How to Make API calls on Blockchain

Most software engineers, when looking to get data from outside their programs, look to get data from API calls or HTTP GET/POST requests. Similarly, on a blockchain, people look to get data from an external API as well. This article will teach you how to do that! Let’s get going, here is step one:

You actually can’t get data from an API call the same way you get data from normal software applications. However, in order for our smart contracts to do anything worthwhile, we would want them to interact with the outside world. So what gives?

The Ethereum blockchain was designed to be entirely deterministic. This means, that if I took the whole history of the network, then replayed it on my computer, I should always end up with the correct state.

Since the internet is non-deterministic and changes over time, then every time I replayed all of the transactions on the network, I would receive a different answer.

Instead of the contract calling the API directly, we need to have it call an application that can interact with the outside world, and have it record its findings on-chain. These are called Oracles. Oracles are any application that interacts with data from the outside world and reports it back on-chain.

If we replayed the chain without oracles and used regular API calls, the API calls might have changed, and we’d get different results.

Thanks for the technicality definition update, but how do I get my data into my solidity application?

Ok ok, let’s get into what you actually came here for. In our example, we will be just trying to get the price of ETH in USD.

For beginners:

Now if you’re a TOTAL NOOB here with this Remix, solidity, Chainlink, and want a step by step guide, feel free to check out my more in-depth guide: Write your first solidity contract or Chainlink’s documentation. We all were imposters once :D

For slightly experienced or advanced users, including remix:

If you want to follow along and just press the buttons without having to choose your own oracles, you can follow along with all the code and deploy it with me in Remix here (code included in the hyperlink). HOWEVER, THAT CODE IS FOR DEMO ONLY AND YOU SHOULD NOT USE IT FOR PRODUCTION. You should put JobIDs and addresses as parameters. You can follow that along with us in Remix here for the more productized version. You’ll notice two of the steps are missing. This is intentional ;)

Go to the gists section. Be sure to send ROPSTEN LINK to your contracts after deploying them, otherwise, you’ll get a gas estimation error. You can get ropsten LINK here. DO NOT SEND YOUR ACTUAL LINK WHEN YOU ARE TESTING. Please. Please. Please. You can use the withdrawLink function if you accidentally send it LINK.

One other note: I tend to use oracle and node interchangeably, while technically not correct, the distinction between the two for this article is negligible.

1. The naive approach

The first approach most blockchain engineers start with is just finding an oracle technology, giving it the URL of our API call, and then having it do its thing of reporting the data on-chain for us to interact with. There are a number of oracles we can use to do this. We will be using Chainlink for reasons you’ll see soon.

To use Chainlink, we first have to pick a Chainlink oracle/node. These are independently operated smart contracts on the blockchain that allow you to interact with the world. To find one, we can go to node listing services like Linkpool’s Market.Link, and just choose a node. We then have to make sure that the node has a “http Get > uint256” job. Not every node can make URL calls and return a Uint256 (basically an Int), but most do!

The node we choose was a Linkpool node at the address in the ORACLE variable. All the magic happens in the requestEthereumPrice function. If you’ve followed Chainlink’s tutorials before, you know that this is the most simple way to get data using Chainlink.

This is great! We can get data. This is fine if you’re just testing and want to quickly develop your code, but please do not use this for a production smart contract. Why? It comes with one major issue:

trustless, decentralized, smart contracts

You are pulling from one oracle and one data provider. Now Linkpool is one of the most trusted Chainlink node services out there, but your application needs to be trustless. Not only that, but you’re also getting your data from one source as well, now you have TWO points of failure in your application. CryptoCompare and LinkPool are both massive liabilities that can be easily remedied. This is a rant worthy point that I will spare you from here, but let me drill it once more.

Your smart contract needs to never have a single point of failure, as that point can be bribed, hacked, be out-of-service at execution time, or many other reasons that leave your smart contract worthless.

So how do we fix this issue?

#api #blockchain #solidity #decentralization #cryptocurrency

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How to Make API calls on Blockchain

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 ='' + 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

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

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

Python Global Variables – How to Define a Global Variable Example

In this article, you will learn the basics of global variables.

To begin with, you will learn how to declare variables in Python and what the term 'variable scope' actually means.

Then, you will learn the differences between local and global variables and understand how to define global variables and how to use the global keyword.

What Are Variables in Python and How Do You Create Them? An Introduction for Beginners

You can think of variables as storage containers.

They are storage containers for holding data, information, and values that you would like to save in the computer's memory. You can then reference or even manipulate them at some point throughout the life of the program.

A variable has a symbolic name, and you can think of that name as the label on the storage container that acts as its identifier.

The variable name will be a reference and pointer to the data stored inside it. So, there is no need to remember the details of your data and information – you only need to reference the variable name that holds that data and information.

When giving a variable a name, make sure that it is descriptive of the data it holds. Variable names need to be clear and easily understandable both for your future self and the other developers you may be working with.

Now, let's see how to actually create a variable in Python.

When declaring variables in Python, you don't need to specify their data type.

For example, in the C programming language, you have to mention explicitly the type of data the variable will hold.

So, if you wanted to store your age which is an integer, or int type, this is what you would have to do in C:

#include <stdio.h>
int main(void)
  int age = 28;
  // 'int' is the data type
  // 'age' is the name 
  // 'age' is capable of holding integer values
  // positive/negative whole numbers or 0
  // '=' is the assignment operator
  // '28' is the value

However, this is how you would write the above in Python:

age = 28

#'age' is the variable name, or identifier
# '=' is the assignment operator
#'28' is the value assigned to the variable, so '28' is the value of 'age'

The variable name is always on the left-hand side, and the value you want to assign goes on the right-hand side after the assignment operator.

Keep in mind that you can change the values of variables throughout the life of a program:

my_age = 28

print(f"My age in 2022 is {my_age}.")

my_age = 29

print(f"My age in 2023 will be {my_age}.")


#My age in 2022 is 28.
#My age in 2023 will be 29.

You keep the same variable name, my_age, but only change the value from 28 to 29.

What Does Variable Scope in Python Mean?

Variable scope refers to the parts and boundaries of a Python program where a variable is available, accessible, and visible.

There are four types of scope for Python variables, which are also known as the LEGB rule:

  • Local,
  • Enclosing,
  • Global,
  • Built-in.

For the rest of this article, you will focus on learning about creating variables with global scope, and you will understand the difference between the local and global variable scopes.

How to Create Variables With Local Scope in Python

Variables defined inside a function's body have local scope, which means they are accessible only within that particular function. In other words, they are 'local' to that function.

You can only access a local variable by calling the function.

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#call function


#The best place to learn to code is with freeCodeCamp!

Look at what happens when I try to access that variable with a local scope from outside the function's body:

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#try to print local variable 'coding_website' from outside the function


#NameError: name 'coding_website' is not defined

It raises a NameError because it is not 'visible' in the rest of the program. It is only 'visible' within the function where it was defined.

How to Create Variables With Global Scope in Python

When you define a variable outside a function, like at the top of the file, it has a global scope and it is known as a global variable.

A global variable is accessed from anywhere in the program.

You can use it inside a function's body, as well as access it from outside a function:

#create a global variable
coding_website = "freeCodeCamp"

def learn_to_code():
    #access the variable 'coding_website' inside the function
    print(f"The best place to learn to code is with {coding_website}!")

#call the function

#access the variable 'coding_website' from outside the function


#The best place to learn to code is with freeCodeCamp!

What happens when there is a global and local variable, and they both have the same name?

#global variable
city = "Athens"

def travel_plans():
    #local variable with the same name as the global variable
    city = "London"
    print(f"I want to visit {city} next year!")

#call function - this will output the value of local variable

#reference global variable - this will output the value of global variable
print(f"I want to visit {city} next year!")


#I want to visit London next year!
#I want to visit Athens next year!

In the example above, maybe you were not expecting that specific output.

Maybe you thought that the value of city would change when I assigned it a different value inside the function.

Maybe you expected that when I referenced the global variable with the line print(f" I want to visit {city} next year!"), the output would be #I want to visit London next year! instead of #I want to visit Athens next year!.

However, when the function was called, it printed the value of the local variable.

Then, when I referenced the global variable outside the function, the value assigned to the global variable was printed.

They didn't interfere with one another.

That said, using the same variable name for global and local variables is not considered a best practice. Make sure that your variables don't have the same name, as you may get some confusing results when you run your program.

How to Use the global Keyword in Python

What if you have a global variable but want to change its value inside a function?

Look at what happens when I try to do that:

#global variable
city = "Athens"

def travel_plans():
    #First, this is like when I tried to access the global variable defined outside the function. 
    # This works fine on its own, as you saw earlier on.
    print(f"I want to visit {city} next year!")

    #However, when I then try to re-assign a different value to the global variable 'city' from inside the function,
    #after trying to print it,
    #it will throw an error
    city = "London"
    print(f"I want to visit {city} next year!")

#call function


#UnboundLocalError: local variable 'city' referenced before assignment

By default Python thinks you want to use a local variable inside a function.

So, when I first try to print the value of the variable and then re-assign a value to the variable I am trying to access, Python gets confused.

The way to change the value of a global variable inside a function is by using the global keyword:

#global variable
city = "Athens"

#print value of global variable
print(f"I want to visit {city} next year!")

def travel_plans():
    global city
    #print initial value of global variable
    print(f"I want to visit {city} next year!")
    #assign a different value to global variable from within function
    city = "London"
    #print new value
    print(f"I want to visit {city} next year!")

#call function

#print value of global variable
print(f"I want to visit {city} next year!")

Use the global keyword before referencing it in the function, as you will get the following error: SyntaxError: name 'city' is used prior to global declaration.

Earlier, you saw that you couldn't access variables created inside functions since they have local scope.

The global keyword changes the visibility of variables declared inside functions.

def learn_to_code():
   global coding_website
   coding_website = "freeCodeCamp"
   print(f"The best place to learn to code is with {coding_website}!")

#call function

#access variable from within the function


#The best place to learn to code is with freeCodeCamp!


And there you have it! You now know the basics of global variables in Python and can tell the differences between local and global variables.

I hope you found this article useful.

You'll start from the basics and learn in an interactive and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you've learned.

Thanks for reading and happy coding!