Presentation from Shane Evans, co-founder of Scrapinghub, Cork Big Data & Analytics Group, 4th April 2016

1. Big Data at Scrapinghub
Shane Evans

2. About Shane
● 9 years web scraping
● Decades with Big Data
● Scrapy, Portia, Frontera,
Scrapy Cloud, etc.
● Co-founded Scrapinghub

3. We turn web content into useful data

4. Founded in 2010, largest 100% remote company based outside of the US
We’re 126 teammates in 41 countries

5. About Scrapinghub
Scrapinghub specializes in data extraction. Our platform is
used to scrape over 4 billion web pages a month.
We offer:
● Professional Services to handle the web scraping for you
● Off-the-shelf datasets so you can get data hassle free
● A cloud-based platform that makes scraping a breeze

6. Who Uses Web Scraping
Used by everyone from individuals to
multinational companies:
● Monitor your competitors’ prices by scraping
product information
● Detect fraudulent reviews and sentiment
changes by scraping product reviews
● Track online reputation by scraping social
media profiles
● Create apps that use public data
● Track SEO by scraping search engine results

7. “Getting information off the
Internet is like taking a drink
from a fire hydrant.”
– Mitchell Kapor

8. Scrapy
Scrapy is a web scraping framework that
gets the dirty work related to web crawling
out of your way.
Benefits
● No platform lock-in: Open Source
● Very popular (13k+ ★)
● Battle tested
● Highly extensible
● Great documentation

9. Introducing Portia
Portia is a Visual Scraping tool that lets you
get data without needing to write code.
Benefits
● No platform lock-in: Open Source
● JavaScript dynamic content generation
● Ideal for non-developers
● Extensible
● It’s as easy as annotating a page

10. How Portia Works
User provides seed URLs:
Follows links
● Users specify which links to follow (regexp, point-and-click)
● Automatically guesses: finds and follows pagination, infinite scroll, prioritizes content
● Knows when to stop
Extracts data
● Given a sample, extracts the same data from all similar pages
● Understands repetitive patterns
● Manages item schemas
Run standalone or on Scrapy Cloud

11. Portia UI

12. Large Scale Infrastructure
Meet Scrapy Cloud , our PaaS for web crawlers:
● Scalable: Crawlers run on our cloud infrastructure
● Crawlera add-on
● Control your spiders: Command line, API or web UI
● Machine learning integration: BigML, MonkeyLearn, among others
● No lock-in: scrapyd, Scrapy or Portia to run spiders on your own
infrastructure

13. Data Growth
● Items, logs and requests are collected in real time
● Millions of web crawling jobs each month
● Now at 4 billion a month and growing
● Thousands of separate active projects

14. ● Browse data as the crawl is running
● Filter and download huge datasets
● Items can have arbitrary schemas
Data Dashboard

15. MongoDB - v1.0
MongoDB was a good fit to get a demo up and
running, but it’s a bad fit for our use at scale
● Cannot keep hot data in memory
● Lock contention
● Cannot order data without sorting, skip+limit
queries slow
● Poor space efficiency
See https://blog.scrapinghub.com/2013/05/13/mongo-bad-for-scraped-data/

16. ● High write volume. Writes are micro-batched
● Much of the data is written in order and immutable (like logs)
● Items are semi-structured nested data
● Expect exponential growth
● Random access from dashboard users, keep summary stats
● Sequential reading important (downloading & analyzing)
● Store data on disk, many TB per node
Storage Requirements - v2.0

17. Bigtable looks good...
Google’s Bigtable provides a sparse,
distributed, persistent
multidimensional sorted map
Can express our requirements in what
Bigtable provides
Performance characteristics should
match our workload
Inspired several open source projects

18. Apache HBase
● Modelled after Google’s Bigtable
● Provides real time random read and write to billions of rows with
millions of columns
● Runs on hadoop and uses HDFS
● Strictly consistent reads and writes
● Extensible via server side filters and coprocessors
● Java-based

19. HBase Architecture

20. HBase Key Selection
Key selection is critical
● Atomic operations are at the row level: we use fat columns, update counts on write
operations and delete whole rows at once
● Order is determined by the binary key: our offsets preserve order

21. HBase Values
● Msgpack is like JSON but fast and small
● Storing entire records as a value has low
overhead (vs. splitting records into multiple
key/values in hbase)
● Doesn’t handle very large values well, requires
us to limit the size of single records
● We need arbitrarily nested data anyway, so we
need some custom binary encoding
● Write custom Filters to support simple queries
We store the entire item record as msgpack encoded data in a single value

22. HBase Deployment
● All access is via a single service that provides a restricted API
● Ensure no long running queries, deal with timeouts everywhere, ...
● Tune settings to work with a lot of data per node
● Set block size and compression for each Column Family
● Do not use block cache for large scans (Scan.setCacheBlocks) and
‘batch’ every time you touch fat columns
● Scripts to manage regions (balancing, merging, bulk delete)
● We host in Hetzner, on dedicated servers
● Data replicated to backup clusters, where we run analytics

23. HBase Lessons Learned
● It was a lot of work
○ API is low level (untyped bytes) - check out Apache Phoenix
○ Many parts -> longer learning curve and difficult to debug. Tools
are getting better
● Many of our early problems were addressed in later releases
○ reduced memory allocation & GC times
○ improved MTTR
○ online region merging
○ scanner heartbeat

24. Broad Crawls

25. Broad Crawls
Frontera allows us to build large scale web crawlers in Python:
● Scrapy support out of the box
● Distribute and scale custom web crawlers across servers
● Crawl Frontier Framework: large scale URL prioritization logic
● Aduana to prioritize URLs based on link analysis (PageRank, HITS)

26. Broad Crawls
Many uses of Frontera:
○ News analysis, Topical crawling
○ Plagiarism detection
○ Sentiment analysis (popularity, likeability)
○ Due diligence (profile/business data)
○ Lead generation (extracting contact information)
○ Track criminal activity & find lost persons (DARPA)

27. Frontera Motivation
Frontera started when we needed to identify frequently changing
hubs
We had to crawl about 1 billion pages per week

28. Frontera Architecture
Supports both local and distributed mode
● Scrapy for crawl spiders
● Kafka for message bus
● HBase for storage and frontier
maintenance
● Twisted.Internet for async primitives
● Snappy for compression

29. Frontera: Big and Small hosts
Ordering of URLs across hosts is important:
● Politeness: a single host crawled by one Scrapy process
● Each Scrapy process crawls multiple hosts
Challenges we found at scale:
Queue flooded with URLs from the same host.
○ Underuse of spider resources.
Additional per-host (per-IP) queue and metering algorithm.
URLs from big hosts are cached in memory.
○ Found a few very huge hosts (>20M docs)
All queue partitions were flooded with huge hosts.
Two MapReduce jobs: queue shuffling, limit all hosts to 100
docs MAX.

30. Breadth-first strategy: huge amount of DNS requests
● Recursive DNS server on every spider node, upstream to
Verizon & OpenDNS
● Scrapy patch for large thread pool for DNS resolving and
timeout customization
Intensive network traffic from workers to services
● Throughput between workers and Kafka/HBase ~ 1Gbit/s
● Thrift compact protocol for HBase
● Message compression in Kafka with Snappy
Batching and caching to achieve performance
Frontera: tuning

31. Duplicate Content
The web is full of duplicate content.
Duplicate Content negatively impacts:
● Storage
● Re-crawl performance
● Quality of data
Efficient algorithms for Near Duplicate Detection, like SimHash, are
applied to estimate similarity between web pages to avoid scraping
duplicated content.

32. Near Duplicate Detection Uses
Compare prices of products scraped from different retailers by finding
near duplicates in a dataset:
Merge similar items to avoid duplicate entries:
Title Store Price
ThinkPad X220 Laptop Lenovo (i7 2.8GHz, 12.5 LED, 320 GB) Acme Store 599.89
Lenovo Thinkpad Notebook Model X220 (i7 2.8, 12.5’’, HDD 320) XYZ Electronics 559.95
Name Summary Location
Saint Fin Barre’s Cathedral Begun in 1863, the cathedral was the first major work of the
Victorian architect William Burges…
51.8944, -8.48064
St. Finbarr’s Cathedral Cork Designed by William Burges and consecrated in 1870, ... 51.894401550293, -8.48064041137695

33. What we’re seeing..
● More data is available than ever
● Scrapinghub can provide web data in a usable format
● We’re combining multiple data sources and analyzing
● The technology to use big data is rapidly improving and
becoming more accessible
● Data Science is everywhere

34. Thank you!
Shane Evans
shane@scrapinghub.com
scrapinghub.com
Thank you!