What is Orchestration?

Liv Wong
Technical Writer
August 23, 2024
Reading Time: 8 mins

Orchestration is the process of coordinating distributed software components and systems so that they execute seamlessly as an automated, repeatable process.

In orchestration, a central platform (known as the orchestrator) coordinates the interactions between different components, such as microservices, APIs, databases, algorithms, eventing systems, LLM models, and other third-party systems so that they can work together efficiently toward an end goal. All sorts of processes can be orchestrated, from microservice-based application flows and IT infrastructure automation to data pipelines and digital user journeys.

Through orchestration, individual components and systems need not interact or integrate directly with each other. Instead, the orchestrator ensures each step in the process gets completed by the responsible component, by scheduling tasks, tracking its state, managing data flow and memory, optimizing resource utilization, and handling failure scenarios.

What is orchestration vs automation?

Orchestration and automation are related but different concepts.

Orchestration is about coordinating different systems so that it is easier to manage complex workflows. Automation is about using machines or technology to make a process automatic, reducing or eliminating the need for human intervention. Some examples of automation are smart home devices, adaptive cruise control, or automatic software updates.

Diagram comparing the differences between orchestration and automation. Orchestration coordinates distributed software components and systems so that they execute seamlessly as an automated, repeatable process, while automation is about using machines or technology to make a process automatic, reducing or eliminating the need for human intervention.
The differences between orchestration and automation.

Automation does not entail orchestration and could involve a single system that doesn’t require coordination across components. On the other hand, orchestration often involves automating a particular process across distributed systems, such as cloud infrastructure upgrades, application flows across microservices, or data pipelines across different sources.

Orchestration is not the only way to automate a process. For example, you could write custom scripts or use RPA (robotic process automation) instead of orchestration. However, orchestration may be more suited in many cases, as it offers multiple benefits other than automation, such as in-built error handling and state tracking (explored further below).

Orchestration also need not involve automation at every step. Some processes could involve human-involved tasks, like shipping and logistics flows or digital onboarding across platforms.

In summary, orchestration is the means to achieve an outcome, which is usually the automation of a process.

What is an orchestration layer?

The orchestration layer is an infrastructure component that coordinates the interactions of different services, databases, and components. It is a central layer that lies beneath and interacts with the user interface or client, so that developers can focus on building each component without worrying about how to integrate them with each other.

The orchestration layer is responsible for scheduling and dispatching calls to the underlying services and databases, based on a predefined sequence of steps and triggers. It manages the flow of data between each component, handles retries and timeouts, and keeps track of the progress at any point in time.

Diagram of an orchestration layer connecting with different components and interfaces.
An orchestration layer coordinates distributed components like databases, APIs, microservices, event systems, LLMs, and other components without the need for integration.

The orchestration layer serves as the headquarters for process delivery and monitoring, with capabilities for service registration, execution logs, failure handling, load balancing, scaling, and versioning. Its primary purpose is to ensure that the overall process runs to completion without needing to know how the service is carried out. In essence, it abstracts away the complexities of the underlying service implementation from the overarching, high-level business process.

Learn more about how orchestration platforms work under the hood to guarantee durable executions.

What are the components in an orchestration layer or platform?

An orchestration layer or platform typically consists of the following:

  • State machine evaluator The core of the orchestrator, it manages the task queues based on the predefined workflow and infrastructure/worker availability.
  • Task queues Used to group tasks according to their task type so that the queues’ corresponding pool of workers can process tasks in a round-robin, first-in-first-out manner.
  • Workers Code units that run and complete tasks in the queue.
  • Scheduler Used to trigger workflow executions based on a set schedule, typically through cron.
  • Persistence stores High-availability storage units that contain all workflow information, including metadata, task queues, and execution history.
Diagram of the different components in an orchestration layer or platform: the state machine evaluator, the task queues, the scheduler, the workers, and the persistence stores.
The components in an orchestration layer or platform.

What are the benefits of orchestration?

Orchestration arose against the backdrop of distributed computing and increasing complexity in the technology stack. Beyond simplifying the development effort of architecting complex processes, orchestration offers a multitude of benefits:

  1. Automation and enhanced efficiency Repetitive but complex flows like video transcoding, cluster upgrades, ELT and ETL pipelines can be automated using orchestration, reducing the amount of manual work and time required, especially at high volumes.
  2. Increased reliability and performance The orchestration layer is typically built to be fault-tolerant, even at high workloads. Through scheduling, task routing, dynamic scaling, parallel processing, and failure-handling mechanisms like rate limits, retries, and alternative flows, orchestration minimizes downtime and ensures continuous service delivery.
  3. Centralized governance and observability The orchestration layer provides clarity into the full process across disparate components, making it easier to build, monitor, and troubleshoot. The system’s state can be tracked, providing visibility into transient states at each moment as it progresses through the coordinated tasks.
  4. Ease of scaling and deploying updates By eliminating the need to integrate different components together, orchestration allows you to switch out databases or models, update code, or change the process completely without breaking it.

What can orchestration be used for?

There are many processes across industries that can benefit from orchestration:

  1. Microservice and API orchestration Microservice and API orchestration involves coordinating separate microservices or APIs to deliver a larger application or service within a distributed architecture. Some examples of application flows and business processes include order processing and supply chain management, digital banking services and investment management, or media transcoding and delivery.
  2. GenAI orchestration GenAI orchestration coordinates between multiple AI models, services, databases, algorithms, and humans to deliver complex AI-enabled services. Some examples include a RAG-based chatbot or recommendation engine, or AI-enabled subtitling or document processing, which can involve different LLMs or frameworks at each juncture.
  3. Data orchestration Data gets stored in disparate systems—data lakes, warehouses, cloud and legacy systems. Data orchestration connects siloed data sources so that data can be combined, transformed, and made available for analysis. By providing visibility across data sets, companies can make more informed decisions and strategic moves, such as reducing costs, ensuring compliance, or detecting anomalies.
  4. Security orchestration Security management and monitoring often involve security teams and specialized tools working in conjunction for threat detection, alert generation, intelligence analysis, containment, remediation, and incident reporting. Security orchestration automates and streamlines the coordination of security systems and teams, in order to improve incident response times, reduce human error, and increase the efficiency of security operations.
  5. DevOps orchestration DevOps orchestration involves coordinating complex workflows for continuous service delivery and automated deployment, minimizing production issues and accelerating time to market. Some examples of orchestrating DevOps processes include cluster provisioning, cloud infrastructure upgrades, or container deployment and monitoring.
  6. User journey orchestration Digital user journeys, such as onboarding, booking, or approval flows, can be very complex, often involving multiple platforms. User journey orchestration joins the different interfaces into a seamless flow representing the intended user experience. For example, a single onboarding flow not only may pull information from government databases and accreditation systems, but also require input from multiple users registered on different platforms.

Orchestrating with Orkes Conductor

Orkes Conductor is a powerful orchestration platform for any use case involving microservices, APIs, event handling, LLM chaining, and human-involved journeys. Orkes Conductor empowers teams to swiftly and easily build resilient, scalable systems.

Developer-first

Build quickly and slash development lifecycles.

  • Build workflows using Conductor SDKs in any language (Python, Java, JavaScript, Go, CSharp, Clojure) or using a visual workflow editor.
  • Create your own task workers or choose from dozens of in-built integrations, tasks, and operators for commonly encountered steps like API calls, database queries, and embedding generation.
  • Inspect workflow execution and logs to debug and get aggregated performance metrics.

With Orkes, developers get to implement each component using specific languages or frameworks without worrying about how to integrate and trace them throughout the workflow.

Scalable and durable

Conductor provides a highly decoupled infrastructure created for durability, speed, and redundancy.

  • Run millions of concurrent workflows with minimal latency.
  • Safely and cleanly version your workflows with no disruption to production runs.
  • Get native support for rate limits, retry policies, timeouts, and more.

Try it out

Get started with orchestration in a few steps:

  1. First, build and define the tasks.
  2. Define the workflow: its sequence of tasks, expected input/outputs, retry and timeout policies, and so on.
  3. Integrate the workflow with a front-end client if necessary.
  4. Schedule and run the workflow using API, cron jobs, or triggers.
  5. Monitor and modify the workflow as needed.

Workflow orchestration example

Taking an order processing flow as an example, let’s explore how orchestration works in practice. In this order processing workflow, the order is first validated. If successful, the workflow proceeds with tax calculation, invoice generation, and an order confirmation notification.

To route the workflow based on the outcome of the order validation task, a Switch task is used. As shown below, the switch evaluation is based on the output returned by the preceding validate_order task.

Screenshot of the Switch task in Conductor
The Switch task evaluates based on the output returned by the preceding order validation task.

Each task can be defined in the orchestration layer. The definition enables developers to configure its execution capabilities, such as number of retries, timeouts, rate limits, and the expected inputs/outputs, which allows for reuse across multiple workflows.

Screenshot of the task definition screen in Conductor
In the tax calculation task definition, the expected order information input is specified as a JSON schema.

When a task is used in a workflow, it inherits the task’s base definition while the task inputs are wired from the workflow’s current state. In our order processing example, the tax calculation task is configured to take the initial workflow input, containing the order information, as its task input.

Screenshot of the tax calculation task in Conductor.
The tax calculation task is configured to take the initial workflow input, containing the order information, as its task input.

The workflow tasks can be any unit of logic, such as a microservice or API coded in any language, which get called by the orchestration layer when the execution reaches it. For example, when the orchestration layer calls the tax calculation task, the Python code below gets executed.

Code sample showing the tax calculation logic in Python.
The underlying code for custom tasks that get called by the orchestration layer.

In Conductor, any existing function can easily become a task worker, facilitating the shift to using an orchestration layer in existing applications. For example, the Python task worker is defined by adding a @worker_task decorator and specifying a task object that contains all the task metadata such as the input values.

Screenshot of the Fork/Join task in Conductor.
The orchestration layer calls tasks to run in parallel as defined.

Lastly, all the order processing workflow runs two notification tasks in parallel using a Fork/Join task.

Orkes Cloud is a fully managed and hosted Conductor service that can scale seamlessly to meet your needs. When you use Conductor via Orkes Cloud, your engineers don’t need to worry about setting up, tuning, patching, and managing high-performance Conductor clusters. Try it out with our 14-day free trial for Orkes Cloud.

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