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Event-driven programming
Event-driven programming
Event-driven programming
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In computer programming, event-driven programming is a programming paradigm in which the flow of the program is determined by external events. UI events from mice, keyboards, touchpads and touchscreens, and external sensor inputs are common cases. Events may also be programmatically generated, such as from messages from other programs, notifications from other threads, or other network events.

Event-driven programming is the dominant paradigm used in graphical user interfaces applications and network servers.

In an event-driven application, there is generally an event loop that listens for events and then triggers a callback function when one of those events is detected.

Event-driven programs can be written in any programming language, although the task is easier in languages that provide high-level abstractions.

Although they do not exactly fit the event-driven model, interrupt handling and exception handling have many similarities.

It is important to differentiate between event-driven and message-driven (aka queue driven) paradigms: Event-driven services (e.g. AWS SNS) are decoupled from their consumers. Whereas queue / message driven services (e.g. AWS SQS) are coupled with their consumers.[1]

Event loop

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Main article: Event loop

Because the event loop that retrieves and dispatches events is common amongst applications, many programming frameworks provide an implementation of an event loop, and the application developer only needs to write the event handlers.

RPG, an early programming language from IBM, whose 1960s design concept was similar to event-driven programming discussed above, provided a built-in main I/O loop (known as the "program cycle") where the calculations responded in accordance with "indicators" (flags) that were set earlier in the cycle.

Event handlers

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The actual logic is contained in event handler routines. These routines handle the events to which the main program will respond. For example, a single mouse-click on a "Save" command button in a GUI program might trigger a routine to save data to a database. An "Exit" button might trigger a routine to exit the program. The event loop receives events from all such command buttons and other GUI elements, dispatching the appropriate event handler routine for each button.

Event handler routines need to be bound to specific events, so the event loop can dispatch the correct routine in response to the event. Many IDEs simplify this process by providing the programmer with an event handling template for each specific event (such as a button click), allowing the programmer to focus on writing the event-handling code.

In a sequential program, keeping track of execution order and history is normally trivial. But in an event-driven program, event handlers execute non-sequentially in response to external events. Special attention and planning is required to correctly structure the event handlers to work when called in any order.

Common uses

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Most existing GUI architectures use event-driven programming.[2] Windows has an event loop. The Java AWT framework processes all UI changes on a single thread, called the Event dispatching thread. Similarly, all UI updates in the Java framework JavaFX occur on the JavaFX Application Thread.[3]

Most network servers and frameworks such as Node.js are also event-driven.[4]

Interrupt and exception handling

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See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Event-driven programming

View on Grokipedia
from Grokipedia
Event-driven programming is a in which the flow of program execution is determined by external events, such as user inputs or system notifications, rather than following a strictly sequential order of instructions. In this model, programs typically operate using an that continuously monitors for incoming events, dispatches them to appropriate handlers, and processes them promptly to maintain responsiveness, particularly in graphical user interfaces (GUIs) and interactive applications. Key concepts include events, which are objects representing actions like mouse clicks or key presses; event handlers or listeners, which are functions or methods that execute in response to specific events; and binding mechanisms that associate events with their handlers. This paradigm contrasts with imperative or procedural programming by decoupling the main program flow from direct control, allowing for modular, reactive code that scales well in multi-threaded environments, such as Java's Event Dispatch Thread (EDT). Advantages include improved user interface responsiveness and easier handling of concurrency without excessive thread management, though it requires careful design to avoid issues like race conditions. Its roots trace back to interrupt handling in operating systems and early interactive graphics systems in the 1960s, and it gained prominence in the 1970s and 1980s with the development of GUI frameworks, such as those in early windowing systems, enabling interactive applications that respond dynamically to user actions. Today, it underpins modern software in domains like , mobile apps, and distributed systems, often integrated with patterns like the observer model for event notification.

Basics

Definition and Principles

Event-driven programming is a computational in which the flow of program execution is determined by the occurrence of , such as user inputs, , messages from other processes, or system signals, rather than a fixed sequential order of instructions. This approach contrasts sharply with traditional , where code executes line by line in a predetermined manner without interruption unless explicitly structured otherwise. A foundational prerequisite for event-driven programming is the distinction between synchronous and asynchronous execution models. In synchronous execution, operations block the program until they complete, potentially leading to inefficiencies when waiting for unpredictable inputs; asynchronous execution, by contrast, allows the program to proceed with other tasks while awaiting event completion, enabling non-blocking behavior essential for responsiveness. Key principles of event-driven programming emphasize reactivity to asynchronous events, where the system continuously monitors and responds to unpredictable triggers without halting overall progress. Another core principle is the inversion of control, in which the event framework or dispatcher dictates the timing and sequence of code execution, rather than the application logic itself driving the flow. Additionally, it supports concurrency through mechanisms like non-blocking I/O, allowing multiple event streams to be handled efficiently without the overhead and complexity of traditional threading models, which often require locks and to avoid race conditions. A basic illustration of event registration and dispatch can be seen in the following , which demonstrates how a handler is associated with an event type and invoked upon occurrence:

# Event registration register_handler("button_click", on_button_click) # Event dispatch (triggered by framework upon event) def dispatch_event(event_type, [data](/page/Data)): if handler := get_handler(event_type): handler([data](/page/Data)) # Example handler def on_button_click([data](/page/Data)): print("Button clicked at", [data](/page/Data).position)

# Event registration register_handler("button_click", on_button_click) # Event dispatch (triggered by framework upon event) def dispatch_event(event_type, [data](/page/Data)): if handler := get_handler(event_type): handler([data](/page/Data)) # Example handler def on_button_click([data](/page/Data)): print("Button clicked at", [data](/page/Data).position)

This mechanism decouples event detection from response logic, with the event loop (detailed in subsequent sections) typically managing the dispatch process.

Historical Development

The roots of event-driven programming lie in the development of interrupt-driven systems within early operating systems, where hardware mechanisms allowed computers to pause normal execution and respond asynchronously to external signals such as operations or timer expirations. Systems like , initiated in 1965 as a collaborative project by MIT, , and , exemplified this approach through its use of interrupts to manage and resource allocation among multiple users, marking an early form of reactive computation. In the 1970s, event-driven concepts evolved significantly with the advent of graphical user interfaces at Xerox PARC, particularly through Alan Kay's work on Smalltalk, which introduced object-oriented event handling to support dynamic, interactive environments. Smalltalk's design incorporated "soft interrupts" and event loops to process user interactions like mouse clicks and window updates, influencing the paradigm's shift toward software-based events in application development. A key milestone occurred in the 1980s with the release of the Apple Macintosh in 1984, which popularized event-driven programming in consumer GUIs by integrating mouse events, keyboard inputs, and menu selections into its operating system, enabling responsive desktop interactions. This built directly on Xerox PARC innovations but made event handling accessible to a broader audience of developers and users. The 1990s saw event-driven programming extend to the web with Brendan Eich's creation of in 1995 for , introducing an event model that allowed scripts to respond to browser events like form submissions and page loads, fundamentally shaping client-side interactivity. In the 2000s, the paradigm gained prominence in server-side applications through , released in 2009 by , which leveraged JavaScript's event-driven architecture for scalable, non-blocking I/O in network programming. Over time, event-driven programming transitioned from low-level hardware interrupts to higher-level software , enabling more modular and decoupled code structures. The rise of multicore processors in the mid-2000s further amplified its relevance, as event-driven models facilitated concurrency without the overhead of traditional threading, improving efficiency in parallel environments.

Key Components

Events and Event Sources

In event-driven programming, an event is a discrete occurrence or signal within a system that indicates a significant change or action, such as a user interaction or system notification, prompting the program to respond accordingly. These events represent asynchronous happenings that disrupt the normal sequential flow, allowing the program to react dynamically rather than proceeding in a predetermined order. Events can be categorized into several types based on their origin and nature. User-generated events arise from direct human interactions, such as keyboard inputs or clicks on graphical elements. System-generated events are produced by underlying operating system or hardware processes, including file completion or arrivals. Custom events, on the other hand, are application-specific signals defined by developers, such as state transitions in a game or messages in a distributed system. Event sources are the entities responsible for detecting and generating these events, originating from hardware, software, or environmental factors. Hardware sources include devices like sensors, keyboards, or controllers that signal physical changes or user actions. Software sources encompass APIs, message queues, or components within the application, such as GUI widgets that monitor user inputs. Environmental sources involve time-based triggers, like expirations, or state changes in the system's context, such as availability updates. Events typically carry specific properties to provide context for handling, including a type identifier, source reference, timestamp, and optional payload data, with some supporting priority levels for queuing. These properties are encapsulated in an event object, which might be structured in as follows:

class Event { [String](/page/String) type; // e.g., "mouseClick" or "timerExpired" Object source; // Reference to the generating entity long timestamp; // Time of occurrence Object [payload](/page/Payload); // Additional data, e.g., coordinates or [message](/page/Message) int priority; // Optional level for processing order (e.g., 0-10) }

class Event { [String](/page/String) type; // e.g., "mouseClick" or "timerExpired" Object source; // Reference to the generating entity long timestamp; // Time of occurrence Object [payload](/page/Payload); // Additional data, e.g., coordinates or [message](/page/Message) int priority; // Optional level for processing order (e.g., 0-10) }

This structure allows events to convey essential details while remaining lightweight.

Event Loop

The event loop serves as a fundamental construct in event-driven programming, continuously waiting for events to occur, retrieving them from an associated queue, and invoking the appropriate handlers to them until the program reaches a termination condition. This mechanism ensures that the program remains responsive by decoupling event detection from , allowing asynchronous handling of inputs such as user interactions or network . Key components of an event loop include the event queue, which operates as a first-in, first-out (FIFO) structure to hold pending events in the order they arrive; the dispatcher, responsible for matching each event to its corresponding handler based on event type or source; and the main loop cycle itself, which repeatedly polls for new events, dequeues them, and dispatches for execution before iterating again. The demultiplexer, often integrated into the loop, uses operating system primitives like select() or poll() to efficiently monitor multiple event sources without busy-waiting. A basic implementation of an event loop can be illustrated in pseudocode as follows:

while (program is running) { events = getEvents(); // Poll or wait for pending events from queue/sources for (each event in events) { handler = findHandler(event); // Dispatch to appropriate handler handler(event); // Invoke the handler } }

while (program is running) { events = getEvents(); // Poll or wait for pending events from queue/sources for (each event in events) { handler = findHandler(event); // Dispatch to appropriate handler handler(event); // Invoke the handler } }

This structure, where getEvents() blocks until events are available and findHandler() matches events to callbacks, exemplifies the loop's role in orchestrating event processing. Event loops exhibit variations in design to suit different concurrency needs, such as single-threaded implementations, which execute all callbacks sequentially within one thread to simplify —as seen in environments where the loop integrates with non-blocking I/O to handle asynchronous operations without spawning threads. In contrast, multi-threaded event loops distribute event handling across multiple threads, one per processor core, using techniques like per-thread queues and work-stealing to enable parallel callback execution while preserving order for dependent events through mechanisms such as "colors" for . Additionally, loops can incorporate blocking I/O, where the poll waits indefinitely, or non-blocking I/O, which uses asynchronous notifications to avoid stalling the cycle. Performance considerations in event loops focus on managing event backlogs to prevent , where high-priority or long-running handlers delay lower-priority events, potentially leading to unresponsiveness. To mitigate this, implementations impose limits on polling durations or queue processing times, ensuring the loop cycles regularly and offloads blocking operations to kernel threads, thereby maintaining fairness across event types.

Event Handlers and Callbacks

In event-driven programming, event handlers are specialized functions or methods designed to execute in response to specific events dispatched by the system. These handlers encapsulate the logic that processes the event, such as updating application state or triggering further actions, and are often implemented as callbacks—functions passed as arguments to be invoked upon event occurrence. Registration of event handlers typically involves binding them to particular event types through provided application programming interfaces (APIs). For instance, in environments, the addEventListener method associates a callback function with an element and event type, allowing multiple handlers to be attached without overwriting existing ones. A example illustrates this binding:

element.addEventListener('click', handleClick); function handleClick(event) { // Process the click event console.log('Button clicked'); }

element.addEventListener('click', handleClick); function handleClick(event) { // Process the click event console.log('Button clicked'); }

This registration ensures the handler is queued for execution when the specified event, such as a user click, is detected. The execution model for event handlers can be either synchronous or asynchronous, depending on the programming environment and design choices. In synchronous invocation, the handler runs immediately and blocks further execution until completion, which is common in single-threaded systems like JavaScript's main thread. Asynchronous models, however, invoke handlers on separate threads or via deferred mechanisms to prevent blocking, as seen in custom event implementations in languages like Visual Basic .NET using BeginInvoke. Handlers receive event parameters, typically in the form of an event object containing details such as the event target (e.g., the clicked element) and associated data (e.g., coordinates or payload). A practical example is a click handler in a graphical interface, where the callback updates the UI without interrupting the event loop. For error handling within handlers, strategies include try-catch blocks to capture exceptions and prevent propagation, alongside retries for transient failures or routing persistent errors to dead-letter queues for later inspection. This ensures system resilience, as unhandled errors in one handler do not crash the entire application. Best practices emphasize avoiding blocking operations in event handlers to maintain application responsiveness. Heavy computations or I/O tasks should be offloaded to asynchronous workers or background threads, preventing delays in processing subsequent events and ensuring the event loop remains efficient.

Practical Applications

In Graphical User Interfaces

Event-driven programming plays a central role in graphical user interfaces (GUIs) by enabling responsive interactions between users and applications. In these systems, user actions such as mouse clicks, keyboard presses, and touch gestures generate that are captured, processed, and used to update the visual state of the interface, such as redrawing windows, highlighting elements, or modifying data views. This approach ensures that the GUI remains interactive and fluid, reacting immediately to inputs without requiring constant polling by the application code. The foundational implementation of event-driven GUIs traces back to the , developed in 1973 at PARC, which introduced a display and mouse-based that handled input events to manage overlapping windows and graphical elements. Modern GUI frameworks build on this model, using event handlers—functions or methods invoked in response to specific events—to bind user interactions to application logic. For instance, in Java's Swing framework, developers register listener interfaces like ActionListener for button clicks or MouseListener for cursor movements, allowing components such as buttons or panels to trigger code execution upon event detection. Similarly, the Qt framework in C++ employs a centralized event system where QEvent objects are dispatched to target widgets, enabling overrides of methods like mousePressEvent to process inputs and update the or layout. Apple's Cocoa framework for uses a responder chain to route events, where NSView subclasses implement methods like keyDown to handle keyboard events and propagate unhandled ones up the hierarchy. Event flow in GUI frameworks often involves propagation mechanisms to determine which component handles an event, akin to hierarchical structures in document object models. In Qt, events are delivered directly to the originating object but can be ignored via the ignore() method, allowing propagation to parent widgets in a bubbling-like fashion until accepted or reaching the top-level window. Cocoa's responder chain operates similarly, forwarding events from the initial responder (e.g., a view under the cursor) to superviews or the application delegate if not consumed, supporting both capture (downward) and bubbling (upward) patterns for complex nested interfaces. Swing dispatches events to the deepest component in the containment hierarchy first, with optional propagation through ancestor listeners if the event is not fully handled. These models facilitate efficient event routing in layered UIs, ensuring interactions like drag-and-drop or menu selections traverse the appropriate component tree. Despite these advantages, event-driven GUIs face challenges in maintaining responsiveness, particularly on the UI thread where all event processing occurs. Blocking operations, such as lengthy computations in response to a button click, can freeze the interface, leading to poor ; frameworks mitigate this by enforcing single-threaded event dispatching. In Swing, the Event Dispatch Thread (EDT) serializes all GUI updates and event handling, requiring developers to use invokeLater or SwingWorker for offloading heavy tasks to background threads to prevent EDT blockage. Qt's runs on the main thread, recommending QTimer or asynchronous signals for non-blocking operations to avoid stalling input processing. Cocoa's NSRunLoop similarly processes events on the main thread, with guidelines to dispatch long-running tasks via Grand Central Dispatch queues to sustain 60 FPS rendering and input latency below 16 ms. Another challenge arises with high-frequency events, such as continuous movements during drags, which can overwhelm the event queue and degrade if every motion triggers a full repaint or . Techniques like debouncing—delaying handler execution until stabilize—or throttling—limiting invocations to a fixed rate—are employed to filter these events. In Swing applications, custom MouseMotionListeners can implement timers to debounce mouseDragged events, executing updates only after a brief pause in movement, thus optimizing resource use without sacrificing interactivity. This approach is essential for smooth scrolling, resizing, or real-time drawing tools in event-driven GUIs.

In Web and Server Applications

In web client-side development, employs an event-driven model to handle interactions with the (DOM) and asynchronous network requests. DOM events, such as clicks or key presses, are dispatched by the browser and captured through event listeners, allowing scripts to respond dynamically without blocking the . For instance, the addEventListener method registers callbacks for these events, enabling reactive updates to page content. Similarly, Asynchronous JavaScript and XML (AJAX) techniques, implemented via the API, use event-driven callbacks like onreadystatechange to process server responses asynchronously, facilitating partial page updates without full reloads. Modern frameworks like React build on this foundation by passing event handler functions as props to components, creating synthetic events that normalize behavior across browsers. In React, attributes such as onClick trigger these handlers when user actions occur, promoting declarative within component lifecycles. This approach integrates seamlessly with the browser's , ensuring efficient handling of user inputs in single-page applications. On the server side, leverages the EventEmitter pattern to manage HTTP requests and real-time connections in an event-driven manner. The http.Server instance emits a 'request' event for each incoming connection, passing request and response objects to registered listeners, which process data non-blockingly. This is underpinned by , a cross-platform library that provides operations, allowing the server to handle multiple concurrent requests without thread blocking. For bidirectional communication, WebSockets extend this model by establishing persistent connections where events like message receipt trigger callbacks on both client and server. Practical examples illustrate these concepts in action. In an event-driven chat application using —a library built on EventEmitter—clients emit events like 'chat message' upon sending text, which the server broadcasts to connected peers via listeners, enabling real-time updates without polling. Similarly, server-side database interactions often treat query completions as asynchronous events; for instance, database drivers invoke callbacks or resolve promises upon result retrieval, allowing non-blocking integration with HTTP handlers. Event-driven architectures enhance scalability in high-concurrency web scenarios, such as , by decoupling services through event publishing and subscription, reducing latency and enabling horizontal scaling. Services react independently to events like user actions or data changes, handling thousands of simultaneous connections efficiently via non-blocking I/O. The evolution of event-driven programming in web applications traces from early (CGI) scripts, which relied on synchronous request-response cycles akin to polling for dynamic content, to AJAX's introduction of asynchronous callbacks in the early 2000s. This progressed to WebSockets in 2011 for full-duplex communication, minimizing overhead from repeated HTTP polls. Modern , exemplified by RxJS—a library for composing asynchronous event sequences—further refine this by treating data flows as observables, allowing declarative handling of streams from user inputs to API responses, supplanting manual polling with efficient, composable operators.

In Embedded and Real-Time Systems

In embedded and real-time systems, event-driven programming is adapted to resource-constrained environments, such as microcontrollers and Internet of Things (IoT) devices, where efficiency and responsiveness are paramount. Lightweight event queues enable non-blocking operation, allowing the system to react to asynchronous inputs without wasting CPU cycles on polling. For instance, finite state machines (FSMs) integrated with event dispatching frameworks minimize overhead, using as little as 164 bytes of code and 8 bytes of data on low-end microcontrollers like the TI MSP430. These adaptations prioritize modularity and reusability, facilitating concurrent event handling in systems with limited processing power. Integration with real-time operating systems (RTOS) like further enhances this paradigm by providing event groups as a lightweight signaling mechanism. event groups use bit flags (up to 24 bits) to synchronize tasks and interrupts, serving as an efficient alternative to full queues for event notification, with built-in safeguards against race conditions via scheduler locking. This event-driven approach ensures no CPU time is expended on inactive waiting, supporting preemptive multitasking in memory-tight setups. Similarly, models like TinyGALS employ globally asynchronous, locally synchronous communication with small FIFO queues (e.g., 10 elements) to manage events across modules, generating compact runtime schedulers of around 112 bytes for platforms like Berkeley motes. Common event types in these systems include hardware , which act as foundational triggers for immediate responses. On Arduino-based microcontrollers, a press on an interrupt-enabled pin (e.g., pin 2) generates a falling-edge event, invoking an interrupt service routine (ISR) to toggle outputs like LEDs without delaying the main loop, ensuring real-time reactivity even during extended operations. Sensor data triggers, such as temperature thresholds from I²C-connected devices, similarly wake the system via interrupts, avoiding continuous polling to conserve power. A representative example is event-driven in smart thermostats, where temperature s initiate actions like setpoint adjustments upon detecting changes. In designs adhering to standards, firmware layers process or events to drift indoor temperatures toward outdoor levels when unoccupied, using event queues for supervisory control and local triggered by inputs. This approach batches readings to reduce wake-ups, integrating with low-power modes for extended battery life in setups. Key constraints include memory efficiency and predictability to meet hard real-time deadlines. Event-driven architectures must limit overhead, such as using hashed event tables to dispatch in ~18 CPU cycles, fitting within tens of kilobytes of RAM and ROM as in 's raw for networked embedded events. For predictability, supervisors queue aperiodic events (e.g., with 6-10 ms deadlines) while enforcing periodic tasks, preventing overruns in safety-critical applications like attitude control. Libraries like support this by processing TCP/IP events (e.g., packet reception callbacks) in a non-blocking manner, enabling efficient handling of network triggers on resource-limited devices without an OS.

Comparisons and Extensions

Relation to Interrupt and Exception Handling

Event-driven programming draws foundational concepts from low-level interrupt and exception handling mechanisms in operating systems and hardware, where asynchronous signals trigger specific responses. Hardware interrupts serve as primitive events, representing asynchronous signals from devices or timers—such as CPU timer interrupts—that prompt the processor to halt normal execution and transfer control to an interrupt service routine (ISR). ISRs act as early forms of event handlers, executing predefined code to address the triggering condition, such as processing incoming data from a peripheral, before returning control to the interrupted task. This model underpins event-driven architectures by providing a reactive paradigm for handling unpredictable hardware events without constant polling. Exceptions, often implemented as software interrupts, extend this reactivity to internal program errors or conditions like or page faults, which trap execution into the operating system's exception . The examines the exception type and routes it to an appropriate handler, mirroring the event loop's role in queuing and dispatching higher-level events to callbacks. For instance, in structured , the system unwinds the stack and invokes handlers in a manner analogous to event propagation, ensuring recovery or termination without crashing the entire . Modern operating systems build event loops atop these interrupt and exception subsystems to abstract low-level details into user-space APIs. In , the mechanism relies on kernel interrupt handling for I/O readiness notifications; hardware interrupts from network devices schedule NAPI (New API) instances, which process events and enable epoll_wait to retrieve them efficiently via polling or interrupt suppression. Similarly, the Windows message pump integrates kernel notifications, where device drivers post to the thread queue in response to interrupts or exceptions, allowing user-mode applications to handle them through the GetMessage-DispatchMessage loop. These mappings enable scalable event-driven applications while leveraging hardware reactivity. Key differences arise in scope and control: interrupts and exceptions operate at preemptive, low-level layers with direct hardware involvement and minimal latency requirements, often disabling further interrupts during handling to avoid nesting. In contrast, event-driven programming provides higher-level abstractions, where events are cooperative, queued, and processed in user-defined loops without immediate preemption, allowing for more complex orchestration at the application level.

Event-Driven vs. Other Paradigms

Event-driven programming differs from in its mechanism. In procedural paradigms, code executes sequentially from a main routine, with the explicitly dictating the order of subroutine calls to process tasks in a linear fashion. This approach suits straightforward, deterministic computations but can lead to inefficient resource use in interactive or asynchronous scenarios, such as repeatedly checking for user input via polling loops. In contrast, event-driven programming decouples execution from a fixed sequence, relying on an to trigger handlers only when external events occur, like a click or data arrival. For procedural programmers, this shift can be disorienting, as the framework manages the flow, inverting the traditional structure where the main routine drives logic. A key example is handling input: polling continuously queries a condition (e.g., state) in a loop, wasting CPU cycles, whereas event-driven waiting registers a callback (e.g., via setOnAction in ) that activates only on the event, promoting efficiency. Compared to (OOP), event-driven approaches build upon and extend OOP principles, particularly through patterns like the observer model. In OOP, encapsulation and organize code into objects that interact via methods, but remains largely synchronous and encapsulated within class hierarchies. Events enhance this by enabling : a subject object notifies multiple observers of state changes without direct dependencies, as seen in GUI toolkits where UI components broadcast events to . This aligns with OOP's open-closed , allowing new event handlers to be added without modifying the core subject code. However, integrating events introduces asynchrony challenges, such as unpredictable notification order among observers, which can complicate and state management in concurrent OOP designs. Event-driven programming relates to functional and reactive paradigms through extensions like (FRP), which reframes events as declarative data rather than imperative callbacks. Traditional event-driven code handles discrete events with mutable state updates (e.g., appending to a list in a .on('event', handler)), requiring manual coordination of side effects like UI renders. FRP, originating from work on functional reactive , models behaviors as continuous functions over time, treating event sequences as composable for operations like mapping or filtering. This declarative style avoids explicit mutation, centralizing updates (e.g., via Bacon.combineTemplate in Bacon.js for aggregating ). The Reactive Extensions (Rx) family, such as Rx.NET, applies this to event-driven systems by providing observables for asynchronous , blending observer patterns with functional composition to simplify complex event flows. Hybrid approaches combine event-driven elements with other models to address limitations like concurrency. For instance, the actor model in frameworks like Akka integrates event-driven messaging with isolated, stateful actors that process asynchronous events without shared memory, enabling scalable distributed systems. Actors receive and respond to messages via an event-driven fabric, supporting features like event sourcing for replayable state changes, which hybridizes reactive principles with concurrent isolation. This mitigates asynchrony issues in pure event-driven code by encapsulating behavior in lightweight units, suitable for resilient applications. Choosing event-driven programming depends on workload characteristics: it excels in I/O-bound tasks, where operations like network requests or file access involve prolonged waiting, allowing a single-threaded to handle concurrency efficiently without blocking. For example, uses event-driven I/O to manage multiple TCP connections asynchronously via , maximizing throughput for web servers. Procedural paradigms, however, are preferable for tasks requiring intensive computation, such as , where multi-threading parallelizes work across cores rather than yielding to an .

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  44. https://www.freertos.org/FreeRTOS-Event-Groups.html
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  64. https://blogg.bekk.no/event-driven-and-functional-reactive-programming-step-by-step-babbd8364150
  65. https://github.com/dotnet/reactive
  66. https://akka.io/how-akka-works
  67. https://blog.risingstack.com/concurrency-and-parallelism-understanding-i-o/
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