glifchits / chalice

Python Serverless Microframework for AWS

Python Serverless Microframework for AWS

The python serverless microframework for AWS allows you to quickly create and deploy applications that use Amazon API Gateway and AWS Lambda. It provides:

• A command line tool for creating, deploying, and managing your app
• A familiar and easy to use API for declaring views in python code
• Automatic IAM policy generation

$ pip install chalice
$ chalice new-project helloworld && cd helloworld
$ cat app.py

from chalice import Chalice

app = Chalice(app_name="helloworld")

@app.route("/")
def index():
    return {"hello": "world"}

$ chalice deploy
...
Your application is available at: https://endpoint/dev

$ curl https://endpoint/dev
{"hello": "world"}

Up and running in less than 30 seconds.

This project is published as a preview project and is not yet recommended for production APIs. Give this project a try and share your feedback with us here on github.

Quickstart

In this tutorial, you'll use the chalice command line utility to create and deploy a basic REST API. First, you'll need to installl chalice. Using a virtualenv is recommended:

$ pip install virtualenv
$ virtualenv ~/.virtualenvs/chalice-demo
$ source ~/.virtualenvs/chalice-demo/bin/activate

Next, in your virtualenv, install chalice:

$ pip install chalice

You can verify you have chalice installed by running:

$ chalice --help
Usage: chalice [OPTIONS] COMMAND [ARGS]...
...

Credentials

Before you can deploy an application, be sure you have credentials configured. If you have previously configured your machine to run boto3 (the AWS SDK for Python) or the AWS CLI then you can skip this section.

If this is your first time configuring credentials for AWS you can follow these steps to quickly get started:

$ mkdir ~/.aws
$ cat >> ~/.aws/config
[default]
aws_access_key_id=YOUR_ACCESS_KEY_HERE
aws_secret_access_key=YOUR_SECRET_ACCESS_KEY
region=YOUR_REGION (such as us-west-2, us-west-1, etc)

If you want more information on all the supported methods for configuring credentials, see the boto3 docs.

Creating Your Project

The next thing we'll do is use the chalice command to create a new project:

$ chalice new-project helloworld

This will create a helloworld directory. Cd into this directory. You'll see several files have been created for you:

$ cd helloworld
$ ls -la
drwxr-xr-x   .chalice
-rw-r--r--   app.py
-rw-r--r--   requirements.txt

You can ignore the .chalice directory for now, the two main files we'll focus on is app.py and requirements.txt.

Let's take a look at the app.py file:

from chalice import Chalice

app = Chalice(app_name='helloworld')


@app.route('/')
def index():
    return {'hello': 'world'}

The new-project command created a sample app that defines a single view, /, that when called will return the JSON body {"hello": "world"}.

Deploying

Let's deploy this app. Make sure you're in the helloworld directory and run chalice deploy:

$ chalice deploy
...
Initiating first time deployment...
https://qxea58oupc.execute-api.us-west-2.amazonaws.com/dev/

You now have an API up and running using API Gateway and Lambda:

$ curl https://qxea58oupc.execute-api.us-west-2.amazonaws.com/dev/
{"hello": "world"}

Try making a change to the returned dictionary from the index() function. You can then redeploy your changes by running chalice deploy.

For the rest of these tutorials, we'll be using httpie instead of curl (https://github.com/jkbrzt/httpie) to test our API. You can install httpie using pip install httpie, or if you're on Mac, you can run brew install httpie. The Github link has more information on installation instructions. Here's an example of using httpie to request the root resource of the API we just created. Note that the command name is http:

$ http https://qxea58oupc.execute-api.us-west-2.amazonaws.com/dev/
HTTP/1.1 200 OK
Connection: keep-alive
Content-Length: 18
Content-Type: application/json
Date: Mon, 30 May 2016 17:55:50 GMT
X-Cache: Miss from cloudfront

{
    "hello": "world"
}

Additionally, the API Gateway endpoints will be shortened to https://endpoint/dev/ for brevity. Be sure to substitute https://endpoint/dev/ for the actual endpoint that the chalice CLI displays when you deploy your API (it will look something like https://abcdefg.execute-api.us-west-2.amazonaws.com/dev/.

Next Steps

You've now created your first app using chalice.

The next few sections will build on this quickstart section and introduce you to additional features including: URL parameter capturing, error handling, advanced routing, current request metadata, and automatic policy generation.

Tutorial: URL Parameters

Now we're going to make a few changes to our app.py file that demonstrate additional capabilities provided by the python serverless microframework for AWS.

Our application so far has a single view that allows you to make an HTTP GET request to /. Now let's suppose we want to capture parts of the URI:

from chalice import Chalice

app = Chalice(app_name='helloworld')

CITIES_TO_STATE = {
    'seattle': 'WA',
    'portland': 'OR',
}


@app.route('/')
def index():
    return {'hello': 'world'}

@app.route('/cities/{city}')
def state_of_city(name):
    return {'state': CITIES_TO_STATE[name]}

In the example above we've now added a state_of_city view that allows a user to specify a city name. The view function takes the city name and returns name of the state the city is in. Notice that the @app.route decorator has a URL pattern of /cities/{city}. This means that the value of {city} is captured and passed to the view function. You can also see that the state_of_city takes a single argument. This argument is the name of the city provided by the user. For example:

GET /cities/seattle   --> state_of_city('seattle')
GET /cities/portland  --> state_of_city('portland')

Now that we've updated our app.py file with this new view function, let's redeploy our application. You can run chalice deploy from the helloworld directory and it will deploy your application:

$ chalice deploy

Let's try it out. Note the examples below use the http command from the httpie package. You can install this using pip install httpie:

$ http https://endpoint/dev/cities/seattle
HTTP/1.1 200 OK

{
    "state": "WA"
}

$ http https://endpoint/dev/cities/portland
HTTP/1.1 200 OK

{
    "state": "OR"
}

Notice what happens if we try to request a city that's not in our CITIES_TO_STATE map:

$ http https://endpoint/dev/cities/vancouver
HTTP/1.1 500 Internal Server Error
Content-Type: application/json
X-Cache: Error from cloudfront

{
    "Code": "ChaliceViewError",
    "Message": "ChaliceViewError: An internal server error occurred."
}

In the next section, we'll see how to fix this and provide better error messages.

Tutorial: Error Messages

In the example above, you'll notice that when our app raised an uncaught exception, a 500 internal server error was returned.

In this section, we're going to show how you can debug and improve these error messages.

The first thing we're going to look at is how we can debug this issue. By default, debugging is turned off, but you can enable debugging to get more information:

from chalice import Chalice

app = Chalice(app_name='helloworld')
app.debug = True

The app.debug = True enables debugging for your app. Save this file and redeploy your changes:

$ chalice deploy
...
https://endpoint/dev/

When you now request the same URL that returned an internal server error, you'll now get back the original stack trace:

$ http https://endpoint/dev/cities/vancouver
{
    "errorMessage": "u'vancouver'",
    "errorType": "KeyError",
    "stackTrace": [
        [
            "/var/task/chalice/__init__.py",
            134,
            "__call__",
            "raise e"
        ]
    ]
}

We can see that the error is caused from an uncaught KeyError resulting from trying to access the vancouver key.

Now that we know the error, we can fix our code. What we'd like to do is catch this exception and instead return a more helpful error message to the user. Here's the updated code:

from chalice import BadRequestError

@app.route('/cities/{city}')
def state_of_city(name):
    try:
        return {'state': CITIES_TO_STATE[name]}
    except KeyError:
        raise BadRequestError("Unknown city '%s', valid choices are: %s" % (
            name, ', '.join(CITIES_TO_STATE.keys())))

Save and deploy these changes:

$ chalice deploy
$ http https://endpoint/dev/cities/vancouver
HTTP/1.1 400 Bad Request

{
    "Code": "BadRequestError",
    "Message": "BadRequestError: Unknown city 'vancouver', valid choices are: portland, seattle"
}

We can see now that we can a Code and Message key, with the message being the value we passed to BadRequestError. Whenver you raise a BadRequestError from your view function, the framework will return an HTTP status code of 400 along with a JSON body with a Code and Message. There's a few additional exceptions you can raise from your python code:

* ChaliceViewError - return a status code of 500
* NotFoundError - return a status code of 404

Tutorial: Additional Routing

So for, our examples have only allowed GET requests. It's actually possible to support additional HTTP methods. Here's an example of a view function that supports PUT:

@app.route('/resource/{value}', methods=['PUT'])
def put_test(value):
    return {"value": value}

We can test this method using the http command:

$ http PUT https://endpoint/dev/resource/foo
HTTP/1.1 200 OK

{
    "value": "foo"
}

Note that the methods kwarg accepts a list of methods. Your view function will be called when any of the HTTP methods you specify are used for the specified resource. For example:

@app.route('/myview', methods=['POST', 'PUT'])
def myview():
    pass

The above view function will be called when either an HTTP POST or PUT is sent to /myview. In the next section we'll go over how you can introspect the given request in order to differentiate between various HTTP methods.

Tutorial: Request Metadata

In the examples above, you saw how to create a view function that supports an HTTP PUT request as well as a view function that supports both POST and PUT via the same view function. However, there's more information we might need about a given request:

• In a PUT/POST, you frequently send a request body. We need some way of accessing the contents of the request body.
• For view functions that support multiple HTTP methods, we'd like to detect which HTTP method was used so we can have different code paths for PUTs vs. POSTs.

All of this and more is handled by the current request object that the chalice library makes available to each view function when it's called.

Let's see an example of this. Suppose we want to create a view function that allowed you to PUT data to an object and retrieve that data via a corresponding GET. We could accomplish that with the following view function:

from chalice import NotFoundError

OBJECTS = {
}

@app.route('/objects/{key}', methods=['GET', 'PUT'])
def myobject(key):
    request = app.current_request
    if request.method == 'PUT':
        OBJECTS[key] = request.json_body
    elif request.method == 'GET':
        try:
            return {key: OBJECTS[key]}
        except KeyError:
            raise NotFoundError(key)

Save this in your app.py file and rerun chalice deploy. Now, you can make a PUT request to /objects/your-key with a request body, and retrieve the value of that body by making a subsequent GET request to the same resource. Here's an example of its usage:

# First, trying to retrieve the key will return a 404.
$ http GET https://endpoint/dev/objects/mykey
HTTP/1.1 404 Not Found

{
    "Code": "NotFoundError",
    "Message": "NotFoundError: mykey"
}

# Next, we'll create that key by sending a PUT request.
$ echo '{"foo": "bar"}' | http PUT https://endpoint/dev/objects/mykey
HTTP/1.1 200 OK

null

# And now we no longer get a 404, we instead get the value we previously
# put.
$ http GET https://endpoint/dev/objects/mykey
HTTP/1.1 200 OK

{
    "mykey": {
        "foo": "bar"
    }
}

You might see a problem with storing the objects in a module level OBJECTS variable. We address this in the next section.

The app.current_request object also has the following properties.

• current_request.query_params - A dict of the query params for the request.
• current_request.headers - A dict of the request headers.
• current_request.uri_params - A dict of the captured URI params.
• current_request.method - The HTTP method (as a string).
• current_request.json_body - The parsed JSON body (json.loads(raw_body))
• current_request.raw_body - The raw HTTP body as bytes.
• current_request.context - A dict of additional context information
• current_request.stage_vars - Configuration for the API Gateway stage

Don't worry about the context and stage_vars for now. We haven't discussed those concepts yet. The current_request object also has a to_dict method, which returns all the information about the current request as a dictionary. Let's use this method to write a view function that returns everything it knows about the request:

@app.route('/introspect')
def introspect():
    return app.current_request.to_dict()

Save this to your app.py file and redeploy with chalice deploy. Here's an example of hitting the /introspect URL. Note how we're sending a query string as well as a custom X-TestHeader header:

$ http 'https://endpoint/dev/introspect?query1=value1&query2=value2' 'X-TestHeader: Foo'
HTTP/1.1 200 OK

{
    "context": {
        ...
        "resource-path": "/introspect",
        "stage": "dev",
        "user-agent": "HTTPie/0.9.3",
        "user-arn": ""
    },
    "headers": {
        "Accept": "*/*",
         ...
        "X-TestHeader": "Foo"
    },
    "json_body": {},
    "method": "GET",
    "query_params": {
        "query1": "value1",
        "query2": "value2"
    },
    "stage_vars": {},
    "uri_params": {}
}

Tutorial: Policy Generation

In the previous section we created a basic rest API that allowed you to store JSON objects by sending the JSON in the body of an HTTP PUT request to /objects/{name}. You could then retrieve objects by sending a GET request to /objects/{name}.

However, there's a problem with the code we wrote:

OBJECTS = {
}

@app.route('/objects/{key}', methods=['GET', 'PUT'])
def myobject(key):
    request = app.current_request
    if request.method == 'PUT':
        OBJECTS[key] = request.json_body
    elif request.method == 'GET':
        try:
            return {key: OBJECTS[key]}
        except KeyError:
            raise NotFoundError(key)

We're storing the key value pairs in a module level OBJECTS variable. We can't rely on local storage like this persisting across requests.

A better solution would be to store this information in Amazon S3. To do this, we're going to use boto3, the AWS SDK for Python. First, install boto3:

$ pip install boto3

Next, add boto3 to your requirements.txt file:

$ echo 'boto3==1.3.1' >> requirements.txt

The requirements.txt file should be in the same directory that contains your app.py file. Next, let's update our view code to use boto3:

import json
import boto3
from botocore.exceptions import ClientError

from chalice import NotFoundError


S3 = boto3.client('s3', region_name='us-west-2')
BUCKET = 'your-bucket-name'


@app.route('/objects/{key}', methods=['GET', 'PUT'])
def s3objects(key):
    request = app.current_request
    if request.method == 'PUT':
        S3.put_object(Bucket=BUCKET, Key=key,
                      Body=json.dumps(request.json_body))
    elif request.method == 'GET':
        try:
            response = S3.get_object(Bucket=BUCKET, Key=key)
            return json.loads(response['Body'].read())
        except ClientError as e:
            raise NotFoundError(key)

Make sure to change BUCKET with the name of an S3 bucket you own. Redeploy your changes with chalice deploy. Now whenver we make a PUT request to /objects/keyname, the data send will be stored in S3. Subsequent GET requests will retrieve this data from S3.

Manually Providing Policies

Whenever your application is deployed using chalice, the auto generated policy is written to disk at <projectdir>/.chalice/policy.json. When you run the chalice deploy command, you can also specify the --no-autogen-policy option. Doing so will result in the chalice CLI loading the <projectdir>/.chalice/policy.json file and using that file as the policy for the IAM role. You can manually edit this file and specify --no-autogen-policy if you'd like to have full control over what IAM policy to associate with the IAM role.

You can also run the chalice gen-policy command from your project directory to print the auto generated policy to stdout. You can then use this as a starting point for your policy.

$ chalice gen-policy
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Action": [
        "s3:ListAllMyBuckets"
      ],
      "Resource": [
        "*"
      ],
      "Effect": "Allow",
      "Sid": "9155de6ad1d74e4c8b1448255770e60c"
    }
  ]
}

Experimental Status

The automatic policy generation is still in the early stages, it should be considered experiemental. You can always disable policy generation with --no-autogen-policy for complete control.

Additionally, you will be prompted for confirmation whenever the auto policy generator detects actions that it would like to add or remove:

$ chalice deploy
Updating IAM policy.

The following action will be added to the execution policy:

s3:ListBucket

Would you like to continue?  [Y/n]:

Backlog

These are features that are in the backlog:

• Adding full support for API gateway stages - issue 20
• Adding support for more than app.py - issue 21

Please share any feedback on the above issues. We'd also love to hear from you. Please create any github issues for additional features you'd like to see: https://github.com/awslabs/chalice/issues

FAQ

Q: How does the Python Serverless Microframework for AWS compare to other similar frameworks?

The biggest difference between this framework and others is that the Python Serverless Microframework for AWS is singularly focused on using a familiar, decorator-based API to write python applications that run on Amazon API Gateway and AWS Lambda. You can think of it as Flask/Bottle for serverless APIs. Its goal is to make writing and deploying these types of applications as simple as possible specifically for Python developers.

To achieve this goal, it has to make certain tradeoffs. Python will always remain the only supported language in this framework. Not every feature of API Gateway and Lambda is exposed in the framework. It makes assumptions about how applications will be deployed, and it has restrictions on how an application can be structured. It does not address the creation and lifecycle of other AWS resources your application may need (Amazon S3 buckets, Amazon DynamoDB tables, etc.). The feature set is purposefully small.

Other full-stack frameworks offer a lot more features and configurability than what this framework has and likely will ever have. Those frameworks are excellent choices for applications that need more than what is offered by this microframework. If all you need is to create a simple rest API in Python that runs on Amazon API Gateway and AWS Lambda, consider giving the Python Serverless Microframework for AWS a try.