gcloud config set project

gcloud config set compute/region 

gcloud config set compute/zone 

export GOOGLE_CLOUD_PROJECT=$(gcloud config get-value core/project)

gcloud container clusters create fancy-prod-268 --num-nodes 3

gcloud compute instances list


gcloud services enable cloudbuild.googleapis.com

# build
cd ~/monolith-to-microservices/monolith
gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/fancy-monolith-220:1.0.0 .

# deploy
kubectl create deployment fancy-monolith-220 --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/fancy-monolith-220:1.0.0

# check
kubectl get all

# Show pods
kubectl get pods

# Show deployments
kubectl get deployments

# Show replica sets
kubectl get rs

#You can also combine them
kubectl get pods,deployments
kubectl get services


# delete
kubectl delete pod/<POD_NAME>

# expose
kubectl expose deployment fancy-monolith-220 --type=LoadBalancer --port 80 --target-port 8080

# Accessing the service
kubectl get service

~/monolith-to-microservices/microservices/src/orders
gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/fancy-orders-430:1.0.0 .

kubectl create deployment fancy-orders-430 --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/fancy-orders-430:1.0.0
kubectl expose deployment fancy-orders-430 --type=LoadBalancer --port 80 --target-port 8081

~/monolith-to-microservices/microservices/src/products
gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/fancy-products-721:1.0.0 .


kubectl delete deployment fancy-products-721
kubectl create deployment fancy-products-721 --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/products-721:1.0.0
kubectl expose deployment fancy-products-721 --type=LoadBalancer --port 80 --target-port 8082


~/monolith-to-microservices/microservices/src/frontend
gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/fancy-frontend-189:1.0.0 .

kubectl create deployment fancy-frontend-189 --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/fancy-frontend-189:1.0.0
kubectl expose deployment fancy-frontend-189 --type=LoadBalancer --port 80 --target-port 8080

------------------


# scale
kubectl scale deployment monolith --replicas=3

kubectl get all

# Make changes to the website
gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/monolith:2.0.0 .

# Update website with zero downtime
kubectl set image deployment/monolith monolith=gcr.io/${GOOGLE_CLOUD_PROJECT}/monolith:2.0.0

kubectl get pods



# delete
# Delete the container image for version 1.0.0 of the monolith
gcloud container images delete gcr.io/${GOOGLE_CLOUD_PROJECT}/monolith:1.0.0 --quiet

# Delete the container image for version 2.0.0 of the monolith
gcloud container images delete gcr.io/${GOOGLE_CLOUD_PROJECT}/monolith:2.0.0 --quiet

# The following command will take all source archives from all builds and delete them from cloud storage

# Run this command to print all sources:
# gcloud builds list | awk 'NR > 1 {print $4}'

gcloud builds list | grep 'SOURCE' | cut -d ' ' -f2 | while read line; do gsutil rm $line; done

kubectl delete service monolith
kubectl delete deployment monolith

gcloud container clusters delete fancy-cluster lab region

# App Build &  Docker Deploy

# artifactregistry Repository
gcloud auth configure-docker us-east1-docker.pkg.dev

gcloud services enable artifactregistry.googleapis.com \
    cloudbuild.googleapis.com \
    run.googleapis.com

# Submit
gcloud builds submit --tag us-east1-docker.pkg.dev/${GOOGLE_CLOUD_PROJECT}/monolith-demo/monolith:1.0.0    


# Cloud Build  에서 진행상태 볼 수 있다.

# Deploy Container
gcloud run deploy monolith --image us-east1-docker.pkg.dev/${GOOGLE_CLOUD_PROJECT}/monolith-demo/monolith:1.0.0 --region us-east1

# check
gcloud run services list

#  Create new revision with lower concurrency ??
gcloud run deploy monolith --image us-east1-docker.pkg.dev/${GOOGLE_CLOUD_PROJECT}/monolith-demo/monolith:1.0.0 --region us-east1 --concurrency 1

gcloud run deploy monolith --image us-east1-docker.pkg.dev/${GOOGLE_CLOUD_PROJECT}/monolith-demo/monolith:1.0.0 --region us-east1 --concurrency 80

# Cloud Run에서 확인가능

# node module 지우고 올리기
cd ~
rm -rf monolith-to-microservices/*/node_modules
gsutil -m cp -r monolith-to-microservices gs://fancy-store-$DEVSHELL_PROJECT_ID/

# 인스턴스 만들기
gcloud compute instances create backend \
    --zone=$ZONE \
    --machine-type=e2-standard-2 \
    --tags=backend \
   --metadata=startup-script-url=https://storage.googleapis.com/fancy-store-$DEVSHELL_PROJECT_ID/startup-script.sh


# ip확인
gcloud compute instances list


# Firewall. 적용하고 3분 기다려야 된다.
gcloud compute firewall-rules create fw-fe \
    --allow tcp:8080 \
    --target-tags=frontend

gcloud compute firewall-rules create fw-be \
    --allow tcp:8081-8082 \
    --target-tags=backend    

# 끄고
gcloud compute instances stop frontend --zone=$ZONE    

gcloud compute instances stop backend --zone=$ZONE


# 인스턴스를 템플릿으로 만든다. 
gcloud compute instance-templates create fancy-fe \
    --source-instance-zone=$ZONE \
    --source-instance=frontend

gcloud compute instance-templates create fancy-be \
    --source-instance-zone=$ZONE \
    --source-instance=backend    

gcloud compute instance-templates list    


# 기존의 것을 지우고
gcloud compute instances delete backend --zone=$ZONE


# managed 로 다시 만든다
gcloud compute instance-groups managed create fancy-fe-mig \
    --zone=$ZONE \
    --base-instance-name fancy-fe \
    --size 2 \
    --template fancy-fe

gcloud compute instance-groups managed create fancy-be-mig \
    --zone=$ZONE \
    --base-instance-name fancy-be \
    --size 2 \
    --template fancy-be    

# set-named-port라는 명령이 있네
gcloud compute instance-groups set-named-ports fancy-fe-mig \
    --zone=$ZONE \
    --named-ports frontend:8080

gcloud compute instance-groups set-named-ports fancy-be-mig \
    --zone=$ZONE \
    --named-ports orders:8081,products:8082        

# 핼스체크 추가
gcloud compute health-checks create http fancy-fe-hc \
    --port 8080 \
    --check-interval 30s \
    --healthy-threshold 1 \
    --timeout 10s \
    --unhealthy-threshold 3

gcloud compute health-checks create http fancy-be-hc \
    --port 8081 \
    --request-path=/api/orders \
    --check-interval 30s \
    --healthy-threshold 1 \
    --timeout 10s \
    --unhealthy-threshold 3

gcloud compute firewall-rules create allow-health-check \
    --allow tcp:8080-8081 \
    --source-ranges 130.211.0.0/22,35.191.0.0/16 \
    --network default        


# 헬스체크한 뒤 업데이트 해야 하나 보다
gcloud compute instance-groups managed update fancy-fe-mig \
    --zone=$ZONE \
    --health-check fancy-fe-hc \
    --initial-delay 300

gcloud compute instance-groups managed update fancy-be-mig \
    --zone=$ZONE \
    --health-check fancy-be-hc \
    --initial-delay 300        


# Create load balancers
gcloud compute http-health-checks create fancy-fe-frontend-hc \
  --request-path / \
  --port 8080    

gcloud compute http-health-checks create fancy-be-orders-hc \
  --request-path /api/orders \
  --port 8081

gcloud compute http-health-checks create fancy-be-products-hc \
  --request-path /api/products \
  --port 8082

# 엥?
# Note: These health checks are for the load balancer, and only handle directing traffic from the load balancer; they do not cause the managed instance groups to recreate instances.      

gcloud compute backend-services create fancy-fe-frontend \
  --http-health-checks fancy-fe-frontend-hc \
  --port-name frontend \
  --global

gcloud compute backend-services create fancy-be-orders \
  --http-health-checks fancy-be-orders-hc \
  --port-name orders \
  --global

gcloud compute backend-services create fancy-be-products \
  --http-health-checks fancy-be-products-hc \
  --port-name products \
  --global

# 로드 밸런서도?... 이렇게 복잡하게 해야하는건가? 보다.
gcloud compute backend-services add-backend fancy-fe-frontend \
  --instance-group-zone=$ZONE \
  --instance-group fancy-fe-mig \
  --global      

gcloud compute backend-services add-backend fancy-be-orders \
  --instance-group-zone=$ZONE \
  --instance-group fancy-be-mig \
  --global  

gcloud compute backend-services add-backend fancy-be-products \
  --instance-group-zone=$ZONE \
  --instance-group fancy-be-mig \
  --global  

# Create a URL map. 악!
gcloud compute url-maps create fancy-map \
  --default-service fancy-fe-frontend  

# 아... 이건 Nginx에서 라우팅 하는 것 처럼 보인다.
#Create a path matcher to allow the /api/orders and /api/products paths to route to their respective services:
gcloud compute url-maps add-path-matcher fancy-map \
   --default-service fancy-fe-frontend \
   --path-matcher-name orders \
   --path-rules "/api/orders=fancy-be-orders,/api/products=fancy-be-products"

# 맵을 만들고... 용어가 달라서 그렇군.
# Create the proxy which ties to the URL map:
gcloud compute target-http-proxies create fancy-proxy \
  --url-map fancy-map   

# 포워딩
# Create a global forwarding rule that ties a public IP address and port to the proxy:
gcloud compute forwarding-rules create fancy-http-rule \
  --global \
  --target-http-proxy fancy-proxy \
  --ports 80  


# Find the IP address for the Load Balancer:  
gcloud compute forwarding-rules list --global


# ip가 바뀌었을테네.. .env 파일 바꿔준다. 음.. 이건 좀.

# 다시 빌드하고 다시 배포 한다.... 음.. .env를 안쓰면 되나? 거참.
cd ~/monolith-to-microservices/react-app
npm install && npm run-script build

cd ~
rm -rf monolith-to-microservices/*/node_modules
gsutil -m cp -r monolith-to-microservices gs://fancy-store-$DEVSHELL_PROJECT_ID/

gcloud compute instance-groups managed rolling-action replace fancy-fe-mig \
    --zone=$ZONE \
    --max-unavailable 100%

# 체크
watch -n 2 gcloud compute backend-services get-health fancy-fe-frontend --global




# 스케일 변경
gcloud compute instance-groups managed set-autoscaling \
  fancy-fe-mig \
  --zone=$ZONE \
  --max-num-replicas 2 \
  --target-load-balancing-utilization 0.60

gcloud compute instance-groups managed set-autoscaling \
  fancy-be-mig \
  --zone=$ZONE \
  --max-num-replicas 2 \
  --target-load-balancing-utilization 0.60

# CDN...은 어떻게 설정되는지?
# Enable content delivery network
gcloud compute backend-services update fancy-fe-frontend \
    --enable-cdn --global    



# 인스턴스 사이즈 변경
gcloud compute instances set-machine-type frontend \
  --zone=$ZONE \
  --machine-type e2-small

gcloud compute instance-templates create fancy-fe-new \
    --region=$REGION \
    --source-instance=frontend \
    --source-instance-zone=$ZONE

gcloud compute instance-groups managed rolling-action start-update fancy-fe-mig \
  --zone=$ZONE \
  --version template=fancy-fe-new

# 와치? 3분 정도 걸린다고 함.
watch -n 2 gcloud compute instance-groups managed list-instances fancy-fe-mig \
  --zone=$ZONE            

gcloud compute instances describe [VM_NAME] --zone=$ZONE | grep machineType  


# ssh
gcloud compute instance-groups list-instances fancy-fe-mig --zone=$ZONE

gcloud compute ssh [INSTANCE_NAME] --zone=$ZONE

gcloud config set project qwiklabs-gcp-02-806cc13914e8

gcloud config list project

bq show bigquery-public-data:samples.shakespeare



bq query --use_legacy_sql=false \
'SELECT
   word,
   SUM(word_count) AS count
 FROM
   `bigquery-public-data`.samples.shakespeare
 WHERE
   word LIKE "%raisin%"
 GROUP BY
   word'


Use the bq ls command to list any existing datasets in your project:



# 현재 내 dataset
bq ls

# 오픈 데이터셋
bq ls bigquery-public-data:


# make dataset
bq mk babynames

curl -LO http://www.ssa.gov/OACT/babynames/names.zip

unzip names.zip

# 스키마 지정해서 업로드
bq load babynames.names2010 yob2010.txt name:string,gender:string,count:integer

# 확인
bq show babynames.names2010


bq query "SELECT name,count FROM babynames.names2010 WHERE gender = 'F' ORDER BY count DESC LIMIT 5"

# 지우는 것도 가능
bq rm -r babynames

gcloud config set project qwiklabs-gcp-00-acf9d7b837de

export GOOGLE_CLOUD_PROJECT=$(gcloud config get-value core/project)

gcloud iam service-accounts create my-natlang-sa \
  --display-name "my natural language service account"

gcloud iam service-accounts keys create ~/key.json \
  --iam-account my-natlang-sa@${GOOGLE_CLOUD_PROJECT}.iam.gserviceaccount.com


gcloud compute ssh [INSTANCE_NAME] --zone=[ZONE]

gcloud compute ssh linux-instance --zone=us-east4-b


gcloud ml language analyze-entities --content="Michelangelo Caravaggio, Italian painter, is known for 'The Calling of Saint Matthew'." > result.json


export API_KEY=<YOUR_API_KEY>

export API_KEY=AIzaSyCO4c7jJCH-ZaMGAld9KLbLUBbrVgpsauI


touch request.json

nano request.json

{
  "config": {
      "encoding":"FLAC",
      "languageCode": "en-US"
  },
  "audio": {
      "uri":"gs://cloud-samples-tests/speech/brooklyn.flac"
  }
}


curl -s -X POST -H "Content-Type: application/json" --data-binary @request.json \
"https://speech.googleapis.com/v1/speech:recognize?key=${API_KEY}"


curl -s -X POST -H "Content-Type: application/json" --data-binary @request.json \
"https://speech.googleapis.com/v1/speech:recognize?key=${API_KEY}" > result.json