#webdatascraper

Before we delve into why one might need a #datascraper in the age of #ai, let’s first understand what #datascraping and AI,

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Introduction

Scraping data from various mobile apps is not something new, but it appears that various approaches have not scaled well. We are working hard at Web Screen Scraping on Mobile Apps Data Extraction on Scale, which is why we created this blog to provide relevant information on the issue.

Reverse Engineering

Now the question is, how are we going to do it? Assume you have to scrape data from a mobile app. Let's say you have an APK of an Android app and you want to scrape 500,000 points of data (UI screens) per day. How can you achieve this and what would it cost?

It's critical to figure out how a client communicates with servers, what protocol he uses, and how they send messages to one another.

Although this appears to be the most scalable and cost-effective option, it may only provide solutions for one application. What should we do if we desire to recurrence the process with more applications? What happens if an API is updated? As you can see, estimating the efforts it should make is difficult.

Following that, we used Android Emulator to install the APK, connect it to the proxy, and monitor the data.

After some hours, we were able to watch traffic from the clients to the server and even mimic calls to a server because everything was done over HTTPS.

Result

Reverse engineering is simple, to begin with, and appears to be the most cost-effective and scalable method of doing so. However, it may take a few lengthy days, as development charges are unpredictable, and don't always acquire the final product.

Appium or Selendroid

The situation is drastically different when utilizing tools like Selendroid or Appium. You may quickly write the situation you wish to test and have it run automatically over and over again. We choose to use Appium in conjunction with Android Emulator.

These have a reputation for being difficult to work with for mobile development, but with the release of x86 emulators, things have begun to operate more smoothly, and it now feels as if the applications running on laptops are faster than the physical devices themselves.

Later, we created a Docker container with Ubuntu 16.04, Appium, and an Android x86 emulator to begin the test of how many of them we could run simultaneously.

So, assuming that one CPU can run one emulator, we'll need 700 CPUs to run 700 emulators! It's a significant demand, and it's also quite costly!

Result

Physical hardware always delivers good performance, but it's difficult to manage on a wide scale.

So, how do you avoid having to deal with physical hardware management?

Well. We can use AWS, which is a public cloud. When we applied this strategy to the cloud, however, things went drastically differently. Linux, Docker, AWS, and Android have all worked successfully together in the past, but not with an emulator. AWS EC2 provides you with a Virtual Machine, and Android Emulator is another Virtual Machine. To take advantage of hardware acceleration while utilizing an x86 Android emulator, the host machine must reveal this capability; however, Amazon, like any other public cloud, does not do so; instead, they use it to serve us with virtual machines, therefore we were unable to even launch an Android x86 emulator!

So, how did we go about doing that? We've already used Ravello.

Cloud Ravello

When running on a public cloud, the Ravello solution supports nested virtualization or Kernel-based Virtual Machines on the host computer.

It has made it possible for us to run x86 Android emulators on the cloud. We tried it and it worked as well, however in terms of performance, the process took three times as long as on physical machines, and the situation worsened as more emulators were used.

Result

The Ravello Cloud solution is functional; however, its performance is lacking.

Cloud Genymotion

The Genymotion Cloud, which offers Android Machine Images (AMI) for Amazon EC2, is another option.

As a result, instead of obtaining a Windows or Ubuntu VM, you'll get an Android VM! It appeared to be the best public cloud-based option. We were able to run the scraping script on the physical hardware as well as the t2.small example (with 1 CPU + 2 GB RAM) using AMI.

The expense of this method is a problem because each instance besides the picture costs 0.149$ per hour, which adds up quickly when you have 700 Android simulators.

Result

Genymotion performs pretty well in the cloud and provides roughly the same work as running on a physical machine, but it's somewhat expensive when used on a large scale.

Bluestacks and Nox

These items were designed specifically for game players, but that doesn't mean we can't use them. To test it, we create a t2.medium Windows VM on AWS EC2.

The installation of Nox failed because the graphic card's driver was out of date. Even after overcoming this, more challenges arose, so we decided to use Bluestacks.

The installation of Bluestacks proceeded smoothly, and it performed admirably.

However, the issue was that we didn't come up with a way to run several Bluestacks applications on the cloud within our Virtual Machine, and our APK test didn't perform well on it either, possibly because Bluestacks operates in tablet mode.

Result

Bluestacks performed incredibly well on the virtual machines, it's free, and it's even visible over ADB, which means we can run tests of Appium on it. However, it can only run on Mac or Windows, and you can only run one instance at a time, and it only works in Tablet mode.

Certain optimizations can assist in speeding the time it takes to scrape the data when using one of the emulator options. To name a few, Use landing URLs, deep links, and other techniques if possible. When used on powerful PCs, the app speed may be faster than on the actual device.

Conclusion

To summarize, if you need to Scrape Mobile Apps Data on a large scale, and reverse engineering performs well and meets your needs, then go for it because Web Screen Scraping claims it is the most cost-effective and scalable method.

Other options for using an Android emulator are limited, and the results are prohibitively expensive. If you have any other ideas for scraping mobile apps on a large scale, please share them with us.

Consider a situation where you need data quickly in large volume from websites. It is tedious and time consuming to collect data manually. Web scraping makes this procedure much faster and easier.

Purpose of Web Scraping

Data extraction is a method of gathering large volume of data through websites. But this data is useful? Let's discuss several web scraping applications:

Comparing Prices: data extracting services is useful in extracting data from shopping websites and use them for comparing prices.

Gathering Email Addresses: data extracting is used by many firms that utilize email as a marketing tool to acquire email addresses and then send mass emails.

Social Media: Web scraping can be used to extract data through social media sites such as Twitter in order to determine trends.

R&D Department: Web scraping can be used to gather a significant amount of data for example, temperature, statistics, general information, and so on through websites, which is then processed and used in surveys or research.

Job Postings: Information about job opportunities and interviews is gathered from several websites and then compiled in one location so it is effortlessly available to user.

How to Define Web Scraping? Is it Legal to Scrape Data?

Data extracting is the process of fetching large volume of data through sites. The data on the webpage is not structured. Web scraping assist in gathering unstructured information and storing them in structured format. There are various methods of

Scraping websites, include using online services, APIs, or developing codes. Now we will discuss web scraping using Python.

When it comes to legal aspects, certain websites permit it while others do not. You may check the "robots.txt" file on a website to see if it enables web scraping or not. By attaching "/robots.txt" to the URL you intend to scrape, you can find this file. For example, to scrape Flipkart website use www.flipkart.com/robots.txt

Web Scraping with Python

Every programing language is excellent and useful as Python. Why to use Python instead of other languages of web scraping?

The following is a list of Python features that makes it unique and can be chosen over other languages:

1. Easy to Use

Python is an easy language to code. There are no curly-braces or semi-colons required anywhere. This makes it easy to use and less confused.

2. Large Pool of Libraries

Python includes a large number of library, such as Matplotlib, Numpy, Pandas, etc. that offer approaches and functions for a several uses. As a result, it's suitable for site scraping and data manipulation.

3. Defined Data Types:

Data types should not be defined for variables using Python. This allows you to save time and helps finishing your work rapidly.

4. Understandable Syntax:

Python language rules is simple and easy to learn, knowing the fact that interpretation of a Python code is rather comparable to understanding a statement of English. The indentation of Python helps users to distinguish between diverse blocks /scopes in code, making it easy-to-read and easy to comprehend.

5. Large Task and Small Code:

Time saving is the main feature of data extracting and you cannot waste more time in writing codes for the tasks. Python solves this problem by allowing to write small codes for large task and hence the time of writing codes is also saved.

6. Community:

Finding difficulties while writing codes? Don’t worry when you have Python. It offers the largest and active communities in which you can seek assistance.

Process of Web Scraping

When web scraping code is executed, the specified request is sent to URL. Data is generated using the requests made, allowing to understand the XML or HTML page. After that the code parses the XML or HTML page, finding data and extracting the same.

Below mentioned is the list of stages involved in web scraping:

Locate the URL of data to scrape.

Examining the Page

Search the information you want to extract

Codes writing.

Execute the code to get the data.

Use appropriate format to store data.

Now let’s see extracting Flipkart website data using Python.

Web Scraping Libraries

Python has many applications and there are different libraries for distinct purpose. The following is the list of libraries used by python:

Selenium:

It is an open-source web developing framework. It's a program that automates browser functions.

BeautifulSoup:

BeautifulSoup is the Python tool that allows you to parse XML and HTML texts. It generates parse trees, which are useful for quickly extracting data.

Pandas:

It is data handling and analysis library and used to fetch information and store in format that you wish.

Let us take an example of scraping Flipkart website data

Pre-Requirements:

Python 2.x and Python 3.x by BeautifulSoup, Selenium, and pandas’ libraries installed

Google-chrome browser

Operating System Ubuntu

Now we will discuss the process,

1. Trace the URL

We will scrape the website of Flipkart to gain the name, price, rating and review of laptops for our example.

https://www.flipkart.com/laptops/buyback-guarantee-on-laptops-/pr?sid=6bo percent 2Cb5g&uniqBStoreParam1=val1&wid=11.productCard.PMU V2 is the URL of the page.

2. Examining the Web Page

In most cases, the data is nested into tags. So that we can study a website to determine the data to be extracted is nested in which tag. Right-click an element and choose "Inspect" from the pull-down menu.

When you select "Inspect" tab, a “Inspector Box" shall appear.

3. Trace the Data to Be Retrieved

Let's take a look at the Name, Price, and Rating tags that are all nested within "div" tag.

4. Compose the Program

Let's start by building a file of Python. For doing this, open Ubuntu's terminal and write gedit < file name> with the extension.py.

My file will be called "web-s." The command is as follows:

gedit web-s.py

let’s write codes for the file.

All the relevant libraries to be imported:

from selenium import webdriver from BeautifulSoup import BeautifulSoup import pandas as pd

Chrome browser is used to configure web driver and set a path for chrome driver.

driver = webdriver.Chrome("/usr/lib/chromium-browser/chromedriver")

Below mentioned are the codes to open URL:

products=[] #List to store name of the product prices=[] #List to store price of the product ratings=[] #List to store rating of the product driver.get("https://www.flipkart.com/laptops/~buyback-guarantee-on-laptops-/pr?sid=6bo%2Cb5g&amp;uniqBStoreParam1=val1&amp;wid=11.productCard.PMU_V2")

Now we will extract data from websites after creating codes to open URL. The information you wish to fetch is nested into div tags. These div tags will fetch data, class names and to be saved in variable. see code:

content = driver.page_source soup = BeautifulSoup(content) for a in soup.findAll('a',href=True, attrs={'class':'_31qSD5'}): name=a.find('div', attrs={'class':'_3wU53n'}) price=a.find('div', attrs={'class':'_1vC4OE _2rQ-NK'}) rating=a.find('div', attrs={'class':'hGSR34 _2beYZw'}) products.append(name.text) prices.append(price.text) ratings.append(rating.text)

5. Execute Code and Retrieve the Information

Use the following to execute code:

python web-s.py

6. The Data to Be in the Proper Format

You wish to save data in desired format after you've extracted it. Based on needs, the format will differ. We will save the fetched information in CSV format for this example. The below lines to be used in the code for storing the data:

df = pd.DataFrame({'Product Name':products,'Price':prices,'Rating':ratings})df.to_csv('products.csv', index=False, encoding='utf-8')

Now, we will run the code again.

The file name products.csv is made and this file contains the data extracted.

Conclusion

Hence this was all about scraping Flipkart data with Python. You can use different libraries, extract the required data, store the data for further analysis.

Get in touch with us to extract Flipkart data using Python.

Contact Web Screen Scraping today!

The Smart Web Data Scraper is capable of saving the extracted data in different formats. It can save them in either .CSV or .TXT formats which can open in excel and notepad respectively. It has greatly helped business organizations in carrying out the online marketing campaigns. It has helped them in gathering the correct data from the Websites.

Web Data Scraper

SMART Web Data Scraper is finest tool to extract data from websites in tabular form. It can automate the scraping task and extract all pages within short span of time and save your time and manual effort. This tool can be used to get list of products, services and other useful data from web pages. You can export extracted data to Excel.