How Keen IO uses Storm

This presentation discusses Storm, the distributed computation system, and how it’s used at Keen IO. Redstorm, which makes it possible to build Storm topologies in Ruby, is also discussed. First given at #sfrails in October 2013.

Transcript

1. How Keen IO
   uses Storm
   Josh Dzielak
   @dzello
   10/18/2013
   #sfrails
   

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2. About me
   * Full stack developer spanning 2 millenia
   * Helped found & build Togetherville (Disney)
   Ruby 1.8.7 and Rails 2.3.8 FTW!
   * Author of mongoid_alize, four & keen-gem.
   * Mentor at HackBright & HackReactor
   * Currently VP Engineering at Keen IO
   

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3. An analytics API for the modern developer.
   http://keen.io
   But is it for me?
   

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4. I’d rather
   spend time
   building
   features
   I know I need
   analytics, but
   Sendgrid
   Iron.io
   Twilio
   Heroku
   Pusher
   I use APIs
   Would I like Keen IO?
   YES
   You
   probably
   would!
   

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5. Tech @ Keen IO
   Tornado API Server
   Flask on the web
   Official & community SDK’s
   Ruby is very popular!
   

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6. Tech @ Keen IO
   Our old backend
   Pros:
   * Fast writes
   * Easy to setup
   * Develop features quickly
   Cons/what we outgrew:
   * Ad-hoc query performance
   * Operational ease
   * Aggregation features
   

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7. Tech @ Keen IO
   Our new backend
   STORM
   

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8. Heard of Storm?
   NO
   

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9. What is Storm?
   a) A project with 7,000+ followers on Github
   b) Low-latency distributed computation system
   c) WNBA team in Seattle
   d) Capable of streaming map-reduce
   Pop quiz! Storm is:
   e) All of the above
   

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10. Storm Primitives
    SPOUT
    pulls from data
    sources
    BOLT
    Does some
    processing
    Username Level Date
    dzello 99 2013-10-17
    TUPLE
    What’s on the
    wire
    

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11. Storm, Deployed
    ExampleTopology
    Host 1 Host 2 Host 3
    Worker 1
    Worker 2
    Worker 3 Worker 4
    Bolt
    Bolt
    Bolt
    Bolt
    Bolt
    Bolt
    Bolt
    Bolt
    Bolt
    Bolt
    Spout
    Spout
    Spout
    Spout Data Source
    Bolt
    

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12. Common Storm Myths
    Myth: Clouds don’t like Storms.
    Storm deploys to any cloud.
    https://github.com/nathanmarz/storm-deploy
    

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13. Storm at Keen IO
    The primary logical layer
    for storing events and performing queries.
    Cassandra distributes the data &
    Storm distributes the computation.
    Because Storm and Cassandra scale
    linearly, we can perform writes and
    queries with low latency, high throughput,
    all while remaining fault tolerant.
    

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14. How fast is this?
    The Write Topology
    Storm Nodes Cassandra Nodes Events/Sec
    3 6 50,000+
    The Query Topology
    Query Type Collection Size (events) Mean Response Time
    Full Count 100M >100ms
    Average w/ groups 100M 300ms
    Sum over a field 600M 800ms
    

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15. The Write Topology
    Tornado API
    Kafka
    Kafka Spout
    Zookeeper
    keeps the
    peace
    EventPartitioner
    Bolt
    EventPartitioner
    Bolt
    PartitionEvent
    Bolt PersistEvent
    Bolt
    PersistEvent
    Bolt
    PersistEvent
    Bolt
    Cassandra
    enforces exactly-once semantics
    splits the work
    Kafka Spout
    Kafka Spout
    keeps the
    data
    fault-tolerance starts here
    

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16. The Query Topology
    Tornado API
    DRPC Spout
    Zookeeper keeping the
    peace
    EventPartitioner
    Bolt
    EventPartitioner
    Bolt
    IndexExpander
    Bolt PersistEvent
    Bolt
    PersistEvent
    Bolt
    BucketReducer
    Bolt
    Cassandra
    emits matching
    buckets
    Storm DRPC Server
    DRPC Spout
    DRPC Spout
    Aggregation Bolt
    keeping the
    data
    reduces each bucket
    returns response
    

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17. Haz Storm for Ruby?
    REDSTORM
    https://github.com/colinsurprenant/redstorm
    Elegant JRuby bindings for Storm.
    Includes batteries:
    CLI scripts to package jars & work
    with storm locally and deploy t a cluster.
    Very easy way to get familiar with Storm.
    Simple twitter streaming example –
    https://github.com/dzello/ontweet
    

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18. Hello, redstorm
    

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19. Thanks #sfrails!
    More resources for Storm & distributed systems
    http://www.michael-noll.com/blog/2012/10/16/understanding-the-
    parallelism-of-a-storm-topology/
    https://speakerdeck.com/dzello/distributed-systems-are-everywhere-
    where-the-full-stack-is-headed
    http://storm-project.net/
    https://github.com/colinsurprenant/redstorm/wiki/Ruby-DSL-Documentation
    

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20. Coming to defrag?
    (November 4th - 6th)
    Check out my talk:
    One Billion Per Second
    The Rise of Designer Data Architectures
    http://defragcon.com/2013/agenda/
    

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21. Thanks!
    Questions?
    Talk at my face
    or email me at
    josh@keen.io
    

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