Sitting in the hotel room, watching this 1 hour before my google interview in New York. Thanks Gaurav!

@gkcs

4 жыл бұрын

All the best!

@gkcs

4 жыл бұрын

@@pranay020692 wow, tough stuff. How'd you reckon it went?

@pranay020692

4 жыл бұрын

@@gkcs I believe, It went well. I have watched most of your system design videos, they were quite helpful. I am on the junior side 3 YOE so I think they went easy on me in Sys Design. Also, I was able to complete all coding questions in time. Google is always a long shot though. 🤞🤞

@karthikmucheli7930

4 жыл бұрын

@@pranay020692 hope you got the job

@Leptoszom

3 жыл бұрын

You got the job, Bajpai?

I used to see your "Competitive Programming" videos before getting into a company and now after getting learning things there ,I am watching your "System Design" it feels good to grow with this channel. Thank you so much 😊

What an outstanding video! No shortage of tutorials on how to code or write algorithms out there buy not enough on Systems design... This is truly outstanding... been writing software 10 years and fringely do I touch these concepts, heck work within them daily yet either forgot or never knew. Thanks so much!!

This channel is like System-Design Wala , far far better than most paid courses, simple explanation

This was amazing! Havent seen other videos that talked about provisioning virtual servers/using multiple hash functions! Hooked!

Knowledgeable and confident presenter!

the best solution is not to use K hash functions, but to generate K replica ids for each server id. Designing K hash functions while maintaining random uniformity and consistency is hard. Generating K replica ids is easy: xxx gives K replicas xxx + '1', xxx + '2', ..., xxx + 'K'. Then you take these replicas and generate K points on the ring with the same hash function and this is what is actually used in practice. Chord algorithm is just an example of this technique to add K replicas for each server id

@gkcs

5 жыл бұрын

That makes sense. K numbers assigned to each server would do the job :)

@pradipacharjee4915

5 жыл бұрын

Hi Andrei, can you just tell me how to choose idle replica count(k) ? for efficiently add or remove servers.

@dudejaa

5 жыл бұрын

The example that you took mentions xxx+1,+2,+3...+k. Correct me if I am wrong but if you assign k consecutive numbers to the same server the load wouldn't distribute (on adding or removing a server) uniformly. That could be one reason to look for different hash functions ?

@charchitpatodi8677

5 жыл бұрын

@@dudejaa Just a thought : he probably not means +1, +2... instead if xxx is id, M is ring capacity and k is number of servers then second position (after hash(xxx) )will be hash(xxx) + (M/k) OR hash(xxx+M/k).. And probably third position will be hash(xxx) + 2*(M/k) and so on till multiple of 'k'

@rishabhmalhotra7058

5 жыл бұрын

@Abhishek Dudeja xxx, xxx+1.. are ids for one server to take a hash on and then reach the respective points on the ring, not the points on the ring itself. And then the hash generated on xxx and on xxx+1.. would be completely different and random, and hence would plot k uniformly random points. @CHARCHIT PATODI I dont think that's the case cause if you think about it , if you add multiple servers each with k different points with that technique -> hash(xxx) + 2*(M/k)..till K, then you're not really randomizing and there would be no difference between adding 1 point or k points per server when it comes to choosing a server for a request. It would be like if you multiplied the ring length into k after choosing one point per server which would not get us what we want.

Thanks for making these videos! I was always unsure about load balancing, but this helped explain a lot of my unanswered questions :)

@gkcs

6 жыл бұрын

Glad it helped :)

I loved the way with ease and simplicity you explained the problem using some pictorial diagram. Good work keep it up!!

@gkcs

5 жыл бұрын

Thank you 😋

After this video, I downloaded the entire playlist!! More love More support!! Gratitude _/\_

You are simply amazing gaurav, system design concepts couldn't be explained better than this!

u r amazing Gaurav! i watched ur video one year ago, i didnt understand then, now i watch again lol ...i understand most of it... thank u !

your content is insanely good. seriously, the best! you were destined to teach others!

Thanks for doing this! Your videos have really helped me understand things better =)

@gkcs

6 жыл бұрын

Thanks Janani!

Simply brilliant and clear explanation . Keep doing such awesome work.

Brilliant explanation! I read the Wikipedia article on this and Cassandra docs and your video clicked everything together!

best explanation of consistent hashing i have seen so far.

When you said try to think of solution, first thing come to my mind is "change the hash function" Thank god i'm understanding it well and its first time I am studying

Awesome explanation. This has truly elevated my understanding of Hashing and Load Balancing in general. Keep up the good work!!!! :)

I am a 3rd sem student and I guess I should not be bothering about these things but your explanations are sooooo gooood that I always wanna watch them :D

Your Videos are very informative. Thanks for making it Gaurav. Your explanation is crisp and Clear

You are a really good teacher, Gaurav! Please keep up your good work! :)

@gkcs

5 жыл бұрын

Thanks!

Excellent video Master. Thanks a lot.

A good video. I am really impressed. Thanks a lot.

Learned a lot from the actual implementation in the attached git repo . Thanks

Awesome, thanks for making this video, really helped me understand. :D

@gkcs

5 жыл бұрын

Glad to hear that :D

That virtual server solution blew my mind I'm so sorry. Geniuses have really paved the way for us in computer science.

many thanks Gaurav for making this concept so clearly explained.

Very nice explanation. Really liked the video. Thanks. Keep making it. 👍

@gkcs

5 жыл бұрын

Thank you!

With multiple hash being applied, can there be case of collisions, i.e. multiple servers ending up on the same bucket? If not , why? If yes, how is it handled?

Thanks Gaurav. Excellent video.

Super helpful, thanks! I never got a CS degree and needed to learn more about sharding.

Great video. One comment is using consistent hashing seems good option for distributed search scenarios (like you pointed for distributed cache, DB search algo) but not for use cases of load balancing where nodes are added to server large numbers of request (like web servers, applications etc). Please comment your view

Great video! I was wondering though, with this architecture, do you have to ensure that the hash functions don't ever collide though right? What would happen if an incoming request suddenly mapped to two servers that fell on the same point?

@gkcs

4 жыл бұрын

It's answered in the other comments 🙂

This video is so helpful! Thanks!

This is great stuff!

Very clear and helpful! Thank you.

@gkcs

5 жыл бұрын

Thanks!

boss code dal na ..!! studying for interviews with your videos, which btw the THE most helpful resource. Thanks for time you put into this !!!

@Arif.Amirov

4 жыл бұрын

how did your interview go?

@gkcs

3 жыл бұрын

A little late to arrive 🙈: github.com/coding-parrot/SystemDesignCourse/blob/master/service-orchestrator/src/main/java/algorithms/ConsistentHashing.java

Great video! Question: where do the requests sit in practice? Is there a node acting as a scheduler dispatching request by request, or the requests are mapped immediately to a server and kept internally in memory? Or both, so that the requests can be rescheduled if the server goes down? (I suppose this would require the scheduler to periodically ping each server, or set a timeout). What happens if the scheduler goes down? Second question: would it be possible to use work-stealing instead do reduce inbalance? Whenever a server is out of work, it would steal a request from the back of the queue of another random server. Or could this skew too much the execution order of the requests?

@gkcs

4 жыл бұрын

Thanks! The load balancer is a service which needs to tell the other services where a request is to be routed. It can either be queried per request (which is very expensive), or a snapshot of the current assignments can be cached by all services. If the snapshot changes at the load balancer, it can notify all interested clients. The service is distributed and backed by a 'reliable' database, so a single failure won't take the system down. Second answer: It sounds complicated and I have never seen it implemented on a large scale system.

Clearly explained. Thank you!

@gkcs

6 жыл бұрын

Thanks!

thx a lot, really helpful for people like me has no sense of system design.

Wow back to back !!!

Amazing videos man!!

Thank you! It was extremely helpful

Awesome stuff man :)

What if we look for the nearest server bidirectionally? Of course if one skewed region is generated, the load between the two distant servers would be somewhat equally distributed. So what if we not only look clockwise but anticlockwise too and choose the nearest server?

fantastic explanation. Thx a ton.

Notes to self: * The previous video gives the impression that there is a mapping from ranges of integers to server ids, and that consistent hashing is about to mapping request ids to integers in ranges resulting in more consistent routing of requests to same servers. -> I did realize that this would not work very well over time, as you would end up completely changing the ranges for higher-index servers with the addition of multiple servers. * In this video, requests ids map to an index in a ring with `M` indices. The "trick" then, is the map the server indices to indices in the ring using the same hash function that also hashes request ids. Now, to assign a server to a request, one simply looks clockwise for the nearest server. * To make it less likely that load will be unbalanced due to (what I would call) unlucky hashing, another idea is used: simply have multiple hash functions for the servers, such as to map them to multiple locations in the index ring! (clever). * @Andrei Marculescu points out that better than using multiple hash functions for server ids, it is easier to maintain multiple aliases for each server id ("...xxx gives K replicas xxx + '1', xxx + '2', ..., xxx + 'K'.") and thus map servers to multiple locations in the index ring.

@Luk3Stein

2 жыл бұрын

Thank you!! I was having so much doubts after watching, reading this made it more clear.

@codingfork6708

2 жыл бұрын

How can we determine the value of `M`? Is [0, M-1] the range of the output of the hash function?

@UlfAslak

2 жыл бұрын

@@codingfork6708 Correct. I think there are good heuristics for choosing M (and probably everyone uses the same standard values). Your hash function has to apply modulus M, otherwise you get an index out of range.

@nxpy6684

Жыл бұрын

Thank you! This helped me a lot!

Great explanation clear and concise

@gkcs

6 жыл бұрын

Thanks!

Thank you! It was clear to understand.

Hi Gaurav, Thanks for sharing such a nice concept.I have one doubt what happen if one server die suppose s1 for 2 hr and then again come back after that so in this case how request are handled.

Very Nice Explanation Sir, Thank you !

Read the article about consistent hashing in wikipedia, this video has clearly articulated the core idea. Thank you!

To be honest this video was more clear than the previos one in the playlist (what is load balancing), the pie chart concept in the previous one made me confused but this hopefully made it clear. Nice work!

@gkcs

3 жыл бұрын

Thanks!

@Gaurav, What would happen to the caching that we talked about in the initial part of the discussion? I understand caching is not going to happen because the requests are too randomized for caching to occur. Is this algorithm so efficient that even without caching it's more efficient than having an algorithm that relies on caching? Also, as a performance engineer, i dealt with load balancing a few times, but never got to see these kinds of algorithms for load balancing. we have implemented algorithms that distributed load across servers based on several parameters such as - geographic proximity of the request to the server, hardware utilization (Server with least CPU, RAM, utilization gets the request), Least connections(Server serving the least active connections gets the new request), etc. Do you have anything to say about those logics, and whether they are related to the hashing algorithm we have seen...

@kaustubhparmar4274

6 ай бұрын

May be late, but I think the caching will happen because the hashes will always return the same output for same input, so if the servers do not change then caching is not affected. But if the number of server changes we need consistent hashing so as to minimise the remapping of the request to the server.

I learned a lot, you're awesome

'n' being the number of servers and 'm' being possible hash values, would spacing out the servers at a value of m/n be a working solution? For ex - with m as 256 and n as 4, first server could be at 64, second be at 128, third at 192 and 4 at 256 - along those lines Understood the possibility of skewed allocations and the need for replicating ids tho. Hooked to your amazing content! kudos

Really well explained man....

Hello Gaurav, when you said 'adding virtual servers' did you mean, adding differently generated hashes of the available servers so that their relative positions on hash ring is uniformly distributed giving us the flexibility of less skewed distribution of requests..? if yes, That implies if 1 physical server goes down, isn't it it's multiple hashed positions will also be off the ring giving more skewed results?

@sauravdas7591

4 жыл бұрын

Yes, it will affect the load, but consider this. If a server has, let's say, 4 points uniformly distributed across the hash ring, so when it crashes it will remove those 4 points, and this being uniformly distributed will increase the load other on other servers by 25%.

Great explanation. Thank you.

great explanation, thanks!

8:33 it was told that k value should be log(M),Is it just a suggestion or its the value we should definitely consider

@roamwithashutosh

4 жыл бұрын

🙂

Hi Gaurov, Wont removing / adding servers to the cluster affects the hash function modulo(%) Example: initially we have 4 servers hash(req for same id) % 4 -> s2 if we remove 1 server :- Hash(req for same id) % 3 -> s1 in this way, still the server 2 have stale cache data right?

Hey gaurav, Thanks for this great video. i have one question, can we achieve the same results using a stick-table (which will keep user/IP and server mapping) in loadbalancer with some nondeterministic load balancing algorithm like RoundRobin or Least connection. if not then can you explain why.?

@gkcs

3 жыл бұрын

The main objective here to reduce the "rebalancing", the total number of cache loads and evictions. This is useful for load balancing on a cache cluster. The RoundRobin or Least connection algorithms are also useful in different scenarios.

Great explanation!

Gaurav, This video is wonderful Have small doubts Let's assume that request R1 is served by server S1. Now we have added a new server S2. Because of this let's assume the request R1 is now coming to S2. How the above scenario gets handled ? Is it like when a new server S2 is added , we have to move some portion of the data from the existing servers (S1) to the new server S2 based on its position on the ring? If it is the case, how can we do the distribution in real time ?

This question might have been asked before, but how do we choose the value of M for the ring? And do we increase M if the no of requests increase such that one slot in the ring can only contain either one request or one server

@Gaurav Sen , Great Video! Thanks a lot ! Question - You mentioned at the end, its used in many many places. Are there places where systems don't use Consistent Hashing at all ? Also, are there systems using some other techniques for consistent hashing? Is this the only approach or one of the approaches to implement consistent hashing?

@gkcs

3 жыл бұрын

Yes, definitely. Consistent hashing has it's own issues, and is usually only used for servers which need to maintain state (caches). Some databases also use consistent hashing. You can also try to reduce data migration by keeping master slaves for DB servers.

Gaurav, I have a question: if the hash function h(x) maps values to the range of (0...M-1), why do you need h(Server Number)%M? %M is redundant here, isn't it?

@vipindixit5532

8 ай бұрын

Same question from my side.

Thank you for Teaching this in such a nice manner.😊

Thanks for the amazing video and describing the ring buffer based design for load balancing. I am wondering how this design will work efficiently when say for an example a subset of users are making too many requests? Because of consistent hashing the requests may land to the same machine , and certain machines might get more work assigned whereas all other machines are starving for the jobs.

God Bless you Gaurav. Love from Pakistan.

Great explanation Gaurav.

Thanks for this video, I just have a small doubt- As there are K hash functions, different hash functions may generate the same key for different servers, how do we deal with this case? A probable case for large M I guess.

@anshulkumar4092

3 жыл бұрын

its always same value for a specific salted hashing algo and we have to make sure salt is different in every k hashes otherwise it would have been a simple random selection algorithm

Thank you so much!

Gaurav, great explanation! thanks, when it comes to implementing this, there needs to be a directory server to store all these mappings for the keys and server points on the virtual ring, is that correct? and if yes then directory server should have replicas too in order to prevent single point failure. is my understanding correct?

@gkcs

3 жыл бұрын

I have a link for sample code in the description. For something useful in production, you have a look at ZooKeeper and how people implement consistent hashing using it. (Yes. You need to store the mappings in DB or a filesystem).

Thanks a lot Gaurav, this was very clear! I was wondering what would happen if there is a clash between different (or the same) hash functions h(x)=h1(y) which server will the load get assigned to?

@vikassaran6430

3 жыл бұрын

same question .....do you know the answer

Legendary tutorial, specially I really like where you try to prove your logicswith mathematical equations, same goes for one of the video called "finding loop in a linked list". Thanks Gaurav again :)

Hey! Thank you for your explanation. Please could you clarify my one doubt:- In a scenario where one server crashes (S4)-> it's respective k point server nodes being removed-> Now load will be distributed to the next upcoming server nodes in the Circle.. which might be say S1,S3 S1 and S2.. My question is that for all these requests blocks we would now need to configure the change of server for upcoming k nodes.. the change will be K Times .. That lands us into the same problem like your previous video. Instead it will be k times. Do let me know if I'm getting confused or this problem will happen ?

@laharibangaru3756

11 ай бұрын

same doubt 🥲could you figure out why ?

Maybe a noob question. In this design. Doesn't a request coming from a user (unique request Id) always map to the same server? Since in hash(requestId)%M hash and M give predictable outputs to each input. Or is there a randomising component to the hash that I'm missing ?

Hi Gaurav, Thanks for the video! How about we divide request into M groups and assign each group to a given server. By say keeping a map? If a server goes down, we can remap its ids to other servers randomly. If new server is added, we can take one group from each server? Is having a mapping somewhere makes requests slow??

Hi Gaurav, thanks a lot for the video. I have one question, so you mean to say that the virtual servers will be mapped to available physical node? Basically a physical node, would have n number of virtual nodes of different hash functions?

@gkcs

4 жыл бұрын

Yes.

This was soooo cool!

Gaurav, thanks so much for your videos! Very informative and easy so follow despite the complexity of the concepts. Just had a couple questions from this video! #1 So one of the original problems in the regular hashing solution was that when you add a new server, you'd have to destroy much of the local caches of the other servers because they become useless, which makes sense. So in this case, less changes occur, but how would you update the local caches to make sure you don't have to clear out the entire cache? Do you need some form of algorithm to determine what cache items should be evicted? #2 Also, how about the algorithm required to determine what the "closest" server is in the ring which will serve the request? Is there a simple mathematical solution for that, or is it somewhat complex? It does seem that the additional complexity in maintaining a consistent hashing system is worth the advantages, just want to understand a bit about how complex it actually is, or if it's simply just a genius solution to a problem.

@soumyajitdas4433

Жыл бұрын

Try looking into Chord Algorithm (en.wikipedia.org/wiki/Chord_(peer-to-peer) for #2 Tl;dr; - every node in the hash ring maintains something called a finger table containing the information around it's predecessor node, successor node and also pointer to nodes (n+2, n+4, n+8 ... n+2^k). This way we can query any node and find the successor node to a particular hash value in O(log n) time.

Please keep on teaching me !

@gkcs

6 жыл бұрын

Sure :D

This was a pretty good video. Thanks Gaurav g!

Loved this video👾

Hey Gaurav... Great video... I'm a non techie and was trying to get familiar with these concepts... Essentially what ur saying is as your requests increase the load balancer should have those many server allocation hash functions so that there is a higher probability of equal distribution of load... Virtual servers here basically mean having those many server hash mapped points on the ring... Am I correct..? :)

Explain in an easy and nice way.

If we add a new server in this consistent hashing ring then again caching problem will remain same? The requests which was going through s3 before adding new server are now handle by s4.. so, s3 cache for those requests will be useless? Please explain

Thanks, Gaurav. Nice work. I have a small doubt. As you told to handle the skewed request by having virtual servers[by having multiple hashing functions for servers], how can we handle the collisions? I mean server S1 and S2 got the same output(say O1) from the hash function. Both will be serving the user request then

@gkcs

5 жыл бұрын

That's rare. If that doesn't work, we can change one of the hash functions and rebalance 😁

@omarraghib905

Жыл бұрын

@@gkcs While hash collision might be rare, but the mod M of hashes may collide more frequently. How do we handle those?

Hi Gaurav, Great series of videos. Thank you for sharing your experiences. I have one question on consistent hashing.. Which component of the distributed system is responsible for implementing this technique. 1) Is it load-balancer's job because it is a load distribution technique? 2) Or is it application's responsibility.? Curious to hear your thoughts. Cheers!

@_romeopeter

Жыл бұрын

I don’t know if you still need answer to this but it’s the Load Balancer’s job because distributes the request and allocate them to the right servers.

What would happen if there is a collusion when you calculate the virtual servers? I mean if h1(S0) = h2(S1) = 1. So there are 2 servers with the same ID right?

how did you add users to the ring ? after taking hash value of request ID, did you took remainder of it by number of servers ? which implies if server count changes, user position will change.

Thank you very much for explaining the concepts in such depth. I just have one question, what would happen if two different servers are hashed to same slot?

@akhilraj9334

4 жыл бұрын

Not an expert , but ideally the hash function you pick should have minimum chance of collision. Another scenario it might collide is when you are adding too many virtual servers into the ring, so should may be have a bigger ring or reduce the number of servers.

Brilliant!!!

Gaurav, In case of a server failure the next server in the ring will serve the load which means the user has to re-login right?, Why don't we use a session store using any in-memory data stores and store the session. So that we don't need to worry about the server going down we will still have the session data in the session store.

Gaurav, The Addition/Deletion of Servers using the k-hash functions with the fixed ring size is a hard problem to solve to ensure the correctness. It could be simplified with generating the multiple ids of the same server.

@gkcs

3 жыл бұрын

That's right 👍

@Gaurav Sen , this is the Cassandra ring architecture. right?