I would like to know how i can extract stats and content updates from various webmaster affiliation programs to display in a single app and or website . I would need the following info , sales and conversions, click stats , webmaster referrals , website affiliate links, content updates ,and payout schedules.
keeping in mind that these affiliates have different logins.
There are besically two ways of doing this:
Use the affiliate program's API's.
Build scrapers for each website. The scraper will basically log on to each site and extract all the info needed on a given interval.
No-1 is preferred but not all affiliate programs have API's. Take a look at current services like this and see a bit how they work. Take a look at StatsRemote. They are storing all the usernames and passwords locally on every users computer.
Related
I want to know what keywords brings users to our website. The result should be such that, every time a user clicks on a link of the company's website, the page URL, timestamp and keywords entered in search are recorded.
I'm not really much of a coder, but I do understand the basics of Google Tag Manager. So I'd appreciate some solutions that can allow me to implement this in GTM's interface itself.
Thanks!
You don't track them. Well, that is unless you can deploy your GTM on Google's search result pages. Which you're extremely unlikely to be able to do.
HTTPS prevents query parameters to get populated for referrers, which is what the core reason for it is.
You still can, technically, track Google search keywords for the extremely rare users who manage to use Google via http, but again, no need to do anything in GTM. GA will automatically track it with its legacy keyword tracking.
Finally, you can use Google Search Console where Google reports what keywords were used to get to your site. That information, however, is so heavily sampled that it's just not joinable to any of the GA data. It is possible, however, to join GSC with GA, but that will only lead to GA having a separate report from GSC and that's it. No real data joins.
Why web crawler must have robustness, politeness, scalablity, quality, freshness and extensibility ?
Robustness: a web crawler must be robust to changes in web site contents. Web search needs to retrieve and index every new web page as soon as possible. if a website just became online, the crawler needs time to go through all the front nodes at the queue in frontier before focusing on this new website. To tackle this web crawler has distributed system which index different web pages with different specification
Politeness: a web search must respect every web server's policies to re-index their web pages. if a web crawler is asked not to crawl a page aggressively by a certain web server, the crawler can put that page into a priority queue and re-index it when the queue is at top
Scalability: new webpages are added every day on the internet, web crawler must index every page asap. for this it needs fault tolerance, distributed systems, extra machines, etc. if a certain node at a web crawler has a fault, other nodes can divide its work and index the particular web pages.
Quality: web search ability to get useful web pages to every user. if the page contains entries which contain content far from user's recent searches or user's interests, the web search must use the previous user experience to predict what kind of content user might like
Freshness: web crawler's ability to fetch and index fresh copies of each page. for eg news websites are updated every second and needed to be re-index urgently. for this web crawler keep a separate priority queue for such priority based contents, to reindex such pages in a small period of time.
Extensibility: during early times, new data formats, languages, and new protocols were introduced. web-crawlers ability to cope with new and unseen data formats and new protocols is called extensibility, this suggests that web crawler architecture must be modular so that changes in one module would not affect others. if a website would contain a new data format unknown to web crawler then the web crawler can fetch the data but requires human intervention to add the data format details to the crawler index module.
How to scrape OTT streaming platforms(Netflix, Prime video, HULU, Hotstar, etc.) catalogue list with details like flixjini, justwatchit and other's do?
Some of the above services used to offer API's to 3rd party search services to help list their content but it does seem that most now do not.
Without this you may find you have to create your own web crawler and also have accounts for every service and region you want to crawl, which may make it unviable commercially. You also probably need to check the applicable laws in any regions you want to do this.
There are some open source web crawling solutions you could look at and also engage with the community on - e.g.
:
https://scrapy.org
I was trying to develop an application, assume that it should list all public events/gatherings happening in Bangalore. For example all food festivals happening in bangalore in next 1 month or a mass movement like marathon race happening in next 4 months. But these details are available in different sites across the web.
Say, suppose I google for "Marathon races in bangalore". Events happening in Bangalore will be listed but the dteails will be in different websites. May be the Marathon organizers have websites of their own or they had put ads in some other websites. I want to get these details from the web. Is there something like a web query or any idea of how to get this data?
I did something like this for my town a long time ago, the short answer is no. The way I implemented it was to contact a bunch of local bars and ask them what their entertainment was for the weekend and then added it my self. After a while They got used to me and I added a feature to have them update the information.
If you find a site that displays events you could do a screen scrape for the information, but it is delicate, if they change the site your application breaks.
I'm a middle school student learning computer programming, and I just have some questions about search engines like Google and Yahoo.
As far as I know, these search engines consist of:
Search algorithm & code
(Example: search.py file that accepts search query from the web interface and returns the search results)
Web interface for querying and showing result
Web crawler
What I am confused about is the Web crawler part.
Do Google's and Yahoo's Web crawlers immediately search through every single webpage existing on WWW? Or do they:
First download all the existing webpages on WWW, save them on their huge server, and then search through these saved pages??
If the latter is the case, then wouldn't the search results appearing on the google search results be outdated, Since I suppose searching through all the webpages on WWW will take tremendous amount of time??
PS. One more question: Actually.. How exactly does a web crawler retrieve all the web pages existing on WWW? For example, does it search through all the possible web addresses, like www.a.com, www.b.com, www.c.com, and so on...? (although I know this can't be true)
Or is there some way to get access to all the existing webpages on world wide web?? (sorry for asking such a silly question..)
Thanks!!
The crawlers search through pages, download them and save (parts of them) for later processing. So yes, you are right that the results that search engines return can easily be outdated. And a couple of years ago they really were quite outdated. Only relatively recently Google and others started to do more realtime searching by collaborating with large content providers (such as Twitter) to get data from them directly and frequently but they took the realtime search again offline in July 2011. Otherwise they for example take notice how often a web page changes so they know which ones to crawl more often than others. And they have special systems for it, such as the Caffeine web indexing system. See also their blogpost Giving you fresher, more recent search results.
So what happens is:
Crawlers retrieve pages
Backend servers process them
Parse text, tokenize it, index it for full text search
Extract links
Extract metadata such as schema.org for rich snippets
Later they do additional computation based on the extracted data, such as
Page rank computation
In parallel they can be doing lots of other stuff such as
Entity extraction for Knowledge graph information
Discovering what pages to crawl happens simply by starting with a page and then its following links to other pages and following their links, etc. In addition to that, they have other ways of learning about new web sites - for example if people use their public DNS server, they will learn about pages that they visit. Sharing links on G+, Twitter, etc.
There is no way of knowing what all the existing web pages are. There may be some that are not linked from anywhere and noone publicly shares a link to them (and doesn't use their DNS, etc.) so they have no way of knowing what these pages are. Then there's the problem of the Deep Web. Hope this helps.
Crawling is not an easy task (for example Yahoo is now outsourcing crawling via Microsoft's Bing). You can read more about it in Page's and Brin's own paper: The Anatomy of a Large-Scale Hypertextual Web Search Engine
More details about storage, architecture, etc. you can find for example on the High Scalability website: http://highscalability.com/google-architecture