sdc - SDC Parent Project Catalog FE and BE

Age	Commit message (Expand)	Author	Files	Lines
2017-08-29	Set up static analysis for on-boarding	vempo	1	-0/+42

.. This work is licensed under a Creative Commons Attribution 4.0 International License. .. http://creativecommons.org/licenses/by/4.0 .. Copyright 2018 Amdocs, Bell Canada .. Links .. _Helm: https://docs.helm.sh/ .. _Helm Charts: https://github.com/kubernetes/charts .. _Kubernetes: https://Kubernetes.io/ .. _Docker: https://www.docker.com/ .. _Nexus: https://nexus.onap.org/#welcome .. _AWS Elastic Block Store: https://aws.amazon.com/ebs/ .. _Azure File: https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction .. _GCE Persistent Disk: https://cloud.google.com/compute/docs/disks/ .. _Gluster FS: https://www.gluster.org/ .. _Kubernetes Storage Class: https://Kubernetes.io/docs/concepts/storage/storage-classes/ .. _Assigning Pods to Nodes: https://Kubernetes.io/docs/concepts/configuration/assign-pod-node/ .. _developer-guide-label: OOM Developer Guide ################### .. figure:: oomLogoV2-medium.png :align: right ONAP consists of a large number of components, each of which are substantial projects within themselves, which results in a high degree of complexity in deployment and management. To cope with this complexity the ONAP Operations Manager (OOM) uses a Helm_ model of ONAP - Helm being the primary management system for Kubernetes_ container systems - to drive all user driven life-cycle management operations. The Helm model of ONAP is composed of a set of hierarchical Helm charts that define the structure of the ONAP components and the configuration of these components. These charts are fully parameterized such that a single environment file defines all of the parameters needed to deploy ONAP. A user of ONAP may maintain several such environment files to control the deployment of ONAP in multiple environments such as development, pre-production, and production. The following sections describe how the ONAP Helm charts are constructed. .. contents:: :depth: 3 :local: .. Container Background ==================== Linux containers allow for an application and all of its operating system dependencies to be packaged and deployed as a single unit without including a guest operating system as done with virtual machines. The most popular container solution is Docker_ which provides tools for container management like the Docker Host (dockerd) which can create, run, stop, move, or delete a container. Docker has a very popular registry of containers images that can be used by any Docker system; however, in the ONAP context, Docker images are built by the standard CI/CD flow and stored in Nexus_ repositories. OOM uses the "standard" ONAP docker containers and three new ones specifically created for OOM. Containers are isolated from each other primarily via name spaces within the Linux kernel without the need for multiple guest operating systems. As such, multiple containers can be deployed with little overhead such as all of ONAP can be deployed on a single host. With some optimization of the ONAP components (e.g. elimination of redundant database instances) it may be possible to deploy ONAP on a single laptop computer. Helm Charts =========== A Helm chart is a collection of files that describe a related set of Kubernetes resources. A simple chart might be used to deploy something simple, like a memcached pod, while a complex chart might contain many micro-service arranged in a hierarchy as found in the `aai` ONAP component. Charts are created as files laid out in a particular directory tree, then they can be packaged into versioned archives to be deployed. There is a public archive of `Helm Charts`_ on GitHub that includes many technologies applicable to ONAP. Some of these charts have been used in ONAP and all of the ONAP charts have been created following the guidelines provided. The top level of the ONAP charts is shown below: .. graphviz:: digraph onap_top_chart { rankdir="LR"; { node [shape=folder] oValues [label="values.yaml"] oChart [label="Chart.yaml"] dev [label="dev.yaml"] prod [label="prod.yaml"] crb [label="clusterrolebindings.yaml"] secrets [label="secrets.yaml"] } { node [style=dashed] vCom [label="component"] } onap -> oValues onap -> oChart onap -> templates onap -> resources oValues -> vCom resources -> environments environments -> dev environments -> prod templates -> crb templates -> secrets } Within the `values.yaml` file at the `onap` level, one will find a set of boolean values that control which of the ONAP components get deployed as shown below: .. code-block:: yaml aaf: # Application Authorization Framework enabled: false <...> so: # Service Orchestrator enabled: true By setting these flags a custom deployment can be created and used during deployment by using the `-f` Helm option as follows:: > helm install local/onap -name development -f dev.yaml Note that there are one or more example deployment files in the `onap/resources/environments/` directory. It is best practice to create a unique deployment file for each environment used to ensure consistent behaviour. To aid in the long term supportability of ONAP, a set of common charts have been created (and will be expanded in subsequent releases of ONAP) that can be used by any of the ONAP components by including the common component in its `requirements.yaml` file. The common components are arranged as follows: .. graphviz:: digraph onap_common_chart { rankdir="LR"; { node [shape=folder] mValues [label="values.yaml"] ccValues [label="values.yaml"] comValues [label="values.yaml"] comChart [label="Chart.yaml"] ccChart [label="Chart.yaml"] mChart [label="Chart.yaml"] mReq [label="requirements.yaml"] mService [label="service.yaml"] mMap [label="configmap.yaml"] ccName [label="_name.tpl"] ccNS [label="_namespace.tpl"] } { cCom [label="common"] mTemp [label="templates"] ccTemp [label="templates"] } { more [label="...",style=dashed] } common -> comValues common -> comChart common -> cCom common -> mysql common -> more cCom -> ccChart cCom -> ccValues cCom -> ccTemp ccTemp -> ccName ccTemp -> ccNS mysql -> mValues mysql -> mChart mysql -> mReq mysql -> mTemp mTemp -> mService mTemp -> mMap } The common section of charts consists of a set of templates that assist with parameter substitution (`_name.tpl` and `_namespace.tpl`) and a set of charts for components used throughout ONAP. Initially `mysql` is in the common area but this will expand to include other databases like `mariadb-galera`, `postgres`, and `cassandra`. Other candidates for common components include `redis` and `kafka`. When the common components are used by other charts they are instantiated each time. In subsequent ONAP releases some of the common components could be a setup as services that are used by multiple ONAP components thus minimizing the deployment and operational costs. All of the ONAP components have charts that follow the pattern shown below: .. graphviz:: digraph onap_component_chart { rankdir="LR"; { node [shape=folder] cValues [label="values.yaml"] cChart [label="Chart.yaml"] cService [label="service.yaml"] cMap [label="configmap.yaml"] cFiles [label="config file(s)"] } { cCharts [label="charts"] cTemp [label="templates"] cRes [label="resources"] } { sCom [label="component",style=dashed] } component -> cValues component -> cChart component -> cCharts component -> cTemp component -> cRes cTemp -> cService cTemp -> cMap cRes -> config config -> cFiles cCharts -> sCom } Note that the component charts may include a hierarchy of components and in themselves can be quite complex. Configuration of the components varies somewhat from component to component but generally follows the pattern of one or more `configmap.yaml` files which can directly provide configuration to the containers in addition to processing configuration files stored in the `config` directory. It is the responsibility of each ONAP component team to update these configuration files when changes are made to the project containers that impact configuration. The following section describes how the hierarchical ONAP configuration system is key to management of such a large system. Configuration Management ======================== ONAP is a large system composed of many components - each of which are complex systems in themselves - that needs to be deployed in a number of different ways. For example, within a single operator's network there may be R&D deployments under active development, pre-production versions undergoing system testing and production systems that are operating live networks. Each of these deployments will differ in significant ways, such as the version of the software images deployed. In addition, there may be a number of application specific configuration differences, such as operating system environment variables. The following describes how the Helm configuration management system is used within the OOM project to manage both ONAP infrastructure configuration as well as ONAP components configuration. One of the artifacts that OOM/Kubernetes uses to deploy ONAP components is the deployment specification, yet another yaml file. Within these deployment specs are a number of parameters as shown in the following mariadb example: .. code-block:: yaml apiVersion: extensions/v1beta1 kind: Deployment metadata: name: mariadb spec: <...> template: <...> spec: hostname: mariadb containers: - args: image: nexus3.onap.org:10001/mariadb:10.1.11 name: "mariadb" env: - name: MYSQL_ROOT_PASSWORD value: password - name: MARIADB_MAJOR value: "10.1" <...> imagePullSecrets: - name: onap-docker-registry-key Note that within the deployment specification, one of the container arguments is the key/value pair image: nexus3.onap.org:10001/mariadb:10.1.11 which specifies the version of the mariadb software to deploy. Although the deployment specifications greatly simplify deployment, maintenance of the deployment specifications themselves become problematic as software versions change over time or as different versions are required for different deployments. For example, if the R&D team needs to deploy a newer version of mariadb than what is currently used in the production environment, they would need to clone the deployment specification and change this value. Fortunately, this problem has been solved with the templating capabilities of Helm. The following example shows how the deployment specifications are modified to incorporate Helm templates such that key/value pairs can be defined outside of the deployment specifications and passed during instantiation of the component. .. code-block:: yaml apiVersion: extensions/v1beta1 kind: Deployment metadata: name: mariadb namespace: "{{ .Values.nsPrefix }}-mso" spec: <...> template: <...> spec: hostname: mariadb containers: - args: image: {{ .Values.image.mariadb }} imagePullPolicy: {{ .Values.pullPolicy }} name: "mariadb" env: - name: MYSQL_ROOT_PASSWORD value: password - name: MARIADB_MAJOR value: "10.1" <...> imagePullSecrets: - name: "{{ .Values.nsPrefix }}-docker-registry-key"apiVersion: extensions/v1beta1 kind: Deployment metadata: name: mariadb namespace: "{{ .Values.nsPrefix }}-mso" spec: <...> template: <...> spec: hostname: mariadb containers: - args: image: {{ .Values.image.mariadb }} imagePullPolicy: {{ .Values.pullPolicy }} name: "mariadb" env: - name: MYSQL_ROOT_PASSWORD value: password - name: MARIADB_MAJOR value: "10.1" <...> imagePullSecrets: - name: "{{ .Values.nsPrefix }}-docker-registry-key" This version of the deployment specification has gone through the process of templating values that are likely to change between deployments. Note that the image is now specified as: image: {{ .Values.image.mariadb }} instead of a string used previously. During the deployment phase, Helm (actually the Helm sub-component Tiller) substitutes the {{ .. }} entries with a variable defined in a values.yaml file. The content of this file is as follows: .. code-block:: yaml nsPrefix: onap pullPolicy: IfNotPresent image: readiness: oomk8s/readiness-check:1.0.0 mso: nexus3.onap.org:10001/openecomp/mso:1.0-STAGING-latest mariadb: nexus3.onap.org:10001/mariadb:10.1.11 Within the values.yaml file there is an image section with the key/value pair mariadb: nexus3.onap.org:10001/mariadb:10.1.11 which is the same value used in the non-templated version. Once all of the substitutions are complete, the resulting deployment specification ready to be used by Kubernetes. Also note that in this example, the namespace key/value pair is specified in the values.yaml file. This key/value pair will be global across the entire ONAP deployment and is therefore a prime example of where configuration hierarchy can be very useful. When creating a deployment template consider the use of default values if appropriate. Helm templating has built in support for DEFAULT values, here is an example: .. code-block:: yaml imagePullSecrets: - name: "{{ .Values.nsPrefix | default "onap" }}-docker-registry-key" The pipeline operator ("|") used here hints at that power of Helm templates in that much like an operating system command line the pipeline operator allow over 60 Helm functions to be embedded directly into the template (note that the Helm template language is a superset of the Go template language). These functions include simple string operations like upper and more complex flow control operations like if/else. ONAP Application Configuration ------------------------------ Dependency Management --------------------- These Helm charts describe the desired state of an ONAP deployment and instruct the Kubernetes container manager as to how to maintain the deployment in this state. These dependencies dictate the order in-which the containers are started for the first time such that such dependencies are always met without arbitrary sleep times between container startups. For example, the SDC back-end container requires the Elastic-Search, Cassandra and Kibana containers within SDC to be ready and is also dependent on DMaaP (or the message-router) to be ready - where ready implies the built-in "readiness" probes succeeded - before becoming fully operational. When an initial deployment of ONAP is requested the current state of the system is NULL so ONAP is deployed by the Kubernetes manager as a set of Docker containers on one or more predetermined hosts. The hosts could be physical machines or virtual machines. When deploying on virtual machines the resulting system will be very similar to "Heat" based deployments, i.e. Docker containers running within a set of VMs, the primary difference being that the allocation of containers to VMs is done dynamically with OOM and statically with "Heat". Example SO deployment descriptor file shows SO's dependency on its mariadb data-base component: SO deployment specification excerpt: .. code-block:: yaml apiVersion: extensions/v1beta1 kind: Deployment metadata: name: {{ include "common.name" . }} namespace: {{ include "common.namespace" . }} labels: app: {{ include "common.name" . }} chart: {{ .Chart.Name }}-{{ .Chart.Version | replace "+" "_" }} release: {{ .Release.Name }} heritage: {{ .Release.Service }} spec: replicas: {{ .Values.replicaCount }} template: metadata: labels: app: {{ include "common.name" . }} release: {{ .Release.Name }} spec: initContainers: - command: - /root/ready.py args: - --container-name - so-mariadb env: ... Kubernetes Container Orchestration ================================== The ONAP components are managed by the Kubernetes_ container management system which maintains the desired state of the container system as described by one or more deployment descriptors - similar in concept to OpenStack HEAT Orchestration Templates. The following sections describe the fundamental objects managed by Kubernetes, the network these components use to communicate with each other and other entities outside of ONAP and the templates that describe the configuration and desired state of the ONAP components. Name Spaces ----------- Within the namespaces are Kubernetes services that provide external connectivity to pods that host Docker containers. ONAP Components to Kubernetes Object Relationships -------------------------------------------------- Kubernetes deployments consist of multiple objects: - **nodes** - a worker machine - either physical or virtual - that hosts multiple containers managed by Kubernetes. - **services** - an abstraction of a logical set of pods that provide a micro-service. - **pods** - one or more (but typically one) container(s) that provide specific application functionality. - **persistent volumes** - One or more permanent volumes need to be established to hold non-ephemeral configuration and state data. The relationship between these objects is shown in the following figure: .. .. uml:: .. .. @startuml .. node PH { .. component Service { .. component Pod0 .. component Pod1 .. } .. } .. .. database PV .. @enduml .. figure:: kubernetes_objects.png OOM uses these Kubernetes objects as described in the following sections. Nodes ~~~~~ OOM works with both physical and virtual worker machines. * Virtual Machine Deployments - If ONAP is to be deployed onto a set of virtual machines, the creation of the VMs is outside of the scope of OOM and could be done in many ways, such as * manually, for example by a user using the OpenStack Horizon dashboard or AWS EC2, or * automatically, for example with the use of a OpenStack Heat Orchestration Template which builds an ONAP stack, Azure ARM template, AWS CloudFormation Template, or * orchestrated, for example with Cloudify creating the VMs from a TOSCA template and controlling their life cycle for the life of the ONAP deployment. * Physical Machine Deployments - If ONAP is to be deployed onto physical machines there are several options but the recommendation is to use Rancher along with Helm to associate hosts with a Kubernetes cluster. Pods ~~~~ A group of containers with shared storage and networking can be grouped together into a Kubernetes pod. All of the containers within a pod are co-located and co-scheduled so they operate as a single unit. Within ONAP Amsterdam release, pods are mapped one-to-one to docker containers although this may change in the future. As explained in the Services section below the use of Pods within each ONAP component is abstracted from other ONAP components. Services ~~~~~~~~ OOM uses the Kubernetes service abstraction to provide a consistent access point for each of the ONAP components independent of the pod or container architecture of that component. For example, the SDNC component may introduce OpenDaylight clustering as some point and change the number of pods in this component to three or more but this change will be isolated from the other ONAP components by the service abstraction. A service can include a load balancer on its ingress to distribute traffic between the pods and even react to dynamic changes in the number of pods if they are part of a replica set. Persistent Volumes ~~~~~~~~~~~~~~~~~~ To enable ONAP to be deployed into a wide variety of cloud infrastructures a flexible persistent storage architecture, built on Kubernetes persistent volumes, provides the ability to define the physical storage in a central location and have all ONAP components securely store their data. When deploying ONAP into a public cloud, available storage services such as `AWS Elastic Block Store`_, `Azure File`_, or `GCE Persistent Disk`_ are options. Alternatively, when deploying into a private cloud the storage architecture might consist of Fiber Channel, `Gluster FS`_, or iSCSI. Many other storage options existing, refer to the `Kubernetes Storage Class`_ documentation for a full list of the options. The storage architecture may vary from deployment to deployment but in all cases a reliable, redundant storage system must be provided to ONAP with which the state information of all ONAP components will be securely stored. The Storage Class for a given deployment is a single parameter listed in the ONAP values.yaml file and therefore is easily customized. Operation of this storage system is outside the scope of the OOM. .. code-block:: yaml Insert values.yaml code block with storage block here Once the storage class is selected and the physical storage is provided, the ONAP deployment step creates a pool of persistent volumes within the given physical storage that is used by all of the ONAP components. ONAP components simply make a claim on these persistent volumes (PV), with a persistent volume claim (PVC), to gain access to their storage. The following figure illustrates the relationships between the persistent volume claims, the persistent volumes, the storage class, and the physical storage. .. graphviz:: digraph PV { label = "Persistance Volume Claim to Physical Storage Mapping" { node [shape=cylinder] D0 [label="Drive0"] D1 [label="Drive1"] Dx [label="Drivex"] } { node [shape=Mrecord label="StorageClass:ceph"] sc } { node [shape=point] p0 p1 p2 p3 p4 p5 } subgraph clusterSDC { label="SDC" PVC0 PVC1 } subgraph clusterSDNC { label="SDNC" PVC2 } subgraph clusterSO { label="SO" PVCn } PV0 -> sc PV1 -> sc PV2 -> sc PVn -> sc sc -> {D0 D1 Dx} PVC0 -> PV0 PVC1 -> PV1 PVC2 -> PV2 PVCn -> PVn # force all of these nodes to the same line in the given order subgraph { rank = same; PV0;PV1;PV2;PVn;p0;p1;p2 PV0->PV1->PV2->p0->p1->p2->PVn [style=invis] } subgraph { rank = same; D0;D1;Dx;p3;p4;p5 D0->D1->p3->p4->p5->Dx [style=invis] } } In-order for an ONAP component to use a persistent volume it must make a claim against a specific persistent volume defined in the ONAP common charts. Note that there is a one-to-one relationship between a PVC and PV. The following is an excerpt from a component chart that defines a PVC: .. code-block:: yaml Insert PVC example here OOM Networking with Kubernetes ------------------------------ - DNS - Ports - Flattening the containers also expose port conflicts between the containers which need to be resolved. Node Ports ~~~~~~~~~~ Pod Placement Rules ------------------- OOM will use the rich set of Kubernetes node and pod affinity / anti-affinity rules to minimize the chance of a single failure resulting in a loss of ONAP service. Node affinity / anti-affinity is used to guide the Kubernetes orchestrator in the placement of pods on nodes (physical or virtual machines). For example: - if a container used Intel DPDK technology the pod may state that it as affinity to an Intel processor based node, or - geographical based node labels (such as the Kubernetes standard zone or region labels) may be used to ensure placement of a DCAE complex close to the VNFs generating high volumes of traffic thus minimizing networking cost. Specifically, if nodes were pre-assigned labels East and West, the pod deployment spec to distribute pods to these nodes would be: .. code-block:: yaml nodeSelector: failure-domain.beta.Kubernetes.io/region: {{ .Values.location }} - "location: West" is specified in the `values.yaml` file used to deploy one DCAE cluster and "location: East" is specified in a second `values.yaml` file (see OOM Configuration Management for more information about configuration files like the `values.yaml` file). Node affinity can also be used to achieve geographic redundancy if pods are assigned to multiple failure domains. For more information refer to `Assigning Pods to Nodes`_. .. note:: One could use Pod to Node assignment to totally constrain Kubernetes when doing initial container assignment to replicate the Amsterdam release OpenStack Heat based deployment. Should one wish to do this, each VM would need a unique node name which would be used to specify a node constaint for every component. These assignment could be specified in an environment specific values.yaml file. Constraining Kubernetes in this way is not recommended. Kubernetes has a comprehensive system called Taints and Tolerations that can be used to force the container orchestrator to repel pods from nodes based on static events (an administrator assigning a taint to a node) or dynamic events (such as a node becoming unreachable or running out of disk space). There are no plans to use taints or tolerations in the ONAP Beijing release. Pod affinity / anti-affinity is the concept of creating a spacial relationship between pods when the Kubernetes orchestrator does assignment (both initially an in operation) to nodes as explained in Inter-pod affinity and anti-affinity. For example, one might choose to co-located all of the ONAP SDC containers on a single node as they are not critical runtime components and co-location minimizes overhead. On the other hand, one might choose to ensure that all of the containers in an ODL cluster (SDNC and APPC) are placed on separate nodes such that a node failure has minimal impact to the operation of the cluster. An example of how pod affinity / anti-affinity is shown below: Pod Affinity / Anti-Affinity .. code-block:: yaml apiVersion: v1 kind: Pod metadata: name: with-pod-affinity spec: affinity: podAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: security operator: In values: - S1 topologyKey: failure-domain.beta.Kubernetes.io/zone podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 100 podAffinityTerm: labelSelector: matchExpressions: - key: security operator: In values: - S2 topologyKey: Kubernetes.io/hostname containers: - name: with-pod-affinity image: gcr.io/google_containers/pause:2.0 This example contains both podAffinity and podAntiAffinity rules, the first rule is is a must (requiredDuringSchedulingIgnoredDuringExecution) while the second will be met pending other considerations (preferredDuringSchedulingIgnoredDuringExecution). Preemption Another feature that may assist in achieving a repeatable deployment in the presence of faults that may have reduced the capacity of the cloud is assigning priority to the containers such that mission critical components have the ability to evict less critical components. Kubernetes provides this capability with Pod Priority and Preemption. Prior to having more advanced production grade features available, the ability to at least be able to re-deploy ONAP (or a subset of) reliably provides a level of confidence that should an outage occur the system can be brought back on-line predictably. Health Checks ------------- Monitoring of ONAP components is configured in the agents within JSON files and stored in gerrit under the consul-agent-config, here is an example from the AAI model loader (aai-model-loader-health.json): .. code-block:: json { "service": { "name": "A&AI Model Loader", "checks": [ { "id": "model-loader-process", "name": "Model Loader Presence", "script": "/consul/config/scripts/model-loader-script.sh", "interval": "15s", "timeout": "1s" } ] } } Liveness Probes --------------- These liveness probes can simply check that a port is available, that a built-in health check is reporting good health, or that the Consul health check is positive. For example, to monitor the SDNC component has following liveness probe can be found in the SDNC DB deployment specification: .. code-block:: yaml sdnc db liveness probe livenessProbe: exec: command: ["mysqladmin", "ping"] initialDelaySeconds: 30 periodSeconds: 10 timeoutSeconds: 5 The 'initialDelaySeconds' control the period of time between the readiness probe succeeding and the liveness probe starting. 'periodSeconds' and 'timeoutSeconds' control the actual operation of the probe. Note that containers are inherently ephemeral so the healing action destroys failed containers and any state information within it. To avoid a loss of state, a persistent volume should be used to store all data that needs to be persisted over the re-creation of a container. Persistent volumes have been created for the database components of each of the projects and the same technique can be used for all persistent state information. Environment Files ~~~~~~~~~~~~~~~~~ MSB Integration =============== The \ `Microservices Bus Project <https://wiki.onap.org/pages/viewpage.action?pageId=3246982>`__ provides facilities to integrate micro-services into ONAP and therefore needs to integrate into OOM - primarily through Consul which is the backend of MSB service discovery. The following is a brief description of how this integration will be done: A registrator to push the service endpoint info to MSB service discovery. - The needed service endpoint info is put into the kubernetes yaml file as annotation, including service name, Protocol,version, visual range,LB method, IP, Port,etc. - OOM deploy/start/restart/scale in/scale out/upgrade ONAP components - Registrator watch the kubernetes event - When an ONAP component instance has been started/destroyed by OOM, Registrator get the notification from kubernetes - Registrator parse the service endpoint info from annotation and register/update/unregister it to MSB service discovery - MSB API Gateway uses the service endpoint info for service routing and load balancing. Details of the registration service API can be found at \ `Microservice Bus API Documentation <https://wiki.onap.org/display/DW/Microservice+Bus+API+Documentation>`__. ONAP Component Registration to MSB ---------------------------------- The charts of all ONAP components intending to register against MSB must have an annotation in their service(s) template. A `sdc` example follows: .. code-block:: yaml apiVersion: v1 kind: Service metadata: labels: app: sdc-be name: sdc-be namespace: "{{ .Values.nsPrefix }}" annotations: msb.onap.org/service-info: '[ { "serviceName": "sdc", "version": "v1", "url": "/sdc/v1", "protocol": "REST", "port": "8080", "visualRange":"1" }, { "serviceName": "sdc-deprecated", "version": "v1", "url": "/sdc/v1", "protocol": "REST", "port": "8080", "visualRange":"1", "path":"/sdc/v1" } ]' ... MSB Integration with OOM ------------------------ A preliminary view of the OOM-MSB integration is as follows: .. figure:: MSB-OOM-Diagram.png A message sequence chart of the registration process: .. uml:: participant "OOM" as oom participant "ONAP Component" as onap participant "Service Discovery" as sd participant "External API Gateway" as eagw participant "Router (Internal API Gateway)" as iagw box "MSB" #LightBlue participant sd participant eagw participant iagw end box == Deploy Servcie == oom -> onap: Deploy oom -> sd: Register service endpoints sd -> eagw: Services exposed to external system sd -> iagw: Services for internal use == Component Life-cycle Management == oom -> onap: Start/Stop/Scale/Migrate/Upgrade oom -> sd: Update service info sd -> eagw: Update service info sd -> iagw: Update service info == Service Health Check == sd -> onap: Check the health of service sd -> eagw: Update service status sd -> iagw: Update service status MSB Deployment Instructions --------------------------- MSB is helm installable ONAP component which is often automatically deployed. To install it individually enter:: > helm install <repo-name>/msb .. note:: TBD: Vaidate if the following procedure is still required. Please note that Kubernetes authentication token must be set at *kubernetes/kube2msb/values.yaml* so the kube2msb registrator can get the access to watch the kubernetes events and get service annotation by Kubernetes APIs. The token can be found in the kubectl configuration file *~/.kube/config* More details can be found here `MSB installation <http://onap.readthedocs.io/en/latest/submodules/msb/apigateway.git/docs/platform/installation.html>`__. .. MISC .. ==== .. Note that although OOM uses Kubernetes facilities to minimize the effort .. required of the ONAP component owners to implement a successful rolling upgrade .. strategy there are other considerations that must be taken into consideration. .. For example, external APIs - both internal and external to ONAP - should be .. designed to gracefully accept transactions from a peer at a different software .. version to avoid deadlock situations. Embedded version codes in messages may .. facilitate such capabilities. .. .. Within each of the projects a new configuration repository contains all of the .. project specific configuration artifacts. As changes are made within the .. project, it's the responsibility of the project team to make appropriate .. changes to the configuration data.