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While we consume content via apps, we use browsers too. From reading articles, bookmarking our favorite websites to sometimes booking tickets or shopping—browsers play a crucial role in how we spend time on the internet. According to Statcounter, people primarily use six browsers that include Chrome, Firefox, Safari, and Edge. But this is not as straightforward as it sounds. When you consider browser versions, there are different versions for mobile, web, and also OS. For instance, let's look at the following graph on Browser Version Market Share by . Statcounter Globally, around 35.06% of users use Chrome for Android, whereas 15.2% use Chrome 89.0. As you can notice, there are multiple other versions, which brings us to a pivotal question — how important is it to perform browser testing? Well, it's quite important if we take into account the fact that our users consume content via browsers on the web, mobile, and on devices with various versions of OS, including outdated versions. For our product to perform uniformly and as intended across different browsers and their versions, browser testing is performed. In this article, we'll explore the best tools for performing browser testing. Before we proceed, please check out our article on . browser testing and when to start Testsigma is a cloud-based end-to-end automation testing ecosystem that offers thousands of real-time devices for you to run tests on. It supports parallel testing, and remote teams can access the tool from anywhere, any time. It can be easily integrated with CI/CD tools. Testsigma It generates screenshots and logs automatically, which are then sent to you via exhaustive reports — you can tag teams and accelerate bug fixing. Besides, it supports responsive and visual comparison testing. Testsigma's tool is codeless and test scripts can be written in simple English, making this tool convenient for even beginners and QA testers without any programming skills. Features Multiple tests can be run parallely It's powered by AI Supports multi-browser testing on the cloud to ensure all websites and web apps are visually uniform across various browsers and versions Tests can be run on local test machines, locally hosted devices, or devices that are hosted on the cloud in various device clouds 1000s of real-time devices and environments are readily available for and cross device testing cross browser Since it's cloud-based, it's scalable. Based on the project requirement, you can scale up or down. This is quite cost-effective too because you pay for what you use. Offers 24/7 support Price $99 upwards (for startups), with enterprise solutions revolving around $249 monthly when billed annually. At the moment, you can access Testsigma for free with its 30-day free trial solution. You can give it a spin . here Cross Browser Testing Smartbear's is perhaps one of the most popular tools out there. It supports manual and live testing on over 2,050 browsers and real mobile devices. It can run automated tests using various frameworks like Appium, Selenium, etc. and it can be integrated with CI/CD tools too. CrossBrowserTesting From Windows XP and Microsoft Edge Mobile, to Maxthon Mobile and Samsung Browser — this tool supports thousands of browsers and its versions. In fact, it gives you access to Mozilla Firefox’s earliest browsers such as 12, 13. Photo Credit: CrossBrowserTesting Features Testing and debugging can be done on remote live devices and browsers Supports visual testing Offers native browser debugging tools and consoles Capable of taking screenshots, logs across multiple browsers or devices Lets us compare screenshots for visual regression testing Price It begins at $39 monthly for live testing and around $169 for unlimited testing. MultiBrowser is an online browser testing tool that offers mobile browser emulators and standalone desktop web browsers. Testing on MultiBrowser is quite straightforward; it supports testing on various versions of Chrome, Edge, Firefox, and Internet Explorer 11. Tests can be performed on local machines eliminating the need for VMs. While tests can be automated, you can also add custom devices to the tool. MultiBrowser Photo Credit: MultiBrowser Features MultiBrowser supports automated functional testing and standalone desktop web browser testing Supports multi-platform site previews Allows us to capture responsive screenshots It comes with an inbuilt video recorder Requires no installations as it can run on local machines Price $199 per user (for professional use); you can access it for free for the first 14 days. BrowserStack offers over 2,000 real-time devices and browsers for you to run tests on. Developers and testers can seamlessly test and automate browser testing on this tool; debugging and shipping becomes effortless as it supports real-time debugging. Besides, tests can be performed on internal dev and staging environments, behind firewalls, etc. Also, this requires zero set-ups and maintenance. BrowserStack Photo Credit: BrowserStack Features Offers native browser experience due to which test results are as accurate as of the tests you perform on local browsers It's cloud-based so you can access it from anywhere, any time Supports CI/CD integrations Design and layouts can be quickly tested with the screenshot generation feature Supports testing on a wide range of screen resolutions, for instance, 800×600 to 2048×1536 Price $29 upwards monthly, if billed annually. Mabl is a cloud-based test automation tool that supports parallel test execution. This tool comes in-built with browsers like Mozilla Firefox, Google Chrome, Safari, and Internet Explorer. Mabl While the tests can be run on emulators, Mabl allows picking desktops or mobile devices, based on the requirement. There's flexibility to choose screen orientation and devices (for instance, Nexus 6P, iPhone 12, etc.) that you'd like to perform tests on. Photo Credit: Mabl Features Mabl doesn't require any additional setup or configuration as it's a cloud-based tool Test workflows can be reused across various test plans Generates reports which let us analyze test reports, statistics, etc. Supports CI/CD integration Supports unlimited parallel test execution Price Mabl offers a custom plan; get in touch with them for a quote. Browsera is an online browser compatibility testing tool. Known for its unique feature of analyzing and comparing screenshots, the tool is equipped with an algorithm that detects any layout issues. Besides, the tool is a pro when it comes to spotting any Javascript errors in your website or web app. It supports testing dynamic pages and technologies such as AJAX and DHTML. Browsera Photo Credit: Browsera Features No extra installations are required as tests can be run on the server Supports testing multiple pages of the website Can take screenshots and analyze them Generates comprehensive reports that state problem areas; this helps in quick bug fixing Supports testing behind login too and supports HTTP Based authentication and app-based logins Price $39 upwards for 14 days; monthly subscription is $49 upwards. Functionize is a cloud-based test automation tool that supports online browser testing via its own test cloud. It comes equipped with an intelligent visual comparison feature that lets even beginners analyze test results via screenshots and logs. This tool also supports parallel test executions and you can schedule tests, based on priority. Functionize Photo Credit: Functionize Features Tests don't have to be modified based on browsers and they can be reused within the browsers Functionize offers Performance Monitoring which helps monitor the UI performance It doesn't require any additional setups or configuration Tests can be written in simple English, which are later converted into automation by NLP Price Functionize has a custom pricing plan; please contact for a quote.

7 tools to use that test your online browser

Multithreading and Multiprocessing are the two most common ways of attaining concurrency and parallelization, however, not many developers understand the difference between them and fail to choose effectively which to use when. In this article, we will be discussing the differences between Multithreading and Multiprocessing and how to decide what to use and how to implement it in Python. What is a thread? A thread is an independent flow of execution. It can be essentially seen as a lightweight individual component of a process, which can run parallely. Threading is a feature usually provided by the operating system. There can be multiple threads in a process, that share the same memory space, which means that they share the code to be executed and the variables declared in the program with each other. To understand this better, let’s consider an example of the programs running on your laptop right now. You are probably reading this article with multiple tabs open in your browser. Meanwhile, you have Spotify desktop app open for listening to music. Now, the browser and the Spotify desktop app are like two distinct processes that can employ several processes or threads to achieve parallelism. So, the different tabs in your browser might be run in different threads. Similarly, Spotify can play music using one thread and use another for downloading your favorite song from the internet, and use a third one to display the user interface. And this is called Multithreading. What is Multithreading in Python? Multithreading, as the name suggests, is a task or an operation that can execute multiple threads at the same time. It is a popular technique that streamlines multiple tasks in quick succession at the same time, and facilitates quick and easy sharing of resources among multiple threads with the main thread. The following image explains Multithreading in Python: Python is a linear language, but we can use the Threading Python module to understand and implement the concept of Multithreading in Python. The threading module offers an intutive API to easily generate multiple threads that can be used when there is more processing power required. It can be used as shown below: import threading
from queue import Queue
import time

def testThread(num):
    print num

if __name__ == '__main__':
    for i in range(5):
        t = threading.Thread(target=testThread, arg=(i,))
        t.start() In the above code snippet, is used as the callable object, to pass parameters to the function and to start the thread. target args start Now, here comes something interesting - the lock. Global Interpreter Lock There are often cases in programming where you would want your threads to be able to modify or use the variables that are common to the threads. However, to do this, you will have to use something known as the or in Python. Lock Global Interpreter Lock (GIL) From the Python : wiki In CPython, the , or , is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once. This lock is necessary mainly because CPython's memory management is not thread-safe. global interpreter lock GIL At the interpreter level, Python basically serializes the instructions. In order for any thread to run any function, it must first get a global lock. Because only one thread may gain that lock at a time, the interpreter must finally execute the instructions serially. This architecture makes memory management thread-safe, but it cannot use multiple CPU cores at all. Simply put, whenever a function wants to use or modify a variable, it locks that variable such that if any other function wants to use or modify that specific variable, it will have to wait until that variable is unlocked. Consider two functions that each iterate a variable by one. You may use the lock to ensure that one function can read the variable, run computations, and write back to it before another function can, so that we can avoid data corruption. Use Cases for Mutlithreading in Python Threading in Python is more helpful for I/O operations or network-bound task such as running scripts for example, in the case of web scraping rather than tasks that can be CPU intensive. Another example is , which uses a thread pool to transform data in parallel. Tensorflow Other than these applications, Graphical User Interfaces (GUIs) use Multithreading all the time to make applications responsive and interactive. A common example could be a text editing program where as soon as the user inputs text, its is displayed on the screen. Here one thread takes care of the user input while the other thread handles the task to display it. We can add more threads for more functionalities such as spell check, autocompletion and so on. Now, having discussed threads in detail, let’s move on to processes. What is a process? A process is simply an instance of the computer program being executed. Each process has its own memory space that is used to store the instructions being run, and any data that it needs to access or store for the execution of the code. Because of this, spawning a process is more time-consuming and slow as compared to a thread. As we discussed earlier, when we are running multiple applications on our desktop, each application is a process and when are executing these processes at the same time, it is called Multiprocessing. What is Multiprocessing in Python? Multiprocessing is the ability of a processor to execute several unrelated tasks simultaneously. It allows you to create programs that can run concurrently, bypassing the Global Interpreter Lock (GIL) and use the entire CPU core for efficient execution of tasks. Although the concept of Multiprocessing is fundamentally different from Multithreading, still their syntax or usage in Python is quite similar. Similar to the Threading module, we have a Multiprocessing module in Python that helps in generating different processes, where each process has its own Python interpreter and a GIL. Since the processes don’t share the same memory, they can’t modify the same memory concurrently, saving us from the risk of running into a deadlock or chances of data corruption. It can be used as shown below: import multiprocessing
def spawn(num):
  print(num)

if __name__ == '__main__':
  for i in range(5):
    p = multiprocessing.Process(target=spawn, args=(i,))
    p.start()
    p.join() # this line allows you to wait for processes Use Cases for Mutliprocessing in Python As we discussed earlier as well, Mutliprocessing is a wiser choice in case the tasks are CPU extensive and don’t have any I/O operations or user interactions. Differences, Merits and Drawbacks Here are some points to summarize the differences, merits and drawbacks of Multiprocessing and Multithreading: Threads share the same memory space whereas each process has its own memory space. Sharing objects between threads is simpler, but you must take extra precautions for object synchronization to ensure that two threads do not write to the same object at the same time and that a race condition does not arise. Multithreaded programming is more prone to bugs rather than Multiprocessing because of the added programming overhead for object synchronization. Spawning process is more time and resource-consuming than threads as they have a lower overhead than processes. Threads cannot achieve complete parallelism by leveraging multiple CPU cores due to GIL constraints in Python. There are no such constraints with multiprocessing. The operating system handles process scheduling, whereas the Python interpreter handles thread scheduling. Child processes can be interrupted and killed, but child threads cannot. You must wait for the threads to finish or join. Conclusion We can draw the following conclusions from this discussion: Threading is recommended for programs that need IO or user interaction. For CPU-bound, computation-intensive apps, multiprocessing should be employed. Now that you understand how Python Multiprocessing and Multithreading operate and how they compare, you can write code effectively and apply the two approaches in a variety of circumstances. I hope you found this article helpful. Keep reading!

Understanding Multiprocessing and Multithreading in Python

Comprender el multiprocesamiento y los subprocesos múltiples en Python

Comprendre le multitraitement et le multithreading en Python

पायथन में मल्टीप्रोसेसिंग और मल्टीथ्रेडिंग को समझना

Python でのマルチプロセッシングとマルチスレッドについて

Compreendendo multiprocessamento e multithreading em Python

Hiểu về Đa xử lý và Đa luồng trong Python

了解 Python 中的多处理和多线程

Object Detection Using Single Shot MultiBox Detector (A Case Study Approach)

While working on a recent project, I realized that heavy processes for python like scrapping could be made easier though python's The documentation and community engaging in multiprocessing is fairly sparse, so I wanted to share some of my learnings through an example project of scrapping the . Below I wrote a bit of code that pulls all of the available pokémon while minding the API's 100 calls per 60 second limits. You'll see that the iteration is fairly slow as there are 964 pokémon the API returns. multiprocessing library. PokéAPI Before Multiprocessing I simily create three calls , , and . The first, , simply returns all the url's from the api for the next process, , to iterate over the urls and pull the information together by using to pull each URL's response data. get_number_pokemon get_pokemon get_all_pokemon get_number_pokemon get_all_pokemon get_pokemon requests req timeit time pandas pd IPython.display Image, HTML random tqdm tqdm ratelimit limits, sleep_and_retry response = req.get(url) response.status_code == : response.status_code != :
        print( , status_code, url) Exception( .format(response.status_code)) response


API_POKEMON = res = req.get(API_POKEMON.format(pokemon= ))
    number_pokemon = res.json()[ ]
    res_url = call_api(API_POKEMON.format(pokemon= .format(limit=str(number_pokemon))))
    pokemon_links_values = [link[ ] link res_url.json()[ ]] pokemon_links_values info = resolved = : resolved:
            

            res = tooManyCalls = :
                res = call_api(link) res == :
                    resolved = Exception e:
                print(e) e == :
                    tooManyCalls = tooManyCalls:
                time.sleep( ) res.status_code < :

                pokemon_info = res.json()

                info = { : pokemon_info[ ][ ], : pokemon_info[ ], : pokemon_info[ ], : pokemon_info[ ], : pokemon_info[ ], : pokemon_info[ ], : pokemon_info[ ][ ]

                }

                resolved = res.status_code == :
                time.sleep( ) :
                sleep_val = random.randint( , )
                time.sleep(sleep_val) Exception e:
        print(e) info : info list_pokemon = [] link tqdm(links_pokemon):
        
        pokemon = get_pokemon(link) pokemon != :
            list_pokemon.append(pokemon)
        time.sleep( )
        
            
    pd.set_option( , )

    df_pokemon = pd.DataFrame(list_pokemon) df_pokemon links_pokemon = get_number_pokemon()

    df_pokemon = get_all_pokemon(links_pokemon=links_pokemon)
    
    df_pokemon.sort_values([ ],inplace= ) df_pokemon, HTML(df_pokemon.iloc[ : ].to_html(formatters={ : image_formatter}, escape= )) import as import import import as from import import from import from import ## Rate limit to help with overcalling ## pokemon api is 100 calls per 60 seconds max @sleep_and_retry @limits(calls=100, period=60) : def call_api (url) if 404 return 'Not Found' if 200 'here' raise 'API response: {}' return 'https://pokeapi.co/api/v2/pokemon/{pokemon}' : def get_number_pokemon () '' 'count' '?offset=0&limit={limit}' 'url' for in 'results' return : def get_pokemon (link= ) '' None False try while not None False try if 'Not Found' True break except as if 'too many calls' True if 60 elif 300 'Image' 'sprites' 'front_default' 'id' 'id' 'name' 'name' 'height' 'height' 'base_experience' 'base_experience' 'weight' 'weight' 'species' 'species' 'name' True elif 429 60 else 1 10 except as return finally return : def get_all_pokemon (links_pokemon=None) for in if None 0.3 'display.max_colwidth' None return : def image_formatter (im) return f'<img src=" ">' {im} : def main_pokemon_run () 'id' True return 0 4 'Image' False As you can see from the response below, the API call took roughly 9 minutes and 30 seconds at a rate of 1.69 iterations per second. We can improve this drastically by adding multiprocessing. With Multiprocessing Now with multiprocessing we can separate the function into a multiprocessing pool function. We use the built in multiprocessing function to define the number of workers needed. Since we we want to get this done as quickly as possible using the full will ensure the program runs the most optimally without taking up all over our . The ensures that this function will never be 0. Next, I use multiprocessing's built in tool to share a global memory of our returned Pokémon. get_all_pokemon cpu_count() cpu_count - 1 CPUs max(cpu_count() -1, 1) Manager While you can use the returned value of the multiprocess map, using a manager is nice way to use multiprocessing for other more complicated tasks. Check the documentation for the available multiprocessing. Manager types. Another step needed is to create a partial function. As the function passes an array to iterate over, we need to add our parameters more specifically to our function before we iterate over it. The library allows us to do this by creating a partial function to send to the pool map. I use tqdm in this example to see the progress of the pool. pool.imap get_pokemon_multiprocess functools.partial I also show in the code the same function without it. Now that the pool has ran, we need to ensure that it is both closed and joined, you'll see I do this immediately after and in the block. This is done to validate that even if there is an error, the process workers are closed and terminated. finally The join function is necessary to have the processes after pooling being joined together. This also validates that the manager is completely finished binding. requests req timeit time pandas pd IPython.display Image, HTML random tqdm tqdm ratelimit limits, sleep_and_retry multiprocessing Pool, Manager, cpu_count functools partial


API_POKEMON = response = req.get(url) response.status_code == : response.status_code != : Exception( .format(response.status_code)) response res = req.get(API_POKEMON.format(pokemon= ))
    number_pokemon = res.json()[ ]
    res_url = call_api(API_POKEMON.format(pokemon= .format(limit=str(number_pokemon))))
    pokemon_links_values = [link[ ] link res_url.json()[ ]] pokemon_links_values link = links_pokemon[process]
    info = resolved = : resolved:

              
            res = tooManyCalls = :
                res = call_api(link) res == :
                    resolved = Exception e:
                print(e) e == :
                    tooManyCalls = tooManyCalls:
                time.sleep( ) res.status_code < :

                pokemon_info = res.json()

                info = { : pokemon_info[ ][ ], :  pokemon_info[ ], : pokemon_info[ ], : pokemon_info[ ], : pokemon_info[ ], : pokemon_info[ ], : pokemon_info[ ][ ]

                }

                resolved = res.status_code == :
                print(res.status_code)
                time.sleep( ) :
                print(res.status_code)
                sleep_val = random.randint( , )
                time.sleep(sleep_val) Exception e:
        print(e) : info != :
            listManager.append(info)
            time.sleep( ) workers = max(cpu_count() , ) manager = Manager() listManager = manager.list()
    pool = Pool(workers) :

        links_pokemon = get_number_pokemon()
        part_get_clean_pokemon = partial(get_pokemon_multiprocess, listManager, links_pokemon) _ tqdm(pool.imap(part_get_clean_pokemon, list(range( , len(links_pokemon)))), total=len(links_pokemon)): pool.close()
        pool.join() :
        pool.close()
        pool.join()
        
    pokemonList = list(listManager)
    
    df_pokemon = pd.DataFrame(pokemonList)
    df_pokemon.sort_values([ ],inplace= ) df_pokemon, HTML(df_pokemon.iloc[ : ].to_html(formatters={ : image_formatter}, escape= )) import as import import import as from import import from import from import from import from import 'https://pokeapi.co/api/v2/pokemon/{pokemon}' #  To see how it ran # def infoDebugger(title): #     print(title) #     print('module name:', __name__) #     if hasattr(os, 'getppid'): #         print('parent process:', os.getppid()) #     print('process id:', os.getpid()) @sleep_and_retry @limits(calls=100, period=60) : def call_api (url) if 404 return 'Not Found' if 200 raise 'API response: {}' return # https://docs.python.org/2/library/multiprocessing.html : def get_number_pokemon () '' 'count' '?offset=0&limit={limit}' 'url' for in 'results' return : def get_pokemon_multiprocess (listManager=None, links_pokemon=None, process= ) 0 #     print('Called Pokemon', process) None False #     print(link) try while not None False try if 'Not Found' True break except as if 'too many calls' True if 60 elif 300 'Image' 'sprites' 'front_default' 'id' 'id' 'name' 'name' 'height' 'height' 'base_experience' 'base_experience' 'weight' 'weight' 'species' 'species' 'name' True elif 429 60 else 1 10 except as finally if None 0.5 return : def image_formatter (im) return f'<img src=" ">' {im} : def main_pokemon_run_multiprocessing () ## cannot be 0, so max(NUMBER,1) solves this -1 1 ## create the pool ## Need a manager to help get the values async, the values will be updated after join try #         could do this the below is visualize the rate success /etc #         pool.imap(part_get_clean_pokemon, list(range(0, len(links_pokemon)))) #         using tqdm to see progress imap works for in 0 pass finally 'id' True return 0 4 'Image' False You'll see in the below snapshot that the pooling worked successfully and improved the runtime and iteration time drastically from 9 minutes to 1 minute and iterations per second to 14.97 iterations per second. Hope this was helpful. To see the full code and check out my notebook on . github Let me know if you come up with any other useful uses for this and please share any feedback on my sample if you have any. Greyson Nevins-Archer

Multiprocessing for heavy API requests with Python and the PokéAPI

I recently had an interviewer ask me about multithreading in NodeJS. I claimed to be proficient in NodeJS but the interviewer, as well as myself, quickly recognized there were a few fundamentals concepts I had not taken the time to research. After the interview I decided to read up on the NodeJS event loop and multithreading. A Node.js application runs on a single thread. In this thread an event loop listens for events and then triggers the events associated callback function upon detection. By this simple illustration we can already see that Node.js does not support multithreading because each application is run on a single thread. Once again if an interviewer asks if Node.js supports multithreading you can confidently say no. Although multithreading is not supported there is a way to harness the power of a multicore system by using processes. Node.js has a module called designed to support a multiprocessing alternative. Let’s view some code from the Node.js . cluster docs const cluster = require('cluster');const http = require('http');const numCPUs = require('os').cpus().length; if (cluster.isMaster) {// Fork workers.for (var i = 0; i < numCPUs; i++) {cluster.fork();} cluster.on('exit', (worker, code, signal) => {console.log(`worker ${worker.process.pid} died`);});} else {// Workers can share any TCP connection// In this case it is an HTTP serverhttp.createServer((req, res) => {res.writeHead(200);res.end('hello world\n');}).listen(8000);} The cluster module allows you to spawn multiple child processes that can share a common port. In this example the worker processes are listening for an http connection on port 8000. With the child processes in place the system can handle greater loads. Hooray! We’ve managed to successfully implement a multicore Node.js application however with every design decision there are trade-offs to evaluate. In this case our trade-offs are associated with the concepts of multithreading and multiprocessing. Threads share the same memory space and creating new threads do not take up much of the system resources. Processes run in separate memory allocations with a complete copy of the program increasing the memory overhead of the application. In situations where memory might be scarce, Node.js might be the least effective tool for application code. If I explained these concepts to my interviewer I would have surely impressed him. No harm done though. In the end the experience gave me an opportunity to learn something new. April 12, 2016 👋🏽Hi! I’m Steven Natera. Follow me on Twitter . I’m actively involved in the , specifically . I like writing about code, startups, React, and Kubernetes. @StevenNatera open source community GatsbyJS

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