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Google Cloud Platform Expert

Google Cloud Platform 专家指南

Expert guidance for Google Cloud Platform services and cloud-native architecture.
为Google Cloud Platform服务及云原生架构提供专家级指导。

Core Concepts

核心概念

  • Compute Engine, App Engine, Cloud Run
  • Cloud Functions (serverless)
  • Cloud Storage
  • BigQuery (data warehouse)
  • Firestore (NoSQL database)
  • Pub/Sub (messaging)
  • Google Kubernetes Engine (GKE)
  • Compute Engine、App Engine、Cloud Run
  • Cloud Functions（无服务器）
  • Cloud Storage
  • BigQuery（数据仓库）
  • Firestore（NoSQL数据库）
  • Pub/Sub（消息队列）
  • Google Kubernetes Engine (GKE)

gcloud CLI

gcloud CLI

bash
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bash
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Initialize

Initialize

gcloud init
gcloud init

Create Compute Engine instance

Create Compute Engine instance

gcloud compute instances create my-instance
--zone=us-central1-a
--machine-type=e2-medium
--image-family=ubuntu-2004-lts
--image-project=ubuntu-os-cloud
gcloud compute instances create my-instance
--zone=us-central1-a
--machine-type=e2-medium
--image-family=ubuntu-2004-lts
--image-project=ubuntu-os-cloud

Deploy App Engine

Deploy App Engine

gcloud app deploy
gcloud app deploy

Create Cloud Storage bucket

Create Cloud Storage bucket

gsutil mb gs://my-bucket-name/
gsutil mb gs://my-bucket-name/

Upload file

Upload file

gsutil cp myfile.txt gs://my-bucket-name/
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gsutil cp myfile.txt gs://my-bucket-name/
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Cloud Functions

Cloud Functions

python
import functions_framework
from google.cloud import firestore

@functions_framework.http
def hello_http(request):
    request_json = request.get_json(silent=True)
    name = request_json.get('name') if request_json else 'World'

    return f'Hello {name}!'

@functions_framework.cloud_event
def hello_pubsub(cloud_event):
    import base64
    data = base64.b64decode(cloud_event.data["message"]["data"]).decode()
    print(f'Received: {data}')
python
import functions_framework
from google.cloud import firestore

@functions_framework.http
def hello_http(request):
    request_json = request.get_json(silent=True)
    name = request_json.get('name') if request_json else 'World'

    return f'Hello {name}!'

@functions_framework.cloud_event
def hello_pubsub(cloud_event):
    import base64
    data = base64.b64decode(cloud_event.data["message"]["data"]).decode()
    print(f'Received: {data}')

BigQuery

BigQuery

python
from google.cloud import bigquery

client = bigquery.Client()
python
from google.cloud import bigquery

client = bigquery.Client()

Query

Query

query = """ SELECT name, COUNT(*) as count FROM 
project.dataset.table
WHERE date >= '2024-01-01' GROUP BY name ORDER BY count DESC LIMIT 10 """
query_job = client.query(query) results = query_job.result()
for row in results: print(f"{row.name}: {row.count}")
query = """ SELECT name, COUNT(*) as count FROM 
project.dataset.table
WHERE date >= '2024-01-01' GROUP BY name ORDER BY count DESC LIMIT 10 """
query_job = client.query(query) results = query_job.result()
for row in results: print(f"{row.name}: {row.count}")

Load data

Load data

dataset_id = 'my_dataset' table_id = 'my_table' table_ref = client.dataset(dataset_id).table(table_id)
job_config = bigquery.LoadJobConfig( source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1, autodetect=True )
with open('data.csv', 'rb') as source_file: job = client.load_table_from_file(source_file, table_ref, job_config=job_config)
job.result()
undefined
dataset_id = 'my_dataset' table_id = 'my_table' table_ref = client.dataset(dataset_id).table(table_id)
job_config = bigquery.LoadJobConfig( source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1, autodetect=True )
with open('data.csv', 'rb') as source_file: job = client.load_table_from_file(source_file, table_ref, job_config=job_config)
job.result()
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Firestore

Firestore

python
from google.cloud import firestore

db = firestore.Client()
python
from google.cloud import firestore

db = firestore.Client()

Create document

Create document

doc_ref = db.collection('users').document('user1') doc_ref.set({ 'name': 'John Doe', 'email': 'john@example.com', 'age': 30 })
doc_ref = db.collection('users').document('user1') doc_ref.set({ 'name': 'John Doe', 'email': 'john@example.com', 'age': 30 })

Query

Query

users_ref = db.collection('users') query = users_ref.where('age', '>=', 18).limit(10)
for doc in query.stream(): print(f'{doc.id} => {doc.to_dict()}')
users_ref = db.collection('users') query = users_ref.where('age', '>=', 18).limit(10)
for doc in query.stream(): print(f'{doc.id} => {doc.to_dict()}')

Real-time listener

Real-time listener

def on_snapshot(doc_snapshot, changes, read_time): for doc in doc_snapshot: print(f'Received document: {doc.id}')
doc_ref.on_snapshot(on_snapshot)
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def on_snapshot(doc_snapshot, changes, read_time): for doc in doc_snapshot: print(f'Received document: {doc.id}')
doc_ref.on_snapshot(on_snapshot)
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Pub/Sub

Pub/Sub

python
from google.cloud import pubsub_v1
python
from google.cloud import pubsub_v1

Publisher

Publisher

publisher = pubsub_v1.PublisherClient() topic_path = publisher.topic_path('project-id', 'topic-name')
data = "Hello World".encode('utf-8') future = publisher.publish(topic_path, data) print(f'Published message ID: {future.result()}')
publisher = pubsub_v1.PublisherClient() topic_path = publisher.topic_path('project-id', 'topic-name')
data = "Hello World".encode('utf-8') future = publisher.publish(topic_path, data) print(f'Published message ID: {future.result()}')

Subscriber

Subscriber

subscriber = pubsub_v1.SubscriberClient() subscription_path = subscriber.subscription_path('project-id', 'subscription-name')
def callback(message): print(f'Received: {message.data.decode("utf-8")}') message.ack()
streaming_pull_future = subscriber.subscribe(subscription_path, callback=callback)
undefined
subscriber = pubsub_v1.SubscriberClient() subscription_path = subscriber.subscription_path('project-id', 'subscription-name')
def callback(message): print(f'Received: {message.data.decode("utf-8")}') message.ack()
streaming_pull_future = subscriber.subscribe(subscription_path, callback=callback)
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Best Practices

最佳实践

  • Use service accounts
  • Implement IAM properly
  • Use Cloud Storage lifecycle policies
  • Monitor with Cloud Monitoring
  • Use managed services
  • Implement auto-scaling
  • Optimize BigQuery costs
  • 使用服务账号
  • 正确配置IAM
  • 启用Cloud Storage生命周期策略
  • 通过Cloud Monitoring进行监控
  • 使用托管服务
  • 实现自动扩缩容
  • 优化BigQuery成本

Anti-Patterns

反模式

❌ No IAM policies ❌ Storing credentials in code ❌ Ignoring costs ❌ Single region deployments ❌ No data backup ❌ Overly broad permissions
❌ 未配置IAM策略 ❌ 在代码中存储凭证 ❌ 忽略成本管控 ❌ 单区域部署 ❌ 未做数据备份 ❌ 权限范围过宽

Resources

参考资源