What is CONSISTENT HASHING and Where is it used?

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Load Balancing is a key concept to system design. One of the popular ways to balance load in a system is to use the concept of consistent hashing. Consistent Hashing allows requests to be mapped into hash buckets while allowing the system to add and remove nodes flexibly so as to maintain a good load factor on each machine.
The standard way to hash objects is to map them to a search space, and then transfer the load to the mapped computer. A system using this policy is likely to suffer when new nodes are added or removed from it.
Consistent Hashing maps servers to the key space and assigns requests(mapped to relevant buckets, called load) to the next clockwise server. Servers can then store relevant request data in them while allowing the system flexibility and scalability.
Some terms you would here in system design interviews are Fault Tolerance, in which case a machine crashes. And Scalability, in which case machines need to be added to process more requests. These two principles are allowed by Consistent Hashing, and hence it is an important building block to a system design architect's toolbox.
Another term used often is request allocation. This means assigning a request to a server. Consistent hashing assigns requests to the servers in a way that the load is balanced are remains close to equal.
Server architecture is a subjective concept, and there are outliers for many cases. Don't think of Consistent Hashing as a silver bullet for fault tolerance and scalability, but a useful concept for request allocation.
Use it to solve software questions in interviews and real life. Best of luck!
Prerequisite: • What is LOAD BALANCING...
Recommended system design video course:
interviewready.io
00:00 Request Hashing
03:00 Request Mapping
06:02 Problems
07:01 Virtual Servers
09:40 Applications
10:18 Thank you!
Along with video lectures, this course has architecture diagrams, capacity planning, API contracts and evaluation tests. It's a complete package.
References:
www.hackerearth.com/practice/...
www.tomkleinpeter.com/2008/03/...
michaelnielsen.org/blog/consis...
• Consistent Hashing - G...
System Design:
highscalability.com/
• What is System Design?
Code:
github.com/coding-parrot/Syst...
#consistent-hashing #system-design #load-balancing

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9 июн 2024

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Комментарии : 649

@pranay020692 4 года назад

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

@gkcs 4 года назад

All the best!

@gkcs 4 года назад

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

@pranay020692 4 года назад

@@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 года назад

@@pranay020692 hope you got the job

@Leptoszom 3 года назад

You got the job, Bajpai?

@shreysom2060 3 года назад

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 😊

@andreimarculescu911 5 лет назад

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 лет назад

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

@pradipacharjee4915 5 лет назад

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

@dudejaa 5 лет назад

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 лет назад

@@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 лет назад

@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.

@headoverbars8750 3 года назад

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!!

@SP-db6sh Год назад

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

@timurmukhtarov1319 4 года назад

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

@UlfAslak 2 года назад

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 года назад

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

@codingfork6708 2 года назад

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

@UlfAslak 2 года назад

@@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 Год назад

Thank you! This helped me a lot!

@Justinkol 6 лет назад

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

@gkcs 6 лет назад

Glad it helped :)

@akshatagrawal3300 3 года назад

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

@jananiravichandran8370 6 лет назад

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

@gkcs 6 лет назад

Thanks Janani!

@AbhishekKumar-ub8co 5 лет назад

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

@gkcs 5 лет назад

Thank you 😋

@jeffruan7701 5 лет назад

Knowledgeable and confident presenter!

@johnleonardo 2 года назад

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

@jrajesh11 3 года назад

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

@user-oy4kf5wr8l 4 года назад

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 !

@krishnasandeep4779 4 года назад

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

@bouzie8000 3 месяца назад

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

@SuiMizu 5 лет назад

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

@gkcs 5 лет назад

Thanks!

@harshdusane8687 5 лет назад

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

@raghuvamsi8740 4 года назад

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

@gautamtyagi8846 3 года назад

many thanks Gaurav for making this concept so clearly explained.

@rishabhagarwal9871 5 лет назад

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

@azeeztaiwo2802 3 года назад

best explanation of consistent hashing i have seen so far.

@SayHelloMeetPatel 5 лет назад

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

@gkcs 5 лет назад

Thank you!

@fiveyearclub6024 5 лет назад

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

@arnabthakuria2243 2 года назад

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

@asafmesika Год назад

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

@mattwilson1845 5 лет назад

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

@gkcs 5 лет назад

Glad to hear that :D

@AbhishekChoudhary-tu7ig 3 года назад

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

@nafeezahid214 5 лет назад

Excellent video Master. Thanks a lot.

@keshavabhamidipaty3126 4 года назад

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 года назад

It's answered in the other comments 🙂

@jatinderarora2261 5 лет назад

Thanks Gaurav. Excellent video.

@alexpanov4678 Год назад

Thank you! It was clear to understand.

@xiuwenzhong7375 4 года назад

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

@gymbeestar 5 лет назад

This video is so helpful! Thanks!

@VishalYadav-gk1kg 6 месяцев назад

Very Nice Explanation Sir, Thank you !

@shishirkumar8335 5 лет назад

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

@nankitable 4 года назад

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?

@i-tingchen439 5 лет назад

Very clear and helpful! Thank you.

@gkcs 5 лет назад

Thanks!

@SOULOFBUU7 6 лет назад

Great explanation clear and concise

@gkcs 6 лет назад

Thanks!

@consistentthoughts826 3 года назад

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

@hellaren 3 года назад

Thank you! It was extremely helpful

@vaibhav8257 4 месяца назад

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

@tanvirt16 3 года назад

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 Год назад

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.

@responsive_random 6 лет назад

Clearly explained. Thank you!

@gkcs 6 лет назад

Thanks!

@chandrakantasaini6438 Год назад

Explain in an easy and nice way.

@giobaldu 4 года назад

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 года назад

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.

@NehaKumari-my7cv 4 года назад

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.

@perfectlyfantastic 4 года назад

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 года назад

🙂

@osamaa.h.altameemi5592 4 года назад

fantastic explanation. Thx a ton.

@rockrock5838 3 года назад

Really well explained man....

@prakharsaxena796 4 года назад

Amazing videos man!!

@deepakrao1100 5 лет назад

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 года назад

how did your interview go?

@gkcs 3 года назад

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

@OmarNg7X 3 года назад

Great explanation. Thank you.

@xbeta84 4 года назад

This is great stuff!

@subhabera5775 4 года назад

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 :)

@phaneendran4208 5 лет назад

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 Год назад

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.

@manveersingh5822 2 года назад

This was a pretty good video. Thanks Gaurav g!

@nehamadaan3328 3 года назад

@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 года назад

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.

@Wise___Man 3 года назад

great explanation, thanks!

@xawnia 4 года назад

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 года назад

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

@sivas09 3 года назад

'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

@SK-ur3hw 5 лет назад

Great video!! I thought that we can add a load factor or load limit like one server can have x requests. So once the load limit is reached, the incoming requests will point to next clockwise server. That way, no server will have too much load. But of course the virtual servers concept is good. Can you please add the code in the desc? Thanks. :)

@gkcs 5 лет назад

Sounds interesting. There are variations on consistent hashing which allow this. Code link: github.com/coding-parrot/SystemDesignCourse/blob/master/service-orchestrator/src/main/java/algorithms/ConsistentHashing.java 😁

@sreeram8942 2 года назад

@@gkcs As you said in previous video about the User's cache data in a particular server , How does consistent hashing solve that issue ?

@sasirekhamsvl9504 2 года назад

Explained very well

@eyalpery8470 2 года назад

I learned a lot, you're awesome

@AbhishekKumar-ky3uc 3 года назад

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 года назад

Thanks!

@RihanPereira 5 лет назад

@Gaurav sen, hashing(applying h2 function) all the servers again, will reshuffle data and request routing of all existing nodes to entirely new nodes.

@satheeshprabhakaran5330 4 года назад

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

@LeSaboteur3981 2 года назад

easy and fast explanation👍👏

@rishabhmalhotra7058 5 лет назад

Awesome stuff man :)

@DarshitSuratwala 5 лет назад

Really well explained mate. Had one question, is it the same approach used by AWS , GCP etc cloud providers?

@gkcs 5 лет назад

Consistent hashing is used by many systems, so a lot of AWS users can ask for routing based on this. I think it's a customer preference.

@goutkannan 5 лет назад

It is based on the load balancing logic u want to be used. One can always ask for a memcache to front any server setup

@imaginationignited7724 Год назад

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?

@yosihashamen1 3 года назад

Great explanation!

@rakeshvarma8091 3 года назад

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 ?

@srinivasasrikanthpodila4376 3 года назад

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 года назад

That's right 👍

@ashishmittal7048 Год назад

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.

@baby_adventures 4 года назад

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

@kollisashank1465 6 лет назад

Hi Gaurav, thanks a lot for creating these videos. It really helps us in understanding somethings which we use day to day. I have question, How do we decide on Search space M? Can we consider it as no of requests per second?

@gkcs 6 лет назад

Thanks Kolli! The choice of M depends on the implementation, but it is usually a large number to have a better distribution of the hash function. Typically, values like 2^64 are used.

@harshulbhaliya193 Год назад

Loved this video👾

@ChandramouliMallampalli99 5 лет назад

very clever solution with multiple hashes

@gkcs 5 лет назад

It's super cool 😋

@crabjuice47 3 года назад

God Bless you Gaurav. Love from Pakistan.

@debjyotibiswas3793 5 лет назад

You should write a book on system design, this is really great.

@abdelrhmansamir1426 2 года назад

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?

@SandeepVerma-yh9ec 5 лет назад

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 лет назад

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

@omarraghib905 Год назад

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

@ankur2443 4 года назад

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 года назад

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.

@ashutoshmishra2328 3 года назад

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 года назад

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.

@smitmandavia5044 4 месяца назад

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??

@notthatguy1923 5 лет назад

Gaurav - Awesome playlist for system design. Can you also include the explanation for when a server goes down and a request comes for a key which was saved in that server, how is the request handled? Are we going to replicate the data to not just one server but multiple servers to ensure availability. And if that is the case how to ensure consistency?

@gkcs 5 лет назад

We can fail the request and retry on the newly assigned server for this request key.

@sridharbalabhadrapatruni1247 3 года назад

@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 месяцев назад

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.

@dacao0711 5 лет назад

Thank you so much!

@soumyaranjanmohanty7839 4 года назад

Great explanation Gaurav.

@saurabh1203 3 года назад

What if the 2 different hashing algorithms for 2 different servers produce same result ? Like for S1, H1 gives 19 and for S4, H2 gives 19. Now both of them will be placed at same location in the ring. What is the solution for this ?

@jeyakumar4728 4 года назад

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?

@romanesterkin 4 года назад

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 месяцев назад

Same question from my side.

@premabhisek 5 лет назад

Gaurav, at 8:23, you mentioned that when we have K points (on performing consistent hashing), 'the load on each server is much much lesser'. Could you explain that? 'coz even if we have 12 points, wouldn't the load on each "server" (considering actual 4 servers) be effectively the same?

@gkcs 5 лет назад

The chance of a skew in load is much lower as the number of points on the ring increase.

@premabhisek 5 лет назад

@@gkcs ok. I got the entire concept correctly now. and got the point you tried to explain. As in the hash ring, (on applying the same hash fn) all request ids having hash value greater than S0, are to be served say by S1, and all request ids> hash of S1, are served by S2 and so on. And that, the difference of S0 - S1 - S2 - S3 may not be same. which means a server might end up serving more requests than one other. So when we increase the no. of server hash ids/replicas id to K points, the chances of load on each virtual point (or to say one specific server) is less.

@premabhisek 5 лет назад

and speed of your response to my question was like as fast as searching a key from a hashtable :) thanks for that!

@gkcs 5 лет назад

@@premabhisek Absolutely!

@jayanthmanklu8642 5 лет назад

@@gkcs Hijacking the pinned thread to ask 2 more clarifications - a) How is the number M chosen? Isn't that number dependent on the traffic load - (a website getting a few hundred visits a day v/s Uber's price for a ride API) b) Can you explain intuition behind using log(M) as the number of hash functions?

@maitrivasa613 3 года назад

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

@krishnareddy3010 6 лет назад

Wow back to back !!!

@JollyBeJolly 3 года назад

Great teacher

@manasdubey8667 4 года назад

Got it Gaurav, Very well explained. I am new to system designing and believe me I am enjoying it thoroughly. What I wanted to ask is, that how would you choose the value of K, to decide the number of hash functions?