sneakpeek-py 0.1.5

Sneakpeek is a framework that helps to quickly and conviniently develop scrapers. It's the best choice for scrapers that have some specific complex scraping logic that needs to be run on a constant basis.

Sneakpeek

Sneakpeek - is a framework that helps to quickly and conviniently develop scrapers. It's the best choice for scrapers that have some specific complex scraping logic that needs to be run on a constant basis.

Quick start

So you want to create a new scraper, first you need to make sure you have installed Sneakpeek:

pip install sneakpeek-py

The next step would be implementing scraper logic (or so called scraper handler):

# file: demo_scraper.py

import json
import logging

from pydantic import BaseModel

from sneakpeek.scraper_context import ScraperContext
from sneakpeek.scraper_handler import ScraperHandler


# This defines model of handler parameters that are defined
# in the scraper config and then passed to the handler
class DemoScraperParams(BaseModel):
    url: str

# This is a class which actually implements logic
# Note that you need to inherit the implementation from
# the `sneakpeek.scraper_handler.ScraperHandler`
class DemoScraper(ScraperHandler):
    # You can have any dependencies you want and pass them
    # in the server configuration
    def __init__(self) -> None:
        self._logger = logging.getLogger(__name__)

    # Each handler must define its name so it later
    # can be referenced in scrapers' configuration
    @property
    def name(self) -> str:
        return "demo_scraper"

    # Some example function that processes the response
    # and extracts valuable information
    async def process_page(self, response: str):
        ...

    # This function is called by the worker to execute the logic
    # The only argument that is passed is `sneakpeek.scraper_context.ScraperContext`
    # It implements basic async HTTP client and also provides parameters
    # that are defined in the scraper config
    async def run(self, context: ScraperContext) -> str:
        params = DemoScraperParams.parse_obj(context.params)
        # Perform GET request to the URL defined in the scraper config
        response = await context.get(params.url)
        response_body = await response.text()

        # Perform some business logic on a response
        result = await self.process_page(response_body)

        # Return meaningful job summary - must return a string
        return json.dumps({
            "processed_urls": 1,
            "found_results": len(result),
        })

Now that we have some scraper logic, let's make it run periodically. To do so let's configure SneakpeekServer:

# file: main.py

from sneakpeek.lib.models import Scraper, ScraperJobPriority, ScraperSchedule
from sneakpeek.lib.storage.in_memory_storage import (
    InMemoryLeaseStorage,
    InMemoryScraperJobsStorage,
    InMemoryScrapersStorage,
)
from sneakpeek.logging import configure_logging
from sneakpeek.plugins.requests_logging_plugin import RequestsLoggingPlugin
from sneakpeek.scraper_config import ScraperConfig
from sneakpeek.server import SneakpeekServer

from demo_scraper import DemoScraper

# For now let's have a static list of scrapers
# but this can as well be a dynamic list which is
# stored in some SQL DB
scrapers = [
    Scraper(
        # Unique ID of the scraper
        id=1,
        # Name of the scraper
        name=f"Demo Scraper",
        # How frequent should scraper be executed
        schedule=ScraperSchedule.EVERY_MINUTE,
        # Our handler name
        handler="demo_scraper",
        # Scraper config, note that params must be successfully
        # deserialized into `DemoScraperParams` class
        config=ScraperConfig(params={"url": url}),
        # Priority of the periodic scraper jobs.
        # Note that manually invoked jobs are always
        # scheduled with `UTMOST` priority
        schedule_priority=ScraperJobPriority.UTMOST,
    )
]

# Define a storage to use to store the list of the scrapers
scrapers_storage = InMemoryScrapersStorage(scrapers)

# Define a jobs storage to use
jobs_storage = InMemoryScraperJobsStorage()

# Define a lease storage for the scheduler to ensure
# that at any point of time there's only 1 active scheduler.
# This eliminates concurrent scrapers execution
lease_storage = InMemoryLeaseStorage()

# Configure server
server = SneakpeekServer.create(
    # List of implemented scraper handlers
    handlers=[DemoScraper()],
    scrapers_storage=scrapers_storage,
    jobs_storage=jobs_storage,
    lease_storage=lease_storage,

    # List of plugins which will be invoked before request
    # is dispatched or after response is received.
    # In the example we use `sneakpeek.plugins.requests_logging_plugin.RequestsLoggingPlugin`
    # which logs all requests and responses being made
    plugins=[RequestsLoggingPlugin()],
)

if __name__ == "__main__":
    configure_logging()
    # Run server (spawns scheduler, API and worker)
    # open http://localhost:8080 and explore UI
    server.serve()

Now, the only thing is left is to actually run the server:

python run main.py

That's it! Now you can open http://localhost:8080 and explore the UI to see how you scraper is being automatically scheduled and executed.

Documentation

For the full documentation please visit sneakpeek-py.readthedocs.io

Contributing

Please take a look at our contributing guidelines if you're interested in helping!