Software Components Overview

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Below is a list of links to and brief descriptions of common software components. It's useful to keep these in mind and reference them during software design as a shorthand for code that will be written to implement the design. Implementing a component could involve just a few or many lines of code.

A suggested way to use these components in solving/coding a computing problem is to include them using a Separation of Concerns design pattern folded into what I call Layered Logic Problem Solving that includes these steps:

  1. Describe in words the problem to be solved. This might include references to software components listed below. You might also include solution acceptance criteria, possibly in the form of formal acceptance tests.
  2. Draw a block diagram of the main problem solution pieces. For more complex problems, other types of diagrams can be used.
  3. Annotate the block diagram with references to components such as those below.
  4. Code the solution in your chosen programming language.

Communications

  • Client-Server Architecture: components include:
  • HTTP Request: indicates the desired action to be performed on an identified resource - actions include: 
    • GET: retrieve data
    • HEAD: retrieve without response body
    • POST: create new subordinate data
    • OPTIONS: retrieve supported actions
    • PUT: create/replace data
    • DELETE: delete the resource
    • CONNECT: convert connection to TCP/IP tunnel, usually to facilitate HTTPS SSL
  • Internet Protocol Suite:
    • Layers: Application/Transport/Internet/Link
    • Levels: Send/Receive, Controls/Routing, Values/Translations
  • Firewall: monitor/control of incoming/outgoing traffic
  • JSON: JavaScript Object Notation, Human Readable, Attribute-Value Pairs, Java JsonObject
  • Load Balancer: distributes workload across computing resources
  • Message: data sent across processes
  • Network: allows computers to exchange data
  • Protocol: Data Formats, Address Formats, Address Mapping, Routing, Error Detection, Acknowledgements, Sequence Control, Flow Control
  • REST: Representational State Transfer, Stateless, Client-Server, JSON data
  • Socket: endpoint of a connection across a computer network

Data

Algorithms & Processes

Objects

  • Class: object template
  • Container: collection of other objects
  • Design Patterns: reusable solutions to common software problems  
  • Object Oriented Design Patterns: reusable solutions to common object oriented software problems
  • Generics: specify type or method to operate on objects
  • Cloud Computing: internet based computing that provides shared processing resources
  • Interface : all abstract methods
  • Mutability: mutable, immutable, strong vs. weak
  • Objects: variables, data structure or function referenced by an identifier
  • OOD: uses inheritance, abstraction, polymorphism, encapsulation, other design patterns

Views

  • Ajax: groups of technologies for client-side asynchronous web applications
  • Android Fragments: UI segmenting, own lifecycle, FragmentManager
  • HTML5 Canvas:  bitmap, javascript
  • CSS:  describes the presentation of information
  • HTML:  head, body, div, script
  • Widget: simple, easy to use applicator
  • XML: markup language that defines a set of rules for encoding documents

B-tree and Binary Search Tree Data Structures

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B-tree and Binary Search Tree data structures are similar but different ways to store data. The advantage of using search trees is that the test for membership can be performed efficiently provided that the tree is reasonably balanced, that is, the leaves of the tree are at comparable depths.

B-tree Implementations

B-tree implementations are normally commercial. Languages don't typically provide direct B-tree support. To find B-tree implementations, search Google for B-tree software.

Binary Search Tree Implementations

Some languages do provide support for Binary Search Trees. In Java, see the TreeMap class, which implements a variant of the Binary Search Tree, the Red-Black Tree.

Binary Tree Data Structure Comparison

Both structures operate in the average in O(log n) time. Note that in the worst case, B-tree, at O(log n), is faster than Binary Search Tree at O(n).

P Versus NP Complexity Theory

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The P Versus NP issue deals with whether every problem whose solution can be quickly verified by a computer can also be quickly solved by a computer. This is a major unsolved problem in computer science.

In common, practical terms, it deals with how to identify problems that are either extremely difficult or impossible to solve.  In order of difficulty from easy to hard, problems are classified as P, NP, NP-Complete and NP-Hard.

So why do we care? When approaching complex problems, it's useful to have at least some idea of whether the problem is precisely solvable, or if an approximation is the best that can be accomplished. 

Big O Notation

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Big O notation is a way to characterize the time or resources needed to solve a computing problem.  It's particularly useful in comparing various computing algorithms under consideration. Below is a table summarizing Big O functions. The four most commonly referenced and important to remember are:

  • O(1)              Constant access time such as the use of a hash table.
  • O(log n)        Logarithmic access time such as a binary search of a sorted table.
  • O(n)              Linear access time such as the search of an unsorted list.
  • O(n log(n))   Multiple of log(n) access time such as using Quicksort or Mergesort.

Sorting Algorithms

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Before delving into the large variety of sorting algorithms, it's important to understand that there are simple ways to perform sorts in most programming languages. For example, in Java, the Collections class contains a sort method that can be implemented as simply as:

Collections.sort(list);

where list is an object such as an ArrayList. Some aspects of the Java sort() method to note are:

  • You can also use the Java Comparator class methods to implement your own list item comparison functions for specialized sorting order needs.
  • The Java Collections class (plural) is different than the Collection class (singular).
  • Which sorting algorithm (see below) is used in the Collections.sort() implementation depends on the implementation approach chosen by the Java language developers. You can find implementation notes for Java Collections.sort() here. It currently uses a modified merge sort that performs in the range of Big O O(n log(n)).

If you don't want to use a built-in sort function and you're going to implement your own sort function, there's a large list of sorting algorithms to choose from.  Factors to consider in choosing a sort algorithm include:

  • Speed of the algorithm for the best, average and worst sort times. Most algorithms have sort times characterized by Big O functions of O(n), O(n log(n)) or O(n^2).
  • The relative importance of the best, average and worst sort times for the sort application.
  • Memory required to perform the sort.
  • Type of data to be sorted (e.g., numbers, strings, documents).
  • The size of the data set to be sorted.
  • The need for sort stability (preserving the original order of duplicate items).
  • Distribution and uniformity of values to be sorted.
  • Complexity of the sort algorithm.

For a comparison of sorting algorithms based on these and other values see: https://en.wikipedia.org/wiki/Sorting_algorithm#Comparison_of_algorithms.

Here's a quick reference for the major algorithms:

  • Exchange Sorts: based on swapping items
    • Bubble sort: for each pair of indices, swap the items if out of order, loop for items and list.
    • Cocktail sort: variation of bubble sort, passes alternately from top to bottom and bottom to top.
    • Comb sort: variation of bubble sort, selective swap of values
    • Gnome sort: also called the stupd sort, moves values back to just above a value less than it
    • Odd-even sort: developed originally for use with parallel processors, examines odd-even pairs and orders them, alternates pairs until list is ordered
    • Quicksort: divide list into two, with all items on the first list coming before all items on the second list.; then sort the two lists. Repeat. Often the method of choice. One of the fastest sort algorithms
  • Hybrid Sorts: mixture of sort techniques
    • Flashsort: Used on data sets with a known distribution, estimates used for where an element should be placed
    • Introsort: begin with quicksort and switch to heapsort when the recursion depth exceeds a certain level
    • Timsort: adaptative algorithm derived from merge sort and insertion sort. 
  • Insertion sorts: builds the final sorted array one item at a time
    • Insertion sort: determine where the current item belongs in the list of sorted ones, and insert it there
    • Library sort: like library shelves, space is created for new entries within groups such as first letters, space is removed at end of sort
    • Patience sorting: based on the solitaire card game, uses piles of "cards"
    • Shell sort: an attempt to improve insertion sort
    • Tree sort (binary tree sort): build binary tree, then traverse it to create sorted list
    • Cycle sort: in-place with theoretically optimal number of writes
  • Merge sorts: takes advantage of the ease of merging already sorted lists into a new sorted list
    • Merge sort: sort the first and second half of the list separately, then merge the sorted lists
    • Strand sort: repeatedly pulls sorted sublists out of the list to be sorted and merges them with the result array
  • Non-comparison sorts
    • Bead sort: can only be used on positive integers, performed using mechanics like beads on an abacus
    • Bucket sort: works by partitioning an array into a number of buckets, each bucket is then sorted individually using the best technique, the buckets are then merged
    • Burstsort: used for sorting strings, employs growable arrays
    • Counting sort: sorts a collection of objects according to keys that are small integers
    • Pigeonhole sort: suitable for sorting lists of elements where the number of elements and number of possible key values are approximately the same, uses auxiliary arrays for grouping values
    • Postman sort: variant of Bucket sort which takes advantage of hierarchical structure
    • Radix sort: sorts strings letter by letter
  • Selection sorts: in place comparison sorts
    • Heapsort: convert the list into a heap, keep removing the largest element from the heap and adding it to the end of the list
    • Selection sort: pick the smallest of the remaining elements, add it to the end of the sorted list
    • Smoothsort
  • Other
  • Unknown class

 

SQL Database Join Operations

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It can be challenging to remember the details of all the SQL Join operations.  A convenient way to do this is to use Venn diagrams shown below. For examples of SQL Joins, go here. 

Commonly used SQL Join operations are:

  • inner join: requires each record in the two joined tables to have matching records.
  • outer join: does not require each record in the two joined tables to have a matching record.

What is HTML5 Canvas?

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The Canvas feature of HTML5 is one of the most exciting new developments in web based capabilities in many years. Here are some key aspects:

HTML5 - Canvas is a part of the latest revision and improvement of the previous version (HTML4, released in the late 1990's) markup language for displaying web pages on the Internet.

Canvas Tag - The <canvas> tag joins the other HTML tags (there are around 100 now) as a way to include single or multiple Canvas areas on a web  page. 

Bit Map Display - The Canvas area you define with a <canvas> tag is a bit map display. You can manipulate individual pixels to draw objects, images, animations and video.

JavaScript Control - The JavaScript language is used to control what is seen on the Canvas. The JavaScript application code is placed between the <head></head> and <script></script> tags of the web page.

Browser Based - The computing needed to create a Canvas display is done within the browser. In other words, it's client based, not server based. The means that creating a Canvas application is simpler than one requiring server based programming. It also means that the computing power needed to generate the Canvas display is not concentrated in single servers. Each user's browser handles its own work.

Animation - Canvas applications can generate animations. This is accomplished using callbacks from the browser. The Canvas application designates one of its functions to be called from the browser at specified time intervals. During each of these callbacks, the application draws a new Canvas display, moving objects slightly. When viewed as a continuous flow of Canvas displays, an animation is generated.

Audio/Video - Audio and video can be incorporated into your Canvas applications. Moving objects can create sounds and video images can be melded into other application objects. This is done without the use of any audio or video plugins.

Gaming - Game developers have all to tools necessary to create compelling HTML5 Canvas games.

Fun - Putting aside the technical aspects of Canvas ... it can be described as just plain fun. An HTML5 Canvas is a place where as a developer you can express your creative energies and as a user you can have expanded and enjoyable experiences.

For a more details look at HTML5 Canvas, watch for my upcoming book: HTML5 Canvas for Dummies.

Re-tooling for the Information Age

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The internet, web sites, smartphones, tablets, social networking, cloud computing ... the Information Age is getting into full swing. Just as the U.S. transitioned completely to the Industrial Age in the 1930's, we are now completing our move into the Information Age.
 
 
Whether as an individual or business, it will become increasingly difficult to compete without using Information Age tools and processes. Emerging markets, with their lower cost base, will take most of the remaining Industrial Age jobs and opportunities. Manufacturing in the U.S. won't disappear, but it too will have to adopt Information Age ways.
 
Some, often those of older generations, regret the popularity of new, fast paced tools such as social networking. It would be more productive to give them a try and leverage their value.
 
The rapid spread of smartphones is accelerating  the use of tools such as social networking. They offer a great way to take the plunge into a new world.

Cloud Computing and Smartphones

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The news is full of stories about interest in and growth of cloud computing ... relying on services hosted on servers owned, operated and maintained by others. Some get nervous about having their information located in places they don't control. The growth of smartphones should, however, accelerate the move to cloud computing and gradually ameliorate those fears.

Relying on cloud computing with a smartphone, or tablet, isn't an option. It's all controlled "in the clouds." Faster and more reliable networks will also help. Over time, whether computing is done on your device or in the cloud should become a somewhat meaningless distinction.