Big Data Architecture at Scrapinghub

In this talk, Shane will discuss the design and architecture of Scrapinghub’s systems for storing and analysing big data. He will introduce HBase, dive into the architecture, and share what he learned designing, implementing and scaling a platform based on it.

Transcript

1. Big Data Architecture at Scrapinghub
   Shane Evans
   

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

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3. About Scrapinghub
   We turn web content into useful data
   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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4. Who Uses Web Data?
   Used by everyone from individuals to large
   corporates:
   ● Monitor your competitors by analyzing product
   information
   ● Detect fraudulent reviews and sentiment changes
   by mining reviews
   ● Create apps that use public data
   ● Track criminal activity
   

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5. Web Crawling and Hadoop
   Heavily influenced by the infrastructure at
   Google
   Initial code for Hadoop was factored out of
   the Nutch web crawler
   

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

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7. Scrapy
   Scrapy is a web scraping framework that
   gets the dirty work related to web crawling
   out of your way.
   Benefits
   ● Open Source
   ● Very popular (16k+ ★)
   ● Battle tested
   ● Highly extensible
   ● Great documentation
   

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8. Scraping 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
   ● Store your data: All data is securely stored in Scrapinghub's
   fault-tolerant database and accessible through the Scrapinghub API
   

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9. MongoDB - v1.0
   ● Quick to prototype
   ● Filtering, indexing, etc.
   ● Easy to work with JSON
   

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10. MongoDB - v1.0
    But for our use, when we started to 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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13. ● Many processes stream data, typically JSON
    ● Which needs to be stored and delivered to
    customers
    ● Order should be preserved (e.g. logs), sometimes
    key-value
    Typical Web Scraping
    

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14. Apache Kafka
    Image from Hortonworks
    

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15. ● Browse data as the crawl is running
    ● Filter datasets
    ● Summary statistics in real time
    ● Share links to items, logs, requests
    Data Dashboard
    

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16. ● High write volume. Writes are micro-batched
    ● Much of the data is written in order and often immutable (e.g
    logs)
    ● Items are semi-structured nested data (like JSON)
    ● 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. Key Selection
    Data ordered by key
    Consider
    ● Key ranges assigned to regions
    ● Tall/Narrow vs. Fat/Wide
    ● Avoid hotspotting
    

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21. Key Selection Examples
    OpenTSDB:
    Row key: [...]
    Google web table:
    Row keys: URLs with reversed domains
    good use of column families
    Facebook inbox search:
    Row key: User
    Column: Word
    Version: message id
    

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22. Key Design: Job Data
    ● Atomic operations are at the row level: we use wide 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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23. Job Data Small Regions
    ● Some IDs gradually increased over time, leaving small or empty regions
    ● Small Regions have overhead - min buffer sizes, time to migrate, etc.
    

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24. Job Data Merged Regions
    

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25. 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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26. HBase Deployment
    ● All access is via a 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 on dedicated servers
    ● Data replicated to backup clusters, where we run analytics
    

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27. 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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28. Uses for HBase Data
    ● Consumed by customers directly
    ● Operational data (e.g. crawl state) used by
    applications
    ● We export data to HDFS and elsewhere
    ● Analysis using different tools, e.g. Spark
    

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29. Hardware failures
    http://xkcd.com/1737/
    ● Drive failures happen, and they matter
    ● Measure cost of replacement, time to
    failover
    ● Poorly performing drive worse than
    failure
    

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30. Hardware failures
    http://xkcd.com/1737/
    Don’t copy a solution that
    works at a much larger
    scale
    

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31. 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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32. Some advice from Jeff Dean
    ● Use back of the envelope calculations
    ● Plan to scale 10-20x, but rewrite by 100x
    

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

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