index.md

Search Service

This service provides endpoints to enable searching in elasticsearch.

• /search/v1.0/index Gets available indexes (filtered by given pattern).
• /search/v1.0/criteria Gets possible filter-criteria within the given index (properties enabled in elasticsearch-mapping), including datatype and possible operators.
• /search/v1.0/resultproperties Gets possible result-properties.
• /search/v1.0 Execute search.

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NOTE on Operators

The following operators are supported:

• EQ look up for '<value>'
• LIKE look up for '*<value>*'
• NOT look up for values not matching '<value>'
• GT look up for values greater than '<value>'
• GTE look up for values greater than or equal to '<value>'
• LT look up for values less than '<value>'
• LTE look up for values less than or equal to '<value>'
• BETWEEN look up for values between '<lowerBound>' and '<upperBound>' (where the bounds are included)

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If multiple Filters are provided, same-property-filters are combined by 'OR', varying properties are combined by 'AND'. Example:

  "filter": [
    {
      "operator": "EQ",
      "property": "metadata.project.projectId",
      "value": "sdk"
    },
    {
      "operator": "EQ",
      "property": "metadata.project.projectId",
      "value": "sdk2"
    },
    {
      "operator": "EQ",
      "property": "metadata.customer.customerId",
      "value": "efs"
    }
  ],

will be interpreted as

"metadata.customer.customerId:efs AND metadata.project.projectId:( sdk OR sdk2 )"

Deployment

For deployment push the service to Azure DevOps, the pipeline will start automatically for development and master branches. For feature branches, please start it manually. The deployment manifest is azure-pipeline.yml.

Build/Deployment steps:

• Build and Push: an image is built using the Dockerfile and pushed to the corresponding ACR (SDK or AICloud).
• Deployment: kubernetes manifests are deployed to the corresponding AKS (SDK or AICloud):
  • config-map.yml writes the spring boot configuration application.yml as a config map
  • rbac.yml gives permission for backend namespace
  • deployment.yml yields the k8 deployment "search", i.e. describes the desired state for Pods and ReplicaSets
  • service.yml yields the corresponding k8 service "search-service", i.e. an abstract way to expose an application running on a set of Pods as a network service.
  • ingress.yml yields the ingress "search" to the service, i.e. manages external http access to the service in the cluster via the public IP https://efs-aicloud.westeurope.cloudapp.azure.com/sdk-frontend/

Service connections

For setting up the pipelines, the following service connections are needed in Azure Devops -> Project Settings:

Docker Registry service connection

• for SDK tenant: sc-efs-sdk-acrsdk (type: Azure Container Registry)

• for AICloud tenant: sc-efs-sdk-acraicloud (type: others)

  • docker registry: https://acraicloud.azurecr.io/
  • docker id: acraicloud
  • docker password: obtained from portal -> ACR -> access keys -> enable admin user -> copy password

Kubernetes service connection

• for SDK tenant: sc-efs-sdk-aks-sdk_devops
• for AICloud tenant: sc-efs-sdk-aks-aicloud_devops

Both are of type Service Account and have the following parameters

• server url: obtained (as described in Azure DevOps) from

  kubectl config view --minify -o jsonpath={.clusters[0].cluster.server}

• secret: obtained from

  kubectl get serviceAccounts <service-account-name> -n <namespace> -o=jsonpath={.secrets[*].name}

  where namespace is default and the service account is e.g. appreg-aicloud-aks-main.

---

Pipeline Variables

the following pipeline variables are required:

name	example
dockerRegistryServiceConnection	sc-efs-sdk-acraicloud
kubernetesServiceConnection	sc-efs-sdk-aks-aicloud_devops
environment	aicloud
elasticsearch-service	elasticsearch-opendistro-es-client-service

The container registry service connection is established during pipeline creation.

TODO

Currently, the documentation is located in usual files like README.md, CHANGELOG.md, CONTRIBUTING.md and LICENSE.md inside the root folder of the repository. That folder is not processed by MkDocs. To build the technical documentation for MkDocs we could follow these steps:

• Move the documentation to Markdown files inside the docs folder.
• Build a proper folder/file structure in docs and update the navigation in mkdocs.yaml.
• Keep the usual files like README.md, CHANGELOG.md, CONTRIBUTING.md and LICENSE.md inside the root folder of the repository (developers expect them to be there, especially in open source projects), but keep them short/generic and just refer to the documentation in the docs folder.