Stateful vs. Stateless: Understanding Key Differences for Apps and IT Systems

The term “stateful” means that information about the state is tracked. In contrast, “stateless” means no information is tracked. These terms play out across applications, IT systems and architecture, data, and transactions between all of them.

Read on for an understanding of state, state-space, and when and where using stateless approaches may work, especially in software development.

What does “state” mean?

State refers to a set of (the smallest possible) variables that can completely and uniquely represent the condition of a system at any given time. The set of discrete state variables gives context to the system’s internal behavior, such as:

• Receding interactions with the user
• Historical transaction events
• Computing environment attributes

A (finite) collection of (all possible) states is called a state-space of the system.

Why state information is useful

Why do you need information about a state? State information is useful especially for:

• Understanding user requirements.
• Guiding compute operations to deliver a desired functionality.

For instance, a user logging in to a financial services app can review their past financial transactions and transfer funds depending on the applicable limits. This is a simple example of a stateful application.

(Related reading: understanding idempotent design for apps and IT systems.)

Stateful vs. stateless architecture and software systems

With those foundational concepts, we can now dig into the IT architecture, systems, and programming practices.

• A stateful software system contains past and present events. A stateful system begins from the state of its last transactional interaction and maintains all subsequent state changes, which is enabled by the stateful architecture.
• A stateless system does not record any information state or variable element. A stateless system instantiates from a same, fixed state and observes a transactional interaction without previous context.

Most software applications (in entirety) are stateful. That’s because they must incorporate historical events and context to deliver a customized service to the end-user. Being stateful itself is not a problem — in fact, it’s necessary in many cases.

The approach to software development, where stateless containers are instantiated and interact in parallel without dependencies, is growing in popularity.

The trend started off as an important driver for DevOps SDLC methodology that focused on rapid and streamlined SDLC pipelines. A stateless code segment is developed, modified, and executed without dependencies. That means that developers and DevOps teams can follow rapid, parallel, and continuous release cycles for their feature assignments.

Concept differences

Let’s review some of the important conceptual differences in stateful versus stateless programming, including what these concepts mean for code, data, and transactions.

Stateful vs. stateless code

Stateful code variables in the application are assigned values either prior to or during execution. These values are maintained and sequentially updated as the application state evolves. The code makes it difficult to change environment variables during a transaction process.

Stateless code does not maintain historical information such as past value assignments. It runs independently and does not have dependencies external to what is already available within an application state (such as packages available within a container running a stateless code component).

New code snippets can be created and appended to existing code without requiring changes to the database. When the updated code build passes its tests, a new container with updated coding functionality is deployed, replacing the old one. 

Stateful vs. stateless data

Stateful data has the properties of:

• Persistence
• Continuity
• Dependence

As the transactional interaction evolves across the state-space, information is stored and updated. The memory update is continuous and sequential. Future states are dependent on past environment interactions. Good examples here include your financial accounts statement or the purchase history for your favorite online/ecommerce store.

Characteristics of stateless data include:

• Ephemeral states
• Independence
• Self-containedness

Changes to a data variable are temporary and last as long as the application is in use, or a transaction is in process. These changes don’t depend on historical data and don’t require external interactions. Common examples include HTTP requests and data caching in CDNs (content delivery networks).

Stateful data required for moving databases, changing formats. When moving between databases and formats, the data must remain stateful. This is a data integrity problem: you can’t have multiple conflicting versions of data stored — you can make changes in the presentation layer and update relevant databases cohesively.

Consider a stateless schema design of the ETL (extract, transform, load) analytics system. Data is extracted from its original schema loaded into a new schema environment where it is transformed, updated, and processed for downstream analytics tasks.

In a stateless system, you do not define the database schema before it is added to a system. In fact, the schema design can be dynamic since the states do not retain information across sessions and requests. Because each request has to be self-contained, the schema design should be decoupled from the source code.

Stateful vs. stateless transactions

Stateful transactions are context-aware: this means they keep track of past states, like a previous action the user or the app took. Stateful transactions do expect a response from servers upon application execution. If the system does not receive a response, or loses the context in another way, it may resend the request to receive context.

In contrast, stateless protocols interact with servers without retaining information between execution sessions. These requests are typically self-contained such that all data and logic necessary is already packaged within the application container. Any external dependency is asynchronous and decoupled from external collaborating components. The stateless transaction uses its own available data and logic where necessary.

In terms of performance, this means that stateless applications are inherently faster as they don’t rely on external systems that may respond slowly and multiple sessions can be executed in parallel. Stateless systems ensure:

• Parallel code execution
• High scalability
• Less frequent incidents due to coding dependencies

This flexibility — feature components are inherently independent — allows DevOps teams to develop fault tolerant code with faster release cycles.

(Related reading: see how your apps perform with app performance monitoring.)

Stateful applications support IT service delivery

The application behavior in its entirety, however, usually remains stateful. Environment variables (such as browser data from cookies and context) is important to deliver most IT services to the end-user.

Stateless programing simply decouples features and code functions that are independent, do not require entire context, and are invoked in real-time when needed.