Modern backend and infrastructure developers frequently grapple with the challenges of orchestrating complex, multi-step processes in distributed systems. These workflows often span multiple services and can run for extended periods, introducing several technical problems: managing the state of long-running processes, ensuring reliability despite failures, coordinating asynchronous steps (workflow orchestration), and implementing robust retries and timeouts.
Many cloud applications involve workflows that are multi-step, distributed across multiple services, and potentially arbitrarily long — bringing “all the hard problems of distributed systems” to the forefront. Traditional approaches using explicit state machines or ad-hoc orchestration code often become complex and error-prone as systems grow. In this writeup, we’ll examine:
- How state machines have traditionally tackled workflow issues
- Why Temporal’s Durable Execution model provides a better solution
- How Temporal lets developers write plain code and often avoid writing explicit state-machine logic while still getting all the benefits of state tracking, retries, and orchestration
Common Challenges in Distributed Applications
Building reliable distributed application logic — like processing an e-commerce order or handling a user sign-up — involves several key challenges:
Orchestration and State Management
Developers need to coordinate multiple steps across distributed services (e.g., charge payment, reserve inventory, send confirmation email) and track progress. The system must “remember” what has been done and what should happen next, even if there are delays or crashes. In a distributed setting, state often must be stored externally so work can resume after failures or restarts.
Retries and Timeouts
Remote calls or tasks will inevitably fail intermittently due to network issues or downtime, so each step requires retry logic with backoff. Some steps might hang, requiring timeouts to avoid waiting indefinitely. Implementing retries and timeouts is non-trivial; developers must carefully avoid duplicating tasks or losing track of attempts..
Distributed Reliability
In a distributed system, any service or machine can fail at any time. The workflow must be robust to partial failures — if a step fails or a service goes down, the overall process should eventually complete or compensate correctly, without ending up in an inconsistent state. This often requires additional logic for error handling, compensating transactions (rollback of previous steps in case of failure, as in the saga pattern), and ensuring exactly-once or at-least-once execution semantics for each step.
Visibility & Debugging
With many moving parts, determining what went wrong when something fails becomes difficult. If a long-running process stops, developers need insight into which step it was executing, what succeeded, what failed, and possibly the ability to resume or replay the process. In home-grown solutions, this typically involves combing through logs or querying state in databases spread across services.
Traditional Solution: State Machines for Workflow Orchestration
A state machine (or finite state machine — FSM) is a well-known paradigm for modeling the process logic by defining all possible states and transitions. For backend workflows, a state machine might include states like “order created,” “payment processed,” “inventory reserved,” “order shipped,” etc., with transitions occurring based on events like payment confirmation, shipment failure, timeouts, and so on.
This approach makes the system’s current state explicit and defines how the system should react to different events. State machines, often event-driven, have traditionally been used to impose structure on complex, dynamic workflows that would otherwise be hard to manage.
How State Machines Address the Problems
By breaking a process into discrete states and transitions, developers can handle each step in isolation and ensure prerequisites are met before moving to the next step. For example, if payment fails, the state machine can transition to a “payment failed” state and trigger a compensating action or retry, rather than proceeding forward.
Many teams implement this by storing the workflow state in a database and writing code or using a framework to move between states based on events or timer triggers. This explicit state tracking helps with reliability: if the system crashes, it can reload the last known state from the database and resume from there. It also helps coordinate distributed steps — one microservice can publish an event that causes another to advance the state.
Examples
AWS Step Functions uses JSON-defined state machines to orchestrate AWS Lambda functions or other services. Similarly, many companies have built bespoke orchestration engines where workflows are represented as state machines in code or configuration. In a simpler form, a cron job plus a database flag can be seen as a two-state (pending/done) machine. The saga pattern for distributed transactions can also use state machines to drive the sequence of steps and compensations.
Limitations and Complexity
While state machines enforce structure, they can become very complex to build, test, and maintain as the number of states and transitions grows. Each new scenario or edge case often means introducing additional states or branches in the state diagram. State machine code grows in complexity and length with each new state, and maintaining or testing these sprawling state graphs becomes challenging.
Developers often write extensive “plumbing” code: saving state transitions to databases, implementing switch/case logic for transitions, handling timeouts by scheduling future events, and so on. This distracts from core business logic.
Furthermore, manually implemented state machines need careful failure handling. Developers must catch exceptions and decide how to update the state or retry, ensuring the system can recover if crashes occur during transitions. Missing corner cases can lead to stuck workflows or data inconsistencies.
A real-world ad-hoc solution might involve multiple moving pieces — storing status in a database, a periodic job (cron) to check for tasks to do, queues to distribute work to workers, and separate handlers for different events like cancellations.
A Temporal engineer described such a solution as a “distributed asynchronous event-driven bespoke system” with multiple durable stores and components. While functional, these solutions represent a “radical departure from the original simple code in terms of complexity.” All that infrastructure is essentially implementing a state machine and workflow engine from scratch, consuming a lot of developer effort, and yet “failure scenarios are numerous” and few implementations get it 100% right.
In summary, explicit state machines can solve workflow orchestration by making state explicit and recoverable, but at the cost of significant complexity. This is where Temporal comes in — to provide a better, more developer-friendly approach.
Temporal’s Approach: Durable, Event-Sourced State Machines as Code
Temporal is an open-source platform designed to abstract away the “plumbing” of distributed workflows, letting developers focus on straightforward business logic. At its core, Temporal is essentially a state machine engine — but one that’s generalized, highly durable, and completely encapsulated behind a programming model that feels like writing simple synchronous code.
Developers can write what looks like a normal function, and the platform ensures that function executes reliably to completion regardless of failures. This is achieved through Temporal’s Durable Execution model, built on concepts like event sourcing, durable state tracking, and automatic retries.
Durable Execution and Event Sourcing
Durable Execution means that application logic can run reliably despite failures or interruptions. Temporal accomplishes this through a simple but powerful approach: it automatically records each significant step of your application logic as it executes.
This creates an append-only log of events stored in Temporal’s persistence layer. Instead of developers having to manually track state in their own databases, Temporal handles this automatically, capturing everything needed to understand what has happened so far.
Because this event log is managed by the Temporal service rather than living on a single machine, your application becomes resilient to infrastructure failures. If the process or machine executing your code crashes, Temporal can seamlessly continue execution on entirely different infrastructure, picking up exactly where it left off.
The result is application logic that appears to run continuously even when the underlying infrastructure is unreliable — a critical feature for business processes that may take minutes, hours, or even days to complete.
Writing Synchronous Code, Getting Asynchronous Benefits
Temporal divides work between Workflow code and Activities. Workflow code is your orchestration code (which runs under Temporal’s control with automatic state persistence and can be replayed), and Activities are the actual tasks that might do things like call external services, perform calculation, send emails, etc.
What makes this powerful is that developers write code that looks completely synchronous:
async function processOrder(orderId) { await activities.verifyPayment(orderId);
await activities.reserveInventory(orderId);
await activities.shipOrder(orderId);
await activities.sendConfirmation(orderId); }
But behind the scenes, Temporal is handling complex distributed messaging automatically. When a Workflow needs to run an Activity, Temporal:
- Puts a task into a task queue
- Worker processes pick up the task to execute the Activity
- The Workflow waits until the activity completes
This architecture (clients start Workflows, Workflows queue tasks, Workers pull tasks) means developers don’t have to build their own queuing or messaging systems. Temporal handles all the complex pub/sub infrastructure while your code remains clean and readable.
Every step is mediated by the Temporal Server, which ensures that only one Worker will execute a given task, and the result or failure of the task is recorded as an event. This eliminates the need to build locking mechanisms to coordinate distributed tasks. Moreover, every action in the Workflow is recorded durably, providing complete visibility into what happened and when, including retries and Workflow decisions.
Why Temporal’s Model Is Better
Temporal’s model offloads the hardest parts of Workflow management to its distributed architecture of Server and SDKs working together. The complete state of your Workflow (local variables, progress, etc.) is autosaved by the platform, giving you all the benefits of a state machine without you having to manually maintain state persistence or transition logic.
This invisible checkpointing happens automatically at critical points like activity calls or timer waits. Your function could be interrupted mid-execution by a server crash, deployment, or even a planned maintenance window lasting days — and when it resumes (potentially on entirely different infrastructure), every local variable, every loop counter, every conditional branch taken is exactly as it was. Your code continues executing from precisely where it left off, with no special recovery logic required.
This eliminates entire categories of bugs and infrastructure that developers typically build: state tables in databases, status fields, manual checkpointing, and complex resume logic all disappear. Your business logic remains pure, focused only on what should happen, not on how to make it reliable.
No Manual State Tracking
With Temporal, there's no need to write code saving Workflow state to databases or checking “if (state == X) then resume from here” — the platform handles this automatically. The complete state of your function and workflow is captured, eliminating the need to track and validate state yourself. This drastically reduces boilerplate and potential bugs.
The Workflow code looks like a normal function that simply calls the next step, but if that function halts and later continues, all local variables and progress remain as if the function never stopped. The Workflow function’s progress effectively becomes the state, dramatically simplifying the mental model.
Adding a new step in the middle of a Workflow only requires adding an activity call in the code at the appropriate place, rather than modifying state definitions and transition logic in multiple locations.
Built-in Retries and Timeouts
Temporal provides native support for retry policies on Activities and built-in timeout handling. You can declaratively specify how an Activity should be retried (e.g., exponential backoff, maximum retry interval), and Temporal performs retries automatically if the Activity fails or times out.
There’s no need to wrap every network call in custom retry loops or worry about transient failures derailing Workflows — Temporal treats retries as first-class concepts. Timeouts and long waits are also simple: you can call Workflow.sleep(30 days) or set a timer, and Temporal will reliably wake the workflow after that duration even if processes restart in the meantime. Implementing this with hand-rolled state machines would be extremely difficult, potentially requiring cron jobs or external schedulers.
Resilience to Failures
Due to the durable event history and replay engine, Temporal Workflows are fault-tolerant by design. If your Workflow code or host machine crashes during execution, the Temporal Server detects the Worker’s failure to report completion, and the Workflow is rescheduled (on a new Worker or when the original Worker returns) by replaying its history. From the developer’s perspective, it’s as if your function is atomically persisted at each await point.
Temporal ensures each step executes at least once and uses event history to avoid duplicating actions. The platform handles state management, queuing, resilience, deduplication, and other safety properties automatically, eliminating the need for complex recovery code. Temporal provides exactly-once execution semantics for Workflow logic and at-least-once for activities, with mechanisms to avoid duplicate side effects in most cases.
Simplified Error Handling and Sagas
Temporal transforms how developers implement patterns like sagas (distributed transactions with compensation). In traditional systems, implementing sagas requires maintaining complex state across services to track which steps completed and which compensations are needed.
With Temporal, this complexity disappears. Developers simply write straightforward try/catch blocks where both the happy path and compensation logic live together in the same function.
For example, in a money transfer scenario, the withdrawal, deposit, and potential refund (compensation) all exist in one cohesive piece of code. This natural coding style dramatically simplifies implementation. The compensation logic lives right alongside the happy path, making it immediately clear what happens in both success and failure scenarios. Developers no longer need to build complex state tracking systems or distributed coordination — they just write simple, readable code that handles both the ideal flow and all the potential error paths in one place.
Visibility and Debugging
Temporal provides tools (and a UI in Temporal Web) to inspect Workflow histories. You can see each event, each Activity call, and the current state of a Workflow at any time. You can even query Workflows or send signals to them (e.g., for human-in-the-loop scenarios). The platform lets you replay executions for debugging, or even “rewind” a running Workflow to a past point if you need to fix something and re-run part of it.
This kind of introspection is usually absent in home-grown state machine systems. Temporal allows you to inspect, replay, and rewind every Workflow execution, step by step, so you never have to guess what’s going on. In contrast, with an ad-hoc distributed state machine spread across services, reproducing the sequence of events that led to a failure can be a nightmare. Temporal centralizes that history.
All these features mean that Temporal acts as a robust state machine engine under the hood — one that is generic and heavily tested — so you don’t have to build your own. Instead of your team writing the 100th variation of a retry loop or state persistence mechanism, you can rely on Temporal’s battle-hardened infrastructure.
Workflow-as-code is much friendlier than trying to express logic in a JSON/YAML state machine definition, and even though systems like Step Functions+Lambda allow expressing some logic, the moment you need anything dynamic or non-trivial, the complexity of those DSL-based state machines grows quickly and becomes painful. Temporal avoids those pitfalls by letting you use a real programming language for your workflow logic, which is infinitely flexible and integrates with normal development workflows.
Orchestration-as-Code: Eliminating the Need for Explicit State Machines
Temporal’s key advantage is its developer experience. You write Workflows as straightforward code in your language of choice (Go, Java, Python, TypeScript, PHP, .NET, or Ruby), using normal control flow constructs and calling services via Activities. Unlike libraries such as Spring State Machine where you explicitly define states and transitions in code, with Temporal you write procedural code while the platform manages state persistence and transitions implicitly.
Consider this money transfer example:
typescript
async function transfer(fromAccount, toAccount, amount) {
await activities.withdraw(fromAccount, amount);
try {
await activities.deposit(toAccount, amount);
} catch (err) {
await activities.deposit(fromAccount, amount); // compensate by refunding
throw err;
}
}
This code functions as a state machine: after withdrawal, it’s waiting for deposit; if deposit fails, it compensates. Temporal ensures that if the process crashes after withdrawal, the Workflow resumes and attempts the deposit. The variables fromAccount, toAccount, amount and the execution state are all preserved automatically.
Temporal captures the complete state of your functions (variables, threads, call stack) so you get the benefits of a state machine without maintaining complex state machine code. Developers no longer need to write code to update status fields in databases or create switch statements over states — the Workflow function’s progress becomes the state. Adding a new step means adding an Activity call at the appropriate place, rather than modifying state definitions and transition logic in multiple locations.
Temporal’s approach can make explicit state machines unnecessary in many cases. Instead of having a separate state machine module or service, the Temporal Workflow acts as the orchestrator. The platform handles making the state durable and trackable. As the Temporal documentation notes, because it captures the entire state and progress of your Workflow, you can often eliminate or avoid state machines altogether. In other words, Temporal itself is a state machine, general-purpose enough that you don’t have to write one. Engineers at Netflix reported spending less time writing glue code for consistency and failure-handling because Temporal “does it for them.”
Another benefit is testability and maintainability. You can unit test Temporal workflow logic using the same tools you use for any other code. Temporal provides testing frameworks to run workflows in-memory to validate their logic deterministically. The “Workflow-as-code” concept feels natural to developers since it’s just code — there’s no new proprietary language to learn for defining workflows.
Temporal doesn’t remove the inherent complexity of business processes, but it localizes that complexity in a single Workflow function and handles error-prone parts automatically. You write straightforward steps, not scattered event handlers or state transition rules. The code becomes easier to understand, with flow explicitly defined in a single place, while Temporal takes care of state management, queuing, and resilience.
Conclusion
Backend developers building distributed systems have long struggled with coordinating complex processes — managing long-running operations, synchronizing services, implementing retries, and recovering from failures. Traditional solutions using explicit state machines can work, but they often become unwieldy and costly to maintain as requirements grow.
Temporal offers a fundamentally better approach by providing a Durable Execution engine that transforms complex distributed processes into straightforward code with built-in state persistence, reliability, and observability. It’s essentially a state machine-as-a-service: you write normal code, and Temporal ensures it executes reliably step by step, even across process restarts, failures, or long delays.
This results in more robust applications with much less boilerplate. Many engineering teams don't even realize a solution like this is possible; they are so used to the norm of manually handling failures that Temporal’s model is paradigm-shifting. By using Temporal, teams can deliver features faster (since they can focus on business logic rather than building infrastructure) and gain confidence that their complex operations will run correctly and resiliently in production.
You can forget you ever worried about building a state machine — you just write your logic in code, and let Temporal handle the rest. The end result is simpler code, fewer bugs from missed edge cases, and a more observable, fault-tolerant system.
Try it out for yourself with a free trial of Temporal Cloud with $1,000 in credits.
FAQ
Is Temporal suitable for any kind of distributed workflow, or are there scenarios where explicit state machines still make sense?
Temporal is versatile enough to handle most distributed workflow scenarios. However, explicit state machines may still be appropriate if your system is simple, static, or requires a strictly defined and enforced state structure. Temporal excels when workflows are dynamic, complex, long-running, or require robust failure handling and state recovery.
Can Temporal Workflows handle extremely long durations, such as weeks or months?
Absolutely. Temporal Workflows are designed to handle very long-running processes effectively. They persist Workflow states across restarts or crashes, using event sourcing to resume workflows seamlessly even after extended periods, such as days, weeks, or months.
What’s the overhead of using Temporal versus custom-built solutions or simpler state machines?
Temporal introduces minimal overhead compared to the benefits it provides. While there is a slight learning curve and initial setup time, the long-term reduction in complexity, fewer bugs from edge cases, and improved maintainability and reliability significantly outweigh initial costs.
How does Temporal guarantee exactly-once execution semantics?
Temporal uses durable event sourcing combined with idempotent execution. Workflow actions and decisions are logged durably, allowing Temporal to replay events to reach the exact state before failures. Activities (external tasks) are retried at least once, and Temporal provides mechanisms like activity IDs and idempotency tokens to ensure side effects are executed exactly once.
What about debugging? How easy is it to debug Workflows running on Temporal?
Temporal greatly simplifies debugging. Its durable event history lets you inspect, replay, or even rewind Workflows step by step. Temporal Web provides detailed visibility into Workflow execution, allowing developers to easily pinpoint failures, view exact states, and debug complex Workflows much more effectively than traditional methods.