I am constructing a hash table mod 17 for example and I am trying to figure out an efficient way to deal with a repeating key value. Suppose I have like a random number generator and I make a 1000 random generated numbers, there is a chance that some of those numbers might occur multiple times. My implementation would have a linked list to an array for each of the slots i.e. 17 slots and keys would be stored in their respective position.
I want to kind of implement a failsafe sort of checker function that insures that there are no repeating keys in the hash table. I have been looking this up on the internet and have not found a most definite answer. MY idea was to keep each linked list sorted and have a lookahead to check if the number is there already. Does anyone know of a better idea?
Any thoughts and comments greatly appreciated.
If I understand, you want multiple values for the same key? I think it is not possible. When you go to retrieve the value, which value would you choose.
Related
https://en.wikipedia.org/wiki/Chord_(peer-to-peer)
I've looked into Chord and i'm having trouble understanding exactly what it does.
It's a protocol for a distributed hash table which stores various keys/values for later usage? Is it just an efficient way to look up in the hash table what value for a given key?
Any help such as a basic example would be much appreciated
An example question is say if I hashed inserting string "Hi" to 3 and there were no peers at 3 it would go to the next available peer and store it there right? Or where does it store it's values to?
I already answered a similar question for bittorrent/kademlia, so just to summarize in a more general sense:
DHTs store the values with some redundancy on N nodes whose ID is closest to the target hash.
Considering the vastness of >= 128bit keyspaces it would be extremely unlikely for nodes to exactly match the key. At least in routing schemes where nodes don't adjust their IDs based on content, and chord is one of those.
It's pretty much the same as regular hash tables, hence distributed hash table. You have a limited set of buckets into which the entries are hashed, where the bucket space is much smaller than the potential input keyspace and thus does not precisely match the keys either.
hashset underlaying data structure is hashtable .how it will identify duplicates and why it is good for if our frequent operation is search operation ?
It uses hash code of the object which is quickly computed integer. This hash code tries to be as even distributed over all potential object values as possible.
As a result it can distribute the inserted values into a array (hashtable) with very low probability of conflict. Then the search operation is quite quick - get the hash code, access the array, compare and get the value - usually constant time. The same actually happens for finding duplicates.
The conflicts of hash code are resolved as well - there can be potentially more values for the same entry within the hash table - there comes the equal into play. But they are rather rare so they don't affect average performance significantly.
Most of us have seen that when we make purchase from Amazon like sites we get a Order Number or Purchase Number(of 10-12 digits) which looks like some random number. Similarly I want generate unique ids for large scale system. What is the best algorithm to generate it?
Some methods which I thought are not efficient or not applicable to large scale system
1) Generating string using rand function ( Array.new(12){rand(10)}.join) and checking
whole table whether it is exists. It is time consuming, inefficient and may struck
in infinite loop.
2) Using time-stamp - I think this cannot be used for large scale system because large
no. user can excess system at same time.
3) Combination of 1) & 2) also creates issue as second when it generates same 1)
Auto increment : I don't want to use.
Is there a reason why a normal UUID won't work? It will produce a longer id, but it's simple, it works, and most languages have generation code built in.
there must be some kind of database involved in this large scale system, so add an auto-incrementing integer to your database. Dish out these unique integers without
worry if some are never used.
I'm trying to create a data set for training a neural network for sports application. I'm trying to capture the impact player substitutions on points scored by a team. I have sets of substitutions (Jones for Smith) (Smith for Davis) etc. that I'm trying to represent with a unique number. For example every time my data set included a Jones for Smith substitution the function/program/hash would produce the same number.
I looked into Hash Codes (MDA, Sha), but these do seem to be the right way to go. I'm sort of stumped on this one. If anyone has come across a similar situation or has some programming wizardry they would care to share I would appreciate it. Thanks.
You could build a string of the primary keys, along the lines of substited,substituted for, next substituted, next substituted for, etc. e.g. "Jones,Smith,Smith,Davis". An MD5 hash of this string, whilst not guaranteed to be unique, is probably going to be unique enough for your purposes.
I have often heard people talking about hashing and hash maps and hash tables. I wanted to know what they are and where you can best use them for.
First you shoud maybe read this article.
When you use lists and you are looking for a special item you normally have to iterate over the complete list. This is very expensive when you have large lists.
A hashtable can be a lot faster, under best circumstances you will get the item you are looking for with only one access.
How is it working? Like a dictionary ... when you are looking for the word "hashtable" in a dictionary, you are not starting with the first word under 'a'. But rather you go straight forward to the letter 'h'. Then to 'ha', 'has' and so on, until you found your word. You are using an index within your dictionary to speed up your search.
A hashtable does basically the same. Every item gets an unique index (the so called hash). You use this hash for lookups. The hash may be an index in a normal linked list. For instance your hash could be a number like 2130 which means that you should look at position 2130 in your list. A lookup at a known index within a normal list is very easy and fast.
The problem of the whole approach is the so called hash function which assigns this index to each item. When you are looking for an item you should be able to calculate the index in advance. Just like in a real dictionary, where you see that the word 'hashtable' starts with the letter 'h' and therefore you know the approximate position.
A good hash function provides hashcodes that are evenly distrubuted over the space of all possible hashcodes. And of course it tries to avoid collisions. A collision happens when two different items get the same hashcode.
In C# for instance every object has a GetHashcode() method which provides a hash for it (not necessarily unique). This can be used for lookups and sorting with in your dictionary.
When you start using hashtables you should always keep in mind, that you handle collisions correctly. It can happen quite easily in large hashtables that two objects got the same hash (maybe your overload of GetHashcode() is faulty, maybe something else happened).
Basically, a HashMap allows you to store items with identifiers. They are stored in a table format with the identifier being hashed using a hashing algorithm.
Typically they are more efficient to retrieve items than search trees etc.
You may find this helpful: http://www.relisoft.com/book/lang/pointer/8hash.html
Hope it helps,
Chris
Hashing (in the noncryptographic sense) is a blanket term for taking an input and then producing an output to identify it with. A trivial example of a hash is adding the sum of the letters of a string, i.e:
f(abc) = 6
Note that this trivial hash scheme would create a collision between the strings abc, bca, ae, etc. An effective hash scheme would produce different values for each string, naturally.
Hashmaps and hashtables are datastructures (like arrays and lists), that use hashing to store data. In a hashtable, a hash is produced (either from a provided key, or from the object itself) that determines where in the table the object is stored. This means that as long as the user of the hashtable is aware of the key, retrieving the object is extremely fast.
In a list, in comparison, you would need to in some way search through the list in order to find your sought object. This also represents the backside of hashtables, which is that it is very complicated to find an object in it without knowing the key, because where the object is stored in the table has no relevance to its value nor when it was inputed.
Hashmaps are similar to hashtables, but only one example of each object is stored in it (hence no key needs to be provided, the object itself is the key).
This is of course a very simple explanation, so I suggest you read in depth from this point on. I hope I didn't make any silly mistakes. =)
Hashmap is used for storing data in key value pairs. We can use a hashmap for storing objects in a application and use it further in the same application for storing, updating, deleting values. Hashmap key and values are stored in a bucket to a specific entry, this entry location is determined using Hashcode function. This hashcode function determines the hash where the value is stored. The detailed explanantion of how hashmap works is described in this video: https://youtu.be/iqYC1odZSNo
Hash maps saves a lot of time as compared to other search criteria. We have a hash key that corresponds to a hash code which further helps to find its index value. In terms of implementation, hash maps takes a string converts it into an integer and remaps it to convert it into an index of an array which helps to find the required value.
To go in detail we can look for handling collisions in hash maps. Like instead of using array we can go with the linked list.
There is a short video available to understand it.
Available here :
Implementation example --> https://www.youtube.com/watch?v=shs0KM3wKv8
Sample:
int hashCode(String s)
{
logic
}