I guess im asking the wrong place but does anybody know of a trick to log back into an Instagram account?? I somehow lost my account password. I love any tricks you can give me!
@Justice Nasir Thanks for your reply. I found the site on google and Im in the hacking process atm. I see it takes a while so I will reply here later with my results.
FINALLY. This made sense. I watched a few others try to explain it in the weirdest way, immediately jumping into details instead of giving a bigger picture first.
How random. Came here looking to learn more about Map Reduce before a Facebook interview next week, and the guy doing the video ends up being one of my old CS professors.
I still refer to this video and I get to understand MR better with every play, if only I could like this video every time I watched it. Thank you for the content.
This video was super helpful for me!! Not only talked about the counting example but also explained that there are some tasks that do not fit in this framework!! Now I think I got the concept!! Thanks!!
Thank you so much for the simple yet effective visuals. Perfect for people who just need the basic knowledge before stepping into all of the complicated parts.
Brilliantly put, I must say, when you explain it like that, it makes our understanding of distributed computing much easier to understand. Saving your video is a play list for future references and sharing. thank you~
if u understand map and reduce u can probably figure out how this work... it is just for distributed system, so ur mapped data will need to be merged/shuffled for individual reducers (also distributed) to see all of relevant keys for someting
MapReduce is distributed sorting Map: splitting data up to multiple computers, and those computers individually conducts the sorting Reduce: combining the sorted results into a lookup table
The Shuffle step groups data with similar key and sends it for reduce. How does Shuffle work? How does it group the data produced by mappers that is spread across multiple nodes?
So, the most complicated work is processed by the map function? Reduce seems to do the simplest job. :/ Since each reduce function is supposed to receive only a specific type of keys, how does the map function knows to which machine should it send the values?
What I think is the intermediate machine shuffler(or shifter) brings all the same keys together and then it passes on to reducer and then the reducer is combining those keys in one.
![WTF is MapReduce?? [Batch Processing] | Systems Design Interview 0 to 1 with Ex-Google SWE](http://i.ytimg.com/vi/lHp7M078nHo/mqdefault.jpg)
![WTF is MapReduce?? [Batch Processing] | Systems Design Interview 0 to 1 with Ex-Google SWE](/img/tr.png)
thanks for explaining this so clearly.
this is one of the rare explanations in youtube that isn't inane or polluted with pointless jargon.
People love complicating simple stuff!
I guess im asking the wrong place but does anybody know of a trick to log back into an Instagram account??
I somehow lost my account password. I love any tricks you can give me!
@Marley Harper Instablaster =)
@Justice Nasir Thanks for your reply. I found the site on google and Im in the hacking process atm.
I see it takes a while so I will reply here later with my results.
@Justice Nasir it did the trick and I now got access to my account again. I am so happy!
Thanks so much, you saved my ass!
FINALLY. This made sense. I watched a few others try to explain it in the weirdest way, immediately jumping into details instead of giving a bigger picture first.
A simple topic made unnecessarily difficult to understand by others poor explanation. Thanks so much, concept is now crystal clear to me.
almost everything is this, ppl wanna seem smart so they spew jargon.
This is the single best explanation on the topic on UA-cam. It helped me 6 years ago and it still holds up now that I'm revising. Thanks!
Finally a good professor on this matter.
Thank you my GTA character
lol
😂
Once you understand the why, it is so much clear. Thank you
straight to the point and specific, great explanation
This the most explicit and best explanation on MapReduce concept ever!!!
How random. Came here looking to learn more about Map Reduce before a Facebook interview next week, and the guy doing the video ends up being one of my old CS professors.
I still refer to this video and I get to understand MR better with every play, if only I could like this video every time I watched it. Thank you for the content.
This video was super helpful for me!! Not only talked about the counting example but also explained that there are some tasks that do not fit in this framework!! Now I think I got the concept!! Thanks!!
Best video on map-reduce on internet
Thank you so much for the simple yet effective visuals. Perfect for people who just need the basic knowledge before stepping into all of the complicated parts.
Thanks a lot! I struggled a bit to learn what is map reduce from documents. This video makes everything so clear!
I just shed a tear. That was beautiful!
Nicely done. Give this man a raise.
This is channel is so underrated.
Brilliantly put, I must say, when you explain it like that, it makes our understanding of distributed computing much easier to understand. Saving your video is a play list for future references and sharing. thank you~
so cool, so consistent! Five minutes and I already understand the essence! Thank u so much!
you mean concise?
Best explanation in the UA-cam! Thanks🙏🏻
High quality explanation in a simple way.
I like the way he speaks and orgnaizes the training material
amazing. cramming for a parallel and distributed systems in an hour. thanks so much!
Got a clear idea about it. Thanks from India.
So clear I could see through the whiteboard.
Well done, precise and elaborate
This is an excellent explanation! thank you so much :D
One of the best explanation ever
Absolutely brilliant explanation. Best I've seen so far.
if u understand map and reduce u can probably figure out how this work... it is just for distributed system, so ur mapped data will need to be merged/shuffled for individual reducers (also distributed) to see all of relevant keys for someting
Thank you so much for the explanation and excellent example
Fantastic introduction. Thanks
Thank you for explaining what my lecturer took 2 hours in 5min
Wonderfully explained
Wow thank you so much for such a simple and crystal clear example..
WOW, what a crystal clear explanation. Thank you!
MapReduce is distributed sorting
Map: splitting data up to multiple computers, and those computers individually conducts the sorting
Reduce: combining the sorted results into a lookup table
Easy to understand, great work done!
Beautifully explained thanks 🙏
Very good and simple explanation!
Excellent explanation, seldom found.
Simply amazed by you dude. Loved it. Thanks !
MapReduce explanation made easy. Good job
Great explanation...Keep up good work.
straight to the point. nice video
You MapReduced the haadop MapReduce concept in 5 minutes.
Very easy explanation. Thanks a lot.
Best explanation so far. Nice one Brof...
AMAZING explanation, thanks!!
When you got a thing for teaching you know you got it. !
simple and to the point , thanks !
Wonderfully explained, thank you!
Thanks for your clear explanations! Very useful 👏🏼
Thank you so much for great and short explain!
Well done!!! I enjoyed listening to a 5 min video and I got the concept of the MapReduce, thank you sooo much :)
thank you for explaining so clearly
this guy woke up and chose th DRIP for his presentation.. love it
great visual - could you make a video on hadoop / hive / spark please?
Very easy to understand, thank you!
Great explanation! Thank you for that!
Great explanation!
Amazing explanation! Thank you.
BEST explanation .
Wonderful explanation! Good job
Finally, someone can make sense!
Thank you! A terrific explanation.
Thanks for the clear explanation, in addition, I got a position of GA. Any advice as I am starting off my career please?
amazingly explained
Thank you that was a great concept explanation video! Easy to understand and right to the point.
Beautifully Explained 👍
Perfectly explained, helped me a lot thank you 😊
Good job, very clear!
Nice explanation, Thank you
Very Good explanation
Very clear explanation. Thanks
your explanation was very helpful, thanks for the video
This was a great explenation. Thanks!
Great explanation
Thanks for this man. You really helped me.
you are good! but I wish you at least mentioned the alternatives of MapReduce framework and what are the scenarios where its not the best for.
Good video! Thanks man!
Very good explanation, Thank You !!
Bless you! Wonderfully explained.
this video is superior to the other five or six i tried
Amazing introduction!
Yes, that was great. Thanks so much.
That's incredible!
very clear explanation
Thank you soo much best explanation !!
The Shuffle step groups data with similar key and sends it for reduce. How does Shuffle work? How does it group the data produced by mappers that is spread across multiple nodes?
Great explanation! Thank you
Excellent tutorial :) Thanks for uploading.
So, how it is done now? if they have left using map reduce?
What are the newer methods he mentioned?
So, the most complicated work is processed by the map function? Reduce seems to do the simplest job. :/
Since each reduce function is supposed to receive only a specific type of keys, how does the map function knows to which machine should it send the values?
What I think is the intermediate machine shuffler(or shifter) brings all the same keys together and then it passes on to reducer and then the reducer is combining those keys in one.
Well explained
Thank you.
Great Video :)
I have a question: Can we use only 1 machine for reduce stage instead of 2? (in case of this example)
How many no. of mappers will run when we fire a query like Select * from table emp where id=10;