#django performance requests per second

[umanologicinc]

Key Advantages of Using a Web Development Framework

In today’s fast digital world, the development of websites and web applications needs to be both efficient and scalable. This is where web development frameworks come into play, offering a structured environment that speeds up the development process while reducing the amount of repetitive coding developers must do. Here are some of the core benefits of using a Web development framework:

1. Efficiency in Coding: Web development frameworks come pre-loaded with a toolbox of components that can be reused for various functions across different projects. This means developers don’t need to write code from scratch every time, leading to faster development cycles. Frameworks like Ruby on Rails, Django, and Laravel, for instance, follow the principle of "convention over configuration," simplifying the coding process further.

2. Enhanced Security: Security is a major concern in web development, and frameworks provide a solid foundation by incorporating built-in security features. These might include protection against common threats such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF). The community support behind these frameworks also ensures regular updates and patches to safeguard against new vulnerabilities.

3. Support for MVC Architecture: Most modern web development frameworks adhere to the Model-View-Controller (MVC) architecture, which separates the data model with business rules from the user interface. This separation helps in managing complex applications, as it allows multiple Web developers to work on the model, controller, and view separately without interfering with each other’s work.

4. Community Support and Resources: One of the biggest advantages of using popular frameworks is the extensive community support they provide. Whether it’s troubleshooting, seeking advice, or sharing expertise, there’s always a community to turn to. Additionally, abundant resources such as plugins and modules can significantly extend the functionality of your web applications without the need to develop these features from scratch.

5. Scalability: With the inherent structure and tools provided by frameworks, scaling up web applications becomes much easier. Frameworks are designed to accommodate growth, handling more requests per second and allowing for further development as user numbers grow.

6. Enhanced Team Collaboration: Web development frameworks enforce best practices and coding standards, ensuring that all developers on a team are working in sync. This is particularly beneficial for larger teams or projects where consistent coding is crucial.  

7. Faster Time-to-Market: By speeding up the development process and simplifying various stages of the lifecycle of web application development, frameworks significantly reduce time-to-market.  

Umano Logic: Effective Web Development for Today's Businesses

Umano Logic specializes in web development, creating powerful, feature-packed, and innovative websites. As a top web development company, you can hire professional full-time developers who will work as if they're part of your own team. Our websites are easy to use, fully functional, secure, and built to grow with your business. We focus on key features to ensure our websites meet current needs and are ready for future changes.

In conclusion, A Web Development Framework offers numerous benefits, such as improved security, increased efficiency, enhanced scalability, and strong community support. These advantages make frameworks essential tools for developers, including those at Umano Logic, who strive to streamline their web development processes and create robust, high-performance web applications.

[codelance]

What are the best ways to improve Django performance?

our app may be different so profile it first, but in my experience an average Django app spends most of the time accessing database. Here's the most typical pain points roughly in the order of potential performance gain per effort spent:

1. Indexes. Make sure you have all the right indexes in place. It's very easy to miss them when evolving schema manually.

2. Generic relationships. Never ever use them unless you are operating very small datasets.

3. Use select_related: http://docs.djangoproject.com/en/dev/ref/models/querysets/#select-related . It's very easy to skip, but it often makes a tremendous difference.

4. Cache template fragments that cause database access. But make sure you understand what you are doing and invalidate your caches at appropriate times.

5. Reuse query results within a single request. In a large app, you may find yourself recreating essentially similar querysets in different places. Django will execute a new query for each new queryset. Sometimes this gets costly.

Also, conventional wisdom says that a typical webapp spends most of its time waiting on i/o, but I noticed (and later seen The Onion dev team reporting the same) that Django ORM is pretty taxing on the CPU when constructing queries, so make sure you have enough of CPU power.

Finally, a generic advice that nevertheless cannot be overstated: do not change anything unless you have confirmed it's slow. Do not preserve your changes unless you have confirmed that they improve performance. An easy but very efficient trick I'm using to profile database access is writing a middleware that outputs the contents of django.db.connection.queries variable to console ordered by execution time and grouped by duplicated queries.

[fireandculture]

Blog Post for CodeMentor: How to Effectively and Politely Critique Code

Originally posted at: https://www.codementor.io/blog/code-review-best-practice-6q5slpm2e0

Programming is often a team effort for introverts. When working as a developer, you will run into decisions other developers have made that you consider mistakes.

It could be a data structure designed with three tightly bound tables. It could be a massive REST API managing all the company’s requests with obsolete technology you need to extend or replace. It could include delicate, untested code that when modified, is likely to cause both personal and technical explosion.

I have built systems that contained all of these flaws, or worked with the (sometimes catastrophic) results. Working with and learning from other programmers is simply easier when you can critique their code effectively without offending them.

Typical Code Review Process

The first step in almost any writing is to 1) write something, 2) have a group of your peers read through it, and 3) adjust to their commentary.

The importance of peer review is mirrored in the fine arts, where feedback on the semantics of your decisions is a significant part of any class. When learning to program, however, you generally receive a series of close-ended problems sized for individual consumption, and peer review of your code is rare. This did not prepare most of us for a professional life in programming.

Reading the code others have written, understanding it, and providing constructive criticism is a subtle skill. Adding to this difficulty, the individuals being critiqued are often introverts. Thus, when I am reading code written by a student or someone on my team, I have three primary concerns:

Team Dynamics: how do I improve my working relationship with this person and their code?

Code Standards: how does this code conform to how it should look?

Reusability/Extensibility: how easily/effectively can this code be expanded upon, maintained, and rebuilt in the future?

Team Dynamics

Team dynamics are important in software development because nobody can know everything. A software development team usually consists of developers with experience and skills enabling them to build approximately the same things.

Most software teams that I have worked on are culturally diverse. Once, my Indian boss told me, “Where I grew up, if you weren’t in the top 5% by the time you were 12, you were screwed.” Thus, as a wide variety of experiences leads to different communication styles, communication and soft skills are an important part of a developer’s skill set.

Soft Skills and Emotional Intelligence

Sometime in the 60’s or 70’s the US Army coined the term ‘soft skills.’ Now, often referred to as ‘Emotional Intelligence,’ (for further reading, the work of Daniel Goleman is fairly approachable) these skills are different from raw, memorized knowledge.

Programming consists of many ‘hard skills’ (e.g. C driver programming, MongoDB querying, and Apache server administration) — most of them are fairly ephemeral in comparison to a career. Anyone can copy JavaScript off the Internet and make a flashing button.

Convincing someone their flashing button is poorly constructed or aesthetically ugly is often more difficult. Soft skills are important in building a consensus motivating forward progression, and maintaining that consensus regarding forward progression. And the easiest, most effective way I have found to convince people to move forward is this: say something nice first.

Say Something Nice First

O’Reilly once assembled a book called, Beautiful Code, and I can’t remember many people in the book citing their own code for samples.

This means if you say something nice to somebody about their code, it may be the first nice thing they’ve ever heard about it. When you start out with, “Wow, this solves problem X very elegantly, right here,” you are building a little goodwill within your team. Also, if you have to look hard for this positive point, you’ve learned something. Often a comparatively awkward approach merely appears that way because it has an entirely different set of priorities and steps. In addition, when an approach looks ineffective to me, when I look for something worth salvaging, I end up making a specific list of issues within the code.

Be Specific in Your Commentary and Criticism

Speaking in very specific terms about awkward code is an effective way to help someone improve it, as well as learn from each other. If you look at the classic FizzBuzz post at Coding Horror, you will see that there are a thousand ways to make even the simplest program go, in all sorts of languages. Here are two pseudocode functions for finding the last day of a month:

Date returnLastDayIterative(DateTime dayIn): Date myDate = dayIn.date while (myDate.month < dayIn.month): myDate.dateAdd (1 day) Return myDate.day.dateAdd(-1 second) Date ldy(DateTime i): Date m = i.date If m.dayOfMonth > 28: m = m - 3 m.month = m.Month + 1 m.day = 1 Return m.dateAdd(-1 second)

What are the good and bad things you see about each?

Algorithm 1 is linear; it’s a single for-loop in code, doesn’t have much logic, and gets the right answer every time. It’s also cross-calendar compatible for non-Gregorian calendars.

Algorithm 2 is probably more efficient computationally, but depends on months being of uniform size within 3 days (Mars or Jupiter months might be longer) and requires two library functions (month_add and date_subtract).

Both find a solution. On balance, since date-time functions can be heavy when applied to large datasets, I personally prefer algorithm 2 for its linear performance results. Semantically, these are simple functions but if I had to maintain one, I’d probably prefer to rewrite the first function over the second. To engender this kind of focused, productive communication, what will often help is code standards.

Code Standards

Not every team has formal code standards (this is an understatement).

The default standard for many teams is: “This is what manager X of programmer Y accepted as gold code on release day.”

But when you are looking at someone’s code and asking them to modify something in a way that requires their effort, code standards help. Most of the time, code standards are about things you don’t want to care about so you can focus on what matters more. Rather than worrying about whether each file is indented with spaces or tabs, you can focus on not dereferencing that NULL pointer.

Code standards will be very different for different environments, though.

When I tested internal loan software at Wells Fargo, we had hundreds of end-to-end tests to run before putting a release on staging servers. In a tiny startup, though, your flagship product may have only a smattering of end-to-end testing (or none at all) that you run once before release. Or, more appropriately, a selective battery of unit tests run by Jeeves, TeamCity, or whatever tool before auto-migration, and a small set of well-considered hand-tests.

Code Standards Will Vary By Environment

Coding standards will change per environment, language, company, end-use, and testing requirements, so a discussion of which standard to use is important and can make communication clearer.

“Hey, I see you used a couple variables here that were declared inline. Do you think we should do that, or declare them at the top of the function? It seems it would be easier to read that way, and agrees with [Agreed Upon Convention X].”

But remember, this will change depending on the situation. When you write Java, it is a completely different animal from when you write SQL.

My team once hired a developer whose experience was extensive in Java but not Python — our primary language. We handed them a standard code-sample request. Instead of the fairly simplistic Django – Python – Postgres result we expected, we received something built with Spring. There were interfaces, and abstractions, and a factory or two.

We examined it closely, because it was…baroque to us (well written Java is almost always going to look baroque to a Python programmer). Java programmers are trained to be relentlessly abstract, so their code is reusable in a wide variety of circumstances.

When you critique a (nowadays, probably logically generated) SQL block, in direct opposition, you want it to be simple.

“Can I read this, run it on a server, and know exactlywhat it does?”

If I see a SQL schema with extra layers to it, or an SQL query nesting arrays in a table, my instinctive response is to remove the complexity. This is directly different from standard Java protocols; primarily for environmental considerations.

Watch for What’s Inappropriate in a Specific Language

A simple JSON block or SQL query can cause a tremendous number of problems. If you think of all programming in state-based terms (almost all programs can be represented as Finite State Machines) the inputs and outputs that come from any program will change the state of all programs and users interacting with it.

Thus, the environment you are working within is extremely important to understand when you look at any code chunk. If you look at someone’s code, some of the easiest/most important questions to answer quickly are, “What are the inputs and outputs, and how did they change?”

Traditional database design started with hand-generated forms and reports representing data, and modern programming technique can learn from this approach. Looking at JSON input block A and comparing it to JSON output block B should allow you to ask important questions of your fellow programmer. Likewise with a series of SQL queries pointing at the tables in question.

Check if the Code Can be Easily Maintained

When you start to talk about environments and tables, reusability and maintainability become fundamentally important. Maintainability presupposes success.

“We will need to be ready to upgrade this code eight months from now because it will be useful enough to keep us all employed,” is quite a confident statement. Telling someone that you believe their code is good enough for someone to pay for it not just today, but tomorrow and next month too is a massive compliment. I suggest saying something along those lines to them, right before you say, “But I can’t tell how or why it does what it does because there is not a single comment inside.”

When you review code, you are reviewing it not just to learn and improve your skills, but to help the creator of said code improve your/their ability to change it later.

Reusability

Would I want to be responsible for modifications to this code?

A great place to start making code more flexible later is in your code standards.

“This would be more readable if your variable names were not all variations of ‘foo1,’ ‘foo2,’ ‘bar1,’ and ‘barA4X,'” is a very good comment that code standards will help with.

Modifying or understanding large chunks of code is much easier when you follow consistent formatting and naming rules, as well as syntactic conventions.

I may not have written a bunch of Perl, but I certainly can cook up regular expressions that look like it, given time to brush the rust off. However, I have found writing three lines of code that do what one line of Perl-code-like regular expressions could do can save me a day of, “Whichever idiot let me do this eight months ago should be keelhauled,” self-recrimination.

Pointing this out to someone, or at least asking them to write five lines of documentation explaining their brilliant line noise can be enlightening for both of you. And once you’re done with the simple stuff in code standards, I strongly suggest looking at the dependencies.

Reinventing the Wheel vs. Inheriting Unreliable Libraries

I don’t particularly enjoy writing code. It’s always buggy, and doesn’t do exactly what I want. It needs to be rewritten repeatedly to handle special cases or additional inputs. Quite often, someone has run into one of my dumb problems and solved it in a way smarter than I am able to:

Conceptualize quickly without effort, and

Implement without a ton of bugs in a reasonable amount of time.

Thus, I don’t write everything in machine language. Or hand-laser wafers to get the exact transistor chains I need onto a chip. Or, sometimes, write as much Python.

Sometimes, however, I pick risky libraries. Or pick libraries that don’t do what is expected, or have insufficient testing for my purposes. When I look at code other people have written, I want to know the libraries and external chunks they’re working with to see if they are appropriate to the task at hand. It all boils down to is the implementer aware of the tradeoffs involved in using the selected references?

Generalizing Code

Expansion vs. Refactoring for Compression

After you understand what a chunk of code does and what it’s built with, the fun part comes in: where can this go?

This can actually be pretty inspiring for both of you. If the code you’re reviewing has done its job properly, you can look at it with an eye for where it can solve other problems, and be generalized. For example, should someone write a function that solves the small problem of setting a default date based upon a date-based input? You can look at the kinds of defaults a system might want: next week, next month, last quarter, etc.

Conclusion

Soft skills can make the difference between shipping a product and your source dissolving into code fiefdoms. Without a doubt, when you are working with a team, you will need to look through their code.

I have found that as I have become more effective at critiquing others’ code more politely, the software I have produced has become better. The primary ways I try to support the teams I work with in critiquing their code are through improving the team dynamics, pushing for more consistent code, and trying to help developers produce code as reusable as possible.

Blog Post for CodeMentor: How to Effectively and Politely Critique Code was originally published on Dylan Brams

[faizrashis1995]

Python vs Java in 2020

Programming Languages are a fundamental part of computer science, they are fundamental tools in a programmer’s toolbox and crucial to almost every programming activity. Choosing between programming languages is often confusing, let alone choosing between the most popular ones.  Python and Java have been battling for the top position on the most popular programming languages out there, with Python making amazing progress in the last few years and Java holding onto its position.

 It often seems that these languages are perfect, and in fact, they are capable of doing most of the tasks out there, however, there are key differences that could help you formulate your decision. We’ll start by explaining each language and key characteristics, then compare them in different fields in computer science to provide more clarity on your choices.

 Java

Java is a statically typed general-purpose programming language, it is an object-oriented and concurrent language. Java was meant to be WORA (write once run anywhere) language, it was designed to run on any platform and with as few dependencies as possible, with the help of the Java Virtual Machine (JVM).

 Python

Python is a dynamically-typed general-purpose programming language. Python’s early development began at a research institute in the Netherlands. The original motivation behind it was to create a higher-level language to bridge the gap between C and the shell, as the author states, creating system administration utilities using C back at that time was pretty complicated. The syntax was also motivated by a few languages like Algol68, Pascal, and ABC and was meant to be readable and clean. You can read more about the history of python on the Python Author’s blog.

 Now let's have a look at key difference between Python and Java.

 Python vs Java: Key Differences

Performance

Languages don't have speed, they have only semantics. If you want to compare speed you must choose specific implementations to compare with each other. You can find a detailed Python vs Java performance comparison on this project called the benchmarks-game (screenshot below), where different languages are benchmarked in different programs.

 Python vs Java performance comparison

 Keep in mind that performance is not only a function of the language’s execution speed, the program’s implementation, and the third party libraries performance is usually the number one factor in the equation.

 Popularity

Popularity has always been a game between these two languages, as they’ve been a close competitor in the top 3 positions of popularity, along with javascript. Before the Javascript revolution, Java was the number one most popular language. When Javascript first came out, the founders chose a name close to Java to make it gain traction.

 As per Github’s Octoverse, Java was the second most used language on Github followed by Python.

 In Stackoverflow’s 2018 developer survey, Python has crowned the fastest growing programming language after taking over C# spot this year and surpassing PHP last year. Java is still ranked above Python being popular with 45% of developers while Python is at 39%, however that gap is closing.

 It is safe to say that both languages reside around the same area in popularity.

 Syntax

Python is a dynamically typed language, when you write Python, you don’t need to determine variable types, as the interpreter will infer these types and the checks will be made at runtime. Which results in an easier syntax that is quite similar to the English Language. Moreover, Python doesn’t use enclosing braces and follows indentation rules ( like how most people right pseudocode) which makes the code quite easy to read and friendly for beginners.

 In this simple class definition, I’ve created a simple class called fruit, with a constructor, which is the code that will be executed when I create an instance of the object, and defined two simple functions as well, each printing one of the object’s attributes.

 class Fruit:

def_init_(mysillyobject, name, color);

mysillyobject.name=name

mysillyobject.color=color

def myfunction(abc) :

print("hello I'm a "+ abc.name)

def mycolor(abc) :

print("hello my Color is " + abc.color)

p1 = Fruit ("Apple", "red")

p1.myfunction()

Java, on the other hand, follows strict syntax rules, it’s a statically typed language where you need to explicitly declare your variable types and shouldn’t an anomaly be spotted, the code will not compile, to begin with. While it’s not the easiest thing for beginners, some developers find comfort with the clarity of statically typed languages, many developers don’t feel comfortable following indentation rules, especially with large code bases.

 public class Fruit {

String name;

String color;

public Fruit(String name, String color){

this.color=color;

this.name=name;

}

public void myfunction()

{

System.out.println("Hello I'm a :" +name);

}

public void mycolor()

{

System.out.println("Hello my color is:" + color);

}

This is the equivalent to the Fruit class we have defined in Python with the exact same functionalities.

 Jobs and Salary

There seems to be no objective difference or comparison between Python vs Java jobs or salary. Both are very popular so if you gain a decent expertise in either, you can start working as a software developer or intern to start your career. Availability of Jobs or Salary should not be be your criterion for choosing either of the programming language, choose the one that you could relate to better.

 Python vs Java: Uses/Applications in various fields

Game Development

We’re not going to talk about general PC game development since neither Python nor Java can really compete with C++/C# in that area with their huge ecosystem. Moreover, game development is a field that requires the highest possible performance to provide seamless experiences to the users, and while Java and Python are not slow, they don’t provide the best performance for game development.

 JMonkeyEngine is a popular open source game development engine with Java, while it’s not on par with Unreal and Unity it is certainly a powerful engine that will help you create some amazing games.

 If you wish to experiment with computer graphics from scratch or build your own engine, OpenGL also provides bindings for the Java language.

 While Python is not a powerful option on its own for game creation, there is Cocos, Panda3d, Pygame and a few other engines/frameworks for building games with Python.

 However, Python isn’t completely ruled out for professional game development, it’s an important tool for a game developer, as Python is a popular scripting-language option for many developers including game developers. Editing Packages like Maya also use Python as a scripting language.

 Web Development

Both languages are used in backend web development. Backend web development is the branch of web development concerned with creating the software that will run on the server. It’s the most popular development field according to StackOverflow’s developer survey.

 Writing your own backend technology from scratch is not only hard, but it’s extremely hard to cover all design requirements from security to reliability and effectiveness. This is why developers have created frameworks which is an abstraction in software that allows you to build your backend technology without reinventing the wheel.

 The most two popular frameworks for Python are Django and Flask. Flask is a micro web framework, it gives you the basic functionalities you’d need like routing requests without much overhead. Django is a more featured option and can help you build a powerful backend while capitalizing on efficiency and security, Django is equipped with a powerful ORM layer which facilitates dealing databases and performing different operations on the data.

 As for Java, Spring is perhaps the most well-known Java backend framework with a massive ecosystem and a huge community around it. Spring is used by Orange, Dell, GE, and many other enterprises, and while it’s not as trending as Django nowadays, it is a powerful option for building enterprise-level applications.

 Machine Learning

Since Python is syntactically very easy yet a fully-fledged general-purpose programming language, it became a popular option for people from different disciplines who wanted to experiment with machine learning and bring the power of AI into their respective fields. That’s why a lot of the development in AI and machine learning is done with Python with a huge ecosystem and libraries.

 There is TensorFlow, Keras, Sickit-Learn, and Facebook’s PyTorch and it’s by far the most popular language in the field.

 Java is also considered a good option when it comes to machine learning, it’s easy to debug and use and it’s already being used for large-scale and enterprise-level applications. Among the libraries, you could use in that area are Weka, Mallet, DeepLearning4, and MOA.[Source]-https://hackr.io/blog/python-vs-java

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