Programming & Web Design Articles

Article / Updated 04-28-2025

For enterprises whose growth depends on the innovation of their developers and data scientists, success moves at the speed of their development environments. Efficiency, effectiveness, and the ability to build without friction — that’s what sets the pace. The question is simple enough: Do your developers and data scientists work their magic in cloud development environments (CDEs), or is their work bogged down by a legacy development environment? Read on to find out why local development environments and other traditional solutions may be holding back your developers, and how cloud development environments can set them free. How traditional development environments are lacking What are some of the ways the wrong development environment may be making your company’s progress sluggish? Local development environments: This was yesterday’s standard, with all developers managing their own environments on their own machines. All that local control gets in the way of getting ahead. It’s complicated and time-consuming to configure tools and dependencies on each individual environment. You wind up with inconsistencies that slow things down. And don’t even think about scaling this approach. Virtual desktop infrastructures (VDIs): VDIs help to centralize environments, but they’re often not really workable for development. They’re expensive and complicated to maintain. They also tend to suffer from sluggish performance, and that’s no way to keep developers happy. Shadow virtual machines (VMs): Developers may get around these issues with ad hoc VMs, but they end up creating different problems. “Shadow” solutions may be lacking in governance and are often overprovisioned. That makes them potential security risks, not to mention wasteful of resources. How cloud development environments can help A fully managed environment is the answer to the challenges of these more disjointed solutions from yesteryear. The CDE may be hosted in the cloud or via on-premises infrastructure, and it brings together all the necessary tools, libraries, and infrastructure needed for efficient development. It’s a development workspace that can be accessed from anywhere, not tied to a specific location or machine. It’s great for remote work, and because of its centralized infrastructure, it’s a lot more secure than storing code and data on developers’ laptops. Developers and data scientists can easily tap into it for coding applications and training machine learning (ML) models, getting up and running in minutes. Provisioning is automatic, so everything developers need is right there and ready to go. It can all be controlled from a centralized interface by authorized team members, ensuring only the right people can access code and data. And the compute and storage resources are easily scalable. How CDEs make developers more productive Traditional local development environments tend to be frustratingly slow when it comes to onboarding new developers or moving developers to new projects (and developers tend to take on new projects multiple times a year, of course). Provisioning environments can take days or even weeks, thanks to all the complexities of dependencies and the need for approvals. Meanwhile, if developers are off working in their own local environments, that also leads to problems requiring time-consuming troubleshooting. Mismatched environments are the enemy of success. What’s more, resource constraints can be a hangup in local development environments. CDEs, on the other hand, can be preconfigured, so developers just log in and get to work. Platform engineers can make global updates and ensure consistencies across the CDE. Configuration drift is no longer a problem. As for resource constraints, the cloud can fix that with high-performance resources that scale as needed. Developers can set up resource-intensive tasks such as ML model training in the cloud, and use their local laptops to get other work done in the meantime. Why cloud development environments provide better security Local development environments and various noncentralized alternatives can pose security threats, due to a lack of adequate governance and a hard-to-manage attack surface. Working with intellectual property and other sensitive data on developer laptops or unapproved VMs is risky. If developers need to pull sensitive data to their laptops for such things as ML model training, you’re tempting fate, because security problems don’t just hold developers back — they can set back the entire organization. CDEs, on the other hand, can live in a cloud environment or be centralized in on-premises infrastructure. They can be air-gapped if need be. Source code and data can stay in secure cloud or on-premise locations, along with any compute resources needed for dealing with them, so sensitive data never needs to leave the safety of home. Compliance teams aren’t fond of local environments because they’re tricky to monitor. CDEs, on the other hand, offer better monitoring and auditing capabilities, without getting in the way of the developers’ work. How CDEs save both money and time Face it — local development environments can be costly. Local hardware isn’t cheap, especially the high-end hardware needed for resource-intensive processes. But if your enterprise tries to trim those costs, developers are likely to be displeased. The reality is, CDEs not only provide a better developer experience and greater security, they also save money and time. Developers can use most cost-effective machines that are less powerful and longer-lasting, because the heavy lifting is happening in the CDE, not on the local machine. What’s more, CDEs can tap into automation to optimize resource utilization. Auto-start and auto-stop features mean unused environments aren’t sitting there wasting money. Bin-packing multiple developer workspaces onto a single VM can reduce infrastructure costs, too. And once again, helping developers be more efficient — by easing onboarding and transitioning, while banishing sluggish equipment — saves money, too. Time is, after all, money. If you can save hours and days and weeks of wasted developer time, you’ll get a whole lot more out of the money you’re paying them. To learn more about the benefits of CDEs, download your free Cloud Development Environments For Dummies, CoderTechnologies, Inc. Special Edition e-book.

Article / Updated 10-28-2024

The simplest data collection in Python is a list. A list is any list of data items, separated by commas, inside square brackets. Typically, you assign a name to the Python list using an = sign, just as you would with variables. If the list contains numbers, then don't use quotation marks around them. For example, here is a list of test scores: scores = [88, 92, 78, 90, 98, 84] If the list contains strings then, as always, those strings should be enclosed in single or double quotation marks, as in this example: To display the contents of a list on the screen, you can print it just as you would print any regular variable. For example, executing print(students) in your code after defining that list shows this on the screen. ['Mark', 'Amber', 'Todd', 'Anita', 'Sandy'] This may not be exactly what you had in mind. But don’t worry, Python offers lots of great ways to access data in lists and display it however you like. Referencing Python list items by position Each item in a list has a position number, starting with zero, even though you don’t see any numbers. You can refer to any item in the list by its number using the name for the list followed by a number in square brackets. In other words, use this syntax: <em>listname</em>[<em>x</em>] Replace <em><code>listname</code></em> with the name of the list you're accessing and replace <em><code>x</code></em> with the position number of item you want. Remember, the first item is always number zero, not one. For example, in the first line below, I define a list named <code>students</code>, and then print item number zero from that list. The result, when executing the code, is that the name <code>Mark</code> is displayed. students = ["Mark", "Amber", "Todd", "Anita", "Sandy"] print(students[0]) Mark When reading access list items, professionals use the word sub before the number. For example, students[0] would be spoken as students sub zero. This next example shows a list named scores. The print() function prints the position number of the last score in the list, which is 4 (because the first one is always zero). scores = [88, 92, 78, 90, 84] print(scores[4]) 84 If you try to access a list item that doesn't exist, you get an “index out of range” error. The index part is a reference to the number inside the square brackets. For example, the image below shows a little experiment in a Jupyter notebook where a list of scores was created and then the printing of score[5] was attempted. It failed and generated an error because there is no scores[5]. There's only scores[0], scores[1], scores[2], scores[3], and scores[4] because the counting always starts at zero with the first one on the list. Looping through a Python list To access each item in a list, just use a for loop with this syntax: for <em>x</em> in <em>list</em>: Replace >x with a variable name of your choosing. Replace list with the name of the list. An easy way to make the code readable is to always use a plural for the list name (such as students, scores). Then you can use the singular name (student, score) for the variable name. You don't need to use subscript numbers (numbers in square brackets) with this approach either. For example, the following code prints each score in the scores list: for score in scores: print(score) Remember to always indent the code that’s to be executed within the loop. This image shows a more complete example where you can see the result of running the code in a Jupyter notebook. Seeing whether a Python list contains an item If you want your code to check the contents of a list to see whether it already contains some item, use in <em>listname</em> in an if statement or a variable assignment. For example, the code in the image below creates a list of names. Then, two variables store the results of searching the list for the names Anita and Bob. Printing the contents of each variable shows True for the one where the name (Anita) is in the list. The test to see whether Bob is in the list proves False. Getting the length of a Python list To determine how many items are in a list, use the len() function (short for length). Put the name of the list inside the parentheses. For example, type the following code into a Jupyter notebook or Python prompt or whatever: students = ["Mark", "Amber", "Todd", "Anita", "Sandy"] print(len(students)) Running that code produces this output: 5 There are indeed five items in the list, though the last one is always one less than the number because Python starts counting at zero. So the last one, Sandy, actually refers to students[4] and not students[5]. Appending an item to the end of a Python list When you want your Python code to add a new item to the end of a list, use the .append() method with the value you want to add inside the parentheses. You can use either a variable name or a literal value inside the quotation marks. For instance, in the following image the line that reads students.append("Goober") adds the name Goober to the list. The line that reads students.append(new_student) adds whatever name is stored in the variable named new_student to the list. The .append() method always adds to the end of the list. So when you print the list you see those two new names at the end. You can use a test to see whether an item is in a list and then append it only when the item isn't already there. For example, the code below won’t add the name Amber to the list because that name is already in the list: student_name = "Amanda" #Add student_name but only if not already in the list. if student_name in students: print (student_name + " already in the list") else: students.append(student_name) print (student_name + " added to the list") Inserting an item into a Python list Although the append() method allows you to add an item to the end of a list, the insert() method allows you to add an item to the list in any position. The syntax for insert() is <em>listname</em>.insert(<em>position</em>, <em>item</em>) Replace listname with the name of the list, position with the position at which you want to insert the item (for example, 0 to make it the first item, 1 to make it the second item, and so forth). Replace item with the value, or the name of a variable that contains the value, that you want to put into the list. For example, the following code makes Lupe the first item in the list: #Create a list of strings (names). students = ["Mark", "Amber", "Todd", "Anita", "Sandy"] student_name = "Lupe" # Add student name to front of the list. students.insert(0,student_name) #Show me the new list. print(students) If you run the code, print(students) will show the list after the new name has been inserted, as follows: ['Lupe', 'Mark', 'Amber', 'Todd', 'Anita', 'Sandy'] Changing an item in a Python list You can change an item in a list using the = assignment operator (check out these common Python operators) just like you do with variables. Just make sure you include the index number in square brackets of the item you want to change. The syntax is: listname[index]=newvalue Replace listname with the name of the list; replace index with the subscript (index number) of the item you want to change; and replace newvalue with whatever you want to put in the list item. For example, take a look at this code: #Create a list of strings (names). students = ["Mark", "Amber", "Todd", "Anita", "Sandy"] students[3] = "Hobart" print(students) When you run this code, the output is as follows, because Anita's name has been changed to Hobart. ['Mark', 'Amber', 'Todd', 'Hobart', 'Sandy'] Combining Python lists If you have two lists that you want to combine into a single list, use the extend() function with the syntax: <em>original_list</em>.extend(<em>additional_items_list</em>) In your code, replace original_list with the name of the list to which you’ll be adding new list items. Replace additional_items_list with the name of the list that contains the items you want to add to the first list. Here is a simple example using lists named list1 and list2. After executing list1.extend(list2), the first list contains the items from both lists, as you can see in the output of the print() statement at the end. # Create two lists of Names. list1 = ["Zara", "Lupe", "Hong", "Alberto", "Jake"] list2 = ["Huey", "Dewey", "Louie", "Nader", "Bubba"] # Add list2 names to list1. list1.extend(list2) # Print list 1. print(list1) ['Zara', 'Lupe', 'Hong', 'Alberto', 'Jake', 'Huey', 'Dewey', 'Louie', 'Nader', 'Bubba'] Easy Parcheesi, no? Removing Python list items Python offers a remove() method so you can remove any value from the list. If the item is in the list multiple times, only the first occurrence is removed. For example, the following code shows a list of letters with the letter C repeated a few times. Then the code uses letters.remove("C") to remove the letter C from the list: # Remove "C" from the list. letters.remove("C") #Show me the new list. print(letters) When you actually execute this code and then print the list, you'll see that only the first letter C has been removed: ['A', 'B', 'D', 'C', 'E', 'C'] If you need to remove all of an item, you can use a while loop to repeat the .remove as long as the item still remains in the list. For example, this code repeats the .remove as long as the “C” is still in the list. #Create a list of strings. letters = ["A", "B", "C", "D", "C", "E", "C"] If you want to remove an item based on its position in the list, use pop() with an index number rather than remove() with a value. If you want to remove the last item from the list, use pop() without an index number. For example, the following code creates a list, one line removes the first item (0), and another removes the last item (pop() with nothing in the parentheses). Printing the list shows those two items have been removed: #Create a list of strings. letters = ["A", "B", "C", "D", "E", "F", "G"] #Remove the first item. letters.pop(0) #Remove the last item. letters.pop() #Show me the new list. print(letters) Running the code shows that the popping the first and last items did, indeed, work: ['B', 'C', 'D', 'E', 'F'] When you pop() an item off the list, you can store a copy of that value in some variable. For example this image shows the same code as above. However, it stores copies of what's been removed in variables named first_removed and last_removed. At the end it prints the Python list, and also shows which letters were removed. Python also offers a del (short for delete) command that deletes any item from a list based on its index number (position). But again, you have to remember that the first item is zero. So, let's say you run the following code to delete item number 2 from the list: # Create a list of strings. letters = ["A", "B", "C", "D", "E", "F", "G"] # Remove item sub 2. del letters[2] print(letters) Running that code shows the list again, as follows. The letter C has been deleted, which is the correct item to delete because letters are numbered 0, 1, 2, 3, and so forth. ['A', 'B', 'D', 'E', 'F', 'G'] You can also use del to delete an entire list. Just don’t use the square brackets and the index number. For example, the code you see below creates a list then deletes it. Trying to print the list after the deletion causes an error, because the list no longer exists when the print() statement is executed. Clearing out a Python list If you want to delete the contents of a list but not the list itself, use .clear(). The list still exists; however, it contains no items. In other words, it's an empty list. The following code shows how you could test this. Running the code displays [] at the end, which lets you know the list is empty: # Create a list of strings. letters = ["A", "B", "C", "D", "E", "F", "G"] # Clear the list of all entries. letters.clear() # Show me the new list. print(letters) Counting how many times an item appears in a Python list You can use the Python count() method to count how many times an item appears in a list. As with other list methods, the syntax is simple: <em>listname</em>.count(<em>x</em>) Replace listname with the name of your list, and x with the value you're looking for (or the name of a variable that contains that value). The code in the image below counts how many times the letter B appears in the list, using a literal B inside the parentheses of .count(). This same code also counts the number of C grades, but that value was stored in a variable just to show the difference in syntax. Both counts worked, as you can see in the output of the program at the bottom. One was added to count the F's, not using any variables. The F’s were counted right in the code that displays the message. There are no F grades, so this returns zero, as you can see in the output. When trying to combine numbers and strings to form a message, remember you have to convert the numbers to strings using the str() function. Otherwise, you get an error that reads something like can only concatenate str (not "int") to str. In that message, int is short for integer, and str is short for string. Finding a Python list item's index Python offers an .index() method that returns a number indicating the position, based on index number, of an item in a list. The syntax is: <em>listname</em>.index(<em>x</em>) As always, replace listname with name of the list you want to search. Replace x what whatever you're looking for (either as a literal or as a variable name, as always). Of course, there’s no guarantee that the item is in the list, and even if it is, there’s no guarantee that the item is in the list only once. If the item isn’t in the list, then an error occurs. If the item is in the list multiple times, then the index of the first matching item is returned. The following image shows an example where the program crashes at the line f_index = grades.index(look_for) because there is no F in the list. An easy way to get around that problem is to use an if statement to see whether an item is in the list before you try to get its index number. If the item isn't in the list, display a message saying so. Otherwise, get the index number and show it in a message. That code is as follows: # Create a list of strings. grades = ["C", "B", "A", "D", "C", "B", "C"] # Decide what to look for look_for = "F" # See if the item is in the list. if look_for in grades: # If it's in the list, get and show the index. print(str(look_for) + " is at index " + str(grades.index(look_for))) else: # If not in the list, don't even try for index number. print(str(look_for) + " isn't in the list.") Alphabetizing and sorting Python lists Python offers a sort() method for sorting lists. In its simplest form, it alphabetizes the items in the list (if they’re strings). If the list contains numbers, they’re sorted smallest to largest. For a simple sort like that, just use sort() with empty parentheses: <em>listname</em>.sort() Replace listname with the name of your list. The following image shows an example using a list of strings and a list of numbers. In the example, a new list was created for each of them simply by assigning each sorted list to a new list name. Then the code prints the contents of each sorted list. If your list contains strings with a mixture of uppercase and lowercase letters, and if the results of the sort don't look right, try replacing .sort() with .sort(key=lambda s:s.lower()) and then running the code again. Dates are a little trickier because you can’t just type them in as strings, like "12/31/2020". They have to be the date data type to sort correctly. This means using the datetime module and the date() method to define each date. You can add the dates to the list as you would any other list. For example, in the following line, the code creates a list of four dates, and the code is perfectly fine. dates = [dt.date(2020,12,31), dt.date(2019,1,31), dt.date(2018,2,28), dt.date(2020,1,1)] The computer certainly won't mind if you create the list this way. But if you want to make the code more readable to yourself or other developers, you may want to create and append each date, one at a time, so just so it’s a little easier to see what’s going on and so you don’t have to deal with so many commas in one line of code. The image below shows an example where an empty list named datelist was created: datelist = [] Then one date at a time was appended to the list using the dt.date(<em>year</em>,<em>month</em>,<em>day</em>) syntax. After the list is created, the code uses datelist.sort() to sort them into chronological order (earliest to latest). You don’t need to use print(datelist) in that code because that method displays the dates with the data type information included, like this: [datetime.date(2018, 2, 28), datetime.date(2019, 1, 31), datetime.date (2020, 1, 1), datetime.date(2020, 12, 31)] Not the easiest list to read. So, rather than print the whole list with one print() statement, you can loop through each date in the list, and printed each one formatted with the f-string %m/%d/%Y. This displays each date on its own line in mm/dd/yyyy format, as you can see at the bottom of the image above. If you want to sort items in reverse order, put reverse=True inside the sort() parentheses (and don't forget to make the first letter uppercase). The image below shows examples of sorting all three lists in descending (reverse) order using reverse=True. Reversing a Python list You can also reverse the order of items in a list using the .reverse method. This is not the same as sorting in reverse, because when you sort in reverse, you still actually sort: Z–A for strings, largest to smallest for numbers, latest to earliest for dates. When you reverse a list, you simply reverse the items in the list, no matter their order, without trying to sort them in any way. The following code shows an example in which you reverse the order of the names in the list and then print the list. The output shows the list items reversed from their original order: # Create a list of strings. names = ["Zara", "Lupe", "Hong", "Alberto", "Jake"] # Reverse the list names.reverse() # Print the list print(names) ['Jake', 'Alberto', 'Hong', 'Lupe', 'Zara'] Copying a Python list If you ever need to work with a copy of a list, use the .copy() method so as not to alter the original list,. For example, the following code is similar to the preceding code, except that instead of reversing the order of the original list, you make a copy of the list and reverse that one. Printing the contents of each list shows how the first list is still in the original order whereas the second one is reversed: # Create a list of strings. names = ["Zara", "Lupe", "Hong", "Alberto", "Jake"] # Make a copy of the list backward_names = names.copy() # Reverse the copy backward_names.reverse() # Print the list print(names) print(backward_names) ['Zara', 'Lupe', 'Hong', 'Alberto', 'Jake'] ['Jake', 'Alberto', 'Hong', 'Lupe', 'Zara'] For future references, the following table summarizes the methods you've learned about. Methods for Working with Lists Method What it Does append() Adds an item to the end of the list. clear() Removes all items from the list, leaving it empty. copy() Makes a copy of a list. count() Counts how many times an element appears in a list. extend() Appends the items from one list to the end of another list. index() Returns the index number (position) of an element within a list. insert() Inserts an item into the list at a specific position. pop() Removes an element from the list, and provides a copy of that item that you can store in a variable. remove() Removes one item from the list. reverse() Reverses the order of items in the list. sort() Sorts the list in ascending order. Put reverse=True inside the parentheses to sort in descending order.

Article / Updated 10-28-2024

The words int and double are examples of primitive types (also known as simple types) in Java. The Java language has exactly eight primitive types. As a newcomer to Java, you can pretty much ignore all but four of these types. (As programming languages go, Java is nice and compact that way.) The types that you shouldn’t ignore are int, double, char, and boolean. The char type Several decades ago, people thought computers existed only for doing big number-crunching calculations. Nowadays, nobody thinks that way. So, if you haven’t been in a cryogenic freezing chamber for the past 20 years, you know that computers store letters, punctuation symbols, and other characters. The Java type that’s used to store characters is called char. The code below has a simple program that uses the char type. This image shows the output of the program in the code below. public class CharDemo { public static void main(String args[]) { char myLittleChar = 'b'; char myBigChar = Character.toUpperCase(myLittleChar); System.out.println(myBigChar); } } In this code, the first initialization stores the letter b in the variable myLittleChar. In the initialization, notice how b is surrounded by single quote marks. In Java, every char literally starts and ends with a single quote mark. In a Java program, single quote marks surround the letter in a char literal. Character.toUpperCase. The Character.toUpperCase method does just what its name suggests — the method produces the uppercase equivalent of the letter b. This uppercase equivalent (the letter B) is assigned to the myBigChar variable, and the B that’s in myBigChar prints onscreen. If you’re tempted to write the following statement, char myLittleChars = 'barry'; //Don't do this please resist the temptation. You can’t store more than one letter at a time in a char variable, and you can’t put more than one letter between a pair of single quotes. If you’re trying to store words or sentences (not just single letters), you need to use something called a String. If you’re used to writing programs in other languages, you may be aware of something called ASCII character encoding. Most languages use ASCII; Java uses Unicode. In the old ASCII representation, each character takes up only 8 bits, but in Unicode, each character takes up 8, 16, or 32 bits. Whereas ASCII stores the letters of the Roman (English) alphabet, Unicode has room for characters from most of the world’s commonly spoken languages. The only problem is that some of the Java API methods are geared specially toward 16-bit Unicode. Occasionally, this bites you in the back (or it bytes you in the back, as the case may be). If you’re using a method to write Hello on the screen and H e l l o shows up instead, check the method’s documentation for mention of Unicode characters. It’s worth noticing that the two methods, Character.toUpperCase and System.out.println, are used quite differently in the code above. The method Character.toUpperCase is called as part of an initialization or an assignment statement, but the method System.out.println is called on its own. The boolean type A variable of type boolean stores one of two values: true or false. The code below demonstrates the use of a boolean variable. public class ElevatorFitter2 { public static void main(String args[]) { System.out.println("True or False?"); System.out.println("You can fit all ten of the"); System.out.println("Brickenchicker dectuplets"); System.out.println("on the elevator:"); System.out.println(); int weightOfAPerson = 150; int elevatorWeightLimit = 1400; int numberOfPeople = elevatorWeightLimit / weightOfAPerson; <strong> boolean allTenOkay = numberOfPeople >= 10;</strong> System.out.println(allTenOkay); } } In this code, the allTenOkay variable is of type boolean. To find a value for the allTenOkay variable, the program checks to see whether numberOfPeople is greater than or equal to ten. (The symbols >= stand for greater than or equal to.) At this point, it pays to be fussy about terminology. Any part of a Java program that has a value is an expression. If you write weightOfAPerson = 150; then 150 ,is an expression (an expression whose value is the quantity 150). If you write numberOfEggs = 2 + 2; then 2 + 2 is an expression (because 2 + 2 has the value 4). If you write int numberOfPeople = elevatorWeightLimit / weightOfAPerson; then elevatorWeightLimit / weightOfAPerson is an expression. (The value of the expression elevatorWeightLimit / weightOfAPerson depends on whatever values the variables elevatorWeightLimit and weightOfAPerson have when the code containing the expression is executed.) Any part of a Java program that has a value is an expression. In the second set of code, numberOfPeople >= 10 is an expression. The expression’s value depends on the value stored in the numberOfPeople variable. But, as you know from seeing the strawberry shortcake at the Brickenchicker family’s catered lunch, the value of numberOfPeople isn’t greater than or equal to ten. As a result, the value of numberOfPeople >= 10 is false. So, in the statement in the second set of code, in which allTenOkay is assigned a value, the allTenOkay variable is assigned a false value. In the second set of code, System.out.println() is called with nothing inside the parentheses. When you do this, Java adds a line break to the program’s output. In the second set of code, System.out.println() tells the program to display a blank line.

Article / Updated 10-28-2024

When coding your app, you will almost inevitably write code that does not behave as you intended. HTML and CSS are relatively forgiving, with the browser even going so far as to insert tags so the page renders properly. However, JavaScript isn’t so forgiving, and the smallest error, such as a missing quotation mark, can cause the page to not render properly. Errors in web applications can consist of syntax errors, logic errors, and display errors. Often, the most likely culprit causing errors in your code will be syntax related. Here are some common errors to check when debugging your code: Opening and closing tags: In HTML, every opening tag has a closing tag, and you always close the most recently opened tag first. Right and left angle brackets: In HTML, every left angle bracket < has a right angle bracket >. Right and left curly brackets: In CSS and JavaScript, every left curly bracket must have a right curly bracket. It can be easy to accidentally delete it or forget to include it. Indentation: Indent your code and use plenty of tabs and returns to make your code as readable as possible. Proper indentation will make it easier for you to identify missing tags, angle brackets, and curly brackets. Misspelled statements: Tags in any language can be misspelled, or spelled correctly but not part of the specification. For example, in HTML, <img scr="image.jpg"> is incorrect because scr should really be src for the image to render properly. Similarly, in CSS font-color looks like it is spelled correctly but no such property exists. The correct property to set font color is just color. Keep these errors in mind when debugging — they may not solve all your problems, but they should solve many of them. If you have tried the steps above and still cannot debug your code, tweet @nikhilgabraham and include the #codingFD hashtag and your codepen.io URL in your tweet.

Article / Updated 05-30-2024

For each web coding issue identified by a validator, you need to determine what course of action to take. Although some culprits that repeatedly crop up are easy to fix, such as missing alt text and <noscript> tags, you’re bound to find coding issues that completely baffle and stump you. For instance, if you get an error message that reads XML Parsing Error: Opening and ending tag mismatch: br line 52 and body, it might be difficult to figure out what that means, let alone why it was caused and how you should fix it. As a strategy then, try to fix the issues within the code from the top down, as they’re listed in the validation results, because sometimes fixing one issue resolves another. With the XML parsing error, that issue might disappear when you correct for an omitted closing element on a <br /> tag listed earlier in the error results. The best way to find out how to code better and make fewer mistakes before validation testing is to make lots of honest mistakes and figure out how to correct them on your own. Most often, you can fix noncompliant code by hand or with the help of a good HTML editor. To help you identify some of the more common coding mistakes, here several code issues along with suggestions about how to fix them. Problem Solution alt text attribute missing from <img> tag Add the alternative text attribute, either with or without a description, as in <img src="images/logo.gif" width="150" height="150" alt="Pete’s Pizza"> <img src="images/flourish.gif" width="200" height="150" alt=""> . <noscript> tags missing from code Add <noscript> tags below each instance when JavaScript is present in in-line JavaScript or at the end of the content before the closing body tag. Between the <noscript> tags, insert HTML content (text, graphics, media files, and so on) that describes the function of the JavaScript and, when appropriate, how visitors can access the information revealed by it, as shown here: <script language="JavaScript" src="bookmark.js" type="text/javascript"></script><noscript>The JavaScript used on this page provides a quick link that allows visitors to automatically bookmark this page. As an alternative, please use your browser’s Bookmark This Page feature.</noscript> Flashing or flickering element(s) detected, such as animated GIFs, Java applets, and other multimedia plug-ins Adjust the speed of any animations to avoid causing the screen to flicker with a frequency between 2 Hz and 55 Hz. Animations that exceed these two measures may cause seizures in visitors with photosensitive epilepsy. No DOCTYPE specified Add a valid DOCTYPE above the opening <head> tag. No HTTP charset parameter specified This special meta tag specifies the character set used in the HTML code. Some HTML editors include it automatically when generating new blank web pages. If validation finds that this tag is missing from your HTML or XHTML code, insert the following code by hand: <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> . For HTML5, insert <meta charset="utf-8"> . No <title> tag specified Add a unique title between <title> tags in the head area on each page. No <meta> tags specified Add meta keywords and meta description tags to the head of each page. These can be identical on every page on the site. If desired, you may also add additional meta tags as needed. No Robots tags specified Add the Robots <meta> tag in the head of the page to instruct web spiders and robots whether to index the page and follow any hyperlinks, such as <meta name="Robots" content="All"> . Deprecated <font> tags detected Move all the presentation markup of the HTML (page, fonts, tables, links, and so on) to an external CSS file and remove all <font> tags and HTML and inline formatting attributes. Deprecated table height attribute detected Control table cell heights, when necessary, with CSS styles. Style attributes detected in the opening <body> tag Move body attributes, like margin attributes and background page color, to a BODY tag redefine style in an external CSS file. type attribute not specified for JavaScript or CSS Add the type="text/css" attribute for <style> tags and the type="text/javascript" attribute for <script> tags: <style type="text/css" ><script type="text/javascript"> . Entity name used instead of entity number Change the entity name to an entity number, such as using $#169; instead of © to create the copyright symbol (c). No background color attribute was specified for a CSS style that specifies text color Provide each style that contains a text color attribute with an attending background color attribute. The background color should match, or closely match, the background color upon which the text will display on. When you’re finished identifying and adjusting all the noncompliant code identified by the validation tools, and have fixed everything that needed fixing, move on to the retesting and acceptable failure phase of the testing process.

Article / Updated 05-30-2024

The success of your DevOps initiative relies heavily on following the process, but it’s also important to use the right tools. Selecting a cloud service provider isn’t an easy choice, especially when DevOps is your driving motivation. GCP (Google Cloud Platform), AWS (Amazon Web Services), and Azure have more in common than they do apart. Often, your decision depends more on your DevOps team’s comfort level with a particular cloud provider or your current stack more than the cloud provider itself. After you’ve decided to move to the cloud, the next decision is to decide on a cloud provider that fits your DevOps needs. Here are some things to consider when evaluating cloud providers with DevOps principles in mind: Solid track record. The cloud you choose should have a history of responsible financial decisions and enough capital to operate and expand large datacenters over decades. Compliance and risk management. Formal structure and established compliance policies are vital to ensure that your data is safe and secure. Ideally, review audits before you sign contracts. Positive reputation. Customer trust is absolutely key. Do you trust that you can rely on this cloud provider to continue to grow and support your evolving DevOps needs? Service Level Agreements (SLAs). What level of service do you require? Typically cloud providers offer various levels of uptime reliability based on cost. For example, 99.9 percent uptime will be significantly cheaper than 99.999 percent uptime. Metrics and monitoring. What types of application insights, monitoring, and telemetry does the vendor supply? Be sure that you can gain an appropriate level of insight into your systems in as close to real-time as possible. Finally, ensure the cloud provider you choose has excellent technical capabilities that provide services that meet your specific DevOps needs. Generally, look for Compute capabilities Storage solutions Deployment features Logging and monitoring Friendly user interfaces You should also confirm the capability to implement a hybrid cloud solution in case you need to at some point, as well as to make HTTP calls to other APIs and services. The three major cloud providers are Google Cloud Platform (GCP), Microsoft Azure, and Amazon web Services (AWS). You can also find smaller cloud providers and certainly a number of private cloud providers, but the bulk of what you need to know comes from comparing the public cloud providers. Amazon Web Services (AWS) As do the other major public cloud providers, AWS provides on-demand computing through a pay-as-you-go subscription. Users of AWS can subscribe to any number of services and computing resources. Amazon is the current market leader among cloud providers, holding the majority of cloud subscribers. It offers a robust set of features and services in regions throughout the world. Two of the most well-known services are Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (Amazon S3). As with other cloud providers, services are accessed and infrastructure is provisioned through APIs. Microsoft Azure Before Microsoft launched this cloud provider as Microsoft Azure, it was called Windows Azure. Microsoft designed it to do just what the name implies — serve as a cloud provider for traditionally Windows IT organizations. But as the market became more competitive and Microsoft started to better understand the engineering landscape, Azure adapted, grew, and evolved. Although still arguably less robust than AWS, Azure is a well-rounded cloud provider focused on user experience. Through various product launches and acquisitions — notably GitHub — Microsoft has invested heavily in Linux infrastructure, which has enabled it to provide more robust services to a wider audience. Google Cloud Platform (GCP) The Google Cloud Platform (GCP) has the least market share of the three major public cloud providers but offers a substantial set of cloud services throughout nearly two dozen geographic regions. Perhaps the most appealing aspect of GCP is that it offers users the same infrastructure Google uses internally. This infrastructure includes extremely powerful computing, storage, analytics, and machine learning services. Depending on your specific product, GCP may have specialized tools that are lacking (or less mature) in AWS and Azure. Finding DevOps tools and services in the cloud Literally hundreds of tools and services are at your disposal through the major cloud providers. Those tools and services are generally separated into the following categories: Compute Storage Networking Resource management Cloud Artificial Intelligence (AI) Identity Security Serverless IoT Following is a list of the most commonly used services across all three of the major cloud providers. These services include app deployment, virtual machine (VM) management, container orchestration, serverless functions, storage, and databases. Additional services are included, such as identity management, block storage, private cloud, secrets storage, and more. It’s far from an exhaustive list but can serve as a solid foundation for you as you begin to research your options and get a feel for what differentiates the cloud providers. App deployment: Platform as a Service (PaaS) solution for deploying applications in a variety of languages including Java, .NET, Python, Node.js, C#, Ruby, and Go Azure: Azure Cloud Services AWS: AWS Elastic Beanstalk GCP: Google App Engine Virtual machine (VM) management: Infrastructure as a Service (IaaS) option for running virtual machines (VMs) with Linux or Windows Azure: Azure Virtual Machines AWS: Amazon EC2 GCP: Google Compute Engine Managed Kubernetes: Enables better container management via the popular orchestrator Kubernetes Azure: Azure Kubernetes Service (AKS) AWS: Amazon Elastic Container Service (ECS) for Kubernetes GCP: Google Kubernetes Engine Serverless: Enables users to create logical workflows of serverless functions Azure: Azure Functions AWS: AWS Lambda GCP: Google Cloud Functions Cloud storage: Unstructured object storage with caching Azure: Azure Blob Storage AWS: Amazon S3 GCP: Google Cloud Storage Databases: SQL and NoSQL databases, on demand Azure: Azure Cosmos DB AWS: Amazon Relational Database Service (RDS) and Amazon DynamoDB (NoSQL) GCP: Google Cloud SQL and Google Cloud BigTable (NoSQL) As you explore the three major cloud providers, you notice a long list of services. You may feel overwhelmed by the hundreds of options at your disposal. If, by chance, you can’t find what you need, the marketplace will likely provide something similar. The marketplace is where independent developers offer services that plug into the cloud — hosted by Azure, AWS or GCP. The table below lists additional services provided by most, if not all, cloud providers. Common Cloud Services Service Category Functionality Block storage Data storage used in storage-area network (SAN) environments. Block storage is similar to storing data on a hard drive. Virtual Private Cloud (VPC) Logically isolated, shared computing resources. Firewall Network security that controls traffic. Content Delivery Network (CDN) Content delivery based on the location of the user. Typically utilizes caching, load balancing and analytics. Domain Name System (DNS) Translator of domain names to IP addresses for browsers. Single Sign-On (SSO) Access control to multiple systems or applications using the same credentials. If you’ve logged into an independent application with your Google, Twitter or GitHub credentials, you’ve used SSO. Identity and Access Management (IAM) Role-based user access management. Pre-determined roles have access to a set group of features; users are assigned roles. Telemetry, monitoring and logging Tools to provide application insights on performance, server load, memory consumption and more. Deployments Configuration, infrastructure and release pipeline management tools. Cloud shell Shell access from a command-line interface (CLI) within the browser. Secrets storage Secure storage of keys, tokens, passwords, certificates and other secrets. Message Queues Dynamically scaled message brokers. Machine Learning (ML) Deep learning frameworks and tools for data scientists. IoT Device connection and management.

Cheat Sheet / Updated 04-12-2024

SQL is a popular and useful programming language. You can make SQL even more useful if you know the phases of SQL development, the criteria for normal forms, the data types used by SQL, a little bit about set and value functions, as well as some tips on how to filter tables with WHERE clauses.

Cheat Sheet / Updated 04-12-2024

Python is a flexible programming language that has become increasingly popular in the past few years. This cheat sheet is designed to give you a handy resource for common Python data types, Python operators, and Python functions. It includes Python data types, operators, special characters, f-strings, and functions for working with robots.

Cheat Sheet / Updated 03-26-2024

If you want to build your own website from start to finish, this book serves as a great resource. It includes many secrets and best practices that web developers know and implement when building any quality website. This cheat sheet includes bits and pieces of what you'll find in the book.

Cheat Sheet / Updated 01-18-2024

Tailor your blog with WordPress software, whether you're writing, editing, or publishing WordPress site content. An understanding of WordPress's dashboard controls and of the types of content available to you helps you get the most out of your website. Also, when all else fails, it's good to know where you can turn to for help with WordPress.