Data Retrieval Web Engine - Queryable Web Scrap engine build on python based on lxml and Selenium package and using JSON as query construct.
Project description
Data Retrieval Web Engine
Context
Multiple technologies are used as web parsers, web scrapers, spider and so forth. Comparative studies can be found in literature that categorise based on methods and technologies. We took a different perspective and looked at querability feature. Our inspiration comes form OXPath where an extension of XPath is used to "query" and extract semi-structured data from the web.
Objectives
Similarly to OXPath, our objective is to create a tool for data retrieval from the web based on a "query" mechanism. We opted for using JSON constructs for our query definitions with augmented keywords, filters and actions.
Technology stack
The tool is written in Python3 and can be included in other python projects by installing it from the python package index
using pip3 install dr-web-engine
or integrating with the tools command line interface by running python3 -m web_engine.runner
The tool is build on top of several other packages which will be automatically installed. These are:
- Selenium
- Geckodriver Autoinstaller
- LXML
- Python Interface
- ArgParse
- XVFBWrapper
XVFB only works on Linux and if the parameter is True on a Windows or MacOX system you will get an error message.
The Python Package page can be found here
Usage
To use the integrated CLI run python3 -m web_engine.runner
. This will display the following help message:
usage: runner.py [-h] [-q QUERY] [-e [ENGINE]] [-ht [HEIGHT]] [-wh [WIDTH]]
[-lat [LAT]] [-lon [LON]] [-img [IMG]] [-l [LOG]]
[-xvfb [XVFB]]
Web Scrap Engine for semi-structured web data retrieval using JSON query constructs
optional arguments:
-h, --help show this help message and exit
-q QUERY, --query QUERY
JSON query
-e [ENGINE], --engine [ENGINE]
Engine: use [lxml] for parser engine (default),
[selenium] for action based web scraping
-ht [HEIGHT], --height [HEIGHT]
specify the browser window height (default is 800,
only used with Selenium engine)
-wh [WIDTH], --width [WIDTH]
specify the browser window width (default is 1280,
only used with Selenium engine)
-lat [LAT], --lat [LAT]
Latitude (not specified by default)
-lon [LON], --lon [LON]
Longitude (not specified by default)
-img [IMG], --img [IMG]
Load images
-l [LOG], --log [LOG]
Set flag to True to see verbose logging output
-xvfb [XVFB], --xvfb [XVFB]
Set flag to False to see Firefox when using Selenium
engine
There is only one required parameter: -q Query
For example, to run the web data retrieval with the following JSON query (supposedly file test.json
):
{
"_doc":"https://www.google.com/search?q=Donald+Duck",
"links":[{
"_base_path": "//div[@id='search'][1]//div[@class='g']",
"_follow": "//a[@id='pnnext'][1]/@href",
"link": "//div[@class='rc']/div[@class='r']/a/@href",
"title": "//h3/text()"
}]
}
use the following command: python3 -m web_engine.runner -q test.json
. The outcome will look like the following JSON result:
{"links": [{"link": ["https://en.wikipedia.org/wiki/Donald_Duck"],
"title": ["Donald Duck - Wikipedia"]},
{"link": ["https://cosleyzoo.org/white-pekin-duck/"],
"title": ["White Pekin Duck – Cosley Zoo"]},
{"link": ["https://www.cheatsheet.com/entertainment/donald-duck-turned-85-years-old.html/"],
"title": ["Donald Duck Turned 85-Years-Old and Disney Fans Are Quacking ..."]},
{"link": ["https://en.wikipedia.org/wiki/Daisy_Duck"],
"title": ["Daisy Duck - Wikipedia"]},
{"link": ["https://www.headstuff.org/culture/history/disney-studios-war-story-donald-duck-became-sgt/"],
"title": ["Disney Studios At War - the story of how Donald Duck became a Sgt ..."]}
In the JSON query provided, the items starting with _
are keywords and can be filters, actions or instructions.
If we remove all the keywords the remaining JSON represents the structure of the expected output.
In another more complex query we use some other keywords and actions:
{
"_doc": "https://www.checkatrade.com/trades/WayreHouseElectricalServices",
"data": {
"ld_data": "//head/script[@type=\"application/ld+json\"][1]"
},
"reviews": [{
"_doc": "https://www.checkatrade.com/trades/WayreHouseElectricalServices/reviews",
"_base_path": "//div[contains(@class, 'ReviewsPage__Content')]//div[contains(@class, 'ReviewsItem__Wrapper')]",
"_key": "review",
"_pre_remove": "//*[contains(@class,'alert-box')]",
"_follow": "//a[contains(@class,\"Chevrons__Wrapper\")][2]/@href",
"_follow_action": "//a[contains(@class,\"Chevrons__Wrapper\")][2]{click }",
"title": "//h3[contains(@class, 'ReviewsItem__Title')]",
"score": "//*[name()='svg']//text()[normalize-space()]",
"verified": "//div[contains(@class, 'ReviewsItem__Verified')]/text()[normalize-space()]",
"content": "//p[contains(@class, 'ReviewsItem__P')]",
"review_by": "//div[contains(@class, 'ReviewsItem__Byline')]/text()[normalize-space()]"
}]
}
Keywords
_doc
: Represents the document to follow. Is usually a URL to a web page. It is compulsory on the top level and can be provided on the lower levels of the hierarchical structure. The _doc
keyword
_base_path
: To be used in an array extraction. Arrays are a lists of element and are defined in the query as an JSON array []
.
When _base_path
is provided, all elements of the query in the array will be looked inside the HTML element as defined by _base_path
.
_key
: Use to assign each element of the array to assigned to the variable _key
_pre_xxx
: All actions that start with _pre_
are to be executed before data extraction.
_pre_remove
: Remove elements from page
_follow
: Follow the link if and when exists
_follow_action
: If element in follow exists, then perform action rather than following the link. The actions are defined as the last part of the XPath query and are always defined between carley brackets. In this case the action {click }
means click on element.
Extendability
The package is intended to be easily extendable. For example the {click }
action is defined in the query as follows:
The corresponding is defined as follows:
class ClickAction(implements(Action)):
def __init__(self, receiver, log: logging = None):
self._log = log
self._receiver = receiver
def execute(self, *args):
if args is None or len(args) != 1:
return
xpath_selector: str = args[0]
wait = WebDriverWait(self._receiver.driver, 10)
elem = wait.until(EC.element_to_be_clickable((By.XPATH, xpath_selector)))
elem.click()
In the Scraper implementations, actions are registered against keywords as follows:
click = ClickAction(self, self.log)
filter_remove = FilterRemoveAction(self, self.log)
self.register('click', click)
self.register('remove', filter_remove)
And invoked by simply matching the action keywords in the query as follows:
def action_get(self, actions: list):
for x in actions:
self.execute(x)
return self.get()
def execute(self, action_composite: str):
action_name, action_path = SeleniumScraper.__get_action(action_composite)
action_name = action_name.strip()
if action_name in self._actions.keys():
self._history.append((time.time(), action_name))
self._actions[action_name].execute(action_path)
else:
self.log.warn(f"Command [{action_name}] not recognised")
@staticmethod
def __get_action(action_composite):
pattern = '{(.+?)}'
matches = re.search(pattern, action_composite)
if not matches:
return None, None
action_name = matches.group(1)
action_xpath = re.sub(pattern, '', action_composite)
return action_name, action_xpath
Future work
This work, whilst it is a working beta, is by no means complete and it's rather focused on a narrow specific problem. However, special effort has been made to keep the solution generic, universal and extendable for it to potentially grow into a mature Data Retrieval Web Engine based on JSON Queries.
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