Daniel  Hughes

Daniel Hughes


Hug: About Embrace The APIs Of The Future


Read Latest Documentation - Browse GitHub Code Repository

hug aims to make developing Python driven APIs as simple as possible, but no simpler. As a result, it drastically simplifies Python API development.

hug's Design Objectives:

  • Make developing a Python driven API as succinct as a written definition.
  • The framework should encourage code that self-documents.
  • It should be fast. A developer should never feel the need to look somewhere else for performance reasons.
  • Writing tests for APIs written on-top of hug should be easy and intuitive.
  • Magic done once, in an API framework, is better than pushing the problem set to the user of the API framework.
  • Be the basis for next generation Python APIs, embracing the latest technology.

As a result of these goals, hug is Python 3+ only and built upon Falcon's high performance HTTP library

HUG Hello World Example

Supporting hug development

Get professionally supported hug with the Tidelift Subscription

Professional support for hug is available as part of the Tidelift Subscription. Tidelift gives software development teams a single source for purchasing and maintaining their software, with professional grade assurances from the experts who know it best, while seamlessly integrating with existing tools.

Installing hug

Installing hug is as simple as:

pip3 install hug --upgrade

Ideally, within a virtual environment.

Getting Started

Build an example API with a simple endpoint in just a few lines.

# filename: happy_birthday.py
"""A basic (single function) API written using hug"""
import hug

def happy_birthday(name, age:hug.types.number=1):
    """Says happy birthday to a user"""
    return "Happy {age} Birthday {name}!".format(**locals())

To run, from the command line type:

hug -f happy_birthday.py

You can access the example in your browser at: localhost:8000/happy_birthday?name=hug&age=1. Then check out the documentation for your API at localhost:8000/documentation

Parameters can also be encoded in the URL (check out happy_birthday.py for the whole example).

def greet(event: str):
    """Greets appropriately (from http://blog.ketchum.com/how-to-write-10-common-holiday-greetings/)  """
    greetings = "Happy"
    if event == "Christmas":
        greetings = "Merry"
    if event == "Kwanzaa":
        greetings = "Joyous"
    if event == "wishes":
        greetings = "Warm"

    return "{greetings} {event}!".format(**locals())

Which, once you are running the server as above, you can use this way:

curl http://localhost:8000/greet/wishes
"Warm wishes!"

Versioning with hug

# filename: versioning_example.py
"""A simple example of a hug API call with versioning"""
import hug

@hug.get('/echo', versions=1)
def echo(text):
    return text

@hug.get('/echo', versions=range(2, 5))
def echo(text):
    return "Echo: {text}".format(**locals())

To run the example:

hug -f versioning_example.py

Then you can access the example from localhost:8000/v1/echo?text=Hi / localhost:8000/v2/echo?text=Hi Or access the documentation for your API from localhost:8000

Note: versioning in hug automatically supports both the version header as well as direct URL based specification.

Testing hug APIs

hug's http method decorators don't modify your original functions. This makes testing hug APIs as simple as testing any other Python functions. Additionally, this means interacting with your API functions in other Python code is as straight forward as calling Python only API functions. hug makes it easy to test the full Python stack of your API by using the hug.test module:

import hug
import happy_birthday

hug.test.get(happy_birthday, 'happy_birthday', {'name': 'Timothy', 'age': 25}) # Returns a Response object

You can use this Response object for test assertions (check out test_happy_birthday.py ):

def tests_happy_birthday():
    response = hug.test.get(happy_birthday, 'happy_birthday', {'name': 'Timothy', 'age': 25})
    assert response.status == HTTP_200
    assert response.data is not None

Running hug with other WSGI based servers

hug exposes a __hug_wsgi__ magic method on every API module automatically. Running your hug based API on any standard wsgi server should be as simple as pointing it to module_name: __hug_wsgi__.

For Example:

uwsgi --http --wsgi-file examples/hello_world.py --callable __hug_wsgi__

To run the hello world hug example API.

Building Blocks of a hug API

When building an API using the hug framework you'll use the following concepts:

METHOD Decorators get, post, update, etc HTTP method decorators that expose your Python function as an API while keeping your Python method unchanged

@hug.get() # <- Is the hug METHOD decorator
def hello_world():
    return "Hello"

hug uses the structure of the function you decorate to automatically generate documentation for users of your API. hug always passes a request, response, and api_version variable to your function if they are defined params in your function definition.

Type Annotations functions that optionally are attached to your methods arguments to specify how the argument is validated and converted into a Python type

def math(number_1:int, number_2:int): #The :int after both arguments is the Type Annotation
    return number_1 + number_2

Type annotations also feed into hug's automatic documentation generation to let users of your API know what data to supply.

Directives functions that get executed with the request / response data based on being requested as an argument in your api_function. These apply as input parameters only, and can not be applied currently as output formats or transformations.

def test_time(hug_timer):
    return {'time_taken': float(hug_timer)}

Directives may be accessed via an argument with a hug_ prefix, or by using Python 3 type annotations. The latter is the more modern approach, and is recommended. Directives declared in a module can be accessed by using their fully qualified name as the type annotation (ex: module.directive_name).

Aside from the obvious input transformation use case, directives can be used to pipe data into your API functions, even if they are not present in the request query string, POST body, etc. For an example of how to use directives in this way, see the authentication example in the examples folder.

Adding your own directives is straight forward:

def square(value=1, **kwargs):
    '''Returns passed in parameter multiplied by itself'''
    return value * value

def tester(value: square=10):
    return value

tester() == 100

For completeness, here is an example of accessing the directive via the magic name approach:

def multiply(value=1, **kwargs):
    '''Returns passed in parameter multiplied by itself'''
    return value * value

def tester(hug_multiply=10):
    return hug_multiply

tester() == 100

Output Formatters a function that takes the output of your API function and formats it for transport to the user of the API.

def my_output_formatter(data):
    return "STRING:{0}".format(data)

def hello():
    return {'hello': 'world'}

as shown, you can easily change the output format for both an entire API as well as an individual API call

Input Formatters a function that takes the body of data given from a user of your API and formats it for handling.

def my_input_formatter(data):
    return ('Results', hug.input_format.json(data))

Input formatters are mapped based on the content_type of the request data, and only perform basic parsing. More detailed parsing should be done by the Type Annotations present on your api_function

Middleware functions that get called for every request a hug API processes

def process_data(request, response):
    request.env['SERVER_NAME'] = 'changed'

def process_data(request, response, resource):
    response.set_header('MyHeader', 'Value')

You can also easily add any Falcon style middleware using:


Parameter mapping can be used to override inferred parameter names, eg. for reserved keywords:

import marshmallow.fields as fields

@hug.get('/foo', map_params={'from': 'from_date'})  # API call uses 'from'
def get_foo_by_date(from_date: fields.DateTime()):
    return find_foo(from_date)

Input formatters are mapped based on the content_type of the request data, and only perform basic parsing. More detailed parsing should be done by the Type Annotations present on your api_function

Splitting APIs over multiple files

hug enables you to organize large projects in any manner you see fit. You can import any module that contains hug decorated functions (request handling, directives, type handlers, etc) and extend your base API with that module.

For example:


import hug

def say_hi():
    return 'hello from something'

Can be imported into the main API file:


import hug
from . import something

def say_hi():
    return "Hi from root"

def something_api():
    return [something]

Or alternatively - for cases like this - where only one module is being included per a URL route:

hug.API(__name__).extend(something, '/something')

Configuring hug 404

By default, hug returns an auto generated API spec when a user tries to access an endpoint that isn't defined. If you would not like to return this spec you can turn off 404 documentation:

From the command line application:

hug -nd -f {file} #nd flag tells hug not to generate documentation on 404

Additionally, you can easily create a custom 404 handler using the hug.not_found decorator:

def not_found_handler():
    return "Not Found"

This decorator works in the same manner as the hug HTTP method decorators, and is even version aware:

def not_found_handler():
    return ""

def not_found_handler():
    return "Not Found"

Asyncio support

When using the get and cli method decorator on coroutines, hug will schedule the execution of the coroutine.

Using asyncio coroutine decorator

def hello_world():
    return "Hello"

Using Python 3.5 async keyword.

async def hello_world():
    return "Hello"

NOTE: Hug is running on top Falcon which is not an asynchronous server. Even if using asyncio, requests will still be processed synchronously.

Using Docker

If you like to develop in Docker and keep your system clean, you can do that but you'll need to first install Docker Compose.

Once you've done that, you'll need to cd into the docker directory and run the web server (Gunicorn) specified in ./docker/gunicorn/Dockerfile, after which you can preview the output of your API in the browser on your host machine.

$ cd ./docker
# This will run Gunicorn on port 8000 of the Docker container.
$ docker-compose up gunicorn

# From the host machine, find your Dockers IP address.
# For Windows & Mac:
$ docker-machine ip default

# For Linux:
$ ifconfig docker0 | grep 'inet' | cut -d: -f2 | awk '{ print $1}' | head -n1

By default, the IP is Assuming that's the IP you see, as well, you would then go to in your browser to view your API.

You can also log into a Docker container that you can consider your work space. This workspace has Python and Pip installed so you can use those tools within Docker. If you need to test the CLI interface, for example, you would use this.

$ docker-compose run workspace bash

On your Docker workspace container, the ./docker/templates directory on your host computer is mounted to /src in the Docker container. This is specified under services > app of ./docker/docker-compose.yml.

bash-4.3# cd /src
bash-4.3# tree
├── __init__.py
└── handlers
    ├── birthday.py
    └── hello.py

1 directory, 3 files

Security contact information

hug takes security and quality seriously. This focus is why we depend only on thoroughly tested components and utilize static analysis tools (such as bandit and safety) to verify the security of our code base. If you find or encounter any potential security issues, please let us know right away so we can resolve them.

To report a security vulnerability, please use the Tidelift security contact. Tidelift will coordinate the fix and disclosure.

Why hug?

HUG simply stands for Hopefully Useful Guide. This represents the project's goal to help guide developers into creating well written and intuitive APIs.

Thanks and I hope you find this hug helpful as you develop your next Python API!

~Timothy Crosley

Author: hugapi
Source Code: https://github.com/hugapi/hug
License: MIT License


What is GEEK

Buddha Community

Hug: About Embrace The APIs Of The Future

Top 10 API Security Threats Every API Team Should Know

As more and more data is exposed via APIs either as API-first companies or for the explosion of single page apps/JAMStack, API security can no longer be an afterthought. The hard part about APIs is that it provides direct access to large amounts of data while bypassing browser precautions. Instead of worrying about SQL injection and XSS issues, you should be concerned about the bad actor who was able to paginate through all your customer records and their data.

Typical prevention mechanisms like Captchas and browser fingerprinting won’t work since APIs by design need to handle a very large number of API accesses even by a single customer. So where do you start? The first thing is to put yourself in the shoes of a hacker and then instrument your APIs to detect and block common attacks along with unknown unknowns for zero-day exploits. Some of these are on the OWASP Security API list, but not all.

Insecure pagination and resource limits

Most APIs provide access to resources that are lists of entities such as /users or /widgets. A client such as a browser would typically filter and paginate through this list to limit the number items returned to a client like so:

First Call: GET /items?skip=0&take=10 
Second Call: GET /items?skip=10&take=10

However, if that entity has any PII or other information, then a hacker could scrape that endpoint to get a dump of all entities in your database. This could be most dangerous if those entities accidently exposed PII or other sensitive information, but could also be dangerous in providing competitors or others with adoption and usage stats for your business or provide scammers with a way to get large email lists. See how Venmo data was scraped

A naive protection mechanism would be to check the take count and throw an error if greater than 100 or 1000. The problem with this is two-fold:

  1. For data APIs, legitimate customers may need to fetch and sync a large number of records such as via cron jobs. Artificially small pagination limits can force your API to be very chatty decreasing overall throughput. Max limits are to ensure memory and scalability requirements are met (and prevent certain DDoS attacks), not to guarantee security.
  2. This offers zero protection to a hacker that writes a simple script that sleeps a random delay between repeated accesses.
skip = 0
while True:    response = requests.post('https://api.acmeinc.com/widgets?take=10&skip=' + skip),                      headers={'Authorization': 'Bearer' + ' ' + sys.argv[1]})    print("Fetched 10 items")    sleep(randint(100,1000))    skip += 10

How to secure against pagination attacks

To secure against pagination attacks, you should track how many items of a single resource are accessed within a certain time period for each user or API key rather than just at the request level. By tracking API resource access at the user level, you can block a user or API key once they hit a threshold such as “touched 1,000,000 items in a one hour period”. This is dependent on your API use case and can even be dependent on their subscription with you. Like a Captcha, this can slow down the speed that a hacker can exploit your API, like a Captcha if they have to create a new user account manually to create a new API key.

Insecure API key generation

Most APIs are protected by some sort of API key or JWT (JSON Web Token). This provides a natural way to track and protect your API as API security tools can detect abnormal API behavior and block access to an API key automatically. However, hackers will want to outsmart these mechanisms by generating and using a large pool of API keys from a large number of users just like a web hacker would use a large pool of IP addresses to circumvent DDoS protection.

How to secure against API key pools

The easiest way to secure against these types of attacks is by requiring a human to sign up for your service and generate API keys. Bot traffic can be prevented with things like Captcha and 2-Factor Authentication. Unless there is a legitimate business case, new users who sign up for your service should not have the ability to generate API keys programmatically. Instead, only trusted customers should have the ability to generate API keys programmatically. Go one step further and ensure any anomaly detection for abnormal behavior is done at the user and account level, not just for each API key.

Accidental key exposure

APIs are used in a way that increases the probability credentials are leaked:

  1. APIs are expected to be accessed over indefinite time periods, which increases the probability that a hacker obtains a valid API key that’s not expired. You save that API key in a server environment variable and forget about it. This is a drastic contrast to a user logging into an interactive website where the session expires after a short duration.
  2. The consumer of an API has direct access to the credentials such as when debugging via Postman or CURL. It only takes a single developer to accidently copy/pastes the CURL command containing the API key into a public forum like in GitHub Issues or Stack Overflow.
  3. API keys are usually bearer tokens without requiring any other identifying information. APIs cannot leverage things like one-time use tokens or 2-factor authentication.

If a key is exposed due to user error, one may think you as the API provider has any blame. However, security is all about reducing surface area and risk. Treat your customer data as if it’s your own and help them by adding guards that prevent accidental key exposure.

How to prevent accidental key exposure

The easiest way to prevent key exposure is by leveraging two tokens rather than one. A refresh token is stored as an environment variable and can only be used to generate short lived access tokens. Unlike the refresh token, these short lived tokens can access the resources, but are time limited such as in hours or days.

The customer will store the refresh token with other API keys. Then your SDK will generate access tokens on SDK init or when the last access token expires. If a CURL command gets pasted into a GitHub issue, then a hacker would need to use it within hours reducing the attack vector (unless it was the actual refresh token which is low probability)

Exposure to DDoS attacks

APIs open up entirely new business models where customers can access your API platform programmatically. However, this can make DDoS protection tricky. Most DDoS protection is designed to absorb and reject a large number of requests from bad actors during DDoS attacks but still need to let the good ones through. This requires fingerprinting the HTTP requests to check against what looks like bot traffic. This is much harder for API products as all traffic looks like bot traffic and is not coming from a browser where things like cookies are present.

Stopping DDoS attacks

The magical part about APIs is almost every access requires an API Key. If a request doesn’t have an API key, you can automatically reject it which is lightweight on your servers (Ensure authentication is short circuited very early before later middleware like request JSON parsing). So then how do you handle authenticated requests? The easiest is to leverage rate limit counters for each API key such as to handle X requests per minute and reject those above the threshold with a 429 HTTP response. There are a variety of algorithms to do this such as leaky bucket and fixed window counters.

Incorrect server security

APIs are no different than web servers when it comes to good server hygiene. Data can be leaked due to misconfigured SSL certificate or allowing non-HTTPS traffic. For modern applications, there is very little reason to accept non-HTTPS requests, but a customer could mistakenly issue a non HTTP request from their application or CURL exposing the API key. APIs do not have the protection of a browser so things like HSTS or redirect to HTTPS offer no protection.

How to ensure proper SSL

Test your SSL implementation over at Qualys SSL Test or similar tool. You should also block all non-HTTP requests which can be done within your load balancer. You should also remove any HTTP headers scrub any error messages that leak implementation details. If your API is used only by your own apps or can only be accessed server-side, then review Authoritative guide to Cross-Origin Resource Sharing for REST APIs

Incorrect caching headers

APIs provide access to dynamic data that’s scoped to each API key. Any caching implementation should have the ability to scope to an API key to prevent cross-pollution. Even if you don’t cache anything in your infrastructure, you could expose your customers to security holes. If a customer with a proxy server was using multiple API keys such as one for development and one for production, then they could see cross-pollinated data.

#api management #api security #api best practices #api providers #security analytics #api management policies #api access tokens #api access #api security risks #api access keys

Autumn  Blick

Autumn Blick


Public ASX100 APIs: The Essential List

We’ve conducted some initial research into the public APIs of the ASX100 because we regularly have conversations about what others are doing with their APIs and what best practices look like. Being able to point to good local examples and explain what is happening in Australia is a key part of this conversation.


The method used for this initial research was to obtain a list of the ASX100 (as of 18 September 2020). Then work through each company looking at the following:

  1. Whether the company had a public API: this was found by googling “[company name] API” and “[company name] API developer” and “[company name] developer portal”. Sometimes the company’s website was navigated or searched.
  2. Some data points about the API were noted, such as the URL of the portal/documentation and the method they used to publish the API (portal, documentation, web page).
  3. Observations were recorded that piqued the interest of the researchers (you will find these below).
  4. Other notes were made to support future research.
  5. You will find a summary of the data in the infographic below.


With regards to how the APIs are shared:

#api #api-development #api-analytics #apis #api-integration #api-testing #api-security #api-gateway

An API-First Approach For Designing Restful APIs | Hacker Noon

I’ve been working with Restful APIs for some time now and one thing that I love to do is to talk about APIs.

So, today I will show you how to build an API using the API-First approach and Design First with OpenAPI Specification.

First thing first, if you don’t know what’s an API-First approach means, it would be nice you stop reading this and check the blog post that I wrote to the Farfetchs blog where I explain everything that you need to know to start an API using API-First.

Preparing the ground

Before you get your hands dirty, let’s prepare the ground and understand the use case that will be developed.


If you desire to reproduce the examples that will be shown here, you will need some of those items below.

  • NodeJS
  • OpenAPI Specification
  • Text Editor (I’ll use VSCode)
  • Command Line

Use Case

To keep easy to understand, let’s use the Todo List App, it is a very common concept beyond the software development community.

#api #rest-api #openai #api-first-development #api-design #apis #restful-apis #restful-api

Marcelle  Smith

Marcelle Smith


What Are Good Traits That Make Great API Product Managers

As more companies realize the benefits of an API-first mindset and treating their APIs as products, there is a growing need for good API product management practices to make a company’s API strategy a reality. However, API product management is a relatively new field with little established knowledge on what is API product management and what a PM should be doing to ensure their API platform is successful.

Many of the current practices of API product management have carried over from other products and platforms like web and mobile, but API products have their own unique set of challenges due to the way they are marketed and used by customers. While it would be rare for a consumer mobile app to have detailed developer docs and a developer relations team, you’ll find these items common among API product-focused companies. A second unique challenge is that APIs are very developer-centric and many times API PMs are engineers themselves. Yet, this can cause an API or developer program to lose empathy for what their customers actually want if good processes are not in place. Just because you’re an engineer, don’t assume your customers will want the same features and use cases that you want.

This guide lays out what is API product management and some of the things you should be doing to be a good product manager.

#api #analytics #apis #product management #api best practices #api platform #api adoption #product managers #api product #api metrics

Autumn  Blick

Autumn Blick


54% of Developers Cite Lack of Documentation as the Top Obstacle to Consuming APIs

Recently, I worked with my team at Postman to field the 2020 State of the API survey and report. We’re insanely grateful to the folks who participated—more than 13,500 developers and other professionals took the survey, helping make this the largest and most comprehensive survey in the industry. (Seriously folks, thank you!) Curious what we learned? Here are a few insights in areas that you might find interesting:

API Reliability

Whether internal, external, or partner, APIs are perceived as reliable—more than half of respondents stated that APIs do not break, stop working, or materially change specification often enough to matter. Respondents choosing the “not often enough to matter” option here came in at 55.8% for internal APIs, 60.4% for external APIs, and 61.2% for partner APIs.

Obstacles to Producing APIs

When asked about the biggest obstacles to producing APIs, lack of time is by far the leading obstacle, with 52.3% of respondents listing it. Lack of knowledge (36.4%) and people (35.1%) were the next highest.

#api #rest-api #apis #api-first-development #api-report #api-documentation #api-reliability #hackernoon-top-story