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Why you need to monitor long-running large-scale scraping projects (and how to do it right)

Hey there!

If you‘re running large-scale web scraping projects that extract data from thousands or even millions of pages per day, you‘ve likely run into some issues that caused headaches. Scraping at scale comes with unique challenges and pitfalls that can sabotage your data quality or waste time and computing resources.

The good news is that carefully monitoring your scrapers can help you avoid and quickly resolve many common problems. In this guide, I‘ll share the top issues that come up in large scraping projects based on my 5 years of experience as a web scraping expert. I‘ve seen these problems first-hand while managing scrapers that extract millions of data points per day.

I‘ll also provide my recommended best practices for monitoring your scrapers to keep them running smoothly. By implementing logging, metrics tracking, alerts, and more, you can stay on top of your scrapers and ensure they deliver timely, high-quality data.

Let‘s get started!

Why monitor your web scraping projects?

Before we get into the specific problems that monitoring helps avoid, it‘s important to understand why monitoring is so critical for large-scale scraping.

More data means more potential for issues

When you‘re extracting thousands or millions of data points from hundreds or thousands of pages, there are simply more opportunities for something to go wrong. A few potential issues include:

  • The website layout changes, breaking your scraper
  • Your IP gets blocked temporarily
  • Server errors or network outages disrupt scraping
  • Data gets parsed or formatted incorrectly

With small-scale scraping, you may be able to spot these types of problems manually. But at large scale, these failures easily fly under the radar. Without monitoring, you won‘t know your data is incomplete or inaccurate.

Resource usage adds up

Scraping millions of pages means you‘re likely running dozens or hundreds of scraping processes simultaneously. Each process consumes computing resources like memory, CPU, and bandwidth.

According to one analysis, a scraper extracting data from 1,000 pages per minute would need:

  • 4 GB of RAM
  • 4 CPU cores
  • 5 Mbps of bandwidth

So a large scraper running across multiple servers could easily burn through terabytes of bandwidth per month and thousands of compute hours.

Careful monitoring helps you provision the right resources for your scraping needs and prevent overages or outages.

Data quality is critical

For most scrapers, the end goal is high-quality, timely data. But data quality issues become increasingly likely at large scale:

  • According to one survey, 60% of companies said poor data quality leads to loss of revenue
  • Inaccurate or outdated data reduces trust and reliability
  • Missing or incomplete data leaves gaps in analysis

By monitoring your scrapers, you can quickly catch any data quality issues and correct them before they impact downstream analytics and decisions.

Watch out for these common web scraping problems

In the following sections, I‘ll cover some of the most frequent pain points and failures I see in large web scraping projects – along with how monitoring helps minimize and resolve them.

Website changes breaking scrapers

This is by far the most common issue in any long-running scraping operation. Over time, sites inevitably change their page structures and layouts in ways that can break scrapers built for the old design.

According to one analysis of over 50 million webpages:

  • On average, pages change every 58 days
  • 93% of pages change within a year

So it‘s not a question of if your target sites will change – it‘s when. Lacking monitoring, your scraper will suddenly stop working with no clear reason why.

By tracking error rates and data volumes, you can immediately notice an unexpected drop and investigate potential site changes. For example, a set of log messages like this would flag a potential issue:

10:05 AM - Extracted 550 items 
10:35 AM - Extracted 0 items
10:45 AM - 0 items extracted

You can then manually review the pages and update your scraper accordingly. Many commercial scraping services also include change detection to automatically flag site changes.

I also recommend periodically re-checking scrapers on sites prone to frequent updates. For sites changing every 2-4 weeks, a monthly re-check can catch layout shifts before they break your scraper.

Getting blocked by websites

As a web scraping expert, I‘m sure you‘re familiar with getting blocked or blacklisted by sites. This is another exceedingly common headache at scale.

The larger the scale of requests you‘re sending to a domain, the more likely they are to employ blocking. Common signs you‘ve been blocked include:

  • HTTP 403 errors
  • CAPTCHAs appearing
  • Complete lack of any response from the server

Blocks can be at the single IP level or apply site-wide. A single IP hitting hundreds of pages per minute is an instant red flag for many sites. Large-scale scraping operations often use thousands of residential IP proxies to avoid wide-ranging blocks.

But proxies aren‘t a complete solution, as individual IPs can still get blocked. By tracking response codes and error rates, blocking becomes obvious:

10:00 AM - 0 errors, 200 pages scraped
10:15 AM - 403 errors on 50% of requests 
10:30 AM - 100% errors, 0 pages scraped

At the first sign of blocks, you can rotate different proxies and IPs to minimize disruption. I also recommend slightly throttling your requests if you‘re seeing frequent blocks. While waiting a few extra seconds between requests sacrifices some speed, it dramatically cuts block rates in my experience.

Parsing and data quality issues

Even if your scraper runs without errors, the extracted data could still have serious quality issues:

  • Missing fields
  • Partial or malformed data
  • Duplicated or outdated data
  • Data formatted incorrectly

Small parsing bugs can fly under the radar but become serious headaches at scale. Just a 2% data error rate in a 1 million record scrape means 20,000 bad records!

By logging a sample of extracted data, you can manually review it for any parsing issues. For example:

Record 1:
   Name: Jane Doe 
   Location: Springfield
   Phone: 555-1234

Record 2:  
   Location: Springfield, VA  

In the above sample, Record 1 looks clean while Record 2 is missing the name and phone. You‘d want to quickly fix bugs causing these data quality issues.

You should also log warnings for any parsing failures, HTTP errors, and other anomalies so they can be corrected:

WARN: Failed to parse phone number for page 

Setting expected value ranges can also help catch outliers that signal problems:

WARN: Parsed price of $987,543 on page Expected max of $2,000.

By being rigorous about data quality from the start, you benefit from clean reliable data downstream.

Server errors and unexpected failures

Servers, networks, APIs, and websites can all suffer sporadic failures that disrupt scraping. These can stem from things like:

  • Peak traffic overwhelming servers
  • Database outages
  • Cascading infrastructure failures

According to one analysis, the average website has 2.5 outages per month, with the average outage lasting 107 minutes.

Scrapers encountering these issues will log a series of timeouts, 500 errors, connection failures, and other warnings:

WARN: Timeout contacting server after 30,000 ms

ERR: API call failed with 500 Server Error

ERR: Connection refused by 

Without monitoring these errors, you could miss entire swaths of data during outages. But catching errors quickly allows you to retry or pause scraping during major failures.

In some cases, you may wish to immediately trigger alerts so problems can be addressed ASAP. If your business relies on near real-time scraped data, outages require an urgent response.

Excessive resource usage and costs

Depending on your infrastructure, web scraping can quickly consume substantial computing resources. Scrapers running on cloud platforms like AWS can rack up hefty bills from:

  • High memory/CPU usage
  • Large amounts of bandwidth usage
  • Constantly scaling up servers

I‘ve seen companies spend thousands extra per month by exceeding projected resource needs. Carefully monitoring usage helps right-size your servers.

For example, you can track metrics like:

  • Peak CPU usage: 85%
  • Peak memory usage: 7.2GB
  • Monthly bandwidth: 18 TB

If peak usage never exceeds 50% of resources, you can likely scale down your servers for cost savings.

Monitoring for spikes in usage also helps catch any runaway scrapers or loops consuming excessive resources. If CPU usage on a server suddenly jumps from 40% to 90%, it warrants investigating.

Best practices for monitoring scraping projects

Now that you know the main problems that monitoring helps avoid, let‘s discuss some best practices for setting up monitoring.

Based on managing tons of large-scale scraping projects, I recommend a combination of:

  • Structured logging
  • Performance tracking
  • Error handling
  • Alerting
  • Data sampling

Together, these give you essential visibility into your scrapers‘ operations and data.

Structured logging

Structured logging means keeping detailed logs not just of errors, but also of key metrics and steps during normal operation. Some key things to log:

Per- scraper stats:

  • Pages scraped
  • Items extracted
  • Errors

Per-page data:

  • URL
  • HTTP status code
  • Time elapsed
  • Data extracted

Global stats:

  • Overall pages scraped
  • Start/end times
  • Any restarts

Logs should provide all key details like URLs and timestamps. Avoid vague logs like "Scraping failed!"

I also recommend logging a sample of full extracted records, which allows spot checking data quality.

Finally, use unique severity levels like INFO, WARN, and ERROR so you can filter by severity.

Performance tracking

In addition to logging, closely track key performance and resource metrics like:

  • CPU usage
  • Memory usage
  • Bandwidth used
  • Scraper latency
  • Errors and block rates

Look for any spikes, dips, or anomalies and log these events for analysis. For example, latency suddenly increasing likely warrants investigation.

Ideally, collect metrics at both the system level and per-scraper level. This helps isolate any specific scrapers consuming excessive resources.

Rigorous error handling

Code your scrapers to catch and handle all possible errors and edge cases, including:

  • HTTP errors like 404 or 503
  • Connection failures
  • Timeout errors
  • Invalid or malformed data
  • Blocked requests

Each error type should:

  1. Be logged for analysis, ideally with the problem URL.
  2. Trigger appropriate retry logic – e.g. backing off after blocks.
  3. If failures persist, raise for manual review.

Analyzing error trends helps identify and address persistent problems.

Make sure to handle unexpected errors safely by skipping and logging, rather than crashing entirely. Crashing loses in-progress work and requires messy restarts.

Smart notifications and alerts

Configure real-time notifications to be aware of issues as they happen. Common notifications include:

  • Email alerts for new critical errors
  • Slack or SMS alerts for scraper failures
  • Notifications when scrapers finish runs

Prioritize and escalate the most important alerts – e.g. text developers about critical failures. For lower-priority notifications like scraper restarts, Slack or email may suffice.

You can also track key metrics like server CPU usage and get alerts when they exceed thresholds. This helps spot problems like under-provisioned servers.

Aim to be notified of issues within 0-60 minutes for the fastest response.

Data sampling and checks

Finally, periodically review samples of your scraped data to spot check quality.

Manual reviews complement automated monitoring to catch issues that slip through the cracks.

Prioritize reviewing samples from any new site or recently changed scraper. Buggy scrapers can churn out bad data for days before you notice odd analytics trends.

You should also randomly review 1-2% of records from well-established scrapers to catch regressions.

For billion record datasets, reviewing every entry is impractical. But sampling 1-2% makes spotting potential parsing bugs manageable while keeping data quality high.

Key takeaways for monitoring scraping success

To wrap up, here are my top recommendations for monitoring and maintaining large-scale scraping projects:

Start right – Test and validate scrapers at small data volumes first. Confirm they collect data properly before scaling up.

Log rigorously – Record key metrics, errors, and data samples to spot issues early.

Handle errors – Employ comprehensive error handling and retries to minimize disruptions.

Monitor proactively – Watch for performance anomalies and trends pointing to problems.

Get alerted – Configure notifications to immediately react to scraping failures or data errors.

Review Samples – Manually check random data samples to confirm quality.

Iterate – Use monitoring insights to constantly improve scrapers.

No scraper is perfect, especially at large scale. But following these steps, you can catch problems fast and keep your data pipelines running smoothly. Scraping problems then become minor bumps rather than major headaches!

Let me know if you have any other questions on large-scale scraping best practices. I‘m always happy to help fellow developers. Stay scrappy!

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