Stateful vs Stateless Architecture: Why Stateless Won

Stateless and stateful architecture defines the user experience in specific ways. See why stateless is the choice for cloud architects.

Stateful services keep track of sessions or transactions and react differently to the same inputs based on that history. Stateless services rely on clients to maintain sessions and center around operations that manipulate resources, rather than the state. 

Two potential points for confusion in considering stateless and stateful architectures are:

• A stateless service as a whole usually still needs to handle state in some form. Stateless simply refers to the fact that this state isn‚Äôt tracked in server-side sessions.
• ‚ÄúState‚Äù is a broad term that can mean different things in different contexts. In this context, it refers specifically to maintaining sessions for authentication and similar multi-action processes. However, this is unrelated to storing data permanently in a database, which stateless services still usually do.

A Brief Historical Context 

Historically, clients or end-user machines were ‚Äúthin‚Äù--very limited in both hardware and software, while all of the heavy lifting was done by powerful servers. Therefore, it  made sense for nearly all complexity and functionality to be handled ‚Äúserver-side‚Äù.

With the explosion of the world wide web and the related growth of the HyperText Transfer Protocol (HTTP), web browsers, PCs, and other end-user software and hardware became more powerful, and clients became “thicker”; they handled more and more functionality that was previously handled by servers.

As software and digital systems grew in complexity, so did the requirements for sessions. Because HTTP is a stateless protocol, it‚Äôs challenging for software to carry out long-running transactions, where system behavior depends on what happened previously. 

For example, when you use online banking, you probably send several instructions to your bank, a simplified version of which, might be similar to:

1. Hi, I’m me and I can prove it by entering this password.
2. Hi, remember me? I just logged in, please transfer $1,000 to John.
3. Yes, I have checked all the details and I would like to confirm this transfer.

In order to correctly execute the last two steps, your bank has to remember that step 1 was successful. Because HTTP (the protocol that your web browser uses to talk to your bank) is stateless, this isn’t as simple as it may initially seem.

If you’ve seen the film Finding Nemo, you can think about using HTTP for any multi-step process as similar to trying to have a conversation with Dory who is incessantly forgetful.

There are two primary ways to handle this problem of “forgetfulness”:

1. Stateful: Store additional information server-side (on the bank’s server), recording the state of the current transaction and waiting for the next instructions.
2. Stateless: Store additional information client-side (in your web browser), passing along additional information with each step ‘reminding’ the server of the previous steps. In practice, this is usually implemented using cookies or localStorage.

There are challenges with each of these approaches. 

With the stateful approach, more care has to be taken so that information stored on the server is consistent and so that a specific server is reserved for a client for the entire transaction.

With either approach, it’s necessary to use cryptography to ensure that the client isn’t lying about the previous steps in the transaction (for example, faking a login).

Prior to the modern enhancements of the internet, interactions across the web were typically done using stateful architectures. This was for a number of reasons, including:

1. Less demand for internet services and therefore fewer scaling requirements. Web services could often be run entirely off a single physical piece of hardware.
2. Clients that were typically very limited in terms of both hardware and software.
3. Simpler use cases for internet-connected applications: state was not as complicated, so maintaining stateful services was less of a burden.

As all of these have changed, there has been a move from stateful to stateless architectures.

Stateful vs Stateless: A Comparison

SOAP-, REST-, and GraphQL-based Web Services

The paradigm shift from stateful to stateless architectures is closely linked to the popularity of different ways to build application programming interfaces (APIs) and web applications. In the early 2000s, SOAP (Simple Object Access Protocol) was the dominant methodology. While it’s possible to create stateless SOAP-based architectures, the protocol also allowed for creating stateful services, maintaining state server-side. Many applications ultimately did this.

REST (REpresentational State Transfer) is a design pattern rather than a protocol like SOAP, and RESTful services can be built in different ways. Following this pattern, which is always fully stateless, RESTful grew in popularity and overtook SOAP.

GraphQL is the new kid on the block, starting to eat REST’s lunch. Like RESTful services, GraphQL-based services are entirely stateless, and give clients even more control over what data is fetched from servers.

The Advantages of Stateless Solutions

Incorporating a stateless solution to a web service or app has a number of advantages over an equivalent stateful implementation. 

For starters, stateful server implementations are complex to configure. They require the ability to reference (potentially multiple) states, which could lead to a number of incomplete transactions and sessions throughout a system. Connection management from the client to the server is also a factor: it requires a “sticky” connection between each client and server pair, which is an inherently a hard-to-scale limitation.

One of the biggest drawbacks of stateful architecture is its inability to scale easily and efficiently. If the state is managed server-side, it means each step in a long-running transaction has to be handled by the same server. 

For small-scale services where only a single physical server is required, it‚Äôs easier to keep track of state. However, if more servers are required to handle the load from thousands or millions of users, it‚Äôs inconvenient to 

1. To have to ensure a specific user’s requests always hit the same server,
2. To synchronize the state for each user across all servers.

With a stateless architecture, a user could hit a different server on each step of the transaction because it doesn’t matter if one of the servers is too busy or goes down; the transaction can continue without issue.

Stateless: Laying Foundations for Cloud-Based Microservices

The independence or loose coupling between client and server allows for more flexibility when developing a solution that would, otherwise, rely heavily on server-side complexity that is characteristic of stateful systems.

What this means for modern-day technology stacks is that they have become far more modular. Stateless architectures have increasingly advocated for the use of smaller, simpler microservices, which help move us away from the monolithic solutions of the past.

While stateless services don’t necessarily equate to microservices or cloud-based solutions, there is a correlated trend. A business that has already implemented stateless services can more easily break separate functionality into different microservices. And a business that has already implemented microservices can more easily do a staged migration to the cloud, moving one service at a time without impacting the others.

Compared to older patterns of monolithic, stateful services, this makes it easier to keep up with more modern demands of scaling globally and following agile methodologies to release new features far more frequently than was previously expected.

This, in turn, allows for easier horizontal scaling of services. Adding more servers to a fleet is far more straightforward if these servers don’t have to worry about tracking state, and this can result in cost savings for the business as they can quickly provision more servers to deal with demand peaks. Following these patterns, an e-commerce site, for example, can double or triple its capacity when there is demand, without having to pay for additional computing power when demand decreases.

Based on these factors, choosing a stateless architecture for any modern-day, online service is almost always the correct path.

A Practical Example: Stateful vs Stateless Banking Service

Taking a look at a practical example is a good way to demonstrate the differences discussed.

While this example may be oversimplified, consider the key overarching takeaways of each implementation.

Online Banking: Stateful vs. Stateless

To add more detail to the previously discussed online banking example, let’s take a look at the steps a stateful server would have to carry out for a basic transaction. For each of these steps, the service would have to maintain an explicit state graph, tracking which state the user is currently in and which states they can access next.

Let’s entertain the transaction:

1. John requests the banking page and is presented with a login prompt. The server records the fact that John is attempting to log in.
2. John enters his password incorrectly. The server records the fact that John has failed to authenticate and moves him to a state where he can only try twice more.
3. John enters his password correctly, and the server records that John is now authenticated.
4. John creates a new payment request and the server records that John is in the state of ‘creating payment’.
5. John adds the details about the recipient and amount and clicks, “pay now”. The server records John as being in a “confirming transaction” state and displays a confirmation page.
6. John presses, “confirm” and the server makes the payment, shows a receipt, and ends the session.

This entire process is confined within a single session. All of the state information is retrieved and tracked in a synchronous way. If there is a connection problem or logical error in any of the steps above, the entire state needs to be recreated in order to pick up where a user left off. 

If any changes were to be made to the client-side application, these would need to be taken into consideration, as it is tightly coupled with the server-side functionality.

Taking the same online banking example, let’s entertain it in a stateless model.

1. John requests the banking page and is presented with a login prompt.
2. John enters his username and password and these are sent to a dedicated authentication server. This server checks John’s credentials against the database and issues a cryptographically signed token to John, which acts as a temporary surrogate for his username and password. This is similar to a certified copy of a passport..
3. John’s web browser then makes a new request to the main banking server (on a third server, both separate from the one that served the initial login page, and from the one that handled authentication). This server validates the signed token and shows John the logged-in page.
4. John creates a new payment request. Again, his browser sends along the signed token with this request. The physical server that handled John‚Äôs previous request is suddenly overloaded, so this new request goes to a fourth physical piece of infrastructure that again checks that the token is valid and gives John a form to enter the recipient and amount.  
5. John enters the payment details and presses, “pay now”. The token is sent again and validated again server-side, even though it’s the same physical machine that handles the request. It returns a page that displays a summary and a “confirm” button.
6. John hits “confirm” and the token is once again sent and validated in the background. The payment is confirmed and John is shown a receipt.

In some ways, this appears similar to the first example. However, the subtle differences have a big impact on the overall robustness of the solution.

By utilizing authentication tokens, there is no dependency on a single session in order for the client to interact with the server. The authentication token effectively carries the state, so we do not rely on any individual server. This means that each step can happen in isolation from the other steps, and different infrastructure can be used in each case.

Looking Forward

Stateful architecture made sense in a server-focused world where clients were merely thin interfaces to more powerful servers. When services only needed to scale to hundreds or thousands of users, having a strong coupling between users and servers wasn’t an issue.

Now that we have powerful client machines and web services are often required to scale to millions or even billions of users, we’ve needed to evolve not only hardware and software, but also design patterns and concepts.