Serverless Web Scraping with Scrapy and AWS Lambda

Hi friend! Let‘s explore how to leverage serverless computing and Python‘s Scrapy framework to build a robust, scalable web scraper on AWS Lambda.

Managing infrastructure for large-scale web scraping can be a major headache. Setting up proxy rotation, scaling server capacity, maintaining software dependencies – it‘s complex and time consuming for any engineering team. Not to mention expensive! Provisioning resources for peak workloads means you end up paying for a lot of idle capacity.

This is where a serverless approach like AWS Lambda shines…

Lambda allows us to handle brief spikes in scraping demand without a single server to manage. We simply deploy our scraper and the service automatically scales up and down based on each request. No idle capacity means lower cost. Lambda‘s pricing model charges only for the compute time we actually use per request.

Even better, by combining Lambda with a battle-tested web scraping library like Scrapy we get enterprise-grade extraction capabilities too. Let‘s look at what makes Scrapy such a robust option.

Why Scrapy Is Built for Serious Web Scraping

While Python has many web scraping libraries to choose from, Scrapy stands out as one of the most fully-featured and industrial strength options available. Here are just a few reasons why:

Powerful parsing – Scrapy includes XPath, CSS selectors and regex to handle complex markup and extract just what you need. Much more robust than simple string matching!
Tunable crawling – Fine-tune scrapy with control over politeness, request rate, concurrency, caching, and more. You‘re in the driver‘s seat.
Built-in tools – Scrapy bundles functionality for scraping images, docs, handling logins and sessions, spider contracts and more out of the box.
Item pipelines – Clean, validate and store scraped data in CSV, JSON, databases without any extra code.
Industrial scale – Companies like Mozilla, Figma, and Compass utilize Scrapy for large-scale data extraction and commercial services.
Large ecosystem – Choose from thousands of open source spiders or utilize services like Scrapy Cloud and scrapyd to automate your workflow.

This combination of robust functionality, tunable performance, and an ecosystem for extensions makes Scrapy a go-to tool for developers building serious web scrapers. Especially when paired with the scale and cost benefits of AWS Lambda.

Now let‘s walk through how we can bring them together into a complete pipeline…

Architecting Our Serverless Pipeline

When combining Scrapy, Lambda, and other services we get a powerful serverless architecture for web scraping. Here is an example pipeline we could build:

This architecture provides a complete workflow – from triggering a scrape to storing and analyzing the extracted data. Let‘s break it down step-by-step:

We use API Gateway to trigger the Lambda function containing our Scrapy spider.
Lambda spins up containers to run the scraper and crawl the sites. Output is saved directly to cloud storage.
Scraped data lands in S3 buckets for persistence across invocations.
Additional AWS data services like Glue and Athena allow querying and analysis.

By orchestrating Scrapy, Lambda, S3, and other AWS tools we get a robust serverless pipeline for scraping at scale, no servers required!

Now let‘s walk through a real working example of deploying Scrapy on Lambda. We‘ll cover:

Setting up our Lambda development environment
Configuring Scrapy for use in Lambda
Packaging and deploying our function
Invoking crawls and analyzing results

Grab your laptop and let‘s get scraping!

Developing Locally with SAM CLI

First, we‘ll need a development environment to build our Lambda function before deploying to AWS.

While there are many options, I recommend using the SAM CLI (Serverless Application Model Command Line Interface). The SAM CLI has everything we need baked in to develop, test, and debug our Lambda function right on our local machine.

Let‘s install SAM CLI to get started:

pip install aws-sam-cli

The CLI includes a number of useful commands:

sam build – Builds code into artifacts that target Lambda
sam local invoke – Runs the function locally
sam deploy – Deploys to AWS

With SAM CLI ready, we have an environment for testing iterations before we‘re ready to deploy. Now let‘s focus on our Scrapy spider next.

Creating Our Serverless Scrapy Spider

Since Scrapy will run in Lambda, we need to build a spider tailored for that environment.

I‘ve created a sample below that crawls a clothing ecommerce site and extracts product info into a JSON file:

import scrapy 

class ProductsSpider(scrapy.Spider):

  name = ‘products‘

  def start_requests(self):
    yield scrapy.Request(url=‘https://www.example.com/new-arrivals‘, 
                         callback=self.parse)

  def parse(self, response):
    for product in response.css(‘div.product‘):
      yield {
        ‘title‘: product.css(‘h2::text‘).get(),
        ‘price‘: product.css(‘.price::text‘).get(),
        ‘sku‘: product.css(‘.sku::text‘).get(),
      }

This gives us a simple Scrapy spider to start with. Next we‘ll get it ready for deployment to Lambda!

Configuring Scrapy for Lambda

Running a complex framework like Scrapy in a serverless environment introduces some constraints:

Ephemeral storage – Lambda provides limited temp storage. We can‘t write files locally.
Cold starts – We want to minimize latency when invoking the function.
Time limits – Lambda functions timeout after 15 minutes max.

To work within these constraints, we can make a few tweaks:

Configure Scrapy to write scraped data directly to cloud storage like S3 rather than local disk.
Import and run the spider on handler invocation to reduce cold starts.
Enable Lambda layers to install PyPi packages like parsel and w3lib our spider needs.
Periodically flush scraped data to S3 and checkpoint progress in case we hit a timeout.

Here is an example lambda_handler implementing these changes:

import scrapy
import boto3
from urllib.parse import urlparse 

from spiders.products import ProductsSpider

s3 = boto3.client(‘s3‘) 

def lambda_handler(event, context):

  spider = ProductsSpider()

  uri = os.environ[‘S3_BUCKET‘] 
  parsed = urlparse(uri)
  bucket = parsed.netloc
  prefix = parsed.path[1:]

  s3.put_object(Bucket=bucket, Key=(f‘{prefix}/execution.log‘))

  spider.settings.set(‘FEED_FORMAT‘, ‘json‘)
  spider.settings.set(‘FEED_URI‘, uri)

  # Crawl & Write to S3
  scrapy.signals.spider_closed(spider)

  return {
    ‘bucket‘: bucket,
    ‘key‘: prefix
  }

With these tweaks, Scrapy will run reliably within a Lambda environment. Time to deploy it!

Packaging & Deploying the Lambda Function

Now that our spider code is ready, we need to bundle it up and deploy to AWS as a Lambda function.

First, we‘ll create our deployment package using SAM CLI:

sam build

This compiles our code and downloads the necessary Docker images to build the Lambda package.

Next we can deploy to AWS with:

sam deploy --guided

The interactive prompts will guide you through providing an S3 bucket for deployment artifacts, an AWS region, and any parameters needed by your application.

Once deployed, our Lambda function containing the Scrapy spider is live! 🎉

SAM CLI makes it incredibly easy to deploy serverless applications as a solo developer. But for larger teams, tools like the Serverless Framework are ideal for managing complex, multi-stage deployments.

Now let‘s look at how we can monitor and orchestrate Lambda scraping jobs.

Running & Monitoring Lambda Web Scraping Jobs

With our function deployed, we need a way to actively trigger and monitor scraping jobs. There are a few options we can leverage:

API Gateway – Create REST API endpoints that invoke the function on demand. Perfect for scripting the scraper.

CloudWatch Events – Schedule recurring scraping jobs using Cron expressions.

Step Functions – Build serverless workflows that orchestrate scraping across many functions.

SQS – Queue scrape requests and have Lambda poll the queue to scale up.

SNS Notifications – Get alerts for failures, throttling or other issues.

Once scraping jobs are running, we‘ll want to monitor progress. Some key metrics to watch include:

Lambda Invocations – How often is the function triggering? Is concurrency config limiting it?
Duration – How long are runtimes? Any risk of timeouts?
Errors – What is the error rate? Are there consistent failures?
S3 Analytics – How many objects are being created? What is the incoming data volume?
Logs – CloudWatch Logs contain stdout/stderr and Scrapy stats that provide rich monitoring.

Combining all these capabilities provides full control over orchestrating and monitoring scraping jobs for optimal data yields.

Now let‘s look at how we can work with the datasets our spider produces.

Analyzing Scraped Datasets with AWS Services

A key benefit of our serverless architecture is all scraped data is automatically stored in S3 buckets. This data lake can be queried using various AWS analytics services:

Athena – Run SQL queries against S3 data instantly. Athena handles schema and serialization so data is query-ready.

Glue – Crawlers can scan S3 data and infer schema for Athena. Glue‘s ETL engine can also transform data.

Elasticsearch – Index and aggregate JSON data for real-time search and metrics.

Redshift – Load & warehouse scraping datasets in a petabyte-scale data warehouse.

SageMaker – Directly access S3 data to train machine learning models at scale.

This combination of serverless data analysis tools paired with Lambda scraping lets us extract insights faster than ever!

To demonstrate, let‘s see a quick example of analyzing our ecommerce product data with Athena:

-- Create Table Mapping JSON in S3
CREATE TABLE products (
  title string,
  price decimal,
  sku string
)
ROW FORMAT SERDE ‘org.openx.data.jsonserde.JsonSerDe‘
WITH SERDEPROPERTIES (
  ‘paths‘=‘title, price, sku‘
)  
LOCATION ‘s3://bucket/ecommerce/‘

-- Example Query
SELECT title, price
FROM products
WHERE price > 50
ORDER BY price DESC
LIMIT 10;

Athena allows running queries like this against JSON data scraped directly into S3 by Scrapy, no loading required!

Wrapping Up

Let‘s recap what we covered:

Why Lambda? Serverless computing offers easy scaling, fine-grained billing, and no server management.
Why Scrapy? A battle-tested web scraping framework with advanced capabilities.
Architecture How to combine Lambda, API Gateway, S3, and other AWS services into a serverless pipeline.
Development Using SAM CLI to build, test, and deploy our scraper function locally.
Monitoring Tracking Lambda performance using CloudWatch, S3, and more during scraping.
Data analysis Querying datasets in S3 with Athena after scraping completes.

This combination of Scrapy, Lambda, and AWS analytics services provides a robust platform for serverless scraping at scale. The cost and engineering benefits are immense compared to traditional infrastructure.

To take this architecture even further, I‘d recommend looking into orchestrating jobs using Step Functions or distributing crawling across fleets of Lambdas. The possibilities are endless!

Hopefully this guide gave you a solid foundation for building your own serverless web scrapers. Feel free to reach out if you have any other questions!

Happy scraping!