Big Data at Scrapinghub

Talk from the Cork Big Data and Analytics meetup
http://www.meetup.com/Cork-Big-Data-Analytics-Group/events/229772532/

Cork-based company Scrapinghub offers tools to turn web-based content into useful data, including a cloud-based web crawling platform, off-the-shelf datasets and turn-key web scraping services. At this meetup, director and co-founder Shane Evans will give an overview and history of the company, discuss the data architecture and provide an insight into their data and analytics plans for the future.

Transcript

1. Big Data at Scrapinghub
   Shane Evans
   

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

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3. We turn web content into useful data
   

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

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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
   

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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
   

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

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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
   

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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
   

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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
    

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11. Portia UI
    

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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
    

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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
    

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

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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/
    

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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
    

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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
    

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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
    

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19. HBase Architecture
    

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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
    

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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
    

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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
    

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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
    

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24. Broad Crawls
    

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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)
    

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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)
    

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

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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
    

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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.
    

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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
    

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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.
    

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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
    

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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
    

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

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