Tom Kralidis
132 lines
3.4 KiB
Markdown
132 lines
3.4 KiB
Markdown
# Using pygeoapi on AWS Lambda Serverless
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## Overview
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AWS Lambda Serverless is a service from Amazon that enables publishing
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code which is executed as on demand functions. The value is here is that
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the server is only working when requests are made, resulting in more efficient
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use of server resources as well as managing costs.
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pygeoapi provides a couple of ways to publish to AWS Lambda depending on your
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environment: zappa and node/serverless.
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## zappa
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[Zappa](https://github.com/zappa/Zappa) provides Python tooling to interact with AWS lambda. Ensure the environment
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variables `AWS_ACCESS_KEY` and `AWS_SECRET_ACCESS_KEY` are set and available.
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```bash
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# install zappa
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pip install zappa
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# set environment variables
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export AWS_ACCESS_KEY_ID=foo
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export AWS_SECRET_ACCESS_KEY=bar
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# deploy pygeoapi to AWS Lambda
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zappa deploy -s zappa_settings.json
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# update
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zappa update -s zappa_settings.json
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# undeploy
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zappa undeploy -s zappa_settings.json
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```
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## node/serverless
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The included `serverless.yml` and `pygeoapi-serverless-config.yml` can be used to deploy pygeoapi
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on AWS Lambda Serverless Environment.
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This requires Amazon Credentials and the Serverless deployment tool.
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AWS Credentials can be created following the instructions at https://serverless.com/framework/docs/providers/aws/guide/credentials/
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Move serverless configs to root directory:
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```bash
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mv serverless.yml ..
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mv pygeoapi-config.yml ..
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cd ..
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```
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To install the Serverless environment
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```bash
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npm install serverless
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```
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The following serverless plugins are also used
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```bash
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serverless plugin install -n serverless-python-requirements
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serverless plugin install -n serverless-wsgi
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```
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To test the application as a lambda locally:
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```bash
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serverless wsgi serve
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```
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To deploy to AWS Lambda:
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```bash
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serverless deploy
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```
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Once deployed, if you only need to update the code and not anything in the serverless configuration, you can update the function using:
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```bash
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serverless deploy function -f app
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```
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When deployed, the output will show the URL the app has been deployed to.
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## node/serverless lambda container
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In the case where your pygeoapi instance is too large to deploy as a lambda function (250MB) you can build and deploy
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a docker image of pygeoapi with the lamda runtime interface installed.
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Move serverless configs to root directory:
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```bash
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mv container/serverless.yml ../..
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mv container/DockerFile ../..
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```
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*note the files below come from the serverless-wsgi node plugin, and ideally this should be part of a build process
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```bash
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cd container/
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npm install serverless
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serverless plugin install -n serverless-wsgi
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mv node_modules/serverless-wsgi/serverless-wsgi.py ../..
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mv node_modules/serverless-wsgi/wsgi_handler.py ../..
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mv container/wsgi.py ../..
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mv container/.serverless-wsgi ../..
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rm -rf container/node_modules
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cd ../..
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```
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# to build docker container
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```bash
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docker build -t pygeo-lambda-container .
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```
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Once built, you need to deploy to ECR. This can also be accomplished with a change to the serverless configuration.
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Depending on environment permissions, you may need to create a ECR repo with appropriate policies first.
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```bash
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AWS_PROFILE=<profile name> aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <ECR repo url>
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docker tag pygeo-lambda-container:latest <ECR repo url>:latest
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docker push <ECR repo url>:latest
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```
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Deploy stack using serverless.
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```
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AWS_PROFILE=<profile name> sls deploy -s <stage name>
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```
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