关于google:在-Google-Kubernetes-Cluster-上使用-HANA-Expression-Database-Service

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咱们晓得,Cluster 是 Google Kubernetes Engine (简称 GKE) 的根底,代表容器化应用程序的 Kubernetes 对象都在集群之上运行。

Google Kubernetes Engine (GKE) 提供了一个托管环境,开发人员能够应用 Google 基础架构在 GKE 中部署、治理和扩缩容器化利用。GKE 环境包含多个 Compute Engine 实例,这些实例组合在一起就造成了 Google Kubernetes Cluster.

SAP HANA Expression 是 SAP HANA 的简化版本,旨在在笔记本电脑和其余主机(包含云托管的虚拟机)上运行,当然也就反对在本文刚刚形容的 Google Kubernetes Cluster 上运行。这个版本除了反对 SAP HANA 传统的内存数据库性能之外,还提供 bring-your-own-language 等多种技术栈,反对微服务、预测剖析和机器学习算法,以及用于构建洞察驱动应用程序的天文空间解决等个性。

本文将具体介绍如何在 Google Kubernetes Cluster 上部署并应用 HANA Expression Database Service.

在 Google Cloud Platform 上创立 Google Kubernetes Cluster 实例

登录 Google Cloud Platform 控制台:

点击控制台左上角的 Hamburger 菜单,创立一个新的 Kubernetes Cluster:

保护 Cluster 的名称,抉择失当的版本,点击 Customize 进行定制化:

为 Cluster 指定 CPU 和内存参数,选定 Ubuntu 作为操作系统。Cluster 的尺寸设置为 1.

Cluster 创立完并胜利部署后,点击 Connect 按钮进行连贯。

连贯胜利之后,就能够应用 Cloud Shell 操作集群了:

Cloud Shell 提供了命令行的形式同 Cluster 进行交互。

在 Google Kubernetes Cluster 上部署 HANA Expression Database Service

应用以下命令创立一个 secret 以获取 Docker 镜像:

kubectl create secret docker-registry docker-secret --docker-server=https://index.docker.io/v1/ --docker-username=xxx --docker-password=yyyyyy --docker-email=jerry@gmail.com

创立一个 yaml 格局的部署配置文件 (Deployment Configuration File), 另存成 hxe.yaml 文件:

kind: ConfigMap
apiVersion: v1
metadata:
  creationTimestamp: 2022-06-25T19:14:38Z
  name: hxe-pass
data:
  password.json: |+
    {"master_password" : "JERRYHana1"}
---
kind: PersistentVolume
apiVersion: v1
metadata:
  name: persistent-vol-hxe
  labels:
    type: local
spec:
  storageClassName: manual
  capacity:
    storage: 150Gi
  accessModes:
    - ReadWriteOnce
  hostPath:
    path: "/data/hxe_pv"
---
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
  name: hxe-pvc
spec:
  storageClassName: manual
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 50Gi
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: hxe
  labels:
    name: hxe
spec:
  selector:
    matchLabels:
      run: hxe
      app: hxe
      role: master
      tier: backend
  replicas: 1
  template:
    metadata:
      labels:
        run: hxe
        app: hxe
        role: master
        tier: backend
    spec:
      initContainers:
        - name: install
          image: busybox
          command: ['sh', '-c', 'chown 12000:79 /hana/mounts']
          volumeMounts:
            - name: hxe-data
              mountPath: /hana/mounts
      volumes:
        - name: hxe-data
          persistentVolumeClaim:
             claimName: hxe-pvc
        - name: hxe-config
          configMap:
             name: hxe-pass
      imagePullSecrets:
      - name: docker-secret
      containers:
      - name: hxe-container
        image: "store/saplabs/hanaexpress:2.00.030.00.20180403.2"
        ports:
          - containerPort: 39013
            name: port1
          - containerPort: 39015
            name: port2
          - containerPort: 39017
            name: port3
          - containerPort: 8090
            name: port4
          - containerPort: 39041
            name: port5
          - containerPort: 59013
            name: port6
        args: ["--agree-to-sap-license", "--dont-check-system", "--passwords-url", "file:///hana/hxeconfig/password.json"]
        volumeMounts:
          - name: hxe-data
            mountPath: /hana/mounts
          - name: hxe-config
            mountPath: /hana/hxeconfig
      - name: sqlpad-container
        image: "sqlpad/sqlpad"
        ports:
        - containerPort: 3000

---
apiVersion: v1
kind: Service
metadata:
  name: hxe-connect
  labels:
    app: hxe
spec:
  type: LoadBalancer
  ports:
  - port: 39013
    targetPort: 39013
    name: port1
  - port: 39015
    targetPort: 39015
    name: port2
  - port: 39017
    targetPort: 39017
    name: port3
  - port: 39041
    targetPort: 39041
    name: port5
  selector:
    app: hxe
---
apiVersion: v1
kind: Service
metadata:
  name: sqlpad
  labels:
    app: hxe
spec:
  type: LoadBalancer
  ports:
  - port: 3000
    targetPort: 3000
    protocol: TCP
    name: sqlpad
  selector:
    app: hxe

这个 yaml 文件里定义了一个 HANA Expression 的 Docker 镜像:store/saplabs/hanaexpress:2.00.030.00.20180403.2

应用如下命令即将这个 Docker 镜像部署到 Kubernetes Cluster 上:

  • kubectl create -f hxe.yaml
  • kubectl describe pods

期待部署胜利完结:

执行命令行 kubectl get pods,确保 pod 状态为 Running,而后进入 Pod 容器外部:

kubectl exec -it <<pod-name>> bash

此时就能够应用 SQL 命令行,连贯运行在 Pod 里的 HANA Expression 实例了:

hdbsql -i 90 -d systemdb -u SYSTEM -p HXEHana1

给数据库增加 document store 的反对:
alter database HXE add 'docstore';

从 SQLPAD service 取得 external IP 地址:

kubectl get services

有了这个内部能够拜访的 IP 地址之后,拜访其 3000 端口,就能够在浏览器里登录 SQLPAD 了:

点击 Sign In,创立一个 Administration account.

应用 Connections 菜单,连贯 HANA Expression 实例里的数据库表:

kubectl get services 命令行后果列表里找到 hxe-connect,抄下其 External IP 地址:

新建一个数据库连贯,保护刚刚抄下来的 External IP 地址,数据库用户名和明码,Tenant 等登录信息:

数据库连贯建设连贯之后,就能够新建一个 Query,对其进行读写操作。

创立一个名叫 quotes 的 document store, 并插入一些测试数据:

create collection quotes;
--Create a collection for document store and insert JSON values
insert into quotes values ({ "FROM" : 'HOMER',   "QUOTE" :  'I want to share something with you: The three little sentences that will get you through life. Number 1: Cover for me. Number 2: Oh, good idea, Boss! Number 3: It wai like that when I got here.', "MOES_BAR" : 'Point(  -86.880306 36.508361)', "QUOTE_ID" : 1  });
insert into quotes values ({ "FROM" : 'HOMER',   "QUOTE" :  'Wait a minute. Bart''s teacher is named Krabappel? Oh, I''ve been calling her Crandall. Why did not anyone tell me? Ohhh, I have been making an idiot out of myself!', "QUOTE_ID" : 2, "MOES_BAR" : 'Point( -87.182708 37.213414)' });
insert into quotes values ({ "FROM" : 'HOMER',   "QUOTE" :  'Oh no! What have I done? I smashed open my little boy''s piggy bank, and for what? A few measly cents, not even enough to buy one beer. Weit a minute, lemme count and make sure…not even close.',"MOES_BAR":'Point( -122.400690 37.784366)',"QUOTE_ID" : 3 });

创立一个 Column 表,开启 Fuzzy Search 的反对:

create column table quote_analysis
(
    id integer,
    homer_quote text FAST PREPROCESS ON FUZZY SEARCH INDEX ON,
    lon_lat nvarchar(200)

);

将插入到 document store collection 的数据拷贝到下面的 Column 表里:

insert into quote_analysis
with doc_store as (select quote_id, quote from quotes)
select doc_store.quote_id as id, doc_store.quote as homer_quote, 'Point(-122.676366 45.535889)'
from doc_store;

查问与 wait 类似度最低的词:

select  id, score() as similarity , lon_lat, TO_VARCHAR(HOMER_QUOTE)
from quote_analysis
where contains(HOMER_QUOTE, 'wait', fuzzy(0.5,'textsearch=compare'))
order by similarity asc

总结

至此,咱们实现了在 Google Kubernetes Cluster 里操作 HANA Expression Database Service 的操作步骤。从整个过程不难感觉出,将蕴含 HANA Expression 的 Docker 镜像部署在 Google Kubernetes Cluster 并运行在 Pod 内,实现了 HANA Expression 服务的开箱即用,从而防止了 On-Premises 部署模式下 HANA Expression 繁琐的装置和配置步骤。

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