Scrapy

This verified source utilizes Scrapy, an open-source and collaborative framework for web scraping. Scrapy enables efficient extraction of required data from websites.

Setup guide

Initialize the verified source

To get started with your data pipeline, follow these steps:

1. Enter the following command:

   dlt init scraping duckdb

   This command will initialize the pipeline example with Scrapy as the source and duckdb as the destination.

2. If you'd like to use a different destination, simply replace duckdb with the name of your preferred destination.

3. After running this command, a new directory will be created with the necessary files and configuration settings to get started.

For more information, read the guide on how to add a verified source.

Add credentials

1. The config.toml, looks like:

   # put your configuration values here
   [sources.scraping]
   start_urls = ["URL to be scraped"] # please set me up!
   start_urls_file = "/path/to/urls.txt" # please set me up!

   When both start_urls and start_urls_file are provided, they will be merged and deduplicated to ensure Scrapy gets a unique set of start URLs.

2. Inside the .dlt folder, you'll find a file called secrets.toml, which is where you can securely store your access tokens and other sensitive information. It's important to handle this file with care and keep it safe.

3. Next, follow the destination documentation instructions to add credentials for your chosen destination, ensuring proper routing of your data to the final destination. For more information, read Secrets and Configs.

Run the pipeline

In this section, we demonstrate how to use the MySpider class defined in "scraping_pipeline.py" to scrape data from "https://quotes.toscrape.com/page/1/".

1. Start by configuring the config.toml as follows:

   [sources.scraping]
   start_urls = ["https://quotes.toscrape.com/page/1/"] # please set me up!

   Additionally, set destination credentials in secrets.toml, as discussed.

2. Before running the pipeline, ensure that you have installed all the necessary dependencies by running the command:

   pip install -r requirements.txt

3. You're now ready to run the pipeline! To get started, run the following command:

   python scraping_pipeline.py

Customization

Create your own pipeline

If you wish to create your data pipeline, follow these steps:

1. The first step requires creating a spider class that scrapes data from the website. For example, the class Myspider below scrapes data from URL: "https://quotes.toscrape.com/page/1/".

   class MySpider(Spider):
       def parse(self, response: Response, **kwargs: Any) -> Any:
           # Iterate through each "next" page link found
           for next_page in response.css("li.next a::attr(href)"):
               if next_page:
                   yield response.follow(next_page.get(), self.parse)
   
           # Iterate through each quote block found on the page
           for quote in response.css("div.quote"):
               # Extract the quote details
               result = {
                   "quote": {
                       "text": quote.css("span.text::text").get(),
                       "author": quote.css("small.author::text").get(),
                       "tags": quote.css("div.tags a.tag::text").getall(),
                   },
               }
               yield result
   

   Define your own class tailored to the website you intend to scrape.

2. Configure the pipeline by specifying the pipeline name, destination, and dataset as follows:

   pipeline = dlt.pipeline(
       pipeline_name="scrapy_pipeline",  # Use a custom name if desired
       destination="duckdb",  # Choose the appropriate destination (e.g., bigquery, redshift)
       dataset_name="scrapy_data",  # Use a custom name if desired
   )

   To read more about pipeline configuration, please refer to our documentation.

3. To run the pipeline with customized scrapy settings:

   run_pipeline(
       pipeline,
       MySpider,
       # you can pass scrapy settings overrides here
       scrapy_settings={
           # How many sub pages to scrape
           # https://docs.scrapy.org/en/latest/topics/settings.html#depth-limit
           "DEPTH_LIMIT": 100,
           "SPIDER_MIDDLEWARES": {
               "scrapy.spidermiddlewares.depth.DepthMiddleware": 200,
               "scrapy.spidermiddlewares.httperror.HttpErrorMiddleware": 300,
           },
           "HTTPERROR_ALLOW_ALL": False,
       },
       write_disposition="append",
   )

   In the above example, scrapy settings are passed as a parameter. For more information about scrapy settings, please refer to the Scrapy documentation.

4. To limit the number of items processed, use the "on_before_start" function to set a limit on the resources the pipeline processes. For instance, setting the resource limit to two allows the pipeline to yield a maximum of two resources.

   def on_before_start(res: DltResource) -> None:
       res.add_limit(2)
   
   run_pipeline(
       pipeline,
       MySpider,
       batch_size=10,
       scrapy_settings={
           "DEPTH_LIMIT": 100,
           "SPIDER_MIDDLEWARES": {
               "scrapy.spidermiddlewares.depth.DepthMiddleware": 200,
               "scrapy.spidermiddlewares.httperror.HttpErrorMiddleware": 300,
           }
       },
       on_before_start=on_before_start,
       write_disposition="append",
   )

5. To create a pipeline using Scrapy host, use create_pipeline_runner defined in helpers.py. As follows:

   scraping_host = create_pipeline_runner(pipeline, MySpider, batch_size=10)
   scraping_host.pipeline_runner.scraping_resource.add_limit(2)
   scraping_host.run(dataset_name="quotes", write_disposition="append")