Serverless 简明教程

npm install -g serverless

serverless config credentials --provider aws --key 1234 --secret 5678 --profile custom-profile

sls create --template aws-python3

import json
def hello(event, context):
   body = {
      "message": "Go Serverless v1.0! Your function executed successfully!", "input": event
   }
   response = {
      "statusCode": 200, "body": json.dumps(body)
   }
   return response
   # Use this code if you don't use the http event with the LAMBDA-PROXY
   # integration
   """
   return {
      "message": "Go Serverless v1.0! Your function executed successfully!", "event": event
   }
   """

service: aws-serverless
frameworkVersion: '2'

provider:
   name: aws
   runtime: python3.8
   lambdaHashingVersion: 20201221
functions:
   hello:
      handler: handler.hello

provider:
   name: aws
   runtime: python3.8
   lambdaHashingVersion: 20201221
   profile: yash-sanghvi

sls deploy -v

sls invoke --function hello

provider:
   name: aws
   runtime: python3.6
   region: us-east-2
   profile: yash-sanghvi

provider:
   name: aws
   runtime: python3.6
   region: us-east-2
   profile: yash-sanghvi
   memorySize: 512 #will be inherited by all functions
   timeout: 50 #will be inherited by all functions

functions:
   custom_timeout: #will override the default timeout
      handler: handler.custom_memory
      timeout: 30
  default_timeout_func: #will inherit the default timeout from provider
      handler: handler.default_memory

cron(minutes hours day-of-month month day-of-week year)

functions:
   cron_triggered_lambda:
      handler: handler.hello
      events:
         - schedule: cron(10 03 * * ? *) #run at 03:10 (UTC) every day.

functions:
   rate_triggered_lambda:
      handler: handler.hello
      events:
         - schedule: rate(10 minutes) #run every 10 minutes

functions:
   user_details_api:
      handler: handler.send_user_details
      events:
         - http:
            path: details/{user_id}
            method: get
            integration: lambda-proxy
            cors: true

   location_api:
      handler: handler.send_location
      events:
         - http:
            path: location/{user_id}
            method: get
            integration: lambda-proxy
            cors: true

import json

def lambda_handler(event, context):
   # TODO implement
   # print(event)
   #helps you see the entire input request. The printed output can be found in CloudWatch logs
      user = event['pathParameters']['user_id']
      return {
         'statusCode': 200,
         'body': json.dumps('Hello ' + str(user))
      }

service: influx-archive-pipeline

provider:
   name: aws
   runtime: python3.6
   stage: prod
   region: us-east-2
   profile: yash-sanghvi
   timeout: 900
   memorySize: 1024

# you can add packaging information here
package:
   include:
      - src/models/config.py
      - src/models/lambda_apis/**
      - src/models/scheduled_lambdas/**

   exclude:
      - docs/**
      - models/**
      - notebooks/**
      - references/**
      - reports/**
      - src/data/**
      - src/visualization/**
      - src/models/**
      - utils/**

functions:
   user_details_api:
      handler: src/models/lambda_apis/user_details_api.sync_user_details
      events:
         - http:
            path: details/{user_id}
            method: get
            integration: lambda
            cors: true

   monitoring_lambda:
      handler: src/models/scheduled_lambdas/monitoring_lambda.periodic_monitoring
      events:
         - schedule: cron(15 16 * * ? *)

aws-psycopg2==1.2.1
boto
boto3==1.7.62
botocore==1.10.62
numpy==1.14.5
pandas==0.25.0
scipy==1.5.2
sqlalchemy==1.2.15

sls plugin install -n serverless-python-requirements

plugins:
   - serverless-python-requirements

import time
import numpy

def hello(event, context):
   print("second update!")
   time.sleep(4)
   print(numpy.random.randint(100))
   return("hello")

Unable to import module 'handler':

IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!

Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.

We have compiled some common reasons and troubleshooting tips at:

   https://numpy.org/devdocs/user/troubleshooting-importerror.html

Please note and check the following:

  * The Python version is: Python3.8 from "/var/lang/bin/python3.8"
  * The NumPy version is: "1.19.4"

and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.

Original error was: No module named 'numpy.core._multiarray_umath'

custom:
   pythonRequirements:
      dockerizePip: true

custom:
   pythonRequirements:
      dockerizePip: true
	useStaticCache: false #not necessary if you will be deploying the code to lambda for the first time.

service: hello-world-python
provider:
   name: aws
   runtime: python3.6
   profile: yash-sanghvi
   region: ap-south-1

functions:
   hello_world:
      handler: handler.hello
      timeout: 6
      memorySize: 128

plugins:
   - serverless-python-requirements

custom:
   pythonRequirements:
      dockerizePip: true
	useStaticCache: false #not necessary if you will be deploying the code to lambda for the first time.

sls plugin install -n serverless-python-requirements

plugins:
   - serverless-python-requirements
custom:
   pythonRequirements:
      dockerizePip: true
      layer: true

functions:
   hello:
      handler: handler.hello
      layers:
         - { Ref: PythonRequirementsLambdaLayer }

functions:
   hello:
      handler: handler.hello
      layers:
         - arn:aws:lambda:region:XXXXXX:layer:LayerName:Y

layers:
   hello:
      path: layer-dir # required, path to layer contents on disk
      name: ${opt:stage, self:provider.stage, 'dev'}-layerName # optional, Deployed Lambda layer name
      description: Description of what the lambda layer does # optional, Description to publish to AWS
      compatibleRuntimes: # optional, a list of runtimes this layer is compatible with
         - python3.8
      licenseInfo: GPLv3 # optional, a string specifying license information
      # allowedAccounts: # optional, a list of AWS account IDs allowed to access this layer.
      #   - '*'
      # note: uncommenting this will give all AWS users access to this layer unconditionally.
      retain: false # optional, false by default. If true, layer versions are not deleted as new ones are created

layers:
   ffmpeg:
      path: layer

layers:
   testLayer:
      path: testLayer
      allowedAccounts:
         - 999999999999 # a specific account ID
         - 000123456789 # a different specific account ID

layers:
   testLayer:
      path: testLayer
      allowedAccounts:
      - '*'