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.
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.
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!!
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.
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
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.
Nope, don't get it. Specifically, how is mapping in this case similar to mapping as we think of it in other cases (like mapping elements to a list, for example)? And the sorted results aren't literally sent to different computers........ right?
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
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.
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.
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?
