hashtable

I'm not an expert in C# and LINQ. I have a Dictionary , which I understand a hash table, that is, keys are not sorted. dataBase = new Dictionary<string, Record>() Record is a user-defined class that holds a number of data for a given key string. I found an interesting example that converts this Dictionary into a sorted dictionary by LINQ: var sortedDict = (from entry in dataBase orderby entry.Key ascending select entry) .ToDictionary(pair => pair.Key, pair => pair.Value); This code works correctly. The resulting sortedDict is sorted by keys. Question : I found that sortedDict is still a hash

var d:Dictionary = new Dictionary(); d["a"] = "b"; d["b"] = "z"; How to get the length/size of the dictionary (which is 2) ? There is no built-in method to get the size/lenght/count of an AS3 dictionary. There are workarounds: for example, you can create a custom dictionary class which extends or wraps the flash.utils.Dictionary class, adding the counter functionality. You can manage the count as entries are added/removed, or count on-demand using a simple For loop iteration: public static function countKeys(myDictionary:flash.utils.Dictionary):int { var n:int = 0; for (var key:* in

I have to store 500M two digit unicode character in memory (RAM). The data structure I use should have: Worst Case Space Complexity: O(n) Worst Case Time Complexity: O(1) <-- insertion, read, update, deletion I was thinking of choosing dict which is implementation of hash in python but then problem is it assures time complexity of O(1) for the required operations only in average cases than worst case. I heard that if number of entries is known, time complexity of O(1) can be achieved in worst case scenarios. How todo that? In case, thats not possible in python can I access memory addresses and

I am new to dynamodb (ddb). I was going through its documentation and it says to add Hash Key and a Hash Range key. In the documentation it says that ddb will create an usorted index on the hash key and a sorted index on the hash range. What is the purpose of having these 2 keys rather than just one key. Is it because the first key is used like : A HashTable which contains : key - range of keys for each value in the hash range 2nd HashTable hash range key - Actual data value. This would help segregate data and make lookup fast. But then why only 2 levels of HashMaps, I could do this for n

I’m trying to work with some json formatted data in C#, but, I’m having some problems determining the proper way to approach the problem. My issue is that the json formatted data will be in an unknown format (I know that sounds odd … please read on). Basically, the json formatted data will be some collection of name/value pairs where the values may or may not be arrays of nested name/value pairs. To make things more fun, the nesting of the name/value pair arrays can continue on ad infinitum. For example: I might have some data that looks like… { "1": { "1.1": { "1.1.1": "value1", "1.1.2":

The "Map types" section of the go language specification describes the interface and general usage of map types and the "Go maps in action" post on The Go Blog casually mentions hash tables and "fast lookups, adds, and deletes". The current runtime/hashmap.go source code describes its implementation as a hashtable (which are typically amortized O(1) ); however, I don't see any guarantee of performance characteristics (such as Big O performance) in the language specification or other materials. Does the go language make any performance guarantees (e.g. constant-time insertion/lookup/deletion?)

What is the complexity of creating a trie of a list of words and what is complexity of searching other set of word in that trie? Should I use trie for string searching, when i have hashtable? The complexity of creating a trie is O(W*L) , where W is the number of words, and L is an average length of the word: you need to perform L lookups on the average for each of the W words in the set. Same goes for looking up words later: you perform L steps for each of the W words. Hash insertions and lookups have the same complexity: for each word you need to check equality, which takes O(L) , for the

Editor's note: This question has a complicated history, but boils down to this: * To learn how to enumerate the entries of a hashtable by its key-value pairs , see the accepted answer . * To learn how to filter a hashtable by a collection of key values , see the other answer . I think I fell into the X Y problem again, my initial question was about Filtering a hash table. I discovered it's easier to filter before creating the hash table. Question answered, right? Nope, the Y problem was looping each Key and using the Values which @briantist helped me with. My goal is to loop over the key names

I am modeling a power subsystem in Java. A simple SQLite database contains a set of Line Replaceable Units (LRUs) and the connections between them. I am writing a Power Model API to simplify queries of the data store, using DDD patterns and repositories. I am seeking an appropriate Java collection to model the query results. There are some special cases in a LRU connection stream that have to be modeled: Initially, there's a Power Distribution Unit (PDU) with multiple ports (<=16) that feeds power to downstream LRUs. Typical connections in a power stream involve a single source LRU where power

I am trying to create a custom hash function for some object that I'll be hashing into a dictionary. The hashing function is unique (not the standard Python one). This is very important to me: to use the unique function. Each key's value is a list. Assuming I override __hash__ and end up coming up with the right hash number for an object. Would: dict = {} dict[number_here] = value hash the value into the position number number_here , or would it still be at the position that Python's hash table would compute for that number? Printing dict only shows the items and not which position they're