Is web2py a good Python framework for web?

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Web2py is one of the best Frameworks with the main goal to ease the use from setup to learning and extending towards distribution, coding and deployment. It is already able to manage things very well towards the verge of achieving goal and thus providing base to one of the successful python development Services.

Web2py: a good python web framework

1.    It offers a very wide variety of features set and allows a lot of flexibility. Day-by-day it is constantly improving with new releases every couple of weeks, and has attracted a very friendly, active and helpful community that is quickly growing.

2.    The documentations of web2py is excellent and is capable of getting support through extremely active mailing list. You are more expected to get a reply from a core developer within an hour or less.

3.    It is seen that many of the web2py modules can be used standalone or in conjunction with other frameworks. In fact a number of people are still using the DAL along with other frameworks, and even with non-web applications. Now Web2py offers gluino, which is a port of a number of the web2py modules for integration with the Flask, Bottle, Torando and Pyramid frameworks as well as wsgiref.

4.    It is totally up to you to decide if python is providing you your needs and web2py sticks to your requirement as a web framework. Web2py is easier to start for fresher candidate or beginners. The document may not be as good as Django but the user group is very helpful.

5.    After knowing web2py, you would find it easier to handle other web frameworks as well. It does not consume lots of time but would surely preach you lot of things.

6.    However, there are a few hindrances in the way of web2py. It is rare to find sites that combine web2py with other tools from the Python ecosystem. It is quite common to see non-web2py sites combine for nearly all sorts of things from the python world.

7.    If you are learning web2py, you must also learn another Python web tool as well. Flask would also be a good choice as it is small, lightweight and can probably be picked up in an evening.

If web2py is fulfilling all your needs, then it is the best choice for you. The python community and ecosystem is fantastic to go. If you only live in web2py, it may land you to a risk of wearing blinders to the bigger and better world. Web2py is excellent in terms of compatibility, gae compatibility, documentation and a lot more.

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Python development Services: How to make python code run faster?

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It is really difficult to answer this without more relevant information regarding the coding. You can try using a profiler to find out which code is using the most time. Then you can use % timeit and perceive if you can elevate that code. If it is still unfortunate, consider looking at the data. Can you make optimizations based on particular characteristics of the data?

Making Python code run faster

You must consider some of the questions to answer along with the following points:

1.    As the data set grows, does your performance increase linearly? If so, you can try to evaluate what you can do to each piece of data and whether you are able to lessen the computation or storage requisite for each of the iterations.

2.    As the data set grows, can you find geometrically increase in the performance? If so, you may be using a hash that doesn’t work well for the size of your data. You may be able to process it in subsets and combine the results.

3.    Check if you have completely used all the memory space. Also check if the process is essentially using swap. If so, increase memory or split your datasets into smaller chunks.

4.    Some algorithms in scikit-learn support multiprocessing, try turn number of jobs up, closer to your number of cores. If your favorite algorithms do not support multiprocessing, then you can split your data sets, train models with similar datasets and average the results.

5.    You must change to faster algorithms. If your disk IO is intensive, use SSD. Use more efficient libraries because it is not possible for algorithm authors to publish only python code. There is usually C or Java implementations available.

6.    Do the operations on series / arrays, or even list comprehension, rather than for loops. You can use built in Python methods, e.g. collections, as they use C which is much faster. Depending on what you work, choose your tool.

The entire step important for coding in python is discussed above, that would help to code faster. Algorithms have their own importance, so you have to necessarily consider it during process. Also, keep the idea of storage and memory, and free the storage to keep it clean. Perform your operation in series and you can expect changes in your coding very soon.


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Python development Services: How do you debug Python code?

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Debugging process varies from one service to another. Python can fairly assist to debug complex code when using loops, and dealing with some data. You must go with test driven development methods and version control technique as the best way to ‘debug’ your code. This can be applied with appropriate discipline and precision that your bugs are instantly obvious and triflingly isolated.

Debug Python Code

1.    Pycharm is one of the best python IDE which has almost all the features that a programmer need. You can download the community version of pycharm for free. It could provide you happy coding or happy indenting.

2.    Python has a debugging module, but it is console based and not visual. It is effective and light weight once you get used to it. There are free IDE’s available for python, notably IDLE and PyDev.there are also commercial IDE’s, notably Komodo from ActiveState.

3.    If you have Visual Studio, there may be plug ins for python. PyDev in eclipse is much easier of syntax-highlighting editor like Notepad++ and python’s native debugger, etc.

4.    Pdb is a good choice for python programs. There is also pydb available which is an enhanced version of pdb which can also be used.

5.    A debugger is simply one tool for actually debugging apps. Error handling, logging and simple print statements can be faster than debuggers. Visual Studio and C# are just one methodlogy of programming. You can certainly continue using best practices from that mindset, but you may find that just like python has different data structures and operations, it also has different ways of handling debugging than C#, of which a debugger app is just one.

6.    Pycharm from JetBrains is a really nice Python IDE. There are a lot more IDE present. You can move to TDD if you want. It makes a big difference for such a dynamic language, and people often end up using the debugger less. The convenience in trying thing interactively is another reason you see less use of debuggers among Python users.

7.    Python has a debugger built into the standard library: the Python Debugger. It is powerful, but the interface is a little arcane as is the documentation. Eclipse works for Python as well as for other languages, so it can also be a better option to use.

You can use Pycharm professional, but if you find comfortable with VS you should be aware that there is a very decent package called Python Tools for Visual Studio. It makes debugging Python fairly easy compared to some other tools. Give a try to few services and select those that fit your work best.

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What are the difficulties in scaling python applications as a Python development Services?

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There are many types of challenges that may arise when the user is scaling python website. Scaling any web application written in Python is almost as similar as any other language taking VM features into account. Most scalable design will hunt for maximizing concurrency which indicates it sets as an advantage of multitasking among which asynchronous code. Among many other difficulties in scaling, few difficulties can be easily chalked out and improved while expert hand is involved for higher tasks for Python development Services.

The difficulties in scaling python applications

1.  Scaling a website application will probably need to first address the more general problem of dealing with larger code base which is generally said to be a weak point of Python as a language. Because of the larger code base, they choose a static typed language first because it is said to be easier to manage.

2. The major setback is suffered when there is reduction of readily available expressiveness power among which meta-programming. This can be mitigated with proper tooling or proper languages like Scala, which finds its place on Twitter to replace Ruby.

3.   Before choosing Python for a web scale, it must go through the same scrutiny that leads some big names to move to a static typed language. You can write database queries in Python for Java based database by choosing Jython. Python is better tool to deal with asynchronous code than JavaScript.

4.  You can expect a much greater scalability boost from async python framework. It would specifically depend on how stateful the application is and on how much communication and synchronization is necessary between web server instances.

5.   It totally depends on the architecture of the website and is not really that difficult.

First you need to follow the following optimizations and then you can think of other optimizations:

•    Use Apache for Dynamic content and Nginx for static
•    Tornado can be used for serving APIs due to its async nature and it can also be scaled easily.
•    For serving content, you can use CDN
•    You can compress CSS and JS
•    You should know the load balancers and auto scaling features
•    You must understand the proper use of vertical and horizontal scaling.

Web applications are very rarely computationally intensive. Python can technically be part and is part of web scale products even if most of the time we cannot be sure that it is not just to replace Makefiles cf. Choosing Python for a web scale application must go through the same scrutiny that lead some big names to move to a static typed language.

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