Arrays and hash tables are two common data structures that you may encounter in software development. They both store collections of values, but they have different strengths and weaknesses. In this article, you will learn how to compare arrays and hash tables based on their properties, operations, and performance.
An array is a data structure that stores a fixed number of values of the same type in a contiguous block of memory. Each value in an array has an index, which is a numeric position that starts from zero. You can access, insert, or delete any value in an array by using its index. Arrays are simple and efficient for storing and retrieving data in a sequential order.
Arrays are like the lockers in a school hallway - numbered storage bins lined up in a row. You can access each "locker" by its index number, store data in any locker you want, and add or remove lockers. The lockers are all the same size and shape, just like array values are the same data type. So arrays store data in an organized, numbered sequence for easy retrieval - like keeping your jackets and lunches handy in school lockers!
An array is a collection of similar data elements stored at contiguous memory locations. It is the simplest data structure where each data element can be accessed directly by only using its index number. Following are types of indexing used in Array Zero Based Indexing- The first element of the array refers to index 0. One Based Indexing- The first element of the array refers to index 1. Following are some the advantages of using Array Arrays represent multiple data elements of the same type using a single name. Accessing or searching an element in an array is easy by using the index number. An array can be traversed easily just by incrementing the index by 1. Arrays allocate memory in contiguous memory locations for all its data elements.
The important thing to remember is that an array has an address for its starting point and sometimes this is used to reference it or it’s first element. You can also do some pointer math. Some languages allow you to reallocate space for the array and some don’t. Using multiple arrays is another way to access data and store information about that data and you can access the data by using the same index.
Well, an array is a collection type in Swift. It can store multiple items. In the majority of cases, the items have to be of the same type.
Arrays can also have a variable number of objects. Modern programming languages allow for arrays to be as large as the amount of data that will fit into memory. Arrays can also be associatively indexed. This means the index key is a named string instead of an integer. This higher level of arrays are used in most modern programming languages to give arrays much flexibility in storing and indexing data.
A hash table is a data structure that stores key-value pairs in an array-like structure. Each key-value pair in a hash table has a unique hash code, which is a numeric representation of the key. A hash function maps each key to an index in the array, where the corresponding value is stored. You can access, insert, or delete any value in a hash table by using its key. Hash tables are flexible and fast for storing and retrieving data in a random order.
Imagine you have a special box with lots of little slots in it. Each slot has a label, like a name tag. When you want to remember something, you put it in the box and attach a name tag to it. Then, if you want to find it later, you just look at the name tag to know which slot it's in. Hash tables work like this special box – they help us remember things and find them quickly by using name tags.
I much prefer these for storing data and working with data. I’ve been told you can often tell what kind of programming a person wants to perform by the kind of data structure they use and whether that is an array or a hashtable. There is no indexing with hash tables, so why bother using an array in modern programming other than to save some time? In many offices, and for many projects this is actually the question, “Why use an indexed collection of anything that doesn’t have any management features control indexing?” I think at the management level. This is a still ongoing debate and most don’t have a need to solve for this question. Proofs for anything are hard for most people.
I think adding a small part to explain how the hashtable is in itself an array and how can we get the hashtable out of an array would be truly beneficial
Different languages implement things in different ways. As I have experienced it, a hash table leverages a hash function to compute the index used to store a value based on some kind of key. The key could be a string name for example. The index value computed is not guaranteed to be in any kind of order. It can appear random without an understanding of the computational function used to generate it. Importantly, the hash table may or may not have a reference to the keys used to lookup any value. Some structures that look similar to and probably act in a similar way are the Dictionary and Associative Array. Depending on language, these may or may not be implemented as hash tables, and performance characteristics may differ.
These questions confused me for a long time. Basically, a hash table is a data structure that uses key-value pairs. In Swift and Python, it is a dictionary, in JavaScript, it is an object.
One way to compare arrays and hash tables is to look at their properties, such as size, order, and uniqueness. Arrays have a fixed size, which means you need to specify the number of elements in advance and cannot change it later. Hash tables have a dynamic size, which means you can add or remove elements as needed and the hash table will resize itself automatically. Arrays maintain the order of insertion, which means you can iterate over the elements in the same order as they were added. Hash tables do not guarantee the order of insertion, which means the elements may appear in a different order than they were added. Arrays allow duplicate values, which means you can have multiple elements with the same value. Hash tables do not allow duplicate keys, which means you can have only one element with the same key.
Arrays have a fixed size, but in C & C++ we use malloc to create new pointer and copy from the old value this will ensure the size change smoothly. Hash table use for a specific case, so to get specific order and have dynamic size there are alternative like Vector.
A hash table is like a dictionary, where you can look up a definition (object or value) by the word that represents it. It could be any value, and doesn't need to be words, however. Some languages do support for duplicate values per key in hash tables (unordered_multimap in C++), so this is not entirely correct.
In summary, arrays are great for situations where you have a fixed sequence of elements and need fast index-based access, while hash tables are better when you have key-value pairs and need efficient lookups, insertions, and deletions based on keys.
Unemployed
You know what’s really annoying is that in some cases you cannot dynamically define the size of an array with a variable even though you can include as many dimensions as you want. Hash tables were the first solution for this problem and later came the list and the array list. So really, they’re only an in between solution, but they were kept because there is some nostalgia to using them however, you will find some developers who are sticking with the efficiency principles, and we’ll find a use for these in between features.
I think the dynamic nature of hash tables is likely to be an illusion. Usually, though not always, a hash table is built on an array of some specific size. Then a hash function is used to compute the index out of the available array locations based on a key value. What this means is that a hash table is not necessarily dynamically sized. It will depend on the implementation provided by the language and library implementing the hash table. The comment about hash tables not allowing duplicate keys is correct with a caveat. Depending on the implementation, keys that compute out to the same index may be stored in a list at that location. This is typically considered to be an index collision. The exact same key may not be allowed again.
Another way to compare arrays and hash tables is to look at their operations, such as access, search, insertion, and deletion. Arrays have constant time access, which means you can get any element in an array by using its index in O(1) time. Hash tables also have constant time access, which means you can get any element in a hash table by using its key in O(1) time. However, hash tables may have collisions, which means two or more keys may map to the same index. To resolve collisions, hash tables use techniques such as chaining or linear probing, which may increase the time complexity of access. Arrays have linear time search, which means you need to scan through the entire array to find an element in O(n) time, where n is the number of elements. Hash tables have constant time search, which means you can find an element in a hash table by using its key in O(1) time. Arrays have linear time insertion and deletion, which means you need to shift the elements to make space or fill the gap when you add or remove an element in O(n) time. Hash tables have constant time insertion and deletion, which means you can add or remove an element in a hash table by using its key in O(1) time.
In addition to chaining and linear probing, there are other collision resolution techniques that can be used with hash tables such as quadratic probing and double hashing. These techniques can help improve the performance of hash tables when dealing with collisions.
Hash table performance was hit by collision, due to collision we need to traverse through linked list to find the value for the given. The new implementation has been improved now, for example after JDK 8 Java use binary tree instead of linked list. This make lookup faster
Honestly, 20 years ago O notation and efficiency became the actual justification for using his tables and so hash tables were great. This was only a step past an extra dimension or an extra array and/or a random value generator. So there you are and I wouldn’t know who McDonald’s drive-thru requested THE hast table “thingy”. I know it was a request by someone and another encapsulation of logic. What are has tables inside though? That’s what I want to know. .
These operations may be true depending on the implementation, though it is not guaranteed. Different languages and implementations can treat this differently. The description here including dynamic sizing seems to imply that the hash table being described is based on a linked list. The characteristics of insertion and deletion are similar as is the dynamic sizing described earlier. Not all hash tables are guaranteed to be built using this approach.
Arrays are faster, especially when reading or writing large numbers of consecutive values, but Hash tables are faster when the values need to be read or written in random order by the key. Arrays that are sorted can be binary-searched in O(log n) time, rather than O(n), but Hash tables are still much faster than that for looking up a value when given a key. The auto-sizing of Hash tables doesn't come for free, when the collisions become too frequent, most hash tables are expanded through a rehash process that takes extra time, and this can happen without warning.
A third way to compare arrays and hash tables is to look at their performance, such as memory usage, scalability, and trade-offs. Arrays have low memory usage, which means they only need enough space to store the elements and their indices. Hash tables have high memory usage, which means they need extra space to store the keys, the values, the hash codes, and the collision resolution structures. Arrays have low scalability, which means they cannot grow or shrink easily and may cause memory wastage or overflow. Hash tables have high scalability, which means they can adjust to the changing number of elements and optimize the space utilization. Arrays have a trade-off between speed and flexibility, which means they are fast for sequential access but inflexible for random access. Hash tables have a trade-off between speed and memory, which means they are fast for random access but memory-intensive for storage.
Hash table storage size differ in different language. This is mainly based on when the language runtime decides to increase the size and what is the rate at which this grows in memory. Most of the run time grows at 2x level except C++ and C# (both of them grow more than 2x)
Accessing elements in an array is fast and constant time (O(1)) because you can directly access an element using its index. Accessing elements in a hash table is generally fast and constant time (O(1)) on average. However, it can degrade to O(n) in the worst case if there are hash collisions (two keys map to the same bucket).
Ancient wisdom states that you can look at this data structure as two big honking arrays with a functional interface, per se. So use it because it is efficient and includes an interface.
Again, the hash table implementation might be different depending on language and implementation. Some hash tables might be constant in memory until they grow beyond the size of their allocations (the backing array). The key characteristic is the hash function that calculates the index in the array. Yes, from a performance perspective that function will take some amount of time to execute, but it is typically very small. I think it generally helps to have a conceptual model that uses an array as the backing for the hash table when trying to understand how they work and the performance differences between them and arrays.
When it comes to choosing the right data structure for your problem, there is no definitive answer. It depends on the requirements and constraints of your application. However, some general guidelines can be followed. Arrays are useful when you need to store a fixed number of elements of the same type, access them by index, and iterate over them in order. Hash tables are ideal when you need to store variable number of elements of different types, access them by key, and perform frequent insertions and deletions. To find the optimal solution, consider the trade-offs between speed, memory, flexibility, and complexity of arrays and hash tables. Comparing their properties, operations, and performance will help you weigh the pros and cons of each data structure.
I'll typically use a hash table (or dictionaries) when I want fast lookup based on a unique key, and don't care about the order of items. Arrays when order matters, or when I know the number of items in advance, and when I need to iterate over items in sequence. It's worth noting that searching for elements requires O(n) in worst-case.
I think this boils down to considerations of both performance and algorithm. Ignore memory differences for now, since that is largely implementation specific. When you have key value pairs and need to access them in random or unordered ways, a hash table is probably the right choice. When you have a set of values with no key, that are accessed as a set or iterated through, maybe an array is the right choice. Some alternatives like dictionaries or associative arrays could land in the middle of these two choices and might not be implemented in the same way that a hash table would be, so the performance expected might not be there, but access characteristics should be largely maintained. A lot of this will come down to implementation.
There are many kinds of data structures tob consider. Breadth Search and Depth Search trees, Hash Maps, etc. There are also multiple ways to implement them to avoid key collisions. So there is no single structure that is best for every situation. This why we also develop metrics for every new algorithm so we can make correct choices
On the whole, I think it is better to conceptualize the array as a base type of data structure, and build upon that with hash table concepts. If someone is learning about the difference between them, this approach builds a better mental model. Later, adding information about the differences between hash tables and dictionaries or associative arrays can help further clarify the underlying mechanics of these structures. Language specific implementations are important. For example, a dictionary in some language might leverage keys and values but not have the performance of a hash table due to the algorithms it leverages. An associative array might be similar to a dictionary, or it might not. These variations need concrete examples and metrics.
How about programming languages? Also about speed of development and urgency of requirements? Memory and speed of data retrieval? Size of data?
Use the right tool for the right job. Sometimes it can be better to use both, if you need the properties of both, and can afford the extra memory consumption. Storing values in an array first can allow a better initial Hash table size to be calculated, so rehashes can be minimized (at the expense of throwing more memory at it).
Other data structures may be better suited for searching data than either an array or a hash table. For instance binary trees give optimal search performance.