Encryption converts regular text into a coded language that only someone with the right key can decode. It is used to secure communication, protect sensitive information, and prevent data breaches. Encryption is routinely used in daily life in ways you may not even notice, such as securing your credit card information during online purchases.

This article will explore encryption theories, types, and practical uses that help keep our digital world safe.

What Is Encryption?

Encryption is the process of encoding readable text into secure code. It’s a fundamental technology for securing information against outside access.

Historically, it has been used in spycraft and wartime for sensitive communications, but the more familiar applications today center on online data.

Personal information, financial data, and confidential documents shared online must be encrypted to secure them properly against cybercrime.

Encryption uses a formula called a “cipher” or an encryption algorithm, which ensures that anyone who tries to intercept information communicated across a digital space cannot read its true contents.

Encryption unlocks information for the intended recipient alone by using a special key that only their device will have. Anyone without this key will be unable to properly decrypt the message.

What Is Encryption in Cybersecurity?

Encryption is a pillar of many cybersecurity protocols and procedures. For example, if cyber attackers breach your network, easily-accessed personal and confidential information could be at risk of theft and either held for ransom or sold to the highest bidder.

However, if your device stores only encrypted information, the data that the hackers access will be rendered useless, as they cannot read it without the correct secret key.

Many regulations now require encryption as part of their set of cybersecurity standards. This is especially true for organizations that store Private Personal Information (PPI), such as financial and healthcare institutions.

What Is the Purpose of Data Encryption?

The fundamental purpose of encryption is to protect sensitive information being seen by those with unauthorized access. Encrypting communications helps you maintain data confidentiality during transmission and storage.

This is especially important to people and organizations whose private data is particularly sensitive or confidential, such as banks, healthcare providers, military organizations, power and energy companies, and insurance providers.

Data encryption allows these types of organizations to hold onto personal information in a secure way that will not compromise your identity. Regular individuals may want to protect their information, too.

Encryption prevents your information from being tampered with. In a digital age that lacks trust, encryption can make you feel more secure that the information you send and receive is authentic. Improving data integrity and authenticity is another of encryption’s core benefits.

Types of Data Encryption

There are many different types of encryption, each with varying levels of security and usability. Let’s explore the most common forms of encryption and the benefits and disadvantages of each of them.

Symmetric Encryption

Symmetric encryption is when a single encryption key is used to encrypt and decrypt information. This means that the key must be shared with both the person sending the information and the person receiving the information.

Symmetric encryption can be created using a block algorithm or a stream algorithm. Using a block algorithm, the system utilizes a unique secret security key to encrypt set lengths of bits in blocks. On the other hand, a stream algorithm does not retain encrypted data in its memory but encrypts it as it flows in.

The benefits of symmetric encryption are that it is a very fast form of encryption and is good to use for bulk encryption needs. However, symmetric keys are difficult to manage at a mass scale and can reduce the security of transmitted messages if their key information has been leaked.

Asymmetric Encryption

Unlike symmetric encryption, asymmetric encryption uses one key for encrypting information and a separate key for decryption.

Asymmetric encryption is also known as public-key encryption, as the key to encryption information is available publicly and can be used by many people. Meanwhile, the person receiving the message has a corresponding private key used to decrypt the message.

Asymmetric encryption is used in many fundamental internet protocols. One application is used in Transport Layer Security (TLS) and Secure Sockets Layer (SSL).

Another common use of asymmetric encryption is in software that requires a connection to be established over an insecure network. By encrypting the information communicated across that connection, browsers and other digital communication devices can maintain security.

It’s important to note that public keys used in encryption do not hide metadata, meaning that information about which computer the message came from or when it was sent will be available.

It is also a much slower form of encryption. Interestingly, one of its common uses is to send the symmetric encryption key to the receiver of a message.

Hashing

Hashing is a process for applying an algorithm that transforms input data into a fixed-length output. The same input will always result in the same hash string output, so comparing hash results is useful for verifying data integrity.

For security purposes, sensitive information can be hashed and stored in ‘hash tables,’ such as when an organization stores passwords in hashed form rather than in plaintext.

Hashing is frequently mislabelled as a type of encryption. While it is a cryptography tool, it is not considered encryption, as hashed information can be recreated without a secret key.

What Is an Encryption Algorithm?

Regardless of whether you are using symmetric or asymmetric encryption, the secret keys exchanged must use an algorithm to encrypt information.

These algorithms are created using a mathematical formula or a set of rules. Using the specific mathematical formula that has been created for that type of encryption, the algorithm converts plaintext into cipher text. Utilizing standardized algorithms ensures that text can always be decrypted in a predictable way.

There are several different common encryption algorithms, each used for different purposes, industries, or required levels of security.

What are Common Encryption Algorithms?

1. Data Encryption Standard (DES)

The Data Encryption Standard (DES) was developed by IBM in the 1970s and was first used by the United States government to send and receive private information.

It is a symmetric-key algorithm for encrypting electronic data. It uses a block algorithm with 56 bits to encrypt information.

As it is an older form of encryption, it is no longer considered secure for most cryptographic functions today. As computers evolved, the 56 bits were not enough to securely protect information because newer devices’ improved computing power could crack the DES algorithm quickly.

However, the DES algorithm paved the way for stronger and more advanced encryption algorithms to follow it.

2. Triple Data Encryption Standard (3DES)

One of the first attempts to improve on the original DES encryption model produced the Triple Data Encryption Standard (3DES).

3DES is also a symmetric encryption block algorithm. Its block cipher uses 64-bit blocks to encrypt information. However, instead of stopping there as DES does, it goes through three rounds of encryption to provide a higher level of security that further hides the original message.

Even so, the National Institute of Standards and Technology (NIST) has stated that as of the end of 2023, 3DES is depreciated. That means while it can still be used for legacy software, it cannot be used to create new cyber-secure applications.

3. Advanced Encryption Standards (AES)

Like DES, Advanced Encryption Standards (AES) is a symmetric encryption algorithm that uses a block cipher to encrypt and decrypt information.

AES differs mainly in its available key sizes. Data can be encrypted using AES with three different key sizes: 128-bit, 192-bit, or 256-bit. These longer bit sizes make it much stronger than DES, as even today’s computers would take an impossibly long time to crack the algorithm. As such, it is used widely and is considered to be one of the most secure encryption methods available today.

AES is used in many common applications, including file encryption, wireless security, processor security, and cloud security protocols such as SSL and TLS.

4. RSA Encryption

Rivest-Shamir-Adleman (RSA) encryption, named after the surnames of its creators, is a type of asymmetric encryption, meaning that you need both a private and public key in order to decrypt the transmitted information.

RSA works by multiplying two very large prime numbers together and relying on the improbability of hackers being able to guess which exact two numbers created the new one.

It also uses extremely large bits to encrypt information, including 1,024-, 2,048-, and sometimes 4,096-bit encryption.

RSA can be applied to different use cases by changing the setup of private and public keys. In the more common configuration, the public key is used for encryption and a private key is required to decrypt the data. This arrangement is commonly to send private information and ensure that it cannot be read if intercepted.

However, RSA encryption can also be used in the reverse arrangement, where the private key encrypts data and the public key decrypts. This method is used to confirm the sender’s authenticity rather than to hide information.

5. Blowfish Encryption

Blowfish encryption is another symmetric-key block cipher algorithm. It was created in the 1990s to replace DES. It can use variable key sizes ranging from 32-bit to 448-bit.

What is distinct about Blowfish is that it is an unpatented algorithm, meaning it can be used by anyone without having to pay for its use. For this reason, it is widely used in software and internet security applications.

Blowfish is slower than some other types of block algorithms, which in some use cases is to its benefit.

6. Twofish Encryption, Threefish Encryption, and More

A demand for increased security has spawned many new encryption algorithms, including Twofish, Threefish, and Macguffin, just to name a few. Each algorithm uses its own unique mathematical formula, and each has its own benefits and drawbacks.

The most important thing is to ensure that the tools you use to encrypt data meet today’s highest standards from the NIST and other regulatory security bodies.

How Is Encryption Used?

Encryption is used every day to protect a variety of data transactions online. You may not even realize some places where it’s used.

Let’s explore the common day-to-day use cases of encryption.

File Encryption

If you’re sending and receiving sensitive information through files such as Word documents, PDFs, or images, file encryption can be used to protect the information contained in those documents.

Using one of the algorithms we discussed in the previous section or another type of encryption method, files can be encoded data in a way that makes them unreadable without a decryption key.

This process provides protection against unauthorized access, theft, and data breaches. There are tools such as Filezilla that allow you to encrypt the documents that you store and send. Making this part of your regular document-sharing process can keep your information much safer.

Disk Encryption

While it’s less common these days, information is sometimes stored and shared on physical devices such as hard drives or USB drives. Ensuring these physical devices have proper cybersecurity procedures implemented in their distribution will help keep the information on them out of the hands of hackers.

Disk encryption uses encryption algorithms to scramble the data on physical storage devices, and only those with the correct secret key can unscramble it. Whereas file encryption is applied to individual files, disk encryption can be applied across the entire disk structure to prevent access to all the files within.

By encrypting your disks, you can protect sensitive data from cyber-attacks or from information falling into the wrong hands.

Email Encryption

A very common and important use of encryption is email encryption.

Email encryption protects the content of your email from being viewed by unauthorized persons. Even if your emails are intercepted by an attacker, encryption can prevent them from being understood by the middleman. Email encryption can also help businesses comply with data protection regulations and maintain the confidentiality of their clients.

When deciding on a secure email provider, you should ensure that the one you choose offers strong encryption capabilities.

Encryption in the Cloud

Cloud security is one of the most important tools in cybersecurity today. Almost everything we do on the web today is stored on servers in the cloud. But when it comes to security, its ease of access is as much of a drawback as it is a benefit.

That’s why cloud encryption is integral to securing data. Encryption in the cloud involves encrypting data before storing it on a cloud server, making it more difficult for hackers or unauthorized users to access it. The encryption keys are typically managed by the cloud provider or the user.

End-to-End Encryption

If you’re using messaging apps today, it’s likely you’re using end-to-end encryption without even realizing it. End-to-end encryption ensures that only the sender and intended recipient can access the content of a text message.

Many popular messaging apps, such as WhatsApp and Signal, use end-to-end encryption to protect their users’ communications.

Encryption has become commonplace in almost all aspects of modern digital life and for good reasons. Let’s explore its main benefits below.

Benefits of Data Encryption

Compliance with Data Protection Regulations

Many organizations and entities are required to be compliant with various data protection standards. Many of these regulations require sensitive data to be stored and transmitted using encryption standards.

One example of this is PCI compliance, which is required by all ecommerce stores. This standard ensures that credit card data is stored and transmitted securely using encryption.

Understanding whether the data you have is properly encrypted or not can save you from fines, lawsuits, or denied insurance claims being found non-compliant. Be sure to check with IT security personnel to ensure you’re meeting the required standards.

Remote Work Protection

While remote work has its many benefits, it can create additional risks when it comes to transmitting sensitive information. With remote work, there’s more information being transmitted over email and instant messaging, all of which are susceptible to interception.

Even though many organizations implement VPNs, firewalls, and other cybersecurity procedures to keep out attackers, the information behind them should still be encrypted in case those protections are breached. Data encryption provides a layer of protection for users working remotely by ensuring that the data is sent in encrypted form and can only be accessed by authorized personnel.

Encryption prevents attackers from capturing network traffic containing sensitive information or exploiting isolated connections over the internet.

Increased Consumer Trust

Using encryption beyond its regulated requirements is also a good idea for many businesses. Being able to promise customers that their data and information will be securely protected with encryption may make them more likely to use your product over another that does not offer similar promises. It shows clients that your company takes data privacy seriously and is committed to protecting its customers.

Furthermore, by using encryption whenever possible, you also reduce the likelihood of being affected by a data or compliance breach. Cyber attacks or compliance violations can cause serious reputational damage to your business and hurt your bottom line.

By using encryption, you can avoid costly and harmful data breaches.

Can Encrypted Data Be Hacked?

Encryption provides strong protection against unauthorized data access, but it is not foolproof. As we’ve explored, some encryption methods are more secure than others. Legacy algorithms are considered less secure because they do not apply enough permutations to avoid being broken by modern-day computers. This problem will increase as computational power continues to increase, and today’s strong encryption could become tomorrow’s weak encryption.

Additionally, there is always a danger that encryption keys can be stolen or lost. Human error plays a role, as encryption keys may be accidentally shared or compromised in other ways.

You should also be aware that encryption also does not categorically protect against every type of cybersecurity risk. Cybercriminals can try to attack your domain from other angles, such as through DDoS attacks, DNS poisoning, phishing, and so on. Therefore, you should harden your security posture with additional tools beyond encryption to ensure your sites and web applications are fully protected.

While these risks do exist, it’s important to remember that cybersecurity is best when layered upon multiple types of security. Encrypted data is still better than unencrypted data, especially if it’s combined with additional types of security procedures to ensure the encryption secrets remain hidden.

Data Encryption FAQs

Encryption is a wide-ranging topic. If you’re interested in diving deeper, here are some commonly asked questions about encryption:

Encryption vs. Tokenization: What’s the Difference?

While encryption is a process that turns intelligible information unintelligible and then back again, tokenization cannot be reversed.

The process of tokenization involves removing key data points from an organization’s data storage and replacing them with placeholder information. Meanwhile, the correct information that was removed is stored elsewhere so as not to be included in the information a hacker may steal if the company is breached.

In Transit vs. At Rest Encryption: What’s the Difference?

The key to understanding the difference between these two types of encryption is understanding the two common states that data can exist in – at rest or in transit.

Data at rest is what we call data that is stored somewhere, on a hard drive, USB, or other digital storage space. This data is in a fixed location, and it doesn’t move. Data in transit is data that is being communicated or transferred. It’s moving between computers, networks, or across the internet. Encryption in transit involves scrambling the information while it’s being moved from one place to another.

Encryption at rest is the process of protecting the data while it is stored at its physical location.

Ensuring your information is encrypted when it is in both states is crucial to protecting the private data of your clients and your company.

What are Encryption Backdoors?

The key to understanding encryption backdoors is to remember that many cybersecurity protocols are built with the knowledge that humans are prone to errors and, on occasion, need a backup plan.

Like the spare house key, you can hide under the mat, encryption backdoors are built-in bypasses that allow authorized personnel to undo the process of encryption in the case of emergencies. However, when not properly protected, these same built-in bypasses can be exploited by attackers and used as backdoors into your encrypted information.

Summary

Encryption is a vital tool in protecting our sensitive information and keeping it safe from cybercriminals. Whether personal data like credit card information or business secrets, encryption ensures that only authorized individuals can access it.

As a website owner, it’s important to understand the different types of encryption, which methods you need to implement to remain in compliance, and how to use them properly to ensure maximum security.

As technology advances, encryption will continue to play a crucial role in safeguarding our data. If you’re curious about how to implement encryption on your web-hosted site, contact us today.

The post What Is Data Encryption? Definition, Types, and Best Practices appeared first on Kinsta®.

You often see this error when pushing to remote repositories when working as part of a team. This complicates the situation somewhat and means you may have to hunt out the source of the issue to make sure you can manage it both now and in the future.

In this tutorial, we look at how you can fix Git’s “error: failed to push some refs to”. Let’s start with what this error means before we move on to the fix.

What the “Error: Failed to Push Some Refs To” Is in Git?

Git’s “error: failed to push some refs to” is a common and sometimes complex issue. In a nutshell, you could see this when you attempt to push changes to a remote repository. The error indicates that the push operation was unsuccessful for some of the references, such as branches or tags.

You can see the error in a few different situations:

• A common scenario is when you try to push changes to a remote repository, but a team member has already pushed changes to the same branch. In this case, Git detects a conflict between the local and remote repositories. As such, you can’t push changes until you resolve the conflict.
• You might also see this error if the remote repository’s branch sees an update or modification, but your local repo is out of date. Git will prevent you from pushing changes to avoid overwriting or losing any changes made by others.

The error message tells you that Git has encountered issues while trying to push some references, usually specific branches, to the remote repo. However, it doesn’t provide specific details about the problems. Instead, it prompts you to investigate further to identify the cause of the failed push.

We’ll give you a full tutorial on how to resolve the “error: failed to push some refs to” later in the article. However, in short, to resolve the error, you need to synchronize your local repository with the changes in the remote one. You would pull the latest changes from remote, merge any conflicting changes, then attempt the push again.

Why “Error: Failed to Push Some Refs To” Occurs?

The “error: failed to push some refs to” is essentially a mismatch in certain references between the local and remote repos. However, there are a few deeper reasons why this error may occur:

• Conflicting changes. Code conflicts represent one of the more common reasons for errors. Here, if someone pushes changes to the same branch before you, Git will detect a conflict and prevent you from overwriting those changes. Git will ask you to pull the latest changes from the remote repository and merge them with your local changes before you retry to push.
• Outdated local repository. If the branch you are trying to push has an update on the remote repo since your last pull or clone, your local repository might be behind. Git recognizes this inconsistency and will refuse a push to avoid losing any changes.
• Insufficient permissions. The “error: failed to push some refs to” message could appear if you don’t have sufficient permissions to push changes to remote. For this, you’ll need to speak with the repo administrator before you can try again.
• Repository configuration. The error can also occur if you misconfigure the remote repository or the Git configuration itself. For instance, you could have incorrect access URLs, authentication issues, or invalid repository settings. All can lead to failed pushes.

Most of the ways to resolve this error involve synchronizing the local and remote repositories. Over the next few sections, we will look at how to fix the error, then look at how you can prevent the issue from appearing in the future.

How To Fix the “Error: Failed to Push Some Refs To” in Git (2 Quick Steps)

While our tutorial on how to fix Git’s “error: failed to push some refs to” looks lengthy, the steps are straightforward. In fact, there are only two. For the first, you want to make sure there are no simple issues you can resolve.

1. Make Sure You’re Not Making a Straightforward Error

As with many other errors you encounter, it’s a good idea to take care of the basics first. It makes sense to ensure the fundamentals are present and correct before you dig into (slightly) more complex solutions.

For this first step, we look at some of the straightforward ways you can resolve the “error: failed to push some refs to” in Git before we consider pushing and pulling options.

Ensure You’re Using the Right Repository Pair

You could consider this check as an equivalent to “Have you turned the computer on at the wall?” It’s important to check whether you are pushing and pulling to and from the right repos before you check anything else.

First, check over the remote repo. Within your preferred Terminal app, use the git remote -v command to view all of the configured remote repos. You want to confirm that the remote repository URL matches the intended repo.

Next, you want to confirm that you’ll push changes to the correct branch. To do this, use git branch, then verify the branch name that shows:

If you need to switch branches, simply use git checkout <branch-name>.

From here, use git status to check for any errors or unresolved conflicts in your local repo changes. Before you attempt to push changes again, make sure you resolve any conflicts or errors you see.

When you’re ready, you can add changes to the staging area using git add <file> for individual files, or git add . to stage all changes.

When you commit the changes, look to give it a descriptive message – one that includes brief details of the error will help create a richer message log for the repo. You can use the git commit -m "Your commit message" command and replace the placeholder with your actual message.

Next, you can execute git pull origin <branch-name> to fetch and merge the latest changes from the remote repository. Again, you should resolve any conflicts that arise during the merge process. When this completes, retry the push using git push origin <branch-name>.

Note that you may need to authenticate the push and provide credentials, which you should do. Regardless, once the push process completes, run git status to ensure there are no uncommitted changes or pending actions that remain.

Check Your Working Directory and Repo Status

Another basic check to help resolve the “error: failed to push some refs to” in Git is to check your working directory and status of the repository.

However, even if you don’t believe you have made a mistake with the command you execute, it’s a good idea to check for typos or other errors here. It may help to test your internet connection too. In short, check everything that could have an impact on the path between your local repo and remote.

From here, you can check on the status of your working directory. This is as simple as executing git status. Once you ensure that you’re staging all the changes you want to push, you can move on to looking at your repo’s status.

As with the other step, you can use git remote -v to verify the remote repository configuration. Here, check that the remote URL is correct. You should also confirm that you will push to the correct branch using git branch:

Once you know everything is in order, git fetch will grab the latest changes from the remote repository. From here, execute git merge origin/<branch-name> to merge the fetched changes into your local branch.

Again, resolve any merge conflicts, then retry the push using git push origin <branch-name>. You might need to authenticate the push, but regardless, run git status after to make sure the working branch is now clean.

2. Carry Out a Simple Git Push and Pull

Once you know that Git’s “error: failed to push some refs to” is not appearing due to simple and fundamental errors, you can begin to deal with your specific scenario. In most situations, you can use a push and pull to put things right again.

However, note that if you believe there’s a permissions issue, you should speak with your remote repo’s administrator. This will be the only way you can resolve the “error: failed to push some refs to” in Git.

For issues where you have conflicting changes or your local repo is behind the remote, you can run a git pull origin <branch-name> to fetch and merge the latest changes from the remote repository.

You may need to resolve any conflicts that arise during the merge process, but once you do this,  commit the changes and run git push origin <branch-name> to push your changes to the remote repo.

However, if you have an incorrect remote repository URL or configuration, you can update it using git remote set-url origin <new-remote-url>.

This will set the correct URL for the remote repository. From here, look to reproduce the “error: failed to push some refs to”, which shouldn’t appear after.

How Can You Prevent “Error: Failed to Push Some Refs To” in Git Before It Becomes a Problem?

While the “error: failed to push some refs to” in Git can be a snap to resolve, you should try to ensure that the error doesn’t appear at all.

Before you begin work, it’s a good idea to verify your permissions. This may have to be through your repo owner or administrator. It’s also a solid idea to have effective communication with other developers working on the same repository. You should look to coordinate and agree on branching strategies, branch naming conventions, and other workflows to minimize conflicts and sync issues.

Apart from these communicative practices, there are a few technical considerations to make too:

• Use branches for collaboration and to reduce conflicts. If you create separate branches for different features or bug fixes, this lets your colleagues work without interfering with each other’s changes.
• Always look to pull the latest changes from the remote repo before you push your changes. As such, your local repository will be up-to-date. It also minimizes the chances of encountering a conflict or outdated reference.
• If a conflict arises during a pull, resolve it locally before attempting to push. Git provides tools to help identify and merge conflicting changes.
• Ensure that the remote repository’s URL is correct in your local repo. What’s more, review this on a regular basis using git remote set-url origin <new-remote-url> if necessary.
• Use staging environments to test and preview changes before you deploy them. This helps identify any issues early on and ensures a smooth deployment process.

From here, you should keep a close eye on the status of your repository and regularly perform maintenance tasks. This could be pulling updates, resolving conflicts, reviewing changes, and more. While you can’t eradicate the issue in full, these typical practices go some way to help minimize any disruptions.

How Kinsta Can Help You Use Git to Deploy Your Website

If you’re a Kinsta user, you have seamless integration and robust support for Git in the box. It’s of big value when it comes to managing your WordPress websites and applications, as well as for deployment.

The process lets you connect your Git repo directly to Kinsta. As such, you can automate deployment processes, streamline collaboration, and maintain a reliable VCS too. It uses Secure Shell (SSH) access to keep your connection safe and secure.

We think using Kinsta and Git offers a number of benefits. For instance, you could set up a continuous integration/continuous deployment (CI/CD) pipeline. For GitLab customers, you can even set up complete automation. This not only reduces human error but ensures your website is always up-to-date.

You also have flexibility when it comes to pushing and deployment. Many Kinsta users turn to WP Pusher, although Beanstalk and DeployBot also have fans.

Using Kinsta’s staging, you can test and preview changes before you deploy them. This is an ideal scenario for Git, as it can happen from the command line and slot into your automated process.

The best way to integrate Git with Kinsta is to locate your SSH credentials on the Info > SFTP/SSH screen.

With these credentials, you can log into your site from the command line. We have a complete guide on using Git with Kinsta within our documentation, and it’s essential reading regardless of whether you need to fix an error or not.

Summary

Git is arguably the best VCS on the market and provides most of the functionality you need to manage the code for your development projects. However, your project’s efficiency could slow to a crawl if you encounter an error. The “error: failed to push some refs to” in Git can be confusing, but it often has a straightforward resolution.

First, check that you don’t make any simple errors, such as using the right repo pair and working directory. From there, you simply need to carry out a push and pull to make sure every file and folder sync correctly.

What’s more, Kinsta is top-tier when it comes to Application and Database Hosting. You can deploy your full stack in minutes to your remote repo without the need to learn new workflows. This means you can minimize errors while you take advantage of our 25 data centers and resource-based pricing.

Do you have any questions about resolving Git’s “error: failed to push some refs to”? Ask away in the comments section below!

The post How To Fix the “Error: Failed to Push Some Refs To” in Git appeared first on Kinsta®.

Today, many B2B companies offer their services via APIs that can be consumed by apps made in any programming language and framework. However, this leaves them vulnerable to DoS and DDoS attacks and can also lead to an uneven distribution of bandwidth between users. To tackle these issues, a technique known as API rate limiting is implemented. The idea is simple — you limit the number of requests that users can make to your API.

In this guide, you will learn what API rate limiting is, the multiple ways it can be implemented, and a few best practices and examples to remember when setting up API rate limits.

What Is API Rate Limiting?

In simple words, API rate limiting refers to setting a threshold or limit over the number of times an API can be accessed by its users. The limits can be decided in multiple ways.

1. User-based Limits

One of the ways to set a rate limit is to reduce the number of times a particular user can access the API in a given timeframe. This can be achieved by counting the number of requests made using the same API key or IP address, and when a threshold is reached, further requests are throttled or denied.

2. Location-based Limits

In many cases, developers want to distribute the available bandwidth for their API equally among certain geographic locations.

The recent ChatGPT preview service is a good example of location-based rate limiting as they started limiting requests based on user locations on the service’s free version once the paid version was rolled out. It made sense since the free preview version was supposed to be used by people worldwide to generate a good sample of usage data for the service.

3. Server-based Limits

Server-based rate limiting is an internal rate limit implemented on the server side to ensure equitable distribution of server resources such as CPU, memory, disk space, etc. It is done by implementing a limit on each server of a deployment.

When a server reaches its limit, further incoming requests are routed to another server with available capacity. If all servers have reached capacity, the user receives a 429 Too Many Requests response. It is important to note that server-based rate limits are applied to all clients irrespective of their geographical location, time of access, or other factors.

Don't make it too easy for attackers to disrupt your APIs! Here are multiple reasons why having an API rate limit can protect you Click to Tweet

Types of API Rate Limits

Apart from the nature of the implementation of the rate limits, you can also classify rate limits based on their effect on the end user. Some common types are:

• Hard limits: These are strict limits that, when crossed, will completely restrict the user from accessing the resource until the limit is lifted.
• Soft limits: These are flexible limits that, when crossed, might still allow the user to access the resource a few more times (or throttle the requests) before shutting access.
• Dynamic limits: These limits depend on multiple factors such as server load, network traffic, user location, user activity, traffic distribution, etc., and are changed in real-time for efficient resource functioning.
• Throttles: These limits do not cut off access to the resource but rather slow down or queue further incoming requests until the limit is lifted.
• Billable limits: These limits do not restrict access or throttle speed but instead charge the user for further requests when the set free threshold is exceeded.

Why Is Rate Limiting Necessary?

There are multiple reasons why you’d need to implement rate limiting in your web APIs. Some of the top reasons are:

1. Protecting Resource Access

The first reason why you should consider implementing an API rate limit in your app is to protect your resources from being overexploited by users with malicious intent. Attackers can use techniques like DDoS attacks to hog up access to your resources and prevent your app from functioning normally for other users. Having a rate limit in place ensures that you are not making it easy for attackers to disrupt your APIs.

2. Splitting Quota Among Users

Apart from protecting your resources, the rate limit allows you to split your API resources among users. This means that you can create tiered pricing models and cater to the dynamic needs of your customers without letting them affect other customers.

3. Enhancing Cost-efficiency

Rate limiting also equates to cost limiting. This means you can make a judicious distribution of your resources among your users. With a partitioned structure, it is easier to estimate the cost required for the system’s upkeep. Any spikes can be handled intelligently by provisioning or decommissioning the right amount of resources.

4. Managing Flow Between Workers

Many APIs rely on a distributed architecture that uses multiple workers/threads/instances to handle incoming requests. In such a structure, you can use rate limits to control the workload passed to each worker node. This can help you ensure that the worker nodes receive equitable and sustainable workloads. You can easily add or remove workers as and when needed without restructuring the entire API gateway.

Understanding Burst Limits

Another common way of controlling API usage is to set a burst limit (also known as throttling) instead of a rate limit. Burst limits are rate limits implemented for a very small time interval, say a few seconds. For instance, instead of setting up a limit of 1.3 million requests per month, you could set a limit of 5 requests per second. While this equates to the same monthly traffic, it ensures that your customers don’t overload your servers by sending in bursts of thousands of requests at once.

In the case of burst limits, requests are often delayed until the next interval instead of denied. It is also often recommended to use both rate and burst limits together for optimum traffic and usage control.

3 Methods of Implementing Rate Limiting

When it comes to implementation, there are a few methods you can use to set up API rate limiting in your app. They include:

1. Request Queues

One of the simplest practical methods of restricting API access is via request queues. Request queues refer to a mechanism in which incoming requests are stored in the form of a queue and processed one after another up to a certain limit.

A common use case of request queues is segregating incoming requests from free and paid users. Here’s how you can do that in an Express app using the express-queue package:

const express = require('express')
const expressQueue = require('express-queue');

const app = express()

const freeRequestsQueue = expressQueue({
    activeLimit: 1, // Maximum requests to process at once
    queuedLimit: -1 // Maximum requests allowed in queue (-1 means unlimited)
});

const paidRequestsQueue = expressQueue({
    activeLimit: 5, // Maximum requests to process at once
    queuedLimit: -1 // Maximum requests allowed in queue (-1 means unlimited)
});

// Middleware that selects the appropriate queue handler based on the presence of an API token in the request
function queueHandlerMiddleware(req, res, next) {
    // Check if the request contains an API token
    const apiToken = req.headers['api-token'];

    if (apiToken && isValidToken(apiToken)) {
        console.log("Paid request received")
        paidRequestsQueue(req, res, next);
    } else {
        console.log("Free request received")
        freeRequestsQueue(req, res, next);
     }
}

// Add the custom middleware function to the route
app.get('/route', queueHandlerMiddleware, (req, res) => {
    res.status(200).json({ message: "Processed!" })
});

// Check here is the API token is valid or not
const isValidToken = () => {
    return true;
}

app.listen(3000);

2. Throttling

Throttling is another technique used to control access to APIs. Instead of cutting off access after a threshold is reached, throttling focuses on evening out the spikes in API traffic by implementing small thresholds for small time ranges. Instead of setting up a rate limit like 3 million calls per month, throttling sets up limits of 10 calls per second. Once a client sends more than 10 calls in a second, the next requests in the same second are automatically throttled, but the client instantly regains access to the API in the next second.

You can implement throttling in Express using the express-throttle package. Here’s a sample Express app that shows how to set up throttling in your app:

const express = require('express')
const throttle = require('express-throttle')

const app = express()

const throttleOptions = {
    "rate": "10/s",
    "burst": 5,
    "on_allowed": function (req, res, next, bucket) {
        res.set("X-Rate-Limit-Limit", 10);
        res.set("X-Rate-Limit-Remaining", bucket.tokens);
        next()
    },
    "on_throttled": function (req, res, next, bucket) {
        // Notify client
        res.set("X-Rate-Limit-Limit", 10);
        res.set("X-Rate-Limit-Remaining", 0);
        res.status(503).send("System overloaded, try again after a few seconds.");
    }
}

// Add the custom middleware function to the route
app.get('/route', throttle(throttleOptions), (req, res) => {
    res.status(200).json({ message: "Processed!" })
});

app.listen(3000);

You can test the app using a load-testing tool like AutoCannon. You can install AutoCannon by running the following command in your terminal:

npm install autocannon -g

You can test the app using the following:

autocannon http://localhost:3000/route

The test uses 10 concurrent connections that send in requests to the API. Here’s the result of the test:

Running 10s test @ http://localhost:3000/route

10 connections

┌─────────┬──────┬──────┬───────┬──────┬─────────┬─────────┬───────┐
│ Stat    │ 2.5% │ 50%  │ 97.5% │ 99%  │ Avg     │ Stdev   │ Max   │
├─────────┼──────┼──────┼───────┼──────┼─────────┼─────────┼───────┤
│ Latency │ 0 ms │ 0 ms │ 1 ms  │ 1 ms │ 0.04 ms │ 0.24 ms │ 17 ms │
└─────────┴──────┴──────┴───────┴──────┴─────────┴─────────┴───────┘
┌───────────┬─────────┬─────────┬────────┬─────────┬────────┬─────────┬─────────┐
│ Stat      │ 1%      │ 2.5%    │ 50%    │ 97.5%   │ Avg    │ Stdev   │ Min     │
├───────────┼─────────┼─────────┼────────┼─────────┼────────┼─────────┼─────────┤
│ Req/Sec   │ 16591   │ 16591   │ 19695  │ 19903   │ 19144  │ 1044.15 │ 16587   │
├───────────┼─────────┼─────────┼────────┼─────────┼────────┼─────────┼─────────┤
│ Bytes/Sec │ 5.73 MB │ 5.73 MB │ 6.8 MB │ 6.86 MB │ 6.6 MB │ 360 kB  │ 5.72 MB │
└───────────┴─────────┴─────────┴────────┴─────────┴────────┴─────────┴─────────┘

Req/Bytes counts sampled once per second.
# of samples: 11
114 2xx responses, 210455 non 2xx responses
211k requests in 11.01s, 72.6 MB read

Since only 10 requests per second were allowed (with an extra burst of 5 requests), only 114 requests were successfully processed by the API, and the remaining requests were responded to with a 503 error code asking to wait for some time.

3. Rate-limiting Algorithms

While rate limiting looks like a simple concept that can be implemented using a queue, it can, in fact, be implemented in multiple ways offering various benefits. Here are a few popular algorithms used to implement rate limiting:

Fixed Window Algorithm

The fixed window algorithm is one of the simplest rate-limiting algorithms. It limits the number of requests that can be handled in a fixed time interval.

You set a fixed number of requests, say 100, that can be handled by the API server in an hour. Now, when the 101st request arrives, the algorithm denies processing it. When the time interval resets (i.e., in the next hour), another 100 incoming requests can be processed.

This algorithm is straightforward to implement and works well in many cases where server-side rate limiting is needed to control bandwidth (in contrast to distributing bandwidth among users). However, it can result in spiky traffic/processing towards the edges of the fixed time interval. The sliding window algorithm is a better alternative in cases where you need even processing.

Sliding Window Algorithm

The sliding window algorithm is a variation of the fixed window algorithm. Instead of using fixed predefined time intervals, this algorithm uses a rolling time window to track the number of processed & incoming requests.

Instead of looking at the absolute time intervals (of, say, 60 seconds each), such as 0s to 60s, 61s to 120s, and so on, the sliding window algorithm looks at the previous 60s from when a request is received. Let’s say a request is received at 82nd second; then the algorithm will count the number of requests processed between 22s and 82s (instead of the absolute interval 60s to 120s) to determine if this request can be processed or not. This can prevent situations in which a large number of requests is processed at both the 59th and 61st seconds, overloading the server for a very short period.

This algorithm is better at handling burst traffic more easily but can be more difficult to implement and maintain compared to the fixed window algorithm.

Token Bucket Algorithm

In this algorithm, a fictional bucket is filled with tokens, and whenever the server processes a request, a token is taken out of the bucket. When the bucket is empty, no more requests can be processed by the server. Further requests are either delayed or denied until the bucket is refilled.

The token bucket is refilled at a fixed rate (known as token generation rate), and the maximum number of tokens that can be stored in the bucket is also fixed (known as bucket depth).

By controlling the token regeneration rate and the depth of the bucket, you can control the maximum rate of traffic flow allowed by the API. The express-throttle package you saw earlier uses the token bucket algorithm to throttle or control the flow of API traffic.

The biggest benefit of this algorithm is that it supports burst traffic as long as it can be accommodated in the bucket depth. This is especially useful for unpredictable traffic.

Leaky Bucket Algorithm

The leaky bucket algorithm is another algorithm for handling API traffic. Instead of maintaining a bucket depth that determines how many requests can be handled in a time frame (like in a token bucket), it allows a fixed flow of requests from the bucket, which is analogous to the steady flow of water from a leaky bucket.

The bucket depth, in this case, is used to determine how many requests can be queued to be processed before the bucket starts overflowing, i.e., denying incoming requests.

The leaky bucket promises a steady flow of requests and, unlike the token bucket, does not handle spikes in traffic.

Best Practices For API Rate Limiting

Now that you understand what API rate limiting is and how it is implemented. Here are a few best practices you should consider when implementing it in your app.

Offer a Free Tier for Users To Explore Your Services

When considering implementing an API rate limit, always try to offer an adequate free tier that your perspective users can use to try out your API. It doesn’t have to be very generous, but it should be enough to allow them to test your API comfortably in their development app.

While API rate limits are vital to maintaining the quality of your API endpoints for your users, a small unthrottled free tier can help you gain new users.

Decide What Happens When Rate Limit Is Exceeded

When a user exceeds your set API rate limit, there are a couple of things you should take care of to ensure that you present a positive user experience while still protecting your resources. Some questions you should ask and considerations you must make are:

What Error Code and Message Will Your Users See?

The first thing you must take care of is informing your users that they have exceeded the set API rate limit. To do this, you need to change the API response to a preset message that explains the issue. It is important that the status code for this response be 429 “Too Many Requests.” It is also customary to explain the issue in the response body. Here’s what a sample response body could look like:

{
    "error": "Too Many Requests",
    "message": "You have exceeded the set API rate limit of X requests per minute. Please try again in a few minutes.",
    "retry_after": 60
}

The sample response body shown above mentions the error name and description and also specifies a duration (usually in seconds) after which the user can retry sending requests. A descriptive response body like this helps the users to understand what went wrong and why they did not receive the response they were expecting. It also lets them know how long to wait before sending another request.

Will New Requests Be Throttled or Completely Stopped?

Another decision point is what to do after the set API rate limit is crossed by a user. Usually, you would limit the user from interacting with the server by sending back a 429 “Too Many Requests” response, as you saw above. However, you should also consider an alternate approach—throttling.

Instead of cutting off access to the server resource completely, you can instead slow down the total number of requests that the user can send in a timeframe. This is useful when you want to give your users a little slap on the wrists but still allow them to continue working if they reduce their request volume.

Consider Caching and Circuit Breaking

API rate limits are unpleasant—they restrict your users from interacting with and using your API services. It is especially worse for users that need to make similar requests again and again, such as accessing a weather forecast dataset that gets updated only weekly or fetching a list of options for a dropdown that might be changed once in a blue moon. In these cases, an intelligent approach would be to implement caching.

Caching is a high-speed storage abstraction implemented in cases where data access volume is high, but the data does not change very often. Instead of making an API call that might invoke multiple internal services and incur heavy expenses, you could cache the most frequently used endpoints so that the second request onwards is served from the static cache, which is usually faster, cheaper, and can reduce the workload from your main services.

There can be another case where you receive an unusually high number of requests from a user. Even after setting a rate limit, they’re consistently reaching their capacity and getting rate limited. Such situations indicate that there is a chance of potential API abuse.

To protect your services from overloading and to maintain a uniform experience for the rest of your users, you should consider restricting the suspect user from the API completely. This is known as circuit breaking, and while it sounds similar to rate limiting, it is generally used when the system faces an overload of requests and needs time to slow down to regain its quality of service.

Monitor Your Setup Closely

While API rate limits are meant to distribute your resources equitably between your users, they can sometimes cause unnecessary trouble to your users or even possibly indicate suspicious activity.

Setting up a robust monitoring solution for your API can help you understand how often the rate limits are being achieved by your users, whether or not you need to reconsider the general limits while keeping the average workload of your users in mind and identify users that hit their limits frequently (which could indicate they’d possibly need an increase in their limits soon or they need to be monitored for suspicious activity). In any case, an active monitoring setup will help you understand the impact of your API rate limits better.

Implement Rate Limiting at Multiple Layers

Rate limiting can be implemented at multiple levels (user, application, or system). Many people make the mistake of setting up rate limits at just one of these levels and expecting it to cover all possible cases. While it is not exactly an anti-pattern, it can turn out to be ineffective in some cases.

If incoming requests overload your system’s network interface, your application level rate limiting might not even be able to optimize workloads. Therefore it’s best to set up the rate limit rules at more than one level, preferably on the topmost layers of your architecture, to ensure no bottlenecks are created.

Working With API Rate Limits

In this section, you will learn how to test the API rate limits for a given API endpoint and how to implement a usage control on your client to ensure you don’t end up exhausting your remote API limits.

How To Test API Rate Limits

To identify the rate limit for an API, your first approach should always be to read the API docs to identify if the limits have been clearly defined. In most cases, the API docs will tell you the limit and how it has been implemented. You should resort to “testing” the API rate limit to identify it only when you can not identify it from the API docs, support, or community. This is because testing an API to find its rate limit means you will end up exhausting your rate limit at least once, which might incur financial costs and/or API unavailability for a certain duration.

If you are looking to manually identify the rate limit, you should first begin with a simple API testing tool like Postman to make requests manually to the API and see if you can exhaust its rate limit. If you can’t, you can then use a load testing tool like Autocannon or Gatling to simulate a large number of requests and see how many requests are handled by the API before it starts responding with a 429 status code.

Another approach can be to use a rate limit checker tool like AppBrokers’ rate-limit-test-tool. Dedicated tools like this automate the process for you and also provide you with a user interface to analyze the test results carefully.

However, if you are not sure of an API’s rate limit, you can always try to estimate your request requirements and set up limits on your client side to ensure that the number of requests from your app doesn’t exceed that number. You’ll learn how to do that in the next section.

How To Throttle API Calls

If you are making calls to an API from your code, you may want to implement throttles on your side to ensure you don’t end up accidentally making too many calls to the API and exhausting your API limit. There are multiple ways to do this. One of the popular ways is to use the throttle method in the lodash utility library.

Before you start throttling an API call, you will need to create an API. Here’s a sample code for a Node.js-based API that prints the average number of requests it receives per minute to the console:

const express = require('express');
const app = express();

// maintain a count of total requests
let requestTotalCount = 0;
let startTime = Date.now();

// increase the count whenever any request is received
app.use((req, res, next) => {
    requestTotalCount++;
    next();
});

// After each second, print the average number of requests received per second since the server was started
setInterval(() => {
    const elapsedTime = (Date.now() - startTime) / 1000;
    const averageRequestsPerSecond = requestTotalCount / elapsedTime;
    console.log(`Average requests per second: ${averageRequestsPerSecond.toFixed(2)}`);
}, 1000);

app.get('/', (req, res) => {
    res.send('Hello World!');
});

app.listen(3000, () => {
    console.log('Server listening on port 3000!');
});

Once this app runs, it will print the average number of requests received every second:

Average requests per second: 0
Average requests per second: 0
Average requests per second: 0

Next, create a new JavaScript file by the name test-throttle.js and save the following code in it:

// function that calls the API and prints the response
const request = () => {
    fetch('http://localhost:3000')
    .then(r => r.text())
    .then(r => console.log(r))
}

// Loop to call the request function once every 100 ms, i.e., 10 times per second
setInterval(request, 100)

Once you run this script, you will notice that the average number of requests for the server jumps up close to 10:

Average requests per second: 9.87
Average requests per second: 9.87
Average requests per second: 9.88

What if this API only allowed 6 requests per second, for instance? You’d want to keep your average requests count below that. However, if your client sends a request based on some user activity, such as the click of a button or a scroll, you might not be able to limit the number of times the API call is triggered.

The throttle() function from the lodash can help here. First of all, install the library by running the following command:

npm install lodash

Next, update the test-throttle.js file to contain the following code:

// import the lodash library
const { throttle } = require('lodash');

// function that calls the API and prints the response
const request = () => {
    fetch('http://localhost:3000')
    .then(r => r.text())
    .then(r => console.log(r))
}

// create a throttled function that can only be called once every 200 ms, i.e., only 5 times every second
const throttledRequest = throttle(request, 200)

// loop this throttled function to be called once every 100 ms, i.e., 10 times every second
setInterval(throttledRequest, 100)

Now, if you look at the server logs, you’ll see a similar output:

Average requests per second: 4.74
Average requests per second: 4.80
Average requests per second: 4.83

This means that even though your app is calling the request function 10 times every second, the throttle function ensures that it gets called only 5 times a second, helping you stay under the rate limit. This is how you can set up client-side throttling to avoid exhausting API rate limits.

Common API Rate Limit Errors

When working with rate-limited APIs, you might encounter a variety of responses that indicate when a rate limit has been exceeded. In most cases, you will receive the status code 429 with a message similar to one of these:

• Calls to this API have exceeded the rate limit
• API rate limit exceeded
• 429 too many requests

However, the message that you receive depends on the implementation of the API you’re using. This implementation can vary, and some APIs might not even use the 429 status code at all. Here are some other types of rate-limit error codes and messages you might receive when working with rate-limited APIs:

• 403 Forbidden or 401 Unauthorized: Some APIs may start treating your requests as unauthorized hence denying you access to the resource
• 503 Service Unavailable or 500 Internal Server Error: If an API is overloaded by incoming requests, it might start sending 5XX error messages indicating that the server is not healthy. This is usually temporary and fixed by the service provider in due time.

How Top API Providers Implement API Rate Limits

When setting the rate limit for your API, it can help to take a look at how some of the top API providers do it:

• Discord: Discord implements rate limiting in two ways: there is a global rate limit of 50 requests per second. Apart from the global limit, there also are route-specific rate limits that you need to keep in mind. You can read all about it in this documentation. When the rate limit is exceeded, you will receive an HTTP 429 response with a retry_after value that you can use to wait before sending another request.
• Twitter: Twitter also has route-specific rate limits that you can find in their documentation. Once the rate limit is exceeded, you will receive an HTTP 429 response with a x-rate-limit-reset header value that will let you know when you can resume access.
• Reddit: Reddit’s archived API wiki states that the rate limit for accessing the Reddit API is 60 requests per minute (via OAuth2 only). The response to each Reddit API call returns the values for X-Ratelimit-Used, X-Ratelimit-Remaining, and X-Ratelimit-Reset headers with which you can determine when the limit might exceed and how lo
• Facebook: Facebook also sets route-based rate limits. For instance, calls made from Facebook-based apps are limited to 200 * (number of app users) requests per hour. You can find the complete details here. Responses from the Facebook API will contain a X-App-Usage or a X-Ad-Account-Usage header to help you understand when your usage will be throttled.

Keep your API from crashing by enforcing a rate limit! Discover why it's a must and learn easy ways to implement it Click to Tweet

Summary

When building APIs, ensuring optimum traffic control is crucial. If you don’t keep a close eye on your traffic management, you will soon end up with an API that is overloaded and non-functional. Conversely, when working with a rate-limited API, it is important to understand how rate-limiting works and how you should use the API to ensure maximum availability and usage.

In this guide, you learned about API rate limiting, why it is necessary, how it can be implemented, and some best practices you should keep in mind when working with API rate limits.

Check out Kinsta’s Application Hosting and spin up your next Node.js project today!

Are you working with a rate-limited API? Or have you implemented rate limiting in your own API? Let us know in the comments below!

The post API Rate Limiting: The Ultimate Guide appeared first on Kinsta®.