Orchestrating Asynchronous Workflows (How Are They Different from Synchronous?)
Asynchronous workflows have become a cornerstone of modern software development, enabling applications to handle complex operations without blocking the main execution thread. This approach is how we can design responsive, scalable, and efficient systems involving distributed components. In this blog, let’s explore what asynchronous workflows are, their use cases, challenges, and best practices for implementing them.
Why use asynchronous workflows?
The modern tech stack is inherently distributed. Your SuperAwesome program might call on a LongProcessing endpoint, receive SuperImportant events streamed from a legacy system, or require SomeRelevant input from a human user. When these processes or events take a long or indefinite time to complete, in a synchronous paradigm, your main execution program is forced to wait, which blocks it from completing anything else.
An asynchronous workflow approach uses design patterns where indefinite or long-running processes are non-blocking yet durable. In other words, asynchronous workflows enable responsive and resilient systems even in the face of uncertainty, like an unscheduled event message or unknown processing time.
One of the biggest benefits of asynchronous workflows is the ability to combine both event-driven and orchestration patterns into a unified system for event-driven workflows. When workflow orchestration is used in conjunction with event-driven architecture, this flexibility unlocks the best of both worlds: real-time responsivity to events while coordinating complex sequences of interactions.
But what exactly does asynchronous mean? What even is an asynchronous workflow? You may already be familiar with these concepts, but let’s have a quick recap.
Synchronous vs asynchronous programming
In software, synchronous and asynchronous are broad concepts that apply across all levels of software architecture. At its core, synchronous tasks or processes are executed in a fashion where the server actively awaits the response before moving on to the next task.
In contrast, asynchronous tasks or processes are executed without the server actively waiting for the required response. In an asynchronous paradigm, the task is executed in a way that doesn’t block the main thread, such as by being completed on a separate thread, worker, or program. The execution is typically a fire-and-forget pattern, where the main thread initiates the task without waiting for the response — a simple implementation could be a function that starts the operation and returns immediately to the main thread, with a callback to return the computed result later. The server can then process another task that isn’t dependent on this pending task.
| Area | Synchronous Programming | Asynchronous Programming |
|---|---|---|
| Code execution | Tasks are executed one after the other only after completion (blocking). | Tasks are executed in a fire-and-forget fashion (can be non-blocking). |
| Implementation | Easier to implement. | More challenging to implement. |
| Program flow | Deterministic, easier to reason about and debug. | Non-deterministic, harder to reason about and debug; potential for race conditions. |
| Scalability | Limited scalability under high concurrency. | Higher scalability under high concurrency due to more efficient resource usage. |
| Uses | Ideal for straightforward projects without unpredictable interactions. | Ideal for complex projects involving real-time interactions with multiple distributed components. |
Challenges of asynchronous implementation
At the program-level, asynchronous programming can be implemented in several ways: callbacks, promises, or async/await. When it comes to communicating between different services, it starts to look more like webhooks, event handlers, or stream processing. For modern-day applications, event-driven architecture has multiple advantages: real-time processing, loose coupling of services, and resilient messaging.
However, an event-driven approach runs into many complications when services inadvertently still need to be executed in a specific order, resulting in implicit dependencies that are difficult to trace and debug. Issues like data consistency, handling transaction rollbacks, and mitigating race conditions end up much more difficult to deal with in a distributed, decoupled event-based architecture. Without careful implementation, the state of your application becomes impossible to maintain or track.
Another related challenge is ensuring the durability of your program flow. If an asynchronous process is in progress but crashes mid-flight, the work may get lost without retries or retried with duplicate side effects. Getting visibility for troubleshooting and handling failures gracefully becomes even more complicated, taking away precious time from developing the main program flow.
Using asynchronous workflows
Enter asynchronous workflows.
A workflow is a sequence of tasks that must be completed in a specific order to achieve a particular goal. On the surface, an “asynchronous workflow” seems oxymoronic: an ordered sequence of tasks can’t possibly be asynchronous. But let’s return to the core of asynchronicity: completing tasks without blocking the program flow. In an asynchronous workflow, asynchronous tasks are triggered and returned immediately, with the actual response returned later on. This is what makes for an asynchronous workflow, even if the tasks are executed in sequential order.
By orchestrating asynchronous workflows, we can easily overcome the issue of state management and durable execution when tracking various asynchronous tasks or processes.
State management with asynchronous workflows
Workflows can be orchestrated and managed using unified platforms like Orkes Conductor. Using workflow orchestration, a central orchestrator manages the execution of a series of tasks pre-determined or dynamically ordered at runtime. The platform tracks each task’s status, so you get a global view of the application state (scheduled, in progress, completed, failed), even when the tasks occur independently of the main flow.
State management ensures application consistency, facilitates graceful recovery, and enables rapid debugging in case of terminal failure. Using orchestration, developers no longer need to spend time building plumbing code to get visibility into the execution flow of distributed components.
When to use asynchronous workflows?
The choice to use a synchronous or asynchronous workflow depends on your project requirements. Asynchronous workflows are often advantageous in the following situations where there is uncertainty in time-bound conditions:
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User-facing applications where responsiveness is critical
An asynchronous workflow enables the application to process long jobs, like file uploads, in the background while the user continues using the application.
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Data processing and analytics pipelines
Since ingesting, transforming, and analyzing large datasets can take a long time, asynchronous orchestration helps manage the flow between various processing stages, ensuring data integrity and handling errors appropriately.
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Long-running business processes that span hours or days
Asynchronous orchestration provides a way to manage the state of business processes, like order fulfillment, approval flows, and claims processing, over an indefinite period.
In these cases where processes take a long or unknown time to complete, using an asynchronous workflow ensures that the process remains durable and persists despite interruptions or failures.
Best practices for asynchronous workflows
When using asynchronous workflows for your applications, keep in mind the following practices:
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Design for resilience.
Configure proper retry mechanisms and design with the expectation that services may fail temporarily. Use compensation flows to handle terminal failures or the saga pattern to safely track reversals or termination in a workflow.
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Make sure to use the appropriate error-handling measures.
An asynchronous workflow that takes a long time to complete will require different timeout, rate limit, or retry strategies compared to fast, synchronous workflows. For example, to avoid unnecessary timeouts, use an appropriate timeout duration based on the recovery requirements.
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Embed observability into your workflows.
Get comprehensive tracing, logging, and monitoring across your asynchronous workflows. This is crucial for diagnosing issues and maintaining overall system health.
Building an asynchronous workflow using Orkes Conductor
Orkes Conductor is an enterprise-grade orchestration platform for process automation, API and microservices orchestration, agentic workflows, and more. With global state tracking, Orkes Conductor’s core platform architecture makes it easy to build asynchronous and event-driven workflows:
1. Easily add asynchronous tasks like Wait tasks and Human tasks.
These tasks enable you to durably pause the workflow for an extended period while waiting on an external signal, such as a form completion.
2. Seamlessly consume and publish events with other event-driven systems.
With in-built integrations with dozens of event systems and the ease of creating and monitoring event handlers, you can quickly build and extend your system’s event-driven architecture.
Trigger workflows from an event, publish events from a workflow, or create event-driven CDC systems—the flexibility is yours to decide.
3. Get webhook integrations with external systems to send or receive webhook events.
Use webhooks from enterprise systems like Slack, Teams, GitHub, or any custom system you need for asynchronous design patterns.
4. Trigger workflows asynchronously in response to tasks, signals, events, and webhooks.
Unlike synchronous workflows, all asynchronous workflows must be invoked asynchronously in a fire-and-forget pattern due to their unknown completion time. Use Conductor’s SDKs or APIs to start workflows asynchronously.
5. Fully native error-handling and observability mechanisms.
Set custom retries, timeouts, rate limits, and more for every task and workflow based on individual needs.
What an asynchronous workflow looks like
An example could look like kicking off a subtitling workflow whenever an UploadSuccess message is registered in the video processing pipeline. An event handler can be easily created in Conductor to start the subtitling workflow from an external trigger. Since the subtitling workflow involves processing time and perhaps a step for human review, it may take some time to complete – making it an asynchronous workflow.
With a fire-and-forget invocation, listeners must be implemented to get notified of an asynchronous workflow completion. In Conductor, a workflow status listener enables you to detect and capture workflow state changes in real-time. These updates can be further transmitted to other systems, such as a message broker, to trigger downstream services or processes. For example, once the subtitling workflow is completed, this change can be captured to make the subtitled video available on the video platform for the end user.
This is how asynchronous workflows in Conductor can be used to manage the complex interactions between multiple processes.
Explore more use cases with our Template Library, try building asynchronous workflows using our online Developer Edition sandbox, or get a demo of Orkes Cloud, a fully managed and hosted Conductor service that can scale seamlessly to meet all enterprise needs.
