Sane Sharding with Akka Cluster

Writing distributed applications is very hard, especially when you start developing them as single-noded ones. Programmers tend to focus on functionalities first, leaving the scalability issues for later. Fortunately, Akka gives us many tools for scaling out and we can use them very early in the development process. I want to show you how to take advantage of these features.

You will learn how to transform a single-noded app into a scalable one. During live coding session I will create both versions from scratch and guide you through the most important architectural decisions. If you are interested in scalability and know basics of message-based concurrency, this talk is for you. First, you will see how to create a web service as a single-noded Akka app. Then we will talk about scalability and availability problems with this approach and introduce sharding as potential solution. We will use this knowledge and Akka Cluster module to make our app more scalable.

Transcript

1. @miciek
   Sane Sharding
   with Akka Cluster
   Live coding & performance analysis
   Michał
   Płachta
   @miciek
   

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2. @miciek
   What’s inside?
   ● Creating a web service using actor model
   ● ...analysing its performance
   ● ...making it scalable
   

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3. @miciek
   Akka Tutorial
   ● actor ~= lightweight thread
   ● actorRef.tell
   ● actorRef.ask
   ● actors create children
   ● actors have mailbox
   ActorRef
   Actor 1 Actor 2
   ask tell
   enqueue
   Mailbox
   Actor 3
   dequeue
   

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4. @miciek
   Scala Tutorial
   case class Junction(id: Int)
   public class Junction {
   private final int id;
   public Junction(int id) {
   this.id = id;
   }
   public int getId() {
   return id;
   }
   // hashCode
   // equals
   // copy
   }
   msg match {
   case Junction(id) => {
   // this will execute when
   // msg is instanceOf Junction
   }
   case SomeOtherType => {}
   }
   JAVA SCALA
   

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5. @miciek
   Our example: Sorter
   scan HTTP push right or not
   See also: http://i.imgur.com/mctb4HC.gifv
   

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6. @miciek
   Sorter Web Service
   http://localhost:8080/junctions//decisionForContainer/
   returns JSON
   { “targetConveyor”: }
   Assumptions:
   ● business logic already defined - focus on performance
   ● the business logic function takes 5-10 ms to make a decision
   

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7. @miciek
   Let’s code it!
   

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8. @miciek
   Step 1: Just REST...
   RestInterface
   HTTP Requests HTTP Responses
   ● One Actor = One Thread
   ● Blocking inside receive method
   ● Low throughput...
   

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9. @miciek
   Throughput testing
   /junctions/1/decisionForContainer/1
   /junctions/2/decisionForContainer/4
   /junctions/3/decisionForContainer/5
   /junctions/4/decisionForContainer/2
   /junctions/5/decisionForContainer/7
   2000 requests
   2000 requests
   2000 requests
   2000 requests
   2000 requests
   in parallel
   cat URLs.txt | parallel -j 5 'ab -ql -n 2000 -c 1 -k {}'
   GNU Parallel ApacheBench
   

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10. @miciek
    Let’s test it!
    

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11. @miciek
    Step 1: Just REST...
    RestInterface
    HTTP Requests HTTP Responses
    ± % cat URLs.txt | parallel -j 5 'ab -ql -n 2000 -c 1 -k {}' | grep 'Requests per second'
    Requests per second: 34.78 [#/sec] (mean)
    Requests per second: 34.22 [#/sec] (mean)
    Requests per second: 33.77 [#/sec] (mean)
    Requests per second: 33.82 [#/sec] (mean)
    Requests per second: 33.98 [#/sec] (mean)
    

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12. @miciek
    Let’s improve the
    performance!
    

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13. @miciek
    Step 2: Logic in another actor
    RestInterface
    HTTP Requests HTTP Responses
    SortingDecider
    

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14. @miciek
    Step 3: One actor per junction
    RestInterface
    HTTP Requests HTTP Responses
    DecidersGuardian
    SortingDecider
    SortingDecider
    SortingDecider
    =1 ... =5
    

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15. @miciek
    Step 3: One actor per junction
    ± % cat URLs.txt | parallel -j 5 'ab -ql -n 2000 -c 1 -k {}' | grep 'Requests per second'
    Requests per second: 67.36 [#/sec] (mean)
    Requests per second: 69.03 [#/sec] (mean)
    Requests per second: 67.75 [#/sec] (mean)
    Requests per second: 66.88 [#/sec] (mean)
    Requests per second: 66.28 [#/sec] (mean)
    

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16. @miciek
    Now what?
    ● non-blocking
    ● concurrent
    ● scaling up works
    ● scaling out?
    RestInterface
    HTTP Requests HTTP Responses
    DecidersGuardian
    SortingDecider
    SortingDecider
    SortingDecider
    =1 ... =5
    

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17. @miciek
    Manual scaling out
    RestInterface
    HTTP Requests HTTP Responses
    DecidersGuardian
    SortingDecider
    SortingDecider
    SortingDecider
    =1 ... =3
    RestInterface
    HTTP Requests HTTP Responses
    DecidersGuardian
    SortingDecider
    SortingDecider
    SortingDecider
    =4 ... =6
    

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18. @miciek
    Enter Sharding
    RestInterface
    HTTP Requests HTTP Responses
    ShardRegion
    SortingDecider
    SortingDecider
    SortingDecider
    =h(m) ... =h(m)
    RestInterface
    HTTP Requests HTTP Responses
    ShardRegion
    SortingDecider
    SortingDecider
    SortingDecider
    =h(m) ... =h(m)
    ...
    

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19. @miciek
    Let’s shard it!
    

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20. @miciek
    Step 4: Sharded web service
    ± % cat URLs.txt | parallel -j 5 'ab -ql -n 2000 -c 1 -k {}' | grep 'Requests per second'
    Requests per second: 106.80 [#/sec] (mean)
    Requests per second: 108.15 [#/sec] (mean)
    Requests per second: 100.60 [#/sec] (mean)
    Requests per second: 99.92 [#/sec] (mean)
    Requests per second: 100.07 [#/sec] (mean)
    

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21. @miciek
    Sharding
    ● automatic distribution
    ● no need to know who is where
    ● no need to know how many nodes are there
    More:
    ● The project: github.com/miciek/akka-sharding-example
    ● Step by step tutorial: michalplachta.com/2016/01/23/scalability-
    using-sharding-from-akka-cluster/
    

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22. @miciek
    Thank you!
    Michał
    Płachta
    @miciek
    Sane Sharding
    with Akka Cluster
    Live coding & performance analysis
    

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