Tag Archives: Assertions

[$] Securing the container image supply chain

Post Syndicated from corbet original https://lwn.net/Articles/754443/rss

“Security is hard” is a tautology, especially in the fast-moving world
of container orchestration. We have previously covered various aspects of
Linux container
security through, for example, the Clear Containers implementation
or the broader question of Kubernetes and
, but those are mostly concerned with container isolation; they do not address the
question of trusting a container’s contents. What is a container running?
Who built it and when? Even assuming we have good programmers and solid
isolation layers, propagating that good code around a Kubernetes cluster
and making strong assertions on the integrity of that supply chain is far
from trivial. The 2018 KubeCon
+ CloudNativeCon Europe
event featured some projects that could
eventually solve that problem.

Performing Unit Testing in an AWS CodeStar Project

Post Syndicated from Jerry Mathen Jacob original https://aws.amazon.com/blogs/devops/performing-unit-testing-in-an-aws-codestar-project/

In this blog post, I will show how you can perform unit testing as a part of your AWS CodeStar project. AWS CodeStar helps you quickly develop, build, and deploy applications on AWS. With AWS CodeStar, you can set up your continuous delivery (CD) toolchain and manage your software development from one place.

Because unit testing tests individual units of application code, it is helpful for quickly identifying and isolating issues. As a part of an automated CI/CD process, it can also be used to prevent bad code from being deployed into production.

Many of the AWS CodeStar project templates come preconfigured with a unit testing framework so that you can start deploying your code with more confidence. The unit testing is configured to run in the provided build stage so that, if the unit tests do not pass, the code is not deployed. For a list of AWS CodeStar project templates that include unit testing, see AWS CodeStar Project Templates in the AWS CodeStar User Guide.

The scenario

As a big fan of superhero movies, I decided to list my favorites and ask my friends to vote on theirs by using a WebService endpoint I created. The example I use is a Python web service running on AWS Lambda with AWS CodeCommit as the code repository. CodeCommit is a fully managed source control system that hosts Git repositories and works with all Git-based tools.

Here’s how you can create the WebService endpoint:

Sign in to the AWS CodeStar console. Choose Start a project, which will take you to the list of project templates.

create project

For code edits I will choose AWS Cloud9, which is a cloud-based integrated development environment (IDE) that you use to write, run, and debug code.

choose cloud9

Here are the other tasks required by my scenario:

  • Create a database table where the votes can be stored and retrieved as needed.
  • Update the logic in the Lambda function that was created for posting and getting the votes.
  • Update the unit tests (of course!) to verify that the logic works as expected.

For a database table, I’ve chosen Amazon DynamoDB, which offers a fast and flexible NoSQL database.

Getting set up on AWS Cloud9

From the AWS CodeStar console, go to the AWS Cloud9 console, which should take you to your project code. I will open up a terminal at the top-level folder under which I will set up my environment and required libraries.

Use the following command to set the PYTHONPATH environment variable on the terminal.

export PYTHONPATH=/home/ec2-user/environment/vote-your-movie

You should now be able to use the following command to execute the unit tests in your project.

python -m unittest discover vote-your-movie/tests

cloud9 setup

Start coding

Now that you have set up your local environment and have a copy of your code, add a DynamoDB table to the project by defining it through a template file. Open template.yml, which is the Serverless Application Model (SAM) template file. This template extends AWS CloudFormation to provide a simplified way of defining the Amazon API Gateway APIs, AWS Lambda functions, and Amazon DynamoDB tables required by your serverless application.

AWSTemplateFormatVersion: 2010-09-09
- AWS::Serverless-2016-10-31
- AWS::CodeStar

    Type: String
    Description: CodeStar projectId used to associate new resources to team members

  # The DB table to store the votes.
    Type: AWS::Serverless::SimpleTable
        # Name of the "Candidate" is the partition key of the table.
        Name: Candidate
        Type: String
  # Creating a new lambda function for retrieving and storing votes.
    Type: AWS::Serverless::Function
      Handler: index.handler
      Runtime: python3.6
        # Setting environment variables for your lambda function.
          TABLE_NAME: !Ref "MovieVoteTable"
          TABLE_REGION: !Ref "AWS::Region"
          !Join ['-', [!Ref 'ProjectId', !Ref 'AWS::Region', 'LambdaTrustRole']]
          Type: Api
            Path: /
            Method: get
          Type: Api
            Path: /
            Method: post

We’ll use Python’s boto3 library to connect to AWS services. And we’ll use Python’s mock library to mock AWS service calls for our unit tests.
Use the following command to install these libraries:

pip install --upgrade boto3 mock -t .

install dependencies

Add these libraries to the buildspec.yml, which is the YAML file that is required for CodeBuild to execute.

version: 0.2


      # Upgrade AWS CLI to the latest version
      - pip install --upgrade awscli boto3 mock


      # Discover and run unit tests in the 'tests' directory. For more information, see <https://docs.python.org/3/library/unittest.html#test-discovery>
      - python -m unittest discover tests


      # Use AWS SAM to package the application by using AWS CloudFormation
      - aws cloudformation package --template template.yml --s3-bucket $S3_BUCKET --output-template template-export.yml

  type: zip
    - template-export.yml

Open the index.py where we can write the simple voting logic for our Lambda function.

import json
import datetime
import boto3
import os

table_name = os.environ['TABLE_NAME']
table_region = os.environ['TABLE_REGION']

VOTES_TABLE = boto3.resource('dynamodb', region_name=table_region).Table(table_name)
CANDIDATES = {"A": "Black Panther", "B": "Captain America: Civil War", "C": "Guardians of the Galaxy", "D": "Thor: Ragnarok"}

def handler(event, context):
    if event['httpMethod'] == 'GET':
        resp = VOTES_TABLE.scan()
        return {'statusCode': 200,
                'body': json.dumps({item['Candidate']: int(item['Votes']) for item in resp['Items']}),
                'headers': {'Content-Type': 'application/json'}}

    elif event['httpMethod'] == 'POST':
            body = json.loads(event['body'])
            return {'statusCode': 400,
                    'body': 'Invalid input! Expecting a JSON.',
                    'headers': {'Content-Type': 'application/json'}}
        if 'candidate' not in body:
            return {'statusCode': 400,
                    'body': 'Missing "candidate" in request.',
                    'headers': {'Content-Type': 'application/json'}}
        if body['candidate'] not in CANDIDATES.keys():
            return {'statusCode': 400,
                    'body': 'You must vote for one of the following candidates - {}.'.format(get_allowed_candidates()),
                    'headers': {'Content-Type': 'application/json'}}

        resp = VOTES_TABLE.update_item(
            Key={'Candidate': CANDIDATES.get(body['candidate'])},
            UpdateExpression='ADD Votes :incr',
            ExpressionAttributeValues={':incr': 1},
        return {'statusCode': 200,
                'body': "{} now has {} votes".format(CANDIDATES.get(body['candidate']), resp['Attributes']['Votes']),
                'headers': {'Content-Type': 'application/json'}}

def get_allowed_candidates():
    l = []
    for key in CANDIDATES:
        l.append("'{}' for '{}'".format(key, CANDIDATES.get(key)))
    return ", ".join(l)

What our code basically does is take in the HTTPS request call as an event. If it is an HTTP GET request, it gets the votes result from the table. If it is an HTTP POST request, it sets a vote for the candidate of choice. We also validate the inputs in the POST request to filter out requests that seem malicious. That way, only valid calls are stored in the table.

In the example code provided, we use a CANDIDATES variable to store our candidates, but you can store the candidates in a JSON file and use Python’s json library instead.

Let’s update the tests now. Under the tests folder, open the test_handler.py and modify it to verify the logic.

import os
# Some mock environment variables that would be used by the mock for DynamoDB
os.environ['TABLE_NAME'] = "MockHelloWorldTable"
os.environ['TABLE_REGION'] = "us-east-1"

# The library containing our logic.
import index

# Boto3's core library
import botocore
# For handling JSON.
import json
# Unit test library
import unittest
## Getting StringIO based on your setup.
    from StringIO import StringIO
except ImportError:
    from io import StringIO
## Python mock library
from mock import patch, call
from decimal import Decimal

class TestCandidateVotes(unittest.TestCase):

    ## Test the HTTP GET request flow. 
    ## We expect to get back a successful response with results of votes from the table (mocked).
    def test_get_votes(self, boto_mock):
        # Input event to our method to test.
        expected_event = {'httpMethod': 'GET'}
        # The mocked values in our DynamoDB table.
        items_in_db = [{'Candidate': 'Black Panther', 'Votes': Decimal('3')},
                        {'Candidate': 'Captain America: Civil War', 'Votes': Decimal('8')},
                        {'Candidate': 'Guardians of the Galaxy', 'Votes': Decimal('8')},
                        {'Candidate': "Thor: Ragnarok", 'Votes': Decimal('1')}
        # The mocked DynamoDB response.
        expected_ddb_response = {'Items': items_in_db}
        # The mocked response we expect back by calling DynamoDB through boto.
        response_body = botocore.response.StreamingBody(StringIO(str(expected_ddb_response)),
        # Setting the expected value in the mock.
        boto_mock.side_effect = [expected_ddb_response]
        # Expecting that there would be a call to DynamoDB Scan function during execution with these parameters.
        expected_calls = [call('Scan', {'TableName': os.environ['TABLE_NAME']})]

        # Call the function to test.
        result = index.handler(expected_event, {})

        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 200

        result_body = json.loads(result.get('body'))
        # Verifying that the results match to that from the table.
        assert len(result_body) == len(items_in_db)
        for i in range(len(result_body)):
            assert result_body.get(items_in_db[i].get("Candidate")) == int(items_in_db[i].get("Votes"))

        assert boto_mock.call_count == 1

    ## Test the HTTP POST request flow that places a vote for a selected candidate.
    ## We expect to get back a successful response with a confirmation message.
    def test_place_valid_candidate_vote(self, boto_mock):
        # Input event to our method to test.
        expected_event = {'httpMethod': 'POST', 'body': "{\"candidate\": \"D\"}"}
        # The mocked response in our DynamoDB table.
        expected_ddb_response = {'Attributes': {'Candidate': "Thor: Ragnarok", 'Votes': Decimal('2')}}
        # The mocked response we expect back by calling DynamoDB through boto.
        response_body = botocore.response.StreamingBody(StringIO(str(expected_ddb_response)),
        # Setting the expected value in the mock.
        boto_mock.side_effect = [expected_ddb_response]
        # Expecting that there would be a call to DynamoDB UpdateItem function during execution with these parameters.
        expected_calls = [call('UpdateItem', {
                                                'TableName': os.environ['TABLE_NAME'], 
                                                'Key': {'Candidate': 'Thor: Ragnarok'},
                                                'UpdateExpression': 'ADD Votes :incr',
                                                'ExpressionAttributeValues': {':incr': 1},
                                                'ReturnValues': 'ALL_NEW'
        # Call the function to test.
        result = index.handler(expected_event, {})
        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 200

        assert result.get('body') == "{} now has {} votes".format(

        assert boto_mock.call_count == 1

    ## Test the HTTP POST request flow that places a vote for an non-existant candidate.
    ## We expect to get back a successful response with a confirmation message.
    def test_place_invalid_candidate_vote(self, boto_mock):
        # Input event to our method to test.
        # The valid IDs for the candidates are A, B, C, and D
        expected_event = {'httpMethod': 'POST', 'body': "{\"candidate\": \"E\"}"}
        # Call the function to test.
        result = index.handler(expected_event, {})
        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 400
        assert result.get('body') == 'You must vote for one of the following candidates - {}.'.format(index.get_allowed_candidates())

    ## Test the HTTP POST request flow that places a vote for a selected candidate but associated with an invalid key in the POST body.
    ## We expect to get back a failed (400) response with an appropriate error message.
    def test_place_invalid_data_vote(self, boto_mock):
        # Input event to our method to test.
        # "name" is not the expected input key.
        expected_event = {'httpMethod': 'POST', 'body': "{\"name\": \"D\"}"}
        # Call the function to test.
        result = index.handler(expected_event, {})
        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 400
        assert result.get('body') == 'Missing "candidate" in request.'

    ## Test the HTTP POST request flow that places a vote for a selected candidate but not as a JSON string which the body of the request expects.
    ## We expect to get back a failed (400) response with an appropriate error message.
    def test_place_malformed_json_vote(self, boto_mock):
        # Input event to our method to test.
        # "body" receives a string rather than a JSON string.
        expected_event = {'httpMethod': 'POST', 'body': "Thor: Ragnarok"}
        # Call the function to test.
        result = index.handler(expected_event, {})
        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 400
        assert result.get('body') == 'Invalid input! Expecting a JSON.'

if __name__ == '__main__':

I am keeping the code samples well commented so that it’s clear what each unit test accomplishes. It tests the success conditions and the failure paths that are handled in the logic.

In my unit tests I use the patch decorator (@patch) in the mock library. @patch helps mock the function you want to call (in this case, the botocore library’s _make_api_call function in the BaseClient class).
Before we commit our changes, let’s run the tests locally. On the terminal, run the tests again. If all the unit tests pass, you should expect to see a result like this:

You:~/environment $ python -m unittest discover vote-your-movie/tests
Ran 5 tests in 0.003s

You:~/environment $

Upload to AWS

Now that the tests have passed, it’s time to commit and push the code to source repository!

Add your changes

From the terminal, go to the project’s folder and use the following command to verify the changes you are about to push.

git status

To add the modified files only, use the following command:

git add -u

Commit your changes

To commit the changes (with a message), use the following command:

git commit -m "Logic and tests for the voting webservice."

Push your changes to AWS CodeCommit

To push your committed changes to CodeCommit, use the following command:

git push

In the AWS CodeStar console, you can see your changes flowing through the pipeline and being deployed. There are also links in the AWS CodeStar console that take you to this project’s build runs so you can see your tests running on AWS CodeBuild. The latest link under the Build Runs table takes you to the logs.

unit tests at codebuild

After the deployment is complete, AWS CodeStar should now display the AWS Lambda function and DynamoDB table created and synced with this project. The Project link in the AWS CodeStar project’s navigation bar displays the AWS resources linked to this project.

codestar resources

Because this is a new database table, there should be no data in it. So, let’s put in some votes. You can download Postman to test your application endpoint for POST and GET calls. The endpoint you want to test is the URL displayed under Application endpoints in the AWS CodeStar console.

Now let’s open Postman and look at the results. Let’s create some votes through POST requests. Based on this example, a valid vote has a value of A, B, C, or D.
Here’s what a successful POST request looks like:

POST success

Here’s what it looks like if I use some value other than A, B, C, or D:



Now I am going to use a GET request to fetch the results of the votes from the database.

GET success

And that’s it! You have now created a simple voting web service using AWS Lambda, Amazon API Gateway, and DynamoDB and used unit tests to verify your logic so that you ship good code.
Happy coding!

YouTuber Convicted For Publishing Video Piracy ‘Tutorials’

Post Syndicated from Andy original https://torrentfreak.com/youtuber-convicted-for-publishing-video-piracy-tutorials-171212/

While piracy-focused tutorials have been around for many years, the advent of streaming piracy coupled with the rise of the YouTube star created a perfect storm online.

Even a cursory search on YouTube now turns up thousands of Kodi addon and IPTV-focused channels, each vying to become the ultimate location for the latest and hottest piracy tips. While these videos don’t appear to be a priority for copyright holders, a channel operator in Brazil has just discovered that they aren’t without consequences.

The case involves Marcelo Otto Nascimento, the operator of YouTube channel Café Tecnológico. It began, strangely, with videos about baking bread but later experimented with videos on technological topics including observations on streaming content without paying for it.

In time, this attracted the negative attention of local TV industry group Associação Brasileira de Televisões por Assinatura (Brazilian Association of Television By Signature / ABTA). The group eventually took legal action, complaining about the nature of Nascimento’s YouTube and Facebook pages.

ABTA told the court that Nascimento had been posting tutorials that “encourage the use of equipment and applications designed to allow access to services and content” of its members, despite that content being protected by copyright. The trade group called for the removal of the content, an injunction against Nascimento, an apology, plus compensation for “material and moral damages.”

In his defense, Nascimento said that he merely comments on IPTV systems, does not breach copyright, doesn’t represent unfair competition, and did not cause the TV companies to incur any losses. Overall, Judge Fernando Henrique de Oliveira Biolcati did not agree with his assertions.

“[T]he plain intention of the defendant was to guide users in order for them to obtain access to the restricted content of the applicant’s associates….while gaining advantages for this, especially via remuneration from the providers of the mentioned applications (YouTube and Facebook), proportional to the volumes of visitors,” the Judge wrote in his ruling.

“This is not a question of mere disinterested comments, in the exercise of freedom of expression,” he added.

As a result, Nascimento was ordered to remove all of his online content that could be deemed instructional for pirates, in order to protect the interests of ABTA’s members and their ability to earn revenue from their content. In addition, the channel operator was forbidden from publishing any more videos of a similar nature.

On top, Nascimento must now pay the copyright holders for material damages, yet to be determined, measured from the posting of the first ‘pirate’ tutorial until such a date when all of the tutorials have been removed.

The ruling (PDF via Mg, Portuguese) also requires Nascimento to pay the equivalent of US$7,600 for “moral damages” plus extra for legal costs, during the next 15 days.

In a statement, ABTA said that following this conviction, more people could fall under the spotlight.

“ABTA is also monitoring the activities of other channels on YouTube and on social networks that publish illegal content such as channel lists, movies and ‘free’ access TV series, as well as tutorials and comparisons of devices or applications intended for illicit use (such as Megabox, HtvBox, Kodi, Dejavu, IPTV, ITVGo, etc.),” the group said.

Meanwhile, Nascimento says that he would’ve taken the videos down if only ABTA had asked him to. He will be appealing the decision, claiming that the videos did not teach people about piracy, they only demonstrated functionality. YouTube declined to comment.

Update: Following publication, a spokesperson for TVAddons – which has previously published instructional videos for Kodi – commented to TorrentFreak on the apparent urgency to take this matter to court, rather than handle via YouTube’s established complaints procedure.

“Taking the matter to courts rather than going through YouTube’s takedown system is part of an increasing pattern of legal bullying in the realm of intellectual property enforcement. Fighting a lawsuit against a major corporation can cost more than buying a house, it’s not a fair playing field for your average individual,” he said.

One of the remaining IPTV-focused videos

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Implementing Canary Deployments of AWS Lambda Functions with Alias Traffic Shifting

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/implementing-canary-deployments-of-aws-lambda-functions-with-alias-traffic-shifting/

This post courtesy of Ryan Green, Software Development Engineer, AWS Serverless

The concepts of blue/green and canary deployments have been around for a while now and have been well-established as best-practices for reducing the risk of software deployments.

In a traditional, horizontally scaled application, copies of the application code are deployed to multiple nodes (instances, containers, on-premises servers, etc.), typically behind a load balancer. In these applications, deploying new versions of software to too many nodes at the same time can impact application availability as there may not be enough healthy nodes to service requests during the deployment. This aggressive approach to deployments also drastically increases the blast radius of software bugs introduced in the new version and does not typically give adequate time to safely assess the quality of the new version against production traffic.

In such applications, one commonly accepted solution to these problems is to slowly and incrementally roll out application software across the nodes in the fleet while simultaneously verifying application health (canary deployments). Another solution is to stand up an entirely different fleet and weight (or flip) traffic over to the new fleet after verification, ideally with some production traffic (blue/green). Some teams deploy to a single host (“one box environment”), where the new release can bake for some time before promotion to the rest of the fleet. Techniques like this enable the maintainers of complex systems to safely test in production while minimizing customer impact.

Enter Serverless

There is somewhat of an impedance mismatch when mapping these concepts to a serverless world. You can’t incrementally deploy your software across a fleet of servers when there are no servers!* In fact, even the term “deployment” takes on a different meaning with functions as a service (FaaS). In AWS Lambda, a “deployment” can be roughly modeled as a call to CreateFunction, UpdateFunctionCode, or UpdateAlias (I won’t get into the semantics of whether updating configuration counts as a deployment), all of which may affect the version of code that is invoked by clients.

The abstractions provided by Lambda remove the need for developers to be concerned about servers and Availability Zones, and this provides a powerful opportunity to greatly simplify the process of deploying software.
*Of course there are servers, but they are abstracted away from the developer.

Traffic shifting with Lambda aliases

Before the release of traffic shifting for Lambda aliases, deployments of a Lambda function could only be performed in a single “flip” by updating function code for version $LATEST, or by updating an alias to target a different function version. After the update propagates, typically within a few seconds, 100% of function invocations execute the new version. Implementing canary deployments with this model required the development of an additional routing layer, further adding development time, complexity, and invocation latency.
While rolling back a bad deployment of a Lambda function is a trivial operation and takes effect near instantaneously, deployments of new versions for critical functions can still be a potentially nerve-racking experience.

With the introduction of alias traffic shifting, it is now possible to trivially implement canary deployments of Lambda functions. By updating additional version weights on an alias, invocation traffic is routed to the new function versions based on the weight specified. Detailed CloudWatch metrics for the alias and version can be analyzed during the deployment, or other health checks performed, to ensure that the new version is healthy before proceeding.

Note: Sometimes the term “canary deployments” refers to the release of software to a subset of users. In the case of alias traffic shifting, the new version is released to some percentage of all users. It’s not possible to shard based on identity without adding an additional routing layer.


The simplest possible use of a canary deployment looks like the following:

# Update $LATEST version of function
aws lambda update-function-code --function-name myfunction ….

# Publish new version of function
aws lambda publish-version --function-name myfunction

# Point alias to new version, weighted at 5% (original version at 95% of traffic)
aws lambda update-alias --function-name myfunction --name myalias --routing-config '{"AdditionalVersionWeights" : {"2" : 0.05} }'

# Verify that the new version is healthy
# Set the primary version on the alias to the new version and reset the additional versions (100% weighted)
aws lambda update-alias --function-name myfunction --name myalias --function-version 2 --routing-config '{}'

This is begging to be automated! Here are a few options.

Simple deployment automation

This simple Python script runs as a Lambda function and deploys another function (how meta!) by incrementally increasing the weight of the new function version over a prescribed number of steps, while checking the health of the new version. If the health check fails, the alias is rolled back to its initial version. The health check is implemented as a simple check against the existence of Errors metrics in CloudWatch for the alias and new version.

GitHub aws-lambda-deploy repo


git clone https://github.com/awslabs/aws-lambda-deploy
cd aws-lambda-deploy


# Rollout version 2 incrementally over 10 steps, with 120s between each step
aws lambda invoke --function-name SimpleDeployFunction --log-type Tail --payload \
  '{"function-name": "MyFunction",
  "alias-name": "MyAlias",
  "new-version": "2",
  "steps": 10,
  "interval" : 120,
  "type": "linear"
  }' output

Description of input parameters

  • function-name: The name of the Lambda function to deploy
  • alias-name: The name of the alias used to invoke the Lambda function
  • new-version: The version identifier for the new version to deploy
  • steps: The number of times the new version weight is increased
  • interval: The amount of time (in seconds) to wait between weight updates
  • type: The function to use to generate the weights. Supported values: “linear”

Because this runs as a Lambda function, it is subject to the maximum timeout of 5 minutes. This may be acceptable for many use cases, but to achieve a slower rollout of the new version, a different solution is required.

Step Functions workflow

This state machine performs essentially the same task as the simple deployment function, but it runs as an asynchronous workflow in AWS Step Functions. A nice property of Step Functions is that the maximum deployment timeout has now increased from 5 minutes to 1 year!

The step function incrementally updates the new version weight based on the steps parameter, waiting for some time based on the interval parameter, and performing health checks between updates. If the health check fails, the alias is rolled back to the original version and the workflow fails.

For example, to execute the workflow:

export STATE_MACHINE_ARN=`aws cloudformation describe-stack-resources --stack-name aws-lambda-deploy-stack --logical-resource-id DeployStateMachine --output text | cut  -d$'\t' -f3`

aws stepfunctions start-execution --state-machine-arn $STATE_MACHINE_ARN --input '{
  "function-name": "MyFunction",
  "alias-name": "MyAlias",
  "new-version": "2",
  "steps": 10,
  "interval": 120,
  "type": "linear"}'

Getting feedback on the deployment

Because the state machine runs asynchronously, retrieving feedback on the deployment requires polling for the execution status using DescribeExecution or implementing an asynchronous notification (using SNS or email, for example) from the Rollback or Finalize functions. A CloudWatch alarm could also be created to alarm based on the “ExecutionsFailed” metric for the state machine.

A note on health checks and observability

Weighted rollouts like this are considerably more successful if the code is being exercised and monitored continuously. In this example, it would help to have some automation continuously invoking the alias and reporting metrics on these invocations, such as client-side success rates and latencies.

The absence of Lambda Errors metrics used in these examples can be misleading if the function is not getting invoked. It’s also recommended to instrument your Lambda functions with custom metrics, in addition to Lambda’s built-in metrics, that can be used to monitor health during deployments.


These examples could be easily extended in various ways to support different use cases. For example:

  • Health check implementations: CloudWatch alarms, automatic invocations with payload assertions, querying external systems, etc.
  • Weight increase functions: Exponential, geometric progression, single canary step, etc.
  • Custom success/failure notifications: SNS, email, CI/CD systems, service discovery systems, etc.

Traffic shifting with SAM and CodeDeploy

Using the Lambda UpdateAlias operation with additional version weights provides a powerful primitive for you to implement custom traffic shifting solutions for Lambda functions.

For those not interested in building custom deployment solutions, AWS CodeDeploy provides an intuitive turn-key implementation of this functionality integrated directly into the Serverless Application Model. Traffic-shifted deployments can be declared in a SAM template, and CodeDeploy manages the function rollout as part of the CloudFormation stack update. CloudWatch alarms can also be configured to trigger a stack rollback if something goes wrong.


  Type: AWS::Serverless::Function
    FunctionName: MyFunction
    AutoPublishAlias: MyFunctionInvokeAlias
      Type: Linear10PercentEvery1Minute
        Fn::GetAtt: [ DeploymentRole, Arn ]
       - { Ref: MyFunctionErrorsAlarm }

For more information about using CodeDeploy with SAM, see Automating Updates to Serverless Apps.


It is often the simple features that provide the most value. As I demonstrated in this post, serverless architectures allow the complex deployment orchestration used in traditional applications to be replaced with a simple Lambda function or Step Functions workflow. By allowing invocation traffic to be easily weighted to multiple function versions, Lambda alias traffic shifting provides a simple but powerful feature that I hope empowers you to easily implement safe deployment workflows for your Lambda functions.

Federate Database User Authentication Easily with IAM and Amazon Redshift

Post Syndicated from Thiyagarajan Arumugam original https://aws.amazon.com/blogs/big-data/federate-database-user-authentication-easily-with-iam-and-amazon-redshift/

Managing database users though federation allows you to manage authentication and authorization procedures centrally. Amazon Redshift now supports database authentication with IAM, enabling user authentication though enterprise federation. No need to manage separate database users and passwords to further ease the database administration. You can now manage users outside of AWS and authenticate them for access to an Amazon Redshift data warehouse. Do this by integrating IAM authentication and a third-party SAML-2.0 identity provider (IdP), such as AD FS, PingFederate, or Okta. In addition, database users can also be automatically created at their first login based on corporate permissions.

In this post, I demonstrate how you can extend the federation to enable single sign-on (SSO) to the Amazon Redshift data warehouse.

SAML and Amazon Redshift

AWS supports Security Assertion Markup Language (SAML) 2.0, which is an open standard for identity federation used by many IdPs. SAML enables federated SSO, which enables your users to sign in to the AWS Management Console. Users can also make programmatic calls to AWS API actions by using assertions from a SAML-compliant IdP. For example, if you use Microsoft Active Directory for corporate directories, you may be familiar with how Active Directory and AD FS work together to enable federation. For more information, see the Enabling Federation to AWS Using Windows Active Directory, AD FS, and SAML 2.0 AWS Security Blog post.

Amazon Redshift now provides the GetClusterCredentials API operation that allows you to generate temporary database user credentials for authentication. You can set up an IAM permissions policy that generates these credentials for connecting to Amazon Redshift. Extending the IAM authentication, you can configure the federation of AWS access though a SAML 2.0–compliant IdP. An IAM role can be configured to permit the federated users call the GetClusterCredentials action and generate temporary credentials to log in to Amazon Redshift databases. You can also set up policies to restrict access to Amazon Redshift clusters, databases, database user names, and user group.

Amazon Redshift federation workflow

In this post, I demonstrate how you can use a JDBC– or ODBC-based SQL client to log in to the Amazon Redshift cluster using this feature. The SQL clients used with Amazon Redshift JDBC or ODBC drivers automatically manage the process of calling the GetClusterCredentials action, retrieving the database user credentials, and establishing a connection to your Amazon Redshift database. You can also use your database application to programmatically call the GetClusterCredentials action, retrieve database user credentials, and connect to the database. I demonstrate these features using an example company to show how different database users accounts can be managed easily using federation.

The following diagram shows how the SSO process works:

  2. Authenticate using Corp Username/Password
  3. IdP sends SAML assertion
  4. Call STS to assume role with SAML
  5. STS Returns Temp Credentials
  6. Use Temp Credentials to get Temp cluster credentials
  7. Connect to Amazon Redshift using temp credentials


Example Corp. is using Active Directory (idp host:demo.examplecorp.com) to manage federated access for users in its organization. It has an AWS account: 123456789012 and currently manages an Amazon Redshift cluster with the cluster ID “examplecorp-dw”, database “analytics” in us-west-2 region for its Sales and Data Science teams. It wants the following access:

  • Sales users can access the examplecorp-dw cluster using the sales_grp database group
  • Sales users access examplecorp-dw through a JDBC-based SQL client
  • Sales users access examplecorp-dw through an ODBC connection, for their reporting tools
  • Data Science users access the examplecorp-dw cluster using the data_science_grp database group.
  • Partners access the examplecorp-dw cluster and query using the partner_grp database group.
  • Partners are not federated through Active Directory and are provided with separate IAM user credentials (with IAM user name examplecorpsalespartner).
  • Partners can connect to the examplecorp-dw cluster programmatically, using language such as Python.
  • All users are automatically created in Amazon Redshift when they log in for the first time.
  • (Optional) Internal users do not specify database user or group information in their connection string. It is automatically assigned.
  • Data warehouse users can use SSO for the Amazon Redshift data warehouse using the preceding permissions.

Step 1:  Set up IdPs and federation

The Enabling Federation to AWS Using Windows Active Directory post demonstrated how to prepare Active Directory and enable federation to AWS. Using those instructions, you can establish trust between your AWS account and the IdP and enable user access to AWS using SSO.  For more information, see Identity Providers and Federation.

For this walkthrough, assume that this company has already configured SSO to their AWS account: 123456789012 for their Active Directory domain demo.examplecorp.com. The Sales and Data Science teams are not required to specify database user and group information in the connection string. The connection string can be configured by adding SAML Attribute elements to your IdP. Configuring these optional attributes enables internal users to conveniently avoid providing the DbUser and DbGroup parameters when they log in to Amazon Redshift.

The user-name attribute can be set up as follows, with a user ID (for example, nancy) or an email address (for example. [email protected]):

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/DbUser">  

The AutoCreate attribute can be defined as follows:

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/AutoCreate">

The sales_grp database group can be included as follows:

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/DbGroups">

For more information about attribute element configuration, see Configure SAML Assertions for Your IdP.

Step 2: Create IAM roles for access to the Amazon Redshift cluster

The next step is to create IAM policies with permissions to call GetClusterCredentials and provide authorization for Amazon Redshift resources. To grant a SQL client the ability to retrieve the cluster endpoint, region, and port automatically, include the redshift:DescribeClusters action with the Amazon Redshift cluster resource in the IAM role.  For example, users can connect to the Amazon Redshift cluster using a JDBC URL without the need to hardcode the Amazon Redshift endpoint:

Previous:  jdbc:redshift://endpoint:port/database

Current:  jdbc:redshift:iam://clustername:region/dbname

Use IAM to create the following policies. You can also use an existing user or role and assign these policies. For example, if you already created an IAM role for IdP access, you can attach the necessary policies to that role. Here is the policy created for sales users for this example:


    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": [
            "Resource": [
            "Effect": "Allow",
            "Action": [
            "Resource": [
            "Condition": {
                "StringEquals": {
                    "aws:userid": "AIDIODR4TAW7CSEXAMPLE:${redshift:DbUser}@examplecorp.com"
            "Effect": "Allow",
            "Action": [
            "Resource": [
            "Effect": "Allow",
            "Action": [
            "Resource": [

The policy uses the following parameter values:

  • Region: us-west-2
  • AWS Account: 123456789012
  • Cluster name: examplecorp-dw
  • Database group: sales_grp
Policy Statement Description

Allow users to retrieve the cluster endpoint, region, and port automatically for the Amazon Redshift cluster examplecorp-dw. This specification uses the resource format arn:aws:redshift:region:account-id:cluster:clustername. For example, the SQL client JDBC can be specified in the format jdbc:redshift:iam://clustername:region/dbname.

For more information, see Amazon Resource Names.


Generates a temporary token to authenticate into the examplecorp-dw cluster. “arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}” restricts the corporate user name to the database user name for that user. This resource is specified using the format: arn:aws:redshift:region:account-id:dbuser:clustername/dbusername.

The Condition block enforces that the AWS user ID should match “AIDIODR4TAW7CSEXAMPLE:${redshift:DbUser}@examplecorp.com”, so that individual users can authenticate only as themselves. The AIDIODR4TAW7CSEXAMPLE role has the Sales_DW_IAM_Policy policy attached.

Automatically creates database users in examplecorp-dw, when they log in for the first time. Subsequent logins reuse the existing database user.
Allows sales users to join the sales_grp database group through the resource “arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/sales_grp” that is specified in the format arn:aws:redshift:region:account-id:dbgroup:clustername/dbgroupname.

Similar policies can be created for Data Science users with access to join the data_science_grp group in examplecorp-dw. You can now attach the Sales_DW_IAM_Policy policy to the role that is mapped to IdP application for SSO.
 For more information about how to define the claim rules, see Configuring SAML Assertions for the Authentication Response.

Because partners are not authorized using Active Directory, they are provided with IAM credentials and added to the partner_grp database group. The Partner_DW_IAM_Policy is attached to the IAM users for partners. The following policy allows partners to log in using the IAM user name as the database user name.


    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": [
            "Resource": [
            "Effect": "Allow",
            "Action": [
            "Resource": [
            "Condition": {
                "StringEquals": {
                    "redshift:DbUser": "${aws:username}"
            "Effect": "Allow",
            "Action": [
            "Resource": [
            "Effect": "Allow",
            "Action": [
            "Resource": [

redshift:DbUser“: “${aws:username}” forces an IAM user to use the IAM user name as the database user name.

With the previous steps configured, you can now establish the connection to Amazon Redshift through JDBC– or ODBC-supported clients.

Step 3: Set up database user access

Before you start connecting to Amazon Redshift using the SQL client, set up the database groups for appropriate data access. Log in to your Amazon Redshift database as superuser to create a database group, using CREATE GROUP.

Log in to examplecorp-dw/analytics as superuser and create the following groups and users:

CREATE GROUP sales_grp;
CREATE GROUP datascience_grp;
CREATE GROUP partner_grp;

Use the GRANT command to define access permissions to database objects (tables/views) for the preceding groups.

Step 4: Connect to Amazon Redshift using the JDBC SQL client

Assume that sales user “nancy” is using the SQL Workbench client and JDBC driver to log in to the Amazon Redshift data warehouse. The following steps help set up the client and establish the connection:

  1. Download the latest Amazon Redshift JDBC driver from the Configure a JDBC Connection page
  2. Build the JDBC URL with the IAM option in the following format:

Because the redshift:DescribeClusters action is assigned to the preceding IAM roles, it automatically resolves the cluster endpoints and the port. Otherwise, you can specify the endpoint and port information in the JDBC URL, as described in Configure a JDBC Connection.

Identify the following JDBC options for providing the IAM credentials (see the “Prepare your environment” section) and configure in the SQL Workbench Connection Profile:

idp_host=demo.examplecorp.com (The name of the corporate identity provider host)
idp_port=443  (The port of the corporate identity provider host)
user=examplecorp\nancy(corporate user name)
password=***(corporate user password)

The SQL workbench configuration looks similar to the following screenshot:

Now, “nancy” can connect to examplecorp-dw by authenticating using the corporate Active Directory. Because the SAML attributes elements are already configured for nancy, she logs in as database user nancy and is assigned the sales_grp. Similarly, other Sales and Data Science users can connect to the examplecorp-dw cluster. A custom Amazon Redshift ODBC driver can also be used to connect using a SQL client. For more information, see Configure an ODBC Connection.

Step 5: Connecting to Amazon Redshift using JDBC SQL Client and IAM Credentials

This optional step is necessary only when you want to enable users that are not authenticated with Active Directory. Partners are provided with IAM credentials that they can use to connect to the examplecorp-dw Amazon Redshift clusters. These IAM users are attached to Partner_DW_IAM_Policy that assigns them to be assigned to the public database group in Amazon Redshift. The following JDBC URLs enable them to connect to the Amazon Redshift cluster:

jdbc:redshift:iam//examplecorp-dw/analytics?AccessKeyID=XXX&SecretAccessKey=YYY&DbUser=examplecorpsalespartner&DbGroup= partner_grp&AutoCreate=true

The AutoCreate option automatically creates a new database user the first time the partner logs in. There are several other options available to conveniently specify the IAM user credentials. For more information, see Options for providing IAM credentials.

Step 6: Connecting to Amazon Redshift using an ODBC client for Microsoft Windows

Assume that another sales user “uma” is using an ODBC-based client to log in to the Amazon Redshift data warehouse using Example Corp Active Directory. The following steps help set up the ODBC client and establish the Amazon Redshift connection in a Microsoft Windows operating system connected to your corporate network:

  1. Download and install the latest Amazon Redshift ODBC driver.
  2. Create a system DSN entry.
    1. In the Start menu, locate the driver folder or folders:
      • Amazon Redshift ODBC Driver (32-bit)
      • Amazon Redshift ODBC Driver (64-bit)
      • If you installed both drivers, you have a folder for each driver.
    2. Choose ODBC Administrator, and then type your administrator credentials.
    3. To configure the driver for all users on the computer, choose System DSN. To configure the driver for your user account only, choose User DSN.
    4. Choose Add.
  3. Select the Amazon Redshift ODBC driver, and choose Finish. Configure the following attributes:
    Data Source Name =any friendly name to identify the ODBC connection 
    user=uma(corporate user name)
    Auth Type-Identity Provider: AD FS
    password=leave blank (Windows automatically authenticates)
    Cluster ID: examplecorp-dw
    idp_host=demo.examplecorp.com (The name of the corporate IdP host)

This configuration looks like the following:

  1. Choose OK to save the ODBC connection.
  2. Verify that uma is set up with the SAML attributes, as described in the “Set up IdPs and federation” section.

The user uma can now use this ODBC connection to establish the connection to the Amazon Redshift cluster using any ODBC-based tools or reporting tools such as Tableau. Internally, uma authenticates using the Sales_DW_IAM_Policy  IAM role and is assigned the sales_grp database group.

Step 7: Connecting to Amazon Redshift using Python and IAM credentials

To enable partners, connect to the examplecorp-dw cluster programmatically, using Python on a computer such as Amazon EC2 instance. Reuse the IAM users that are attached to the Partner_DW_IAM_Policy policy defined in Step 2.

The following steps show this set up on an EC2 instance:

  1. Launch a new EC2 instance with the Partner_DW_IAM_Policy role, as described in Using an IAM Role to Grant Permissions to Applications Running on Amazon EC2 Instances. Alternatively, you can attach an existing IAM role to an EC2 instance.
  2. This example uses Python PostgreSQL Driver (PyGreSQL) to connect to your Amazon Redshift clusters. To install PyGreSQL on Amazon Linux, use the following command as the ec2-user:
    sudo easy_install pip
    sudo yum install postgresql postgresql-devel gcc python-devel
    sudo pip install PyGreSQL

  1. The following code snippet demonstrates programmatic access to Amazon Redshift for partner users:
    #!/usr/bin/env python
    python redshift-unload-copy.py <config file> <region>
    * Copyright 2014, Amazon.com, Inc. or its affiliates. All Rights Reserved.
    * Licensed under the Amazon Software License (the "License").
    * You may not use this file except in compliance with the License.
    * A copy of the License is located at
    * http://aws.amazon.com/asl/
    * or in the "license" file accompanying this file. This file is distributed
    * express or implied. See the License for the specific language governing
    * permissions and limitations under the License.
    import sys
    import pg
    import boto3
    REGION = 'us-west-2'
    CLUSTER_IDENTIFIER = 'examplecorp-dw'
    DB_NAME = 'sales_db'
    DB_USER = 'examplecorpsalespartner'
    options = """keepalives=1 keepalives_idle=200 keepalives_interval=200
    set_timeout_stmt = "set statement_timeout = 1200000"
    def conn_to_rs(host, port, db, usr, pwd, opt=options, timeout=set_timeout_stmt):
        rs_conn_string = """host=%s port=%s dbname=%s user=%s password=%s
                             %s""" % (host, port, db, usr, pwd, opt)
        print "Connecting to %s:%s:%s as %s" % (host, port, db, usr)
        rs_conn = pg.connect(dbname=rs_conn_string)
        return rs_conn
    def main():
        # describe the cluster and fetch the IAM temporary credentials
        global redshift_client
        redshift_client = boto3.client('redshift', region_name=REGION)
        response_cluster_details = redshift_client.describe_clusters(ClusterIdentifier=CLUSTER_IDENTIFIER)
        response_credentials = redshift_client.get_cluster_credentials(DbUser=DB_USER,DbName=DB_NAME,ClusterIdentifier=CLUSTER_IDENTIFIER,DurationSeconds=3600)
        rs_host = response_cluster_details['Clusters'][0]['Endpoint']['Address']
        rs_port = response_cluster_details['Clusters'][0]['Endpoint']['Port']
        rs_db = DB_NAME
        rs_iam_user = response_credentials['DbUser']
        rs_iam_pwd = response_credentials['DbPassword']
        # connect to the Amazon Redshift cluster
        conn = conn_to_rs(rs_host, rs_port, rs_db, rs_iam_user,rs_iam_pwd)
        # execute a query
        result = conn.query("SELECT sysdate as dt")
        # fetch results from the query
        for dt_val in result.getresult() :
            print dt_val
        # close the Amazon Redshift connection
    if __name__ == "__main__":

You can save this Python program in a file (redshiftscript.py) and execute it at the command line as ec2-user:

python redshiftscript.py

Now partners can connect to the Amazon Redshift cluster using the Python script, and authentication is federated through the IAM user.


In this post, I demonstrated how to use federated access using Active Directory and IAM roles to enable single sign-on to an Amazon Redshift cluster. I also showed how partners outside an organization can be managed easily using IAM credentials.  Using the GetClusterCredentials API action, now supported by Amazon Redshift, lets you manage a large number of database users and have them use corporate credentials to log in. You don’t have to maintain separate database user accounts.

Although this post demonstrated the integration of IAM with AD FS and Active Directory, you can replicate this solution across with your choice of SAML 2.0 third-party identity providers (IdP), such as PingFederate or Okta. For the different supported federation options, see Configure SAML Assertions for Your IdP.

If you have questions or suggestions, please comment below.

Additional Reading

Learn how to establish federated access to your AWS resources by using Active Directory user attributes.

About the Author

Thiyagarajan Arumugam is a Big Data Solutions Architect at Amazon Web Services and designs customer architectures to process data at scale. Prior to AWS, he built data warehouse solutions at Amazon.com. In his free time, he enjoys all outdoor sports and practices the Indian classical drum mridangam.


SAML for Your Serverless JavaScript Application: Part II

Post Syndicated from Bryan Liston original https://aws.amazon.com/blogs/compute/saml-for-your-serverless-javascript-application-part-ii/

Contributors: Richard Threlkeld, Gene Ting, Stefano Buliani

The full code for both scenarios—including SAM templates—can be found at the samljs-serverless-sample GitHub repository. We highly recommend you use the SAM templates in the GitHub repository to create the resources, opitonally you can manually create them.

This is the second part of a two part series for using SAML providers in your application and receiving short-term credentials to access AWS Services. These credentials can be limited with IAM roles so the users of the applications can perform actions like fetching data from databases or uploading files based on their level of authorization. For example, you may want to build a JavaScript application that allows a user to authenticate against Active Directory Federation Services (ADFS). The user can be granted scoped AWS credentials to invoke an API to display information in the application or write to an Amazon DynamoDB table.

Part I of this series walked through a client-side flow of retrieving SAML claims and passing them to Amazon Cognito to retrieve credentials. This blog post will take you through a more advanced scenario where logic can be moved to the backend for a more comprehensive and flexible solution.


As in Part I of this series, you need ADFS running in your environment. The following configurations are used for reference:

  1. ADFS federated with the AWS console. For a walkthrough with an AWS CloudFormation template, see Enabling Federation to AWS Using Windows Active Directory, ADFS, and SAML 2.0.
  2. Verify that you can authenticate with user example\bob for both the ADFS-Dev and ADFS-Production groups via the sign-in page.
  3. Create an Amazon Cognito identity pool.

Scenario Overview

The scenario in the last blog post may be sufficient for many organizations but, due to size restrictions, some browsers may drop part or all of a query string when sending a large number of claims in the SAMLResponse. Additionally, for auditing and logging reasons, you may wish to relay SAML assertions via POST only and perform parsing in the backend before sending credentials to the client. This scenario allows you to perform custom business logic and validation as well as putting tracking controls in place.

In this post, we want to show you how these requirements can be achieved in a Serverless application. We also show how different challenges (like XML parsing and JWT exchange) can be done in a Serverless application design. Feel free to mix and match, or swap pieces around to suit your needs.

This scenario uses the following services and features:

  • Cognito for unique ID generation and default role mapping
  • S3 for static website hosting
  • API Gateway for receiving the SAMLResponse POST from ADFS
  • Lambda for processing the SAML assertion using a native XML parser
  • DynamoDB conditional writes for session tracking exceptions
  • STS for credentials via Lambda
  • KMS for signing JWT tokens
  • API Gateway custom authorizers for controlling per-session access to credentials, using JWT tokens that were signed with KMS keys
  • JavaScript-generated SDK from API Gateway using a service proxy to DynamoDB
  • RelayState in the SAMLRequest to ADFS to transmit the CognitoID and a short code from the client to your AWS backend

At a high level, this solution is similar to that of Scenario 1; however, most of the work is done in the infrastructure rather than on the client.

  • ADFS still uses a POST binding to redirect the SAMLResponse to API Gateway; however, the Lambda function does not immediately redirect.
  • The Lambda function decodes and uses an XML parser to read the properties of the SAML assertion.
  • If the user’s assertion shows that they belong to a certain group matching a specified string (“Prod” in the sample), then you assign a role that they can assume (“ADFS-Production”).
  • Lambda then gets the credentials on behalf of the user and stores them in DynamoDB as well as logging an audit record in a separate table.
  • Lambda then returns a short-lived, signed JSON Web Token (JWT) to the JavaScript application.
  • The application uses the JWT to get their stored credentials from DynamoDB through an API Gateway custom authorizer.

The architecture you build in this tutorial is outlined in the following diagram.


First, a user visits your static website hosted on S3. They generate an ephemeral random code that is transmitted during redirection to ADFS, where they are prompted for their Active Directory credentials.

Upon successful authentication, the ADFS server redirects the SAMLResponse assertion, along with the code (as the RelayState) via POST to API Gateway.

The Lambda function parses the SAMLResponse. If the user is part of the appropriate Active Directory group (AWS-Production in this tutorial), it retrieves credentials from STS on behalf of the user.

The credentials are stored in a DynamoDB table called SAMLSessions, along with the short code. The user login is stored in a tracking table called SAMLUsers.

The Lambda function generates a JWT token, with a 30-second expiration time signed with KMS, then redirects the client back to the static website along with this token.

The client then makes a call to an API Gateway resource acting as a DynamoDB service proxy that retrieves the credentials via a DeleteItem call. To make this call, the client passes the JWT in the authorization header.

A custom authorizer runs to validate the token using the KMS key again as well as the original random code.

Now that the client has credentials, it can use these to access AWS resources.

Tutorial: Backend processing and audit tracking

Before you walk through this tutorial you will need the source code from the samljs-serverless-sample Github Repository. You should use the SAM template provided in order to streamline the process but we’ll outline how you you would manually create resources too. There is a readme in the repository with instructions for using the SAM template. Either way you will still perform the manual steps of KMS key configuration, ADFS enablement of RelayState, and Amazon Cognito Identity Pool creation. The template will automate the process in creating the S3 website, Lambda functions, API Gateway resources and DynamoDB tables.

We walk through the details of all the steps and configuration below for illustrative purposes in this tutorial calling out the sections that can be omitted if you used the SAM template.

KMS key configuration

To sign JWT tokens, you need an encrypted plaintext key, to be stored in KMS. You will need to complete this step even if you use the SAM template.

  1. In the IAM console, choose Encryption Keys, Create Key.
  2. For Alias, type sessionMaster.
  3. For Advanced Options, choose KMS, Next Step.
  4. For Key Administrative Permissions, select your administrative role or user account.
  5. For Key Usage Permissions, you can leave this blank as the IAM Role (next section) will have individual key actions configured. This allows you to perform administrative actions on the set of keys while the Lambda functions have rights to just create data keys for encryption/decryption and use them to sign JWTs.
  6. Take note of the Key ID, which is needed for the Lambda functions.

IAM role configuration

You will need an IAM role for executing your Lambda functions. If you are using the SAM template this can be skipped. The sample code in the GitHub repository under Scenario2 creates separate roles for each function, with limited permissions on individual resources when you use the SAM template. We recommend separate roles scoped to individual resources for production deployments. Your Lambda functions need the following permissions:

    "Version": "2012-10-17",
    "Statement": [
            "Sid": "Stmt1432927122000",
            "Effect": "Allow",
            "Action": [
            "Resource": [

Lambda function configuration

If you are not using the SAM template, create the following three Lambda functions from the GitHub repository in /Scenario2/lambda using the following names and environment variables. The Lambda functions are written in Node.js.

  • GenerateKey_awslabs_samldemo
  • ProcessSAML_awslabs_samldemo
  • SAMLCustomAuth_awslabs_samldemo

The functions above are built, packaged, and uploaded to Lambda. For two of the functions, this can be done from your workstation (the sample commands for each function assume OSX or Linux). The third will need to be built on an AWS EC2 instance running the current Lambda AMI.


This function is only used one time to create keys in KMS for signing JWT tokens. The function calls GenerateDataKey and stores the encrypted CipherText blob as Base64 in DynamoDB. This is used by the other two functions for getting the PlainTextKey for signing with a Decrypt operation.

This function only requires a single file. It has the following environment variables:

  • KMS_KEY_ID: Unique identifier from KMS for your sessionMaster Key
  • ENC_CONTEXT: ADFS (or something unique to your organization)

Navigate into /Scenario2/lambda/GenerateKey and run the following commands:

zip –r generateKey.zip .

aws lambda create-function --function-name GenerateKey_awslabs_samldemo --runtime nodejs4.3 --role LAMBDA_ROLE_ARN --handler index.handler --timeout 10 --memory-size 512 --zip-file fileb://generateKey.zip --environment Variables={SESSION_DDB_TABLE=SAMLSessions,ENC_CONTEXT=ADFS,RAND_HASH=us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX,KMS_KEY_ID=<kms key="KEY" id="ID">}


This is an API Gateway custom authorizer called after the client has been redirected to the website as part of the login workflow. This function calls a GET against the service proxy to DynamoDB, retrieving credentials. The function uses the KMS key signing validation of the JWT created in the ProcessSAML_awslabs_samldemo function and also validates the random code that was generated at the beginning of the login workflow.

You must install the dependencies before zipping this function up. It has the following environment variables:

  • ENC_CONTEXT: ADFS (or whatever was used in GenerateKey_awslabs_samldemo)

Navigate into /Scenario2/lambda/CustomAuth and run:

npm install

zip –r custom_auth.zip .

aws lambda create-function --function-name SAMLCustomAuth_awslabs_samldemo --runtime nodejs4.3 --role LAMBDA_ROLE_ARN --handler CustomAuth.handler --timeout 10 --memory-size 512 --zip-file fileb://custom_auth.zip --environment Variables={SESSION_DDB_TABLE=SAMLSessions,ENC_CONTEXT=ADFS,ID_HASH= us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX }


This function is called when ADFS sends the SAMLResponse to API Gateway. The function parses the SAML assertion to select a role (based on a simple string search) and extract user information. It then uses this data to get short-term credentials from STS via AssumeRoleWithSAML and stores this information in a SAMLSessions table and tracks the user login via a SAMLUsers table. Both of these are DynamoDB tables but you could also store the user information in another AWS database type, as this is for auditing purposes. Finally, this function creates a JWT (signed with the KMS key) which is only valid for 30 seconds and is returned to the client as part of a 302 redirect from API Gateway.

This function needs to be built on an EC2 server running Amazon Linux. This function leverages two main external libraries:

  • nJwt: Used for secure JWT creation for individual client sessions to get access to their records
  • libxmljs: Used for XML XPath queries of the decoded SAMLResponse from AD FS

Libxmljs uses native build tools and you should run this on EC2 running the same AMI as Lambda and with Node.js v4.3.2; otherwise, you might see errors. For more information about current Lambda AMI information, see Lambda Execution Environment and Available Libraries.

After you have the correct AMI launched in EC2 and have SSH open to that host, install Node.js. Ensure that the Node.js version on EC2 is 4.3.2, to match Lambda. If your version is off, you can roll back with NVM.

After you have set up Node.js, run the following command:

yum install -y make gcc*

Now, create a /saml folder on your EC2 server and copy up ProcessSAML.js and package.json from /Scenario2/lambda/ProcessSAML to the EC2 server. Here is a sample SCP command:

cd ProcessSAML/


package.json    ProcessSAML.js

scp -i ~/path/yourpemfile.pem ./* [email protected]:/home/ec2-user/saml/

Then you can SSH to your server, cd into the /saml directory, and run:

npm install

A successful build should look similar to the following:


Finally, zip up the package and create the function using the following AWS CLI command and these environment variables. Configure the CLI with your credentials as needed.

  • ENC_CONTEXT: ADFS (or whatever was used in GenerateKeyawslabssamldemo)
  • PRINCIPAL_ARN: Full ARN of the AD FS IdP created in the IAM console
  • REDIRECT_URL: Endpoint URL of your static S3 website (or CloudFront distribution domain name if you did that optional step)
zip –r saml.zip .

aws lambda create-function --function-name ProcessSAML_awslabs_samldemo --runtime nodejs4.3 --role LAMBDA_ROLE_ARN --handler ProcessSAML.handler --timeout 10 --memory-size 512 --zip-file fileb://saml.zip –environment Variables={USER_DDB_TABLE=SAMLUsers,SESSION_DDB_TABLE= SAMLSessions,REDIRECT_URL=<your S3 bucket and test page path>,ID_HASH=us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX,ENC_CONTEXT=ADFS,PRINCIPAL_ARN=<your ADFS IdP ARN>}

If you built the first two functions on your workstation and created the ProcessSAML_awslabs_samldemo function separately in the Lambda console before building on EC2, you can update the code after building on with the following command:

aws lambda update-function-code --function-name ProcessSAML_awslabs_samldemo --zip-file fileb://saml.zip

Role trust policy configuration

This scenario uses STS directly to assume a role. You will need to complete this step even if you use the SAM template. Modify the trust policy, as you did before when Amazon Cognito was assuming the role. In the GitHub repository sample code, ProcessSAML.js is preconfigured to filter and select a role with “Prod” in the name via the selectedRole variable.

This is an example of business logic you can alter in your organization later, such as a callout to an external mapping database for other rules matching. In this tutorial, it corresponds to the ADFS-Production role that was created.

  1. In the IAM console, choose Roles and open the ADFS-Production Role.
  2. Edit the Trust Permissions field and replace the content with the following:

      "Version": "2012-10-17",
      "Statement": [
          "Effect": "Allow",
          "Principal": {
            "Federated": [
          "Action": "sts:AssumeRoleWithSAML"

If you end up using another role (or add more complex filtering/selection logic), ensure that those roles have similar trust policy configurations. Also note that the sample policy above purposely uses an array for the federated provider matching the IdP ARN that you added. If your environment has multiple SAML providers, you could list them here and modify the code in ProcessSAML.js to process requests from different IdPs and grant or revoke credentials accordingly.

DynamoDB table creation

If you are not using the SAM template, create two DynamoDB tables:

  • SAMLSessions: Temporarily stores credentials from STS. Credentials are removed by an API Gateway Service Proxy to the DynamoDB DeleteItem call that simultaneously returns the credentials to the client.
  • SAMLUsers: This table is for tracking user information and the last time they authenticated in the system via ADFS.

The following AWS CLI commands creates the tables (indexed only with a primary key hash, called identityHash and CognitoID respectively):

aws dynamodb create-table \
    --table-name SAMLSessions \
    --attribute-definitions \
        AttributeName=group,AttributeType=S \
    --key-schema AttributeName=identityhash,KeyType=HASH \
    --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5
aws dynamodb create-table \
    --table-name SAMLUsers \
    --attribute-definitions \
        AttributeName=CognitoID,AttributeType=S \
    --key-schema AttributeName=CognitoID,KeyType=HASH \
    --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5

After the tables are created, you should be able to run the GenerateKey_awslabs_samldemo Lambda function and see a CipherText key stored in SAMLSessions. This is only for convenience of this post, to demonstrate that you should persist CipherText keys in a data store and never persist plaintext keys that have been decrypted. You should also never log plaintext keys in your code.

API Gateway configuration

If you are not using the SAM template, you will need to create API Gateway resources. If you have created resources for Scenario 1 in Part I, then the naming of these resources may be similar. If that is the case, then simply create an API with a different name (SAMLAuth2 or similar) and follow these steps accordingly.

  1. In the API Gateway console for your API, choose Authorizers, Custom Authorizer.
  2. Select your region and enter SAMLCustomAuth_awslabs_samldemo for the Lambda function. Choose a friendly name like JWTParser and ensure that Identity token source is method.request.header.Authorization. This tells the custom authorizer to look for the JWT in the Authorization header of the HTTP request, which is specified in the JavaScript code on your S3 webpage. Save the changes.


Now it’s time to wire up the Lambda functions to API Gateway.

  1. In the API Gateway console, choose Resources, select your API, and then create a Child Resource called SAML. This includes a POST and a GET method. The POST method uses the ProcessSAML_awslabs_samldemo Lambda function and a 302 redirect, while the GET method uses the JWTParser custom authorizer with a service proxy to DynamoDB to retrieve credentials upon successful authorization.
  2. lambdasamltwo_4.png

  3. Create a POST method. For Integration Type, choose Lambda and add the ProcessSAML_awslabs_samldemo Lambda function. For Method Request, add headers called RelayState and SAMLResponse.


  4. Delete the Method Response code for 200 and add a 302. Create a response header called Location. In the Response Models section, for Content-Type, choose application/json and for Models, choose Empty.


  5. Delete the Integration Response section for 200 and add one for 302 that has a Method response status of 302. Edit the response header for Location to add a Mapping value of integration.response.body.location.


  6. Finally, in order for Lambda to capture the SAMLResponse and RelayState values, choose Integration Request.

  7. In the Body Mapping Template section, for Content-Type, enter application/x-www-form-urlencoded and add the following template:

    "SAMLResponse" :"$input.params('SAMLResponse')",
    "RelayState" :"$input.params('RelayState')",
    "formparams" : $input.json('$')

  8. Create a GET method with an Integration Type of Service Proxy. Select the region and DynamoDB as the AWS Service. Use POST for the HTTP method and DeleteItem for the Action. This is important as you leverage a DynamoDB feature to return the current records when you perform deletion. This simultaneously allows credentials in this system to not be stored long term and also allows clients to retrieve them. For Execution role, use the Lambda role from earlier or a new role that only has IAM scoped permissions for DeleteItem on the SAMLSessions table.


  9. Save this and open Method Request.

  10. For Authorization, select your custom authorizer JWTParser. Add in a header called COGNITO_ID and save the changes.


  11. In the Integration Request, add in a header name of Content-Type and a value for Mapped of ‘application/x-amzn-json-1.0‘ (you need the single quotes surrounding the entry).

  12. Next, in the Body Mapping Template section, for Content-Type, enter application/json and add the following template:

        "TableName": "SAMLSessions",
        "Key": {
            "identityhash": {
                "S": "$input.params('COGNITO_ID')"
        "ReturnValues": "ALL_OLD"

Inspect this closely for a moment. When your client passes the JWT in an Authorization Header to this GET method, the JWTParser Custom Authorizer grants/denies executing a DeleteItem call on the SAMLSessions table.


If it is granted, then there needs to be an item to delete the reference as a primary key to the table. The client JavaScript (seen in a moment) passes its CognitoID through as a header called COGNITO_ID that is mapped above. DeleteItem executes to remove the credentials that were placed there via a call to STS by the ProcessSAML_awslabs_samldemo Lambda function. Because the above action specifies ALL_OLD under the ReturnValues mapping, DynamoDB returns these credentials at the same time.


  1. Save the changes and open your /saml resource root.
  2. Choose Actions, Enable CORS.
  3. In the Access-Control-Allow-Headers section, add COGNITO_ID into the end (inside the quotes and separated from other headers by a comma), then choose Enable CORS and replace existing CORS headers.
  4. When completed, choose Actions, Deploy API. Use the Prod stage or another stage.
  5. In the Stage Editor, choose SDK Generation. For Platform, choose JavaScript and then choose Generate SDK. Save the folder someplace close. Take note of the Invoke URL value at the top, as you need this for ADFS configuration later.

Website configuration

If you are not using the SAM template, create an S3 bucket and configure it as a static website in the same way that you did for Part I.

If you are using the SAM template this will automatically be created for you however the steps below will still need to be completed:

In the source code repository, edit /Scenario2/website/configs.js.

  1. Ensure that the identityPool value matches your Amazon Cognito Pool ID and the region is correct.
  2. Leave adfsUrl the same if you’re testing on your lab server; otherwise, update with the AD FS DNS entries as appropriate.
  3. Update the relayingPartyId value as well if you used something different from the prerequisite blog post.

Next, download the minified version of the AWS SDK for JavaScript in the Browser (aws-sdk.min.js) and place it along with the other files in /Scenario2/website into the S3 bucket.

Copy the files from the API Gateway Generated SDK in the last section to this bucket so that the apigClient.js is in the root directory and lib folder is as well. The imports for these scripts (which do things like sign API requests and configure headers for the JWT in the Authorization header) are already included in the index.html file. Consult the latest API Gateway documentation if the SDK generation process updates in the future

ADFS configuration

Now that the AWS setup is complete, modify your ADFS setup to capture RelayState information about the client and to send the POST response to API Gateway for processing. You will need to complete this step even if you use the SAM template.

If you’re using Windows Server 2008 with ADFS 2.0, ensure that Update Rollup 2 is installed before enabling RelayState. Please see official Microsoft documentation for specific download information.

  1. After Update Rollup 2 is installed, modify %systemroot%\inetpub\adfs\ls\web.config. If you’re on a newer version of Windows Server running AD FS 3.0, modify %systemroot%\ADFS\Microsoft.IdentityServer.Servicehost.exe.config.
  2. Find the section in the XML marked <Microsoft.identityServer.web> and add an entry for <useRelayStateForIdpInitiatedSignOn enabled="true">. If you have the proper ADFS rollup or version installed, this should allow the RelayState parameter to be accepted by the service provider.
  3. In the ADFS console, open Relaying Party Trusts for Amazon Web Services and choose Endpoints.
  4. For Binding, choose POST and for Invoke URL,enter the URL to your API Gateway from the stage that you noted earlier.

At this point, you are ready to test out your webpage. Navigate to the S3 static website Endpoint URL and it should redirect you to the ADFS login screen. If the user login has been recent enough to have a valid SAML cookie, then you should see the login pass-through; otherwise, a login prompt appears. After the authentication has taken place, you should quickly end up back at your original webpage. Using the browser debugging tools, you see “Successful DDB call” followed by the results of a call to STS that were stored in DynamoDB.


As in Scenario 1, the sample code under /scenario2/website/index.html has a button that allows you to “ping” an endpoint to test if the federated credentials are working. If you have used the SAM template this should already be working and you can test it out (it will fail at first – keep reading to find out how to set the IAM permissions!). If not go to API Gateway and create a new Resource called /users at the same level of /saml in your API with a GET method.


For Integration type, choose Mock.


In the Method Request, for Authorization, choose AWS_IAM. In the Integration Response, in the Body Mapping Template section, for Content-Type, choose application/json and add the following JSON:

    "status": "Success",
    "agent": "${context.identity.userAgent}"


Before using this new Mock API as a test, configure CORS and re-generate the JavaScript SDK so that the browser knows about the new methods.

  1. On the /saml resource root and choose Actions, Enable CORS.
  2. In the Access-Control-Allow-Headers section, add COGNITO_ID into the endpoint and then choose Enable CORS and replace existing CORS headers.
  3. Choose Actions, Deploy API. Use the stage that you configured earlier.
  4. In the Stage Editor, choose SDK Generation and select JavaScript as your platform. Choose Generate SDK.
  5. Upload the new apigClient.js and lib directory to the S3 bucket of your static website.

One last thing must be completed before testing (You will need to complete this step even if you use the SAM template) if the credentials can invoke this mock endpoint with AWS_IAM credentials. The ADFS-Production Role needs execute-api:Invoke permissions for this API Gateway resource.

  1. In the IAM console, choose Roles, and open the ADFS-Production Role.

  2. For testing, you can attach the AmazonAPIGatewayInvokeFullAccess policy; however, for production, you should scope this down to the resource as documented in Control Access to API Gateway with IAM Permissions.

  3. After you have attached a policy with invocation rights and authenticated with AD FS to finish the redirect process, choose PING.

If everything has been set up successfully you should see an alert with information about the user agent.

Final Thoughts

We hope these scenarios and sample code help you to not only begin to build comprehensive enterprise applications on AWS but also to enhance your understanding of different AuthN and AuthZ mechanisms. Consider some ways that you might be able to evolve this solution to meet the needs of your own customers and innovate in this space. For example:

  • Completing the CloudFront configuration and leveraging SSL termination for site identification. See if this can be incorporated into the Lambda processing pipeline.
  • Attaching a scope-down IAM policy if the business rules are matched. For example, the default role could be more permissive for a group but if the user is a contractor (username with –C appended) they get extra restrictions applied when assumeRoleWithSaml is called in the ProcessSAML_awslabs_samldemo Lambda function.
  • Changing the time duration before credentials expire on a per-role basis. Perhaps if the SAMLResponse parsing determines the user is an Administrator, they get a longer duration.
  • Passing through additional user claims in SAMLResponse for further logical decisions or auditing by adding more claim rules in the ADFS console. This could also be a mechanism to synchronize some Active Directory schema attributes with AWS services.
  • Granting different sets of credentials if a user has accounts with multiple SAML providers. While this tutorial was made with ADFS, you could also leverage it with other solutions such as Shibboleth and modify the ProcessSAML_awslabs_samldemo Lambda function to be aware of the different IdP ARN values. Perhaps your solution grants different IAM roles for the same user depending on if they initiated a login from Shibboleth rather than ADFS?

The Lambda functions can be altered to take advantage of these options which you can read more about here. For more information about ADFS claim rule language manipulation, see The Role of the Claim Rule Language on Microsoft TechNet.

We would love to hear feedback from our customers on these designs and see different secure application designs that you’re implementing on the AWS platform.

Assert() in the hands of bad coders

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/03/assert-in-hands-of-bad-coders.html

Using assert() creates better code, as programmers double-check assumptions. But only if used correctly. Unfortunately, bad programmers tend to use them badly, making code worse than if no asserts were used at all. They are a nuanced concept that most programmers don’t really understand.

We saw this recently with the crash of “Bitcoin Unlimited”, a version of Bitcoin that allows more transactions. They used an assert() to check the validity of input, and when they received bad input, most of the nodes in the network crashed.

The Bitcoin Classic/Unlimited code is full of bad uses of assert. The following examples are all from the file main.cpp.

Example #1this line of code:

  1.     if (nPos >= coins->vout.size() || coins->vout[nPos].IsNull())
  2.         assert(false); 

This use of assert is silly. The code should look like this:

  1.     assert(nPos < coins->vout.size());
  2.     assert(!coins->vout[nPos].IsNull());

This is the least of their problems. It understandable that as code ages, and things are added/changed, that odd looking code like this appears. But still, it’s an example of wrong thinking about asserts. Among the problems this would cause is that if asserts were ever turned off, you’d have to deal with dead code elimination warnings in static analyzers.

Example #2line of code:

  1.     assert(view.Flush());

The code within assert is supposed to only read values, not change values. In this example, the Flush function changes things. Normally, asserts are only compiled into debug versions of the code, and removed for release versions. However, doing so for Bitcoin will cause the program to behave incorrectly, as things like the Flush() function are no longer called. That’s why they put at the top of this code, to inform people that debug must be left on.

  1. #if defined(NDEBUG)
  2. # error “Bitcoin cannot be compiled without assertions.”
  3. #endif

Example #3: line of code:

  1.     CBlockIndex* pindexNew = new CBlockIndex(block);
  2.     assert(pindexNew);

The new operator never returns NULL, but throws its own exception instead. Not only is this a misconception about what new does, it’s also a misconception about assert. The assert is supposed to check for bad code, not check errors.

Example #4: line of code

  1.     BlockMap::iterator mi = mapBlockIndex.find(inv.hash);
  2.     CBlock block;
  3.     const Consensus::Params& consensusParams = Params().GetConsensus();
  4.     if (!ReadBlockFromDisk(block, (*mi).second, consensusParams))
  5.         assert(!“cannot load block from disk”);

This is the feature that crashed Bitcoin Unlimited, and would also crash main Bitcoin nodes that use the “XTHIN” feature. The problem comes from parsing input (inv.hash). If the parsed input is bad, then the block won’t exist on the disk, and the assert will fail, and the program will crash.

Again, assert is for checking for bad code that leads to impossible conditions, not checking errors in input, or checking errors in system functions.


The above examples were taken from only one file in the Bitcoin Classic source code. They demonstrate the typically wrong ways bad programmers use asserts. It’d be a great example to show students of programming how not to write bad code.

More generally, though, it shows why there’s a difference between 1x and 10x programmers. 1x programmers, like those writing Bitcoin code, make the typical mistake of treating assert() as error checking. The nuance of assert is lost on them.

Updated to reflect that I’m refering to the “Bitcoin Classic” source code, which isn’t the “Bitcoin Core” source code. However, all the problems above appear to also be problems in the Bitcoin Core source code.

SAML Identity Federation: Follow-Up Questions, Materials, Guides, and Templates from an AWS re:Invent 2016 Workshop (SEC306)

Post Syndicated from Quint Van Deman original https://aws.amazon.com/blogs/security/saml-identity-federation-follow-up-questions-materials-guides-and-templates-from-an-aws-reinvent-2016-workshop-sec306/

As part of the re:Source Mini Con for Security Services at AWS re:Invent 2016, we conducted a workshop focused on Security Assertion Markup Language (SAML) identity federation: Choose Your Own SAML Adventure: A Self-Directed Journey to AWS Identity Federation Mastery. As part of this workshop, attendees were able to submit their own federation-focused questions to a panel of AWS experts. In this post, I share the questions and answers from that workshop because this information can benefit any AWS customer interested in identity federation.

I have also made available the full set of workshop materials, lab guides, and AWS CloudFormation templates. I encourage you to use these materials to enrich your exploration of SAML for use with AWS.

Q: SAML assertions are limited to 50,000 characters. We often hit this limit by being in too many groups. What can AWS do to resolve this size-limit problem?

A: Because the SAML assertion is ultimately part of an API call, an upper bound must be in place for the assertion size.

On the AWS side, your AWS solution architect can log a feature request on your behalf to increase the maximum size of the assertion in a future release. The AWS service teams use these feature requests, in conjunction with other avenues of customer feedback, to plan and prioritize the features they deliver. To facilitate this process you need two things: the proposed higher value to which you’d like to see the maximum size raised, and a short written description that would help us understand what this increased limit would enable you to do.

On the AWS customer’s side, we often find that these cases are most relevant to centralized cloud teams that have broad, persistent access across many roles and accounts. This access is often necessary to support troubleshooting or simply as part of an individual’s job function. However, in many cases, exchanging persistent access for just-in-time access enables the same level of access but with better levels of visibility, reduced blast radius, and better adherence to the principle of least privilege. For example, you might implement a fast, efficient, and monitored workflow that allows you to provision a user into the necessary backend directory group for a short duration when needed in lieu of that user maintaining all of those group memberships on a persistent basis. This approach could effectively resolve the limit issue you are facing.

Q: Can we use OpenID Connect (OIDC) for federated authentication and authorization into the AWS Management Console? If so, does it have a similar size limit?

A: Currently, AWS support for OIDC is oriented around providing access to AWS resources from mobile or web applications, not access to the AWS Management Console. This is possible to do, but it requires the construction of a custom identity broker. In this solution, this broker would consume the OIDC identity, use its own logic to authenticate and authorize the user (thus being subject only to any size limits you enforce on the OIDC side), and use the sts:GetFederatedToken call to vend the user an AWS Security Token Service (STS) token for either AWS Management Console use or API/CLI use. During this sts:GetFederatedToken call, you attach a scoping policy with a limit of 2,048 characters. See Creating a URL that Enables Federated Users to Access the AWS Management Console (Custom Federation Broker) for additional details about custom identity brokers.

Q: We want to eliminate permanent AWS Identity and Access Management (IAM) access keys, but we cannot do so because of third-party tools. We are contemplating using HashiCorp Vault to vend permanent keys. Vault lets us tie keys to LDAP identities. Have you seen this work elsewhere? Do you think it will work for us?

A: For third-party tools that can run within Amazon EC2, you should use EC2 instance profiles to eliminate long-term credentials and their associated management (distribution, rotation, etc.). For third-party tools that cannot run within EC2, most customers opt to leverage their existing secrets-management tools and processes for the long-lived keys. These tools are often enhanced to make use of AWS APIs such as iam:GetCredentialReport (rotation information) and iam:{Create,Update,Delete}AccessKey (rotation operations). HashiCorp Vault is a popular tool with an available AWS Quick Start Reference Deployment, but any secrets management platform that is able to efficiently fingerprint the authorized resources and is extensible to work with the previously mentioned APIs will fill this need for you nicely.

Q: Currently, we use an Object Graph Navigation Library (OGNL) script in our identity provider (IdP) to build role Amazon Resource Names (ARNs) for the role attribute in the SAML assertion. The script consumes a list of distribution list display names from our identity management platform of which the user is a member. There is a 60-character limit on display names, which leaves no room for IAM pathing (which has a 512-character limit). We are contemplating a change. The proposed solution would make AWS API calls to get role ARNs from the AWS APIs. Have you seen this before? Do you think it will work? Does the AWS SAML integration support full-length role ARNs that would include up to 512 characters for the IAM path?

A: In most cases, we recommend that you use regular expression-based transformations within your IdP to translate a list of group names to a list of role ARNs for inclusion in the SAML assertion. Without pathing, you need to know the 12-digit AWS account number and the role name in order to be able to do so, which is accomplished using our recommended group-naming convention (AWS-Acct#-RoleName). With pathing (because “/” is not a valid character for group names within most directories), you need an additional source from which to pull this third data element. This could be as simple as an extra dimension in the group name (such as AWS-Acct#-Path-RoleName); however, that would multiply the number of groups required to support the solution. Instead, you would most likely derive the path element from a user attribute, dynamic group information, or even an external information store. It should work as long as you can reliably determine all three data elements for the user.

We do not recommend drawing the path information from AWS APIs, because the logic within the IdP is authoritative for the user’s authorization. In other words, the IdP should know for which full path role ARNs the user is authorized without asking AWS. You might consider using the AWS APIs to validate that the role actually exists, but that should really be an edge case. This is because you should integrate any automation that builds and provisions the roles with the frontend authorization layer. This way, there would never be a case in which the IdP authorizes a user for a role that doesn’t exist. AWS SAML integration supports full-length role ARNs.

Q: Why do AWS STS tokens contain a session token? This makes them incompatible with third-party tools that only support permanent keys. Is there a way to get rid of the session token to make temporary keys that contain only the access_key and the secret_key components?

A: The session token contains information that AWS uses to confirm that the AWS STS token is valid. There is no way to create a token that does not contain this third component. Instead, the preferred AWS mechanisms for distributing AWS credentials to third-party tools are EC2 instance profiles (in EC2), IAM cross-account trust (SaaS), or IAM access keys with secrets management (on-premises). Using IAM access keys, you can rotate the access keys as often as the third-party tool and your secrets management platform allow.

Q: How can we use SAML to authenticate and authorize code instead of having humans do the work? One proposed solution is to use our identity management (IDM) platform to generate X.509 certificates for identities, and then present these certificates to our IdP in order to get valid SAML assertions. This could then be included in an sts:AssumeRoleWithSAML call. Have you seen this working before? Do you think it will work for us?

A: Yes, when you receive a SAML assertion from your IdP by using your desired credential form (user name/password, X.509, etc.), you can use the sts:AssumeRoleWithSAML call to retrieve an AWS STS token. See How to Implement a General Solution for Federated API/CLI Access Using SAML 2.0 for a reference implementation.

Q: As a follow-up to the previous question, how can we get code using multi-factor authentication (MFA)? There is a gauth project that uses NodeJS to generate virtual MFAs. Code could theoretically get MFA codes from the NodeJS gauth server.

A: The answer depends on your choice of IdP and MFA provider. Generically speaking, you need to authenticate (either web-based or code-based) to the IdP using all of your desired factors before the SAML assertion is generated. This assertion can then include details of the authentication mechanism used as an additional attribute in role-assumption conditions within the trust policy in AWS. This lab guide from the workshop provides further details and a how-to guide for MFA-for-SAML.

This blog post clarifies some re:Invent 2016 attendees’ questions about SAML-based federation with AWS. For more information presented in the workshop, see the full set of workshop materials, lab guides, and CloudFormation templates. If you have follow-up questions, start a new thread in the IAM forum.

– Quint

A Rebuttal For Python 3

Post Syndicated from Eevee original https://eev.ee/blog/2016/11/23/a-rebuttal-for-python-3/

Zed Shaw, of Learn Python the Hard Way fame, has now written The Case Against Python 3.

I’m not involved with core Python development. The only skin I have in this game is that I like Python 3. It’s a good language. And one of the big factors I’ve seen slowing its adoption is that respected people in the Python community keep grouching about it. I’ve had multiple newcomers tell me they have the impression that Python 3 is some kind of unusable disaster, though they don’t know exactly why; it’s just something they hear from people who sound like they know what they’re talking about. Then they actually use the language, and it’s fine.

I’m sad to see the Python community needlessly sabotage itself, but Zed’s contribution is beyond the pale. It’s not just making a big deal about changed details that won’t affect most beginners; it’s complete and utter nonsense, on a platform aimed at people who can’t yet recognize it as nonsense. I am so mad.

The Case Against Python 3

I give two sets of reasons as I see them now. One for total beginners, and another for people who are more knowledgeable about programming.

Just to note: the two sets of reasons are largely the same ideas presented differently, so I’ll just weave them together below.

The first section attempts to explain the case against starting with Python 3 in non-technical terms so a beginner can make up their own mind without being influenced by propaganda or social pressure.

Having already read through this once, this sentence really stands out to me. The author of a book many beginners read to learn Python in the first place is providing a number of reasons (some outright fabricated) not to use Python 3, often in terms beginners are ill-equipped to evaluate, but believes this is a defense against propaganda or social pressure.

The Most Important Reason

Before getting into the main technical reasons I would like to discuss the one most important social reason for why you should not use Python 3 as a beginner:


Python 3’s adoption is really only at about 30% whenever there is an attempt to measure it.

Wait, really? Wow, that’s fantastic.

I mean, it would probably be higher if the most popular beginner resources were actually teaching Python 3, but you know.

Nobody is all that interested in finding out what the real complete adoption is, despite there being fairly simple ways to gather metrics on the adoption.

This accusatory sentence conspicuously neglects to mention what these fairly simple ways are, a pattern that repeats throughout. The trouble is that it’s hard to even define what “adoption” means — I write all my code in Python 3 now, but veekun is still Python 2 because it’s in maintenance mode, so what does that say about adoption? You could look at PyPI download stats, but those are thrown way off by caches and system package managers. You could look at downloads from the Python website, but a great deal of Python is written and used on Unix-likes, where Python itself is either bundled or installed from the package manager.

It’s as simple as that. If you learn Python 2, then you can still work with all the legacy Python 2 code in existence until Python dies or you (hopefully) move on. But if you learn Python 3 then your future is very uncertain. You could really be learning a dead language and end up having to learn Python 2 anyway.

You could use Python 2, until it dies… or you could use Python 3, which might die. What a choice.

By some definitions, Python 2 is already dead — it will not see another major release, only security fixes. Python 3 is still actively developed, and its seventh major release is next month. It even contains a new feature that Zed later mentions he prefers to Python 2’s offerings.

It may shock you to learn that I know both Python 2 and Python 3. Amazingly, two versions of the same language are much more similar than they are different. If you learned Python 3 and then a wizard cast a spell that made it vanish from the face of the earth, you’d just have to spend half an hour reading up on what had changed from Python 2.

Also, it’s been over a decade, maybe even multiple decades, and Python 3 still isn’t above about 30% in adoption. Even among the sciences where Python 3 is touted as a “success” it’s still only around 25-30% adoption. After that long it’s time to admit defeat and come up with a new plan.

Python 3.0 came out in 2008. The first couple releases ironed out some compatibility and API problems, so it didn’t start to gain much traction until Python 3.2 came out in 2011. Hell, Python 2.0 came out in 2000, so even Python 2 isn’t multiple decades old. It would be great if this trusted beginner reference could take two seconds to check details like this before using them to scaremonger.

The big early problem was library compatibility: it’s hard to justify switching to a new version of the language if none of the libraries work. Libraries could only port once their own dependencies had ported, of course, and it took a couple years to figure out the best way to maintain compatibility with both Python 2 and Python 3. I’d say we only really hit critical mass a few years ago — for instance, Django didn’t support Python 3 until 2013 — in which case that 30% is nothing to sneeze at.

There are more reasons beyond just the uncertain future of Python 3 even decades later.

In one paragraph, we’ve gone from “maybe even multiple decades” to just “decades”, which is a funny way to spell “eight years”.

Not In Your Best Interests

The Python project’s efforts to convince you to start with Python 3 are not in your best interest, but, rather, are only in the best interests of the Python project.

It’s bad, you see, for the Python project to want people to use the work it produced.

Anyway, please buy Zed Shaw’s book.

Anyway, please pledge to my Patreon.

Ultimately though, if Python 3 were good they wouldn’t need to do any convincing to get you to use it. It would just naturally work for you and you wouldn’t have any problems. Instead, there are serious issues with Python 3 for beginners, and rather than fix those issues the Python project uses propaganda, social pressure, and marketing to convince you to use it. In the world of technology using marketing and propaganda is immediately a sign that the technology is defective in some obvious way.

This use of social pressure and propaganda to convince you to use Python 3 despite its problems, in an attempt to benefit the Python project, is morally unconscionable to me.

Ten paragraphs in, Zed is telling me that I should be suspicious of anything that relies on marketing and propaganda. Meanwhile, there has yet to be a single concrete reason why Python 3 is bad for beginners — just several flat-out incorrect assertions and a lot of handwaving about how inexplicably nefarious the Python core developers are. You know, the same people who made Python 2. But they weren’t evil then, I guess.

You Should Be Able to Run 2 and 3

In the programming language theory there is this basic requirement that, given a “complete” programming language, I can run any other programming language. In the world of Java I’m able to run Ruby, Java, C++, C, and Lua all at the same time. In the world of Microsoft I can run F#, C#, C++, and Python all at the same time. This isn’t just a theoretical thing. There is solid math behind it. Math that is truly the foundation of computer science.

The fact that you can’t run Python 2 and Python 3 at the same time is purely a social and technical decision that the Python project made with no basis in mathematical reality. This means you are working with a purposefully broken platform when you use Python 3, and I personally can’t condone teaching people to use something that is fundamentally broken.

The programmer-oriented section makes clear that the solid math being referred to is Turing-completeness — the section is even titled “Python 3 Is Not Turing Complete”.

First, notice a rhetorical trick here. You can run Ruby, Java, C++, etc. at the same time, so why not Python 2 and Python 3?

But can you run Java and C# at the same time? (I’m sure someone has done this, but it’s certainly much less popular than something like Jython or IronPython.)

Can you run Ruby 1.8 and Ruby 2.3 at the same time? Ah, no, so I guess Ruby 2.3 is fundamentally and purposefully broken.

Can you run Lua 5.1 and 5.3 at the same time? Lua is a spectacular example, because Lua 5.2 made a breaking change to how the details of scope work, and it’s led to a situation where a lot of programs that embed Lua haven’t bothered upgrading from Lua 5.1. Was Lua 5.2 some kind of dark plot to deliberately break the language? No, it’s just slightly more inconvenient than expected for people to upgrade.

Anyway, as for Turing machines:

In computer science a fundamental law is that if I have one Turing Machine I can build any other Turing Machine. If I have COBOL then I can bootstrap a compiler for FORTRAN (as disgusting as that might be). If I have FORTH, then I can build an interpreter for Ruby. This also applies to bytecodes for CPUs. If I have a Turing Complete bytecode then I can create a compiler for any language. The rule then can be extended even further to say that if I cannot create another Turing Machine in your language, then your language cannot be Turing Complete. If I can’t use your language to write a compiler or interpreter for any other language then your language is not Turing Complete.

Yes, this is true.

Currently you cannot run Python 2 inside the Python 3 virtual machine. Since I cannot, that means Python 3 is not Turing Complete and should not be used by anyone.

And this is completely asinine. Worse, it’s flat-out dishonest, and relies on another rhetorical trick. You only “cannot” run Python 2 inside the Python 3 VM because no one has written a Python 2 interpreter in Python 3. The “cannot” is not a mathematical impossibility; it’s a simple matter of the code not having been written. Or perhaps it has, but no one cares anyway, because it would be comically and unusably slow.

I assume this was meant to be sarcastic on some level, since it’s followed by a big blue box that seems unsure about whether to double down or reverse course. But I can’t tell why it was even brought up, because it has absolutely nothing to do with Zed’s true complaint, which is that Python 2 and Python 3 do not coexist within a single environment. Implementing language X using language Y does not mean that X and Y can now be used together seamlessly.

The canonical Python release is written in C (just like with Ruby or Lua), but you can’t just dump a bunch of C code into a Python (or Ruby or Lua) file and expect it to work. You can talk to C from Python and vice versa, but defining how they communicate is a bit of a pain in the ass and requires some level of setup.

I’ll get into this some more shortly.

No Working Translator

Python 3 comes with a tool called 2to3 which is supposed to take Python 2 code and translate it to Python 3 code.

I should point out right off the bat that this is not actually what you want to use most of the time, because you probably want to translate your Python 2 code to Python 2/3 code. 2to3 produces code that most likely will not work on Python 2. Other tools exist to help you port more conservatively.

Translating one programming language into another is a solidly researched topic with solid math behind it. There are translators that convert any number of languages into JavaScript, C, C++, Java, and many times you have no idea the translation is being done. In addition to this, one of the first steps when implementing a new language is to convert the new language into an existing language (like C) so you don’t have to write a full compiler. Translation is a fully solved problem.

This is completely fucking ludicrous. Translating one programming language to another is a common task, though “fully solved” sounds mighty questionable. But do you know what the results look like?

I found a project called “Transcrypt”, which puts Python in the browser by “translating” it to JavaScript. I’ve never used or heard of this before; I just googled for something to convert Python to JavaScript. Here’s their first sample, a demo using jQuery:

def start ():
    def changeColors ():
        for div in S__divs:
            S (div) .css ({
                'color': 'rgb({},{},{})'.format (* [int (256 * Math.random ()) for i in range (3)]),

    S__divs = S ('div')
    changeColors ()
    window.setInterval (changeColors, 500)

And here’s the JavaScript code it compiles to:

(function () {
    var start = function () {
        var changeColors = function () {
            var __iterable0__ = $divs;
            for (var __index0__ = 0; __index0__ < __iterable0__.length; __index0__++) {
                var div = __iterable0__ [__index0__];
                $ (div).css (dict ({'color': 'rgb({},{},{})'.format.apply (null, function () {
                    var __accu0__ = [];
                    for (var i = 0; i < 3; i++) {
                        __accu0__.append (int (256 * Math.random ()));
                    return __accu0__;
                } ())}));
        var $divs = $ ('div');
        changeColors ();
        window.setInterval (changeColors, 500);
    __pragma__ ('<all>')
        __all__.start = start;
    __pragma__ ('</all>')
}) ();

Well, not quite. That’s actually just a small piece at the end of the full 1861-line file.

You may notice that the emitted JavaScript effectively has to emulate the Python for loop, because JavaScript doesn’t have anything that works exactly the same way. And this is a basic, common language feature translated between two languages in the same general family! Imagine how your code would look if you relied on gritty details of how classes are implemented.

Is this what you want 2to3 to do to your code?

Even if something has been proven to be mathematically possible, that doesn’t mean it’s easy, and it doesn’t mean the results will be pretty (or fast).

The 2to3 translator fails on about 15% of the code it attempts, and does a poor job of translating the code it can handle. The motivations for this are unclear, but keep in mind that a group of people who claim to be programming language experts can’t write a reliable translator from one version of their own language to another. This is also a cause of their porting problems, which adds up to more evidence Python 3’s future is uncertain.

Writing a translator from one language to another is a fully proven and fundamental piece of computer science. Yet, the 2to3 translator cannot translate code 100%. In my own tests it is only about 85% effective, leaving a large amount of code to translate manually. Given that translation is a solved problem this seems to be a decision bordering on malice rather than incredible incompetence.

The programmer-oriented section doubles down on this idea with a title of “Purposefully Crippled 2to3 Translator” — again, accusing the Python project of sabotaging everyone. That doesn’t even make sense; if their goal is to make everyone use Python 3 at any cost, why would they deliberately break their tool that reduces the amount of Python 2 code and increases the amount of Python 3 code?

2to3 sucks because its job is hard. Python is dynamically typed. If it sees d.iteritems(), it might want to change that to d.items(), as it’s called in Python 3 — but it can’t always be sure that d is actually a dict. If d is some user-defined type, renaming the method is wrong.

But hey, Turing-completeness, right? It must be mathematically possible. And it is! As long as you’re willing to see this:

for key, value in d.iteritems():

Get translated to this:

__d = d
for key, value in (__d.items() if isinstance(__d, dict) else __d.iteritems()):

Would Zed be happier with that, I wonder?

The JVM and CLR Prove It’s Pointless

Yet, for some reason, the Python 3 virtual machine can’t run Python 2? Despite the solidly established mathematics disproving this, the countless examples of running one crazy language inside a Russian doll cascade of other crazy languages, and huge number of languages that can coexist in nearly every other virtual machine? That makes no sense.

This, finally, is the real complaint. It’s not a bad one, and it comes up sometimes, but… it’s not this easy.

The Python 3 VM is fairly similar to the Python 2 VM. The problem isn’t the VM, but the core language constructs and standard library.

Consider: what happens when a Python 2 old-style class instance gets passed into Python 3, which has no such concept? It seems like a value would have to always have the semantics of the language version it came from — that’s how languages usually coexist on the same VM, anyway.

Now, I’m using Python 3, and I load some library written for Python 2. I call a Python 2 function that deals with bytestrings, and I pass it a Python 3 bytestring. Oh no! It breaks because Python 3 bytestrings iterate as integers, whereas the Python 2 library expects them to iterate as characters.

Okay, well, no big deal, you say. Maybe Python 2 libraries just need to be updated to work either way, before they can be used with Python 3.

But that’s exactly the situation we’re in right now. Syntax changes are trivially fixed by 2to3 and similar tools. It’s libraries that cause the subtler issues.

The same applies the other way, too. I write Python 3 code, and it gets an int from some Python 2 library. I try to use the .to_bytes method on it, but that doesn’t exist on Python 2 integers. So my Python 3 code, written and intended purely for Python 3, now has to deal with Python 2 integers as well.

Perhaps “primitive” types should convert automatically, on the boundary? Okay, sure. What about the Python 2 buffer type, which is C-backed and replaced by memoryview in Python 3?

Or how about this very fundamental problem: names of methods and other attributes are str in both versions, but that means they’re bytestrings in Python 2 and text in Python 3. If you’re in Python 3 land, and you call obj.foo() on a Python 2 object, what happens? Python 3 wants a method with the text name foo, but Python 2 wants a method with the bytes name foo. Text and bytes are not implicitly convertible in Python 3. So does it error? Somehow work anyway? What about the other way around?

What about the standard library, which has had a number of improvements in Python 3 that don’t or can’t exist in Python 2? Should Python ship two entire separate copies of its standard library? What about modules like logging, which rely on global state? Does Python 2 and Python 3 code need to set up logging separately within the same process?

There are no good solutions here. The language would double in size and complexity, and you’d still end up with a mess at least as bad as the one we have now when values leak from one version into the other.

We either have two situations here:

  1. Python 3 has been purposefully crippled to prevent Python 2’s execution alongside Python 3 for someone’s professional or ideological gain.
  2. Python 3 cannot run Python 2 due to simple incompetence on the part of the Python project.

I can think of a third.

Difficult To Use Strings

The strings in Python 3 are very difficult to use for beginners. In an attempt to make their strings more “international” they turned them into difficult to use types with poor error messages.

Why is “international” in scare quotes?

Every time you attempt to deal with characters in your programs you’ll have to understand the difference between byte sequences and Unicode strings.

Given that I’m reading part of a book teaching Python, this would be a perfect opportunity to drive this point home by saying “Look! Running exercise N in Python 3 doesn’t work.” Exercise 1, at least, works fine for me with a little extra sprinkle of parentheses:

print("Hello World!")
print("Hello Again")
print("I like typing this.")
print("This is fun.")
print('Yay! Printing.')
print("I'd much rather you 'not'.")
print('I "said" do not touch this.')

Contrast with the actual content of that exercise — at the bottom is a big red warning box telling people from “another country” (relative to where?) that if they get errors about ASCII encodings, they should put an unexplained magical incantation at the top of their scripts to fix “Unicode UTF-8”, whatever that is. I wonder if Zed has read his own book.

Don’t know what that is? Exactly.

If only there were a book that could explain it to beginners in more depth than “you have to fix this if you’re foreign”.

The Python project took a language that is very forgiving to beginners and mostly “just works” and implemented strings that require you to constantly know what type of string they are. Worst of all, when you get an error with strings (which is very often) you get an error message that doesn’t tell you what variable names you need to fix.

The complaint is that this happens in Python 3, whereas it’s accepted in Python 2:

>>> b"hello" + "hello"
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: can't concat bytes to str

The programmer section is called “Statically Typed Strings”. But this is not static typing. That’s strong typing, a property that sets Python’s type system apart from languages like JavaScript. It’s usually considered a good thing, because the alternative is to silently produce nonsense in some cases, and then that nonsense propagates through your program and is hard to track down when it finally causes problems.

If they’re going to require beginners to struggle with the difference between bytes and Unicode the least they could do is tell people what variables are bytes and what variables are strings.

That would be nice, but it’s not like this is a new problem. Try this in Python 2.

>>> 3 + "hello"
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'int' and 'str'

How would Python even report this error when I used literals instead of variables? How could custom types hook into such a thing? Error messages are hard.

By the way, did you know that several error messages are much improved in Python 3? Python 2 is somewhat notorious for the confusing errors it produces when an argument is missing from a method call, but Python 3 is specific about the problem, which is much friendlier to beginners.

However, when you point out that this is hard to use they try to claim it’s good for you. It is not. It’s simple blustering covering for a poor implementation.

I don’t know what about this is hard. Why do you have a text string and a bytestring in the first place? Why is it okay to refuse adding a number to a string, but not to refuse adding bytes to a string?

Imagine if one of the Python core developers were just getting into Python 2 and messing around.

# -*- coding: utf8 -*-
print "Hi, my name is Łukasz Langa."
print "Hi, my name is Łukasz Langa."[::-1]
Hi, my name is Łukasz Langa.
.agnaL zsaku�� si eman ym ,iH

Good luck figuring out how to fix that.

This isn’t blustering. Bytes are not text; they are binary data that could encode anything. They happen to look like text sometimes, and you can get away with thinking they’re text if you’re not from “another country”, but that mindset will lead you to write code that is wrong. The resulting bugs will be insidious and confusing, and you’ll have a hard time even reasoning about them because it’ll seem like “Unicode text” is somehow a different beast altogether from “ASCII text”.

Exercise 11 mentions at the end that you can use int() to convert a number to an integer. It’s no more complicated to say that you convert bytes to a string using .decode(). It shouldn’t even come up unless you’re explicitly working with binary data, and I don’t see any reading from sockets in LPTHW.

It’s also not statically compiled as strongly as it could be, so you can’t find these kinds of type errors until you run the code.

This comes a scant few paragraphs after “Dynamic typing is what makes Python easy to use and one of the reasons I advocate it for beginners.”

You can’t find any kinds of type errors until you run the code. Welcome to dynamic typing.

Strings are also most frequently received from an external source, such as a network socket, file, or similar input. This means that Python 3’s statically typed strings and lack of static type safety will cause Python 3 applications to crash more often and have more security problems when compared with Python 2.

On the contrary — Python 3 applications should crash less often. The problem with silently converting between bytestrings and text in Python 2 is that it might fail, depending on the contents. "cafe" + u"hello" works fine, but "café" + u"hello" raises a UnicodeDecodeError. Python 2 makes it very easy to write code that appears to work when tested with ASCII data, but later breaks with anything else, even though the values are still the same types. In Python 3, you get an error the first time you try to run such code, regardless of what’s in the actual values. That’s the biggest reason for the change: it improves things from being intermittent value errors to consistent type errors.

More security problems? This is never substantiated, and seems to have been entirely fabricated.

Too Many Formatting Options

In addition to that you will have 3 different formatting options in Python 3.6. That means you’ll have to learn to read and use multiple ways to format strings that are all very different. Not even I, an experienced professional programmer, can easily figure out these new formatting systems or keep up with their changing features.

I don’t know what on earth “keep up with their changing features” is supposed to mean, and Zed doesn’t bother to go into details.

Python 3 has three ways to format strings: % interpolation, str.format(), and the new f"" strings in Python 3.6. The f"" strings use the same syntax as str.format(); the difference is that where str.format() uses numbers or names of keyword arguments, f"" strings just use expressions. Compare:

number = 133

This isn’t “very different”. A frequently-used method is being promoted to syntax.

I really like this new style, and I have no idea why this wasn’t the formatting for Python 3 instead of that stupid .format function. String interpolation is natural for most people and easy to explain.

The problem is that beginner will now how to know all three of these formatting styles, and that’s too many.

I could swear Zed, an experienced professional programmer, just said he couldn’t easily figure out these new formatting systems. Note also that str.format() has existed in Python 2 since Python 2.6 was released in 2008, so I don’t know why Zed said “new formatting systems“, plural.

This is a truly bizarre complaint overall, because the mechanism Zed likes best is the newest one. If Python core had agreed that three mechanisms was too many, we wouldn’t be getting f"" at all.

Even More Versions of Strings

Finally, I’m told there is a new proposal for a string type that is both bytes and Unicode at the same time? That’d be fantastic if this new type brings back the dynamic typing that makes Python easy, but I’m betting it will end up being yet another static type to learn. For that reason I also think beginners should avoid Python 3 until this new “chimera string” is implemented and works reliably in a dynamic way. Until then, you will just be dealing with difficult strings that are statically typed in a dynamically typed language.

I have absolutely no idea what this is referring to, and I can’t find anyone who does. I don’t see any recent PEPs mentioning such a thing, nor anything in the last several months on the python-dev mailing list. I don’t see it in the Python 3.6 release notes.

The closest thing I can think of is the backwards-compatibility shenanigans for PEP 528 and PEP 529 — they switch to the Windows wide-string APIs for console and filesystem encoding, but pretend under the hood that the APIs take UTF-8-encoded bytes to avoid breaking libraries like Twisted. That’s a microscopic detail that should never matter to anyone but authors of Twisted, and is nothing like a new hybrid string type, but otherwise I’m at a loss.

This paragraph really is a perfect summary of the whole article. It speaks vaguely yet authoritatively about something that doesn’t seem to exist, it doesn’t bother actually investigating the thing the entire section talks about, it conjectures that this mysterious feature will be hard just because it’s in Python 3, and it misuses terminology to complain about a fundamental property of Python that’s always existed.

Core Libraries Not Updated

Many of the core libraries included with Python 3 have been rewritten to use Python 3, but have not been updated to use its features. How could they given Python 3’s constant changing status and new features?

What “constant changing status”? The language makes new releases; is that bad? The only mention of “changing” so far was with string formatting, which makes no sense to me, because the only major change has been the addition of syntax that Zed prefers.

There are several libraries that, despite knowing the encoding of data, fail to return proper strings. The worst offender seems to be any libraries dealing with the HTTP protocol, which does indicate the encoding of the underlying byte stream in many cases.

In many cases, yes. Not in all. Some web servers don’t send back an encoding. Some files don’t have an encoding, because they’re images or other binary data. HTML allows the encoding to be given inside the document, instead. urllib has always returned bytes, so it’s not all that unreasonable to keep doing that, rather than… well, I’m not quite sure what this is proposing. Return strings sometimes?

The documentation for urllib.request and http.client both advise using the higher-level Requests library instead, in a prominent yellow box right at the top. Requests has distinct mechanisms for retrieving bytes versus text and is vastly easier to use overall, though I don’t think even it understands reading encodings from HTML. Alas, computers.

Good luck to any beginner figuring out how to install Requests on Python 2 — but thankfully, Python 3 now comes bundled with pip, which makes installing libraries much easier. Contrast with the beginning of exercise 46, which apologizes for how difficult this is to explain, lists four things to install, warns that it will be frustrating, and advises watching a video to help figure it out.

What’s even more idiotic about this is Python has a really good Chardet library for detecting the encoding of byte streams. If Python 3 is supposed to be “batteries included” then fast Chardet should be baked into the core of Python 3’s strings making it cake to translate strings to bytes even if you don’t know the underlying encoding. … Call the function whatever you want, but it’s not magic to guess at the encoding of a byte stream, it’s science. The only reason this isn’t done for you is that the Python project decided that you should be punished for not knowing about bytes vs. Unicode, and their arrogance means you have difficult to use strings.

Guessing at the encoding of a byte stream isn’t so much science as, well, guessing. Guessing means that sometimes you’re wrong. Sometimes that’s what you want, and I’m honestly ambivalent about having chardet in the standard library, but it’s hardly arrogant to not want to include a highly-fallible heuristic in your programming language.

Conclusions and Warnings

I have resisted writing about these problems with Python 3 for 5 versions because I hoped it would become usable for beginners. Each year I would attempt to convert some of my code and write a couple small tests with Python 3 and simply fail. If I couldn’t use Python 3 reliably then there’s no way a total beginner could manage it. So each year I’d attempt it, and fail, and wait until they fix it. I really liked Python and hoped the Python project would drop their stupid stances on usability.

Let us recap the usability problems seen thusfar.

  • You can’t add b"hello" to "hello".
  • TypeErrors are phrased exactly the same as they were in Python 2.
  • The type system is exactly as dynamic as it was in Python 2.
  • There is a new formatting mechanism, using the same syntax as one in Python 2, that Zed prefers over the ones in Python 2.
  • urllib.request doesn’t decode for you, just like in Python 2.
  • 档牡敤㽴 isn’t built in. Oh, sorry, I meant chardet.

Currently, the state of strings is viewed as a Good Thing in the Python community. The fact that you can’t run Python 2 inside Python 3 is seen as a weird kind of tough love. The brainwashing goes so far as to outright deny the mathematics behind language translation and compilation in an attempt to motivate the Python community to brute force convert all Python 2 code.

Which is probably why the Python project focuses on convincing unsuspecting beginners to use Python 3. They don’t have a switching cost, so if you get them to fumble their way through the Python 3 usability problems then you have new converts who don’t know any better. To me this is morally wrong and is simply preying on people to prop up a project that needs a full reset to survive. It means beginners will fail at learning to code not because of their own abilities, but because of Python 3’s difficulty.

Now that we’re towards the end, it’s a good time to say this: Zed Shaw, your behavior here is fucking reprehensible.

Half of what’s written here is irrelevant nonsense backed by a vague appeal to “mathematics”. Instead of having even the shred of humility required to step back and wonder if there are complicating factors beyond whether something is theoretically possible, you have invented a variety of conflicting and malicious motivations to ascribe to the Python project.

It’s fine to criticize Python 3. The string changes force you to think about what you’re doing a little more in some cases, and occasionally that’s a pain in the ass. I absolutely get it.

But you’ve gone out of your way to invent a conspiracy out of whole cloth and promote it on your popular platform aimed at beginners, who won’t know how obviously full of it you are. And why? Because you can’t add b"hello" to "hello"? Are you kidding me? No one can even offer to help you, because instead of examples of real problems you’ve had, you gave two trivial toys and then yelled a lot about how the whole Python project is releasing mind-altering chemicals into the air.

The Python 3 migration has been hard enough. It’s taken a lot of work from a lot of people who’ve given enough of a crap to help Python evolve — to make it better to the best of their judgment and abilities. Now we’re finally, finally at the point where virtually all libraries support Python 3, a few new ones only support Python 3, and Python 3 adoption is starting to take hold among application developers.

And you show up to piss all over it, to propagate this myth that Python 3 is hamstrung to the point of unusability, because if the Great And Wise Zed Shaw can’t figure it out in ten seconds then it must just be impossible.

Fuck you.

Sadly, I doubt this will happen, and instead they’ll just rant about how I don’t know what I’m talking about and I should shut up.

This is because you don’t know what you’re talking about, and you should shut up.