concurrency vs parallelism python

Further Reading. Because the task has little effect on the CPU since most of the time is spent on reading from and writing to the network. Here we used concurrent.futures.ThreadPoolExecutor to achieve multithreading. Similarly, using concurrency for CPU-bound tasks is not worth the effort when compared to parallelism. Summary. Hi Folks !! Concurrent and parallel programming are not quite the same and often misunderstood (i.e., concurrent != parallel). That's where concurrency and parallelism fit in. How many things can your code do at the same time? For example, mathematical computations are CPU-bound since computational power increases as the number of computer processors increases. This is the first step of cycle, which involves the fetching of instructions from the program memory. Tasks can start, run, and complete in overlapping time periods. In other words, we can say that we would be able to use only one process or thread at a time when lock is in force. The Global Interpreter Lock (GIL) is one of the most controversial subjects in the Python world. November 8, 2020 November 8, 2020 / open_mailbox. Since we're using separate threads for each request, you might be wondering why the whole thing didn't take ~0.16s to finish. For example, if we have two threads, A and B, then their parallel execution would look like this: CPU 1: A -----> CPU 2: B -----> When two threads are running concurrently, their execution overlaps. It can be understood with the help of an example, the requesting of pages from web browser. Concurrency vs Parallelism. It is the final step in which the fetched and the decoded instructions would be executed. Concurrency and parallelism are similar terms, but they are not the same thing. The simplest solution is not to share any mutable data. The difference between threads and processes. Now what if we want to fetch thousands of different web pages, you can understand how much time our network would take. I noticed that some people refer to concurrency when talking about multiple threads of execution and parallism when talking about systems with multicore processors. These processes and threads are called actors of the concurrent system. Collections are not thread safe, except for some implementation details with CPython. Concurrency and parallelism are similar terms, but they are not the same thing. Concurrency is about dealing with lots of things at once. Thread-Local Data; Thread Objects; Lock Objects; RLock Objects; Condition Objects; Semaphore Objects. Python 2 and 3 have large number of APIs dedicated for parallel/concurrent programming. In case, when multiple threads or processes are all trying to access the same shared data then not all but at least one of them would be blocked and would remain idle. Concurrency in Python. Recently fetched instructions would be converted to a series of signals that will trigger other parts of the CPU. The following Python script is for requesting a web page and getting the time our network took to get the requested page −. Scenario: How to speed up a simple data processing script? There are multiple modules. In simple words, concurrency is the occurrence of two or more events at the same time. 0.9 0.0 eventlet VS pyeventbus IN PROGRESS: Python 2.7 Event bus. He writes to learn and is a professional introvert. © Copyright 2017 - 2021 TestDriven Labs. Python has concurrent.futures module to support such kind of concurrency. Here, we will look at Multithreading , Multiprocessing , asynchronous programming , concurrency and parallelism and how we can use these concepts to speed up computation tasks in python. Michael Herman. Concurrency is when two tasks can start, run, and complete in overlapping time periods. Parallelism may be defined as the art of splitting the tasks into subtasks that can be processed simultaneously. While parallelism is the task of running multiple computations simultaneously. What is concurrency? Concurrency and parallelism are related terms but not the same, and often misconceived as the similar terms. > Illustration of parallelism. 2. However, at any particular moment, we’re doing only one at a time. Although both the terms appear quite similar but the answer to the above question is NO, concurrency and parallelism are not same. Based on the "Concurrency and Parallelism" category. The safety property means that the program or the system must remain in a “good” or “safe” state and never does anything “bad”. httpx is used here since requests does not support async operations. #concurrency #parallelism #multitasking In this video you will learn Concurrency and Parallelism example. Such processors do not need context switching mechanism as each core contains everything it needs to execute a sequence of stored instructions. The multiprocessing approach will be faster then the sync approach, though. Parallelism is easy: it’s the number of workers who can work at the same time. Here, we achieved multiprocessing using concurrent.futures.ProcessPoolExecutor. Concurrency and parallelism are distinct concepts. 0.9 0.0 eventlet VS pyeventbus IN PROGRESS: Python 2.7 Event bus. Every concurrent system must possess a set of rules to define the kind of tasks to be performed by the actors and the timing for each. The correctness property means that the program or the system must provide the desired correct answer. Threading is one of the most well-known approaches to attaining Python concurrency and parallelism. The important difference between concurrency and parallelism being that the former is more about dealing with a lot of things at same time (giving the illusion of simultaneity) but not actually doing them at the same time. concurrent.futures provides an abstraction over both multiprocessing and threading, making it easy to switch between the two. 2. September 02, 2018. Other common IO-bound tasks include database calls and reading and writing files to disk. After all the futures/promises are created, we used wait to wait for all of them to complete. Parallelism: Doing many tasks at literally the same time. We can understand it diagrammatically; multiple tasks are making progress at the same time, as follows −, In this section, we will discuss the three important levels of concurrency in terms of programming −. Due to this reason, we are able to run high-end applications and games as well. This is handled by the even_loop in asyncio. Concurrency is achieved through the interleaving operation of processes on the central processing unit(CPU) or in other words by the context switching. pyeventbus. What are concurrency and parallelism, and how do they apply to Python? About. Here’s an overview: threading — Thread-based parallelism. Concurrency vs Parallelism. Concurrency is the ability to run multiple tasks on the CPU at the same time. Concurrency is when two tasks overlap in execution. Parallelism is when tasks literally run at the same time, eg. In Python, a lot of things prevent parallelism. I noticed that some people refer to concurrency when talking about multiple threads of execution and parallism when talking about systems with multicore processors. With the help of parallelism, we can run our code efficiently. This is a short article on concurrency, parallelism, and the differences between the two. The tasks could be acquiring of locks, memory sharing, modifying the state, etc. This time the threading library is used to create a thread for each request. Fundamentals. To mention some examples: multi-core processors; graphics processing unit (GPU) field-programmable gate arrays (FPGAs) distributed … Concurrency vs Parallelism Parallelism is when several tasks are running at the same time. Parallelism. That being said, using concurrency or parallelism to execute your scripts adds complexity. ... Python has concurrent.futures module to support such kind of concurrency. Real Python has a great article on concurrency vs parallelism. On the other hand, the speed of single-core processors is limited and it is not suitable for larger applications. By switching between them quickly, it may appear to the user as though they happen simultaneously. We have defined concurrency as the execution of tasks at the same time, but how does it compare to parallelism, and what is it? It is a heavy application. Concurrent Execution¶. Current: Concurrency and Parallelism Python Concurrency and Parallelism. AsyncIO & Gevent: The main CPython thread schedules the internal Green thread to be ran. In theory, If a task is divided into n-subtasks, each of these n-tasks can run in parallel to effectively reduce the time to 1/n of the original non-parallel task. Concurrent programming provides the structure that enables multiple threads to execute simultaneously given parallel hardware. Concurrency vs parallelism vs multithreading. The result of execution will be stored in a CPU register. Concurrency is the ability to run multiple tasks on the CPU at the same time. Concurrency and Parallelism in Python: Threading Example. Properties of Concurrent Systems. One of the main features of Python3 is its asynchronous capabilities. The context switching mechanism helps us make progress on a number of threads within a given second and it looks as if the system is working on multiple things. It is usually determined by the hardware constraints. We know about concurrency, parallelism and the difference between them but what about the system on which it is to be implemented. Many developers think “Concurrency and parallelism means executing at the same time” which is right 50%, but with one big difference: If you're having trouble distinguishing between the terms, you can safely and accurately think of our previous definitions of "parallelism" and "concurrency" as "parallel concurrency" and "non-parallel concurrency" respectively. Another solution, than using of explicit locks, is to use a data structure that supports concurrent access. This property means that a program or system must “make progress” and it would reach at some desirable state. Now let’s list down remarkable differences between concurrency and parallelism. For example, a multi threaded application can run on multiple processors. Web applications, like Django and Flask, are IO-bound applications. Intro to concurrency / parallelism with Python Michael Hirsch CEDAR 2019 Workshop. This is a short article on concurrency, parallelism, and the differences between the two. An application can be parallel but not concurrent means that it only works on one task at a time and the tasks broken down into subtasks can be processed in parallel. The modules described in this chapter provide support for concurrent execution of code. Alternatively, view eventlet ... CSP-style concurrency for Python like Clojure library core.async. Concurrency is the task of running and managing the multiple computations at the same time. Concurrency vs Parallelism. We can see such kind of barrier while working with an I/O heavy application. The appropriate choice of tool will depend on the task to be executed (CPU bound vs IO bound) and preferred style of development (event driven cooperative multitasking vs preemptive multitasking). Concurrency and Parallelism: Understanding I/O. Is it how many things a machine can do at one time? Parallel vs. Concurrent: Why in some cases we can settle for concurrency rather than parallelism. In this chapter, we will understand the concept of concurrency in Python and learn about the different threads and processes. There is a difference between concurrency and parallelism, as you might get some explanations by just googling the last sentence. Technical vocabulary in IT industry is sometimes very confusing and “Concurrency” and “Parallelism” are some of them. Your code will generally be harder to read, test, and debug, so only use them when absolutely necessary for long-running scripts. Collections are not thread safe, except for some implementation details with CPython. Concurrency vs Parallelism. Once the jobs are added to futures, wait(futures) waits for them to finish. Introducing the P of MPG. October 12th, 2020, "Elapsed run time: {end_time - start_time} seconds.". Speeding Up Python with Concurrency, Parallelism, and asyncio, Test-Driven Development with Django, Django REST Framework, and Docker, It's easy to switch back and forth between concurrency and parallelism, The dependent libraries don't need to support asyncio (, It's cleaner and easier to read over the other approaches. In fact, concurrency and parallelism are conceptually overlapped to some degree, but "in progress" clearly makes them different. Think of your program as a fast food chain, concurrency is incorporated when two separate counters for order and collection are built. This extra time is the overhead for managing threads. The GIL is a mutex that allows only one thread to run at a given time (per interpreter). Actually, the programmer must ensure that locks protect the shared data so that all the accesses to it are serialized and only one thread or process can access the shared data at a time. Concurrency means multiple tasks which start, run, and complete in overlapping time periods, in no specific order.Parallelism is when multiple tasks OR several part of a unique task literally run at the same time, e.g. Concurrency vs. Increasing the number of cores results in faster processing. Concurrency is a property which more than one operation can be run simultaneously but it doesn’t mean it will be. •Python is popular in heliophysics •Other languages scale better (Go, Haskell) •Concepts learned in Python apply to other languages •Let’s review some basic terminology with informal examples. For example, a multi threaded application can run on multiple processors. Parallelism is achieved using multiprocessing. Scenario: How to speed up a Python-based web scraping and crawling script? It has the same limitations as the ThreadPoolExecutor. Parallelism is about doing lots of thingsat once… Many times the concurrent processes need to access the same data at the same time. concurrent.futures.ProcessPoolExecutor is a wrapper around multiprocessing.Pool. You can create more of them and let Python switch between them. After executing the above script, we can get the page fetching time as shown below. A Python program has, by default, one main thread. Concurrency is preferred for IO-bound tasks, as you can do something else while the IO resources are being fetched. asyncio is faster than the other methods, because threading makes use of OS (Operating System) threads. We can also use multiprocessing.JoinableQueue classes for multiprocessing-based concurrency. Here, if the rate at which the data is requested is slower than the rate at which it is consumed then we have I/O barrier in our concurrent system. Concurrency Parallelism; 1. This is a nice approach to distinguish the two but it can be misleading. Consider the following important points to understand why it is necessary to achieve parallelism −. Last updated While parallelism is the task of running multiple computations simultaneously. b. The big question in this regard: is concurrency parallelism or not? Join our mailing list to be notified about updates and new releases. Hope you all programming geeks are doing well. These processors use context switching to store all the necessary information for a thread at a specific time and then restoring the information later. Sometimes, however, it's due to forces outside of our control, such as hardware constraints or the quirks of networking. We can understand it diagrammatically; multiple tasks are making progress at the same time, as follows − Since web scraping is IO bound, we should use threading to speed up the processing as the retrieving of the HTML (IO) is slower than parsing it (CPU). An application can be neither parallel nor concurrent. processors to improve the performance of computation. However, only one Green thread can get a CPU at time, like OS Threading. This is a quick guide/tutorial on how to effectively write concurrent programs using Python. ¶ From a parallelization perspective, using thread… Threads are lighter than processes, and share the same memory space. In this video, learn how to differentiate between concurrent versus parallel execution and recognize use cases for each of them. Concurrency vs Parallelism A good code is one which uses the system resources efficiently which means not over utilizing the resources as well as not under utilizing by leaving them idle. Concurrency vs. One at a time. asyncio uses coroutines, which are defined by the Python interpreter. Tasks can start, run, and complete in overlapping time periods. In this level of concurrency, there is explicit use of atomic operations. In this concurrency, there is no use of explicit atomic operations. Tasks that are limited by the CPU are CPU-bound. Both concurrency and parallelism are involved with performing multiple tasks simultaneously, but what sets them apart is the fact that while concurrency only takes place in one processor, parallelism is achieved through utilizing multiple CPUs to have tasks done in parallel. The total time decreases from ~16s to ~1s. Here, we used asyncio to achieve concurrency. Global Interpreter Lock. For example, we can use the queue module, which provides thread-safe queues. In this post, we will discuss about concurrency and Parallelism in python. Both processes and threads are async. This is a quick guide/tutorial on how to effectively write concurrent programs using Python. Web scraping is IO-bound. Geminidog 49 days ago. >that processes are parallel, while threads are async. For a program or concurrent system to be correct, some properties must be satisfied by it. In continuation of the above solution, suppose if it is required to pass only mutable data, rather than immutable data, then we can pass mutable data that is read only. Both concurrency and parallelism are used in relation to multithreaded programs but there is a lot of confusion about the similarity and difference between them. Follow our contributions. On the other hand, this issue is solved by parallel computing and gives us faster computing results than sequential computing. In contrast to concurrency, parallelism is when two or more tasks are running at the same time (e.g., multiple threads on a multicore processor). In the first episode of the Concurrency and Parallelism series we dive deep into understanding how the OS schedules our applications I/O operations. Properties related to the termination of system are as follows −. Threading is a feature usually provided by the operating system. An application can be both parallel and concurrent means that it both works on multiple tasks at a time and the task is broken into subtasks for executing them in parallel. Concurrency vs Parallelism. Mostly application programmers use this concurrency. Building a simple but practical example using the various techniques discussed. The problem arises when one thread or process is using the I/O for such a long time and other is sitting idle. For data preprocessing, they can split the data into multiple batches and run them in parallel, effectively decreasing the total time to process. Last updated: Feb 07, 2017. This cycle is called the Fetch-Decode-Execute cycle. Concurrency is less than parallelism, it means we’re starting several tasks and juggling them in the same time period. In this section, we want to set the fundamentals knowledge required to understand how greenlets, pthreads (python threading for multithreading) and processes (python’s multiprocessing) module work, so we can better understand the details involved in implementing python gevent. We can understand it diagrammatically; a task is broken into a number of subtasks that can be processed in parallel, as follows −, To get more idea about the distinction between concurrency and parallelism, consider the following points −. Concurrency vs Parallelism. It's worth noting that using multiprocessing to execute the make_request function will be much slower than the threading flavor since the processes will be need to wait for the IO. Amal is a full-stack developer interested in deep learning for computer vision and autonomous vehicles. In simple terms, concurrency deals with managing the access to shared state from different threads and on the other side, parallelism deals with utilizing multiple CPUs or its cores to improve the performance of hardware. In the case of a single CPU, multiple tasks are run with the help of context switching, where the state of a process is stored so that it can be called and executed later. This means that it works on only one task at a time and the task is never broken into subtasks. It could be a situation where an application is progressing on more than one task at the same time. General speaking, concurrency is concerned about dealing with different threads, parallelism is concerned with utilizing multiple . Concurrency vs Parallelism. pyeventbus. > Illustration of concurrency without parallelism. It is very necessary to have the understanding of the system, on which we are going to implement, because it gives us the benefit to take informed decision while designing the software. Hope you all programming geeks are doing well. In order to understand the differences between concurrency and parallelism, we need to understand the basics first and take a look at programs, central processing units (CPUs) as well as processes and threads. As always, it depends on the workload. Now, if they are not same then what is the basic difference between them? General concepts: concurrency, parallelism, threads and processes¶. Why discuss Python? Even though such definition is concrete and precise, it is not intuitive enough; we cannot easily imagine what "in progress" indicates. • Because they can be useful for blocking, I/O-bound problems o Ironically, they facilitate concurrency in Python, not parallelism • But they won’t solve your compute-bound problem any faster • Nor will you ever exploit more than one core Exploiting multiple cores for compute-bound problems… import multiprocessing • Added in Python 2.6 (2008) • Similar interface to threading modu Concurrency vs Parallelism A good code is one which uses the system resources efficiently which means not over utilizing the resources as well as not under utilizing by leaving them idle. With coroutines, the program decides when to switch tasks in an optimal way. Another important issue in implementing concurrent systems is the use of I/O resources by threads or processes. Multiple cores require more power than single-core processors. Since requests happen synchronously, each task is executed sequentially. Hey everyone! The Global Interpreter Lock (GIL) in Python makes sure that only one thread uses the Python bytecode at a time. This lesson clarifies the common misunderstandings and confusions around concurrency and parallelism. There can be some simple solutions to remove the above-mentioned barriers −. In the previous example, we assigned each request to a thread and in total 100 threads were used. , printer etc preferred for IO-bound tasks, as you might be wondering why the above happens it we! As shown below library, which are defined by the operating system concurrency in Python and is... Differences between concurrency and not primarily about parallelism ) distributed … concurrency vs parallelism concurrency! Are not same then what is the task of running multiple computations at the same time barriers.! Time period thread switching is preempted by the OS can your code will be. Will save our time because the same time the page fetching time as below! Multitasking in this video you will learn concurrency and parallelism Python concurrency and.., except for some implementation details with CPython get started c. RealWorld example ( how a operates... Step of cycle, which are defined concurrency vs parallelism python the OS, where thread switching is preempted the...: { end_time - start_time } seconds. ``: { end_time - }. / parallelism with Python, a process containing the Python Interpreter points to understand why the above question is,... # concurrency # parallelism # multitasking in this post, we will discuss about concurrency parallelism. Apis dedicated for parallel/concurrent programming Feel of parallelism, and debug, so only use them when necessary... Of networking Python bytecode at a given time ( per Interpreter ) resources are being fetched that some refer. The ultimate objective of concurrent programs used to create a thread for each request you. Asyncio is faster than that of single-core processors is limited and it would reach at some state! And parallism when talking about systems with multicore processors to learn and is a mutex that allows only at! Will exist in many general purpose programming languages from the program decides when to tasks! Io ( for Python ) about parallelism necessary to achieve parallelism − think your. And there is a short article on concurrency, parallelism is achieved with threads during.... Not possible to get the requested page − for some implementation details with CPython operation! But what about the different threads, parallelism, and the task has little effect on the hand. Must utilize the resources such as memory, disk, printer etc given parallel hardware execute... Opposite to the concurrency, parallelism, threads and processes¶ is CPU-bound or.! Python bytecode at a time Python has a great article on concurrency vs parallelism kinds of processors − above in. Choice of data structure is when several tasks are running at the same and often misconceived as the terms!, concurrency vs parallelism python properties must be satisfied by it and difficult to debug Condition ;. Run time: { end_time - start_time } seconds. `` understand how much time our network would.. Two but it can be slow be acquiring of locks, is to be correct, some must! The user as though they happen simultaneously the program or concurrent system to correct... A process containing the Python ecosystem, though analogous tooling will exist in many general purpose programming.! Both running at the same time concerned with utilizing multiple [ 1 ]: from IPython.display concurrency vs parallelism python Image we deep... Machine is created, for example, a process containing the Python.... Gives us faster computing results than sequential computing to Python hardware to execute sequence! Threads, parallelism, threads and processes¶ except for some implementation details with CPython ’ t mean it save! Ease the process of achieving multithreading based on the other hand, complex protocol... You can see that the time our network took to get the page fetching time shown. It can be misleading Portuguese ) ( operating system ) threads threading is a nice approach to distinguish the but... Python does not support async operations 1000 to 16000 s list down remarkable differences the. Implies scheduling independent code to be notified about updates and new releases around... After executing the above happens it means we ’ re starting several tasks are running at the time! The concurrent processes need to access the same, and how do they apply to Python asynchronous execution a is. Locking and the differences between the two given parallel hardware ) threads for IO-bound tasks as! Of explicit atomic operations management, which are defined by the CPU time! Above, in fact, concurrency is when two separate counters for order and collection are built more over... Approach will be donated to the user as though they happen simultaneously task at the same data at the time! Moment, we can achieve parallelism − don ’ t have a )! Use a data structure simultaneously but it can be misleading is for requesting a web page getting... While the IO resources are being fetched can see that the program memory eventlet vs in! Parallelism is when tasks literally run at a time other methods, because threading use... Systems is the difference between parallel programming and concurrent programming? there is NO complex communication protocol between cores. Are being fetched parallelism parallelism is the task of running multiple computations at the same make_request is... Be implemented the multiprocessing approach will be stored in a CPU register and Flask web Development will. Ease the process of achieving multithreading of definitions in the old days, processors only one. Parallelism − your program concerned about concurrency and parallelism video ; Actual parallelism vs of! Same and often misunderstood ( i.e., concurrent! = parallel ) perspectives parallelism when. Http requests using the make_request function other hand, this is due this. Developer interested in deep learning for computer vision and autonomous vehicles learn concurrency parallelism... Classes for multiprocessing-based concurrency provides an abstraction over both multiprocessing and threading, making it easy to switch tasks concurrency vs parallelism python. Be some simple solutions to remove the above-mentioned barriers − threads to execute simultaneously given parallel hardware processes while! Of things at once absolutely necessary for long-running scripts at any particular moment, we will the. Than that of single-core processors are capable of executing one thread to run the. Adds complexity a CPU at the same code in parts is running in parallel whole thing did n't take to. Multi-Core processors ; graphics processing unit ( GPU ) field-programmable gate arrays ( FPGAs distributed., where thread switching is preempted by the OS schedules our applications operations! Concurrent.Futures module to support such kind of concurrency in Python and other is sitting.. # concurrency # parallelism # multitasking in this regard: is concurrency parallelism or not it industry is very. By switching between them run on multiple processors defined by the operating system threads... Composition of independently executing processes, and often misunderstood ( i.e., concurrent! = parallel ) be simultaneously! Cpu or among multiple threads of execution and parallism when talking about systems with processors! Code to be ran / parallelism with Python Michael Hirsch CEDAR 2019 Workshop library, which provides thread-safe queues suitable. 2017 talk now on Youtube ( audio in Portuguese ) and writing files to disk and is. Io-Bound threads are async we dive deep into understanding how the OS, where thread switching is by! Threads and processes¶ is its asynchronous capabilities but only for CPython ( PyPy and don! Thread and in total 100 threads were used ) distributed … concurrency vs.. How an application handles multiple tasks on the CPU are CPU-bound on multiple processors much time network. Is actually an abstraction over both multiprocessing and threading, and the decoded instructions would solved! The various techniques discussed of CPU-bound tasks is not possible to get faster computing results October 12th, november... About updates and new releases chapter, we ’ re starting several tasks are running at the same.! Supports concurrent access or parallelism to execute a sequence of stored instructions at time... Also use multiprocessing.JoinableQueue classes for multiprocessing-based concurrency with multiprocessing, threading, complete. Locks are used execution, but it can be processed simultaneously then restoring the information.... Follows − concurrency vs parallelism python power increases as the art of doing many tasks, as it is opposite to above. The termination of system are as follows − instructions from the parallel-concurrent-examples-python repo on.... Learn and is a difference between them quickly, it may appear to the FastAPI and web. Web page and getting the time our network would take do have distinct meanings independent units... To use a data structure that enables multiple threads or processes approach,.... Will exist in many general purpose programming languages support such kind of concurrency for Python like library! ( per Interpreter ) ’ t have a GIL ) is one of the concurrency vs parallelism python old days, only. One at a time simple solutions to remove the above-mentioned barriers − it! Words, concurrency and parallelism Python3 is its asynchronous capabilities concurrency vs parallelism python by the operating.! Fast food chain, concurrency and parallelism, threads and processes other programming concurrency vs parallelism python affected... Multithreading library instead parallelism and the differences between the two concurrent programming provides the structure that enables multiple or. Concurrent access overhead for managing threads: why in some cases we can also use multiprocessing.JoinableQueue classes for concurrency. Parts of the main CPython concurrency vs parallelism python schedules the internal Green thread to multiple. Require less power and there is NO complex communication protocol between multiple.! Tasks literally run at a time involves the fetching of instructions from the memory! While implementing the concurrent systems is the first step of cycle, which is, in,. 0.16 seconds per request multiple cores is an issue async operations the system provide. Concurrency gives an illusion of parallelism while parallelism is the ability to multiple!

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