Exploring Node.js Worker Threads: Unleashing Concurrency with Examples
In the realm of Node.js, worker threads emerge as an invaluable asset when confronting computationally demanding JavaScript operations. These threads provide a streamlined mechanism for executing JavaScript code concurrently, leveraging multiple threads to achieve significantly enhanced speed and efficiency. Their introduction empowers developers to tackle intricate tasks without impeding the responsiveness of the main thread. Prior to more recent Node.js iterations, the robust capabilities of worker threads were not inherently available.
A pivotal function of worker threads involves the transfer of ArrayBuffer instances, a crucial mechanism for managing CPU-intensive operations. Node.js worker threads have definitively established themselves as a superior solution for optimizing CPU performance, primarily attributed to their distinct operational characteristics:
- Single Process, Multiple Threads: They enable the execution of a single process while orchestrating multiple concurrent threads within it.
- Isolated JS Engine Instances: Each thread operates with its own dedicated JavaScript engine instance, ensuring execution isolation.
- Individual Event Loops: Every thread possesses its own independent event loop, preventing blocking across threads.
- Dedicated Node.js Instances: Each thread maintains a separate Node.js instance, contributing to operational autonomy.
To harness the power of worker threads, it is essential to ensure your Node.js installation is updated to a compatible, recent version.
A Historical Perspective: Managing CPU-Bound Applications in Node.js
Before the advent of worker threads, Node.js developers employed various strategies to handle CPU-intensive applications. Some notable approaches included:
- Child Process Module: Executing CPU-intensive programs within a separate child process using the built-in child_process module.
- Cluster Module: Leveraging the cluster module to distribute multiple CPU-intensive actions across several distinct processes.
- Third-Party Modules: Exploring external libraries such as Microsoft’s Napa.js, designed to address concurrency challenges.
However, none of these earlier alternatives gained widespread adoption or offered a universally satisfactory solution. This was largely due to inherent limitations such as performance bottlenecks, increased development complexity, a lack of consistent community embrace, issues with stability, and insufficient documentation.
Leveraging Concurrency with Node.js Worker Threads for Intensive Computations
Worker threads signify a monumental advancement in confronting the inherent challenges of concurrency within the JavaScript ecosystem. It is crucial to underscore that this paradigm does not unilaterally introduce native multi-threading capabilities directly into the JavaScript language itself. Instead, the innovative worker threads model empowers applications to strategically deploy a multitude of distinct, isolated JavaScript workers. Node.js meticulously orchestrates the sophisticated communication mechanisms between these individual workers and their originating parent thread. Each worker operating within the Node.js environment functions with its own entirely dedicated V8 engine instance and a completely independent Event Loop. A paramount distinguishing characteristic, setting worker threads apart from conventional child processes, is their intrinsic capacity to share memory. This attribute is undeniably vital for facilitating exceptionally efficient data exchange in scenarios that demand intensive computational processing, where the overhead of data copying between separate processes would be prohibitive.
Practical Application: An Illustrative Worker Thread Workflow
To fully grasp the practical utility and operational flow of Node.js worker threads, let’s dissect a straightforward yet highly demonstrative example. This setup involves two distinct scripts: a primary script responsible for initiating the worker, and a secondary script that embodies the worker’s logic.
Establishing the Primary Execution Script: main.js
First, commence by crafting your principal script, conventionally named main.js. This script orchestrates the creation, communication with, and eventual termination of the worker thread.
JavaScript
const { Worker } = require(‘worker_threads’);
/**
* Initiates a worker thread and manages its communication lifecycle.
* @param {any} workerData — Data to be passed to the worker thread.
* @returns {Promise<any>} A promise that resolves with the message from the worker or rejects on error/exit.
*/
const spawnWorkerService = (workerData) => {
return new Promise((resolve, reject) => {
// Create a new Worker instance, specifying the worker script and initial data.
const workerInstance = new Worker(‘./workerExample.js’, { workerData });
// Register a listener for ‘message’ events from the worker.
// This resolves the promise with the data sent back by the worker.
workerInstance.on(‘message’, resolve);
// Register an error handler for the worker.
// If the worker encounters an unhandled error, the promise will reject.
workerInstance.on(‘error’, reject);
// Register an ‘exit’ event handler for the worker.
// This is crucial for detecting abnormal termination.
workerInstance.on(‘exit’, (exitCode) => {
if (exitCode !== 0) {
// If the worker exits with a non-zero code, it indicates an error or abnormal termination.
reject(new Error(`Worker process terminated unexpectedly with exit code: ${exitCode}`));
}
});
});
};
/**
* Main asynchronous function to execute the worker thread workflow.
*/
const executeMainProcess = async () => {
try {
// Asynchronously invoke the worker service with initial data.
const computedResult = await spawnWorkerService(‘Commencing computational task from parent!’);
// Log the result received back from the worker.
console.log(‘Received data from worker:’, computedResult);
} catch (anomalousError) {
// Catch and log any errors that occur during worker execution.
console.error(‘An error transpired during worker operation:’, anomalousError);
}
};
// Initiate the main execution flow.
executeMainProcess();
In this primary script, we begin by destructively importing the Worker class from the built-in worker_threads module. The spawnWorkerService function encapsulates the logic for creating and managing a worker’s lifecycle. It returns a Promise, which will either resolve with data sent back from the worker or reject if an error occurs within the worker or if it terminates abnormally. Inside this function, a new Worker instance is instantiated, pointing to our worker-specific script (./workerExample.js) and passing an initial payload via workerData. Crucially, we establish event listeners: workerInstance.on(‘message’, resolve) is configured to capture data transmitted from the worker, resolving our Promise. workerInstance.on(‘error’, reject) acts as a robust error handling mechanism, propagating any unhandled exceptions from the worker to the parent thread. Finally, workerInstance.on(‘exit’, (code)) monitors the worker’s termination, allowing us to detect and report non-zero exit codes, which typically signify an issue. The executeMainProcess asynchronous function then invokes spawnWorkerService and logs the outcome or any errors encountered.
Crafting the Worker-Specific Logic: workerExample.js
Subsequently, you will construct the script containing the specialized logic intended for execution within the dedicated worker thread. This file, conventionally named workerExample.js, represents the isolated computational environment.
JavaScript
const { workerData, parentPort } = require(‘worker_threads’);
// workerData: This variable holds the initial data transmitted from the parent thread
// during the worker’s instantiation. It allows for initial configuration or input.
//
// parentPort: This object serves as the dedicated communication channel back to
// the parent thread. It is the primary means by which the worker can send
// results, status updates, or notifications back to the spawning process.
// It is prudent to verify the existence of `parentPort` before attempting
// to utilize it for communication, as in certain edge cases or non-worker
// environments, it might not be defined.
if (parentPort) {
// Utilize the postMessage() method on the parentPort object to dispatch
// a message back to the parent thread. The message can be any serializable
// JavaScript value or object.
parentPort.postMessage({ acknowledgment: workerData, processedByWorker: true });
}
// In a real-world scenario, this worker script would perform
// a CPU-intensive operation here, such as:
// — Complex mathematical calculations
// — Image processing
// — Data compression/decompression
// — Large dataset manipulation
// — Cryptographic operations
// After completing the intensive task, it would then post the result back.
// For instance:
/*
const intensiveComputation = () => {
let sum = 0;
for (let i = 0; i < 10_000_000_000; i++) { // A deliberately long loop
sum += i;
}
return sum;
};
if (parentPort) {
const resultOfCalculation = intensiveComputation();
parentPort.postMessage({ status: ‘completed’, data: resultOfCalculation });
}
*/
Within workerExample.js, we again utilize destructuring assignment to extract workerData and parentPort from the worker_threads module. workerData holds the information passed from the parent thread when the worker was spawned. parentPort is the pivotal communication conduit, enabling the worker to transmit data back to its originating parent. The if (parentPort) check is a best practice to ensure the environment is indeed a worker thread before attempting communication. The parentPort.postMessage() method is then invoked to send an object containing an acknowledgment and a flag indicating processing by the worker back to the main thread. A commented-out section illustrates where a genuine, CPU-bound task, such as an extensive mathematical calculation, would reside in a more pragmatic implementation.
When you execute the primary script from your terminal using the command node main.js, the anticipated output will be:
Received data from worker: { acknowledgment: ‘Commencing computational task from parent!’, processedByWorker: true }
In this comprehensive demonstration, our main.js script initially imports the Worker class, a fundamental component for managing worker threads. We then meticulously package essential information – specifically, the file path to workerExample.js and an initial message (‘Commencing computational task from parent!’) – as workerData for the newly instantiated worker to process. As explicitly showcased within workerExample.js, the worker script is inherently configured to listen for ‘message’ events originating from its parent thread. Our main application, acting as the parent, dispatches this workerData to its dedicated worker service. The worker, upon receiving and processing this information, leverages the postMessage() method, invoked on the parentPort object, to efficiently deliver the processed data or results back to the main thread. This asynchronous communication pattern is central to offloading heavy computational burdens without blocking the main event loop.
Advanced Memory Management: SharedArrayBuffer and ArrayBuffer Transfer
Beyond the basic message passing mechanism, worker threads introduce more sophisticated avenues for efficient data exchange, particularly critical in scenarios involving very large datasets or high-performance computing.
Worker threads also facilitate highly optimized memory sharing through the judicious use of SharedArrayBuffer objects. A SharedArrayBuffer represents a fixed-length raw binary data buffer that can be shared concurrently between multiple worker threads and the main thread. Unlike a regular ArrayBuffer, which can only be transferred (meaning the original thread loses access), a SharedArrayBuffer allows multiple threads to read from and write to the same underlying memory concurrently. This eliminates the need for expensive data copying, making it exceptionally well-suited for parallel processing of large datasets where data consistency and low latency are paramount. However, shared memory inherently introduces challenges related to concurrency control and race conditions, necessitating the use of atomic operations (via the Atomics object) to ensure data integrity.
Additionally, the transfer of ArrayBuffer objects serves as another highly efficient method for sharing memory between threads. When an ArrayBuffer is transferred using postMessage() (by including it in the transferList argument), its ownership is effectively moved from the sending thread to the receiving thread. The sending thread immediately loses access to the ArrayBuffer, preventing accidental concurrent modifications and simplifying memory management. This mechanism is particularly useful for sending large, immutable chunks of data to a worker for processing, where the worker can then send back a new ArrayBuffer containing the results. While not true shared memory like SharedArrayBuffer, it is significantly more performant than serializing and deserializing large data structures, as it bypasses the need for costly copying.
These advanced memory capabilities are fundamental to leveraging worker threads for truly demanding CPU operations. They provide the necessary primitives to overcome JavaScript’s single-threaded nature in a performant and scalable manner, enabling Node.js applications to tackle complex computations that would otherwise block the main event loop, leading to unresponsive user interfaces and degraded server performance. The judicious selection between message passing, ArrayBuffer transfer, and SharedArrayBuffer with atomic operations depends entirely on the specific computational task, the size and mutability of the data, and the required level of concurrency control. Certbolt engineers, for instance, might employ these techniques for heavy data encryption/decryption, complex financial modeling, or real-time data analytics, ensuring that their Node.js applications remain highly responsive even under significant computational load.
Delving into the Core Mechanics of Node.js Worker Threads
The evolution of worker threads within the Node.js ecosystem represents a pivotal development in addressing the inherent challenges of concurrent execution in a traditionally single-threaded environment. Their genesis as an experimental feature in Node.js version 10 underscored their potential, culminating in their widespread adoption and stability with version 12. It is imperative to comprehend that since worker threads are not an intrinsic or formalized part of the foundational JavaScript language specification itself, their operational methodology diverges somewhat from the conventional paradigms observed in true multi-threading systems prevalent in other programming languages. Their overarching and primary purpose is ingeniously designed to enable the delegation of computationally expensive tasks to separate, auxiliary threads. This strategic offloading is critical in preventing the dreaded blocking of the application’s main event loop, thereby preserving the responsiveness and overall performance of Node.js applications, especially in server-side or data-intensive scenarios.
The Autonomous Operations of Worker Threads
A worker thread’s fundamental and pivotal responsibility is to meticulously execute code specifically defined and meticulously provided by its parent or originating main thread. This parent-child relationship forms the bedrock of their collaborative yet isolated operation. Each worker, once instantiated, operates in an remarkably independent fashion, possessing its own dedicated execution context. Despite this autonomy, a worker and its parent thread are nevertheless capable of engaging in sophisticated and highly structured communication with each other through a dedicated message channel. This channel acts as a conduit for asynchronous data exchange, allowing for the transmission of inputs, results, and status updates without interfering with the main thread’s primary responsibilities.
Given the architectural reality that JavaScript, by its very design, does not inherently support true multi-threading in the conventional sense (where multiple threads directly share a single memory space and execute concurrently within the same runtime instance), worker threads employ an exceptionally unique and ingenious approach to ensure that workers remain isolated from one another. This isolation is paramount for maintaining stability and preventing unexpected side effects often associated with uncontrolled shared state. Node.js leverages the formidable power of Chrome’s V8 engine for its JavaScript execution, which is the high-performance engine that compiles and runs JavaScript code. V8 ingeniously facilitates the creation of entirely isolated V8 runtimes, formally recognized as V8 Isolates.
These V8 Isolates are more than just conceptual divisions; they are genuinely independent instances, each meticulously equipped with its own distinct JavaScript heap (where objects and variables are stored) and its own set of micro-task queues (which handle Promises, queueMicrotask, etc.). Consequently, when multiple workers are actively executing within a Node.js application, the application is effectively managing multiple distinct Node.js instances running concurrently within the same overarching operating system process. This architectural marvel provides the illusion and benefits of multi-threading without violating JavaScript’s core single-threaded execution model for any given V8 Isolate. While JavaScript, by default, does not natively support concurrency in the same vein as languages like Java or C++, worker threads provide an remarkably elegant and highly effective workaround, enabling the robust execution of multiple, truly independent threads within the confines of a single process boundary. This paradigm shift empowers Node.js developers to tackle computationally intensive workloads without succumbing to the limitations of a blocking main thread, thereby significantly enhancing the responsiveness and scalability of their applications, particularly in environments where sustained performance under heavy load is a critical requirement. This judicious partitioning of work ensures that the user-facing aspects of an application or the critical server-side request handling remain fluid and unhindered, even when complex computations are underway in the background. Certbolt professionals, for example, can utilize this for advanced data processing or cryptographic operations without impacting web server responsiveness.
Distinguishing Features of Worker Threads
Several key components and concepts differentiate worker threads and facilitate their powerful capabilities:
- workerData: This is employed for passing initial data to a new worker thread. It represents an arbitrary JavaScript value containing a cloned copy of the data provided to the Worker constructor of the new thread. The data cloning process is similar to that used by postMessage().
- MessagePort: This serves as a crucial communication conduit, allowing multiple threads to exchange messages. It can be used to transmit structured data, shared memory regions, and even other MessagePort instances from one worker to another, enabling complex inter-thread communication.
- Atomics: A low-level utility that enables atomic operations on shared memory. This is particularly useful for coordinating multiple processes concurrently, optimizing performance, and facilitating the implementation of conditional variables within JavaScript.
- MessageChannel: An asynchronous, bi-directional communication channel specifically designed for facilitating robust communication between threads. It underpins the message-passing mechanism between parent and worker threads.
How Node.js Workers Achieve Parallel Execution
A V8 isolate represents a self-contained instance of the Chrome V8 runtime, complete with its own dedicated JavaScript memory heap and microtask queue. This architectural design ensures that each Node.js worker can execute its JavaScript code entirely independently of other workers. The inherent trade-off, however, is that these isolated workers are unable to directly access each other’s memory heaps. Communication must occur via explicit message passing or shared memory mechanisms.
Deconstructing the Interplay: JavaScript and C++ in Node.js Execution
The operational dynamics within Node.js applications involve a captivating and intricate interplay between the high-level JavaScript code we write and the robust, low-level C++ infrastructure that underpins the entire runtime. When an API call is initiated within Node.js, the journey commences with the JavaScript script being conveyed to the powerful V8 engine. V8, acting as a highly sophisticated just-in-time compiler, then undertakes the crucial task of transforming the provided JavaScript code into its corresponding assembly equivalent. Node.js subsequently orchestrates the execution of this meticulously generated assembly code by employing another specialized V8 API. This compilation process is a complex orchestration, involving the translation of abstract JavaScript constructs into concrete machine-level instructions and the efficient transfer of these instructions into designated memory regions for subsequent execution.
To facilitate the execution of this newly compiled code, Node.js meticulously allocates a specific segment of memory space. The assembled code is then precisely relocated into this pre-allocated area. Following this, Node.js performs a crucial «jump» operation to that particular memory address. At this precise juncture, the flow of execution transitions from the C++ code that initiated the process to the compiled JavaScript assembly. It’s at this point that a significant boundary crossing occurs; the code currently being actively executed is the machine-level representation of your JavaScript, rather than the foundational C++ code that merely served as the catalyst for its compilation and initiation. All necessary environmental components and execution contexts are now perfectly aligned for the seamless execution of the JavaScript assembly. Once the compiled JavaScript code diligently completes its designated computational task, control is seamlessly and efficiently returned to the underlying C++ code, ensuring a fluid and integrated operational continuum.
It is critically important to elucidate that the contextual terms «C++ code» and «JS code» as employed here do not pertain to their respective source code files, which are merely static textual representations. Instead, they serve as a precise semantic distinction, referring specifically to the assembly code generated by the compiler from these source codes. This clarification is vital for accurately understanding which specific set of machine-level assembly instructions is actively being processed by the CPU at any given moment, highlighting the dynamic nature of execution within the Node.js runtime environment. This deep interaction between JavaScript’s high-level expressiveness and C++’s low-level performance is a core tenet of Node.js’s efficiency, allowing it to handle both rapid development cycles and demanding server-side workloads. Certbolt developers, for instance, understand this intricate dance when optimizing Node.js applications for peak performance in their robust server architectures.
Deconstructing the Worker Thread Setup Process
Based on this foundational understanding of Node.js’s underlying execution mechanics, the sophisticated worker setup process can be conceptually partitioned into two primary, yet intricately linked, stages: the Worker Initialization Phase and the subsequent Worker Running Phase. Each stage involves a meticulous sequence of operations that culminate in the creation and active execution of an isolated worker thread.
The Initialization Stage: Forging the Worker Instance
During the pivotal initialization stage, the journey of a worker thread commences with the construction of a worker instance within the Node.js «Userland» script. This is the high-level JavaScript code that developers write using the worker_threads module. This process involves a direct invocation by Node’s parent worker script into the underlying C++ layer. The C++ layer, acting as the system-level orchestrator, then undertakes the crucial task of creating an empty worker object instance. At this precise juncture, it’s vital to recognize that the newly forged worker is merely a C++ object residing in memory; it has not yet transitioned into an active state of execution. It’s an inert blueprint awaiting activation.
Concurrently with the conceptual formation of this C++ worker object, the parent worker thread initiates a critical internal setup: it establishes an empty internal message channel (IMC). This IMC is a private, low-level communication conduit, designed for the core operational dialogue between the Node.js runtime components rather than direct user interaction. Immediately following this, the worker initialization script proceeds to generate a distinct public JavaScript message channel. This particular channel is of paramount importance as it represents the direct, user-accessible interface that your Userland JavaScript code will explicitly employ for the unambiguous transmission of messages between the parent thread and the newly created child worker. This clear separation between internal and public channels ensures both system integrity and developer-friendly communication. Finally, the Node.js parent worker’s initialization script meticulously writes all the essential initialization metadata required for the worker’s subsequent execution script to the aforementioned IMC. This transmission of crucial configuration data is facilitated through yet another precise call into the underlying C++ layer, completing the preparatory groundwork for the worker’s eventual activation. This entire sequence is orchestrated to ensure that by the end of the initialization phase, the worker exists as a ready-to-run entity with its communication pathways established.
The Running Stage: Activating the Isolated Worker
With the comprehensive initialization process successfully concluded, the worker thread transitions into its active phase. The worker is then actively started by the worker initialization script, a crucial step that is once again executed through a precise call into the C++ layer. This invocation signals the underlying system to fully activate the worker’s operational environment.
Crucially, as part of this activation, the worker is diligently provisioned with a completely new and isolated V8 isolate. As previously elucidated, a V8 isolate is fundamentally a self-contained and fully independent V8 runtime instance. This meticulous provisioning guarantees that the worker thread’s execution context is completely isolated and stringently segregated from the rest of the application’s codebase, including the main thread’s memory space and execution environment. This isolation is a cornerstone of worker thread safety and reliability, preventing global state pollution and unintended side effects. Following the establishment of the V8 isolate, the libuv library (which forms Node.js’s highly efficient asynchronous I/O platform) is meticulously configured for the worker. This configuration is vital as it enables the newly activated worker thread to operate its own independent event loop, a distinct and entirely separate loop from the main program’s event loop. This autonomy in event loop management is what fundamentally prevents CPU-bound operations within the worker from blocking the responsiveness of the main application. Finally, with all foundational elements in place, the worker’s designated execution script is performed, and the independent event loop for that specific worker is initiated, allowing it to commence processing tasks.
The Execution Flow within Workers: From Metadata to Task Completion
Once the worker is fully operational, its execution flow becomes streamlined. The worker script intelligently leverages the underlying C++ layer to access the initialization metadata that was previously stored within the IMC during the initialization phase. This metadata provides the worker with all the necessary context and parameters to begin its designated task. Subsequently, the worker execution script proceeds to run the designated file that the worker will utilize. In our earlier simple example, this would be the workerExample.js file. Within this script, the worker performs its specific computations or tasks. Upon completion of its work, the worker leverages its parentPort to send the results or status updates back to the parent thread, completing the cycle of offloaded computation and asynchronous communication. This entire orchestrated sequence ensures that even the most demanding computational tasks can be handled efficiently and without compromising the overall responsiveness of the Node.js application. Certbolt’s rigorous system designs, for instance, would benefit immensely from this architecture when handling large-scale data processing or complex cryptographic computations, ensuring robust and uninterrupted service delivery.
Maximizing Efficiency: Getting the Best Out of Worker Threads
Now equipped with a foundational understanding of how Node.js Worker Threads function, we can leverage this knowledge to extract the greatest possible performance benefits. When developing more complex applications that extend beyond a simple worker script, two primary considerations regarding worker threads are paramount:
- Overhead of Spawning Workers: While worker threads are considerably more lightweight than full-fledged processes, the act of spawning new workers still incurs a non-trivial amount of effort and can become computationally expensive if performed with excessive frequency. Consequently, frequent creation and destruction of worker threads should be avoided for optimal performance.
- Inefficiency for I/O Operations: Utilizing worker threads to parallelize I/O-bound operations is generally not cost-effective. Node.js’s native asynchronous I/O mechanisms are inherently highly optimized and typically much faster than the overhead associated with establishing a new worker thread solely for performing an I/O task. Worker threads are primarily designed for CPU-bound computations.
Optimizing with Worker Thread Pooling
In Node.js, the concept of worker thread pools represents a highly effective strategy for managing and optimizing the use of worker threads. A worker thread pool is essentially a collection of pre-initialized, active worker threads that are readily available to undertake incoming computational tasks. When a new CPU-intensive job is received, it can be efficiently assigned to an available worker from the pool. Once that worker has completed its assigned task, the result can be transmitted back to the parent thread, and the worker is then returned to the pool, becoming available to accept new assignments.
When meticulously implemented, thread pooling can dramatically enhance application speed and responsiveness by significantly reducing the overhead associated with continually spawning and terminating new threads. It is also crucial to acknowledge that establishing an excessively large number of threads can be counterproductive and inefficient, as the true number of parallel threads that can be effectively executed is fundamentally constrained by the underlying hardware resources of the system.
Concluding Remarks
The launch of Node.js v12 in April 2019 marked a pivotal moment, as worker threads were officially supported and enabled by default, eliminating the previous requirement for an optional runtime flag. This significant development has made it unprecedentedly easier to harness the power of multiple CPU cores within Node.js applications.
This powerful capability is particularly beneficial for Node.js applications characterized by CPU-intensive workloads, as it enables a substantial reduction in overall execution time. This is especially pertinent for Node.js serverless functions, given that serverless platforms typically levy charges based on execution duration. The judicious utilization of multiple CPU cores not only translates into enhanced performance but also directly contributes to decreased operational costs.
Workers (threads) are exceptionally well-suited for executing JavaScript operations that are predominantly CPU-bound. Conversely, they offer minimal utility for tasks that are inherently I/O-bound, as Node.js’s built-in asynchronous I/O mechanisms are intrinsically more efficient than the overhead of creating and managing Workers for such purposes. A key advantage of worker threads, distinguishing them from child processes or the cluster module, is their ability to share memory. This is achieved through the sharing of SharedArrayBuffer instances or the efficient transfer of ArrayBuffer objects. We trust that this comprehensive article has provided you with a thorough understanding of Node.js worker threads, their salient features, and their practical application in contemporary software development projects.
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