Thanks much. All tutorials just draw the ring and explain this concept that is hard to understand. First time I understood what consistent hashing is after watching this video!!!

Consistent Hashing begins at 12:35

One correction or clarification. The hash for two keys might not be the same for two different keys but the modulo value can be same, which is where bucket collisions occur. Though there are hash collisions they are extremely rare due to possible large values available but the mod of hash will find many collision and that's where the linked list in a bucket helps. Good video.

Best explanation of consistent hashing I've seen. Thank you!

Went through many vedios to understand this. But your explaination is very good and in laymen terms so any new person like me can also understand correctly. Thanks for posting this.

Out of all other resources out there, your explanation on consistent hashing is the most concise. Thank you !!

I have seen many videos on consistent hashing but this is the best s far! Running trough an examples help retain concepts so well!!!

Your explanation is very clear and nice. Thank you! One point is I think I like the explanation of consistent hashing using a circle diagram better, but this is very useful too esp when you're thinking about implementing it.

So great video, would like to add one thing here: We have two challenges which we try to solve with consistent hashing: 1. Minimize data movement/rehashing when we add/remove servers. 2. Evenly distribute data across servers. Your video talks about the first problem and how to fix it. For the second one, we can use the concept of Virtual Servers, which allows each server to occupy multiple portions distributed throughout your hash ring. This helps with skewed data; for example, if you add a server in between 2 existing servers, then the load on other servers is technically more than this new server, and the server after this. Basically, data isn't evenly distributed amongst servers, hence skewed. Having the same server hashed in different locations over the hash ring helps solve this problem as well

Thank You! Great words and very nice vertical stack explanation! One of the humble and clearest videos so far on CH unlike some of the flamboyant ones on YT! Subscribed! \../

You have explained in a very clear and easy way without using so many typical words.

Nicely explained. Honeatly I saw many videos but none of them come even closer to this one. Thanks a lot

best explanation on KZfaq, thanks man!

Clearest video on this topic that I've found. Thanks!

Nicely explained. I had seen the circular implementation of Hash Ring and was wondering how will it be implemented in the system. In theory we can have a ring but while implementing it we'll have the table like structure which confused me. This explanation was a great help to me. Thanks!!

Good Work. Thanks for sharing your knowledge. Keep it up. (Y)

great explanation :) and also the thing is the person who does know how Hashing works how the hash map works also able to understand because you covered from A to Z. Thanks for the Video :)

your explanation is the easiest to understand. I really have huge respect for you and given a chance would definitely return the favor in whatever I can. Stay blessed and keep being awesome

Excellent stuff! Coincidentally, the dog started barking exactly when called for it at 15:31 😂

thanks so much, not sure if I can really thank enough with the kind of knowledge I am gaining from all your videos, please keep up a good work like this.

I have a doubt on the failover part where you mentioned that if the last node is unavailable key-value will go get saved in the first node by following the cyclic order. If this is how it actually works, what happens when the last node comes back up and we make a query for the same key. Will there not be a situation that the system is trying to find this key in the last node but the key is actually saved in the first node?

Very clear and solid explanation! Thanks a ton!

Woaah... This is awesome. First few minutes I was wondering why are we learning hashmap here..and then all of a sudden everything made sense...boom..!! ❤️ Super explanation. Thanks! 🙏

@Narendra You are the best. I see many videos on consistent hashing but the way you explained is superb

It's so clear! Thanks for the efforts

Great introduction presentation on consistent hashing.

Dude, I really enjoy listening to you. Excellent teaching skills.

clear and concise explanation on hashing and consistent hashing,kudos.

you make things so easier to understand! thanks a lot !

Thanks man for good explanation, I understood if one of the location mede unavailable, rehashing is easy i.e. move data of this location to next location(Drawback is next location will have double load now) and no need to rehash whole table but how does the rehashing happen when a new location added in the ring?

Hi I need your help to understand implementation of concurrent hashmap after Java 8 as it uses RB Tree instead of linked list

as mentioned when a Node goes does what happens to the data stored in it? & if during that node downtime data(ex-ABC) intended to be saved on the particular node(Ex-NodeX) is instead stored to the next Node(NodeA) in clock-wise direction what happens when another node takes dead node place? Now when ABC is searched it will check in NodeX but data for it was saved in NodeA.

Excellent, Easy to understand explanation.

What about the keys present in the 9900 ? when that location is down. are these keys distributed amoung available location ?

how dose the rehasing happen when a new location added in the ring ?

Execellent explanation! Thank you for the links too.

Is there a load balancer out there which uses this? I cannot see any of them using it. They use only round robin etc.

Very good explanation. Nicely done. Thanks man!

Much better & clear, Thank you :)

Really great explanation, as usual. Thanks

Thanks for the video. Is there any way you could use a better mic?

Because if I withdraw money from atm but still reflects to core banking db,internet bank db,atm db,mobile banking db,imps/neft/rtgs db/upi db/mobile banking db etc...,how this will happen in. Back ground if I do transaction in one db through one of payment method as described earlier

Thanks, man, Nicely explained. I got one question though If a system goes down or removed new value coming will be stored in the next range or cycle back to the first range, But what about if it comes back online which was failed or we want to reduce the load of first server(range) suppose lot of load coming to it. now the search will go to the new range, while the data was stored in a different range. how adding of new range works.

What happens when that location that failed comes back but some keys were written to the alternative locations. When lookups are performed, wouldn't they go back to the original location where they should have been? Does some reconciliation process occur when brining the failed locations back?

very very good explanation, thanks narendra

thankyou soo much for this wonderful presentation

When Computer 2 goes down, and at that time if an entry is made (say above 9,500 key) that will get stored in the starting key. But what after Computer 2 becme available later on?

Nice Explanation, Thank you. I have one Question, What if one more server needs to be added in the hashtable. How we will assign the # function numbers. Like if we go for number 11k for e.g. we know that values for that could have been stored in the first row earlier. it Would make inconsistent as in getting that value will look for the 11k key but it was in the first raw not the last. please Explain that.

One question on Consistent Hash Ring example, what you explained here. Suppose for (key,value) -> (Dog:Max) , the hash function gave 19234 and then at memory address 2022 we saved the following (key,value). Now if I am retrieving data using map.get(Dog), how does it internally finds out the 2022 address, as the hash function will again return 19234? Where is the mapping of 19234 and 2022 is done internally ?

Didn't understand the last part for 10115 no, greater key is not present and we go back and we put on first key but the rule was to find key which is greater than current hash key we got...... ???

Hi, What happens when the node comes back on, how does the data stored on that node behave? Also, when the node comes back up, is that node added with the same random number, if it is a different random number how do we make sure the data is still consistent that was stored prior to node creation?

What happens when you add another computer to the hash set? In your last example, if you add a computer with memory address 13000, it will be greater than 9000, so data will be stored there. So the data which was earlier going to the first node/computer, will now go to the last one. This will cause inconsistencies in retrieval, because data which was earlier stored in first computer will now be searched in the last one, causing old data to be never retrieved.

9:40 Use hash to store key-values pairs, what if for N inputs we get same hashKey and appending N values by nodes. White retrieval time complexity would become linear O(N). How do we get values in constant time though? Any suggestion on that would be highly appreciated.

Clear explanation. Thanks Man!

Great explanation. Thank you.

Could you please do video about bank design

Performance gonna take a hit, retrieval and put gonna take log(n) instead of n in traditional hash tables. But it is the best solution for consistent systems

Thanks for taking time to create this

Nice explanation. I have a doubt on consistent hash ring. In order to find the key in the Hash table it sounds like we have to do the liner search starting from the start of the hash, compare it to decide on the location to insert the key-value pair. Is it making better in terms of search time if we chose to have the array solution here?

Thanks for explaining the concept but would it change the time of find from O(1) to O(log n)? Or you have build a second map between key and index?

Doesn't 1011 location will be overwhelmed with values when a key does not fit in 9900 bucket?

Excellent. I was just thinking, how the value will be retrieved which is stored earlier (in 1011) for keys greater than 9900, when one new location will be added, let's say 10010 or 11000.

Excellent, thank you.

Very good explanation👍👍

Dont you think in this case if last 2 goes down, all load will be on 1st 2 nodes.

What's the purpose of wearing a cap? Is this helps to explain hashing?

Brilliant explanation! So what happens if the node that was down comes back up? Will we have to remap the key that was moved to the first node? Or does it mean we would have to search all the nodes for a key?

what will happen if the location is getting added back in hashtable once the node are up again in distributed env? e.g 10115 got stored in 1011 as there was no location more than it available in hashtable. but suppose 10300 got added in hashtable then what will happen? will it remap all key values?

To find a bucket in Consistent Hashing, linear search is needed. Is it O(1)?

legendary content! thank you sir!

Clever! Thanks!

Gr8 content. Thank you

In case of consistent # ring, when a node is not available, how is the data retrieved? Also, when data is inserted, it will go to the next node. And when the non available node becomes available, how does the system knows where to find the data when queried? Wouldn't it look for the data in the now available node?

Its still not clear whats happens when a node which owns certain rows of the tables goes down and comes back up ?

How do we perform the correct bucket search in CH? I can think of some variation of binary search, but isn't it slower than direct lookup in a simple hashtable (O(logn) vs O(n))?

Good one brother.. Thank you ..

Please add elevator system design. I didn't find good system design on the elevator. Or you share ant good link??. It's really help please do it

Hi, with Consistent hash ring, the fact that we need to loop through the key to find the one that is larger than hash value, makes this is not looking up with O(1) anymore but instead O(n), right?

Well Explained. Thanks.

I did not get how can we retrieve value if not storing key/value pair in consistence hashing. In this case storing only hai-max then how do get(hey) and get(dog) works. Both are at same bucket location. Please explain if i am missing anything

Hi Narendra, I have some confusion at 18:15, suppose Pair (Rat, Grt) whose hash# 9015 is mapped at bucket #9900. However, due to issue bucket, #9900 is down, If we do mapping again Pair (Rat, Grt), it will give the same hash# 9015, because bucket #9900 is down, it will be mapped to bucket #1011, which is the first bucket. So my question is what happens if bucket #9900 is online, now if we try to get Pair (Rat, Grt) whose hash# 9015 will try to locate on Bucket #9900, and finds that not available so return null, however, this Pair (Rat, Grt) is available on bucket #1011. So how this consistent mapping is helping on this. Waiting for your reply. Thank for your nice tutorial. Regards Neelabh Singh

very good explanation!! thank you

So, the scenario is, if we try to save any object with hash greater than 9900, say '9950' when 9900 is down then it (9950) will be saved at 1011 and whenever we try to fetch the object it will be available. But, if the location 9900 comes up and then we are trying to find the 9950 our logic will hit 9900 and return with nothing, so how this will be handled.

Thank you for the explanation.

I finally now understand it.

what is we want to retrieve the value from the unavailable node? how consistent hashing solves that issue?

good example, thank you

Hey , What if while inserting a key, some locations were not available and due to hash ring it goes to next location (circular way) . What will happen when those locations comes back and i am looking for the key I inserted ( When those locations were down ).Will not it give me the wrong answer ?

This is amazing!

explained better than my college professor

Thanks for video, Can you do a video on "Debugger" System Design.

Great Explanation

Great tutorial

Great job.

12:28 : Consistent Hash

can you or somebody explain the black box here which is hash function ? How is the number generated or regenerated are same ?

Thank you sir , huge fan of your videos . Superb content . Quick question:Complexity will be increased now with consistent hashing rt ? Earlier it was O(1) now it will be logn . Correct me sir If I am wrong ?

At 12:01 when he is saying that we get wrong value. How we get wrong value because with equals key will not match so still it will return null. This same problem will occur in consistent hashing as well.

How it is O(1). we have to search in keys to find a fit that's O(n)

Hi, Sir instead of consistent hashing we also can use range. example range (1, 1000) store in key 1 and (1001, 2000) store in key 2 and any disadvantage of consistent hashing?