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.
Related
Everyone was telling me that a List is heavy on performance, so I was wondering is it the same with a dictionary? Because a dictionary doesn't have a fixed size. Is there also a dictionary with a fixed size, just like a normal array?
Thanks in advance!
A list can be heavy on performance, but it depends on your use case.
If your use case is the indexing of a very large data set, in which you plan to search for elements during runtime, then a Dictionary will behave with O(1) Time Complexity for retrievals (which is great!).
If you plan to insert/remove a little bit of data here and there at runtime then that's okay. But, if you plan to do constant insertions at runtime then you will be taking a hit on performance due to the hashing and collision handling functions.
If your use case requires a lot of insertions, removals, iteration through the consecutive data, then a list would be and fast. But if you are planning to search constantly at runtime, then a list could take a hit performance-wise.
Regarding the Dictionary and size:
If you know the size/general range of your data set then you could technically account for that and initialize accordingly. Or you could write your own Dictionary and Hash Table implementation.
In all:
Each data structure has it's advantages and disadvantages. So think about what you plan to do with the data at runtime, then pick accordingly.
Also, keeping a data structure time and space complexity table is always handy :P
This is depends on your needs.
If you just add and then iterate items in a List in sequental way - this is a good choice.
If you have a key for every item and need fast random access by key - use Dictionary.
In both cases you can specify the initial size of the collection to reduce memory allocation.
If you have a varying number of items in the collection, you'll want to use a list vs recreating an array with the new number of items in the collection.
With a dictionary, it's a little easier to get to specific items in the collection, given you have a key and just need to look it up, so performance is a little better when getting an item from the collection.
List and dictionary are part of the System.Collections namespace, which are mutable types. There is a System.Collections.Immutable namespace, but it's not yet supported in Unity.
Ran into an annoying problem - I need some way to tell if the bucket I'm trying to fill is empty or not (the buckets are stored as an array of value type structs for key-value pairs).
If I were to reserve a key value for marking things empty then that would just mean that some data unfortunate enough to stumble on that hash value would never be accessible.
On the other hand, including a boolean in the KVP struct would increase the size of the struct from 16 to 24, (such a waste and I'm tight on memory as it is). Has anybody figured out a good solution for this?
This is a problem that is as intrinsic to hash tables as collisions. A related problem is dealing with deleting from a hash table, again, in the context of collisions. There's no solution that doesn't involve some compromise in performance, so it's pretty common to see hash table implementations that have a particular key that is illegal.
By far the most direct solution is to just special-case the key value that you're using to mean empty. That is, if the user is trying to store a key value 0, you just put it in a special array you keep around for that purpose.
Really lame hash tables that only work with pointers don't usually have this issue, since you can always find a pointer value which the caller can't pass in (such as a pointer to an object you own). Obviously hash tables using linked lists or array elements don't have this problem either, but then, there's a massive performance penalty for those.
You could probably find some clever way to encode it inside the table itself, by using multiple elements. The only way this would be better is if its somehow unified with deleted element handling or something else, so it would be free or faster than checking some separate list.
I have a table that will contain large amounts of data. The purpose of this table is user transactions.
I will be inserting into this table from a web-service, which a third party will be calling, frequently.
The third party will be supplying a reference code (most probably a string).
The requirement here is that I will need to check whether this reference code has already been inserted. If it exists, just return the details and do nothing else. If it doesn't create the transaction as expected. The reasoning behind this is the possibility of loss of communication with the service after the request is received.
I have some performance concerns with this, as the search will be done on a string value, and also on a large table. Most of the time the transaction will not exist in the database, as this is just a precaution.
I am not asking for code here, but for the best approach for performance.
AS your subject indicates if you are trying to look for evaluating EXISTS (SELECT 1 from Sometable) then there will not be much of performance penality. This is because you will not be writing just a bunch of 1s (means the inner query) to evaluate the result to boolean.
Other aspect to this is the non clustered indexing provided on the reference code field.If the length of the reference code lets say its a fixed length string (CHAR(50) then also the B -tree will be optimum .
I am not sure about the data consistency requirements hence exepct the normal readcommitted will wont do any harm unless you have highly transnational read writes.
I've decided to use GUID as primary key for many of my project DB tables. I think it is a good practice, especially for scalability, backup and restore in mind. The problem is that I don't want to use the regular GUID and search for an alternative approach. I was actually interested to know what Pinterest i using as primary key. When you look at the URL you see something like this:
http://pinterest.com/pin/275001120966638272/
I prefer the numerical representation, even it it is stores as string. Is there any way to achieve this?
Furthermore, youtube also use a different kind of hashing technique which I can't figure it out:
http://www.youtube.com/watch?v=kOXFLI6fd5A
This reminds me shorten url like scheme.
I prefer the shortest one, but I know that it won't guarantee to be unique. I first thought about doing something like this:
DateTime dt1970 = new DateTime(1970, 1, 1);
DateTime current = DateTime.Now;
TimeSpan span = current - dt1970;
Result Example:
1350433430523.66
Prints the total milliseconds since 1970, But what happens if I have hundreds thousands of writes per second.
I mainly prefer the non BIGINT Auto-Increment solution because it makes a lot less headache to scale the DB using 3rd party tools as well as less problematic backup/restore functionality because I can transfer data between servers and such if I want.
Another sophisticated approach is to tailor the solution towards my application. In the database, the primary key will also contain the username (unique and can't be changed by the user), so I can combine the numerical value of the name with the millisecond number which will give me a unique numerical string. Because the user doesn't insert data as such a high rate, the numerical ID is guarantee to be unique. I can also remove the last 5 figures and still get a unique ID, because I assume that the user won't insert data at more than 1 per second the most, but I would probably won't do that (what do you think about this idea?)
So I ask for your help. My data is assumes to grow very big, 2TB a year with ten of thousands new rows each second. I want URLs to look as "friendly" as possible, and prefer not to use the 'regular' GUID.
I am developing my app using ASP.NET 4.5 and MySQL
Thanks.
Collision Table
For YouTube like GUID's you can see this answer. They are basically keeping a database table of all random video ID's they are generating. When they request a new one, they check the table for any collisions. If they find a collision, they try to generate a new one.
Long Primary Keys
You could use a long (e.g. 275001120966638272) as a primary key, however if you have multiple servers generating unique identifiers you'll have to partition them somehow or introduce a global lock, so each server doesn't generate the same unique identifier.
Twitter Snowflake ID's
One solution to the partitioning problem with long ID's is to use snowflake ID's. This is what Twitter uses to generate it's ID's. All generated ID's are made up of the following parts:
Epoch timestamp in millisecond precision - 41 bits (gives us 69 years with a custom epoch)
Configured machine id - 10 bits (gives us up to 1024 machines)
Sequence number - 12 bits (A local counter per machine that rolls over every 4096)
One extra bit is reserved for future purposes. Since the ID's use timestamp as the first component, they are time sortable (which is very important for query performance).
Base64 Encoded GUID's
You can use ShortGuid which encodes a GUID as a base64 string. The downside is that the output is a little ugly (e.g. 00amyWGct0y_ze4lIsj2Mw) and it's case sensitive which may not be good for URL's if you are lower-casing them.
Base32 Encoded GUID's
There is also base32 encoding of GUID's, which you can see this answer for. These are slightly longer than ShortGuid above (e.g. lt7fz44kdqlu5pt7wnyzmu4ov4) but the advantage is that they can be all lower case.
Multiple Factors
One alternative I have been thinking about is to introduce multiple factors e.g. If Pintrest used a username and an ID for extra uniqueness:
https://pinterest.com/some-user/1
Here the ID 1 is unique to the user some-user and could be the number of posts they've made i.e. their next post would be 2. You could also use YouTube's approach with their video ID but specific to a user, this could lead to some ridiculously short URL's.
The first, simplest and practical scenario for unique keys
is the increasing numbering sequence of the write order,
This represent the record number inside one database providing unique numbering on a local scale : this is the -- often met -- application level requirement.
Next, the numerical approach based on a concatenation of time and counters is commonly used to ensure that concurrent transactions in same wagons will have unique ids before writing.
When the system gets highly threaded and distributed, like in highly concurrent situations, do some constraints need to be relaxed, before they become a penalty for scaling.
Universally unique identifier as primary key
Yes, it's a good practice.
A key reference system can provide independence from the underlying database system.
This provides one more level of integrity for the database when the evoked scenario occurs : backup, restore, scale, migrate and perhaps prove some authenticity.
This article Generating Globally Unique Identifiers for Use with MongoDB
by Alexander Marquardt (a Senior Consulting Engineer at MongoDB) covers the question in detail and gives some insight about database and informatics.
UUID are 128 bits length. They introduce an amount of entropy
high enough to ensure a practical uniqueness of labels.
They can be represented by a 32 hex character strings.
Enough to write several thousands of billions of billions
of decimal number.
Here are a few more questions that can occur when considering the overall principle and the analysis:
should primary keys of database
and Unique Resource Location be kept as two different entities ?
does this numbering destruct the sequentiality in the system ?
Does providing a machine host number (h),
followed by a user number (u) and time (t) along a write index (i)
guarantee the PK huti to stay unique ?
Now considering the DB system:
primary keys should be preserved as numerical (be it hexa)
the database system relies on it and this implies performance considerations.
their size should be fixed,
the system must answer rapidly to tell if it's potentially dealing with a PK or not.
Hashids
The hashing technique of Youtube is hashids.
It's a good choice :
the hash are shorts and the length can be controlled,
the alphabet can be customized,
it is reversible (and as such interesting as short reference to the primary keys),
it can use salt.
it's design to hash positive numbers.
However it is a hash and as such the probability exists that a collision happen. They can be detected : unique constraint is violated before they are stored and in such case, should be run again.
Consider the comment to this answer to figure out how much entropy it's possible to get from a shorten sha1+b64 recipe.
To anticipate on the colliding scenario,
calls for the estimation of the future dimension of the database, that is, the potential number of records. Recommended reading : Z.Bloom, How Long Does An ID Need To Be ?
Milliseconds since epoch
Cited from the previous article, which provides most of the answer to the problem at hand with a nice synthetic style
It may not be necessary for you to encode every time since 1970
however. If you are only interested in keeping recent records close to
each other, you only need enough values to ensure that you don’t have
more values with the same prefix than your database can cache at once
What you could do is convert a GUID into only numeric by converting all the letters into numbers in the guid. Here is a example of what that would look like. It's abit long but if that is not a problem this could be one way of going about generating the keys.
1004234499987310234371029731000544986101469898102
Here is the code i used to generate the string above. But i would probably recommend you using a long primary key insteed although it can be abit of a pain it's probably a safer way to do it then the function below.
string generateKey()
{
Guid guid = Guid.NewGuid();
string newKey = "";
foreach(char c in guid.ToString().Replace("-", "").ToCharArray())
{
if(char.IsLetter(c))
{
newKey += (int)c;
}
else
{
newKey += c;
}
}
return newKey;
}
Edit:
I did some testing with only taking the 20 first numbers and out of 5000000 generated keys 4999978 was uniqe. But when using 25 first numbers it is 5000000 out of 5000000. I would recommend you to do some more testing if going with this method.
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
}