threading vs multiprocessing in python

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  • čas přidán 22. 06. 2024
  • A comparative look between threading and multiprocessing in python.
    I will show activity plots of 4,8,16 threads vs 4,8,16 processes and discuss the differences between the two modules.
    In summary: threads in python are concurrent and not parallel, so no two threads can execute at the same time. The way to get around this isto use the core module multiprocessing and spawn child python processes to each run work in parallel.

Komentáře • 330

  • @sorcerer_of_supreme
    @sorcerer_of_supreme Před 2 lety +627

    I can't imagine the effort and time you have invested for making this video.. Very informative

    • @DavesSpace
      @DavesSpace  Před 2 lety +32

      Thank you very much!

    • @ErikS-
      @ErikS- Před rokem +17

      I fully agree.
      It is one of the best videos showing the issues with multithreading and how it compares to multiprocessing. It really deserves a higher piority in youtube search.

    • @anonfourtyfive
      @anonfourtyfive Před 10 měsíci

      @@ErikS- yup, 18:24 clear my mind about it.
      I was always wondering the real impact of it.
      damn right I was for using threading, but yet, I understand now the utility of multiprocessing.
      +1
      🦾

    • @mohammadjavadebrahimi5895
      @mohammadjavadebrahimi5895 Před 7 měsíci

      Where can we have full code? Can you give a GitHub link plz? Also explain any risk of doing this multiprocessing

    • @jarrodhroberson
      @jarrodhroberson Před 7 měsíci +1

      Too bad this video is incorrect on almost ever level.

  • @EbonySeraphim
    @EbonySeraphim Před 2 lety +177

    Very nice full presentation. The short of it is that "Python" doesn't support parallel execution. For most programmers, when you talk about having multiple threads, the assumption is that those threads can and will execute in parallel. Unfortunately, Python was designed with single core CPU in mind so even though the idea of threads have existed for a while in computing, code wasn't likely to be run on a multithreaded/multicore/multicpu machine to do anything in parallel. It was just the operating system giving out small slices of time to execute one thread or another and it was perceived like both were happening at the same time -- very much like your graphs show.
    Python, like most interpreted languages, cannot get over this problem because of the synchronization and locking needed to share access to data across threads so they inherently can only allow one "Python interpreted" thread to run at a time. Only library implementations in C can get around this under the hood by spawning real threads on Python's behalf to do work. Or this "multiprocess" approach, which creates a new process and an independent Python interpreter with entirely separate program state and memory. This approach isn't really a Python solution because any programming language can spawn a new OS process (provided a library is available to access fork() and exec*() system calls) and then the OS will execute that process in parallel on a multicore machine. But the thing about multiple processes is that it's harder and slower to share and synchronize data between processes than it is threads. It may not be an issue in some cases if not much synchronization is needed (the case if only an end result matters at the end of parallel work), but it can be a severe a limitation.
    The last thing I'll say is that often times IO driven or IO heavy applications don't really need a performance boost of true parallel execution. The wait for IO (disk and network for example) are so slow compared to CPU execution that most threads would be waiting for IO anyways. With proper async-io setup (kqueue, select, epoll, IO completion ports) you can use a single thread to handle and dispatch thousands of IO requests and still be bottlenecked by IO. This is how/why people can still write "performance intensive" applications with interpreted languages and compete with a language like C or C++. Maximizing IO efficiency is simply something that sometimes C/C++ won't offer any benefit for so much "slower" languages appear to be just as fast.

    • @sylvianblade75
      @sylvianblade75 Před 2 lety +2

      I was able to combine multithreading under multiprocessing using threadpoolexecutor and processpoolexecutor thinking I could achieve true parallelism. But you’re right there is no real benefit doing multiprocessing 99% if your program is IO bound. The extra overhead and slowdown spawning processes is simply not worth it.

    • @alessandropolidori9895
      @alessandropolidori9895 Před rokem +3

      What i can’t understand from the video is why multi-threading (even if non parallel) should help, in theory, in IO heavy applications. Can you help me?

    • @sephirot7581
      @sephirot7581 Před rokem +2

      ​@@alessandropolidori9895 When it comes to IO the operating system is doing this in the background. So this means, you can schedule an other thread and the operating system will still do the work in the background and if your thread is scheduled again, the OS maybe finished his work and you are able to continue your work

    • @janekschleicher9661
      @janekschleicher9661 Před rokem +4

      @@alessandropolidori9895 In theory, one thing where it can help is if the hot data that is processed, can e.g. mostly be cached in some of the direct for a cpu available cache line in L1 or L2 cache. An example could be a redis like implementation with a bloom filter (very small memory that can definitly deny if data is not in the slow data store behind and 99% or so sure if it is there). And for such a scenario, it's of course helpful if for different data stores, each one works on a different cpu core, so that the bloom filter is already in the ultrafast L1 or L2 cache in case. To be honest, for python scenarios, this is a bit far off - as you would usually implement such things in a system language like C, C++, Rust, Ada, or even Golang (there indeed exists a redis clone). The latter is an example of a language that still has its own run time and garbage collection, but optimized for such tasks.
      The more practical example is that in IO heavy tasks, some individual tasks will block. (Classic example fetch data from a SQL database or an url). Now you certainly don't want all other tasks to wait for it. The modern approach for it is async - but this is relatively new in Python (something like 4 years "young") and multithreading were the answer before the async implementations were available and production ready.
      It's also nowadays a simple way (but slightly less performant than the async implementations in most cases) to alter code and have this (mostly) non-blocking behaviour if you don't want or can't refactor the implementation.
      In general, nowadays I'd recommend either optimize the program to just run on one cpu core or to run on all, nothing in between. You can't really mix the use cases and still be performant anyway. In python, you'd end up fighting a lot with GIL (global interpreter lock) and if you have to put both use cases into one application, I'd suggest to have two different programs that communicate e.g. with a message queue asynchronously. I remember a lot of headaches with machine learning optimized implementations (e.g. Spacy) in combinations with a web server (running with wsgi). Short story: don't do it - separate them 🙂

    • @DR_1_1
      @DR_1_1 Před 7 měsíci

      I notice a 20-50% slower file copy with Python compared to system, for example with shutil.move() on Windows, I'm running the file copy on a separate pyQt thread... juste renaming the file which should not take any noticeable processing time.
      Do you think another method might be faster? I'm asking because I expect C++ would be as fast as the system in this case, not 20-50% slower...

  • @calloq1035
    @calloq1035 Před 7 měsíci +3

    What an incredible video. I’ve just been blindly picking one or the other, not sure the differences between either one, but this makes everything so clear. I’m so glad I found it!!

  • @ninjahkz4078
    @ninjahkz4078 Před 2 lety +96

    this video is amazing, honestly one of the best I've ever seen, thank you from the bottom of my heart for dedicating so much time to creating it❤️

  • @MrHvfan
    @MrHvfan Před rokem +4

    Thankyou Dave, i'm so glad youtube algo's bought your video to my daily feed. A really fascinating insight into thread and processes and the presentation style was perfect. Best wishes.

  • @neelshah1943
    @neelshah1943 Před 5 měsíci +4

    Excellent explaination about the most complicated questions that I have ever come across in an interview setting. Even though, this is an after math I am super glad to learn in with such a thought clarity. This is how you become fear-less!!! Thank you Dave ❤!

  • @falwk
    @falwk Před 6 měsíci +2

    Absolutely the best video on youtube describing how threading works in Python, with concise demonstrations and a well thought of script and presentation. 10/10, subscribed

  • @rampage_sl
    @rampage_sl Před rokem +3

    Brilliant work!! Best video on multithreading/processing I've seen in a while

  • @javedalam7383
    @javedalam7383 Před 2 lety +4

    Brilliant representation of the concept. Thanks for all you effort.

  • @NONAME-ey6qs
    @NONAME-ey6qs Před rokem +1

    This is hands down the most thorrow video on a topic. And youtube shows me this exactly 1 year after I desperately needed it.
    Better late than never i guess.

  • @CherifRahal
    @CherifRahal Před 3 měsíci

    The amount of work done here is unblievable. Thank you so much

  • @nj6553
    @nj6553 Před rokem

    2 minutes into this I already understand it better than all other readings I did online. Nice!

  • @TON-vz3pe
    @TON-vz3pe Před rokem +3

    Massively underrated video. Saved it in my library. Thank you sir.

  • @aramshojaei8490
    @aramshojaei8490 Před rokem +11

    This is the most comprehensive video I've ever seen on multithreading and multiprocessing. Great job!

  • @MultiMojo
    @MultiMojo Před rokem

    Incredible video and crystal clear explanations. Hope to see more !

  • @mehulaggarwal7776
    @mehulaggarwal7776 Před 2 lety +3

    Literally the best video ive seen yet on this topic. Keep posting man!

  • @maply007
    @maply007 Před rokem +1

    The best video in CZcams explaining the concept! Thanks

  • @DChoi5815
    @DChoi5815 Před rokem +6

    This is by far the most comprehensive and easily consumable video on any CS learning I've ever seen. Great job! Giving you a sub for sure.
    Keep it up Dave!

  • @meme-ge8tq
    @meme-ge8tq Před rokem +24

    This video was so informative, even for someone who is unfamiliar with the concept
    You deserve a lot more recognition

  • @aeronesto
    @aeronesto Před měsícem

    This is the very best explanation of threading vs multiprocessing that I have ever seen. Well done!

  • @alexandrepv
    @alexandrepv Před 2 lety +15

    Very well explained :) I can see your number of subscribers growing at a steady pace mate. Keep it up! Good stuff!

    • @DavesSpace
      @DavesSpace  Před 2 lety +1

      Thank you very much! Yes steady growth is encouraging 😊

  • @AmanKumar-tu2og
    @AmanKumar-tu2og Před rokem +33

    The best I have ever watched on multiprocessing v/s threading!! The visualizations were a complete treat ❤

  • @yeahthisismyhandleyouknow
    @yeahthisismyhandleyouknow Před 7 měsíci +1

    Yay, it's so interesting to see a visual representation of something that I have been figuring out during my work for a few years with threading\multiproc.
    When you understand it on instincts but not so visualized and vivid.

  • @rustyelectron
    @rustyelectron Před 2 lety +1

    Woah, just found your channel. This is truly a goldmine.

  • @jerrylu532
    @jerrylu532 Před 2 lety +3

    This channel is a hidden gem!

  • @weiao7276
    @weiao7276 Před 2 lety +2

    It is my first time to figure out the multiprocess and threading in Python. Thanks a lot.

  • @kedardeshmukh1168
    @kedardeshmukh1168 Před rokem +2

    This sooooooo great.... probably the best explanation on CZcams

  • @Subbestionix
    @Subbestionix Před 7 měsíci

    Awesome, no pressure and yet informative! Good work! Thanks a lot! allthough i knew the topic well from uni, i could deepen my understanding with this!

  • @retrogamessocietybrasil3372

    One of the best lectures on multiprocessing and threading that I ever saw. Thanks for the guide and info, this will help me improve my own lectures on the subject

  • @Nielsx
    @Nielsx Před rokem

    Great video. The best multiprocessing v/s threading graphical explanation on the hole internet. Thanks for the dedication. New subscriber.

  • @tinkeringengr
    @tinkeringengr Před rokem

    Thanks for this -- looking forward to more of your work!

  • @Adamreir
    @Adamreir Před 2 lety +13

    I really, really don’t understand why you don’t have more followers. Keep up the good work. This is really well done! Informative and straight out fun!

  • @sarthaknarayan2159
    @sarthaknarayan2159 Před rokem

    Hands down best video on python multithreading and multiprocessing.

  • @jylpah
    @jylpah Před 7 měsíci

    Outstanding video. “Like” is an understatement. So clear and informative.

  • @Matlockization
    @Matlockization Před rokem +4

    I am impressed with your use of visual aids in explaining how all this works. It definitely makes a lot more sense.

  • @thahrimdon
    @thahrimdon Před 8 měsíci

    As someone who does data analysis and plotting with Python, thank you. So much.

  • @marzhanzhylkaidarova3994
    @marzhanzhylkaidarova3994 Před 6 měsíci

    Thank you so much for your content! very useful and I really enjoyed the way you structure and visualize your video! Thank you!

  • @trustytrojan
    @trustytrojan Před rokem +3

    fantastic data visualization with the activity charts, i will be checking out more of your videos

  • @daymaker_bybit
    @daymaker_bybit Před 6 měsíci

    This was a super quality educational video, thanks so much!

  • @burnthewitch7286
    @burnthewitch7286 Před rokem

    This is the best video explanation on this topic, WOW

  • @user-jt5nd3yq4u
    @user-jt5nd3yq4u Před 3 měsíci

    Excellent work, very informative! Thanks a ton for your time!

  • @daveys
    @daveys Před 6 měsíci

    Excellent video, superbly made. Thanks for posting.

  • @ivankudinov4153
    @ivankudinov4153 Před 11 měsíci

    This video is a marvellous craftsmanship

  • @abhinav_mzn
    @abhinav_mzn Před 10 měsíci

    One of the best video that I have seen on the internet...This video forced me to subscribe this channel.

  • @wonyoungjung3268
    @wonyoungjung3268 Před 6 měsíci +2

    🎯 Key Takeaways for quick navigation:
    00:15 🐍 *Python is multi-threaded but not simultaneously multi-threaded, meaning two threads cannot execute simultaneously within the same process.*
    03:36 🚧 *Python's Global Interpreter Lock (GIL) ensures thread safety by allowing only one thread to execute at a time within a process.*
    06:00 🔄 *Multi-threading is suitable for I/O-bound tasks, where threads can perform other tasks while waiting for I/O operations.*
    11:12 🚀 *Multi-processing is effective for CPU-bound tasks, allowing processes to run simultaneously and utilize multiple CPU cores.*
    18:11 📊 *Choose multi-threading for I/O-bound tasks and multi-processing for CPU-bound tasks, considering the nature of your application.*
    Made with HARPA AI

  • @Exce11ent22
    @Exce11ent22 Před rokem

    I wish growth to your channel. A very informative video with amazing visualization. There would be more of this in my recommendations.

  • @brpawankumariyengar4227
    @brpawankumariyengar4227 Před 9 měsíci

    Awesome video and so very well explained. Thank you so very much. It was excellent.

  • @robmoore423
    @robmoore423 Před rokem +1

    This was incredibly helpful!

  • @etienneboutet7193
    @etienneboutet7193 Před 2 lety +2

    Very informative video. Thanks a lot !

  • @SubhamSharma-ei3vs
    @SubhamSharma-ei3vs Před 2 lety +2

    Very nice explanation . keep up the good work.

  • @NileGold
    @NileGold Před rokem

    I love this video, the explanation is perfect

  • @kemoxplus
    @kemoxplus Před 7 měsíci

    Great explanation! Thanks for clarifying.

  • @a.for.arun_
    @a.for.arun_ Před 2 lety

    Awesome video. Those visuals are helpful. Thank you

  • @daniiltroshkov6081
    @daniiltroshkov6081 Před 3 měsíci

    Excellent video! Thank you!

  • @azmatullah2847
    @azmatullah2847 Před 4 měsíci

    Thanks for the really great information.❤

  • @giladfuchs2377
    @giladfuchs2377 Před 6 měsíci

    amazing explanation!!
    thank you!!

  • @Kattemageren
    @Kattemageren Před rokem

    This is a brilliant video, thank you

  • @ravithejaburugu8926
    @ravithejaburugu8926 Před rokem

    Thanks for the detailing. Excellent

  • @mengisi
    @mengisi Před rokem

    Unbelievable I found this video! Now open my mind about Python! Please make video like this agaiinn!

  • @karaca_ahmet
    @karaca_ahmet Před 6 měsíci

    It was a very good and impressive presentation. listening to it made me feel as if David Attenborough was describing the lyrebird like in the bbc documentary. :) Thank you for your effort...

  • @ajblondell5853
    @ajblondell5853 Před 7 měsíci +3

    This video is amazing! I don't usually go to youtube for programming content because its all just copy paste. This is one of the most informative and useful vids I've come across in a long time. I love the graphics/ visuals. I don't know how you managed to make multithreading and multiprocessing so engaging but bravo! 👏 Keep up the great work and thank you for the content!

  • @roark45
    @roark45 Před 2 lety

    Wow! Really well explained

  • @fabricio_patrocinio
    @fabricio_patrocinio Před rokem +3

    Você tá de parabéns, um dos vídeos mais bem didáticos que vi sobre python. Bom trabalho e certamente irei ver mais vídeo seu!

  • @hassaanshah9819
    @hassaanshah9819 Před rokem

    Awesome attention to details 😀

  • @TheRailroad99
    @TheRailroad99 Před 6 měsíci +2

    A few things to note:
    The GIL (and therefore sequential thread execution within a process) are only an issue in CPython, not in (most) other python interpreters.
    Jython for example has true parallel threads. Also most other languages have them. This is mostly a python problem

  • @greob
    @greob Před 7 měsíci

    Thanks for sharing this nice presentation!

  • @linuxguy1199
    @linuxguy1199 Před 3 měsíci

    One major improvement I've found is taking your CPU intensive Python code and writing it in this language called "C". Joking aside, great video!

  • @ImSidgr
    @ImSidgr Před 2 lety +1

    Very high quality!

  • @-_Nuke_-
    @-_Nuke_- Před 4 měsíci

    Thank you so much for this!

  • @peterstark9381
    @peterstark9381 Před rokem +2

    Is there a preferred way to have the OS do the multiprocessing for you? Meaning, not using one control process of python to kick-off all processes and waiting for them, but rather starting them loosely (e.g. using os.fork(), os.setsid, function() and then sys.exit)? I want to avoid the controlling process to get stuck waiting for the threads/processes.

  • @user-is5vn8ie5v
    @user-is5vn8ie5v Před 8 měsíci

    Great job and thank you so much !

  • @aRWorldDJ
    @aRWorldDJ Před 7 měsíci +2

    This is a masterpiece, honestly. Content-wise is very informative, but the way you represent everything is like watching a sci-fi movie.

  • @AmitKB00
    @AmitKB00 Před 10 měsíci

    Great description!

  • @romangaranin2675
    @romangaranin2675 Před 8 měsíci

    Amazing video! Thanks a lot!

  • @soy_terricola
    @soy_terricola Před rokem

    Excelent content!

  • @AlexandreSiedschlag
    @AlexandreSiedschlag Před rokem

    1ºclass work, Congratz

  • @emersontavera9362
    @emersontavera9362 Před rokem

    thank you so much, it was a great video

  • @alexengineering3754
    @alexengineering3754 Před rokem

    Good Explanation, next time i know exactly which one is better for my purpose.

  • @Simorenarium
    @Simorenarium Před rokem +4

    That explains the one intern I had, who wouldn't want to believe that threads are simultaneous. He said he had some python experience, but we use java.

  • @TurboLoveTrain
    @TurboLoveTrain Před 6 měsíci

    You can run parallel threads using PdP (Parallel distributed Processing) if you have a process that can run non serial...obviously there is networking overhead. Great video--lots of ground to cover.

  • @jayaganthan1
    @jayaganthan1 Před 10 měsíci

    Awesome video. Thanks

  • @thenoseplays2488
    @thenoseplays2488 Před rokem +1

    This has been the best explanation of the differences between the two I have seen.
    My only gripe is I really wanted to see this same data but also include a column for true single threaded work with no threading or multiprocessing enabled. How much lower than the 2million is it?
    That would have been helpful to see. Otherwise this was excellent and helped clarify what I need yo use when. Thanks so much.

    • @OMGclueless
      @OMGclueless Před rokem +2

      You should expect it is more than 2 million, not less. Threading has some overhead, and since none of the operations in this example are i/o bound you never get that overhead back.

  • @Mrslykid1992
    @Mrslykid1992 Před 2 lety +1

    HOLY CRAP THIS IS A GREAT USE CASE!

  • @osogrande4999
    @osogrande4999 Před 7 měsíci

    Fantastic video.

  • @TusharPal93
    @TusharPal93 Před rokem

    Really nice explanation.

  • @felixfourcolor
    @felixfourcolor Před 7 měsíci +2

    PEP 703 go brrr! I'm excited to try it on python 3.13

  • @VorpalForceField
    @VorpalForceField Před 4 měsíci

    excellent info .. Thank You .. Cheers :)

  • @Schlumpfpirat
    @Schlumpfpirat Před rokem

    Knew all of that already (wish it was more tl;dw - like 2mins) but think it's super extensive + informative for a beginner.

  • @gastonarevalo1237
    @gastonarevalo1237 Před 3 měsíci

    Really informative video¡¡ I struggled a bit with the accent and speed but it's really good¡

  • @lovebroman9335
    @lovebroman9335 Před 10 měsíci

    Great video!

  • @Shontushontu
    @Shontushontu Před 2 lety +1

    I love your channel :) you are a 3 blue 1 brown in the making, if not better

  • @blazingentertainment5420
    @blazingentertainment5420 Před 11 měsíci

    i appreciate this work

  • @qinglinxiao4692
    @qinglinxiao4692 Před 7 měsíci

    Thanks, so good video

  • @michaelmueller9635
    @michaelmueller9635 Před 11 měsíci

    This video is completely underrated.

  • @jcashion123
    @jcashion123 Před rokem +1

    Just a few weeks ago I went through this discovery myself when writing a wordle solver in python. This video would have been very helpful at that time. Everything explained here is spot on.

  • @not_amanullah
    @not_amanullah Před 3 měsíci

    This is helpful ❤

  • @UmbertoFontanazza
    @UmbertoFontanazza Před 7 měsíci

    Amazing video

  • @nathanhelmburger
    @nathanhelmburger Před 7 měsíci +2

    My rule of thumb from trial and error is that you should always leave about 1 core free for each set of 8 (using python and Linux). So 2 cores free for 16 cores would be 14 max. Otherwise the system just bogs down and you get less performance and greater chance of hanging up.

  • @maxbezrukov7711
    @maxbezrukov7711 Před 2 lety +1

    Perfect visualisation and well presented content. Thank you for your efforts!

  • @flipflap4673
    @flipflap4673 Před 10 měsíci

    Very impressive!