Serverless applications

Developers on OpenShift Serverless can use the provided Kubernetes-native APIs, as well as familiar languages and frameworks, to deploy applications and container workloads. For information about installing OpenShift Serverless, see Installing OpenShift Serverless.

OpenShift Serverless on OpenShift Container Platform enables stateful, stateless, and serverless workloads to all run on a single multi-cloud container platform with automated operations. Developers can use a single platform for hosting their microservices, legacy, and serverless applications.

OpenShift Serverless is based on the open source Knative project, which provides portability and consistency across hybrid and multi-cloud environments by enabling an enterprise-grade serverless platform.

Applications are created using Custom Resource Definitions (CRDs) and associated controllers in Kubernetes, and are packaged as OCI compliant Linux containers that can be run anywhere.

To deploy applications in OpenShift Serverless, you must create Knative Services. For more information see Getting started with Knative Services.

Knative Serving on OpenShift Container Platform builds on Kubernetes and Istio to support deploying and serving serverless applications.

It creates a set of Kubernetes Custom Resource Definitions (CRDs) that are used to define and control the behavior of serverless workloads on an OpenShift Container Platform cluster.

These CRDs can be used as building blocks to address complex use cases, such as rapid deployment of serverless containers, automatic scaling of Pods, routing and network programming for Istio components, or viewing point-in-time snapshots of deployed code and configurations.

The Knative Client (kn) extends the functionality of the oc or kubectl tools to enable interaction with Knative components on OpenShift Container Platform. kn allows developers to deploy and manage applications without editing YAML files directly.

A developer preview version of Knative Eventing is available for use with OpenShift Serverless. However, this is not included in the OpenShift Serverless Operator and is not currently supported as part of this Technology Preview. For more information about Knative Eventing, including installation instructions and samples, see the Knative Eventing on OpenShift Container Platform documentation.

The cluster must be sized appropriately to ensure that OpenShift Serverless can run correctly. You can use the MachineSet API to manually scale your cluster up to the desired size.

An OpenShift cluster with 10 CPUs and 40 GB memory is the minimum requirement for getting started with your first serverless application. This usually means you must scale up one of the default MachineSets by two additional machines.

For more advanced use-cases, such as using OpenShift logging, monitoring, metering, and tracing, you must deploy more resources. Recommended requirements for such use-cases are 24 vCPUs and 96GB of memory.

The OpenShift Serverless Operator can be installed using the OpenShift Container Platform instructions for installing Operators.

You can install the OpenShift Serverless Operator in the host cluster by following the OpenShift Container Platform instructions on installing an Operator.

For details, see the OpenShift Container Platform documentation on adding Operators to a cluster.

You must create a KnativeServing object to install Knative Serving using the OpenShift Serverless Operator.

apiVersion: v1
kind: Namespace
metadata:
 name: knative-serving
---
apiVersion: operator.knative.dev/v1alpha1
kind: KnativeServing
metadata:
 name: knative-serving
 namespace: knative-serving

To uninstall Knative Serving, you must remove its custom resource and delete the knative-serving namespace.

You can remove the OpenShift Serverless Operator from the host cluster by following the OpenShift Container Platform instructions on deleting an Operator.

For details, see the OpenShift Container Platform documentation on deleting Operators from a cluster.

After uninstalling the OpenShift Serverless Operator, the Operator CRDs and API services remain on the cluster. Use this procedure to completely uninstall the remaining components.

To create a service, you must create the service.yaml file.

You can copy the sample below. This sample will create a sample golang application called helloworld-go and allows you to specify the image for that application.

apiVersion: serving.knative.dev/v1alpha1 1
kind: Service
metadata:
  name: helloworld-go 2
  namespace: default 3
spec:
  template:
    spec:
      containers:
        - image: gcr.io/knative-samples/helloworld-go 4
          env:
            - name: TARGET 5
              value: "Go Sample v1"

To deploy a serverless application, you must apply the service.yaml file.

Now that service has been created and the application has been deployed, Knative will create a new immutable revision for this version of the application.

Knative will also perform network programming to create a route, ingress, service, and load balancer for your application, and will automatically scale your pods up and down based on traffic, including inactive pods.

Knative Services can call a Kubernetes deployment in any namespace, provided that there are no existing additional network barriers.

A Kubernetes deployment can call a Knative Service if:

Before application developers can monitor their applications, the human operator of the cluster needs to configure the cluster accordingly. This procedure shows how to.

Manual configuration for monitoring by an administrator is not required, but you can carry out these steps to verify that monitoring is installed correctly.

This section provides example instructions for the visualization of Knative Serving Pod autoscaling metrics by using the OpenShift Container Platform monitoring tools.

For information about installing metering on OpenShift Container Platform, see Installing Metering.

The following ReportDataSources are examples of how Knative Serving can be used with OpenShift Container Platform metering.

apiVersion: metering.openshift.io/v1
kind: ReportDataSource
metadata:
  name: knative-service-cpu-usage
spec:
  prometheusMetricsImporter:
    query: >
      sum
          by(namespace,
             label_serving_knative_dev_service,
             label_serving_knative_dev_revision)
          (
            label_replace(rate(container_cpu_usage_seconds_total{container_name!="POD",container_name!="",pod_name!=""}[1m]), "pod", "$1", "pod_name", "(.*)")
            *
            on(pod, namespace)
            group_left(label_serving_knative_dev_service, label_serving_knative_dev_revision)
            kube_pod_labels{label_serving_knative_dev_service!=""}
          )

apiVersion: metering.openshift.io/v1
kind: ReportDataSource
metadata:
  name: knative-service-memory-usage
spec:
  prometheusMetricsImporter:
    query: >
      sum
          by(namespace,
             label_serving_knative_dev_service,
             label_serving_knative_dev_revision)
          (
            label_replace(container_memory_usage_bytes{container_name!="POD", container_name!="",pod_name!=""}, "pod", "$1", "pod_name", "(.*)")
            *
            on(pod, namespace)
            group_left(label_serving_knative_dev_service, label_serving_knative_dev_revision)
            kube_pod_labels{label_serving_knative_dev_service!=""}
          )

$ oc apply -f <datasource-name>.yaml

$ oc apply -f knative-service-memory-usage.yaml

The following ReportQuery resources reference the example DataSources provided.

apiVersion: metering.openshift.io/v1
kind: ReportQuery
metadata:
  name: knative-service-cpu-usage
spec:
  inputs:
  - name: ReportingStart
    type: time
  - name: ReportingEnd
    type: time
  - default: knative-service-cpu-usage
    name: KnativeServiceCpuUsageDataSource
    type: ReportDataSource
  columns:
  - name: period_start
    type: timestamp
    unit: date
  - name: period_end
    type: timestamp
    unit: date
  - name: namespace
    type: varchar
    unit: kubernetes_namespace
  - name: service
    type: varchar
  - name: data_start
    type: timestamp
    unit: date
  - name: data_end
    type: timestamp
    unit: date
  - name: service_cpu_seconds
    type: double
    unit: cpu_core_seconds
  query: |
    SELECT
      timestamp '{| default .Report.ReportingStart .Report.Inputs.ReportingStart| prestoTimestamp |}' AS period_start,
      timestamp '{| default .Report.ReportingEnd .Report.Inputs.ReportingEnd | prestoTimestamp |}' AS period_end,
      labels['namespace'] as project,
      labels['label_serving_knative_dev_service'] as service,
      min("timestamp") as data_start,
      max("timestamp") as data_end,
      sum(amount * "timeprecision") AS service_cpu_seconds
    FROM {| dataSourceTableName .Report.Inputs.KnativeServiceCpuUsageDataSource |}
    WHERE "timestamp" >= timestamp '{| default .Report.ReportingStart .Report.Inputs.ReportingStart | prestoTimestamp |}'
    AND "timestamp" < timestamp '{| default .Report.ReportingEnd .Report.Inputs.ReportingEnd | prestoTimestamp |}'
    GROUP BY labels['namespace'],labels['label_serving_knative_dev_service']

apiVersion: metering.openshift.io/v1
kind: ReportQuery
metadata:
  name: knative-service-memory-usage
spec:
  inputs:
  - name: ReportingStart
    type: time
  - name: ReportingEnd
    type: time
  - default: knative-service-memory-usage
    name: KnativeServiceMemoryUsageDataSource
    type: ReportDataSource
  columns:
  - name: period_start
    type: timestamp
    unit: date
  - name: period_end
    type: timestamp
    unit: date
  - name: namespace
    type: varchar
    unit: kubernetes_namespace
  - name: service
    type: varchar
  - name: data_start
    type: timestamp
    unit: date
  - name: data_end
    type: timestamp
    unit: date
  - name: service_usage_memory_byte_seconds
    type: double
    unit: byte_seconds
  query: |
    SELECT
      timestamp '{| default .Report.ReportingStart .Report.Inputs.ReportingStart| prestoTimestamp |}' AS period_start,
      timestamp '{| default .Report.ReportingEnd .Report.Inputs.ReportingEnd | prestoTimestamp |}' AS period_end,
      labels['namespace'] as project,
      labels['label_serving_knative_dev_service'] as service,
      min("timestamp") as data_start,
      max("timestamp") as data_end,
      sum(amount * "timeprecision") AS service_usage_memory_byte_seconds
    FROM {| dataSourceTableName .Report.Inputs.KnativeServiceMemoryUsageDataSource |}
    WHERE "timestamp" >= timestamp '{| default .Report.ReportingStart .Report.Inputs.ReportingStart | prestoTimestamp |}'
    AND "timestamp" < timestamp '{| default .Report.ReportingEnd .Report.Inputs.ReportingEnd | prestoTimestamp |}'
    GROUP BY labels['namespace'],labels['label_serving_knative_dev_service']

$ oc apply -f <query-name>.yaml

$ oc apply -f knative-service-memory-usage.yaml

You can run metering reports against Knative Serving by creating Report resources. Before you run a report, you must modify the input parameter within the Report resource to specify the start and end dates of the reporting period.

apiVersion: metering.openshift.io/v1
kind: Report
metadata:
  name: knative-service-cpu-usage
spec:
  reportingStart: '2019-06-01T00:00:00Z' 1
  reportingEnd: '2019-06-30T23:59:59Z' 2
  query: knative-service-cpu-usage 3
runImmediately: true

$ oc apply -f <report-name>.yml

$ kubectl get report

NAME                        QUERY                       SCHEDULE   RUNNING    FAILED   LAST REPORT TIME       AGE
knative-service-cpu-usage   knative-service-cpu-usage              Finished            2019-06-30T23:59:59Z   10h

OpenShift Container Platform cluster administrators can deploy cluster logging using a few CLI commands and the OpenShift Container Platform web console to install the Elasticsearch Operator and Cluster Logging Operator. When the operators are installed, create a Cluster Logging Custom Resource (CR) to schedule cluster logging pods and other resources necessary to support cluster logging. The operators are responsible for deploying, upgrading, and maintaining cluster logging.

You can configure cluster logging by modifying the Cluster Logging Custom Resource (CR), named instance. The CR defines a complete cluster logging deployment that includes all the components of the logging stack to collect, store and visualize logs. The Cluster Logging Operator watches the ClusterLogging Custom Resource and adjusts the logging deployment accordingly.

Administrators and application developers can view the logs of the projects for which they have view access.

OpenShift Container Platform cluster logging is designed to be used with the default configuration, which is tuned for small to medium sized OpenShift Container Platform clusters.

The installation instructions that follow include a sample Cluster Logging Custom Resource (CR), which you can use to create a cluster logging instance and configure your cluster logging deployment.

If you want to use the default cluster logging install, you can use the sample CR directly.

If you want to customize your deployment, make changes to the sample CR as needed. The following describes the configurations you can make when installing your cluster logging instance or modify after installation. See the Configuring sections for more information on working with each component, including modifications you can make outside of the Cluster Logging Custom Resource.

spec:
  logStore:
    elasticsearch:
      resources:
        limits:
          cpu:
          memory:
        requests:
          cpu: 1
          memory: 16Gi
      type: "elasticsearch"
  collection:
    logs:
      fluentd:
        resources:
          limits:
            cpu:
            memory:
          requests:
            cpu:
            memory:
        type: "fluentd"
  visualization:
    kibana:
      resources:
        limits:
          cpu:
          memory:
        requests:
          cpu:
          memory:
     type: kibana
  curation:
    curator:
      resources:
        limits:
          memory: 200Mi
        requests:
          cpu: 200m
          memory: 200Mi
      type: "curator"

  spec:
    logStore:
      type: "elasticsearch"
      elasticsearch:
        nodeCount: 3
        storage:
          storageClassName: "gp2"
          size: "200G"

  spec:
    logStore:
      type: "elasticsearch"
      elasticsearch:
        nodeCount: 3
        storage: {}

  spec:
    curation:
    type: "curator"
    resources:
    curator:
      schedule: "30 3 * * *"

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
  namespace: "openshift-logging"
spec:
  managementState: "Managed"
  logStore:
    type: "elasticsearch"
    elasticsearch:
      nodeCount: 2
      resources:
        limits:
          memory: 2Gi
        requests:
          cpu: 200m
          memory: 2Gi
      storage: {}
      redundancyPolicy: "SingleRedundancy"
  visualization:
    type: "kibana"
    kibana:
      resources:
        limits:
          memory: 1Gi
        requests:
          cpu: 500m
          memory: 1Gi
      replicas: 1
  curation:
    type: "curator"
    curator:
      resources:
        limits:
          memory: 200Mi
        requests:
          cpu: 200m
          memory: 200Mi
      schedule: "*/5 * * * *"
  collection:
    logs:
      type: "fluentd"
      fluentd:
        resources:
          limits:
            memory: 1Gi
          requests:
            cpu: 200m
            memory: 1Gi

With OpenShift Cluster Logging, the logs that your applications write to the console are collected in Elasticsearch. The following procedure outlines how to apply these capabilities to applications deployed by using Knative Serving.

You can specify the number of concurrent requests that should be handled by each instance of an application (revision container) by adding the target annotation or the containerConcurrency field in the revision template.

Here is an example of target being used in a revision template:

apiVersion: serving.knative.dev/v1alpha1
kind: Service
metadata:
  name: myapp
spec:
  template:
    metadata:
      annotations:
        autoscaling.knative.dev/target: 50
    spec:
      containers:
      - image: myimage

Here is an example of containerConcurrency being used in a revision template:

apiVersion: serving.knative.dev/v1alpha1
kind: Service
metadata:
  name: myapp
spec:
  template:
    metadata:
      annotations:
    spec:
      containerConcurrency: 100
      containers:
      - image: myimage

Adding a value for both target and containerConcurrency will target the target number of concurrent requests, but impose a hard limit of the containerConcurrency number of requests.

For example, if the target value is 50 and the containerConcurrency value is 100, the targeted number of requests will be 50, but the hard limit will be 100.

If the containerConcurrency value is less than the target value, the target value will be tuned down, since there is no need to target more requests than the number that can actually be handled.

autoscaling.knative.dev/target: 50

containerConcurrency: 0 | 1 | 2-N

The minScale and maxScale annotations can be used to configure the minimum and maximum number of Pods that can serve applications. These annotations can be used to prevent cold starts or to help control computing costs.

minScale and maxScale can be configured as follows in the revision template:

spec:
  template:
    metadata:
      autoscaling.knative.dev/minScale: "2"
      autoscaling.knative.dev/maxScale: "10"

Using these annotations in the revision template will propagate this confguration to PodAutoscaler objects.

Knative Client does not have its own log in mechanism. To log in to the cluster you must install the oc CLI and use oc login.

Installation options for the oc CLI will vary depending on your operating system. For more information on installing the oc CLI for your operating system and logging in with oc, see the CLI getting started documentation.

Use this basic workflow to create, read, update, delete (CRUD) operations on a service. The following example deploys a simple Hello World service that reads the environment variable TARGET and prints its output.

You can access autoscaling capabilities by using kn to modify Knative services without editing YAML files directly.

Use the service create and service update commands with the appropriate flags to configure the autoscaling behavior.

kn helps you control which revisions get routed traffic on your Knative service.

Knative service allows for traffic mapping, which is the mapping of revisions of the service to an allocated portion of traffic. It offers the option to create unique URLs for particular revisions and has the ability to assign traffic to the latest revision.

With every update to the configuration of the service, a new revision is created with the service route pointing all the traffic to the latest ready revision by default.

You can change this behavior by defining which revision gets a portion of the traffic.

$ kn service update svc --traffic @latest=10 --traffic svc-vwxyz=90

If you experience difficulty with a procedure described in this documentation, visit the Customer Portal to learn more about support for Technology Preview features.

OpenShift Serverless is based on the open source Knative project.