services also creates some jobs that request 200m CPU and 256Mi Memory.
The following jobs are created at the beginning of the installation and do not last long:
- ads-ltpa-creation
- ads-runtime-bai-registration
- ads-ads-runtime-zen-translation-job
- ads-designer-zen-translation-job
The ads-rr-integration and ads-ads-rr-as-runtime-synchro jobs are started every 15 minutes, and are also short-lived.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of Replicas | Pods are licensed for production/nonproduction | Ephemeral storage Limit |
|---|---|---|---|---|---|---|---|
| OCR Extraction | 200 | 1000 | 1024 | 2560 | With Object Detection: 3 Without Object Detection: 6 |
Yes | 3072Mi |
| Classify Process | 200 | 500 | 400 | 2048 | 2 | Yes | 3072Mi |
| Processing Extraction | 500 | 1000 | 1024 | 6656 | With Object Detection: 8 Without Object Detection: 5 |
Yes | With Object Detection: 5Gi Without Object Detection: 3Gi |
| Natural Language Extractor | 200 | 500 | 600 | 1440 | 2 | Yes | 3072Mi |
| PostProcessing | 200 | 600 | 400 | 1229 | 2 | No | 3072Mi |
| Setup | 200 | 600 | 600 | 1440 | 4 | No | 3072Mi |
| Deep Learning | 1000 | 2000 | 3072 | 15360 | 2 | No | 7.5Gi |
| Backend | 200 | 1000 | 400 | 2048 | 4 | No | 4608Mi |
| Redis | 100 | 250 | 100 | 640 | 1 | No | 500Mi |
| RabbitMQ | 100 | 1000 | 100 | 1024 | 3 | No | 3072Mi |
| One Conversion | 200 | 1000 | 100 | 4096 | 2 | Yes | 3072Mi |
| Common Git Gateway Service (git-service) | 500 | 1000 | 512 | 1536 | 1 | No | Not applicable |
| Content Designer Repo API (CDRA) | 500 | 1000 | 1024 | 3072 | 2 | No | Not applicable |
| Content Designer UI and REST (CDS) | 500 | 1000 | 512 | 3072 | 2 | No | Not applicable |
| Content Project Deployment Service (CPDS) | 500 | 1000 | 512 | 3072 | 2 | No | Not applicable |
| Mongo database (mongodb) | 500 | 1000 | 512 | 1024 | 1 | No | Not applicable |
| Viewer service (viewone) | 500 | 2000 | 1024 | 4096 | 2 | No | Not applicable |
Important:
- Document Processing - The Deep
Learning optional container has the ability to use NVIDIA GPU if it is available. NVIDIA is the only
supported GPU for Deep Learning in the Document Processing pattern. The GPU worker
nodes must have a unique label, for example
ibm-cloud.kubernetes.io/gpu-enabled:true. You add this label value to the deployment script or to the YAML file of your custom resource when you configure the YAML for deployment. To install the NVIDIA GPU operator, follow these installation instructions. For high-availability, you need a minimum of 2 GPU so that 2 replicas of Deep Learning pods can be started. You can change the replica to 1 if you have 1 GPU on the node. - For Document Processing, the CPU of the worker nodes must meet TensorFlow AVX requirements. For more information, see Hardware requirements for TensorFlow with pip.
- The One Conversion (optional) container uses Scene Text Recognition (STR) to recognize identity cards.
Note:
- Document Processing requires databases for project configuration and processing. These databases must be Db2. The hardware and storage requirements for the databases depend on the system load for each document processing project.
- If you process only fixed-format documents, you might improve performance by disabling deep learning object detection. For more information about the system requirements for Document Processing engine components in this scenario, see IBM Automation Document Processing system requirements when disabling deep learning object detection for fixed-format documents.
- If you deploy with only the
document_processingpattern, you can reduce the sizing for some of the required components. For more information, see IBM Automation Document Processing system requirements for a light production deployment (document_processing pattern only). - It is recommended to use 70% of the available space for projects, for example if you have 5000 Mb, use 3500 Mb for your projects. Because the model size is 153 Mb, it means you should create a maximum of 22 projects. If you want to set up more than 22 projects, you should increase the ephemeral storage for both the Deep Learning and Processing Extraction containers.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Workflow Server | 500 | 2000 | 2560 | 3512 | 2 | Yes |
| Java Message Service | 200 | 1000 | 512 | 2048 | 1 | No |
| Process Federation Server operator | 100 | 500 | 20 | 1024 | 1 | No |
| Process Federation Service | 200 | 1000 | 512 | 1024 | 2 | No |
| Process Federation Service-dbareg | 50 | 100 | 512 | 512 | 1 | No |
| Elasticsearch Service | 500 | 1000 | 3512 | 5120 | 3 | No |
Notes:
Automation Workstream Services also creates some jobs that request 200m CPU and 128Mi Memory:
- basimport-job is created only with Business Automation Studio.
- content-init-job
- db-init-job-pfs
- ltpa-job
- oidc-registry-job
- workplace-init-job
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| App Engine | 300 | 500 | 256 | 1024 | 3 | Yes/No |
| Resource Registry | 100 | 500 | 256 | 512 | 3 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Business Performance Center | 100 | 4000 | 512 | 2000 | 1 | Yes/No |
| Flink task managers | 1000 | 1000 | 1728 | 1728 | Default parallelism 2 |
Yes/No |
| Flink job manager | 1000 | 1000 | 1728 | 1728 | 1 | No |
| Administration REST API | (Optional) 100 | (Optional) 500 | 50 | 120 | 2 | No |
| Management REST API | 100 | 1000 | 50 | 120 | 2 | No |
| Management back end (second container of the same management pod as the previous one) | 100 | 500 | 350 | 512 | 2 | No |
Note: Business Automation Insights relies on Kafka,
Apicurio, and Elasticsearch from IBM Automation foundation. For information about their
system requirements, see the System requirements page of the IBM Automation foundation documentation. Business Automation Insights also creates the
bai-setup and iaf-insights-engine-application-setup Kubernetes
jobs and requests 200m for CPU and 350Mi for memory. The CPU and memory limits are set equal to the
requests. The pods of these Kubernetes jobs run for a short time at the beginning of the
installation, then complete, thus freeing the resources.| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Navigator | 2000 | 3000 | 4096 | 4096 | 2 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| App Engine playback | 300 | 500 | 256 | 1024 | 2 | No |
| BAStudio | 1000 | 2000 | 1752 | 3072 | 2 | No |
| Resource Registry | 100 | 500 | 256 | 512 | 3 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Workflow Server | 500 | 2000 | 2560 | 3512 | 2 | Yes |
| Workflow Authoring | 500 | 4000 | 1024 | 3072 | 1 | No |
| Java Message Service | 100 | 1000 | 512 | 1024 | 1 | No |
| Process Federation Server operator | 100 | 500 | 20 | 1024 | 1 | No |
| Process Federation Service | 200 | 1000 | 512 | 1024 | 2 | No |
| Process Federation Service-dbareg | 50 | 100 | 512 | 512 | 1 | No |
| Elasticsearch Service | 500 | 1000 | 3512 | 5120 | 3 | No |
| Intelligent Task Prioritization | 500 | 2000 | 1024 | 2560 | 2 | No |
| Workforce Insights | 500 | 2000 | 1024 | 2560 | 2 | No |
Notes:
Intelligent Task Prioritization and Workforce Insights are optional and are not supported on all platforms. For more information, see Detailed system requirements.
Business Automation Workflow also creates
some jobs that request 200m CPU and 128Mi Memory:
- basimport-job is created only with Business Automation Studio.
- case-init-job
- content-init-job
- db-init-job-pfs
- ltpa-job
- oidc-registry-job
- oidc-registry-job-for-webpd is created only with workflow center.
- workplace-init-job
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| CPE | 1500 | 2000 | 3072 | 3072 | 2 | Yes |
| CSS | 1000 | 2000 | 8192 | 8192 | 2 | Yes |
| Enterprise Records (ER) | 500 | 1000 | 1536 | 1536 | 2 | Yes |
| Content Collector for SAP (CC4SAP) | 500 | 1000 | 1536 | 1536 | 2 | Yes |
| CMIS | 500 | 1000 | 1536 | 1536 | 2 | No |
| GraphQL | 500 | 2000 | 3072 | 3072 | 3 | No |
| Task Manager | 500 | 1000 | 1536 | 1536 | 2 | No |
Note: Not all containers are used in every workload. If a feature like the Content Services GraphQL API is not used, that container requires less resources or is
optionally not deployed.
In high-volume indexing scenarios, where ingested docs are full-text indexed, the CSS utilization can exceed the CPE utilization. In some cases, this might be 3 - 5 times larger.
For optional processing such as thumbnail generation or text filtering, at least 1 GB of native memory is required by the CPE for each. If both types of processing are expected, add at least 2 GB to the memory requests/limits for the Content Platform Engine (CPE).
With the processing of content, resource requirements increase with the complexity and size of the content. Increase both memory and CPU for the CPE and CSS services to reflect the type and size of documents in your system. Resource requirements might also increase over time as the amount of data in the system grows.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction | Ephemeral storage Request | Ephemeral storage Limit |
|---|---|---|---|---|---|---|---|---|
| Decision Center | 1000 | 1000 | 4096 | 8192 | 2 | Yes | 1G | 2G |
| Decision Runner | 500 | 2000 | 2048 | 2048 | 2 | Yes | 200Mi | 1G |
| Decision Server Runtime | 2000 | 2000 | 2048 | 2048 | 3 | Yes | 200Mi | 1G |
| Decision Server Console | 500 | 2000 | 512 | 2048 | 1 | No | 200Mi | 1G |
Note: Operational Decision Manager also creates an
odm-oidc-job-registration job that requests 200m CPU and 256Mi Memory. The pod is created at the
beginning of the installation and does not last long.
To achieve high availability, you must adapt the cluster configuration and physical resources. You can set up a Db2® High Availability Disaster Recovery (HADR) database. For more information, see Preparing your environment for disaster recovery. For high availability and fault tolerance to be effective, set the number of replicas that you need for the respective configuration parameters in your custom resource file. The operator then manages the scaling.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Workflow Process Service Authoring | 1000 | 2000 | 1752 | 3072 | 2 | No |
Large profile hardware requirements
- Table 26 Cloud Pak for Business Automation operator default requirements for a large profile
- Table 27 Automation Decision Services default requirements for a large profile
- Table 28 Automation Document Processing default requirements for a large profile
- Table 29 Automation Workstream Services default requirements for a large profile
- Table 30 Business Automation Application default requirements for a large profile
- Table 31 Business Automation Insights default requirements for a large profile
- Table 32 Business Automation Navigator default requirements for a large profile
- Table 33 Business Automation Studio default requirements for a large profile
- Table 34 Business Automation Workflow default requirements with or without Automation Workstream Services for a large profile
- Table 35 FileNet Content Manager default requirements for a large profile
- Table 36 Operational Decision Manager default requirements for a large profile
- Table 37 Workflow Process Service Authoring default requirements for a large profile
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| ibm-cp4a-operator | 500 | 1000 | 256 | 1024 | 1 | No |
Note: If you plan to install the operator in all namespaces for more than one instance, add more
resources. You can use the
oc patch csv command to add more
resources:oc patch csv ibm-cp4a-operator.v22.1.0 --type=json -p '[
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/cpu",
"value": "4"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/memory",
"value": "8Gi"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/cpu",
"value": "1500m"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/memory",
"value": "1600Mi"
},
]'| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction | Ephemeral storage Request | Ephemeral storage Limit |
|---|---|---|---|---|---|---|---|---|
| ads-runtime | 1000 | 2000 | 2048 | 3072 | 2 | Yes | 2.1Gi | 2.6Gi |
| ads-credentials | 250 | 2000 | 800 | 1536 | 2 | No | 300Mi | 700Mi |
| ads-embedded-build | 500 | 2000 | 1024 | 2048 | 2 | No | 1.1Gi | 1.5Gi |
| ads-download | 100 | 300 | 200 | 200 | 2 | No | 300Mi | 700Mi |
| ads-front | 100 | 300 | 256 | 256 | 2 | No | 300Mi | 700Mi |
| ads-gitservice | 500 | 2000 | 800 | 1536 | 2 | No | 400Mi | 800Mi |
| ads-parsing | 250 | 2000 | 800 | 1536 | 2 | No | 300Mi | 700Mi |
| ads-restapi | 500 | 2000 | 800 | 1536 | 2 | No | 300Mi | 1.2Gi |
| ads-run | 500 | 2000 | 800 | 1536 | 2 | No | 300Mi | 1Gi |
Note: Automation Decision Services also creates some jobs that request 200m CPU and 256Mi Memory.
The following jobs are created at the beginning of the installation and do not last long:
- ads-ltpa-creation
- ads-runtime-bai-registration
- ads-ads-runtime-zen-translation-job
- ads-designer-zen-translation-job
The ads-rr-integration and ads-ads-rr-as-runtime-synchro jobs are started every 15 minutes, and are also short-lived.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of Replicas | Pods are licensed for production/nonproduction | Ephemeral storage Limit |
|---|---|---|---|---|---|---|---|
| OCR Extraction | 200 | 1000 | 1024 | 2560 | 5 | Yes | 3072Mi |
| Classify Process | 200 | 500 | 400 | 2048 | 2 | Yes | 3072Mi |
| Processing Extraction | 500 | 1000 | 1024 | 6656 | 15 | Yes | With Object Detection: 5Gi Without Object Detection: 3Gi |
| Natural Language Extractor | 200 | 500 | 600 | 1440 | 2 | Yes | 3072Mi |
| PostProcessing | 200 | 600 | 400 | 1229 | 2 | No | 3072Mi |
| Setup | 200 | 600 | 600 | 1440 | 6 | No | 3072Mi |
| Deep Learning | 1000 | 2000 | 3072 | 15360 | 2 | No | 7.5Gi |
| Backend | 200 | 1000 | 400 | 2048 | 6 | No | 4608Mi |
| Redis | 100 | 250 | 100 | 640 | 1 | No | 500Mi |
| RabbitMQ | 100 | 1000 | 100 | 1024 | 3 | No | 3072Mi |
| One Conversion | 200 | 1000 | 100 | 4096 | 2 | Yes | 3072Mi |
| Common Git Gateway Service (git-service) | 500 | 1000 | 512 | 1536 | 2 | No | Not applicable |
| Content Designer Repo API (CDRA) | 500 | 1000 | 1024 | 3072 | 3 | No | Not applicable |
| Content Designer UI and REST (CDS) | 500 | 1000 | 512 | 3072 | 3 | No | Not applicable |
| Content Project Deployment Service (CPDS) | 500 | 1000 | 512 | 3072 | 3 | No | Not applicable |
| Mongo database (mongodb) | 500 | 1000 | 512 | 1024 | 1 | No | Not applicable |
| Viewer service (viewone) | 1000 | 3000 | 3072 | 6144 | 2 | No | Not applicable |
Important:
- Document Processing - The Deep
Learning optional container has the ability to use NVIDIA GPU if it is available. NVIDIA is the only
supported GPU for Deep Learning in the Document Processing pattern. The GPU worker
nodes must have a unique label, for example
ibm-cloud.kubernetes.io/gpu-enabled:true. You add this label value to the deployment script or to the YAML file of your custom resource when you configure the YAML for deployment. To install the NVIDIA GPU operator, follow these installation instructions. For high-availability, you need a minimum of 2 GPU so that 2 replicas of Deep Learning pods can be started. You can change the replica to 1 if you have 1 GPU on the node. - For Document Processing, the CPU of the worker nodes must meet TensorFlow AVX requirements. For more information, see Hardware requirements for TensorFlow with pip.
- The One Conversion (optional) container uses Scene Text Recognition (STR) to recognize identity cards.
Note:
- Document Processing requires databases for project configuration and processing. These databases must be Db2. The hardware and storage requirements for the databases depend on the system load for each document processing project.
- If you process only fixed-format documents, you might improve performance by disabling deep learning object detection. For more information about the system requirements for Document Processing engine components in this scenario, see IBM Automation Document Processing system requirements when disabling deep learning object detection for fixed-format documents.
- If you deploy with only the
document_processingpattern, you can reduce the sizing for some of the required components. For more information, see IBM Automation Document Processing system requirements for a light production deployment (document_processing pattern only). - It is recommended to use 70% of the available space for projects, for example if you have 5000 Mb, use 3500 Mb for your projects. Because the model size is 153 Mb, it means you should create a maximum of 22 projects. If you want to set up more than 22 projects, you should increase the ephemeral storage for both the Deep Learning and Processing Extraction containers.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Workflow Server | 1000 | 2000 | 3060 | 4000 | 4 | Yes |
| Java Message Service | 500 | 1000 | 512 | 1024 | 1 | No |
| Process Federation Server operator | 100 | 500 | 20 | 1024 | 1 | No |
| Process Federation Service | 200 | 1000 | 512 | 1024 | 2 | No |
| Process Federation Service-dbareg | 50 | 100 | 512 | 512 | 1 | No |
| Elasticsearch Service | 1000 | 2000 | 3512 | 5128 | 3 | No |
Notes:
Automation Workstream Services also creates some jobs that request 200m CPU and 128Mi Memory:
- basimport-job is created only with Business Automation Studio.
- content-init-job
- db-init-job-pfs
- ltpa-job
- oidc-registry-job
- workplace-init-job
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| App Engine | 300 | 500 | 256 | 1024 | 6 | Yes/No |
| Resource Registry | 100 | 500 | 256 | 512 | 1 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Business Performance Center | 100 | 4000 | 512 | 2000 | 1 | Yes/No |
| Flink task managers | 1000 | 1000 | 1728 | 1728 | Default parallelism 2 |
Yes/No |
| Flink job manager | 1000 | 1000 | 1728 | 1728 | 1 | No |
| Administration REST API | (Optional) 100 | (Optional) 500 | 50 | 120 | 2 | No |
| Management REST API | 100 | 1000 | 50 | 120 | 2 | No |
| Management back end (second container of the same management pod as the previous one) | 100 | 500 | 350 | 512 | 2 | No |
Note: Business Automation Insights relies on Kafka,
Apicurio, and Elasticsearch from IBM Automation foundation. For information about their
system requirements, see the System requirements page of the IBM Automation foundation documentation. Business Automation Insights also creates the
bai-setup and iaf-insights-engine-application-setup Kubernetes
jobs and requests 200m for CPU and 350Mi for memory. The CPU and memory limits are set equal to the
requests. The pods of these Kubernetes jobs run for a short time at the beginning of the
installation, then complete, thus freeing the resources.| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Navigator | 2000 | 4000 | 6144 | 6144 | 6 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| App Engine playback | 300 | 500 | 256 | 1024 | 4 | No |
| BAStudio | 2000 | 4000 | 1752 | 3072 | 2 | No |
| Resource Registry | 100 | 500 | 256 | 512 | 3 | No |
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Workflow Server | 1000 | 2000 | 3060 | 4000 | 4 | Yes |
| Workflow Authoring | 1000 | 2000 | 2000 | 3000 | 2 | No |
| Java Message Service | 500 | 1000 | 512 | 1024 | 1 | No |
| Process Federation Server operator | 100 | 500 | 20 | 1024 | 1 | No |
| Process Federation Service | 200 | 1000 | 512 | 1024 | 2 | No |
| Process Federation Service-dbareg | 50 | 100 | 512 | 512 | 1 | No |
| Elasticsearch Service | 1000 | 2000 | 3512 | 5128 | 3 | No |
| Intelligent Task Prioritization | 500 | 2000 | 1024 | 2560 | 2 | No |
| Workforce Insights | 500 | 2000 | 1024 | 2560 | 2 | No |
Notes:
Intelligent Task Prioritization and Workforce Insights are optional and are not supported on all platforms. For more information, see Detailed system requirements.
Business Automation Workflow also creates
some jobs that request 200m CPU and 128Mi Memory:
- basimport-job is created only with Business Automation Studio.
- case-init-job
- content-init-job
- db-init-job-pfs
- ltpa-job
- oidc-registry-job
- oidc-registry-job-for-webpd is created only with workflow center.
- workplace-init-job
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| CPE | 3000 | 4000 | 8192 | 8192 | 2 | Yes |
| CSS | 2000 | 4000 | 8192 | 8192 | 2 | Yes |
| Enterprise Records (ER) | 500 | 1000 | 1536 | 1536 | 2 | Yes |
| Content Collector for SAP (CC4SAP) | 500 | 1000 | 1536 | 1536 | 2 | Yes |
| CMIS | 500 | 1000 | 1536 | 1536 | 2 | No |
| GraphQL | 1000 | 2000 | 3072 | 3072 | 6 | No |
| Task Manager | 500 | 1000 | 1536 | 1536 | 2 | No |
Note: Not all containers are used in every workload. If a feature like the Content Services GraphQL API is not used, that container requires less resources or is
optionally not deployed.
In high-volume indexing scenarios, where ingested docs are full-text indexed, the CSS utilization can exceed the CPE utilization. In some cases, this might be 3 - 5 times larger.
For optional processing such as thumbnail generation or text filtering, at least 1 GB of native memory is required by the CPE for each. If both types of processing are expected, add at least 2 GB to the memory requests/limits for the CPE.
With the processing of content, resources required increase with the complexity and size of the content. Increase both memory and CPU for the CPE and CSS services to reflect the type and size of documents in your system. Resource requirements might also increase over time as the amount of data in the system grows.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction | Ephemeral storage Request | Ephemeral storage Limit |
|---|---|---|---|---|---|---|---|---|
| Decision Center | 2000 | 2000 | 4096 | 16384 | 2 | Yes | 1G | 2G |
| Decision Runner | 500 | 4000 | 2048 | 2048 | 2 | Yes | 200Mi | 1G |
| Decision Server Runtime | 2000 | 2000 | 4096 | 4096 | 6 | Yes | 200Mi | 1G |
| Decision Server Console | 500 | 2000 | 512 | 4096 | 1 | No | 200Mi | 1G |
Note: Operational Decision Manager also creates an
odm-oidc-job-registration job that requests 200m CPU and 256Mi Memory. The pod is created at the
beginning of the installation and does not last long.
To achieve high availability, you must adapt the cluster configuration and physical resources. You can set up a Db2 High Availability Disaster Recovery (HADR) database. For more information, see Preparing your environment for disaster recovery. For high availability and fault tolerance to be effective, set the number of replicas that you need for the respective configuration parameters in your custom resource file. The operator then manages the scaling.
| Component | CPU Request (m) | CPU Limit (m) | Memory Request (Mi) | Memory Limit (Mi) | Number of replicas | Pods are licensed for production/nonproduction |
|---|---|---|---|---|---|---|
| Workflow Process Service Authoring | 2000 | 4000 | 1752 | 3072 | 2 | No |