First, the load distribution across the nodes can still be uneven. Then for example, for any string hash function will always return a value between 0 to 100. 2. Yes they are well distributed but they are also too expensive to compute — there are much cheaper options available. Consistent Hashing is a distributed hashing scheme that operates independently of the number of servers or objects in a distributed hash table by assigning them a position on a hash ring. MPCH provides O(n) space (one entry per node), and O(1) addition and removal of nodes. A lookup hashes the key and checks the entry at that location. Your hash function should be fast. This sort of variability makes capacity planning tricky. Consistent hashing idea was introduced in paper Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web which was released in the year 1997. I want to distribute the keys across the servers so I can find them again. A similar approach is described in this blog post from Amazon on “shuffle sharding”. Is there a way to have flexible ring resizing and low variance without the memory overhead? Hash function and Array:Here is where hash function and hash table comes to rescue which provides constant time for all three operations. What are the downsides of this approach? But for some reason suppose one of the servers (S3) crashed, it’s no longer able to accept a request. One of the popular ways to balance load in a system is to use the concept of consistent hashing. This tends to rule out cryptographic ones like SHA-1 or MD5. consistent hashing basic. In computer science, consistent hashing is a special kind of hashing such that when a hash table is resized, only n / m {\displaystyle n/m} keys need to be remapped on average where n {\displaystyle n} is the number of keys and m {\displaystyle m} is the number of slots. In general, only the K/N number of keys are needed to remapped when a server is added or removed. This hashing strategy, multiplying an incoming key by a prime number, is actually relatively common. Ring hashing provides arbitrary bucket addition and removal at the cost of high memory usage to reduce load variance. These combined make Jump Hash better suited for data storage applications where you can use replication to mitigate node failure. And I want to do this without having to store a global directory. Searches in the bucket are linear but a properly size hashed table will have a small number of objects per bucket resulting in constant time access. consistent Hashing 1013 RepliesWhen working on distributed systems, we often have to distribute some kind of workload on different machines (nodes) of a cluster so we have to rely on a predi. First, we will describe the main concepts. And those keys should be evenly chosen from the 9 “old” servers. It works particularly well when the number of machines storing data may change. Here are all the repositories implementing the algorithms I discuss: unsigned int k_limit = floorf(pct * 40.0 * ketama->numbuckets); limit := int(float32(float64(pct) * 40.0 * float64(numbuckets))), func Hash(key uint64, numBuckets int) int32 {, func (r *Rendezvous) Lookup(k string) string {, Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web, Dynamo: Amazon’s Highly Available Key-value Store, A Fast, Minimal Memory, Consistent Hash Algorithm”, Maglev: A Fast and Reliable Software Network Load Balancer, Predictive Load-Balancing: Unfair But Faster & More Robust, Actionable advice to start learning to code. I needed a compatible Go implementation and came across this problem. (A brief note on terminology. This comes with significant memory cost. When you do an image search for “consistent hashing”, this is what you get: You can think of the circle as all integers 0 ..2³²-1. Here’s the code taken from github.com/dgryski/go-jump, translated from the C++ in the paper. The main limitation is that it only returns an integer in the range 0..numBuckets-1. The algorithm was actually included in the 2011 release of the Guava libraries and indicates it was ported from the C++ code base. This makes it a useful trick for system design questions involving large, distributed databases, which have many machines and must account for machine failure. All keys and servers are hashed using the same hash function and placed on the edge of the circle. K is the number of keys and N is the number of servers ( to be specific, maximum of the initial and final number of servers), Dynamo: Amazon’s Highly Available Key-Value Datastore, Great Developer Experiences and the People Who Make Them, Automating Deployments with Bitbucket Pipelines. We will first start with hashing and why it is required. So far so good. With 100 replicas (“vnodes”) per server, the standard deviation of load is about 10%. The simplest solution for this is to take the hash modulo of the number of servers. Something like MurmurHash is good, but there are slightly better ones out there now. As a node joins the cluster, it picks a random number, and that number determines the data it's going to be responsible for. The 99% confidence interval for bucket sizes is 0.76 to 1.28 of the average load (i.e., total keys / number of servers). As you can see, there is no perfect consistent hashing algorithm. Each existing algorithm has its own specification: MD5 produces 128-bit hash values. Since there will be many keys which will map to the same index, a list or a bucket is attached to each index to store all objects mapping to the same index. For example, server = hash(key) modulo N where N is the number of servers. That year saw two works published: These cemented consistent hashing’s place as a standard scaling technique. What’s the catch? You can’t use it for distributing keys among a set of memcached instances where one of them might crash — there’s no way to remove the crashed node from the list of possible destinations. Hopefully you didn’t just skip down to the bottom of the article and ignore all the caveats and tradeoffs that each consistent hashing function has. A function is usually used for mapping objects to hash code known as a hash function. In 2016, Google released Maglev: A Fast and Reliable Software Network Load Balancer. Consistent hashing algorithm vary in how easy and effective it is to add servers with different weights. For two overviews, see. Each call to GetNode() costs only 1 or 2 macro-seconds. Consistent Hashing is one of the most asked questions during the tech interview and this is also one of the widely used concepts in the distributed system, caching, databases, and etc. This is known as rehashing problem. Consistent hashing solves the problem of rehashing by providing a distribution scheme which does not directly depend on the number of servers. They were only assigned to server S1 which will increase the load on server S1. Luckily, there’s a paper that solves this. There is a plethora of excellent articles online that does that. It’s now used by Cassandra, Riak, and basically every other distributed system that needs to distribute load over servers. Then, we will dig into existing algorithms to understand the challenges associated with consistent hashing. Suppose our hash function output range in between zero to 2**32 or INT_MAX, then this range is mapped onto the hash ring so that values are wrapped around. This reduces the load variance among servers. This paper described the approach used by Akamai in their distributed content delivery network. Let’s explore different data structure for the above use-case. What is “hashing” all about? In short — consistent hashing is the algorithm that helps to figure out which node has the key. The basic idea is that each server is mapped to a point on a circle with a hash function. And once I had this sorted out for my go-ketama implementation, I immediately wrote my own ring hash library (libchash) which didn’t depend on floating point round-off error for correctness. In that situation, we will try to distribute the hash table to multiple servers to avoid memory limitation of one server. I have a set of keys and values. Objects (and their keys) are distributed among several servers. Lesson: avoid implicit floating point conversions, and probably floating point in general, if you’re building anything that needs to be cross-language. The first operation is to create the ring. This algorithm is the popular ring-based consistent hashing. Let’s rehash all the keys and see how it looks like. What’s the Go equivalent of this line of C? Weighted rendezvous hashing is a one-line fix to add weights to rendezvous: choose the highest combined hash scaled by -weight / math.Log(h) . Ketama is a memcached client that uses a ring hash to shard keys across server instances. We compare our system to other Web caching systems in Section 4. A method, system, computer-readable storage medium and apparatus for balanced and consistent placement of resource management responsibilities within a multi-computer environment, such as a cluster, that are both scalable and make efficient use of cluster resources are provided. Like everything else in this post, choosing a replication strategy is filled with trade-offs. Consistent hashing may help solve such problems. My library is also slightly faster because it doesn’t use MD5 for hashing. Akamai distributed content delivery network uses the approach described in the paper. With a tricky data structure you can get the total lookup cost from O(k log n) down to just O(k). If we need to store a new key, we can do the same and store it in one of the server depending on the output of server = hash (key) modulo 3. One section of the paper described a new consistent hashing algorithm which has come to be known as “maglev hashing”. And now what you’ve all been waiting for. But like the above they’re all struggling to balance distribution, memory usage, lookup time, and construction time (including node addition and removal cost). First, consistent hashing is a relatively fast operation. Linked list:If we will use linked list to store employee records then worst-case time for insert will be O(1) and search and delete will be O(n). Nó cho phép việc thêm hay xóa các node trên một cụm server (cluster) mà ít gây ra sự xáo trộn dữ liệu, do đó nó các hệ thống caching system sẽ dễ dàng scale-up hay scale down. consistent hashing. This allows servers and objects to scale without affecting the overall system. consistent-hash. This is bad. Suppose our hash function output range in between zero to 2**32 or INT_MAX, then thi… Here’s a problem. For example, if server S3 is removed then, all keys from server S3 will be moved to server S1 but keys stored on server S1 and S2 are not relocated. This will increase the load on origin in case of caching servers as there will be cache miss of keys and all of them needs to be rehashed. For 100 nodes, this translates into more than a megabyte of memory. 1. of tons of major companies. The prime number reduces the likelihood that the output hash code shares a common factor with the size of the array, reducing the chance of a collision. For a peak-to-mean-ratio of 1.05 (meaning that the most heavily loaded node is at most 5% higher than the average), k is 21. To find a key we do the same thing, find the position of the key on the circle and then move forward until you find a server replica. Another paper from Google “Multi-Probe Consistent Hashing” (2015) attempts to address this. You need to know these types and also C’s promotion rules: And the reason is because of C’s arithmetic promotion rules and because the 40.0 constant is a float64. If we will use balanced binary search tree to store these employee records then worst-case time for each operation will be O(log n). It then uses the random numbers to “jump forward” in the list of buckets until it falls off the end. This can be either to protect against node failure, or simply as a second node to query to reduce tail latency. Load Balancing is a key concept to system design. In contrast, in most traditional hash tables, a change in the number of array slots causes nearly all keys to be remapped because the … To add a new object, we hash the key, find the index and check the bucket at that index. Common solutions for handling collision are Chaining and Open Addressing. First, it’s very easy to explain. Rendezvous you take the next highest (or lowest). consistent hash in C#. Assuming 50 data centers across different regions. So instead of server labels S1, S2 and S3, we will have S10 S11…S19, S20 S21…S29 and S30 S31…S39. Jump Hash addresses the two disadvantages of ring hashes: it has no memory overhead and virtually perfect key distribution. The sorted nodes slice is searched to see find the smallest node hash value larger than the key hash (with a special case if we need to wrap around to the start of the circle). Some algorithms have straightforward ways to choose multiple nodes for fallback or replication. Case closed? It represents the resource requestors (which we shall refer to as ‘requests’ from now on, for the purpose of this blog post) and the server nodes in some kind of a virtual ring structure, known as a hashring. Luckily (again from Google) we have two consistent hashing approaches for load balancing in addition to Maglev. Maglev hashing also aims for “minimal disruption” when nodes are added and removed, rather than optimal. Ring hashing presents a solution to our initial problem. Consistent Hashing — Load balancer decides which instance to send the request to. My implementation optimizes the multiple hashing by pre-hashing the nodes and using an xorshift random number generator as a cheap integer hash function. Similar things happen if we add a server. The algorithm works by using a hash of the key as the seed for a random number generator. Here is an awesome video on what, why and how to cook delicious consistent hashing. With a ring hash, you can scale the number of replicas by the desired load. Things You Wanted to Know About Networking, Managing Azure Subscriptions and Resources (Part 1), Build Your First CI/CD Pipeline using Azure DevOps, A Quick Guide To Understanding RabbitMQ & AMQP, Search or fetch an employee details by email. The first, from 2016, Consistent Hashing with Bounded Loads. To lookup the server for a given key, you hash the key and find that point on the circle. Suppose three servers are S1, S2, and S3, each will have an equal number of keys. Similarly, if we need to remove a server (say, because it crashed), then the keys should be evenly distributed across the remaining live servers. Fast Virtual Functions: Hacking the VTable for Fun and Profit, Functional Programming in Swift: An Introduction. consistent hashing made one thing a lot easier: replicating data across several nodes. It took until 2007 for the ideas to seep into the popular consciousness. Of course, choosing this random number again can be done using a hash function but the s… The server location for almost all keys changed, not only for the keys from S3. For clients in a choosing which set of backends to connect to, Google’s SRE Book outlines an algorithm called “deterministic subsetting”. The original consistent hashing paper called servers “nodes”. To expand on the first point, if we’re moving from 9 servers to 10, then the new server should be filled with 1/10th of all the keys. There are two kinds of hash functions cryptographic and non-cryptographic which are used for different purpose. Hash function can be used to hash object key (which is email) to an integer number of fixed size. You can always add a second “shadow” node that refers back to an original node, but this approach fails when the load multiple is not an integer. Jump Hash and Multi-Probe consistent hashing are trickier to use and maintain their existing performance guarantees. Making Configurable Angular Feature Modules Using Strategy Pattern. The number of locations is no longer fixed, but the ring is considered to have an infinite number of points and the server nodes can be placed at random locations on this ring. In a nutshell, consistent hashing is a solution for the rehashing problem in a load distribution process. In 2007, consistent hashing was used in two published works. Here’s a simple implementation taken from groupcache (slightly modified for clarity): To add the list of nodes to the ring hash, each one is hashed m.replicas times with slightly different names ( 0 node1, 1 node1, 2 node1, …). Consistent Hashing is a distributed hashing scheme that operates independently of the number of servers or objects in a distributed hash table by assigning them a position on an abstract circle, or hash ring. Let’s dive into it. To find an object by key, hash the key and get the index and looks for the key in the bucket at that index. the primary means for replication is to ensure data survives single or multiple machine failures. All keys originally assigned to S1 and S2 will not be moved. It was also first published in 1997. We timed the dynamic step of consistent hashing on a Pentium II 266MHz chip. We conclude in Section 6. Suppose a number of employees kept growing and it becomes difficult to store all employee information in a hash table which can fit on a single computer. Papers will generally talk about“nodes”, “servers”, or “shards”. In comparison to the algorithm of Karger et al., jump There’s a detailed post detailing how it was added to HAProxy at Vimeo, (with a cameo by Yours Truly :). Example use case #1 Database instances distribution DB1 DB2 DB3 DB4 client A client B client C client D. It’s a trick question: you can’t answer it in isolation. We can then use array to store the employee details in such a way that, index i has employee details whose key hash value is i. To evenly distribute the load among servers when a server is added or removed, it creates a fixed number of replicas ( known as virtual nodes) of each server and distributed it along the circle. If there is a request for john@example.com, then server number will be S2 ( 89 modulo 2 = 1) and it will be a cache miss and that object will be again fetched from the origin and stored in S2. Like most hashing schemes, consistent hashing assigns a set of items to buck-ets so that each bin receives roughly the same number of items. Consistent Hashing is a distributed hashing scheme that operates independently of the number of servers or objects in a distributed hash table by assigning them a position on an abstract circle, orhash ring. A better way to think of Jump Hash is as providing a shard number, not a server name. Main Concepts Hashing. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. For maglev’s use case as a software load balancer, this is sufficient. The algorithm effectively produces a lookup table that allows finding a node in constant time. Consistent Hashing. It is based on a ring (an end-to-end connected array). That is, send more (or less) load to one server as to the rest. When adding or removing servers, only 1/nth of the keys should move. All keys which are mapped to replicas Sij are stored on server Si. This allows servers and objects to scale without affecting the overall system. We have three servers and employees with the following emails. Hash table or Hash Map is a common data structure in computer science which is used for constant time lookup. If we will use an array data structure to store that information, the worst-case time complexity for each operation would be O(n). Lookups get slower. What if one of the queue partitions goes down? It may be the fastest consistent hashing in C#. To find out which server to ask for a given key or store a given key, we need to first locate the key on the circle and move in a clockwise direction until we find a server. Hash functions are used in combination with the hash table. Not quite. Non-cryptographic hash functions like xxHash, MetroHash or SipHash1–3 are all good replacements. It is widely used for scaling application caches. This allows servers and objects to scale without affecting the overall system. It’s fast and splits the load evenly. ∙ Rice University ∙ 0 ∙ share . To store a key, first hash the key to get the hash code, then apply modulo of the number of server to get the server in which we need to store the key. Consistent hashing gave birth to Akamai, which to this day is a major player in the Internet (market cap ˇ$10B), managing the Web presence c2015{2016, Tim Roughgarden and Gregory Valiant. For a more in-depth description of how the table is built, see the original paper or the summary at The Morning Paper . Hashing is the process to map data of arbitrary size to fixed-size values. This kind of setup is very common for in-memory caches like Memcached, Redis etc. Let’s consider what an “optimal” function would do here. Load balancing is a huge topic and could easily be its own book. Even thought rendezvous hashing is O(n) lookups, the inner loop isn’t that expensive. Design a HashMap without using any built-in hash table libraries. That node hash is then looked up in the map to determine the node it came from. Consistent Hashing is quite useful when dealing with the cache distributed issue in a dynamic environment (The servers keep adding/removing) compares with the Mod-Hashing. Jump is a bit tricky, but it can be done. If the number of concurrent users of your application doesn’t run into a few hundred million then an In-memory data store is a good solution. For our testing environment, we set up a cache view using 100 caches and created 1000 copies of each cache on the unit circle. Let’s use the above example and place them on the hash ring. Consistent Hashing. Ring hashing still has some problems. My implementation uses the tricky data structure. Replication is using the consistent hash to choose secondary (or more) nodes for a given key. The two downsides is that generating a new table on node failure is slow (the paper assumes backend failure is rare), and this also effectively limits the maximum number of backend nodes. This study mentioned for the first time the term consistent hashing. To fix that we can use a hash table. 一般的数据库进行horizontal shard的方法是指，把 id 对 数据库服务器总数 n 取模，然后来得到他在哪台机器上。这种方法的缺点是，当数据继续增加，我们需要增加数据库服务器，将 n 变为 n+1 时，几乎所有的数据都要移动，这就造成了不 consistent。 (The standard deviation of buckets is 0.000000764%, giving a 99% confidence interval of 0.99999998 to1.00000002). As the keys are distributed across servers, the load is checked and a node is skipped if it’s too heavily loaded already. If the object is not in the bucket then add it. Consistent Hashing, a .Net/C# implementation. The factor for a number of replicas is also known as weight, depends on the situation. They all have trade-offs. You need to know these types and also C’s promotion rules:The answer is this:And the reason is because of C’s arithmetic promotion rules and because the 40.0 c… The last bucket it lands in is the result. The table is effectively a random permutation of the nodes. In this case, the minimum value on the circle is 0 and the maximum value is 100. Since there will be multiple servers, how do we determine which server will store a key? This is called collision. 2. Another early attempt at solving the consistent hashing problem is called rendezvous hashing or “highest random weight hashing”. Using consistent hashing for load balancing seems like an appealing idea. This is not an in-depth analysis of consistent hashing as a concept. Dynamic load balancing lies at the heart of distributed caching. In addi- Revisiting Consistent Hashing with Bounded Loads. Consistent hashing is (mostly) stateless - Given list of servers and # of virtual nodes, client can locate key - Worst case unbalanced, especially with zipf Add a small table on each client - Table maps: virtual node -> server - Shard master reassigns table entries to balance load (With ring hash, even if two different instances receive their server lists in a different order, the resulting key mapping will still be the same.) Redis is a fast In-memory solution for caching. Suppose server S3 is removed, then all S3 replicas with labels S30 S31 … S39 must be removed. In 2014, Google released the paper “A Fast, Minimal Memory, Consistent Hash Algorithm” known as “Jump Hash”. We mention other positive aspects of our Web caching system, such as fault tolerance and load balancing, in Section 5. In 1997, the paper “Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web” was released. What is the ‘copyWith()’ Operation in Flutter? Now we are only left with two servers. It can also be tricky to use with node weights. The downside is that it’s hard to avoid the O(n) lookup cost of iterating over all the nodes. For example, a hash function can be used to map random size strings to some fixed number between 0 … N. Given any string it will always try to map it to any integer between 0 to N. Suppose N is 100. Maglev hashing approaches weights by having altering the table construction procedure so that more heavily weighted nodes choose entries in the lookup table more frequently. This could be handled by partition logic/implementation such as consistent hashing using unique node attributes (ip/mac addresses/hardware id etc..) Multi DC. The paper has a more complete explanation of how it works and a derivation of this optimized loop. Consistent Hashing can be described as follows: 1. This article will use all three interchangeably.). Some strategies use full node replication (i.e, having two full copies of each server), while others replicate keys across the servers. To be specific, your design should include these functions: put(key, value): Insert a (key, value) pair into the HashMap.If the value already exists in the HashMap, update the value. The hash values are added to the m.nodes slice and the mapping from hash value back to node is stored in m.hashMap. This is O(1) with a small constant (just the time to hash the key). It’s also very cheap to compute. But there is one problem when server S3 is removed then keys from S3 are not equally distributed among remaining servers S1 and S2. Merriam-Webster defines the noun hash as “ The idea is that you hash the node and the key together and use the node that provides the highest hash value. It’s also available as a standalone package. First, choose a hash function to map a key (string) to an integer. You may have seen a “points-on-the-circle” diagram. These extra points are called “virtual nodes”, or “vnodes”. Consistent hashing helps us to distribute data across a set of nodes/servers in such a way that reorganization is minimum. Suppose we want to add a server S4 as a replacement of S3 then we need to add labels S40 S41 … S49. I also have some servers for a key-value store. Finally the m.nodes slice is sorted so we can use a binary search during lookup. 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