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https://github.com/bomquote/transistor
Transistor, a Python web scraping framework for intelligent use cases.
https://github.com/bomquote/transistor
beautifulsoup4 framework headless-browsers lxml mechanicalsoup python-3 requests scraping
Last synced: 25 days ago
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Transistor, a Python web scraping framework for intelligent use cases.
- Host: GitHub
- URL: https://github.com/bomquote/transistor
- Owner: bomquote
- License: mit
- Created: 2018-11-12T15:25:51.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-09-20T20:23:47.000Z (about 2 months ago)
- Last Synced: 2024-10-07T19:11:56.873Z (about 1 month ago)
- Topics: beautifulsoup4, framework, headless-browsers, lxml, mechanicalsoup, python-3, requests, scraping
- Language: Python
- Homepage:
- Size: 2.16 MB
- Stars: 212
- Watchers: 10
- Forks: 21
- Open Issues: 246
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGES
- Contributing: contributing.rst
- License: LICENSE
- Authors: AUTHORS
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README
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**Web data collection and storage for intelligent use cases.**
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=============
*transistor*
=============
About
-----
The web is full of data. Transistor is a web scraping framework for collecting, storing, and using targeted data from structured web pages.
Transistor's current strengths are in being able to:
- provide an interface to use `Splash `_ headless browser / javascript rendering service.
- includes *optional* support for using the scrapinghub.com `Crawlera `_ 'smart' proxy service.
- ingest keyword search terms from a spreadsheet or use RabbitMQ or Redis as a message broker, transforming keywords into task queues.
- scale one ``Spider`` into an arbitrary number of workers combined into a ``WorkGroup``.
- coordinate an arbitrary number of ``WorkGroups`` searching an arbitrary number of websites, into one scrape job.
- send out all the ``WorkGroups`` concurrently, using gevent based asynchronous I/O.
- return data from each website for each search term 'task' in our list, for easy website-to-website comparison.
- export data to CSV, XML, JSON, pickle, file object, and/or your own custom exporter.
- save targeted scrape data to the database of your choice.
Suitable use cases include:
- comparing attributes like stock status and price, for a list of ``book titles`` or ``part numbers``, across multiple websites.
- concurrently process a large list of search terms on a search engine and then scrape results, or follow links first and then scrape results.
Development of Transistor is sponsored by `BOM Quote Manufacturing `_. Here is a Medium story from the author about creating Transistor: `That time I coded 90-hours in one week `_.
**Primary goals**:
1. Enable scraping targeted data from a wide range of websites including sites rendered with Javascript.
2. Navigate websites which present logins, custom forms, and other blockers to data collection, like captchas.
3. Provide asynchronous I/O for task execution, using `gevent `_.
4. Easily integrate within a web app like `Flask `_, `Django `_ , or other python based `web frameworks `_.
5. Provide spreadsheet based data ingest and export options, like import a list of search terms from excel, ods, csv, and export data to each as well.
6. Utilize quick and easy integrated task work queues which can be automatically filled with data search terms by a simple spreadsheet import.
7. Able to integrate with more robust task queues like `Celery `_ while using `rabbitmq `_ or `redis `_ as a message broker as desired.
8. Provide hooks for users to persist data via any method they choose, while also supporting our own opinionated choice which is a `PostgreSQL `_ database along with `newt.db `_.
9. Contain useful abstractions, classes, and interfaces for scraping and crawling with machine learning assistance (wip, timeline tbd).
10. Further support data science use cases of the persisted data, where convenient and useful for us to provide in this library (wip, timeline tbd).
11. Provide a command line interface (low priority wip, timeline tbd).
Quickstart
----------
First, install ``Transistor`` from pypi:
.. code-block:: rest
pip install transistor
If you have previously installed ``Transistor``, please ensure you are using the latest version:
.. code-block:: rest
pip-install --upgrade transistor
Next, setup Splash, following the Quickstart instructions. Finally, follow the minimal abbreviated Quickstart example ``books_to_scrape`` as detailed below.
This example is explained in more detail in the source code found in the ``examples/books_to_scrape`` folder, including fully implementing object persistence with ``newt.db``.
Quickstart: Setup Splash
-------------------------
Successfully scraping is now a complex affair. Most websites with useuful data will rate limit, inspect headers, present captchas, and use javascript that must be rendered to get the data you want.
This rules out using simple python requests scripts for most serious use. So, setup becomes much more complicated.
To deal with this, we are going to use `Splash `_,
"A Lightweight, scriptable browser as a service with an HTTP API".
Transistor also supports the **optional** use of a *smart proxy service* from `scrapinghub `_ called `Crawlera `_.
The crawlera smart proxy service helps us:
- avoid getting our own server IP banned
- enable regional browsing which is important to us, because data can differ per region on the websites we want to scrape, and we are interested in those differences
The minimum monthly cost for the smallest size crawlera `C10` plan is $25 USD/month. This level is useful but can easily be overly restrictive. The next level up is $100/month.
The easiest way to get setup with Splash is to use `Aquarium `_ and that is what we are going to do. Using Aquarium requires Docker and Docker Compose.
**Windows Setup**
On Windows, the easiest way to get started with Docker is to use `Chocolately `_ to install docker-desktop (the successor to docker-for-windows, which has now been depreciated). Using Chocolately requires
`installing Chocolately `_.
Then, to install docker-desktop with Chocolately:
.. code-block:: rest
C:\> choco install docker-desktop
You will likely need to restart your Windows box after installing docker-desktop, even if it doesn't tell you to do so.
**All Platforms**
Install Docker for your platform. For Aquarium, follow the `installation instructions `_.
After setting up Splash with Aquarium, ensure you set the following environment variables:
.. code-block:: python
SPLASH_USERNAME = ''
SPLASH_PASSWORD = ''
Finally, to run Splash service, *cd to the Aquarium repo on your hard drive*, and then run ``docker-compose up`` in your command prompt.
**Troubleshooting Aquarium and Splash service**:
1. Ensure you are in the ``aquarium`` folder when you run the ``docker-compose up`` command.
2. You may have some initial problem if you did not share your hard drive with Docker.
3. Share your hard drive with docker (google is your friend to figure out how to do this).
4. Try to run the ``docker-compose up`` command again.
5. Note, upon computer/server restart, you need to ensure the Splash service is started, either daemonized or with ``docker-compose up``.
At this point, you should have a splash service running in your command prompt.
**Crawlera**
Using crawlera is optional and not required for this ``books_to_scrape`` quickstart.
But, if you want to use Crawlera with Transistor, first, register for the service and buy a subscription at `scrapinghub.com `_.
After registering for Crawlera, create accounts in scrapinghub.com for each region you would like to present a proxied ip address from. For our case, we are setup to handle three regions, ALL for global, China, and USA.
Next, you should set environment variables on your computer/server with the api key for each region you need, like below:
.. code-block:: python
CRAWLERA_ALL = ''
CRAWLERA_CN = ''
CRAWLERA_USA = ''
CRAWLERA_REGIONS = 'CRAWLERA_ALL,CRAWLERA_USA,CRAWLERA_CN'
There are some utility functions which are helpful for working with crawlera found in ``transistor/utility/crawlera.py`` which require the ``CRAWLERA_REGIONS`` environment variable to be set. ``CRAWLERA_REGIONS`` should just be a comma separated string of whatever region environment variables you have set.
Finally, to use Crawlera, you will need to pass a keyword arg like ``crawlera_user=`` into your custom Scraper spider which has been subclassed from the ``SplashScraper`` class.
Alternately, you can directly set ``crawlera_user`` in your custom subclassed Scraper's ``__init__()`` method like ``self.crawlera_user = os.environ.get('CRAWLERA_USA', None)``.
Last, you must pass in a Lua script in the ``script`` argument which supports the Crawlera service. We have included two Lua scripts in ``transistor\scrapers\scripts`` folder which will be helpful to work out-of-the-box.
Of course, to get the full power of Splash + Crawlera you will need to read their documentations and also come up to speed on how to customize the Lua script to fully use Splash, to do things like fill out forms and navigate pages.
Quickstart: ``books_to_scrape`` example
---------------------------------------
See ``examples/books_to_scrape`` for a fully working example with more detailed notes in the source code. We'll go through an abbreviated setup here, without many of the longer notes and database/persistence parts that you can find in the ``examples`` folder source code.
In this abbreviated example, we will create a ``Spider`` to crawl the books.toscrape.com website to search for 20 different book titles, which the titles are ingested from an excel spreadsheet. After we find the book titles, we will export the targeted data to a different csv file.
The ``books_to_scrape`` example assumes we have a column of 20 book titles in an excel file, with a column heading in the spreadsheet named *item*. We plan to scrape the domain ``books.toscrape.com`` to find the book titles. For the book titles we find, we will scrape the sale price and stock status.
First, let's setup a custom scraper Spider by subclassing ``SplashScraper``. This will enable it to use the Splash headless browser.
Next, create a few custom methods to parse the html found by the ``SplashScraper`` and saved in the ``self.page`` attribute, with beautifulsoup4.
.. code-block:: python
from transistor.scrapers import SplashScraper
class BooksToScrapeScraper(SplashScraper):
"""
Given a book title, scrape books.toscrape.com/index.html
for the book cost and stock status.
"""
def __init__(self, book_title: str, script=None, **kwargs):
"""
Create the instance with a few custom attributes and
set the baseurl
"""
super().__init__(script=script, **kwargs)
self.baseurl = 'http://books.toscrape.com/'
self.book_title = book_title
self.price = None
self.stock = None
def start_http_session(self, url=None, timeout=(3.05, 10.05)):
"""
Starts the scrape session. Normally, you can just call
super().start_http_session(). In this case, we also want to start out
with a call to self._find_title() to kickoff the crawl.
"""
super().start_http_session(url=url, timeout=timeout)
return self._find_title()
# now, define your custom books.toscrape.com scraper logic below
def _find_title(self):
"""
Search for the book title in the current page. If it isn't found, crawl
to the next page.
"""
if self.page:
title = self.page.find("a", title=self.book_title)
if title:
return self._find_price_and_stock(title)
else:
return self._crawl()
return None
def _next_page(self):
"""
Find the url to the next page from the pagination link.
"""
if self.page:
next_page = self.page.find('li', class_='next').find('a')
if next_page:
if next_page['href'].startswith('catalogue'):
return self.baseurl + next_page['href']
else:
return self.baseurl + '/catalogue/' + next_page['href']
return None
def _crawl(self):
"""
Navigate to the next url page using the SplashScraper.open() method and
then call find_title again, to see if we found our tasked title.
"""
if self._next_page():
self.open(url=self._next_page())
return self._find_title()
return print(f'Crawled all pages. Title not found.')
def _find_price_and_stock(self, title):
"""
The tasked title has been found and so now find the price and stock and
assign them to class attributes self.price and self.stock for now.
"""
price_div = title.find_parent(
"h3").find_next_sibling(
'div', class_='product_price')
self.price = price_div.find('p', class_='price_color').text
self.stock = price_div.find('p', class_='instock availability').text.translate(
{ord(c): None for c in '\n\t\r'}).strip()
print('Found the Title, Price, and Stock.')
Next, we need to setup two more subclasses from baseclasses ``SplashScraperItem`` and ``ItemLoader``. This will allow us to export the data from the ``SplashScraper`` spider to the csv spreadsheet.
Specifically, we are interested to export the ``book_title``, ``stock`` and ``price`` attributes. See more detail in ``examples/books_to_scrape/persistence/serialization.py`` file.
.. code-block:: python
from transistor.persistence.item import Field
from transistor.persistence import SplashScraperItems
from transistor.persistence.loader import ItemLoader
class BookItems(SplashScraperItems):
# -- names of your customized scraper class attributes go here -- #
book_title = Field() # the book_title which we searched
price = Field() # the self.price attribute
stock = Field() # the self.stock attribute
def serialize_price(value):
"""
A simple serializer used in BookItemsLoader to ensure USD is
prefixed on the `price` Field, for the data returned in the scrape.
:param value: the scraped value for the `price` Field
"""
if value:
return f"UK {str(value)}"
class BookItemsLoader(ItemLoader):
def write(self):
"""
Write your scraper's exported custom data attributes to the
BookItems class. Call super() to also capture attributes
built-in from the Base ItemLoader class.
Last, ensure you assign the attributes from `self.items` to
`self.spider.` and finally you must return
self.items in this method.
"""
# now, define your custom items
self.items['book_title'] = self.spider.book_title
self.items['stock'] = self.spider.stock
# set the value with self.serialize_field(field, name, value) as needed,
# for example, `serialize_price` below turns '£50.10' into 'UK £50.10'
# the '£50.10' is the original scraped value from the website stored in
# self.scraper.price, but we think it is more clear as 'UK £50.10'
self.items['price'] = self.serialize_field(
field=Field(serializer=serialize_price),
name='price',
value=self.spider.price)
# call super() to write the built-in SplashScraper Items from ItemLoader
super().write()
return self.items
Finally, to run the scrape, we will need to create a main.py file. This is all we need for the minimal example to scrape and export targeted data to csv.
So, at this point, we've:
1. Setup a custom scraper ``BooksToScrapeScraper`` by subclassing ``SplashScraper``.
2. Setup ``BookItems`` by subclassing ``SplashScraperItems``.
3. Setup ``BookItemsLoader`` by subclassing ``ItemLoader``.
4. Wrote a simple ``serializer`` with the ``serialize_price`` function, which appends 'UK' to the returned `price` attribute data.
Next, we are ready to setup a ``main.py`` file as the final entry point to run our first scrape and export the data to a csv file.
The first thing we need to do is perform some imports.
.. code-block:: python
# -*- coding: utf-8 -*-
# in main.py, monkey patching for gevent must be done first
from gevent import monkey
monkey.patch_all()
# you probably need to add your project directory to the pythonpath like below
import sys
sys.path.insert(0, "C:/Users//repos/books_to_scrape")
# finally, import from transistor and your own custom code
from transistor import StatefulBook, WorkGroup, BaseWorkGroupManager
from transistor.persistence.exporters import CsvItemExporter
from import BooksToScrapeScraper
from import BookItems, BookItemsLoader
Second, setup a ``StatefulBook`` which will read the ``book_titles.xlsx`` file and transform the book titles from the spreadsheet "titles" column into task queues for our ``WorkGroups``.
.. code-block:: python
# we need to get the filepath to your book_titles.xlsx excel file, you can copy it
# from transistor/examples/books_to_scrape/schedulers/stateful_book/book_titles.xlsx
# need a variable like below:
# filepath = 'your/path/to/book_titles.xlsx'
# including some file path code here as a hint because it's not so straightforward
from pathlib import Path
from os.path import dirname as d
from os.path import abspath
root_dir = d(d(abspath(__file__)))
def get_file_path(filename):
"""
Find the book_titles excel file path.
"""
root = Path(root_dir)
filepath = root / 'files' / filename
return r'{}'.format(filepath)
# now we can use get_file_path to set the variable named `filepath`
filepath = get_file_path('book_titles.xlsx')
trackers = ['books.toscrape.com']
tasks = StatefulBook(filepath, trackers, keywords="titles")
Third, setup a list of exporters which than then be passed to whichever ``WorkGroup`` objects you want to use them with. In this case, we are just going to use the built-in ``CsvItemExporter`` but we could also use additional exporters to do multiple exports at the same time, if desired.
.. code-block:: python
exporters=[
CsvItemExporter(
fields_to_export=['book_title', 'stock', 'price'],
file=open('c:/book_data.csv', 'a+b'))
]
Fourth, setup the ``WorkGroup`` in a list we'll call *groups*. We use a list here because you can setup as many ``WorkGroup`` objects with unique target websites and as many individual workers, as you need:
.. code-block:: python
groups = [
WorkGroup(
name='books.toscrape.com',
url='http://books.toscrape.com/',
spider=BooksToScrapeScraper,
items=BookItems,
loader=BookItemsLoader,
exporters=exporters,
workers=20, # this creates 20 Spiders and assigns each a book as a task
kwargs={'timeout': (3.0, 20.0)})
]
Fifth, setup the ``WorkGroupManager`` and prepare the file to call the ``manager.main()`` method to start the scrape job:
.. code-block:: python
# If you want to execute all the scrapers at the same time, ensure the pool is
# marginally larger than the sum of the total number of workers assigned in the
# list of WorkGroup objects. However, sometimes you may want to constrain your pool
# to a specific number less than your scrapers. That's also OK. This is useful
# like Crawlera's C10 instance, only allows 10 concurrent workers. Set pool=10.
manager = BaseWorkGroupManager(job_id='books_scrape', tasks=tasks, workgroups=groups, pool=25)
if __name__ == "__main__":
manager.main() # call manager.main() to start the job.
Finally, run ``python main.py`` and then **profit**. After a brief Spider runtime to crawl the books.toscrape.com website and write the data, you should have a newly exported csv file in the filepath you setup, 'c:/book_data.csv' in our example above.
To summarize what we did in ``main.py``:
We setup a ``BaseWorkGroupManager``, wrapped our spider ``BooksToScrapeScraper`` inside a list of ``WorkGroup`` objects called *groups*. Then we passed the *groups* list to the ``BaseWorkGroupManager``.
- Passing a list of ``WorkGroup`` objects allows the ``WorkGroupManager`` to run multiple jobs targeting different websites, concurrently.
- In this simple example, we are only scraping ``books.toscrape.com``, but if we wanted to also scrape ``books.toscrape.com.cn``, then we'd setup two ``BaseGroup`` objects and wrap them each in their own ``WorkGroup``, one for each domain.
NOTE-1: A more robust use case will also subclass the ``BaseWorker`` class. Because, it provides several methods as hooks for data persistence and post-scrape manipulation.
Also, one may also consider to subclass the ``WorkGroupManager`` class and override it's ``monitor`` method. This is another hook point to have access to the ``BaseWorker`` object before it shuts down for good.
Refer to the full example in the ``examples/books_to_scrape/workgroup.py`` file for an example of customizing ``BaseWorker`` and ``WorkGroupManager`` methods. In the example, we show how to to save data to postgresql with newt.db but you can use whichever db you choose.
NOTE-2: If you do try to follow the more detailed example in ``examples/books_to_scrape``, including data persistence with postgresql and newt.db, you may need to set the environment variable:
.. code-block:: python
TRANSISTOR_DEBUG = 1
Whether or not you actually need to set this ``TRANSISTOR_DEBUG`` environment variable will depend on how you setup your settings.py and newt_db.py files.
If you copy the files verbatim as shown in the ``examples/books_to_scrape`` folder, then you will need to set it.
Directly Using A SplashScraper
--------------------------------
Perhaps you just want to do a quick one-off scrape?
It is possible to just use your custom scraper subclassed from ``SplashScraper`` directly, without going through all the work to setup a ``StatefulBook``, ``BaseWorker``, ``BaseGroup``, ``WorkGroup``, and ``WorkGroupManager``.
Just fire it up in a python repl like below and ensure the ``start_http_session`` method is run, which can generally be done by setting ``autorun=True``.
.. code-block:: python
>>> from my_custom_scrapers.component.mousekey import MouseKeyScraper
>>> ms = MouseKeyScraper(part_number='C1210C106K4RACTU', autorun=True)
After the scrape completes, various methods and attributes from ``SplashScraper`` and ``SplashBrowser`` are available, plus your custom attributes and methods from your own subclassed scraper, are available:
.. code-block:: python
>>> print(ms.stock())
'4,000'
>>> print(ms.pricing())
'{"1" : "USD $0.379", "10" : "USD $0.349"}'
Architecture Summary
--------------------
Transistor provides useful layers and objects in the following categories:
**Layers & Services**
1. **javascript rendering service / headless browser layer**:
- Transistor uses `Splash `_ implemented with `Aquarium `_ cookicutter docker template.
- Splash provides a programmable headless browser to render javascript and Aquarium provides robust concurrency with multiple Splash instances that are load balanced with `HAProxy `_ .
- Transistor provides integration with Splash through our ``SplashBrowser`` class found in ``transistor/browsers/splash_browser.py``.
2. **smart proxy service**:
- Transistor supports use of `Crawlera `_ , which is a paid *smart proxy service* providing robust protection against getting our own ip banned while scraping sites that actively present challenges to web data collection.
- Crawlera use is optional. It has a minimum monthly cost of $25 USD for starter package and next level up is currently $100 USD/month.
- in using Crawlera, the concurrency provided by gevent for asynchronous I/O along with Splash running with Aquarium, is absolutely required, because a single request with Splash + Crawlera is quite slow, taking up to **15 minutes** or more to successfully return a result.
**Spiders**
1. **browsers**
- see: ``transistor/browsers``
- wrap `python-requests `_ and `beautifulsoup4 `_ libraries to serve our various scraping browser needs.
- browser API is generally created by subclassing and overriding the well known `mechanicalsoup `_ library to work with Splash and/or Splash + Crawlera.
- if Javascript support is not needed for a simple scrape, it is nice to just use mechanicalsoup's ``StatefulBrowser`` class directly as a Scraper, like as shown in ``examples/cny_exchange_rate.py`` .
- a ``Browser`` object is generally instantiated inside of a ``Scraper`` object, where it handles items like fetching the page, parsing headers, creating a ``self.page`` object to parse with beautifulsoup4, handling failures with automatic retries, and setting class attributes accessible to our ``Scraper`` object.
2. **scrapers**
- see ``transistor/scrapers``
- instantiates a browser to grab the ``page`` object, implements various html filter methods on ``page`` to return the target data, can use Splash headless browser/javascript rendering service to navigate links, fill out forms, and submit data.
- for a Splash or Splash + Crawlera based scraper ``Spider``, the ``SplashScraper`` base class provides a minimal required Lua script and all required connection logic. However, more complex use cases will require providing your own custom modified Lua script.
- the scraper design is built around gevent based asynchronous I/O, and this design allows to send out an arbitrarily large number of scraper workers, with each scraper worker assigned a specific scrape task to complete.
- the current core design, in allowing to send out an arbitrarily large number of scraper workers, is not necessarily an optimal design to 'crawl' pages in search of targeted data. Where it shines is when you need to use a webpage search function on an arbitrarily large list of search tasks, await the search results for each task, and finally return a scraped result for each task.
3. **crawlers** (wip, on the to-do list)
- see ``transistor/crawlers`` (not yet implemented)
- this crawling ``Spider`` will be supported through a base class called ``SplashCrawler``.
- while it is straightforward to use the current Transistor scraper ``SplashScraper`` design to do basic crawling (see ``examples/books_to_scrape/scraper.py`` for an example) the current way to do this with Transistor is not optimal for crawling. So we'll implement modified designs for crawling spiders.
- specifics TBD, may be fully custom or else may reuse some good architecture parts of `scrapy `_, although if we do that, it will be done so we don't need a scrapy dependency and further it will be using gevent for asynchronous I/O.
**Program I/O**
1. **schedulers**:
*BOOKS*
- see ``transistor/schedulers/books``
- a ``StatefulBook`` object provides an interface to work with spreadsheet based data.
- for example, a book facilitates importing a column of keyword search term data, like 'book titles' or 'electronic component part numbers', from a designated column in an .xlsx file.
- after importing the keyword search terms, the book will transform each search term into a task contained in a ``TaskTracker`` object
- each ``TaskTracker`` will contain a queue of tasks to be assigned by the ``WorkGroupManager``, and will ultimately allow an arbitrarily large number of ``WorkGroups`` of ``BaseWorkers`` to execute the tasks, concurrently.
*RabbitMQ & Redis*
- see ``transistor/schedulers/brokers``
- provides the ``ExchangeQueue`` class in transistor.scheulers.brokers.queues which can be passed to the ``tasks`` parameter of ``BaseWorkGroupManager``
- Just pass the appropriate connection string to ``ExchangeQueue`` and ``BaseWorkGroupManager`` and you can use either RabbitMQ or Redis as a message broker, thanks to `kombu `_.
- in this case, the ``BaseWorkGroupManager`` also acts as a AMQP ``consumer`` which can receive messages from RabbitMQ message broker
2. **workers**:
- a ``BaseWorker`` object encapsulates a ``Spider`` object like the ``SplashScraper`` or ``SplashCrawler`` objects, which has been customized by the end user to navigate and extract the targeted data from a structured web page.
- a ``BaseGroup`` object can then be created, to encapsulate the ``BaseWorker`` object which contains the ``Spider`` object.
- The purpose of this ``BaseGroup`` object is to enable concurrency and scale by being able to spin up an arbitrarily large number of ``BaseWorker`` objects, each assigned a different scrape task for execution.
- the ``BaseGroup`` object can then receive tasks to execute, like individual book titles or electronic component part numbers to search, delegated by a ``WorkGroupManager`` class.
- each ``BaseWorker`` in the ``BaseGroup`` also processes web request results, as they are returned from it's wrapped ``SplashScraper`` object. ``BaseWorker`` methods include hooks for exporting data to mutiple formats like csv/xml or saving it to the db of your choice.
- each ``BaseGroup`` should be wrapped in a ``WorkGroup`` which is passed to the ``WorkGroupManager``. Objects which the ``BaseWorker`` will use to process the ``Spider`` after it returns from the scrape should also be specified in ``WorkGroup``, like ``Items``, ``ItemLoader``, and ``Exporter``.
3. **managers**:
- the overall purpose of the ``WorkGroupManager`` object is to provide yet more scale and concurrency through asynchronous I/O.
- The ``WorkGroupManager`` can spin up an arbitrarily large number of ``WorkGroup`` objects while assigning each ``BaseWorker/Spider`` in each of the ``WorkGroup`` objects, individual scrape tasks.
- This design approach is most useful when you have a finite pipeline of scrape tasks which you want to search and compare the same terms, across multiple different websites, with each website targeted by one ``WorkGroup``.
- for example, we may have a list of 50 electronic component part numbers, which we want to search each part number in ten different regional websites. The ``WorkGroupManager`` can spin up a ``WorkGroup`` for each of the 10 websites, assign 50 workers to each ``WorkGroup``, and send out 500 ``BaseWorkers`` each with 1 task to fill, concurrently.
- to further describe the ``WorkGroupManager``, it is a middle-layer between ``StatefulBook`` and ``BaseGroup``. It ingests ``TaskTracker`` objects from the ``StatefulBook`` object. It is also involved to switch states for ``TaskTracker`` objects, useful to track the task state like completed, in progress, or failed (this last detail is a work-in-progress).
**Persistence**
1. **exporters**
- see ``transistor/persistence/exporters``
- export data from a ``Spider`` to various formats, including *csv*, *xml*, *json*, *xml*, *pickle*, and *pretty print* to a *file* object.
**Object Storage, Search, and Retrieval**
Transistor can be used with the whichever database or persistence model you choose to implement. But, it will offer some open-source code in support of below:
1. **SQLAlchemy**
- we use `SQL Alchemy `_ extensively and may include some contributed code as we find appropriate or useful to keep in the Transistor repository. At least, an example for reference will be included in the `examples` folder.
2. **object-relational database** using `PostgreSQL `_ with `newt.db `_.
- persist and store your custom python objects containing your web scraped data, directly in a PostgreSQL database, while also converting your python objects to JSON, *automatically* indexing them for super-quick searches, and making it available to be used from within your application or externally.
- leverage PostgreSQL's strong JSON support as a document database while also enabling "ease of working with your data as ordinary objects in memory".
- this is accomplished with `newt.db `_ which turns `PostgreSQL `_ into an object-relational database while leveraging PostgreSQL's well integrated JSON support.
- newt.db is itself a wrapper built over the battle tested `ZODB `_ python object database and `RelStorage `_ which integrates ZODB with PostgreSQL.
- more on newt.db here [1]_ and here [2]_
.. [1] `Why Postgres Should Be Your Document Database (blog.jetbrains.com) `_
.. [2] `Newt DB, the amphibious database (newtdb.org) `_.
Database Setup
---------------
Transistor maintainers prefer to use PostgreSQL with newt.db. Below is a quick setup walkthrough.
After you have a valid PostgreSQL installation, you should install newt.db:
.. code-block:: rest
pip install newt.db
After installation of newt.db you need to provide a URI connection string for newt.db to connect to PostgreSQL. An example setup might use two files for this, with a URI as shown
in ``examples/books_to_scrape/settings.py`` and a second file to setup newt.db as shown in ``examples/books_to_scrape/persistence/newt_db.py`` as shown below:
1. ``examples/books_to_scrape/settings.py``
- not recreated here, check the source file
2. ``examples/books_to_scrape/newt_db.py``:
.. code-block:: python
import os
import newt.db
from examples.books_to_scrape.settings import DevConfig, ProdConfig, TestConfig
from transistor.utility.utils import get_debug_flag
def get_config():
if 'APPVEYOR' in os.environ:
return TestConfig
return DevConfig if get_debug_flag() else ProdConfig
CONFIG = get_config()
ndb = newt.db.connection(CONFIG.NEWT_DB_URI)
Next, we need to store our first two python objects in newt.db, which are:
1. A list collection object, so we have a place to store our scrapes.
2. An object to hold our list collection object, so that we can have a list of lists
.. code-block:: python
from transistor.persistence.newt_db.collections import SpiderList, SpiderLists
Now, from your python repl:
.. code-block:: python
from transistor.newt_db import ndb
>>> ndb.root.spiders = SpiderLists() # Assigning SpiderLists() is only required during initial setup. Or else, when/if you change the SpiderLists() object, for example, to provide more functionality to the class.
>>> ndb.root.spiders.add('first-scrape', SpiderList()) # You will add a new SpiderList() anytime you need a new list container. Like, every single scrape you save. See ``process_exports`` method in ``examples/books_to_scrape/workgroup.py``.
>>> ndb.commit() # you must explicitly commit() after each change to newt.db.
At this point, you are ready-to-go with newt.db and PostgreSQL.
Later, when you have a scraper object instance, such as ``BooksToScrapeScraper()`` which has finished it's web scrape cycle, it will be stored in the ``SpiderList()`` named ``first-scrape`` like such:
.. code-block:: python
>>> ndb.root.spiders['first-scrape'].add(BooksToScrapeScraper(name="books.toscrape.com", book_title="Soumission"))
More on StatefulBook
--------------------
Practical use requires multiple methods of input and output. ``StatefulBook`` provides a method for reading an excel file
with one column of search terms, *part numbers* in the below example, which we would like to search and scrape data from multiple websites which sell such components:
.. code-block:: python
>>> from transistor import StatefulBook
>>> filepath = '/path/to/your/file.xlsx'
>>> trackers = ['mousekey.cn', 'mousekey.com', 'digidog.com.cn', 'digidog.com']
This will create four separate task trackers for each of the four websites to search with the part numbers:
.. code-block:: python
>>> book = StatefulBook(filepath, trackers, keywords="part_numbers")
>>> book.to_do()
Output:
.. code-block:: python
deque([, , , ])
So now, each website we intend to scrape, has it's own task queue. To work with an individual tracker and see what is in it's individual to_do work queue:
.. code-block:: python
>>> for tracker in book.to_do():
>>> if tracker.name == 'mousekey.cn':
>>> ms_tracker = tracker
>>> print(ms_tracker)
>>> ms_tracker.to_do()
deque(['050R30-76B', '1050170001', '12401598E4#2A', '525591052', '687710152002', 'ZL38063LDG1'])
Testing
-------------
The easiest way to test your scraper logic is to download the webpage html and then pass in the html file with a test dict.
Below is an example:
.. code-block:: python
from pathlib import Path
data_folder = Path("c:/Users//repos//tests/scrapers/component/mousekey")
file_to_open = data_folder / "mousekey.cn.html"
f = open(file_to_open, encoding='utf-8')
page = f.read()
test_dict = {"_test_true": True, "_test_page_text": page, "_test_status_code": 200, "autostart": True}
from my_custom_scrapers.component.mousekey import MouseKeyScraper
ms = MouseKeyScraper(part_number='GRM1555C1H180JA01D', **test_dict)
assert ms.stock() == '17,090'
assert ms.pricing() == '{"1": "CNY ¥0.7888", "10": "CNY ¥0.25984", "100": "CNY ¥0.1102", ' \
'"500": "CNY ¥0.07888", "10,000": "CNY ¥0.03944"}'