Provide a brief, high-level overview of what the final product (artifact) of this goal is. Include any relevant resources or dependencies here.
Write tests and implementations for common data structures.
Fork the core-data-structures repository and use the fork as your project artifact.
Use the list below as a reference for each data structure's interface.
The classic LIFO (Last-In-First-Out) one-dimensional list.
From Wikipedia [edited]:
An abstract data type that serves as a collection of elements, with two principal operations: push, which adds an element to the collection, and pop, which removes the most recently added element that was not yet removed. The order in which elements come off a stack gives rise to its alternative name, LIFO (for last in, first out). Additionally, a peek operation may give access to the top without modifying the stack.
const stack = new Stack()
stack.push("foo") // push an element (the string "foo") to the top of the stack.
stack.pop() // returns and removes the top element in the stack or null if the stack is empty.
stack.peek() // returns the top element in the stack or null if the stack is empty.
stack.isEmpty() // returns true if the stack is empty or false if not.
stack.length() // returns the number of elements in the stack.The classic FIFO (First-In-First-Out) one-dimensional list.
From Wikipedia [edited]:
A particular kind of abstract data type or collection in which the entities in the collection are kept in order and the principal (or only) operations on the collection are the addition of entities to the rear terminal position, known as enqueue, and removal of entities from the front terminal position, known as dequeue.
const queue = new Queue()
queue.enqueue("foo") // adds an element (the string "foo") to the back of the queue.
queue.dequeue() // returns and removes the front element in the queue or null if the queue is empty.
queue.front() // returns the front element in queue or null if the queue is empty.
queue.back() // returns the back element in the queue or null if the queue is empty.
queue.isEmpty() // returns true if the queue is empty or false if not.
queue.length() // returns the number of elements in the queueLike a queue, but with priorities.
From Wikipedia [edited]:
An abstract data type which is like a regular queue or stack data structure, but where additionally each element has a "priority" associated with it. An element with high priority is served before an element with low priority. If two elements have the same priority, they are served according to their order in the queue.
const pQueue = new PriorityQueue()
pQueue.enqueue("pizza", 100) // adds an element with priority (number) to the back of the queue.
pQueue.front() // returns the front element (highest priority) in the queue or null if the queue is empty.
pQueue.back() // returns the back element (lowest priority) in the queue or null if the queue is empty.
pQueue.dequeue() // returns and removes the front element (highest priority) in the queue or null if the queue is empty.
pQueue.isEmpty() // returns true if the queue is empty or false if not.
pQueue.length() // returns the number of elements in the queue.Collection of things, without repetition.
From Wikipedia [edited]:
An abstract data type that can store certain values, without any particular order, and no repeated values. It is a computer implementation of the mathematical concept of a finite set. Unlike most other collection types, rather than retrieving a specific element from a set, one typically tests a value for membership in a set.
const set = new Set(['A', 'B', 'C'])
const otherSet = new Set(['B', 'C', 'E'])
set.add('D') // adds an element to the set.
set.isEmpty() // returns true if the set is empty or false if not.
set.contains('B') // returns true the set contains the element or false if not.
set.remove('C') // removes an element (if it exists) from the set.
set.forEach(elem => console.log(elem)) // takes a callback function and passes it each element in sequence.
set.size() // returns the number of elements in the set.
set.union(otherSet) // unions the set with another set and returns the resulting set.
set.intersect(otherSet) // intersects the set with another set and returns the resulting set.
set.difference(otherSet) // returns a set that contains the elements found in the set but not in otherSet.
set.isSubset(otherSet) // returns true if the set is a subset of otherSet or false if not.
set.clone() // returns a cloned set.A list of nodes that link to each other, like a daisy-chain.
From Wikipedia [edited]:
A linear collection of data elements, called nodes, each pointing to the next node by means of a pointer. It is a data structure consisting of a group of nodes which together represent a sequence.
Specifically, the following is a singly-linked list (as opposed to the doubly-linked list below).
const linkedList = new LinkedList()
linkedList.getHeadNode() // Returns the first node in the list
linkedList.getTailNode() // Returns the last node in the list
linkedList.contains("bananas") // Determines whether or not the list contains the provided data
linkedList.find("bananas") // Returns the first node containing the provided data, or -1 if not found
linkedList.insert("bananas") // Inserts a node (with the provided data) to the tail of the list
linkedList.insertFirst("bananas") // Inserts a node (with the provided data) to the head of the list
linkedList.insertBefore("bananas", "apples") // Inserts a node (with data "apples") before the first node containing "bananas"
linkedList.insertAfter("apples", "bananas") // Inserts a node (with data "bananas") after the first node containing "apples"
linkedList.remove() // Removes the tail node from the list
linkedList.removeFirst() // Removes the head node from the list
linkedList.isEmpty() // Determines if the list is empty or not
linkedList.size() // Returns the size of the list (number of nodes)
linkedList.clear() // Clears the list of all nodes/data
const node = linkedList.find("apple")
node.data() // Returns the data ("apple") of the node
node.next() // Returns the next node, or null if no next nodeThe interface for the Doubly-Linked List is the same as the Linked List above, except that the nodes also have a .prev() method, pointing to the previous node in the sequence, or null if it is the head of the list.
From Wikipedia [edited]:
A linked data structure that consists of a set of sequentially linked records called nodes. Each node contains two fields, called links, that are references to the previous and to the next node in the sequence of nodes.
Maps keys to values, like a dictionary or a phone book. Or an object in JavaScript...
From Wikipedia [edited]:
A data structure used to implement an associative array, a structure that can map keys to values. A hash table uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found.
const ht = new HashTable()
ht.put("name", "Zanzibar") // adds a key-value pair to the hash table.
ht.get("name") // returns the data associated with key.
ht.contains("name") // returns true if the hash table contains the key.
ht.iterate((k, v) => console.log(`${k}: ${v}`)) // takes a callback function and passes it each key and value in sequence.
ht.remove("name") // removes a key-value pair by key.
ht.size() // returns the number of key-value pairs in the hash table.
HashTable.hash("name") // generates a hash for the key "name"Maps keys to values, like a dictionary or a phone book. Or an object in JavaScript...
From Wikipedia [edited]:
A particular type of container that allows fast lookup, addition and removal of items, and can be used to implement either dynamic sets of items, or lookup tables that allow finding an item by its key (e.g., finding the phone number of a person by name).
Binary search trees keep their keys in sorted order, so that lookup and other operations can use the principle of binary search: when looking for a key in a tree (or a place to insert a new key), they traverse the tree from root to leaf, making comparisons to keys stored in the nodes of the tree and deciding, based on the comparison, to continue searching in the left or right subtrees.
const bst = new BinarySearchTree()
bst.insert(3) // inserts a node with the specified value into the tree.
bst.search(3) // returns a node object or null if not found.
bst.remove(3) // removes an value's node (if exists) from the tree.
bst.traverse('inOrder', (val) => console.log(val)) // traverse the tree in the defined order (either 'preOrder', 'inOrder', or 'postOrder') and apply function on each node's value.
bst.count() // return the number of nodes in the tree.Nodes connected by vertices with a direction.
From Wikipedia [edited]:
A graph (that is a set of vertices connected by edges), where the edges have a direction associated with them.
const diGraph = new DirectedGraph()
diGraph.addVertex('v1') // adds a vertex to the graph.
diGraph.hasVertex('v1') // returns true if the graph contains the vertex or false if not.
diGraph.addDirection('v1', 'v2' , 3) // adds a direction from 'v1' to 'v2' with a weight (number).
diGraph.hasDirection('v1', 'v2') // returns true if there's a direction from 'v1' to 'v2'.
diGraph.getDirectionWeight('v1', 'v2') // returns direction weight between v1 & v2 or null if no direction exists.
diGraph.visit( 'v1', vertex => console.log(vertex)) // visit all the connected vertices in the graph starting with v1 and apply function on the reached vertex.
diGraph.findShortestPath('v1', 'v2') // returns an array of all the shortest paths between two vertices based on the sum of weights.
diGraph.removeDirection('v1', 'v2') // removes an existing direction between 'v1' and 'v2'.
diGraph.getSeparatedVertices() // returns an array of all the vertices that are separated from the graph.
diGraph.removeVertex('v1') // removes an existing vertex and all its directions (the incoming and outgoing).
diGraph.count() // returns the number of vertices in the graph.Most of the below was shamelessly borrowed from Wikipedia and these libraries:
Why is this goal important? How is it useful? What questions will it raise?
If you spend most of your time programming in high-level languages, you may not realize how often you use data structures or how they are built. Sometimes it's useful to peek under the hood and see how the engine works.
The nice thing is, most data structures are actually quite simple when you get down to it. They have straight-forward, relatively small interfaces.
In a larger sense, being more familiar with data structures is helpful for you ability to think about data more abstractly, and to design better software.
List of specifications (specs) for the completed goal. These are declarative sentences (statements) describing a feature of the final product.
This is a core goal. The specifications are non-negotiable. To complete this goal, you must complete all specs listed below.
- Artifact produced is a fork of the core-data-structures repo.
- Can run all tests with
npm test. - All tests are passing.
- For each data structure identified above, there exists:
- a test file with unit tests for each method and property.
- an implementation file with a correct implementation of the data structure.
Do not remove these specs - they are required for all goals.
- The artifact produced is properly licensed, preferably with the MIT license.
Pick a different programming language from JavaScript (e.g. Ruby, Swift, Python, C, Java...) and write tests & implementations for each.
- Can run all non-JavaScript tests with a single command.
- For each data structure identified above, there exists:
- a test file with unit tests for each method and property in a language other than JavaScript.
- an implementation file with a correct implementation of the data structure in a language other than JavaScript.
Well formatted code
- Code uses a linter, which can be invoked with a command (e.g.
npm run lint). [50 points] - Running the linter on all source code files generates no linting errors. [50 points]
Clear and useful README
- Repository includes a README file with installation and setup instructions. [25 points]
- Repository includes a README file with usage instructions and at least one example use case. [25 points]
Proper dependency management
- There is a command to install dependencies (e.g.
npm install) and it is specified in the installation and setup instructions of the README. [50 points]
Good project management
- Commit messages are concise and descriptive. [25 points]
- All features are added via pull requests. [25 points]
- Every pull request has a description summarizing the changes made. [25 points]
- Every pull request has been reviewed by at least one other person. [25 points]
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.