Hashcodes, however, represent only half the picture. Sometimes, however, multiple objects may map to the same bucket, an event known as a collision. In Java, the Hashtable responds to a collision by placing multiple values into the same bucket other implementations may handle collisions differently.
Figure 2 shows what a Hashtable might look like after a few collisions. Now imagine that you call get with a key that maps to Bucket 0. To perform this lookup, the Hashtable executes the following steps:. A long answer to a short question I know, but it gets worse. Properly overriding equals and hashCode is a nontrivial exercise. You must take extreme care to guarantee both methods' contracts.
In Effective Java , Joshua Bloch offers a five-step recipe for writing an effective equals method. Here's the recipe in code form:. Note: You need not perform a null check since null instanceof EffectiveEquals will evaluate to false. Finally, for Step 5, go back to equals 's contract and ask yourself if the equals method is reflexive, symmetric, and transitive.
If not, fix it! Creating a properly working hashCode method proves difficult; it becomes even more difficult if the object in question is not immutable. You can calculate a hashcode for a given object in many ways. The most effective method bases the number upon the object's fields.
Moreover, you can combine these values in various ways. Here are two popular approaches:. As a reminder, chained hashing kinda sorta looks like a clothing dresser - each bucket drawer can hold multiple items, and when you do a lookup you check all of those items.
However, this isn't the only way to build a hash table. There's another family of hash tables that use a strategy called open addressing. The basic idea behind open addressing is to store an array of slots , where each slot can either be empty or hold exactly one item. In open addressing, when you perform an insertion, as before, you jump to some slot whose index depends on the hash code computed.
If that slot is free, great! You put the item there, and you're done. But what if the slot is already full? In that case, you use some secondary strategy to find a different free slot in which to store the item. The most common strategy for doing this uses an approach called linear probing.
In linear probing, if the slot you want is already full, you simply shift to the next slot in the table. If that slot is empty, great! You can put the item there. But if that slot is full, you then move to the next slot in the table, etc. If you hit the end of the table, just wrap back around to the beginning. Linear probing is a surprisingly fast way to build a hash table. CPU caches are optimized for locality of reference , so memory lookups in adjacent memory locations tend to be much faster than memory lookups in scattered locations.
Since a linear probing insertion or deletion works by hitting some array slot and then walking linearly forward, it results in few cache misses and ends up being a lot faster than what the theory normally predicts.
And it happens to be the case that the theory predicts it's going to be very fast! Another strategy that's become popular recently is cuckoo hashing.
I like to think of cuckoo hashing as the "Frozen" of hash tables. Instead of having one hash table and one hash function, we have two hash tables and two hash functions. Each item can be in exactly one of two places - it's either in the location in the first table given by the first hash function, or it's in the location in the second table given by the second hash function.
This means that lookups are worst-case efficient, since you only have to check two spots to see if something is in the table. Insertions in cuckoo hashing use a different strategy than before. We start off by seeing if either of the two slots that could hold the item are free.
If so, great! We just put the item there. But if that doesn't work, then we pick one of the slots, put the item there, and kick out the item that used to be there. That item has to go somewhere, so we try putting it in the other table at the appropriate slot. If that works, great! If not, we kick an item out of that table and try inserting it into the other table. This process continues until everything comes to rest, or we find ourselves trapped in a cycle. That latter case is rare, and if it happens we have a bunch of options, like "put it in a secondary hash table" or "choose new hash functions and rebuild the tables.
There are many improvements possible for cuckoo hashing, such as using multiple tables, letting each slot hold multiple items, and making a "stash" that holds items that can't fit anywhere else, and this is an active area of research! Then there are hybrid approaches.
Hopscotch hashing is a mix between open addressing and chained hashing that can be thought of as taking a chained hash table and storing each item in each bucket in a slot near where the item wants to go.
This strategy plays well with multithreading. The Swiss table uses the fact that some processors can perform multiple operations in parallel with a single instruction to speed up a linear probing table. Extendible hashing is designed for databases and file systems and uses a mix of a trie and a chained hash table to dynamically increase bucket sizes as individual buckets get loaded.
Robin Hood hashing is a variant of linear probing in which items can be moved after being inserted to reduce the variance in how far from home each element can live. For more information about the basics of hash tables, check out these lecture slides on chained hashing and these follow-up slides on linear probing and Robin Hood hashing. You can learn more about cuckoo hashing here and about theoretical properties of hash functions here.
To understand a hash table, the direct address table is the first concept we should understand. The direct address table uses the key directly as an index to a slot in an array.
The size of universe keys is equal to the size of the array. It is really fast to access this key in O 1 time because an array supports random access operations. In fact, not a lot of situations in real life fit the above requirements, so a hash table comes to the rescue. Instead of using the key directly, a hash table first applies a mathematical hash function to consistently convert any arbitrary key data to a number, then using that hash result as the key.
The length of the universe keys can be large than the length of the array, which means two different keys can be hashed to the same index called a hash collision? Actually, there are a few different strategies to deal with it.
Here is a common solution: instead of storing the actual values in the array, we store a pointer to a linked list holding the values for all the keys that hash to that index.
If you still have interests to know to how implement a hashmap from scratch, please read the following post. For all those looking for programming parlance, here is how it works. Bucket is any space where the values are stored - for here I have kept it int as an array index, but it maybe a memory location as well. Stack Overflow for Teams — Collaborate and share knowledge with a private group.
Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. How does a hash table work? Ask Question. Asked 12 years, 7 months ago. Active 7 months ago. Viewed k times. Could anyone clarify the process? Improve this question. Isma Arec Barrwin Arec Barrwin Recently, I have written this en. You could think of a hash table as an extended version of an array, that's not just limited to consecutive integer keys.
Here is another one: intelligentjava. Add a comment. Active Oldest Votes. Here's an explanation in layman's terms. Ok, so that's basically how a hash table works. Technical stuff follows. Thus, you put the book in slot number 3. I hope this explanation was a bit more down to earth than buckets and functions :. Improve this answer. Lasse V. Karlsen Lasse V. Karlsen k 94 94 gold badges silver badges bronze badges.
Thanks for such a great explanation. Do you know where I can find more technical details regarding how it's implemented in 4. Net framework? No, it is just a number. You would just number each shelf and slot starting at 0 or 1 and increasing by 1 for each slot on that shelf, then continue numbering on the next shelf. It is just another algorithm? OK, so suppose we use another algorithm that outputs a different number based on the book name.
Then later on, if I were to find that book, how would I know which algorithm to use? I'd use first algorithm, second algorithm and so on until I find the book whose title is the one I'm looking for? KyleDelaney: No for closed hashing where collisions are handled by finding an alternative bucket, which means the memory usage is fixed but you spend more time searching across buckets.
KyleDelaney: need the " Tony" thing to get notified of your comments. Regardless of whether there are collisions when inserting, the memory usage is fixed. Kinda, kinda not. Same total memory used. Show 6 more comments. Usage and Lingo: Hash tables are used to quickly store and retrieve data or records. Records are stored in buckets using hash keys Hash keys are calculated by applying a hashing algorithm to a chosen value the key value contained within the record.
This chosen value must be a common value to all the records. Each bucket can have multiple records which are organized in a particular order. So in the example above, the 30, possible clients or so are mapped to a smaller space. Hope this helps, Jeach! David Cary 4, 6 6 gold badges 50 50 silver badges 63 63 bronze badges. Jeach Jeach 7, 7 7 gold badges 45 45 silver badges 58 58 bronze badges. Might be worth an edit. You will end up with a generated value of something that looks like e5dcfbc8bcf77eed8c.
This is nothing more than a large hexadecimal number of bits bytes. You can then use this to determine which bucket a limited quantity will be used to store your record. TonyD, I'm not sure where the term "hash key" is referred in a conflicting matter? If so, please point out the two or more locations.
Or are you saying that "we" use the term "hash key" while other sites such as Wikipedia uses "hash values, hash codes, hash sums, or simply hashes"?
If so, who cares as long as the term used is consistent within a group or an organization. Programmers often use the "key" term. I would personally argue that another good option would be "hash value". But I would rule out using "hash code, hash sum or simply hashes".
Focus on the algorithm and not the words! TonyD, I've changed the text to "they would module the hash key by ", hoping it will be cleaner and clearer for everyone. Show 1 more comment. This turns out to be a pretty deep area of theory, but the basic outline is simple. Balancing these two properties and a few others has kept many people busy! There are lots of schemes and tricks to make this work better, but that's the basics. A hash table has O 1 time complexity.
However, once you use a sparse array, aren't you left needing to do a binary search to find your value? At that point doesn't the time complexity become O log n? TonyDelroy - that's true it is oversimplification but the idea was to give a overview of what they are and why, not a practical implementation. The details of the latter are more nuanced, as you nod to in your expansion. A few words on hash functions Strong hashing Tony Delroy Tony Delroy Allow me to just say: fantastic answer.
Tony Delroy Thanks for the amazing answer. I still have one open point in my mind though. You say that even if there are million buckets,lookup time would be O 1 with load factor 1 and a cryptographic strength hash function.
But what about finding the right bucket in million? Even if we have all the buckets sorted,isn't it O log How can finding the bucket be O 1? Thinking the complexity is O log,, implies you imagine doing a binary search through sorted buckets, but that's not how hashing works.
Maybe read a few of the other answers and see if it starts to make more sense. TonyDelroy Indeed,"sorted buckets" are the best-case scenario that I imagine. Hence O log,, But if this is not the case,how can the application find related bucket among millions? Does hash function generate a memory location somehow? By the way, due to this reason, it's a requirement for the key object in HashMap to implement both equals and hashCode , in fact, that is also one of the popular questions among experienced Java programmers, asked as what is the requirement for an object to be used as a key in hash-based Maps like HashMap , Hashtable , and ConcurrentHashMap.
Another optional, but worth mentioning requirement of keys in a hash-based collection is being an Immutable object because if you use a mutable object then if the state of an object is changed while the object is stored in Map then it would be impossible to find the same bucket location as hashCode will return a different value and equals will also not match with the original object. That's all on these two HashMap questions guys. Remember to mention the key. A value object is not used, it just exists in Entry so that get can return it.
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